Home Journals Articles Updates About
CN
image
收藏切换
Multi-omics in colorectal cancer liver metastasis: applications and research advances
收藏切换
PDF
Kexue Zhou1, Chengxiang Yang1, Yan Li1, 2
Cancer Biology & Medicine | 2025, 22(6) : 618 - 638
Less
收藏切换
Cancer Biology & Medicine | 2025, 22(6): 618-638
• Review •
Multi-omics in colorectal cancer liver metastasis: applications and research advances
Full
Kexue Zhou1, Chengxiang Yang1, Yan Li1, 2
Affiliations
  • 1School of Medicine Chongqing University, Chongqing 400030, China
  • 2Department of Medical Oncology, Chongqing University Cancer Hospital, Chongqing 400030, China
About Author:

Author contributions

Conceived and designed the analysis: Kexue Zhou, Yan Li.

Collected the data: Kexue Zhou.

Contributed data or analysis tools: Kexue Zhou, Chengxiang Yang.

Performed the analysis: Kexue Zhou, Yan Li.

Wrote the paper: Kexue Zhou.

Published: 2025-06-15 doi: 10.20892/j.issn.2095-3941.2025.0066
Outline
收藏切换

Colorectal cancer (CRC) is a common malignant tumor with a high mortality rate worldwide. Advanced CRC often leads to liver metastasis, which has a poor prognosis, highlighting the need to investigate the underlying mechanisms. Omics, encompassing genomics, epigenomics, transcriptomics, proteomics, metabolomics, and microbiomics, enables comprehensive molecular analysis of cells and tissues. Tumor-omics research has advanced rapidly, with growing attention on CRC-related omics. However, systematic reviews on omics research specific to colorectal cancer liver metastasis (CRLM) are limited. This review summarizes the current status and progress of multi-omics research on CRLM and discusses the application of multi-omics technologies in basic research and the significant clinical implications.

