Aug 2026· International Journal of Molecular Sciences· Vol 27· 0 citations· 71 references
Medicine
Abstract
Hepatocellular carcinoma (HCC) is one of the malignant tumors with high incidence and mortality rates worldwide. Given the poor prognosis of patients with HCC, it is crucial to explore the molecular mechanisms underlying HCC development and to evaluate prognostic markers. Differential expression analysis followed by univariate Cox, LASSO, and multivariate Cox regression identified four genes (EPO, SOCS2, IL18RAP, and KPNA2), and a Cox-based risk score was evaluated in the TCGA-LIHC cohort and externally in GSE14520 using Kaplan–Meier and time-dependent ROC analyses. Bulk, single-cell, and protein resources provided convergent expression context. Survival machine-learning analysis using observed overall-survival time and censoring status identified Cox–Ridge as the best-performing model in TCGA-LIHC, with more modest performance in GSE14520, and immune profiling revealed risk-group-associated differences in estimated immune and stromal components, immune-cell composition, and immune-checkpoint expression. The oncoPredict/GDSC2 screen highlighted five potential drug candidates for experimental prioritization. Because the drug screen is based on computationally predicted sensitivities, these findings should be regarded as hypothesis-generating and require validation in prospective cohorts and experimental systems before clinical translation.
A seven-gene immune-related prognostic signature that, combined with clinicopathological parameters, provides a robust tool for individualized survival prediction and may guide precision management in CRC patients is developed and validated.
ABSTRACT Hepatocellular carcinoma (HCC) is a leading cause of cancer-related deaths, its progression and treatment heterogeneity are mainly influenced by driver gene and tumor micro-environment (TME) interactions. Nevertheless, the mechanisms of this process at the single-cell level remain unclear. This study integrated TCGA and multi-center single-cell transcriptome data to identify a 575 genes HCC-specific core set, developing a single-cell “oncogene scoring” system to quantify individual carcinogenic activity. This score is significantly elevated in malignant and proliferative T cells and is closely associated with metabolic reprogramming, aberrant cell‒cell communication, and immunosuppressive phenotypes. Based on these characteristics, we constructed a machine learning-based Random Survival Forest (RSF) prognostic model validated in multiple independent cohorts, which classifies patients into distinct risk subtypes. The high-risk group exhibits genomic instability, increased tumor stemness, and immune evasion, while the low-risk group was more sensitive to drugs such as sorafenib. This study highlights the potential pathways by which high oncogenic activity is associated with HCC progression, suggesting a profound link with single-cell metabolic‒immune crosstalk. The constructed RSF model offers a promising computational framework for risk stratification and provides hypothesis-generating insights that may inform future personalized treatment strategies for HCC patients.
Background Hepatocellular carcinoma (HCC) demonstrates significant prognostic variability that is not entirely accounted for by traditional staging systems. Necrosis by sodium overload (NECSO) is an emerging programmed cell death pathway, but its clinical relevance in HCC remains undefined. Therefore, this study aimed to identify TRPM4-associated core genes, develop and validate a prognostic signature, and investigate its relationship with the tumor immune microenvironment, tumor mutational burden, and single-cell expression patterns in HCC. Methods We integrated transcriptomic, clinical, and mutational datasets from The Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA-LIHC) (n=421) and Gene Expression Omnibus (GEO) cohorts (n=115) to identify genes co-expressed with TRPM4—a key NECSO mediator—and those differentially expressed in HCC. A prognostic signature was developed using least absolute shrinkage and selection operator (LASSO)-Cox regression and validated through survival analysis, time-dependent receiver operating characteristic (ROC) curves, and multivariate Cox regression analysis. The immune landscape was characterized using CIBERSORT, somatic mutation data were used to calculate tumor mutational burden (TMB) and assess its correlation with the risk score, and single-cell RNA sequencing (scRNA-seq) resolved cell-type-specific expression patterns. Results From 294 TRPM4-associated core genes, we identified an 11-gene signature (BRSK1, MMP1, GRIN2D, GP6, MYOM2, N4BP3, CCDC112, TSEN54, MAP3K9, SPP1, B3GNT4) that independently predicted overall survival (OS) (hazard ratio =5.419, P<0.001) with areas under the curve (AUCs) of 0.779, 0.693, and 0.701 at 1, 3, and 5 years. These values were superior or comparable to conventional clinicopathologic variables after direct comparison. High-risk patients exhibited an immunosuppressive microenvironment, characterized by enrichment of M0 macrophage, a higher M2/M1 ratio (P<0.001) and distinct immune checkpoint profiles. When integrated with TMB, the prognostic stratification was further refined: high-TMB/high-risk patients had poorest outcomes (median OS, 15.3 months), while low-TMB/low-risk patients had the most favorable survival (median OS, 68.7 months). Single-cell analysis revealed that MMP1 was induced in cancer-associated fibroblasts (CAFs) and SPP1 was downregulated in macrophages, single-cell risk scores confirmed TAFs and macrophages as the main contributors to the prognostic model. Conclusions The TRPM4-centered 11-gene signature provides robust and independent prognostic stratification in HCC by integrating immune, mutational, and single-cell features. This signature serves as a potential tool for prognostic evaluation and may help inform immunotherapeutic strategies for HCC.
