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

作者：Ze Zhang, Wei Yan-yan, Qiongmei Guo, Changhao Zhou, Nan Li, Jinfang Wu, Yating Li, Weiwei Gao, Hui-Li Li · 发表于：Journal of Oncology · 年份：2022 · DOI：10.1155/2022/2559258 · 被引用次数：11 · 研究领域：Ferroptosis and cancer prognosis、Kruppel-like factors research、Renal cell carcinoma treatment

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 progno...