Colorectal cancer liver metastasis  /  genomics  /  transcriptomics  /  epigenomics  /  proteomics  /  metabolomics  /  microbiomics
Kexue Zhou, Chengxiang Yang, Yan Li. Multi-omics in colorectal cancer liver metastasis: applications and research advances[J]. Cancer Biology & Medicine, 2025 , 22 (6) : 618 -638 . DOI: 10.20892/j.issn.2095-3941.2025.0066
Full Text
Less
Introduction
Less
Global Cancer Observatory (GLOBOCAN) data from 2022 report 1.9 million colorectal cancer (CRC) cases and 904,000 deaths globally, ranking 3rd in incidence and 2nd in mortality worldwide1. Between 25% and 30% of patients with CRC develop liver metastases with a poor prognosis and a 5-year survival rate < 10%2,3. Studies on isolated cellular pathways and tissue metabolism offer limited insight into the pathogenesis of colorectal cancer liver metastasis (CRLM), diagnostic accuracy, recurrence, metastasis, therapeutic efficacy, and patient survival. Therefore, omics research significantly contributes to advancing our understanding of liver metastasis in patients with CRC.
Omics analysis involves statistical processing, and comparative and correlation analyses of bulk data across various molecular levels, such as genomics, epigenomics, transcriptomics, proteomics, metabolomics, and microbiomics. Omics analysis establishes relationships among molecular data and reveals the interconnections4. For example, multi-omics research on CRLM integrates omics data to identify key genes, metabolites, proteins, and microorganisms involved in the process for further experimental analysis and potential applications.
Although there are numerous reports on omics research in CRC, systematic reviews on omics in liver metastasis are lacking. This study provides a comprehensive review of genomics, epigenomics, transcriptomics, proteomics, metabolomics, microbiomics, and multi-omics CRLM studies and explores the current state of omics research in this context (Figure 1).
Genomic insights into CRLM
Less
Genomics explores the structure, dynamics, and functions of the genome. Ki et al.5 utilized complementary (c)DNA microarrays to identify 46 genes potentially involved in CRLM progression. Similarly, Yamasaki et al.6 showed that the potential for liver metastasis in CRC is encoded within the primary tumor and detected by gene expression profiling. Studies in China identified frequent mutations in patients with CRC with liver metastasis, including tumor protein (TP) 53 (82%), adenomatous polyposis coli (APC) (76%), Kirsten rat sarcoma viral oncogene homolog (KRAS) (42%), SMAD family member (SMAD) 4 (14%), filaggrin (FLG) (13%), and f-box and wd repeat domain-containing (FBXW) 7 (11%)7. These findings link high-frequency mutations to poor patient prognosis, thereby highlighting the clinical implications of genomic research for CRLM treatment and prognosis.
Genomic differences between primary CRC lesions and liver metastasis
Although significant research has focused on the genomics of primary CRC tumors, studies involving liver metastasis are limited. Evidence suggests that differential gene expression between primary and metastatic sites is a key genomic feature of liver metastasis. Lin et al.8 performed gene ontology (GO) analysis and showed that genes associated with tissue remodeling and immune responses are upregulated in metastatic tumors, whereas genes related to proliferation and oxidative phosphorylation are downregulated. The study also identified 10 genes with higher expression in CRLM, 5 of which encoded “matrix proteins” involved in extracellular matrix (ECM) interactions, indicating a potential association with liver metastasis9. Notably, genomic profiles in patients with CRC vary significantly by metastasis site, with greater genomic divergence between primary tumors and liver metastases than between primary tumors and lung metastases10. Comparative genomic hybridization (CGH) and other techniques demonstrated a greater number of mutations in liver metastases than in primary tumors, which likely reflects increased genetic instability11. Additionally, patients with sporadic CRLM exhibited specific transcriptional dysregulation in liver metastases, whereas primary tumor expression remained normal. This finding indicates increased genomic instability in metastatic cells or adaptive changes to the liver microenvironment12,13.
Application of genomics in the tumor microenvironment (TME) of CRLM
Traditional sequencing methods treat tumor samples as a whole, whereas single-cell technologies differentiate cellular components, particularly immune cells. This shift has redirected cancer genomics from focusing solely on cancer cells to exploring the TME. Genomic analyses have revealed that immune cell changes within the TME may contribute to tumor progression and liver metastasis. Jackstadt et al.14 found that intestinal epithelial neurogenic locus notch homolog protein (NOTCH) 1 signaling drives metastasis through transforming growth factor (TGF)-dependent neutrophil recruitment, highlighting the role of NOTCH1 in CRC progression. Sathe et al.15 identified distinct intercellular communication programs in which macrophages and fibroblasts regulate the pre-metastatic niche in the liver, thereby forming a “soil” for tumor seeding. Specifically, TME-associated macrophages expressing secreted phosphoprotein (SPP) 1 promote primary CRC progression. Furthermore, Rong et al.16 used single-cell RNA sequencing to identify matrix gla protein (MGP) as a driver of CD8+ T cell exhaustion via NF-κB activation for CRLM. MGP knockout combined with anti-PD1 therapy synergistically resist liver metastasis. These findings highlight the role of genomics in understanding CRLM mechanisms and treatment outcomes.
Application of genomics in imaging diagnosis of CRLM
Studies linking genomics and imaging have proven valuable in diagnosing CRLM. Using next-generation sequencing (NGS) technology, a study analyzed weak target tumor enhancement (TTE) and normal tissues using CRC magnetic resonance imaging (MRI) and revealed correlations between TTE and genetic mutations in CRLM17. Wang et al.18 identified six genes (NRAS, KRAS, BRAF, PIK3CA, MAPK1, and STAT1) linked to specific imaging features, such as shape-elongation and gradient, thus providing a foundation for non-invasive diagnosis of liver metastasis.
Application of genomics in the treatment of CRLM
Surgery remains a viable option for patients with CRLM who meet resectability criteria. However, tumor molecular behavior, rather than surgical technique, ultimately determines patient prognosis19. Chan et al.20 reported no statistically significant differences in patient outcomes across surgical approaches. Chan et al.20 emphasized the importance of integrated management strategies based on cancer genomics to assess prognosis. Genomic analysis can guide patient selection, surgical timing, and technique for metastatic lesions21. Sun et al.22 demonstrated that P-cadherin regulates CRLM. This finding suggested P-cadherin inhibition as a potential therapeutic strategy. Liu et al.23 identified ankyrin repeat domain 42 (ANKRD42) as a key regulatory gene promoting CRLM through genome-wide sgRNA library screening. Knockdown of ANKRD42 significantly attenuates tumor cell migration and invasion, highlighting its potential as a therapeutic target. Additionally, combining oxaliplatin with mTOR inhibitors shows synergistic effects, highlighting the potential of integrating chemotherapy with targeted therapy24. While genomic analysis offers significant insights for CRC treatment, further research is warranted to advance clinical application.
Application of genomics in prognosis of CRLM
Genomic research can reveal the clonality and timing of metastatic CRC25. Recent studies have focused on identifying biomarkers in bodily fluids, such as blood and urine, including serum proteins (peptides), plasma miRNAs, circulating tumor cells, and nucleic acids. When combined with technologies, such as whole-genome sequencing and circulating tumor cell DNA analysis, genomic analysis can help predict CRLM occurrence. Notably, undetectable circulating tumor DNA following liver metastasis resection suggests a better prognosis for recurrence-free survival26.
The core of tumor genomics is mapping cancer-driving factors and providing personalized therapeutic strategies27. Genomics is crucial in understanding CRLM origins, molecular differences between primary and metastatic lesions, genetic profiling, treatment decisions, and survival prognosis.
Epigenomics of CRLM
Less
Although every human body cell contains the same genome, distinct cellular phenotypes arise from different gene expression patterns, driven by cell type-specific transcription factors and epigenome structures28. Epigenomics primarily focuses on DNA and histone modifications that regulate chromatin structure, gene activity, and gene expression without altering the DNA sequence4. Studies have suggested that epigenetic regulation may contribute to tumor metastasis29, and DNA methylation and histone modifications are reversible. Investigating epigenetics in CRLM could reveal new therapeutic targets for CRLM diagnosis and treatment (Figure 2).
Relationship between DNA methylation and the occurrence, development, treatment, and prognosis of CRLM
DNA methylation occurs at cytosine residues preceding guanine in humans, forming CpG dinucleotides (C-phosphate-G bonds)30. While most CpG dinucleotides are methylated, CpG islands, found in the promoter regions of 40%–60% of tumor suppressor genes, remain unmethylated in healthy cells. These islands, spanning 200–2,000 base pairs with a CG content ≥ 50%, contribute to gene expression regulation30–32. Epigenetic mutations silence tumor suppressor genes or activate oncogenes in human cancers.
The methylation of various tumor suppressor genes is associated with CRLM. Zhang et al.33 noted that long non-coding RNA (lnc-LALC) promotes CRLM by epigenetically silencing leucine zipper tumor suppressor gene 1 (LZTS1). Upregulation of genes, such as SLC10A1, MAPT, SHANK2, PTH1R, and C2, and downregulation of genes, including CAB39, CFLAR, CTSC, THBS1, and TRAPPC3, are linked to DNA methylation and may act as biomarkers for CRLM34. Differential DNA methylation in primary CRC can predict risk of liver metastasis, with several regions, including TNNI2, PAX8, GUF1, KLF4, EVI2B, CEP112, and lncRNA AC011298, identified as prognostic biomarkers35,36.
Numerous studies have observed hypermethylation of genes in metastatic liver lesions of CRC. Ebert et al.37 found frequent hypermethylation of TPEF, which encodes a transmembrane protein with epidermal growth factor and follistatin domains, in primary CRC lesions and liver metastases. This finding suggested that epigenetic alterations are common in the early and late stages of carcinogenesis. Therefore, TPEF methylation may act as a diagnostic target. Several studies have identified different gene methylation mechanisms associated with CRLM. ALX4 is hypermethylated in primary colorectal adenocarcinomas and liver metastases38, and MUC2 promoter methylation is linked to liver and lymph node metastases39. Lu et al.40 showed that B3GNT7 methylation enhances CRC cell metastasis to the liver. Tommasi et al.41 found that RASSF1A methylation contributes to liver metastasis progression. Ju et al.42 suggested that DNA methylation-induced silencing of UPK3A enhances liver metastasis CRLM. Rodger et al.43 observed differential CpG methylation patterns, with hypomethylation in normal colon and primary CRC, but hypermethylation in liver metastases, which correlated with methylation levels and chromatin activity44.
