Integrative analysis of genetic variability and functional traits in lung adenocarcinoma epithelial cells via single-cell RNA sequencing, GWAS, bayesian deconvolution, and machine learning.

doi:10.1007/s13258-025-01621-2

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Article

Integrative analysis of genetic variability and functional traits in lung adenocarcinoma epithelial cells via single-cell RNA sequencing, GWAS, bayesian deconvolution, and machine learning.

Genes & genomics - 1 Apr 2025

Gao Chenggen, Wu Jintao, Zhong Fangyan, Yang Xianxin, Liu Hanwen, Lai Junming, Cai Jing, Mao Weimin, Xu Huijuan

Abstract excerpt

BACKGROUND: Lung adenocarcinoma remains a leading cause of cancer-related mortality worldwide, characterized by high genetic and cellular heterogeneity, especially within the tumor microenvironment. OBJECTIVE: This study integrates single-cell RNA sequencing (scRNA-seq) with genome-wide association studies (GWAS) using Bayesian deconvolution and machine learning techniques to unravel the genetic and functional...

Read the complete abstract on PubMed

Topics

Humans
Machine Learning
Adenocarcinoma of Lung
Bayes Theorem
Lung Neoplasms
Genome-Wide Association Study

Methodology Data sources Editorial policy Privacy Delete account & data About Contact Helena Bioinformatics

Single-Cell Analysis

Epithelial Cells

Tumor Microenvironment

Sequence Analysis, RNA

Biomarkers, Tumor

Prognosis

Genetic Variation

B7-H1 Antigen

Source

Open in PubMed Open full text

Identifiers

PMID: 39992528
DOI: 10.1007/s13258-025-01621-2
PMCID: PMC12000210

Genes

F12
GOLM1
S100P
SLC2A1

Variants

Provenance

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