Jul 2026· Journal of Clinical Medicine· Vol 15, pp. 5382· 0 citations· 37 references
Medicine
TL;DR
A novel three-gene immune-related prognostic signature comprising TNFRSF4, PPARG, and PDGFA provides insights into immune-related mechanisms in LSCC, presenting potential targets for therapeutic intervention.
Abstract
Background: Laryngeal squamous cell carcinoma (LSCC) is a highly aggressive malignancy with poor prognosis, particularly in advanced stages. While traditional treatments have improved survival rates, reliable biomarkers for prognosis remain limited. Methods: We analyzed RNA-seq data of LSCC patients from the Cancer Genome Atlas (TCGA) and validated the results using the Gene Expression Omnibus (GEO) dataset (GSE27020), clinical samples, and LSCC cell lines. Differentially expressed immune-related genes (DEIRGs) were identified using the “limma” R package. A prognostic signature was developed by integrating univariate Cox analysis, least absolute shrinkage and selection operator (LASSO) regression, and multivariate Cox analysis. The signature’s predictive performance was validated using Kaplan–Meier survival analysis and receiver operating characteristic (ROC) curves. Results: A three-gene immune-related prognostic signature comprising TNFRSF4, PPARG, and PDGFA was established. In the training cohort, the model stratified patients into high- and low-risk groups with significantly different overall survival (HR = 5.81, 95% CI: 2.56–13.22, p < 0.001), with apparent 1-, 2-, and 3-year AUC values of 0.838, 0.895, and 0.947, respectively. Predictive performance was further evaluated in the TCGA testing cohort, the full TCGA cohort, and the GSE27020 cohort. Functional enrichment analysis revealed that the signature genes are involved in immune regulation and tumor progression. Conclusions: This study identified and validated a novel three-gene immune-related prognostic signature for LSCC, offering a practical tool for individualized prognosis and personalized treatment strategies. The signature provides insights into immune-related mechanisms in LSCC, presenting potential targets for therapeutic intervention.
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.
Yuxian Liu, Xing-Jie Chen, Junyuan Zhang et al.· International Journal of Mol...· 0 citations
: Backgrounds: Colorectal cancer (CRC) prognosis remains difficult due to molecular heterogeneity and interaction between tumor cells and the immune microenvironment. This study aimed to identify transcriptomic and immune-cell patterns associated with overall survival (OS) and to develop an integrated prognostic model to improve risk stratification. Methods: RNA-sequencing was performed on 131 primary CRC samples and matched normal tissues. Differentially expressed genes (DEGs) were identified and functionally characterized through gene ontology, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment, and protein–protein interaction (PPI) network analysis. Immune-cell composition was estimated using CIBERSORTx deconvolution and evaluated for its association with OS. Prognostic DEGs were screened using univariate Cox regression and Least Absolute Shrinkage and Selection Operator (LASSO) analysis to construct a risk score. The model was validated in the Cancer Genome Atlas Database (TCGA)-COAD (colon cancer) and READ (rectal cancer) cohorts. A nomogram integrating molecular and clinicopathological variables were generated. Results: A total of 5589 DEGs were identified between CRC and normal tissues, enriched in pathways related to cell cycle, Tumor Protein P53 (TP53), WNT Family Member (WNT), Janus kinase/signal transducer and activator of transcription 3 (JAK/STAT), calcium signaling, metabolism, and immune regulation. PPI analysis highlighted ten upregulated hub genes involved in mitotic spindle formation and chromosomal stability. Immune infiltration analysys indicated that higher proportions of plasma cells ( p = 6.9 × 10 − 4 ), naïve B cells ( p = 0.019), resting CD4 + memory T cells ( p = 0.02), M0 macrophages ( p = 0.0077), and activated dendritic cells ( p = 1.89 × 10 − 5 ) were associated with improved OS, whereas monocytes ( p = 0.012), neutrophils ( p = 0.041), activated mast cells ( p = 0.0066), and M2 macrophages ( p = 0.014) were linked to poorer survival. A seven-gene signature including aspartate beta-hydroxylase (ASPH), bradykinin receptor B1 (BDKRB1), calcium voltage-gated channel auxiliary subunit beta 1 (CACNB1), C-C motif chemokine receptor 8 (CCR8), cyclic nucleotide gated channel subunit alpha 3 (CNGA3), microtubule associated protein 1A (MAP1A) and oxytocin receptor (OXTR) stratified patients into high-and low-risk groups with significant OS differences. The model demonstrated strong predictive performance (AUC: 0.84 at 1 year) and was validated in TCGA cohorts. Multivariate analysis confirmed the risk score as an independent prognostic factor. The integrated nomogram accurately predicted 1, 3-, and 5-year survival (C-index = 0.757; 95% CI 0.724–0.791). Conclusions: We developed and validated 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.
