Integrating machine learning and single-cell analysis to uncover lung adenocarcinoma progression and prognostic biomarkers

Pengpeng Zhang; Jiaqi Feng; Min Rui; Jiping Xie; Lianmin Zhang; Zhenfa Zhang

doi:10.1111/jcmm.18516

Integrating machine learning and single-cell analysis to uncover lung adenocarcinoma progression and prognostic biomarkers

J Cell Mol Med. 2024 Jul;28(13):e18516. doi: 10.1111/jcmm.18516.

Authors

Pengpeng Zhang¹, Jiaqi Feng¹, Min Rui², Jiping Xie¹, Lianmin Zhang¹, Zhenfa Zhang¹

Affiliations

¹ Department of Lung Cancer, Tianjin Lung Cancer Center, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.
² Department of Pathology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin, China.

Abstract

The progression of lung adenocarcinoma (LUAD) from atypical adenomatous hyperplasia (AAH) to invasive adenocarcinoma (IAC) involves a complex evolution of tumour cell clusters, the mechanisms of which remain largely unknown. By integrating single-cell datasets and using inferCNV, we identified and analysed tumour cell clusters to explore their heterogeneity and changes in abundance throughout LUAD progression. We applied gene set variation analysis (GSVA), pseudotime analysis, scMetabolism, and Cytotrace scores to study biological functions, metabolic profiles and stemness traits. A predictive model for prognosis, based on key cluster marker genes, was developed using CoxBoost and plsRcox (CPM), and validated across multiple cohorts for its prognostic prediction capabilities, tumour microenvironment characterization, mutation landscape and immunotherapy response. We identified nine distinct tumour cell clusters, with Cluster 6 indicating an early developmental stage, high stemness and proliferative potential. The abundance of Clusters 0 and 6 increased from AAH to IAC, correlating with prognosis. The CPM model effectively distinguished prognosis in immunotherapy cohorts and predicted genomic alterations, chemotherapy drug sensitivity, and immunotherapy responsiveness. Key gene S100A16 in the CPM model was validated as an oncogene, enhancing LUAD cell proliferation, invasion and migration. The CPM model emerges as a novel biomarker for predicting prognosis and immunotherapy response in LUAD patients, with S100A16 identified as a potential therapeutic target.

Keywords: S100A16; immunotherapy response; lung adenocarcinoma; machine learning; single‐cell analysis.

MeSH terms

Adenocarcinoma of Lung* / genetics
Adenocarcinoma of Lung* / pathology
Biomarkers, Tumor* / genetics
Biomarkers, Tumor* / metabolism
Disease Progression*
Gene Expression Profiling
Gene Expression Regulation, Neoplastic
Humans
Immunotherapy / methods
Lung Neoplasms* / genetics
Lung Neoplasms* / pathology
Machine Learning*
Prognosis
Single-Cell Analysis* / methods
Tumor Microenvironment* / genetics

Substances

Biomarkers, Tumor

Grants and funding

21JCYBJC01020/Natural Science Foundation of Tianjin Municipality