[Development of a 36-gene signature for predicting recurrence in non-muscle-invasive bladder cancer and validation in a Chinese cohort].

Cross-Platform Gene Signature to Predict Survival Outcomes for Nasopharyngeal Carcinoma.

PURPOSE Nasopharyngeal carcinoma (NPC) is a multifactorial malignancy often diagnosed at an advanced stage due to nonspecific early symptoms. Accurate prognostic stratification is essential for individualized therapy but remains challenging because of biological and clinical heterogeneity. This study aimed to develop and validate a gene expression-based prognostic signature for locally advanced NPC. METHODS This retrospective biomarker investigation combined transcriptomic profiling and survival analyses. A prognostic model was constructed using LASSO-Cox regression in the discovery cohort (N = 99) from the multicenter, randomized phase III trial NPC-0501, and validated in independent cohorts (N = 133) from Queen Elizabeth Hospital (QEH), Hong Kong, and Sun Yat-sen University Cancer Center (SYS), Guangzhou, using different profiling methods. Mechanisms underlying the signature were explored using single-cell RNA-seq (scRNA-seq) data. The primary outcome was 3-year progression-free survival (PFS), with 5-year overall survival (OS) as a secondary end point. Performance was evaluated using hazard ratios (HRs) and survival probabilities (SP). RESULTS A 5-gene signature (SPP1, IL18BP, PALMD, WDR35, and SOCS6) predicted 3-year PFS in the NPC-0501 cohort (HR, 0.046 [95% CI, 0.011 to 0.190]; P < .001), separating high-risk from low-risk patients (SP, 42.9% v 96.0%; P < .0001). It predicted survival in both validation cohorts, with associations with 5-year OS in QEH (SP, 72.7% v 100%; P < .001) and 3-year PFS in SYS (SP, 70.5% v 93.2%; P = .0059). It outperformed a published metastasis-related model. Single-cell analyses showed SPP1 enrichment in M2 macrophages, linking high risk with an immunosuppressive tumor microenvironment. CONCLUSION A robust five-gene signature was established and validated for prognostic stratification of locally advanced NPC. Reproducibility across transcriptomic platforms and biological relevance support clinical application to guide personalized treatment.

LAG3 as an independent TME biomarker in Chinese colorectal cancer: Validation of a lung cancer-derived subtyping signature

Abstract Objective Commercially available next-generation sequencing (NGS) platforms in China routinely adopt a lung cancer-derived tumor microenvironment (TME) subtyping signature from a European cohort to classify colorectal cancer (CRC), yet its diagnostic performance in Chinese CRC patients remains unvalidated. This study aimed to evaluate the subtyping efficiency of the lung cancer TME signature in a Chinese CRC cohort, screen CRC-specific immune mRNA biomarkers for TME subtyping, and explore the clinical utility of IRF1, CD8A and LAG3 for distinguishing immune-enriched (IE) and immune-desert plus fibrotic (D+F) subtypes. Methods A total of 87 FFPE CRC specimens with complete NGS and clinicopathological data were retrospectively enrolled, including 15 IE subtype and 72 D+F subtype patients. Thirty-one mRNA transcripts covering 13 immune-metabolic homeostasis genes and 18 immune checkpoint/infiltration-related genes were divided into two functional modules. Spearman correlation analysis was performed to assess co-expression patterns among candidate genes. Receiver operating characteristic (ROC) curves combined with five-fold cross-validation were used to compare the discriminatory efficacy of single-gene markers and the three-gene combined panel. Results Strong positive co-expression was observed between IRF1, CD8A and LAG3 (IRF1-CD8A: r=0.93; IRF1-LAG3: r=0.84; CD8A-LAG3: r=0.73). Nominal P-values indicated elevated expression of IRF1, CD8A and LAG3 in IE subtype, though no intergroup significance remained after Benjamini-Hochberg FDR correction, largely attributed to the limited sample size of IE cases. Single-gene ROC analysis showed AUC values of 0.763 (IRF1), 0.752 (CD8A) and 0.771 (LAG3), with LAG3 exhibiting the best individual discriminatory capacity. The three-gene combined panel yielded a cross-validated AUC of 0.717, inferior to single LAG3, due to severe collinearity that generated redundant predictive information. Conclusions The lung cancer-originated TME subtyping system cannot be directly extrapolated to Chinese CRC patients. LAG3 serves as a promising independent transcriptomic candidate marker for distinguishing CRC TME subtypes. The robust collinearity among IRF1, CD8A and LAG3 eliminates additional predictive benefits of the combined signature. Large independent multi-center Chinese CRC cohorts are required to construct population-specific immune transcriptomic biomarkers for standardized clinical NGS TME stratification.

