Prognostic Characteristics and Immune Effects of N6-Methyladenosine and 5-Methylcytosine-Related Regulatory Factors in Clear Cell Renal Cell Carcinoma

In recent years, methylation modification regulators have been found to have essential roles in various tumor mechanisms. However, the relationships between N6-methyladenosine (m6A) and 5-methylcytosine (m5C) regulators and clear cell renal cell carcinoma (ccRCC) remain unknown. This study investiga...

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Main Authors: Lei Li, Zijia Tao, Yiqiao Zhao, Mingyang Li, Jianyi Zheng, Zeyu Li, Xiaonan Chen
Format: Article
Language:English
Published: Frontiers Media S.A. 2022-04-01
Series:Frontiers in Genetics
Subjects:
Online Access:https://www.frontiersin.org/articles/10.3389/fgene.2022.864383/full
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author Lei Li
Zijia Tao
Yiqiao Zhao
Mingyang Li
Jianyi Zheng
Zeyu Li
Xiaonan Chen
author_facet Lei Li
Zijia Tao
Yiqiao Zhao
Mingyang Li
Jianyi Zheng
Zeyu Li
Xiaonan Chen
author_sort Lei Li
collection DOAJ
description In recent years, methylation modification regulators have been found to have essential roles in various tumor mechanisms. However, the relationships between N6-methyladenosine (m6A) and 5-methylcytosine (m5C) regulators and clear cell renal cell carcinoma (ccRCC) remain unknown. This study investigated these relationships using the data from The Cancer Genome Atlas database. We calculated risk scores using a Lasso regression analysis and divided the patient samples into two risk groups (tumor vs. normal tissues). Furthermore, we used univariate and multivariate Cox analyses to determine independent prognostic indicators and explore correlations between the regulatory factors and immune infiltrating cell characteristics. Finally, quantitative reverse transcriptase–polymerase chain reaction (PCR) and The Human Protein Atlas were used to verify signature-related gene expression in clinical samples. We identified expression differences in 35 regulatory factors between the tumor and normal tissue groups. Next, we constructed a five-gene risk score signature (NOP2 nucleolar protein [NOP2], methyltransferase 14, N6-adenosine-methyltransferase subunit [METTL14], NOP2/Sun RNA methyltransferase 5 [NSUN5], heterogeneous nuclear ribonucleoprotein A2/B1 [HNRNPA2B1], and zinc finger CCCH-type containing 13 [ZC3H13]) using the screening criteria (p < 0.01), and then divided the cases into high- and low-risk groups based on their median risk score. We also screened for independent prognostic factors related to age, tumor grade, and risk score. Furthermore, we constructed a Norman diagram prognostic model by combining two clinicopathological characteristics, which demonstrated good prediction efficiency with prognostic markers. Then, we used a single-sample gene set enrichment analysis and the cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) method to evaluate the tumor microenvironment of the regulatory factor prognostic characteristics. Moreover, we evaluated five risk subgroups with different genetic signatures for personalized prognoses. Finally, we analyzed the immunotherapy and immune infiltration response and demonstrated that the high-risk group was more sensitive to immunotherapy than the low-risk group. The PCR results showed that NSUN5 and HNRNPA2B1 expression was higher in tumor tissues than in normal tissues. In conclusion, we identified five m6A and m5C regulatory factors that might be promising biomarkers for future research.
