ArticleJournal of oncology2022
Inflammation-Related Gene Signature: An Individualized Risk Prediction Model for Kidney Renal Clear Cell Carcinoma.
Article in Journal of oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 11 citations in OpenAlex.
- Methyltransferase-like 3 is a target for the diagnose and therapy of clear cell renal carcinoma.Frontiers in pharmacology · 2025Review
- Article
- Analysis of Inflammation-Related Genes in Patients with Stanford Type A Aortic Dissection.Journal of personalized medicine · 2023Article
- Alteration of pro-carcinogenic gut microbiota is associated with clear cell renal cell carcinoma tumorigenesis.Frontiers in microbiology · 2023Article
- MicroRNA-28-5p as a potential diagnostic biomarker for chronic periodontitis and its role in cell proliferation and inflammatory response.Journal of dental sciences · 2022Article
- Construction of an immune-related gene signature for the prognosis and diagnosis of glioblastoma multiforme.Frontiers in oncology · 2022Article
Corrections and comments
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Authors and funding
9 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThere 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.
methodsThe 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.
resultsHerein, 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.
conclusionsIn conclusion, a novel inflammation-related gene signature comprehensively constructed in the current study may help stratify patients with KIRC.
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