Integrated RNA sequencing analysis and machine learning identifies a metabolism-related prognostic signature in clear cell renal cell carcinoma
Abstract The connection between metabolic reprogramming and tumor progression has been demonstrated in an increasing number of researches. However, further research is required to identify how metabolic reprogramming affects interpatient heterogeneity and prognosis in clear cell renal cell carcinoma...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-85618-7 |
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| author | Yunxun Liu Zhiwei Yan Cheng Liu Rui Yang Qingyuan Zheng Jun Jian Minghui Wang Lei Wang Xiaodong Weng Zhiyuan Chen Xiuheng Liu |
| author_facet | Yunxun Liu Zhiwei Yan Cheng Liu Rui Yang Qingyuan Zheng Jun Jian Minghui Wang Lei Wang Xiaodong Weng Zhiyuan Chen Xiuheng Liu |
| author_sort | Yunxun Liu |
| collection | DOAJ |
| description | Abstract The connection between metabolic reprogramming and tumor progression has been demonstrated in an increasing number of researches. However, further research is required to identify how metabolic reprogramming affects interpatient heterogeneity and prognosis in clear cell renal cell carcinoma (ccRCC). In this work, single-cell RNA sequencing (scRNA-seq) based deconvolution was utilized to create a malignant cell hierarchy with metabolic differences and to investigate the relationship between metabolic biomarkers and prognosis. Simultaneously, we created a machine learning-based approach for creating metabolism-related prognostic signature (MRPS). Gamma-glutamyltransferase 6 (GGT6) was further explored for deep biological insights through in vitro experiments. Compared to 51 published signatures and conventional clinical features, MRPS showed substantially higher accuracy. Meanwhile, high MRPS-risk samples demonstrated an immunosuppressive phenotype with more infiltrations of regulatory T cell (Treg) and tumour-associated macrophage (TAM). Following the administration of immune checkpoint inhibitors (ICIs), MRPS showed consistent and strong performance and was an independent risk factor for overall survival. GGT6, an essential metabolic indicator and component of MRPS, has been proven to support proliferation and invasion in ccRCC. MRPS has the potential to be a highly effective tool in improving the clinical results of patients with ccRCC. |
| format | Article |
| id | doaj-art-776bda6e95684cdf96146b76bd8e7915 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-776bda6e95684cdf96146b76bd8e79152025-01-12T12:22:21ZengNature PortfolioScientific Reports2045-23222025-01-0115111610.1038/s41598-025-85618-7Integrated RNA sequencing analysis and machine learning identifies a metabolism-related prognostic signature in clear cell renal cell carcinomaYunxun Liu0Zhiwei Yan1Cheng Liu2Rui Yang3Qingyuan Zheng4Jun Jian5Minghui Wang6Lei Wang7Xiaodong Weng8Zhiyuan Chen9Xiuheng Liu10Department of Urology, Renmin Hospital of Wuhan UniversityDepartment of Urology, Renmin Hospital of Wuhan UniversityDepartment of Gynecology and Obstetrics, Renmin Hospital of Wuhan UniversityDepartment of Urology, Renmin Hospital of Wuhan UniversityDepartment of Urology, Renmin Hospital of Wuhan UniversityDepartment of Urology, Renmin Hospital of Wuhan UniversityDepartment of Urology, Renmin Hospital of Wuhan UniversityDepartment of Urology, Renmin Hospital of Wuhan UniversityDepartment of Urology, Renmin Hospital of Wuhan UniversityDepartment of Urology, Renmin Hospital of Wuhan UniversityDepartment of Urology, Renmin Hospital of Wuhan UniversityAbstract The connection between metabolic reprogramming and tumor progression has been demonstrated in an increasing number of researches. However, further research is required to identify how metabolic reprogramming affects interpatient heterogeneity and prognosis in clear cell renal cell carcinoma (ccRCC). In this work, single-cell RNA sequencing (scRNA-seq) based deconvolution was utilized to create a malignant cell hierarchy with metabolic differences and to investigate the relationship between metabolic biomarkers and prognosis. Simultaneously, we created a machine learning-based approach for creating metabolism-related prognostic signature (MRPS). Gamma-glutamyltransferase 6 (GGT6) was further explored for deep biological insights through in vitro experiments. Compared to 51 published signatures and conventional clinical features, MRPS showed substantially higher accuracy. Meanwhile, high MRPS-risk samples demonstrated an immunosuppressive phenotype with more infiltrations of regulatory T cell (Treg) and tumour-associated macrophage (TAM). Following the administration of immune checkpoint inhibitors (ICIs), MRPS showed consistent and strong performance and was an independent risk factor for overall survival. GGT6, an essential metabolic indicator and component of MRPS, has been proven to support proliferation and invasion in ccRCC. MRPS has the potential to be a highly effective tool in improving the clinical results of patients with ccRCC.https://doi.org/10.1038/s41598-025-85618-7Clear cell renal cell carcinomaSingle-cell RNA sequencingCell metabolic reprogrammingMachine learningPrognosis |
| spellingShingle | Yunxun Liu Zhiwei Yan Cheng Liu Rui Yang Qingyuan Zheng Jun Jian Minghui Wang Lei Wang Xiaodong Weng Zhiyuan Chen Xiuheng Liu Integrated RNA sequencing analysis and machine learning identifies a metabolism-related prognostic signature in clear cell renal cell carcinoma Scientific Reports Clear cell renal cell carcinoma Single-cell RNA sequencing Cell metabolic reprogramming Machine learning Prognosis |
| title | Integrated RNA sequencing analysis and machine learning identifies a metabolism-related prognostic signature in clear cell renal cell carcinoma |
| title_full | Integrated RNA sequencing analysis and machine learning identifies a metabolism-related prognostic signature in clear cell renal cell carcinoma |
| title_fullStr | Integrated RNA sequencing analysis and machine learning identifies a metabolism-related prognostic signature in clear cell renal cell carcinoma |
| title_full_unstemmed | Integrated RNA sequencing analysis and machine learning identifies a metabolism-related prognostic signature in clear cell renal cell carcinoma |
| title_short | Integrated RNA sequencing analysis and machine learning identifies a metabolism-related prognostic signature in clear cell renal cell carcinoma |
| title_sort | integrated rna sequencing analysis and machine learning identifies a metabolism related prognostic signature in clear cell renal cell carcinoma |
| topic | Clear cell renal cell carcinoma Single-cell RNA sequencing Cell metabolic reprogramming Machine learning Prognosis |
| url | https://doi.org/10.1038/s41598-025-85618-7 |
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