ArticleTranslational cancer research2026
Circadian-immune-related gene signature for lung squamous cell carcinoma: machine learning and multi-omics analysis.
Article in Translational cancer research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- External validation of a nomogram for de novo bone metastasis in breast cancer: a single-center Mexican cohort.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Article
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Authors and funding
9 authors.
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Abstract
Background: Circadian disruption promotes tumor progression and therapy resistance, but its prognostic value and impact on the tumor immune microenvironment in lung squamous cell carcinoma (LUSC) remain unclear. This study aimed to develop a circadian-immune-related gene signature to improve LUSC risk stratification and therapy guidance. Methods: We integrated multi-omics data from LUSC patients (n=494) across The Cancer Genome Atlas and Gene Expression Omnibus (GEO) database GSE73403 (n=69). A circadian-immune-related gene prognostic signature (CIGPS) was constructed from 1,677 circadian rhythm-related genes through weighted gene co-expression network analysis (WGCNA) and ten machine learning algorithms (StepCox + GBM optimized) and externally validated. Bioinformatics analyses assessed the tumor immune microenvironment, mutation landscape, and therapy response. Single-cell RNA sequencing data (GSE148071) explored gene expression at cellular resolution. Results: A six-gene CIGPS was developed, comprising Conclusions: The CIGPS serves as a potent prognostic tool that elucidates the heterogeneous tumor immune microenvironment in LUSC. It holds significant promise for guiding risk assessment and informing personalized therapeutic strategies based on distinct molecular subtypes.
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