Inflammation-Related Gene Signature: An Individualized Risk Prediction Model for Kidney Renal Clear Cell Carcinoma

Ze Zhang; Yan-Yan Wei; Qiong-Mei Guo; Chang-Hao Zhou; Nan Li; Jin-Fang Wu; Ya-Ting Li; Wei-Wei Gao; Hui-Li Li

doi:10.1155/2022/2559258

Inflammation-Related Gene Signature: An Individualized Risk Prediction Model for Kidney Renal Clear Cell Carcinoma

J Oncol. 2022 Jan 31:2022:2559258. doi: 10.1155/2022/2559258. eCollection 2022.

Authors

Ze Zhang¹, Yan-Yan Wei¹, Qiong-Mei Guo¹, Chang-Hao Zhou¹, Nan Li², Jin-Fang Wu², Ya-Ting Li³, Wei-Wei Gao¹, Hui-Li Li¹

Affiliations

¹ Department of Anesthesiology, The First Hospital of Hebei Medical University, Shijiazhuang, Hebei, China.
² Department of Operating Room, The First Hospital of Hebei Medical University, Shijiazhuang, Hebei, China.
³ Pharmacy Intravenous Admixture Services, The First Hospital of Hebei Medical University, Shijiazhuang, Hebei, China.

Abstract

Background: There is much evidence that confirms the inextricable link between inflammation and malignancy. Inflammation-related regulators were involved in the progression of kidney renal clear cell carcinoma (KIRC). However, the predictive role of single gene biomarkers is inadequate, and more accurate prognostic models are necessary. We undertook the current research to construct a robust inflammation-related gene signature that could stratify patients with KIRC.

Methods: The transcriptome sequencing data along with clinicopathologic information of KIRC were obtained from TCGA. A list of inflammation-related genes was acquired from the Molecular Signatures Database. Using the RNA-seq and survival time data from the TCGA training cohort, an inflammation-related gene signature was built using bioinformatic methods, and its performance in predicting patient prognosis was assessed by Kaplan-Meier and ROC curve analyses. Furthermore, we explored the association of risk score with immune score, stromal score, tumor immune-infiltrating cells (TIICs), immunosuppressive molecules, m6A regulators, and autophagy-related biomarkers.

Results: Herein, nine inflammation-related hub genes (ROS1, PLAUR, ACVR2A, KLF6, GABBR1, APLNR, SPHK1, PDPN, and ADORA2B) were determined and used to build a predictive model. All sets, including training set, four testing sets, and the entire TCGA group, were divided into two groups (low and high risk), and Kaplan-Meier curves all showed an adverse prognosis for patients in the high-risk group. ESTIMATE algorithm revealed a higher immune score in the high-risk subgroup. CIBERSORT algorithm illustrated that the high-risk group showed higher-level immune infiltrates. Furthermore, LAG3, TIGIT, and CTLA4 were overexpressed in the high-risk subgroup and positively associated with risk scores. Moreover, except for METTL3 and ALKBH5, the other m6A regulators decreased in the high-risk subgroup.

Conclusions: In conclusion, a novel inflammation-related gene signature comprehensively constructed in the current study may help stratify patients with KIRC.