Table2_Prognostic Characteristics and Immune Effects of N6-Methyladenosine and 5-Methylcytosine-Related Regulatory Factors in Clear Cell Renal Cell Ca.xlsx (10.94 kB)
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Table2_Prognostic Characteristics and Immune Effects of N6-Methyladenosine and 5-Methylcytosine-Related Regulatory Factors in Clear Cell Renal Cell Carcinoma.xlsx

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posted on 27.04.2022, 04:51 by Lei Li, Zijia Tao, Yiqiao Zhao, Mingyang Li, Jianyi Zheng, Zeyu Li, Xiaonan Chen

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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