Jun-Ze Chen, Jia-Mei Li, Zhi-Yong Lin et al.· Journal of Gastrointestinal...· 0 citations
This approach establishes a biologically informed framework for stratifying recurrence risk based on RNA-seq data, potentially enhancing riskadapted surveillance and postoperative management for HCC.
Hyeran Shim, Hoang Bao Khanh Chu, Jiun Kim et al.· BMB Reports· 0 citations
LINC01615, a long noncoding RNA, plays a pivotal role in the progression of kidney renal clear cell carcinoma (KIRC). This study aimed to assess the prognostic value of LINC01615-associated genes in KIRC by developing a risk model. Differential expression analysis in the The Cancer Genome Atlas-KIRC dataset identified differentially expressed genes between high and low LINC01615 expression groups, as well as between KIRC and control groups. Signature genes were subsequently selected through protein-protein interaction (PPI) network analysis, while prognostic genes were identified via Cox regression. The risk model was then constructed and validated using the E-MTAB-1980 dataset. Furthermore, an independent prognostic analysis identified key risk factors, and a nomogram was created for clinical application. Additional analyses, including enrichment analysis, immune-related analysis, drug sensitivity evaluation, and regulatory network construction, were performed to explore the underlying mechanisms in high and low-risk groups. The GSE40435 dataset was employed for the validation of prognostic gene expression. Reverse transcription-quantitative PCR (RT-qPCR) was conducted to confirm the expression levels of prognostic genes and LINC01615 in clinical samples. LINC01615 expression was found to differ significantly between KIRC and control groups, with notable survival differences observed between high and low expression groups. A total of 757 candidate genes were identified. Among these, COL4A4, COL5A1, and COL15A1 were screened as prognostic genes, and a risk model with better accuracy was constructed. Age and risk score were recognized as independent risk factors, and the nomogram demonstrated enhanced predictive accuracy. Twelve drugs showed a significant negative correlation with risk scores. Additionally, the high-risk group exhibited an increased likelihood of immune escape. A regulatory relationship between hsa-miR-3163 and COL4A4/LINC01615 was identified. In both The Cancer Genome Atlas-KIRC and GSE40435 datasets, COL5A1 and COL15A1 were overexpressed in the KIRC group. RT-qPCR results for COL5A1 and COL4A4 were consistent with the above findings, while COL15A1 showed no significant differences in clinical samples, possibly due to the small sample size. COL4A4, COL5A1, and COL15A1 were identified as prognostic biomarkers through bioinformatics analysis. The developed risk model offers valuable insights for clinical prognostic prediction and immunotherapy in KIRC.
Shi-Bin Guo, Shuangqin Xu, Peng Song et al.· Medicine· 0 citations
Background Prostate cancer (PCa) is the second most prevalent malignancy in men worldwide, and accurate stratification of biochemical recurrence (BCR) risk remains challenging using conventional clinicopathological parameters alone. Identification of robust molecular biomarkers and integrated prognostic models is therefore of high clinical priority. Methods RNA-seq count data and clinical annotations for 554 TCGA-PRAD samples were obtained and normalized to log2(CPM+1). Weighted gene co-expression network analysis (WGCNA) identified co-expression modules correlated with Gleason score, PSA, and pathologic T stage. Protein-protein interaction (PPI) network analysis with CytoHubba topological scoring defined consensus hub genes. Four machine learning algorithms - LASSO Cox regression, random forest, SVM, and XGBoost - were applied to construct and validate a prognostic risk model. Immune cell infiltration was quantified and a prognostic nomogram was constructed and evaluated by decision curve analysis. Hub gene expression was experimentally validated by qRT-PCR and ELISA in prostate cancer and normal prostatic epithelial cell lines. Results Five hub genes - EZH2, CDK1, AURKA, TOP2A, and CCNB1 - were identified within the turquoise WGCNA module, which showed the strongest correlations with Gleason score (r = 0.78), PSA (r = 0.68), and pathologic T stage (r = 0.62). LASSO Cox regression and random forest consensus selected EZH2, CDK1, and AURKA for a three-gene risk score (Risk Score = 0.312xEZH2 + 0.285xCDK1 + 0.241xAURKA). High-risk patients demonstrated markedly inferior BCR-free survival (HR = 3.21, 95% CI: 2.05–5.03; log-rank P < 0.0001), with time-dependent AUCs of 0.821, 0.842, and 0.836 at 1, 3, and 5 years, respectively. Multivariate Cox regression confirmed the risk score as an independent prognostic factor (HR = 2.87; P < 0.001). A nomogram integrating the risk score with clinical parameters showed superior net benefit by decision curve analysis. Hub-high tumors exhibited reduced CD8+ T cell infiltration, elevated M2 macrophage abundance, and upregulated immune checkpoints (PD-L1, CTLA4, TIM-3, LAG3). All hub genes were confirmed overexpressed at both mRNA and protein levels in PCa cell lines by qRT-PCR and ELISA. Conclusion EZH2, CDK1, and AURKA constitute an internally validated prognostic risk signature in PCa that links cell cycle dysregulation to an immunosuppressive tumor microenvironment. This signature provides clinically actionable risk stratification and highlights candidate therapeutic targets in prostate cancer.
Gu-Quan Chen, Jie-Feng Zhang, Lin-Fu Zhao et al.· Frontiers in Genetics· 0 citations
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