DNA methylation is a valuable tool for the prognostic assessment of CRLM. For example, multiplex DNA methylation profiling of circulating free DNA (cfDNA) enables patient stratification and treatment monitoring in advanced CRC with liver metastasis45. Furthermore, methylation profiling has been used to identify effective methylation biomarkers, such as the significant increase in trichohyaline (TCHH) gene methylation, which correlates strongly with liver metastasis tumor volume. This finding suggested the potential of TCHH as a biomarker for liver metastasis46.
Epigenetic regulation is reversible and restoring normal epigenomic profiles could serve as a potential therapeutic target for cancer. CXCL14, a tumor suppressor gene in CRC, is activated but silenced during progression, primarily because of promoter hypermethylation. Therefore, reversing CXCL14 hypermethylation may offer an epigenetic therapeutic strategy for CRC treatment47. However, research on epigenetic therapies to intervene in CRLM is limited and further exploration is needed.
Relationship between histone modifications and the occurrence, development, treatment, and prognosis of CRLM
DNA wraps around nucleosome particles to form the organizational foundation for eukaryotic genomes. Core histones (H2A, H2B, H3, and H4) undergo post-translational modifications that help define specific chromatin regions and states48. These modifications include methylation, acetylation, ubiquitination, and phosphorylation with methylation and acetylation being most common49. CRLM has been reported to be closely linked to histone modifications50.
Histone methylation and demethylation in CRLM
Histone methylation involves adding methyl groups to lysine (K) residues on H3 and H4, and is a key post-translational modification51. Regulation of histone methylation is controlled by two enzyme groups [histone methyltransferases (HMTs), which add methylation marks, and histone demethylases (HDMs), which remove methylation marks]52,53. Each lysine residue can undergo three distinct methylation states (monomethyl, dimethyl, or trimethyl)54.
Histone methylation modifications are reportedly involved in CRC initiation and progression55–57. However, research on the role of histone methylation modifications in liver metastasis is limited. Tamagawa et al.50 found that liver metastasis in CRC is associated with histone modifications with H3K27me2 and H3K4me2 methylation levels potentially serving as independent prognostic factors58. Specifically, lower H3K4me2 levels correlate with poorer survival outcomes50. Further research is required to determine the role of histone methylation in CRLM.
Additionally, histone methylation modifications can serve as diagnostic markers for CRC59 and predictors of chemotherapy sensitivity60. Nair et al.61 compared the epigenetic modification mechanisms of immune checkpoints in CRC tissues and normal intestinal mucosa. Nair et al.61 found high expression of PD-1, CTLA-4, TIM-3, TIGIT, PD-L1, and Galectin-9 in CRC tissues. Nair et al.61 also observed a significant reduction in H3K9me3 binding to the PD-1 and TIGIT promoters and H3K27me3 binding to the CTLA-4 promoter in CRC tissues, as well as lower binding of both repressive histones to the TIM-3 promoter. These findings suggested that H3K9me3 and H3K27me3 are involved in upregulating CTLA-4, TIGIT, and PD-1, making these modifications potential diagnostic biomarkers for CRC.
Along with the gene methylation detected in tissues, histone methylation of circulating nucleosomes (H3K27me3 and H4K20me3) can serve as a biomarker for CRC diagnosis59. Kang et al.60 found that 5-fluorouracil (5-FU)-resistant colon cancer cells exhibit oxidative stress, which induces histone methylation-related proteins and DNA demethylases, thereby contributing to 5-FU resistance. However, the role of histone methylation modifications in predicting liver metastasis and survival prognosis remains unexplored.
Histone acetylation and deacetylation in CRLM
Histone acetylation is regulated by a balance between histone acetyltransferases (HATs) and histone deacetylases (HDACs)62. Shen et al.63 compared acetylation patterns in metastatic liver tumors and primary CRC tissues, and identified 31 downregulated acetylation sites on 22 proteins and 40 upregulated acetylation sites on 32 proteins. The most significantly altered acetylated histones were HIST2H3AK19Ac and H2BLK121Ac, which provides new insights into CRC metastatis. HDAC6-dependent deacetylation of A-kinase-anchoring protein 12 (AKAP12) leads to its ubiquitin-mediated degradation and promotes colon cancer metastasis64. These studies suggest that histone acetylation and deacetylation are involved in CRC metastasis. However, liver metastasis-specific research has been limited.
CRLM transcriptomics
Less
Although each distinct cell type shares an identical genome in multicellular organisms, not all genes are transcriptionally active. Different cell types exhibit unique gene expression profiles. Transcriptomics classifies all types of transcripts, including mRNAs, ncRNAs, and miRNAs. The transcriptional structure of genes is determined by factors, such as the transcriptional start sites, 5′ and 3′ ends, splicing patterns, and other post-transcriptional modifications. Additionally, transcriptomics quantifies changes in the level of each transcript expression during development and under various conditions65.
Transcriptomic study on the occurrence and development of CRLM
Researchers have elucidated the mechanisms driving CRLM based on transcriptomic analysis, which has provided insights into potential diagnostic biomarkers and therapeutic targets. Sha et al.66 demonstrated that single-cell transcriptomic analysis can identify the potential cellular origin and driving factors of CRLM. Additionally, whole exome and RNA sequencing of CRC tumors and liver metastases revealed that copy number variations primarily follow several evolutionary patterns, such as clonal–clonal evolution 20q amplification (amp), 17p deletion (del), 18q del, 8p del, subclonal–clonal evolution (8q amp, 13q amp, and 8p del), and metastasis-specific evolution (8q amp).
These findings suggested a novel evolutionary process for CRLM67 and offer deeper insights into the mechanisms underlying liver metastasis through transcriptomics. The liver-specific transcription factors, FOXA2 and HNF1A, can reportedly bind to acquired enhancers and activate the transcription of liver-specific genes, thereby driving CRLM68. Cai et al.69 reported that polyclonal dissemination and transcriptional repressor GATA binding 1 (TRPS1) gene mutations may be key drivers of CRLM through single-cell exome sequencing. Hur et al.70 concluded that upregulated miR-885 and miR-122 and downregulated miR-10b in CRLM tissues suggests the potential as metastasis-specific biomarkers71. Furthermore, high expression of miR-20a is closely associated with the liver metastatic state of CRC72. Xu et al.73 found that circRNA_0001178 and circRNA_0000826 are significantly upregulated in CRLM tissues, suggesting that these circRNAs may serve as potential biomarkers for predicting CRLM.
Hematogenous dissemination is the primary route for CRLM, followed by two histopathologic growth patterns [sprouting angiogenesis and vessel co-option (VCO)]. Transcriptomic research has revealed distinct mechanisms for both metastatic patterns. Most liver metastases promote hematogenous spread through angiogenesis. Fleischer et al.74 found specific metabolic changes and WNT signaling activation in VCO-related metastatic cancer cells, providing potential therapeutic targets for VCO-based CRLM. Tumor pericytes (TPCs) play a pivotal role in this invasion. Li et al.75 used single-cell RNA sequencing (scRNA-seq) to identify 13 TPC subpopulations and a novel TCF21high “matrix–pericytes” phenotype, which promotes extracellular matrix stiffness, collagen remodeling, and metastasis. Rad et al.76 found that runt-related transcription factor-1 (RUNX1) overexpression in replacement lesions drives cancer cell motility in VCO-type CRLM.
Transcriptional differences between primary CRC lesions and liver metastases
Various researchers have compared the transcriptomic profiles of primary CRC lesions and liver metastases to determine transcriptional heterogeneity and cellular diversity. Liver metastases exhibit lower expression of miR-99b-5p, miR-377, miR-143, miR-10b, miR-28-5p, and miR-200c compared to primary tumor tissues, while miR-196b-5p, miR-122, miR-122*, and miR-885-5p are upregulated71,77. Four lncRNAs (GAS5, H19, MEG3, and Yiya) also exhibit significant differences78. Wang et al.79 identified increased CD8_CXCL13 and CD4_CXCL13 subpopulations in liver metastases, which are linked to better prognosis. Wang et al.79 also observed that distinct MCAM(+) fibroblasts in liver metastases may activate CD8 + CXCL13 cells through Notch signaling. Other studies showed that intestinal epithelial Notch1 signaling drives metastasis formation in genomic research14. This finding suggested a dual role of the Notch pathway in CRC progression and patient survival by regulating immune cells. These studies highlight gene expression differences between primary and metastatic CRC lesions.
Application of transcriptomics in CRLM treatment
Researchers have conducted mRNA expression profiling studies to analyze the correlation between gene expression and therapeutic strategies80. Simultaneously, differences in drug sensitivity can be detected at the transcriptomic level, potentially leading to the development of therapeutic agents based on variations in gene expression81. Transcriptomic sequencing of endothelial cells from CRLM and adjacent normal liver tissues revealed molecular differences, one of which was upregulation of the checkpoint kinase, WEE1. Subsequent experiments confirmed that WEE1 inhibitors could be promising therapeutic options for the treatment of CRLM82. Using single-cell transcriptomic analysis, Zhang et al.83 found that secreted protein acidic and rich in cysteine (SPARC) was significantly upregulated in CRLM. SPARC knockdown reduced CRC cell spheroid and colony formation, invasion, and migration. Sun et al.84 analyzed transcriptomic data from primary adjacent tissues, colorectal tumors, and metastases in patients, which revealed that SLITRK4 inhibition suppressed tumor growth and liver metastasis. Pharmacologic inhibition using lipid-polymer hybrid nanoparticles (NPs) to deliver siRNA effectively inhibited CRLM. A transcriptomic analysis is valuable for developing new therapeutic approaches for patients with CRC, with and without liver metastasis. However, current research on the application of transcriptomics in the treatment of liver metastasis is limited.
Application of transcriptomics in the prognostic analysis of CRLM
Transcriptomics and other methods have identified factors that are negatively correlated with CRLM prognosis, as follows:
1.

