ArchiveProstate cancer is a significant global health issue with inflammation emerging as a critical driver of progression. The prostate tumor microenvironment (TME) is comprised of tumor cells, mesenchymal stem cells, immune cells, cancer-associated fibroblasts, adipocytes, and the extracellular matrix. All of these TME components interact via soluble factors, such as growth factors, cytokines, and chemokines. These interactions remodel the TME and drive inflammation and tumor progression. Prolonged inflammation leads to dysregulated activation and infiltration of immune cells in the TME. This process maintains an immunosuppressive environment and facilitates epithelial-to-mesenchymal transition, migration, and invasion. Chronic inflammation causes inflammatory mediators to enter the circulation over time, as evidenced by systemic biomarkers, such as the systemic immune-inflammation index, which links inflammation to disease severity. Interactions between the prostate gland and adipose tissues further exacerbate systemic inflammation. Inflammation in the prostate gland confers resistance to therapy, primes distant metastatic niches, and promotes metastatic spread, resulting in poor clinical outcomes. Therapeutic strategies, such as anti-inflammatory agents and immunotherapies, hold promise in mitigating disease burden. This review explored the immune landscape of systemic inflammation in prostate cancer, discussed the role of the immune landscape in resistance to therapy and metastasis, and offered insights into potential interventions for targeting inflammation to limit prostate cancer burden.
Organoids are three-dimensional stem cell-derived models that offer a more physiologically relevant representation of tumor biology compared to traditional two-dimensional cell cultures or animal models. Organoids preserve the complex tissue architecture and cellular diversity of human cancers, enabling more accurate predictions of tumor growth, metastasis, and drug responses. Integration with microfluidic platforms, such as organ-on-a-chip systems, further enhances the ability to model tumor-environment interactions in real-time. Organoids facilitate in-depth exploration of tumor heterogeneity, molecular mechanisms, and the development of personalized treatment strategies when coupled with multi-omics technologies. Organoids provide a platform for investigating tumor-immune cell interactions, which aid in the design and testing of immune-based therapies and vaccines. Taken together, these features position organoids as a transformative tool in advancing cancer research and precision medicine.
Hepatocellular carcinoma (HCC), a highly aggressive liver cancer, poses a large medical care burden worldwide. The prognosis of patients with HCC is poor, owing to recurrence and metastasis after common treatment methods. Therefore, identifying new targets to eliminate HCC cells is critical for treatment of HCC without recurrence. PANoptosis, a novel inflammatory cell death pathway, has become an intensively investigated area in recent years. The concept of PANoptosis has brought new hope for HCC therapy, given recent evidence implicating this form of programmed cell death in cancer progression, prognosis, and resistance to chemotherapy and immunotherapy. Despite increasing reviews describing the role of PANoptosis in various cancer types, to our knowledge, no systematic review has examined the implications of PANoptosis in HCC. Therefore, we sought to provide the first systematic review of the regulatory mechanisms and therapeutic potential of PANoptosis in HCC. We summarize recent progress in exploration of the role of PANoptosis in HCC, particularly regulation of the HCC tumor microenvironment by PANoptosis. Finally, we highlight the potential of PANoptosis-based diagnostic and therapeutic strategies for HCC.
The key molecular events signifying the Helicobacter pylori-induced gastric carcinogenesis process are largely unknown.
Bulk tissue-proteomics profiling were leveraged across multi-stage gastric lesions from Linqu (n = 166) and Beijing sets (n = 99) and single-cell transcriptomic profiling (n = 18) to decipher key molecular signatures of H. pylori-related gastric lesion progression and gastric cancer (GC) development. The association of key proteins association with gastric lesion progression and GC development were prospectively studied building on follow-up of the Linqu set and UK Biobank (n = 48,529).
Concordant proteomics signatures associated with H. pylori infection and gastric carcinogenesis (ρ = 0.784, correlation P = 1.80 × 10−36) were identified. RNA expression of genes encoding 13 up- and 15 down-regulated key proteins displayed trending alterations in the transition from normal gastric epithelium to intestinal metaplasia, then to malignant cells. A 15-tissue protein panel integrating these signatures demonstrated potential for targeting individuals at high risk for progressing to gastric neoplasia (OR = 7.22, 95% CI: 1.31–39.72 for the high-score group). A 4-circulating protein panel may be used as non-invasive markers predicting the risk of GC development (hazard ratio = 3.73, 95% confidence interval: 1.63–8.54, high-risk vs. low-risk populations, area under the curve = 0.75).
Concordant proteomics signatures associated with H. pylori infection and gastric carcinogenesis were unveiled with potential as biomarkers for targeted prevention strategies.
Hyper-progression recurrence (HPR) after hepatectomy is a specific recurrence pattern associated with extremely poor prognosis in patients with hepatocellular carcinoma (HCC). This study was aimed at investigating the probable risk factors and establishing comprehensive models for formulating clinical strategies.
Overall, 16,158 patients with HCC from 8 hospitals were screened, among whom 3,125 patients who underwent R0 resection were included, and divided into development (n = 2,113) and validation (n = 1,012) cohorts. A comprehensive study of HPR predictive models and biological features was conducted.
Among the 3,125 enrolled patients, 506 (16.19%) developed HPR. The influence of HPR on extremely poor prognosis was reflected by recurrence features, adverse effects on systemic and liver function, and limited therapeutic options. Nine variables closely associated with HPR were identified, and incorporated into nomogram and conditional inference tree models, which successfully achieved pre- and post-operative HPR risk stratification and facilitated clinical decision-making. Multi-dimensional verification also confirmed the predictive accuracy of model combinations and their reliability in clinical applications. Furthermore, biological analyses revealed that HCCs with HPR exhibited hyperactive biological processes, inactive metabolism, and immune exhaustion features, together with high MYCN/HMGA2 co-expression, thereby enhancing understanding of the molecular events leading to HPR and providing valuable knowledge for HPR management.
HPR after hepatectomy is associated with extremely poor prognosis and requires substantial attention. We constructed comprehensive predictive models and propose a clinical strategy for guiding HPR prevention and management.