Aug 2026· Discover Oncology· Vol 17· 0 citations· 35 references
Medicine
TL;DR
A four-gene hypoxia-related prognostic signature was established and successfully stratified patients into high- and low-risk groups and was associated with distinct metabolic and immune characteristics in HCC.
Abstract
This study aimed to develop and validate a hypoxia-related gene signature for prognostic assessment in hepatocellular carcinoma (HCC) and to explore its associated biological characteristics and immune features. In addition, a self-established human tissue cohort was used to verify the expression stability of the identified signature genes. The TCGA-LIHC cohort was used as the training set to identify differentially expressed hypoxia-related genes associated with overall survival. Least absolute shrinkage and selection operator (LASSO) regression and Cox regression analyses were subsequently applied to construct a prognostic model. The predictive performance of the model was externally validated using the GSE14520 and GSE116174 cohorts. Functional enrichment and immune microenvironment analyses were performed to characterize the biological differences between risk groups. Furthermore, quantitative real-time PCR (qRT-PCR) was conducted in a human tissue cohort, including normal liver tissues, adjacent non-tumorous tissues, and HCC tissues, to validate the expression patterns of the four signature genes (TMEM45A, PPARGC1A, EFNA3, and STC2). A four-gene hypoxia-related prognostic signature was established and successfully stratified patients into high- and low-risk groups. Patients in the high-risk group exhibited significantly poorer overall survival in both the training and validation cohorts. Functional enrichment analyses revealed that high-risk tumors were associated with activation of pathways related to cell-cycle progression, MYC targets, epithelial–mesenchymal transition, and metabolic reprogramming. Immune analyses demonstrated increased M0 macrophage infiltration and elevated expression of multiple immune checkpoint genes in the high-risk group. qRT-PCR validation further confirmed the differential expression patterns of the four signature genes in human HCC tissues. This four-gene hypoxia-related signature demonstrated robust prognostic performance across multiple independent cohorts and was associated with distinct metabolic and immune characteristics in HCC. qRT-PCR validation further supported the expression stability of the identified genes in human tissues. These findings provide a useful framework for prognostic stratification and future investigation of hypoxia-related biological mechanisms and therapeutic strategies in HCC.
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.
Yu-Xian Liu, Xing-Jie Chen, Junyuan Zhang et al.· International Journal of Mol...· 0 citations
A six-gene-fibrosis-based prognostic model based on six genes stratifies survival risk and correlates with immune features and drug sensitivity, but provides a preliminary framework requiring prospective clinical validation.
Yanyan Qiu, Cui Lv, Shu-Bo Ding· Clinical and Translational O...· 0 citations
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
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.
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
The proposed glutathione metabolism-related signature demonstrated prognostic value in both training and validation cohorts and was associated with immune characteristics, pathway enrichment patterns, genomic alterations, and tumor mutation burden in LUAD.
Z. Sheng, Si-Yu Chen, Su Chen· Cancer Informatics· 0 citations
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