Sci Rep. 2025 Jan 30;15(1):3843. doi: 10.1038/s41598-025-87444-3.

ABSTRACT

Stomach adenocarcinoma (STAD) is a common malignancy with high heterogeneity and a lack of highly precise treatment options. We downloaded the multiomics data of STAD patients in The Cancer Genome Atlas (TCGA)-STAD cohort, which included mRNA, microRNA, long non-coding RNA, somatic mutation, and DNA methylation data, from the sxdyc website. We synthesized the multiomics data of patients with STAD using 10 clustering methods, construct a consensus machine learning-driven signature (CMLS)-related prognostic models by combining 10 machine learning methods, and evaluated the prognosis models using the C-index. The prognostic relationship between CMLS and STAD was assessed using Kaplan-Meier curves, and the independent prognostic value of CMLS was determined by univariate and multivariate regression analyses. we also evaluated the immune characteristics, immunotherapy response, and drug sensitivity of different CMLS groups. The results of the multiomics analysis classified STAD into three subtypes, with CS1 resulting in the best survival outcome. In total, 10 hub genes (CES3, AHCYL2, APOD, EFEMP1, CYP1B1, ASPN, CPE, CLIP3, MAP1B, and DKK1) were screened and constructed the CMLS was significantly correlated with prognosis in patients with STAD and was an independent prognostic factor for patients with STAD. Using the CMLS risk score, all patients were divided into a high CMLS group and a low CMLS group. Patients in the low-CMLS group had better survival, more enriched immune cells, and higher tumor mutation load scores, suggesting better immunotherapy responsiveness and a possible "hot tumor" phenotype. Patients in the high-CMLS group had a significantly poorer prognosis and were less sensitive to immunotherapy but were likely to benefit more from chemotherapy and targeted therapy. In this study, 10 clustering methods and 10 machine learning methods were combined to analyze the multiomics of STAD, classify STAD into three subtypes, and constructed CMLS-related prognostic model features, which are important for accurate management and effective treatment of STAD.

PMID:39885324 | DOI:10.1038/s41598-025-87444-3

Cancer Lett. 2025 Mar 31;613:217502. doi: 10.1016/j.canlet.2025.217502. Epub 2025 Jan 24.

ABSTRACT

Recent advancements in multi-omics and big-data technologies have facilitated the discovery of numerous cancer prognostic biomarkers and gene signatures. However, their clinical application remains limited due to poor reproducibility and insufficient independent validation. Despite the availability of high-quality datasets, achieving reliable biomarker identification across multiple cohorts continues to be a significant challenge. To address these issues, we developed a comprehensive platform, SurvivalML, designed to support the discovery and validation of prognostic biomarkers and gene signatures using large-scale and harmonized data from 21 cancer types. Through SurvivalML, we identified DCLRE1B as a novel prognostic biomarker for hepatocellular carcinoma, with experimental confirmation of its role in promoting tumor progression. Additionally, we developed the Chinese glioblastoma prognostic signature (CGPS) and its simplified version, SCGPS, a three-gene model. Both demonstrated superior predictive performance compared to other glioblastoma signatures in our in-house cohort and five independent Chinese datasets. The SCGPS model was further validated in 109 clinical samples using multiplex immunofluorescence, showing strong consistency with the original CGPS model. Overall, SurvivalML provides a robust platform for the identification and validation of prognostic biomarkers and gene signatures, offering a valuable resource for advancing cancer research and clinical application.

PMID:39864538 | DOI:10.1016/j.canlet.2025.217502