Transcriptome Analysis of Human Endogenous Retroviruses at Locus-Specific Resolution in Non-Small Cell Lung Cancer

SIMPLE SUMMARY: Lung cancer is the leading cause of cancer deaths worldwide. Most lung cancer patients are diagnosed with locally advanced or metastatic diseases, and their prognosis is relatively poor, with 5-year survival rates ranging from 4 to 17%. Consequently, the identification of novel diagnostic lung cancer biomarkers remains crucial. Recently, human endogenous retroviruses (HERVs) have been found to be implicated in cancer development and later employed as novel diagnostic and prognostic cancer biomarkers. In this study, we present the first-ever locus-specific analysis of HERV expression in 515 lung adenocarcinoma (LUAD) and 497 lung squamous cell carcinoma (LUSC) patients’ samples from the TCGA repository. In our study, we identified the differentially expressed HERVs in both TCGA-LUAD and TCGA-LUSC cohorts, we examined their impact on signaling pathways using in silico models, and we described HERVs’ association with overall survival (OS) and relapse-free survival (RFS). ABSTRACT: Lung cancer is the second most commonly diagnosed cancer and the leading cause of cancer deaths worldwide. Among its subtypes, lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) are the most common, accounting for more than 85% of lung cancer diagnoses. Despite the incredible efforts and recent advances in lung cancer treatments, patients affected by this condition still have a poor prognosis. Therefore, novel diagnostic biomarkers are needed. Recently, a class of transposable elements called human endogenous retroviruses (HERVs) has been found to be implicated in cancer development and later employed as novel biomarkers for several tumor types. In this study, we first ever characterized the expression of HERVs at genomic locus-specific resolution in both LUAD and LUSC cohorts available in The Cancer Genome Atlas (TCGA). Precisely, (i) we profiled the expression of HERVs in TCGA-LUAD and TCGA-LUSC cohorts; (ii) we identified the dysregulated HERVs in both lung cancer subtypes; (iii) we evaluated the impact of the dysregulated HERVs on signaling pathways using neural network-based predictions; and (iv) we assessed their association with overall survival (OS) and relapse-free survival (RFS). In conclusion, we believe this study may help elucidate another layer of dysregulation that occurs in lung cancer involving HERVs, paving the way for identifying novel lung cancer biomarkers.