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

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 acc...

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Autores principales: Zhang, Ze, Wei, Yan-Yan, Guo, Qiong-Mei, Zhou, Chang-Hao, Li, Nan, Wu, Jin-Fang, Li, Ya-Ting, Gao, Wei-Wei, Li, Hui-Li
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8820901/
https://www.ncbi.nlm.nih.gov/pubmed/35140786
http://dx.doi.org/10.1155/2022/2559258
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author Zhang, Ze
Wei, Yan-Yan
Guo, Qiong-Mei
Zhou, Chang-Hao
Li, Nan
Wu, Jin-Fang
Li, Ya-Ting
Gao, Wei-Wei
Li, Hui-Li
author_facet Zhang, Ze
Wei, Yan-Yan
Guo, Qiong-Mei
Zhou, Chang-Hao
Li, Nan
Wu, Jin-Fang
Li, Ya-Ting
Gao, Wei-Wei
Li, Hui-Li
author_sort Zhang, Ze
collection PubMed
description 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.
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spelling pubmed-88209012022-02-08 Inflammation-Related Gene Signature: An Individualized Risk Prediction Model for Kidney Renal Clear Cell Carcinoma Zhang, Ze Wei, Yan-Yan Guo, Qiong-Mei Zhou, Chang-Hao Li, Nan Wu, Jin-Fang Li, Ya-Ting Gao, Wei-Wei Li, Hui-Li J Oncol Research Article 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. Hindawi 2022-01-31 /pmc/articles/PMC8820901/ /pubmed/35140786 http://dx.doi.org/10.1155/2022/2559258 Text en Copyright © 2022 Ze Zhang et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Zhang, Ze
Wei, Yan-Yan
Guo, Qiong-Mei
Zhou, Chang-Hao
Li, Nan
Wu, Jin-Fang
Li, Ya-Ting
Gao, Wei-Wei
Li, Hui-Li
Inflammation-Related Gene Signature: An Individualized Risk Prediction Model for Kidney Renal Clear Cell Carcinoma
title Inflammation-Related Gene Signature: An Individualized Risk Prediction Model for Kidney Renal Clear Cell Carcinoma
title_full Inflammation-Related Gene Signature: An Individualized Risk Prediction Model for Kidney Renal Clear Cell Carcinoma
title_fullStr Inflammation-Related Gene Signature: An Individualized Risk Prediction Model for Kidney Renal Clear Cell Carcinoma
title_full_unstemmed Inflammation-Related Gene Signature: An Individualized Risk Prediction Model for Kidney Renal Clear Cell Carcinoma
title_short Inflammation-Related Gene Signature: An Individualized Risk Prediction Model for Kidney Renal Clear Cell Carcinoma
title_sort inflammation-related gene signature: an individualized risk prediction model for kidney renal clear cell carcinoma
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8820901/
https://www.ncbi.nlm.nih.gov/pubmed/35140786
http://dx.doi.org/10.1155/2022/2559258
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