Shuo Pang, Shuo Zhao, Yuxi Dongye, Yidong Fan, Jikai Liu
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Our prognostic genes predicted ccRCC treatment drugs.</p><p>We found 37 DEMFRGs by comparing 1913 differentially expressed ccRCC genes to 202 m6A RNA methylation FRGs. Functional enrichment analysis showed that hypoxia-induced cell death and metabolism pathways were the most differentially expressed methylation functional regulating genes. Five prognostic genes were found by machine learning: TRIB3, CHAC1, NNMT, EGFR, and SLC1A4. An advanced renal cell carcinoma nomogram with age and risk score accurately predicted the outcome. These five prognostic genes were linked to various cancers. Immunological cell number and checkpoint expression differed between high- and low-risk groups. The risk model successfully predicted immunotherapy outcome, showing high-risk individuals had poor results. NIACIN, TAE-684, ROCILETINIB, and others treat ccRCC. We found ccRCC prognostic genes that work. This discovery may lead to new ccRCC treatments.</p>","PeriodicalId":9806,"journal":{"name":"Cell Biology International","volume":"48 6","pages":"777-794"},"PeriodicalIF":3.3000,"publicationDate":"2024-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Identification and validation of m6A-associated ferroptosis genes in renal clear cell carcinoma\",\"authors\":\"Shuo Pang, Shuo Zhao, Yuxi Dongye, Yidong Fan, Jikai Liu\",\"doi\":\"10.1002/cbin.12146\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Urinary cancer is synonymous with clear cell renal cell carcinoma (ccRCC). Unfortunately, existing treatments for this illness are ineffective and unpromising. Finding novel ccRCC biomarkers is crucial to creating successful treatments.</p><p>The Cancer Genome Atlas provided clear cell renal cell carcinoma transcriptome data. Functional enrichment analysis was performed on ccRCC and control samples' differentially expressed N6-methyladenosine RNA methylation and ferroptosis-related genes (DEMFRGs). Machine learning was used to find and model ccRCC patients' predicted genes. A nomogram was created for clear cell renal cell carcinoma patients. Prognostic genes were enriched. We examined patients' immune profiles by risk score. Our prognostic genes predicted ccRCC treatment drugs.</p><p>We found 37 DEMFRGs by comparing 1913 differentially expressed ccRCC genes to 202 m6A RNA methylation FRGs. Functional enrichment analysis showed that hypoxia-induced cell death and metabolism pathways were the most differentially expressed methylation functional regulating genes. Five prognostic genes were found by machine learning: TRIB3, CHAC1, NNMT, EGFR, and SLC1A4. An advanced renal cell carcinoma nomogram with age and risk score accurately predicted the outcome. These five prognostic genes were linked to various cancers. Immunological cell number and checkpoint expression differed between high- and low-risk groups. The risk model successfully predicted immunotherapy outcome, showing high-risk individuals had poor results. NIACIN, TAE-684, ROCILETINIB, and others treat ccRCC. We found ccRCC prognostic genes that work. 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Identification and validation of m6A-associated ferroptosis genes in renal clear cell carcinoma
Urinary cancer is synonymous with clear cell renal cell carcinoma (ccRCC). Unfortunately, existing treatments for this illness are ineffective and unpromising. Finding novel ccRCC biomarkers is crucial to creating successful treatments.
The Cancer Genome Atlas provided clear cell renal cell carcinoma transcriptome data. Functional enrichment analysis was performed on ccRCC and control samples' differentially expressed N6-methyladenosine RNA methylation and ferroptosis-related genes (DEMFRGs). Machine learning was used to find and model ccRCC patients' predicted genes. A nomogram was created for clear cell renal cell carcinoma patients. Prognostic genes were enriched. We examined patients' immune profiles by risk score. Our prognostic genes predicted ccRCC treatment drugs.
We found 37 DEMFRGs by comparing 1913 differentially expressed ccRCC genes to 202 m6A RNA methylation FRGs. Functional enrichment analysis showed that hypoxia-induced cell death and metabolism pathways were the most differentially expressed methylation functional regulating genes. Five prognostic genes were found by machine learning: TRIB3, CHAC1, NNMT, EGFR, and SLC1A4. An advanced renal cell carcinoma nomogram with age and risk score accurately predicted the outcome. These five prognostic genes were linked to various cancers. Immunological cell number and checkpoint expression differed between high- and low-risk groups. The risk model successfully predicted immunotherapy outcome, showing high-risk individuals had poor results. NIACIN, TAE-684, ROCILETINIB, and others treat ccRCC. We found ccRCC prognostic genes that work. This discovery may lead to new ccRCC treatments.
期刊介绍：
Each month, the journal publishes easy-to-assimilate, up-to-the minute reports of experimental findings by researchers using a wide range of the latest techniques. Promoting the aims of cell biologists worldwide, papers reporting on structure and function - especially where they relate to the physiology of the whole cell - are strongly encouraged. Molecular biology is welcome, as long as articles report findings that are seen in the wider context of cell biology. In covering all areas of the cell, the journal is both appealing and accessible to a broad audience. Authors whose papers do not appeal to cell biologists in general because their topic is too specialized (e.g. infectious microbes, protozoology) are recommended to send them to more relevant journals. Papers reporting whole animal studies or work more suited to a medical journal, e.g. histopathological studies or clinical immunology, are unlikely to be accepted, unless they are fully focused on some important cellular aspect.
These last remarks extend particularly to papers on cancer. Unless firmly based on some deeper cellular or molecular biological principle, papers that are highly specialized in this field, with limited appeal to cell biologists at large, should be directed towards journals devoted to cancer, there being very many from which to choose.
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