Multi-omics data-based modeling reveals tumorigenesis- and prognosis-associated genes with clinical potential in lung adenocarcinoma

Abstract This study aims to utilize multi-omics high-throughput sequencing data, including ATAC-seq and RNA-seq data from TCGA, GTEx, and GEO databases, to construct predictive and prognostic models for lung adenocarcinoma (LUAD) and identify potential biomarkers. We first obtained LUAD ATAC-seq dat...

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Автори:	Zhendong Lu, Pengfei Bao, Taiwei Wang, Kairui Hu, Lina Zhang, Ling Yi, Yuanming Pan, Weiying Li, Zhi John Lu, Jinghui Wang, Junzhong Ruan
Формат:	Artigo
Мова:	Inglês
Опубліковано:	BMC 2025-11-01
Серія:	BMC Cancer
Предмети:	Deep learning ATAC-seq LUAD Tumorigenesis prognosis Early cancer detection ScRNA-seq
Онлайн доступ:	https://doi.org/10.1186/s12885-025-14943-x
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