Title:Identification of a 15 DNA Damage Repair-Related Gene Signature as a
Prognostic Predictor for Lung Adenocarcinoma
Volume: 25
Issue: 9
Author(s): Linping Gu, Yuanyuan Xu and Hong Jian*
Affiliation:
- Department of Oncology, Shanghai Chest Hospital, Shanghai Jiaotong University, Shanghai, 200030, China
Keywords:
Adenocarcinoma of lung, DNA repair, prognosis, transcriptome, DDR-related gene, malignancy.
Abstract:
Background: Lung adenocarcinoma (LUAD) is a common malignancy with a poor
prognosis due to the lack of predictive markers. DNA damage repair (DDR)-related genes are
closely related to cancer progression and treatment.
Introduction: To identify a reliable DDR-related gene signature as an independent predictor of
LUAD.
Methods: DDR-related genes were obtained using combined analysis of TCGA-LUAD data and
literature information, followed by the identification of DDR-related prognostic genes. The DDRrelated
molecular subtypes were then screened, followed by Kaplan-Meier analysis, feature gene
identification, and pathway enrichment analysis of each subtype. Moreover, Cox and LASSO
regression analyses were performed for the feature genes of each subtype to construct a prognostic
model. The clinical utility of the prognostic model was confirmed using the validation dataset
GSE72094 and nomogram analysis.
Results: Eight DDR-related prognostic genes were identified from 31 DDR-related genes. Using
consensus cluster analysis, three molecular subtypes were screened. Cluster2 had the best
prognosis, while cluster3 had the worst. Compared to cluster2, clusters 1 and 3 consisted of more
stage3 - 4, T2-T4, male, and older samples. The feature genes of clusters1, 2, and 3 were mainly
enriched in the cell cycle, arachidonic acid metabolism, and ribosomes. Furthermore, a 15-feature
gene signature was identified for improving the prognosis of LUAD patients.
Conclusion: The 15 DDR-related feature gene signature is an independent and powerful
prognostic biomarker for LUAD that may improve risk classification and provide supplementary
information for a more accurate evaluation and personalized treatment.