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spelling doaj.art-81470bdb3322472fa46bf2710ac1c3062022-12-22T00:09:59ZengFrontiers Media S.A.Frontiers in Genetics1664-80212022-04-011310.3389/fgene.2022.864383864383Prognostic Characteristics and Immune Effects of N6-Methyladenosine and 5-Methylcytosine-Related Regulatory Factors in Clear Cell Renal Cell CarcinomaLei LiZijia TaoYiqiao ZhaoMingyang LiJianyi ZhengZeyu LiXiaonan ChenIn recent years, methylation modification regulators have been found to have essential roles in various tumor mechanisms. However, the relationships between N6-methyladenosine (m6A) and 5-methylcytosine (m5C) regulators and clear cell renal cell carcinoma (ccRCC) remain unknown. This study investigated these relationships using the data from The Cancer Genome Atlas database. We calculated risk scores using a Lasso regression analysis and divided the patient samples into two risk groups (tumor vs. normal tissues). Furthermore, we used univariate and multivariate Cox analyses to determine independent prognostic indicators and explore correlations between the regulatory factors and immune infiltrating cell characteristics. Finally, quantitative reverse transcriptase–polymerase chain reaction (PCR) and The Human Protein Atlas were used to verify signature-related gene expression in clinical samples. We identified expression differences in 35 regulatory factors between the tumor and normal tissue groups. Next, we constructed a five-gene risk score signature (NOP2 nucleolar protein [NOP2], methyltransferase 14, N6-adenosine-methyltransferase subunit [METTL14], NOP2/Sun RNA methyltransferase 5 [NSUN5], heterogeneous nuclear ribonucleoprotein A2/B1 [HNRNPA2B1], and zinc finger CCCH-type containing 13 [ZC3H13]) using the screening criteria (p < 0.01), and then divided the cases into high- and low-risk groups based on their median risk score. We also screened for independent prognostic factors related to age, tumor grade, and risk score. Furthermore, we constructed a Norman diagram prognostic model by combining two clinicopathological characteristics, which demonstrated good prediction efficiency with prognostic markers. Then, we used a single-sample gene set enrichment analysis and the cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) method to evaluate the tumor microenvironment of the regulatory factor prognostic characteristics. Moreover, we evaluated five risk subgroups with different genetic signatures for personalized prognoses. Finally, we analyzed the immunotherapy and immune infiltration response and demonstrated that the high-risk group was more sensitive to immunotherapy than the low-risk group. The PCR results showed that NSUN5 and HNRNPA2B1 expression was higher in tumor tissues than in normal tissues. In conclusion, we identified five m6A and m5C regulatory factors that might be promising biomarkers for future research.https://www.frontiersin.org/articles/10.3389/fgene.2022.864383/fullrenal clear cell cancerN6-methyladenoseprognosisThe Cancer Genome Atlas5-methylcytosinetumor microenvironment
spellingShingle Lei Li
Zijia Tao
Yiqiao Zhao
Mingyang Li
Jianyi Zheng
Zeyu Li
Xiaonan Chen
Prognostic Characteristics and Immune Effects of N6-Methyladenosine and 5-Methylcytosine-Related Regulatory Factors in Clear Cell Renal Cell Carcinoma
Frontiers in Genetics
renal clear cell cancer
N6-methyladenose
prognosis
The Cancer Genome Atlas
5-methylcytosine
tumor microenvironment
title Prognostic Characteristics and Immune Effects of N6-Methyladenosine and 5-Methylcytosine-Related Regulatory Factors in Clear Cell Renal Cell Carcinoma
title_full Prognostic Characteristics and Immune Effects of N6-Methyladenosine and 5-Methylcytosine-Related Regulatory Factors in Clear Cell Renal Cell Carcinoma
title_fullStr Prognostic Characteristics and Immune Effects of N6-Methyladenosine and 5-Methylcytosine-Related Regulatory Factors in Clear Cell Renal Cell Carcinoma
title_full_unstemmed Prognostic Characteristics and Immune Effects of N6-Methyladenosine and 5-Methylcytosine-Related Regulatory Factors in Clear Cell Renal Cell Carcinoma
title_short Prognostic Characteristics and Immune Effects of N6-Methyladenosine and 5-Methylcytosine-Related Regulatory Factors in Clear Cell Renal Cell Carcinoma
title_sort prognostic characteristics and immune effects of n6 methyladenosine and 5 methylcytosine related regulatory factors in clear cell renal cell carcinoma
topic renal clear cell cancer
N6-methyladenose
prognosis
The Cancer Genome Atlas
5-methylcytosine
tumor microenvironment
url https://www.frontiersin.org/articles/10.3389/fgene.2022.864383/full
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