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Decoding temporal heterogeneity in NSCLC through machine learning and prognostic model construction.

Junpeng Cheng¹, Meizhu Xiao¹, Qingkang Meng¹

¹Department of Pharmacogenomics, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, P. R. China.

World Journal of Surgical Oncology

|June 13, 2024

Summary

This summary is machine-generated.

This study identifies key genes driving non-small cell lung cancer (NSCLC) progression using single-cell analysis, developing a risk score model for potential personalized treatment strategies.

Keywords:

Machine learning Non-small cell lung cancer Temporal heterogeneity

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Area of Science:

Oncology
Genomics
Bioinformatics

Background:

Non-small cell lung cancer (NSCLC) is a heterogeneous disease with distinct genomic profiles in early versus advanced stages.
Identifying key genes and pathways is crucial for improving NSCLC diagnosis and treatment outcomes.

Purpose of the Study:

To characterize malignant NSCLC cells using single-cell transcriptome analysis.
To identify genes and pathways driving NSCLC progression and develop a risk assessment tool.

Main Methods:

Single-cell transcriptome analysis of 93,406 NSCLC cells from 22 patients.
Clustering using cNMF to identify molecular modules and pseudotime analysis for temporal gene expression.
XGBoost and LASSO regression for marker gene selection and risk score model development, validated with TCGA and GEO data.

Main Results:

Malignant NSCLC cells classified into metabolic reprogramming, cell cycle, and stemness modules.
Metabolism, cell cycle, and stemness identified as key drivers of NSCLC malignant evolution.
Marker genes (e.g., CHCHD2, GAPDH, CD24) correlated with NSCLC progression, validated by a robust eight-gene risk score model.

Conclusions:

Identified temporal heterogeneous biomarkers in NSCLC, providing insights into disease progression.
Potential therapeutic targets and a promising workflow for clinical application in NSCLC risk assessment and personalized treatment.