Characterization of a new genetic signature associated with copy number variation that determines outcome in lung adenocarcinoma patients

Background: The occurrence of lung adenocarcinoma (LUAD) is a complicated process, involving the genetic and epigenetic changes of proto-oncogenes and oncogenes. The objective of this study was to establish new predictive signatures of lung adenocarcinoma based on copy number variation (CNVs) and gene expression data. Methods: Next-generation sequencing was implemented to obtain gene expression and CNV information. According to univariate, multivariate survival Cox regression analysis and Lasso analysis, the expression profiles of lung adenocarcinoma patients were screened and a risk score formula was established and experimentally validated in a local cohort. The model was evaluated by three independent cohorts, and then validated by clinical samples from LUAD patients. Results: A total of 844 CNV-related differential expressed genes (CNV related DEGs) were identified. An CNV associated-six gene signature was dramatically linked to overall survival in lung adenocarcinoma samples from both training and validation groups. Functional enrichment analysis further revealed involvement of genes in p53 signaling pathway and cell cycle, as well as the mismatch repair pathway. Risk score is an independent marker considering clinical parameters and had better prediction in clinical sub-population. The same signature also classified tumors tissues of clinical patients those with CNV detected from their corresponding non-tumorous tissues with an accuracy of 0.92. Conclusions: In conclusion, we identified a new class of 6 CNV-related gene markers that may act as efficient prognostic predictors of lung adenocarcinoma, thus contributing to individualized treatment decisions in patients. Overall design: In total, 20 pairs of tumor and matched adjacent tumor-free tissues were collected during the surgery from LUAD patients admitted to The Maoming People's Hospital between 2020 and 2021. None of the patients had preoperative chemotherapy or preoperative radiotherapy. Tumor staging was determined according to AJCC TNM system. All tumor infomations were histologically classified based on World Health Organization criteria. Pathologic, clinical and follow-up patient informations were obtained from hospital medical records