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, Shubo Ding· Clinical and Translational O...· 0 citations
Hypoxia-associated transcription is an important component of the esophageal squamous cell carcinoma (ESCC) microenvironment. We developed an exploratory four-gene signature and evaluated its prognostic performance, internal stability, cross-cohort transferability, and biological context.
TCGA, GTEx, and GEO transcriptomic data were analyzed. A four-gene signature was derived using univariate Cox screening, 10-fold cross-validated LASSO Cox regression, and stepwise multivariable Cox regression. Internal validation used 1,000 bootstrap resamples, optimism correction, and out-of-bag calibration. Fixed-coefficient external survival testing was conducted in GSE53624 and GSE53622. We additionally audited clinical heterogeneity across cohorts, performed exploratory continuous-score subgroup Cox analyses and interaction tests, and synthesized 16 reviewer-directed hypoxia-associated pathways using the original database-wide FDR values. TCGA-only differential-expression sensitivity, paired GSE23400 expression, patient/sample-level GSE160269 single-cell, immune functional scoring, and exploratory in silico drug-sensitivity analyses were also performed.
The signature comprised ATF3, EGFR, KDELR3, and PNRC1 in 91 ESCC patients. High-risk patients had poorer overall survival (log-rank
P
= 0.0031), but the complete feature-selection pipeline was unstable under bootstrap resampling. Apparent 1-, 3-, and 5-year AUCs were 0.730, 0.696, and 0.567; optimism-corrected AUCs were 0.691, 0.657, and 0.546, respectively, and the corrected C-index was 0.660. Direct application in GSE53624 (
n
= 119) and GSE53622 (
n
= 60) did not reproduce derivation-cohort performance. TNM-stage and follow-up distributions differed across cohorts, and TCGA treatment data were unavailable. Exploratory subgroup analyses did not demonstrate consistent external performance. Focused GSEA identified heterogeneous directions across hypoxia-adjacent inflammatory, angiogenic, metabolic, and redox processes.
The four-gene signature showed moderate short-term discrimination in the derivation cohort but weak long-term performance, selection instability, and limited cross-cohort reproducibility. It should be regarded as a hypothesis-generating research signature rather than a platform-independent or clinically deployable model, and requires prospective multicenter and experimental validation.
Yubo Liu, Surina Wu, Gang Wu· Discover Oncology· 0 citations
Keratins (KRTs) are intermediate filament proteins expressed in epithelial cells and serve as diagnostic cancer biomarkers. They serve pivotal roles in tumor progression and metastasis, but their prognostic value in lung adenocarcinoma (LUAD) and relationship with the tumor immune microenvironment remain unclear. Gene expression of KRTs in The Cancer Genome Atlas (TCGA)-LUAD was analyzed; candidate genes were selected using LASSO and random forest. An XGBoost classifier with SHAP interpretation distinguished tumor from normal tissues. Prognostic models were developed using 101 algorithms with 10-fold cross-validation and validated in two independent GEO cohorts. Immune cell composition and pathway activity were assessed by CIBERSORT, MCP-counter, ssGSEA and GSEA; the TIDE score estimated potential immunotherapy response. Functional validation of KRT81 was performed via shRNA knockdown in LUAD cell lines, followed by proliferation, apoptosis and migration assays. A KRT-based diagnostic and prognostic signature was established. The XGBoost model achieved high accuracy (TCGA AUC=0.996; GSE31210 AUC=0.860); SHAP identified KRT81 as a key contributor. A four-gene prognostic model (KRT27, KRT80, KRT16, KRT81) stratified patients into high- and low-risk groups. The risk score was associated with advanced tumor stage and independently predicted overall survival (multivariate HR=2.16, 95% CI 1.43–3.28, P=2.0×10-4). High-risk tumors enriched proliferation/stroma pathways (cell cycle, DNA repair, ECM-receptor interaction, focal adhesion, p53 signaling); low-risk tumors enriched immune pathways. Immune profiling revealed reduced T/B cell infiltration, increased endothelial cells and higher T-cell exclusion scores in high-risk patients, indicating an immunosuppressive microenvironment. Higher risk scores also associated with chemotherapy resistance. Functional validation confirmed that shRNA-mediated KRT81 knockdown reduced LUAD cell proliferation, migration and invasion, while promoting apoptosis. These findings link KRT expression to clinical outcomes, the tumor microenvironment and therapeutic response in LUAD, suggesting roles for KRTs in cancer progression, chemotherapy resistance and predictive potential for immunotherapy response. This study provides new insights for prognostic evaluation and therapeutic decision-making in LUAD.
Aohui Chen, Ting Gao, Fengqi Liu et al.· Oncology Letters· 0 citations
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.
Chengting Wu, Yuanqin Du, Juhong Jia et al.· Discover Oncology· 0 citations