Latest ArticlesErianin has potential anticancer activities, especially against lung cancer. The specific mechanisms underlying the anti-cancer effects, including the molecular targets and signaling pathways in lung cancer, remain poorly understood and necessitate further investigation.
Lung cancer cell viability was evaluated using the CCK-8 assay. Flow cytometry was used to examine the effects of erianin on apoptosis and cell cycle progression. mRNA sequencing and metabolomics analysis were utilized to explore erianin-induced biological changes. Potential targets were identified and validated through molecular docking and Western blot analysis. The roles of mammalian target of rapamycin (mTOR) and carbamoyl-phosphate synthetase/aspartate transcarbamylase/dihydroorotase (CAD) in erianin-induced growth inhibition were studied using gene overexpression/knockdown techniques with uridine and aspartate supplementation confirming pyrimidine metabolism involvement. Additionally, lung cancer-bearing nude mouse models were established to evaluate the anti-lung cancer effects of erianin in vivo.
Erianin significantly inhibits the proliferation of lung cancer cells, induces apoptosis, and causes G2/M phase cell cycle arrest. Integrative analysis of mRNA sequencing and metabolomics data demonstrated that erianin disrupts pyrimidine metabolism in lung cancer cells. Notably, uridine supplementation mitigated the inhibitory effects of erianin, establishing a connection between pyrimidine metabolism and anticancer activity. Network pharmacology analyses identified mTOR as a key target of erianin. Erianin inhibited mTOR phosphorylation, thereby blocking downstream effectors (S6K and CAD), which are essential regulators of pyrimidine metabolism.
Erianin is a promising therapeutic candidate for lung cancer. Erianin likely inhibits lung cancer cell growth by disrupting pyrimidine metabolism by suppressing mTOR activation.
The aim of the study was to evaluate the feasibility of functional MR in predicting the clinical response to chemotherapy in patients with colorectal liver metastases (CLM).
A total of 196 eligible patients were enrolled in the study between August 2016 and January 2023. Functional MR was performed at baseline and after one cycle of chemotherapy. The diffusion kurtosis radiomic texture features were extracted and a signature model was built using the R package. The initial 100 cases were designated as the training set, the following 48 cases were designated as the validation set, and the final 48 cases were designated as the intervention validation set.
Good performance for the response prediction (AUC = 0.818 in the training set and 0.755 in the validation set) was demonstrated. The objective response rates (ORRs) in the high-risk subgroup were significantly lower than the low-risk subgroup in the training and validation sets. Worse progression-free survival and overall survival rates were noted in the high-risk population. In the intervention set 22.9% (11/48) of the chemotherapy regimens for patients were changed in response to the model-predicted results and the ORR reached 77.1% (37/48), which was significantly higher than the training and validation sets [47.97% (71/148); P = 0.000].
A functional MR signature effectively predicted the chemotherapy response and long-term survival. The adjustment of the regimen guided by the model significantly improved the ORR.
Our previous studies have indicated potentially higher proliferative activity of tumor cells in Chinese patients with mantle-cell lymphoma (MCL) than those in Western. Given the success and tolerability of R-DA-EDOCH immunochemotherapy in treating aggressive B-cell lymphomas, we designed a prospective, phase 3 trial to explore the efficacy and safety of alternating R-DA-EDOCH/R-DHAP induction therapy for young patients with newly diagnosed MCL. The primary endpoint was the complete remission rate (CRR) at the end of induction (EOI).
A total of 55 patients were enrolled. The CRR at the EOI was 89.1% [95% confidence interval (CI) 78%–96%], and the overall response rate was 98.1% (95% CI 90%–100%). Most patients with bone marrow involvement quickly attained minimal residual disease (MRD) negative status, with a 95.7% rate at the EOI.
The 3-year progression-free survival (PFS) and overall survival rates were 66.3% and 83.2%, respectively. No patients discontinued treatment because of adverse events. Univariate analysis identified pathologic morphology and TP53 mutations as risk factors for PFS. However, high tumor proliferative activity and certain cytogenetic abnormalities showed no significant adverse prognostic significance.
Intensive therapy based on a high cytarabine dose and continuously administered EDOCH achieved a high MRD-negative rate and provides an optional induction choice for young patients with MCL with high-risk factors.
Artificial intelligence (AI) is significantly advancing precision medicine, particularly in the fields of immunogenomics, radiomics, and pathomics. In immunogenomics, AI can process vast amounts of genomic and multi-omic data to identify biomarkers associated with immunotherapy responses and disease prognosis, thus providing strong support for personalized treatments. In radiomics, AI can analyze high-dimensional features from computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography/computed tomography (PET/CT) images to discover imaging biomarkers associated with tumor heterogeneity, treatment response, and disease progression, thereby enabling non-invasive, real-time assessments for personalized therapy. Pathomics leverages AI for deep analysis of digital pathology images, and can uncover subtle changes in tissue microenvironments, cellular characteristics, and morphological features, and offer unique insights into immunotherapy response prediction and biomarker discovery. These AI-driven technologies not only enhance the speed, accuracy, and robustness of biomarker discovery but also significantly improve the precision, personalization, and effectiveness of clinical treatments, and are driving a shift from empirical to precision medicine. Despite challenges such as data quality, model interpretability, integration of multi-modal data, and privacy protection, the ongoing advancements in AI, coupled with interdisciplinary collaboration, are poised to further enhance AI’s roles in biomarker discovery and immunotherapy response prediction. These improvements are expected to lead to more accurate, personalized treatment strategies and ultimately better patient outcomes, marking a significant step forward in the evolution of precision medicine.