high exosomal CXCL10 expression is linked to poor prognosis and shorter progression-free survival85;

2.

overexpression of lncRNA-SNHG15 is associated with lower survival rates in patients with CRLM86;

3.

high miR-221 or miR-222 expression in cancer stroma correlates with liver metastasis, distant metastasis, and short overall survival87;

4.

miR-200c and miR-196b-5p expression are associated with short overall survival in patients with CRLM77;

5.

miR-125, miR-127, miR-145, miR-192, miR-194, miR-199-5p, miR-215, and miR-429 expression is associated with poor survival in liver metastases88;

6.

increased miR-210 and miR-133b expression correlates with reduced survival89; and

7.

high CIB1 expression is associated with poor progression-free survival and OS90.

Additionally, transcriptomic studies have identified the following factors that correlate with CRLM prognosis:
1.

low let-7i expression in primary CRC tissues predicts a worse prognosis70; and

2.

low miR-340 expression is linked to short 5-year disease-free survival and OS91.

Proteomic studies in CRLM
Less
The concept of a proteome was first proposed by Wilkins et al.92 in 1995. A proteome refers to the entire set of proteins expressed in the genome of a cell, tissue, or organism with the respective forms and functional states93. This chapter analyzes the occurrence and progression of liver metastasis in CRC from a proteomic perspective, highlighting the differences between primary colorectal tumors and metastatic liver lesions. We also discuss the implications of proteomic findings in guiding the diagnosis, treatment, and prognosis of CRC.
Proteomic studies on CRLM occurrence and progression
The development and progression of CRC are closely associated with the ECM, which is composed of various proteins, including collagen, proteoglycans, and glycoproteins. Proteomic analyses have identified upregulated collagen types (COL10A1, COL12A1, COL14A1, and COL15A1) in CRLM, which are also present in primary tumors, suggesting ECM alterations involved in tumor proliferation and liver metastasis94. In a study involving 3 recurrent liver metastases from 1 patient, Voß et al.95 identified 56 upregulated ECM-related proteins in the 3rd metastatic lesion, 26 of which were linked to poor prognosis in CRC. This finding indicated varying malignancy levels in liver metastases.
Epithelial-mesenchymal transition (EMT) is a prerequisite for metastasis. Thrombospondin-1 (THBS1) was shown to promote CRLM by facilitating EMT based on a proteomic analysis of primary CRC specimens96. Unstable epithelial/mesenchymal (E/M)-type tumor cells exhibit the strongest potential for liver metastasis. The proportion of E/M-type circulating tumor cells is associated with distant metastasis. Furthermore, EMT-related protein clusters (ERBB2, COL6A1, and CAVIN1) are likely implicated in EMT-mediated metastasis97.
Other studies utilizing proteomic approaches have identified key proteins associated with CRC and liver metastasis, including heat shock protein family D member 1 (HSPD1), eukaryotic translation elongation factor 1 gamma, heterogeneous nuclear ribonucleoprotein A2/B1, fibrinogen beta chain (FGB), Talin 1, adaptor related protein complex 2 subunit alpha 2, serrate RNA effector molecule homolog, apolipoprotein C3, phosphoglucomutase 598, filamin A-interacting protein 1-like (FILIP1L), and plasminogen (PLG)99, as well as overexpression of apolipoprotein E100. However, the relationship between these proteins and CRLM requires further validation.
Overall, studies employing proteomic approaches have unveiled the mechanisms underlying CRLM, which are important for advancing our understanding of the initiation and progression of liver metastasis.
Proteomic differences between CRLM and primary tumors
Retrospective studies have revealed molecular differences between primary and metastatic CRC lesions, including the CAM-EMT, RAS-RAF-MEK, PI3K-AKT-PTEN, TGFBRII/SMAD4, apoptotic, and transcriptional error regulation pathways. These findings suggest that overall protein marker expression profiles may exhibit significant differences101. Farhner et al.102 compared the proteomes of seven specimens from primary and liver metastatic CRC lesions and revealed several upregulated proteins in liver metastasis, particularly those involved in glucose metabolism, including pyruvate carboxylase, fructose-bisphosphate aldolase B, and fructose-1,6-bisphosphatase 1. Moreover, Kim et al.103 performed a proteomic analysis of primary and liver metastatic CRC lesions. Several proteins were significantly upregulated in liver metastases, including SERPINA1, APOA1, ITLN1, DES, DBI, SDHA, and CA1 1, as well as arginase and glutathione S-transferase A3104. Fahrner et al.102 discovered that liver metastases exhibit a loss of several structural proteins associated with muscle contraction and cell junction assembly, such as vinculin, synaptopodin, and filamin C, compared to primary lesions. This study highlighted the proteomic differences between primary and metastatic liver CRC, providing insights into the clinical challenges of treating liver metastases.
Application of proteomic analysis in diagnosing CRLM
Some studies have identified CRLM predictive factors using proteomic analyses. For example, immunoproteomic profiling of tumor tissues revealed that OLFM4, CD11b, and ITGA2 are overexpressed in both primary colon tumors and liver metastases, suggesting that OLFM4, CD11b, and ITGA2 are potential CRC biomarkers with autoantigenic properties105. Additionally, proteomic analysis of urine samples identified the following two promising peptides derived from type I collagen: AGPP(-OH)GEAGKP(-OH)GEQGVP(-OH)GDLGAP(-OH)GP; and KGNSGEP(-OH)GAPGSKGDTGAKGEP(-OH)GPVG. This study provided a technical reference for the non-invasive detection of CRLM106. Similarly, other researchers have found that the combination of collagen-derived urinary AGP peptides and serum CEA levels may significantly improve the detection of patients at high risk of CRLM107. Furthermore, based on a study of collagen biomarkers in urine, the following two naturally occurring peptides (NOPs) were identified: GPP [collagen α-1(I)]; and GND [collagen α-1(III)]. The development of a molecular model combining these NOPs with CEA has enhanced the sensitivity and specificity of diagnosing high-risk patients for CRLM108.
Application of proteomic analysis in treating CRLM
Proteomic analysis may aid in identifying effective individualized cancer therapeutic targets. Receptor tyrosine kinases (RTKs), which are cell surface receptors involved in the regulation of key biological pathways, are critical targets for cancer treatment. One study quantified RTKs using proteomics to assess the protein abundance of 21 RTKs and demonstrated significant differences between metastatic and normal liver tissues. RET kinase was the most abundant in non-tumor tissues (approximately 35%), while PGFRB was the most abundant RTK in tumors (approximately 47%)109, highlighting the potential of RTK as a therapeutic target for tyrosine kinase inhibition. Turtoi et al.110 found that the proteomic characteristics of different regions of CRC liver metastases vary. Among the antigens that can uniformly target the entire liver lesion, LTBP2, and IGFBI are particularly suitable for liver metastasis-targeted therapy. Wertenbroek et al.111 conducted thermal ablation treatment (either cryoablation or radiofrequency ablation) in patients with CRC with liver metastases, followed by proteomic analysis. The results indicated that radiofrequency ablation is superior to cryoablation, with longer survival and a lower incidence of complications. Therefore, proteomic-based phenotypic analysis provides valuable support for the localized treatment of CRLM.
Application of proteomic analysis in the prognosis of CRLM
Proteomic analysis has been shown to predict the prognosis of patients with liver metastases. Upregulation of gankyrin (a small molecular protein) is positively correlated with disease progression and liver metastasis in patients with CRC112. Additionally, positive expression of the polymeric immunoglobulin receptor (pIgR) is significantly associated with poor prognosis in patients with CRLM113. In patients with CRC and liver metastasis, an elevated level of extracellular vesicle protein is significantly correlated with shortened OS both before and after surgery114. Zhu et al.115 developed a decision-tree model using eight proteomic features (m/z 3315, 6637, 1207, 1466, 4167, 4210, 2660, and 4186) and demonstrated the potential value in predicting liver metastasis in patients undergoing radical CRC surgery. Kirana et al.116 showed that the proteins, HLAB, ADAMTS 2, LTBP 3, JAG 2, and NME 2, are significantly associated with tumor progression, vascular invasion, and distant liver metastasis based on differential protein expression analysis. Spatial mapping of the tumor proteome in CRLM revealed that three specific peptides (histone H4, hemoglobin subunit alpha, and inosine-5′-monophosphate dehydrogenase 2) and two unknown m/z values (1305.840 and 1661.060) are significantly elevated in patients with shorter survival. These molecules may serve as potential prognostic biomarkers117.
Overall, proteomic analysis to study CRLM can assist in the diagnosis, treatment, and improvement of prognosis. Proteomics can be used to identify new drug targets, enabling the design of novel therapies targeting the different pathways involved in CRLM.
Applications of metabolomics in the progression, treatment, and prognosis of CRLM
Less
Metabolomics involves qualitative and quantitative analyses of all metabolites within an organism to determine the relationships with physiologic or pathologic changes4. Metabolomics can identify potential biomarkers for CRLM, monitor metastatic progression, and assess treatment responses.
Serum metabolomic profiling can serve as a tool for distinguishing different stages of CRC. Notably, serum metabolomic features differ between patients with locally advanced CRC and patients with liver-only metastases. Furthermore, significant differences in serum metabolomic profiles were observed between patients with liver-only metastases and patients with extrahepatic metastases, indicating that serum metabolomics can be used for cancer staging and guiding treatment strategies118. The literature is replete with studies showing that metabolomics contributes to the non-invasive diagnosis and staging of CRC119,120. However, research on gut metabolomics with liver metastasis is limited and requires further investigation.
