A Hypoxia-Related Signature for Predicting Prognosis, Cellular Processes, Immune Microenvironment and Targeted Compounds in Lung Squamous Cell Carcinoma

Abstract

Background: Lung squamous cell carcinoma (LUSC) is a malignant tumour of the lung epithelium. A hypoxic environment can promote tumour cell proliferation and invasion. Therefore, this study aims to explore hypoxia-related genes and construct reliable models to predict the prognosis, cellular processes, immune microenvironment and target compounds of lung squamous carcinoma. Methods: The transcriptome data and matched clinical information of LUSC were retrieved from The Cancer Genome Atlas (TCGA) database. The GSVA algorithm calculated each LUSC patient’s hypoxia score, and all LUSC patients were divided into the high hypoxia score group and low hypoxia score group. Weighted gene co-expression network analysis (WGCNA) and differential expression analysis were performed to screen out differentially expressed hypoxia-related genes (DE-HRGs) in LUSC microenvironment, and the underlying regulatory mechanism of DE-HRGs in LUSC was explored through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Hereafter, we established a prognosis-related genetic signature for DE-HRGs using univariate and multivariate Cox regression analyses. The relationship between gene signature and immune cells was further evaluated. Finally, the Comparative Toxicogenomics Database (CTD) was utilized to predict the targeted drugs for the prognostic genes. Results: We obtained 376 DE-HRGs. Functional enrichment analysis indicated that the DE-HRGs were involved in the cell cyclerelated regulatory processes. Next, we developed and validated 3 HRGs-based prognostic signature for LUSC, including HELLS, GPRIN1, and FAM83A. Risk score is an independent prognostic factor for LUSC. Functional enrichment analysis and immune landscape analysis suggested that the risk scoring system might be involved in altering the immune microenvironment of LUSC patients to influence patient outcomes. Ultimately, a total of 92 potential compounds were predicted for the three prognostic genes. Conclusion: In summary, we developed and validated a hypoxia-related model for LUSC, reflecting the cellular processes and immune microenvironment characteristics and predicting the prognostic outcomes and targeted compounds.