Identification of a Prognostic Model Based on Immune-Related Genes of Lung Squamous Cell Carcinoma

Rui Li; Xiao Liu; Xi-Jia Zhou; Xiao Chen; Jian-Ping Li; Yun-Hong Yin; Yi-Qing Qu

doi:10.3389/fonc.2020.01588

Identification of a Prognostic Model Based on Immune-Related Genes of Lung Squamous Cell Carcinoma

Front Oncol. 2020 Sep 2:10:1588. doi: 10.3389/fonc.2020.01588. eCollection 2020.

Authors

Rui Li¹, Xiao Liu¹, Xi-Jia Zhou¹, Xiao Chen^{1

2}, Jian-Ping Li¹, Yun-Hong Yin³, Yi-Qing Qu³

Affiliations

¹ Department of Pulmonary and Critical Care Medicine, Cheeloo College of Medicine, Qilu Hospital, Shandong University, Jinan, China.
² Department of Respiratory Medicine, Tai'an City Central Hospital, Tai'an, China.
³ Department of Pulmonary and Critical Care Medicine, Qilu Hospital of Shandong University, Jinan, China.

Abstract

Immune-related genes (IRGs) play considerable roles in tumor immune microenvironment (IME). This research aimed to discover the differentially expressed immune-related genes (DEIRGs) based on the Cox predictive model to predict survival for lung squamous cell carcinoma (LUSC) through bioinformatics analysis. First of all, the differentially expressed genes (DEGs) were acquired based on The Cancer Genome Atlas (TCGA) using the limma R package, the DEIRGs were obtained from the ImmPort database, whereas the differentially expressed transcription factors (DETFs) were acquired from the Cistrome database. Thereafter, a TFs-mediated IRGs network was constructed to identify the candidate mechanisms for those DEIRGs in LUSC at molecular level. Moreover, Gene Ontology (GO), together with Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, was conducted for exploring those functional enrichments for DEIRGs. Besides, univariate as well as multivariate Cox regression analysis was conducted for establishing a prediction model for DEIRGs biomarkers. In addition, the relationship between the prognostic model and immunocytes was further explored through immunocyte correlation analysis. In total, 3,599 DEGs, 223 DEIRGs, and 46 DETFs were obtained from LUSC tissues and adjacent non-carcinoma tissues. According to multivariate Cox regression analysis, 10 DEIRGs (including CALCB, GCGR, HTR3A, AMH, VGF, SEMA3B, NRTN, ENG, ACVRL1, and NR4A1) were retrieved to establish a prognostic model for LUSC. Immunocyte infiltration analysis showed that dendritic cells and neutrophils were positively correlated with IRGs, which possibly exerted an important part within the IME of LUSC. Our study identifies a prognostic model based on IRGs, which is then used to predict LUSC prognosis and analyze immunocyte infiltration. This may provide a novel insight for exploring the potential IRGs in the IME of LUSC.

Keywords: a Cox prediction model; immune-related genes (IRGs); lung squamous cell carcinoma; prognostic biomarkers; transcription factors (TFs) mediated IRGs network.