Development and Validation of an Immune-Related Prognostic Signature for Laryngeal Squamous Cell Carcinoma

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.

A Platform-Independent Binary Gene-Pair Signature Derived from CRPC-Enriched Single-Cell Transcriptomics for Predicting Recurrence-Free Survival in Prostate Cancer

Background Recurrence-free survival (RFS) following radical prostatectomy is a pivotal measure of therapeutic success in prostate cancer (PCa), yet conventional clinicopathological tools offer limited discriminative accuracy. We sought to construct a platform-independent prognostic signature to predict RFS by capturing early molecular traces of advanced disease potential. Methods Single-cell RNA sequencing data were analyzed to identify malignant epithelial subclusters and evaluate their compositional changes during the transition to castration-resistant prostate cancer (CRPC). We benchmarked 12 machine learning algorithms and 104 algorithmic combinations to develop a robust binary gene-pair signature in TCGA-PRAD cohort (n = 493) and validated in five external cohorts (n = 694). Downstream analyses included functional enrichment, immune and mutational profiling, drug sensitivity prediction and virtual knockouts. Results A 36-gene-pair signature was established, showing robust performance in predicting RFS across five external validation cohorts, with an average C-index of 0.725. Distinct signatures in signaling and metabolic processes were identified between the two risk groups through enrichment analysis. High-risk patients exhibited an immune-inflamed microenvironment with elevated TP53 mutation frequency and greater tumor mutational burden, and shared significant transcriptional similarities with responders to anti-PD-1 immunotherapy. These immunotherapy-related findings are hypothesis-generating and require prospective validation. Virtual knockout identified CKS2 as a risk-associated candidate gene linked to an androgen-responsive network, suggesting CKS2’s potential role in the molecular reprogramming associated with PCa progression. Conclusion The 36-gene-pair binary signature provides robust RFS risk stratification. High-risk individuals exhibit transcriptional similarity to reported anti-PD-1 therapy responders, and CKS2 emerges as a prognostic hub warranting validation.

A radiotherapy-related fibrosis gene signature-based risk model for predicting prognosis and immunological features in lung adenocarcinoma.

Cross-Cohort Evaluation of NanoString nCounter Data for Recurrence Prediction in Colorectal Cancer

Gene expression-based prognostic models have shown promise for predicting recurrence in colorectal cancer (CRC), but their clinical implementation remains limited. The NanoString nCounter platform provides a practical alternative to RNA sequencing and microarrays through standardized, cost-effective gene expression profiling that is compatible with routine clinical samples. In this study, we evaluated whether NanoString nCounter gene expression data improve prediction of recurrence following curative CRC surgery. Gene expression profiles from the NanoString PanCancer IO 360 panel were analyzed in two independent CRC cohorts (cohort A, n = 189; cohort B, n = 131). Differential gene expression analyses and Cox proportional hazards models were used to assess the prognostic value of gene expression alone and in combination with established clinical risk factors. Model performance was evaluated by five-fold cross-validation and external validation between cohorts using the concordance index (C-index) and Kaplan-Meier risk stratification. The two cohorts differed significantly in recurrence-free survival, and differential expression analysis demonstrated marked cohort-specific transcriptional patterns. Ninety-one recurrence-associated genes were identified in cohort A, whereas no significant genes were detected in cohort B, with poor agreement in gene-level differential expression between cohorts (Pearson r = 0.128). Across all prediction models, external performance was modest, and inclusion of gene expression data did not improve prediction beyond clinical variables. The clinical baseline model, incorporating age, UICC stage, and tumor site, consistently achieved the highest cross-cohort performance, with UICC stage emerging as the strongest predictor of recurrence. Although overall discrimination was moderate, the baseline model successfully stratified patients into significantly different high- and low-risk groups across cohorts. These findings indicate that prognostic gene expression signatures derived from NanoString data showed limited reproducibility across independent cohorts and provided little additional predictive value beyond established clinical factors. The results highlight the importance of external validation and suggest that robust clinical variables remain the most reliable predictors of recurrence risk in this setting.