Integrated WGCNA and Machine Learning Uncover ECM/Cell Cycle Hub Genes as Prognostic Biomarkers in Lung Adenocarcinoma for Improved Health Care

Moksha Pradha Prabhakar 1, Narmadha Ramasamy 1, Rizwana Rohoofur Rahman 1, Kannan Muthu 1 *
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1 Department of Bioinformatics, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, India
* Corresponding Author
J CLIN MED KAZ, Volume 23, Issue 5, pp. 27-40. https://doi.org/10.23950/jcmk/19212
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Author Contributions: Conceptualization, M.K.; methodology, M.P., R.R, and M.K; validation, M.K. and M.P ; formal analysis, M.P.; investigation, M.P and R.R.; data curation, M.P., R.R., and R.N; writing – original draft preparation, M.P. and R.N ; writing – review and editing, R.N. and M.P ; visualization, M.P. and K.M ; supervision, M.K., project administration, M.K., ;All authors have read and agreed to the published version of the manuscript.

Data availability statement: The data presented in this study are available on request from the corresponding author due to the protection of primary data.

Ethical statement: All information utilized in this research was sourced from publicly available data collections in the GEO database. No human subjects or animal participants were directly engaged, and ethical consent was not necessary.

Declaration on the Use of Generative AI and AI-assisted Tools: The authors declare that no generative AI or AI-assisted tools were used in the writing or preparation of this manuscript.

ABSTRACT

Introduction: Lung adenocarcinoma (LUAD) is the most common lung cancer subtype, carrying high mortality in global health. Dependable molecular risk assessment tools are urgently needed to complement current clinicopathological staging and improve prognostic accuracy.
Methods: Using dataset GSE75307, we performed differential expression and weighted gene co-expression network analysis (WGCNA). Functional insights were gained via GO/KEGG over-representation and pre-ranked gene set enrichment analyses. A protein–protein interaction (PPI) network was built using STRING, and Cytoscape/cytoHubba identified hub genes. Consensus machine learning-based feature selection was applied to isolate key prognostic genes, which were then validated using the TCGA-LUAD cohort (n = 515).
Results: We identified 442 unique differentially expressed genes, combining them with tumor-related WGCNA modules to yield 945 candidates enriched in extracellular matrix organization and cell cycle pathways. PPI analysis revealed 17 hub genes, ultimately narrowed down to a robust three-gene signature: TOP2A, CCNB1, and CDK1. In the TCGA-LUAD validation cohort, the model's risk score successfully stratified participant survival risk. The clinicopathological nomogram incorporating the three-gene risk score achieved a concordance index (C-index) of 0.784, although the standalone three-gene signature showed only modest discrimination in the time-dependent ROC analysis. High-risk tumors exhibited an immunosuppressive microenvironment, characterized by elevated M2 macrophage infiltration, upregulation of immune checkpoints (PDCD1, CTLA4), and TGF- β pathway activation.
Conclusion: This study established and validated a dependable three-gene transcriptomic signature (TOP2A, CCNB1, CDK1) that successfully stratifies LUAD survival risk and aligns with immunosuppressive tumor microenvironments, offering a valuable molecular tool to enhance lung cancer prognosis.
Keywords: Lung adenocarcinoma, Healthcare, WGCNA, Machine learning, Cell cycle, Extracellular matrix, Prognostic biomarkers.

CITATION

Prabhakar MP, Ramasamy N, Rohoofur Rahman R, Muthu K. Integrated WGCNA and Machine Learning Uncover ECM/Cell Cycle Hub Genes as Prognostic Biomarkers in Lung Adenocarcinoma for Improved Health Care. J CLIN MED KAZ. 2026;23(5):27-40. https://doi.org/10.23950/jcmk/19212

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