ArticleAging2024
The integration of multi-omics analysis and machine learning for the identification of prognostic assessment and immunotherapy efficacy through aging-associated genes in lung cancer.
Article in Aging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Identification of super-enhancer-based biomarkers for predicting survival and immunotherapy efficacy in colorectal cancer.Journal of Cancer · 2026Article
- Biological organ ages associate with risk of chronic diseases in a community-based population.JCI insight · 2025Article
- Immunosenescence and cancer: molecular hallmarks, tumor microenvironment remodeling, and age-specific immunotherapy challenges.Journal of hematology & oncology · 2025Review
- Multiomics of Aging and Aging-Related Diseases.International journal of molecular sciences · 2024Review
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Authors and funding
6 authors.
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Abstract
backgroundRecent years revealed key molecules in lung cancer research, yet their exact roles in disease onset and progression remain uncertain. Lung cancer's heterogeneity complicates prognosis prediction. This study integrates pivotal molecules to evaluate patient prognosis and immunotherapy efficacy.
methodsThe WGCNA algorithm identified module genes linked to immunity. The Lasso-Cox method built a prognostic model for outcome prediction. GO and KEGG analyses explored gene pathways. ssGSEA quantified immune cell types and functions. The riskScore predicts the effectiveness of immunotherapy based on its correlation with DNA repair and immune checkpoint genes. Single-cell sequencing examined key gene expression across cell types.
resultsUsing WGCNA, we identified the MEbrown module related to immunity. Lasso-Cox selected "BLK," "ITGB4," "PRKCH," and "SNAI1" for the prognostic model. MF analysis revealed enriched functions including antigen binding, GTPase regulator activity. In terms of BP, processes like immune signaling and mitotic division were enriched. CC enrichment included immunoglobulin complexes and chromosomal regions. Enriched pathways encompassed Cell cycle, Focal adhesion, Cellular senescence, and p53 signaling. ssGSEA evaluated immune cell abundance. RiskScore correlated with CTLA4 and PD1 through MMR and immune checkpoint analysis. Single-cell analysis indicated gene expression across cell types for BLK, ITGB4, PRKCH, and SNAI1.
conclusionIn summary, our developed prognostic model utilizing age-related genes effectively predicts lung cancer prognosis and the efficacy of immune therapy.
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