Numerous genes contribute to CRC progression through metabolite alterations and targeting these pathways can prolong survival in CRLM. Research has indicated that the EFNB2/EPHB4 axis increases cholesterol levels in CRLM with hypercholesterolemia linked to tumor growth. A high-cholesterol diet significantly shortens survival in mice, whereas blocking the EFNB2/EPHB4 axis extends the OS121,122. Metabolomic analysis suggests that chondroitin sulfate synthase 1 (CHSY1) gene activation promotes CD8+ T cell exhaustion via succinate metabolism, leading to liver metastasis. Artemisinin significantly inhibits CHSY1 and combined with anti-PD1 therapy, enhances treatment efficacy123. Aerobic glycolysis, which is a key metabolic pathway in tumor metabolism, alters pyruvate entry into mitochondria, which is targeted in cancer research124. Mitochondrial uncouplers, such as niclosamide ethanolamine (NEN) and oxyclozanide, show effective anticancer activity in mouse models of metastatic liver tumors124.
Metabolomics has also been explored in the treatment of liver metastasis in CRC. Metabolomic analysis of circulating tumor cells (CTCs) derived from CRLM revealed that the metabolite profiles and associated gene expression in CTC-derived metastatic tissue, particularly the specific upregulation of mRNAs involved in metabolic pathways, such as the folate one-carbon pool, folate biosynthesis, and histidine metabolism, differed significantly from those in parental cells. Additionally, tumors and metastatic sites derived from CTCs exhibit significantly higher invasiveness and chemoresistance than tumors and metastatic sites from cell lines. Therefore, identifying different metabolites could provide valuable insight into cancer progression and metastasis, thereby offering potential targets for treating metastatic CRC125. Mass spectrometry-based metabolomic analysis was used to characterize the metabolic profile of tumor-associated macrophages (TAMs), which showed that riboflavin is positively correlated with the morphology of larger TAMs (L-TAMs). Riboflavin regulates the expression and activity of lysine-specific demethylase 1A (LSD1) in L-TAMs and in vitro M2-polarized macrophages. The riboflavin-LSD1 axis influences TAM morphology in CRLM, making LSD1 a potential target for reprogramming TAM subtypes and offering new directions for anti-tumor therapy126.
Metabolomics is valuable for assessing the prognosis of liver metastases in CRC. Jonas et al.127 identified preoperative plasma metabolites, such as lysophosphatidylcholines (lysoPCs) and phosphatidylcholines (PCs), as potential predictors for postoperative disease recurrence in patients with CRLM. Costantini et al. 128 demonstrated that combined metabolic and lipidomic biomarkers help predict the prognosis of patients with metastatic CRC undergoing liver metastasis resection after induction therapy. Wu et al.129 categorized the metabolic phenotypes of CAFs within the TME and showed that scoring this classification could be used to evaluate chemotherapy sensitivity and survival prognosis in patients with liver metastases.
Applications of microbiomics in the diagnosis, treatment, progression, and prognosis of CRLM
Less
Microbiomics focuses on the structure and function of microbial communities, interactions within these communities, and relationships between microbes and their environment or host. Microbiomics also explores regulation of microbial community growth, metabolism, and cooperative processes4. The intestinal microbiota significantly contributes to waste degradation, nutrient transport, and gut homeostasis maintenance. Dysbiosis and ecological imbalance in the colon can lead to physiologic disorders130, including the development of CRC131,132.
Microbiomics has been extensively explored in the context of pathogenesis, diagnosis, treatment, and prognosis. However, the role of CRLM is the subject of active research. Na et al. 133 reported that an increase in Proteus mirabilis and a decrease in Bacteroides vulgatus significantly influence CRLM, potentially due to a reduction in Kupffer cells in the liver. Studies have shown that cytolethal distending toxin (CDT) produced by Campylobacter jejuni promotes CRC metastasis to the liver and lungs via the JAK2-STAT3-MMP9 signaling pathway134. However, the specific mechanisms linking the gut microbiome to CRLM, as well as potential applications in diagnosing and predicting liver metastasis, require further investigation.
Studies have shown that microbiomics can be applied in treating liver metastasis. Antibiotic therapy is a potential strategy for CRC. Studies have suggested that changes in the oral microbiota may affect treatment outcomes and prognosis of primary CRC and CRLM radiotherapy. Fusobacterium nucleatum (Fn) in the oral cavity may migrate and colonize CRC lesions, contributing to radiotherapy resistance. However, combining antibiotics [e.g., metronidazole (MTZ)] can prevent this resistance135. Fn in CRC liver metastatic tissues correlates with a lower density of CD8(+) T cells and more myeloid-derived suppressor cells. These findings offer critical insights for the development of microbiota- and immune-targeted therapeutic strategies aimed at preventing and treating colorectal cancer metastasis to the liver.136. Wang et al.137 reported that MTZ inhibits tumor growth and reduces CRLM malignancy by modulating gut Fn and altering microbiota composition. Additionally, augmenting the microbiome has potential therapeutic value. Huang et al. 138 found fecal microbiota transplantation enhances the efficacy of PD-1 blockade in murine models of colorectal cancer. Xu et al.139 developed a nanodrug-engineered bacterium (LR-S-CD/CpG@LNP) in which oral administration of CD/CpG@LNPs induced the production of cytotoxic reactive oxygen species (ROS) at the CRC tumor site through photothermal and photodynamic effects, triggering immunogenic cell death (ICD) in CRC cells. This finding offered a new strategy for oral combination therapy in CRLM. Therefore, gut microbiota transplantation may serve as an adjunctive therapy for advanced CRC, particularly for patients with liver metastasis, by potentially enhancing treatment efficacy through modulation of the TME (Figure 3).
Integrated application of multi-omics in the mechanisms, diagnosis, and treatment of CRLM
Less
The integration of multi-omic approaches, including genomics, transcriptomics, epigenomics, microbiomics, metabolomics, and proteomics, offers promise for enhancing biomarker sensitivity and addressing population heterogeneity (Figure 4). Chen et al.140 used genomics, proteomics, and phosphoproteomics to investigate CRC cohorts and found that metastatic tissues are genetically like primary tumors, while significant differences emerged at the proteomic level. Proteomic and phosphoproteomic analyses of primary tumors effectively distinguished metastatic cases with kinase network analysis revealing heterogeneity between primary tumors and liver metastases. Researchers analyzing multi-omics data (genome, epigenome, and transcriptome) from cfDNA and cfRNA samples observed that multi-omics approaches outperformed single-omic data in identifying cancer-associated genes. Additionally, plasma cfRNA was strongly correlated with tumor RNA for specific cancer- and immune-related features, suggesting that cfRNAs is a non-invasive approach for the clinical monitoring cancer progression and patient status141. Therefore, multi-omics research is crucial for advancing the diagnosis and treatment of CRC.
Numerous studies have used multi-omics approaches to demonstrate the differences and mechanisms underlying primary and metastatic lesions in CRLM. Ham-Karim et al.101 conducted an integrated multi-omics (genomics, transcriptomics, and proteomics) analysis and found that the mutation status of 54 of 60 loci was identical between primary and metastatic tumors, whereas 40 of 58 gene loci exhibited heterogeneity. Furthermore, miRNA expression profiles differed between primary and metastatic tumors. Integrated genomics, transcriptomics, and proteomics analysis identified BRCA 1 as 1 of 20 transcripts simultaneously upregulated in all 3 types of TME liver cells during CRLM. The most likely sequence of cellular activation during metastasis involves endothelial (Ito) followed by Kupffer cells. Immunohistochemical analysis of human liver metastases revealed BRCA1 protein co-localization with Ito, Kupffer, and endothelial cells in 81.8% of early or synchronous metastases. This finding suggested that BRCA1 co-expression in these cells may contribute to the early stages of CRLM, thus highlighting the potential of BRCA1 as a TME biomarker142.
Multi-omics analysis holds promise for elucidating the mechanisms underlying treatment resistance in CRC. Shi et al.143 revealed changes in genes and the microenvironment during chemotherapy (bevacizumab combined with chemotherapy) for CRLM based on multi-omics analysis. Shi et al.143 found that 92% of cases exhibited minimal change at the genomic level but significant differences at the transcriptomic level. Specifically, the SMAD4 R361H/C mutation mediates resistance to bevacizumab and 5-FU via the STAT3 signaling pathway. Combining the STAT3 inhibitor, GB201, with the initial treatment restored the therapeutic sensitivity of SMAD4 R361H/C mutated cancer cells. Thus, multi-omics analysis reveals resistance mechanisms during treatment and enables effective intervention.
Studies on genomics and transcriptomics as prognostic biomarkers for CRLM have shown that CTAG1A, CSTL1, FJX1, IER5L, and KLHL35 expression is higher in metastatic lesions than primary tumors. These genes may contribute to cancer cell metastasis. Specifically, FJX1 mRNA expression is elevated in CRC tissues at various T stages and in metastatic lymph nodes than in normal mucosa or lymphatic tissues. Gene expression analysis suggested that FJX1 regulates chromatin-modifying enzymes, the Notch signaling pathway, cellular senescence, and other signaling pathways. Therefore, FJX1 may be a crucial target for CRLM and a potential novel biomarker for its prediction and treatment144.
Multi-omics analysis aids in prognosis monitoring for patients with CRLM. Wong et al.145 used genomics and proteomics to accurately identify patients at risk of early relapse after curative treatment. Wong et al.145 found the somatic mutations most associated with early relapse were TP53 (88%), APC (71%), KRAS (38%), and SMAD4 (21%). Additionally, proteins associated with early relapse included calponin-2, versican core protein, glutathione peroxidase 3, fibulin-5, and amyloid-β precursor protein. Further analysis indicated that SMAD4, calponin-2, and glutathione peroxidase 3 may have predictive potential for early relapse, which aids in optimizing prognosis evaluation for CRLM.
