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November 10, 2025BMC CancerOpen Access

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

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Authors

ZLZhendong LuPBPengfei BaoTWTaiwei Wang

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Overview

Multi-omics analysis identifies predictive and prognostic genes in lung adenocarcinoma, suggesting potential drug targets and biomarkers for early detection.

Key Points

  • This research aims to identify genes associated with tumorigenesis and prognosis in lung adenocarcinoma using multi-omics data.
  • Utilized ATAC-seq and RNA-seq data from TCGA, GTEx, and GEO for analysis.
  • Identified differential chromatin regions and expressed genes leading to the construction of predictive models.
  • Built a prognostic Cox model to evaluate prognosis-related genes and validated findings with external datasets.
  • Identified 337 consensus genes through intersection of differential peak genes and differentially expressed genes.
  • Developed predictive models with nine predictive-related genes linked to lung adenocarcinoma outcomes.
  • Found significant prognostic-related genes suggesting their potential as biomarkers and targets for drug development.

Cite This Study

Lu et al. (2025) studied this question.

synapsesocial.com/papers/69253a29c0ce034ddc357497https://doi.org/10.1186/s12885-025-14943-x
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Also Consider

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  1. 1Integrating Functional Genomic Screens and Multi-Omics Data to Construct a Prognostic Model for Lung Adenocarcinoma and Validating SPC252025
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  3. 3B21-04 A Prognostic Risk Model Based on the Tumor Microenvironment in Lung Adenocarcinoma2026
  4. 4A robust gene model developed based on the features of ADME genes for predicting lung adenocarcinoma prognosis2026
  5. 5Identifying potential therapeutic targets in lung adenocarcinoma: a multi-omics approach integrating bulk and single-cell RNA sequencing with Mendelian randomization2024 · 12 citations