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

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.

MicroRNA-28-5p as a potential diagnostic biomarker for chronic periodontitis and its role in cell proliferation and inflammatory response

“…SPHK1, a kinase in sphingolipid metabolism, is recognized as a critical factor in the regulation of inflammatory response and cellular immunity. 27 For example, SPHK1 promoted inflammatory responses in renal clear cell carcinoma 28 and cerebral ischemia/reperfusion injury, 29 and its reduced levels alleviated inflammatory factor release in ethanol-intoxicated septic mice 30 and was involved in the ameliorative effect of atractylenolide-1 in mice with colitis. 31 The transcriptional activation of pro-inflammatory cytokines (IL-6), chemokines, and adhesion molecules requires the activation of SPHK1.…”

Pan-Cancer Identification of Prognostic-Associated Metabolic Pathways

“…Previous work revealed that high expression of glutamate dehydrogenase 1 (GLUD1) is detrimental to the survival of renal cancer cells in unfavorable nutritional environments (such as amino acid deficiency) and is significantly associated with good prognosis in KIRC patients [ 18 ]. High expression levels of adenosine A2b receptor (ADORA2B) and solute carrier family 1 member 5 (SLC1A5) were also correlated with poor prognosis in KIRC patients [ 19 , 20 ]. Further, the high expression of ACAT1 (HR = 0.44), GLUD1 (HR = 0.41), SLC38A1 (HR = 0.61), and SLC38A2 (HR = 0.63) are significantly correlated with good prognosis in LGG patients, while high expression of AHR (HR = 1.78), ENO1 (HR = 1.69), HK2 (HR = 3.56), MTOR (HR = 1.71), NFATC2 (HR = 2.16), SLC16A4 (HR = 2.92), SLC1A5 (HR = 3.00), SLC36A4 (HR = 1.67), and XBP1 (HR = 1.82) are correlated with poor prognosis in LGG patients.…”

Alteration of pro-carcinogenic gut microbiota is associated with clear cell renal cell carcinoma tumorigenesis

“…Consequently, we screened out 22 inflammatory genes such as Gabbr1 , by univariate Cox regression and different expression analysis in the TCGA database. Gabbr1 was classical macrophage-related genes which have been confirmed to be the inextricable link between inflammation and ccRCC ( Kovaleva et al, 2016 ; Zhang et al, 2022 ). Other overlapped genes, such as FZD5 and STAB1, which are known as scavenger receptors also participate in carcinogenesis, and their potential functions in ccRCC need to be further revealed ( Katoh, 2007 ; Hollmén et al, 2020 ).…”