Overall, multi-omics data related to tumor cells and tissues provide a more comprehensive understanding of the pathogenesis, diagnostic and therapeutic strategies, drug resistance mechanisms, and prognostic evaluation of the corresponding diseases compared to single-omics data. However, research applying multi-omicss approaches to CRLM is limited.
Summary and future directions
Less
Advances in genomics, transcriptomics, epigenomics, proteomics, metabolomics, and microbiomics have paved a solid foundation for precise oncologic research. CRLM is a key factor for poor prognosis in CRC. Applying omics approaches to CRLM can guide the diagnosis, treatment, and prognosis of advanced CRC. This strategy offers more effective management and improved survival outcomes for patients with liver metastasis (Table 1).
Proteomic-related research on CRLM has focused extensively on pathogenesis, diagnosis, metastatic vs. primary tumors, treatment, and prognosis. Future studies should explore clinical translation to provide more therapeutic options for patients with liver metastasis.
Current omics research on CRLM requires further basic studies to explore underlying mechanisms. Genomic studies mostly focus on differences between primary tumors and metastatic sites but miss mechanisms directly linked to liver metastasis onset and therapeutic strategies. Epigenomic research, especially on DNA methylation, lacks studies on treatments for CRLM. Research on histone methylation and demethylation is limited with few studies investigating specific mechanisms. Although transcriptomic studies on CRLM are abundant, progress in improving the diagnosis of liver metastasis is limited. Metabolomics research in CRC mainly targets lipid and gut microbiota metabolites, with few studies on liver metastasis. Microbiological research has focused on CRC initiation and progression, treatment, and prognosis. However, research into the role of the microbiota in CRLM has been minimal. These gaps in knowledge present significant opportunities for further exploration.
Funding
Less
  • This study was supported by grants from the Natural Science Foundation of Chongqing (Grant No. CSTB2024NSCQ-MSX0478).
References
Less
1.
Bray F,Laversanne M,Sung H,Ferlay J,Siegel RL,Soerjomataram I, et al.Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries.CA Cancer J Clin. 2024; 74: 229–63.
2.
Manfredi S,Lepage C,Hatem C,Coatmeur O,Faivre J,Bouvier AM.Epidemiology and management of liver metastases from colorectal cancer.Ann Surg. 2006; 244: 254–9.
3.
Gunderson LL,Jessup JM,Sargent DJ,Greene FL,Stewart A.Revised tumor and node categorization for rectal cancer based on surveillance, epidemiology, and end results and rectal pooled analysis outcomes.J Clin Oncol. 2010; 28: 256–63.
4.
Zhao J,Yang J.Multi-omics research for precision medicine.Beijing: Science Press; 2021.
5.
Ki DH,Jeung HC,Park CH,Kang SH,Lee GY,Lee WS, et al.Whole genome analysis for liver metastasis gene signatures in colorectal cancer.Int J Cancer. 2007; 121: 2005–12.
6.
Yamasaki M,Takemasa I,Komori T,Watanabe S,Sekimoto M,Doki Y, et al.The gene expression profile represents the molecular nature of liver metastasis in colorectal cancer.Int J Oncol. 2007; 30: 129–38.
7.
Wang HW,Yan XL,Wang LJ,Zhang MH,Yang CH,Wei L, et al.Characterization of genomic alterations in Chinese colorectal cancer patients with liver metastases.J Transl Med. 2021; 19: 313.
8.
Lin HM,Chatterjee A,Lin YH,Anjomshoaa A,Fukuzawa R,McCall JL, et al.Genome wide expression profiling identifies genes associated with colorectal liver metastasis.Oncol Rep. 2007; 17: 1541–9.
9.
Shiomi A,Kusuhara M,Sugino T,Sugiura T,Ohshima K,Nagashima T, et al.Comprehensive genomic analysis contrasting primary colorectal cancer and matched liver metastases.Oncol Lett. 2021; 21: 466.
10.
Hu CT,Wang JL,Hou T,Yan ZW,Zu LD,Fu GH, et al.Comprehensive mutation profiling of colorectal cancer patients with lung or liver metastasis by targeted next-generation sequencing.Technol Cancer Res Treat. 2023; 22: 15330338231185285.
11.
Diep CB,Parada LA,Teixeira MR,Eknaes M,Nesland JM,Johansson B, et al.Genetic profiling of colorectal cancer liver metastases by combined comparative genomic hybridization and G-banding analysis.Genes Chromosomes Cancer. 2003; 36: 189–97.
12.
Sayagués JM,Corchete LA,Gutiérrez ML,Sarasquete ME,Del Mar Abad M,Bengoechea O, et al.Genomic characterization of liver metastases from colorectal cancer patients.Oncotarget. 2016; 7: 72908–22.
13.
Muñoz-Bellvis L,Fontanillo C,González-González M,Garcia E,Iglesias M,Esteban C, et al.Unique genetic profile of sporadic colorectal cancer liver metastasis versus primary tumors as defined by high-density single-nucleotide polymorphism arrays.Mod Pathol. 2012; 25: 590–601.
14.
Jackstadt R,van Hooff SR,Leach JD,Cortes-Lavaud X,Lohuis JO,Ridgway RA, et al.Epithelial NOTCH signaling rewires the tumor microenvironment of colorectal cancer to drive poor-prognosis subtypes and metastasis.Cancer Cell. 2019; 36: 319–36.e7.
15.
Sathe A,Mason K,Grimes SM,Zhou Z,Lau BT,Bai X, et al.Colorectal cancer metastases in the liver establish immunosuppressive spatial networking between tumor-associated SPP1+ macrophages and fibroblasts.Clin Cancer Res. 2023; 29: 244–60.
16.
Rong D,Sun G,Zheng Z,Liu L,Chen X,Wu F, et al.MGP promotes CD8+ T cell exhaustion by activating the NF-κB pathway leading to liver metastasis of colorectal cancer.Int J Biol Sci. 2022; 18: 2345–61.
17.
Seth A,Amemiya Y,Cheung H,Hsieh E,Law C,Milot L.Delayed MRI enhancement of colorectal cancer liver metastases is associated with metastatic mutational profile.Cancer Genomics Proteomics. 2021; 18: 627–35.
18.
Wang X,Li N,Guo H,Yin X,Zheng Y.Correlation analysis of gene and radiomic features in colorectal cancer liver metastases.Comput Math Methods Med. 2022; 2022: 8559011.
19.
Margonis GA,Sasaki K,Kim Y,Samaha M,Buettner S,Amini N, et al.Tumor biology rather than surgical technique dictates prognosis in colorectal cancer liver metastases.J Gastrointest Surg. 2016; 20: 1821–9.
20.
Chan AKC,Mason JM,Baltatzis M,Siriwardena AK.Management of colorectal cancer with synchronous liver metastases: an inception cohort study (CoSMIC).Ann Surg Oncol. 2022; 29: 1939–51.
21.
Brudvik KW,Shindoh J.Limitations of molecular biomarkers in patients with resectable colorectal liver metastases.Chin Clin Oncol. 2019; 8: 48.
22.
Sun L,Hu H,Peng L,Zhou Z,Zhao X,Pan J, et al.P-cadherin promotes liver metastasis and is associated with poor prognosis in colon cancer.Am J Pathol. 2011; 179: 380–90.
23.
Liu S,Zhang Z,Wang Z,Li J,Shen L.Genome-wide CRISPR screening identifies the pivotal role of ANKRD42 in colorectal cancer metastasis through EMT regulation.IUBMB Life. 2024; 76: 803–19.
24.
Lu M,Zessin AS,Glover W,Hsu DS.Activation of the mTOR pathway by oxaliplatin in the treatment of colorectal cancer liver metastasis.PLoS One. 2017; 12: e0169439.
25.
Alves JM,Prado-López S,Tomás L,Valecha M,Estévez-Gómez N,Alvariño P, et al.Clonality and timing of relapsing colorectal cancer metastasis revealed through whole-genome single-cell sequencing.Cancer Lett. 2022; 543: 215767.
26.
Bolhuis K,van’t Erve I,Mijnals C,Delis-Van Diemen PM,Huiskens J,Komurcu A, et al.Postoperative circulating tumour DNA is associated with pathologic response and recurrence-free survival after resection of colorectal cancer liver metastases.EBioMedicine. 2021; 70: 103498.
27.
Wang D,Liu B,Zhang Z.Accelerating the understanding of cancer biology through the lens of genomics.Cell. 2023; 186: 1755–71.
28.
Weichenhan D,Lipka DB,Lutsik P,Goyal A,Plass C.Epigenomic technologies for precision oncology.Semin Cancer Biol. 2022; 84: 60–8.
29.
Patel SA,Vanharanta S.Epigenetic determinants of metastasis.Mol Oncol. 2017; 11: 79–96.
30.
Herman JG,Baylin SB.Gene silencing in cancer in association with promoter hypermethylation.N Engl J Med. 2003; 349: 2042–54.
31.
Weber M,Hellmann I,Stadler MB,Ramos L,Pääbo S,Rebhan M, et al.Distribution, silencing potential and evolutionary impact of promoter DNA methylation in the human genome.Nat Genet. 2007; 39: 457–66.
32.
Antequera F,Bird A.Number of CpG islands and genes in human and mouse.Proc Natl Acad Sci U S A. 1993; 90: 11995–9.
33.
Zhang C,Wang L,Jin C,Zhou J,Peng C,Wang Y, et al.Long non-coding RNA Lnc-LALC facilitates colorectal cancer liver metastasis via epigenetically silencing LZTS1.Cell Death Dis. 2021; 12: 224.
34.
Liu J,Li H,Sun L,Shen S,Zhou Q,Yuan Y, et al.Epigenetic alternations of microRNAs and DNA methylation contribute to liver metastasis of colorectal cancer.Dig Dis Sci. 2019; 64: 1523–34.
35.
Li W,Guo L,Tang W,Ma Y,Wang X,Shao Y, et al.Identification of DNA methylation biomarkers for risk of liver metastasis in early-stage colorectal cancer.Clin Epigenetics. 2021; 13: 126.
36.
Gai W,Ji L,Lam WKJ,Sun K,Jiang P,Chan AWH, et al.Liver- and colon-specific DNA methylation markers in plasma for investigation of colorectal cancers with or without liver metastases.Clin Chem. 2018; 64: 1239–49.
37.
Ebert MP,Mooney SH,Tonnes-Priddy L,Lograsso J,Hoffmann J,Chen J, et al.Hypermethylation of the TPEF/HPP1 gene in primary and metastatic colorectal cancers.Neoplasia. 2005; 7: 771–8.
38.
Ebert MP,Model F,Mooney S,Hale K,Lograsso J,Tonnes-Priddy L, et al.Aristaless-like homeobox-4 gene methylation is a potential marker for colorectal adenocarcinomas.Gastroenterology. 2006; 131: 1418–30.
39.
Hanski C,Riede E,Gratchev A,Foss HD,Böhm C,Klussmann E, et al.MUC2 gene suppression in human colorectal carcinomas and their metastases: in vitro evidence of the modulatory role of DNA methylation.Lab Invest. 1997; 77: 685–95.
40.
Lu CH,Wu WY,Lai YJ,Yang CM,Yu LC.Suppression of B3GNT7 gene expression in colon adenocarcinoma and its potential effect in the metastasis of colon cancer cells.Glycobiology. 2014; 24: 359–67.
41.
Tommasi S,Pinto R,Petriella D,Pilato B,Lacalamita R,Santini D, et al.Oncosuppressor methylation: a possible key role in colon metastatic progression.J Cell Physiol. 2011; 226: 1934–9.
42.
Ju HX,An B,Okamoto Y,Shinjo K,Kanemitsu Y,Komori K, et al.Distinct profiles of epigenetic evolution between colorectal cancers with and without metastasis.Am J Pathol. 2011; 178: 1835–46.
43.
Rodger EJ,Gimenez G,Ajithkumar P,Stockwell PA,Almomani S,Bowden SA, et al.An epigenetic signature of advanced colorectal cancer metastasis.iScience. 2023; 26: 106986.
44.
Orjuela S,Menigatti M,Schraml P,Kambakamba P,Robinson MD,Marra G.The DNA hypermethylation phenotype of colorectal cancer liver metastases resembles that of the primary colorectal cancers.BMC Cancer. 2020; 20: 290.
45.
Pulverer W,Kruusmaa K,Schönthaler S,Huber J,Bitenc M,Bachleitner-Hofmann T, et al.Multiplexed DNA methylation analysis in colorectal cancer using liquid biopsy and its diagnostic and predictive value.Curr Issues Mol Biol. 2021; 43: 1419–35.
46.
Chen S,Liu T,Bu D,Zhu J,Wang X,Pan Y, et al.Methylome profiling identifies TCHH methylation in CfDNA as a noninvasive marker of liver metastasis in colorectal cancer.FASEB J. 2021; 35: e21720.
47.
Wang Y,Wang S,Niu Y,Ma B,Li J.Data mining suggests that CXCL14 gene silencing in colon cancer is due to promoter methylation.Int J Mol Sci. 2023; 24: 16027.
48.
Tan M,Luo H,Lee S,Jin F,Yang JS,Montellier E, et al.Identification of 67 histone marks and histone lysine crotonylation as a new type of histone modification.Cell. 2011; 146: 1016–28.
49.
Kouzarides T.Chromatin modifications and their function.Cell. 2007; 128: 693–705.
50.
Tamagawa H,Oshima T,Shiozawa M,Morinaga S,Nakamura Y,Yoshihara M, et al.The global histone modification pattern correlates with overall survival in metachronous liver metastasis of colorectal cancer.Oncol Rep. 2012; 27: 637–42.
51.
Greer EL,Shi Y.Histone methylation: a dynamic mark in health, disease and inheritance.Nat Rev Genet. 2012; 13: 343–57.
52.
Ruthenburg AJ,Allis CD,Wysocka J.Methylation of lysine 4 on histone H3: intricacy of writing and reading a single epigenetic mark.Mol Cell. 2007; 25: 15–30.
53.
Klose RJ,Zhang Y.Regulation of histone methylation by demethylimination and demethylation.Nat Rev Mol Cell Biol. 2007; 8: 307–18.
54.
Kouzarides T.Histone methylation in transcriptional control.Curr Opin Genet Dev. 2002; 12: 198–209.
55.
Boonsanay V,Mosa MH,Looso M,Weichenhan D,Ceteci F,Pudelko L, et al.Loss of SUV420H2-dependent chromatin compaction drives right-sided colon cancer progression.Gastroenterology. 2023; 164: 214–27.
56.
Chen X,Xu M,Xu X,Zeng K,Liu X,Pan B, et al.METTL14-mediated N6-methyladenosine modification of SOX4 mrna inhibits tumor metastasis in colorectal cancer.Mol Cancer. 2020; 19: 106.
57.
Ding J,Zhang ZM,Xia Y,Liao GQ,Pan Y,Liu S, et al.LSD1-mediated epigenetic modification contributes to proliferation and metastasis of colon cancer.Br J Cancer. 2013; 109: 994–1003.
58.
Tamagawa H,Oshima T,Numata M,Yamamoto N,Shiozawa M,Morinaga S, et al.Global histone modification of H3K27 correlates with the outcomes in patients with metachronous liver metastasis of colorectal cancer.Eur J Surg Oncol. 2013; 39: 655–61.
59.
Gezer U,Yörüker EE,Keskin M,Kulle CB,Dharuman Y,Holdenrieder S.Histone methylation marks on circulating nucleosomes as novel blood-based biomarker in colorectal cancer.Int J Mol Sci. 2015; 16: 29654–62.
60.
Kang KA,Piao MJ,Ryu YS,Kang HK,Chang WY,Keum YS, et al.Interaction of DNA demethylase and histone methyltransferase upregulates Nrf2 in 5-fluorouracil-resistant colon cancer cells.Oncotarget. 2016; 7: 40594–620.
61.
Sasidharan Nair V,Toor SM,Taha RZ,Shaath H,Elkord E.DNA methylation and repressive histones in the promoters of PD-1, CTLA-4, TIM-3, LAG-3, TIGIT, PD-L1, and galectin-9 genes in human colorectal cancer.Clin Epigenetics. 2018; 10: 104.
62.
Zhang Y,Sun Z,Jia J,Du T,Zhang N,Tang Y, et al.Overview of histone modification.Adv Exp Med Biol. 2021; 1283: 1–16.
63.
Shen Z,Wang B,Luo J,Jiang K,Zhang H,Mustonen H, et al.Global-scale profiling of differential expressed lysine acetylated proteins in colorectal cancer tumors and paired liver metastases.J Proteomics. 2016; 142: 24–32.
64.
Deng Y,Gao J,Xu G,Yao Y,Sun Y,Shi Y, et al.HDAC6-dependent deacetylation of AKAP12 dictates its ubiquitination and promotes colon cancer metastasis.Cancer Lett. 2022; 549: 215911.
65.
Wang Z,Gerstein M,Snyder M.RNA-Seq: a revolutionary tool for transcriptomics.Nat Rev Genet. 2009; 10: 57–63.
66.
Sha Z,Gao Q,Wang L,An N,Wu Y,Wei D, et al.Investigating the cell origin and liver metastasis factors of colorectal cancer by single-cell transcriptome analysis.Onco Targets Ther. 2024; 17: 345–58.
67.
Li C,Xu J,Wang X,Zhang C,Yu Z,Liu J, et al.Whole exome and transcriptome sequencing reveal clonal evolution and exhibit immune-related features in metastatic colorectal tumors.Cell Death Discov. 2021; 7: 222.
68.
Teng S,Li YE,Yang M,Qi R,Huang Y,Wang Q, et al.Tissue-specific transcription reprogramming promotes liver metastasis of colorectal cancer.Cell Res. 2020; 30: 34–49.
69.
Cai J,Zhang W,Lu Y,Liu W,Zhou H,Liu M, et al.Single-cell exome sequencing reveals polyclonal seeding and TRPS1 mutations in colon cancer metastasis.Signal Transduct Target Ther. 2024; 9: 247.
70.
Hur K,Toiyama Y,Schetter AJ,Okugawa Y,Harris CC,Boland CR, et al.Identification of a metastasis-specific microRNA signature in human colorectal cancer.J Natl Cancer Inst. 2015; 107: dju492.
71.
Vychytilova-Faltejskova P,Pesta M,Radova L,Liska V,Daum O,Kala Z, et al.Genome-wide microRNA expression profiling in primary tumors and matched liver metastasis of patients with colorectal cancer.Cancer Genomics Proteomics. 2016; 13: 311–6.
72.
Makondi PT,Wei PL,Huang CY,Chang YJ.Development of novel predictive miRNA/target gene pathways for colorectal cancer distance metastasis to the liver using a bioinformatic approach.PLoS One. 2019; 14: e0211968.
73.
Xu H,Wang C,Song H,Xu Y,Ji G.RNA-Seq profiling of circular RNAs in human colorectal cancer liver metastasis and the potential biomarkers.Mol Cancer. 2019; 18: 8.
74.
Fleischer JR,Schmitt AM,Haas G,Xu X,Zeisberg EM,Bohnenberger H, et al.Molecular differences of angiogenic versus vessel co-opting colorectal cancer liver metastases at single-cell resolution.Mol Cancer. 2023; 22: 17.
75.
Li X,Pan J,Liu T,Yin W,Miao Q,Zhao Z, et al.Novel TCF21high pericyte subpopulation promotes colorectal cancer metastasis by remodelling perivascular matrix.Gut. 2023; 72: 710–21.
76.
Rada M,Kapelanski-Lamoureux A,Petrillo S,Tabariès S,Siegel P,Reynolds AR, et al.Runt related transcription factor-1 plays a central role in vessel co-option of colorectal cancer liver metastases.Commun Biol. 2021; 4: 950.
77.
Li W,Chang J,Tong D,Peng J,Huang D,Guo W, et al.Differential microRNA expression profiling in primary tumors and matched liver metastasis of patients with colorectal cancer.Oncotarget. 2017; 8: 35783–91.
78.
Kong H,Wu Y,Zhu M,Zhai C,Qian J,Gao X, et al.Long non-coding RNAs: novel prognostic biomarkers for liver metastases in patients with early stage colorectal cancer.Oncotarget. 2016; 7: 50428–36.
79.
Wang F,Long J,Li L,Wu ZX,Da TT,Wang XQ, et al.Single-cell and spatial transcriptome analysis reveals the cellular heterogeneity of liver metastatic colorectal cancer.Sci Adv. 2023; 9: eadf5464.
80.
Ji D,Song C,Li Y,Xia J,Wu Y,Jia J, et al.Combination of radiotherapy and suppression of tregs enhances abscopal antitumor effect and inhibits metastasis in rectal cancer.J Immunother Cancer. 2020; 8: e000826.
81.
Bruun J,Kryeziu K,Eide PW,Moosavi SH,Eilertsen IA,Langerud J, et al.Patient-derived organoids from multiple colorectal cancer liver metastases reveal moderate intra-patient pharmacotranscriptomic heterogeneity.Clin Cancer Res. 2020; 26: 4107–19.
82.
Webster PJ,Littlejohns AT,Gaunt HJ,Young RS,Rode B,Ritchie JE, et al.Upregulated WEE1 protects endothelial cells of colorectal cancer liver metastases.Oncotarget. 2017; 8: 42288–99.
83.
Zhang H,Zhuo C,Lin R,Ke F,Wang M,Yang C.Identification and verification of key genes in colorectal cancer liver metastases through analysis of single-cell sequencing data and TCGA data.Ann Surg Oncol. 2024; 31: 8664–79.
84.
Sun X,Zhang J,Dong B,Xiong Q,Wang X,Gu Y, et al.Targeting SLITRK4 restrains proliferation and liver metastasis in colorectal cancer via regulating PI3K/AKT/NFκB pathway and tumor-associated macrophage.Adv Sci (Weinh). 2025; 12: e2400367.
85.
Lee S,Park YS,Kim JH,Lim AR,Hyun MH,Kim B, et al.Identification of biomarkers associated with liver metastasis progression from colorectal cancer using exosomal RNA profiling.Cancers (Basel). 2022; 14: 4723.
86.
Huang L,Lin H,Kang L,Huang P,Huang J,Cai J, et al.Aberrant expression of long noncoding RNA SNHG15 correlates with liver metastasis and poor survival in colorectal cancer.J Cell Physiol. 2019; 234: 7032–9.
87.
Iida M,Hazama S,Tsunedomi R,Tanaka H,Takenouchi H,Kanekiyo S, et al.Overexpression of miR-221 and miR-222 in the cancer stroma is associated with malignant potential in colorectal cancer.Oncol Rep. 2018; 40: 1621–31.
88.
Pecqueux M,Liebetrau I,Werft W,Dienemann H,Muley T,Pfannschmidt J, et al.A comprehensive microRNA expression profile of liver and lung metastases of colorectal cancer with their corresponding host tissue and its prognostic impact on survival.Int J Mol Sci. 2016; 17: 1755.
89.
Ellermeier C,Vang S,Cleveland K,Durand W,Resnick MB,Brodsky AS.Prognostic microRNA expression signature from examination of colorectal primary and metastatic tumors.Anticancer Res. 2014; 34: 3957–67.
90.
Fan S,Fleischer JR,Dokshokova L,Böhme LS,Haas G,Schmitt AM, et al.High CIB1 expression in colorectal cancer liver metastases correlates with worse survival and the replacement histopathological growth pattern.Mol Ther Oncol. 2024; 32: 200828.
91.
Takeyama H,Yamamoto H,Yamashita S,Wu X,Takahashi H,Nishimura J, et al.Decreased miR-340 expression in bone marrow is associated with liver metastasis of colorectal cancer.Mol Cancer Ther. 2014; 13: 976–85.
92.
Wasinger VC,Cordwell SJ,Cerpa-Poljak A,Yan JX,Gooley AA,Wilkins MR, et al.Progress with gene-product mapping of the Mollicutes: mycoplasma genitalium.Electrophoresis. 1995; 16: 1090–4.
93.
The promise of proteomics.Nature. 1999; 402: 703.
94.
van Huizen NA,Coebergh van den Braak RRJ,Doukas M,Dekker LJM,IJzermans JNM,Luider TM.Up-regulation of collagen proteins in colorectal liver metastasis compared with normal liver tissue.J Biol Chem. 2019; 294: 281–9.
95.
Voß H,Wurlitzer M,Smit DJ,Ewald F,Alawi M,Spohn M, et al.Differential regulation of extracellular matrix proteins in three recurrent liver metastases of a single patient with colorectal cancer.Clin Exp Metastasis. 2020; 37: 649–56.
96.
Liu X,Xu D,Liu Z,Li Y,Zhang C,Gong Y, et al.THBS1 facilitates colorectal liver metastasis through enhancing epithelial-mesenchymal transition.Clin Transl Oncol. 2020; 22: 1730–40.
97.
Huang MS,Fu LH,Yan HC,Cheng LY,Ru HM,Mo S, et al.Proteomics and liquid biopsy characterization of human EMT-related metastasis in colorectal cancer.Front Oncol. 2022; 12: 790096.
98.
Yang W,Shi J,Zhou Y,Liu T,Li J,Hong F, et al.Co-expression network analysis identified key proteins in association with hepatic metastatic colorectal cancer.Proteomics Clin Appl. 2019; 13: e1900017.
99.
Ku X,Xu Y,Cai C,Yang Y,Cui L,Yan W.In-depth characterization of mass spectrometry-based proteomic profiles revealed novel signature proteins associated with liver metastatic colorectal cancers.Anal Cell Pathol (Amst). 2019; 2019: 7653230.
100.
Zhao Z,Zou S,Guan X,Wang M,Jiang Z,Liu Z, et al.Apolipoprotein E overexpression is associated with tumor progression and poor survival in colorectal cancer.Front Genet. 2018; 9: 650.
101.
Ham-Karim H,Negm O,Ahmad N,Ilyas M.Investigating genomic, proteomic, and post-transcriptional regulation profiles in colorectal cancer: a comparative study between primary tumors and associated metastases.Cancer Cell Int. 2023; 23: 192.
102.
Fahrner M,Bronsert P,Fichtner-Feigl S,Jud A,Schilling O.Proteome biology of primary colorectal carcinoma and corresponding liver metastases.Neoplasia. 2021; 23: 1240–51.
103.
Kim EK,Song MJ,Jung Y,Lee WS,Jang HH.Proteomic analysis of primary colon cancer and synchronous solitary liver metastasis.Cancer Genomics Proteomics. 2019; 16: 583–92.
104.
Bai X,Li SY,An P,Yu B,Cai HY.Proteome study of colorectal cancer genesis and hepatic metastasis.Zhonghua Yi Xue Za Zhi. 2005; 85: 2128–31.
105.
Yang Q,Bavi P,Wang JY,Roehrl MH.Immuno-proteomic discovery of tumor tissue autoantigens identifies olfactomedin 4, CD11b, and integrin alpha-2 as markers of colorectal cancer with liver metastases.J Proteomics. 2017; 168: 53–65.
106.
Bröker ME,Lalmahomed ZS,Roest HP,van Huizen NA,Dekker LJ,Calame W, et al.Collagen peptides in urine: a new promising biomarker for the detection of colorectal liver metastases.PLoS One. 2013; 8: e70918.
107.
Lalmahomed ZS,Bröker ME,van Huizen NA,Coebergh van den Braak RR,Dekker LJ,Rizopoulos D, et al.Hydroxylated collagen peptide in urine as biomarker for detecting colorectal liver metastases.Am J Cancer Res. 2016; 6: 321–30.
108.
van Huizen NA,van Rosmalen J,Dekker LJM,Coebergh van den Braak RRJ,Verhoef C,IJzermans JNM, et al.Identification of a collagen marker in urine improves the detection of colorectal liver metastases.J Proteome Res. 2020; 19: 153–60.
109.
Vasilogianni AM,Al-Majdoub ZM,Achour B,Peters SA,Rostami-Hodjegan A,Barber J.Proteomic quantification of receptor tyrosine kinases involved in the development and progression of colorectal cancer liver metastasis.Front Oncol. 2023; 13: 1010563.
110.
Turtoi A,Blomme A,Debois D,Somja J,Delvaux D,Patsos G, et al.Organized proteomic heterogeneity in colorectal cancer liver metastases and implications for therapies.Hepatology. 2014; 59: 924–34.
111.
Wertenbroek MW,Schepers M,Kamminga-Rasker HJ,Bottema JT,Muller Kobold AC,Roelofsen H, et al.Clinical outcome, proteome kinetics and angiogenic factors in serum after thermoablation of colorectal liver metastases.BMC Cancer. 2013; 13: 266.
112.
Wang C,Li X,Ren L,Ma C,Wu M,Liang W, et al.Gankyrin as potential biomarker for colorectal cancer with occult liver metastases.Front Oncol. 2021; 11: 656852.
113.
Liu F,Ye P,Bi T,Teng L,Xiang C,Wang H, et al.COLORECTAL polymeric immunoglobulin receptor expression is correlated with hepatic metastasis and poor prognosis in colon carcinoma patients with hepatic metastasis.Hepatogastroenterology. 2014; 61: 652–9.
114.
Lin K,Baenke F,Lai X,Schneider M,Helm D,Polster H, et al.Comprehensive proteomic profiling of serum extracellular vesicles in patients with colorectal liver metastases identifies a signature for non-invasive risk stratification and early-response evaluation.Mol Cancer. 2022; 21: 91.
115.
Zhu D,Zhong Y,Wu H,Ye L,Wang J,Li Y, et al.Predicting metachronous liver metastasis from colorectal cancer using serum proteomic fingerprinting.J Surg Res. 2013; 184: 861–6.
116.
Kirana C,Peng L,Miller R,Keating JP,Glenn C,Shi H, et al.Combination of laser microdissection, 2D-DIGE and MALDI-TOF MS to identify protein biomarkers to predict colorectal cancer spread.Clin Proteomics. 2019; 16: 3.
117.
Li CMY,Briggs MT,Lee YR,Tin T,Young C,Pierides J, et al.Use of tryptic peptide MALDI mass spectrometry imaging to identify the spatial proteomic landscape of colorectal cancer liver metastases.Clin Exp Med. 2024; 24: 53.
118.
Farshidfar F,Weljie AM,Kopciuk K,Buie WD,Maclean A,Dixon E, et al.Serum metabolomic profile as a means to distinguish stage of colorectal cancer.Genome Med. 2012; 4: 42.
119.
Coker OO,Liu C,Wu WKK,Wong SH,Jia W,Sung JJY, et al.Altered gut metabolites and microbiota interactions are implicated in colorectal carcinogenesis and can be non-invasive diagnostic biomarkers.Microbiome. 2022; 10: 35.
120.
Kim M,Vogtmann E,Ahlquist DA,Devens ME,Kisiel JB,Taylor WR, et al.Fecal metabolomic signatures in colorectal adenoma patients are associated with gut microbiota and early events of colorectal cancer pathogenesis.mBio. 2020; 11: e03186–19.
121.
Liu W,Chakraborty B,Safi R,Kazmin D,Chang CY,McDonnell DP.Dysregulated cholesterol homeostasis results in resistance to ferroptosis increasing tumorigenicity and metastasis in cancer.Nat Commun. 2021; 12: 5103.
122.
Xu C,Gu L,Kuerbanjiang M,Jiang C,Hu L,Liu Y, et al.Adaptive activation of EFNB2/EPHB4 axis promotes post-metastatic growth of colorectal cancer liver metastases by LDLR-mediated cholesterol uptake.Oncogene. 2023; 42: 99–112.
123.
Sun G,Zhao S,Fan Z,Wang Y,Liu H,Cao H, et al.CHSY1 promotes CD8+ T cell exhaustion through activation of succinate metabolism pathway leading to colorectal cancer liver metastasis based on CRISPR/CAS9 screening.J Exp Clin Cancer Res. 2023; 42: 248.
124.
Alasadi A,Chen M,Swapna GVT,Tao H,Guo J,Collantes J, et al.Effect of mitochondrial uncouplers niclosamide ethanolamine (NEN) and oxyclozanide on hepatic metastasis of colon cancer.Cell Death Dis. 2018; 9: 215.
125.
Li M,Wu S,Zhuang C,Shi C,Gu L,Wang P, et al.Metabolomic analysis of circulating tumor cells derived liver metastasis of colorectal cancer.Heliyon. 2023; 9: e12515.
126.
Soldani C,De Simone G,Polidoro MA,Morabito A,Franceschini B,Colombo FS, et al.Riboflavin-LSD1 axis participates in the in vivo tumor-associated macrophage morphology in human colorectal liver metastases.Cancer Immunol Immunother. 2024; 73: 63.
127.
Jonas JP,Hackl H,Pereyra D,Santol J,Ortmayr G,Rumpf B, et al.Circulating metabolites as a concept beyond tumor biology determining disease recurrence after resection of colorectal liver metastasis.HPB (Oxford). 2022; 24: 116–29.
128.
Costantini S,Di Gennaro E,Capone F,De Stefano A,Nasti G,Vitagliano C, et al.Plasma metabolomics, lipidomics and cytokinomics profiling predict disease recurrence in metastatic colorectal cancer patients undergoing liver resection.Front Oncol. 2022; 12: 1110104.
129.
Wu C,Yu S,Wang Y,Gao Y,Xie X,Zhang J.Metabolic-suppressed cancer-associated fibroblasts limit the immune environment and survival in colorectal cancer with liver metastasis.Front Pharmacol. 2023; 14: 1212420.
130.
Thomas S,Izard J,Walsh E,Batich K,Chongsathidkiet P,Clarke G, et al.The host microbiome regulates and maintains human health: a primer and perspective for non-microbiologists.Cancer Res. 2017; 77: 1783–812.
131.
Nakatsu G,Li X,Zhou H,Sheng J,Wong SH,Wu WK, et al.Gut mucosal microbiome across stages of colorectal carcinogenesis.Nat Commun. 2015; 6: 8727.
132.
Kwong TNY,Wang X,Nakatsu G,Chow TC,Tipoe T,Dai RZW, et al.Association between bacteremia from specific microbes and subsequent diagnosis of colorectal cancer.Gastroenterology. 2018; 155: 383–90.e8.
133.
Yuan N,Li X,Wang M,Zhang Z,Qiao L,Gao Y, et al.Gut microbiota alteration influences colorectal cancer metastasis to the liver by remodeling the liver immune microenvironment.Gut Liver. 2022; 16: 575–88.
134.
He Z,Yu J,Gong J,Wu J,Zong X,Luo Z, et al.Campylobacter jejuni-derived cytolethal distending toxin promotes colorectal cancer metastasis.Cell Host Microbe. 2024; 32: 2080–91.e6.
135.
Dong J,Li Y,Xiao H,Zhang S,Wang B,Wang H, et al.Oral microbiota affects the efficacy and prognosis of radiotherapy for colorectal cancer in mouse models.Cell Rep. 2021; 37: 109886.
136.
Sakamoto Y,Mima K,Ishimoto T,Ogata Y,Imai K,Miyamoto Y, et al.Relationship between fusobacterium nucleatum and antitumor immunity in colorectal cancer liver metastasis.Cancer Sci. 2021; 112: 4470–7.
137.
Wang M,Li Y,Yang X,Liu Z,Wang K,Gong D, et al.Effects of metronidazole on colorectal cancer occurrence and colorectal cancer liver metastases by regulating Fusobacterium nucleatum in mice.Immun Inflamm Dis. 2023; 11: e1067.
138.
Huang J,Zheng X,Kang W,Hao H,Mao Y,Zhang H, et al.Metagenomic and metabolomic analyses reveal synergistic effects of fecal microbiota transplantation and anti-PD-1 therapy on treating colorectal cancer.Front Immunol. 2022; 13: 874922.
139.
Xu H,Wang Y,Liu G,Zhu Z,Shahbazi MA,Reis RL, et al.Nano-armed limosilactobacillus reuteri for enhanced photo-immunotherapy and microbiota tryptophan metabolism against colorectal cancer.Adv Sci (Weinh). 2025; 12: e2410011.
140.
Li C,Sun YD,Yu GY,Cui JR,Lou Z,Zhang H, et al.Integrated omics of metastatic colorectal cancer.Cancer Cell. 2020; 38: 734–47.e9.
141.
Tao Y,Xing S,Zuo S,Bao P,Jin Y,Li Y, et al.Cell-free multi-omics analysis reveals potential biomarkers in gastrointestinal cancer patients’ blood.Cell Rep Med. 2023; 4: 101281.
142.
Gerovska D,Larrinaga G,Solano-Iturri JD,Márquez J,García Gallastegi P,Khatib AM, et al.An integrative omics approach reveals involvement of BRCA1 in hepatic metastatic progression of colorectal cancer.Cancers (Basel). 2020; 12: 2380.
143.
Shi M,Yang Y,Huang N,Zeng D,Mo Z,Wang J, et al.Genetic and microenvironmental evolution of colorectal liver metastases under chemotherapy.Cell Rep Med. 2024; 5: 101838.
144.
Zou J,Zhang H,Huang Y,Xu W,Huang Y,Zuo S, et al.Multi-omics analysis of the tumor microenvironment in liver metastasis of colorectal cancer identified FJX1 as a novel biomarker.Front Genet. 2022; 13: 960954.
145.
Wong GYM,Li J,McKay M,Castaneda M,Bhimani N,Diakos C, et al.Proteogenomic characterization of early intrahepatic recurrence after curative-intent treatment of colorectal liver metastases.J Proteome Res. 2024; 23: 4523–37.
Appendix
Less
Year 2025 volume 22 Issue 6
PDF
4
2
Cite this Article
BibTeX
Article Info
doi: 10.20892/j.issn.2095-3941.2025.0066
  • Receive Date：2025-02-11
  • Online Date：2026-11-07
  • Published：2025-06-15
Article Data
Affiliations
History
  • Received：2025-02-11
  • Accepted：2025-05-14
Funding
This study was supported by grants from the Natural Science Foundation of Chongqing (Grant No. CSTB2024NSCQ-MSX0478).
Affiliations
    1School of Medicine Chongqing University, Chongqing 400030, China
    2Department of Medical Oncology, Chongqing University Cancer Hospital, Chongqing 400030, China

Corresponding:

Correspondence to: Yan Li, E-mail:
References
Share
https://castjournals.cast.org.cn/joweb/cbm/EN/10.20892/j.issn.2095-3941.2025.0066
Share to
QR

Scan QR to access full text

Cite this article
BibTeX
Citations
表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
关闭全屏
  • BibTeX
  • EndNote
  • RefWorks
  • TxT
Links
Articles
Latest Articles
Most Read
Collections
Updates
Events
News
Multimedia
About
About Us
Contact
No. 86 Xueyuan South Road, Haidian District, Beijing
100081
010-62662699(Operation and Technical Support)
qkjq@cast.org.cn

Copyright © 2025 China Association for Science and Technology. All rights reserved. For all open access content, the relevant licensing terms apply.
Sponsored by the Office of the Leading Group for Cybersecurity and Informatization of CAST, and supported by Science and Technology Review Publishing House