Machine learning-based prognosis signature for survival prediction of patients with clear cell renal cell carcinoma
The sole clinicopathological characteristic is not enough for the prediction of survival of patients with clear cell renal cell carcinoma (ccRCC). However, the survival prediction model constructed by machine learning technology for patients with ccRCC using clinicopathological features is rarely re...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-09-01
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| Series: | Heliyon |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844022018667 |
| _version_ | 1811254999084171264 |
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| author | Siteng Chen Tuanjie Guo Encheng Zhang Tao Wang Guangliang Jiang Yishuo Wu Xiang Wang Rong Na Ning Zhang |
| author_facet | Siteng Chen Tuanjie Guo Encheng Zhang Tao Wang Guangliang Jiang Yishuo Wu Xiang Wang Rong Na Ning Zhang |
| author_sort | Siteng Chen |
| collection | DOAJ |
| description | The sole clinicopathological characteristic is not enough for the prediction of survival of patients with clear cell renal cell carcinoma (ccRCC). However, the survival prediction model constructed by machine learning technology for patients with ccRCC using clinicopathological features is rarely reported yet. In this study, a total of 5878 patients diagnosed as ccRCC from four independent patient cohorts were recruited. The least absolute shrinkage and selection operator analysis was implemented to identify optimal clinicopathological characteristics and calculate each coefficient to construct the prognosis model. In addition, weighted gene co-expression network and gene enrichment analysis associated with risk score were also carried out. Three clinicopathologic features were selected for the construction of the prognosis risk score model as the prognostic factors of ccRCC, including tumor size, tumor grade, and tumor stage. In the CPTAC (Clinical Proteomic Tumor Analysis Consortium) cohort, the General cohort, the SEER (Surveillance, Epidemiology, and End Results) cohort, and the Huashan cohort, patients with high-risk score had worse clinical outcomes than patients with low-risk score (hazard ratio 5.15, 4.64, 3.96, and 5.15, respectively). Further functional enrichment analysis demonstrated that our machine learning-based risk score was significantly connected with some cell proliferation-related pathways, consisting of DNA repair, cell division, and cell cycle. In summary, we developed and validated a machine learning-based prognosis prediction model, which might contribute to clinical decision-making for patients with ccRCC. |
| first_indexed | 2024-04-12T17:16:09Z |
| format | Article |
| id | doaj.art-a7faa8cc506746aa9fbb2ac341cf6932 |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-04-12T17:16:09Z |
| publishDate | 2022-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-a7faa8cc506746aa9fbb2ac341cf69322022-12-22T03:23:38ZengElsevierHeliyon2405-84402022-09-0189e10578Machine learning-based prognosis signature for survival prediction of patients with clear cell renal cell carcinomaSiteng Chen0Tuanjie Guo1Encheng Zhang2Tao Wang3Guangliang Jiang4Yishuo Wu5Xiang Wang6Rong Na7Ning Zhang8Department of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaDepartment of Urology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaDepartment of Urology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaDepartment of Urology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaDepartment of Urology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaDepartment of Urology, Huashan Hospital, Fudan University, Shanghai, ChinaDepartment of Urology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Corresponding author.Department of Surgery, Queen Mary Hospital, The University of Hong Kong, Hong Kong SAR, China; Corresponding author.Department of Urology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Corresponding author.The sole clinicopathological characteristic is not enough for the prediction of survival of patients with clear cell renal cell carcinoma (ccRCC). However, the survival prediction model constructed by machine learning technology for patients with ccRCC using clinicopathological features is rarely reported yet. In this study, a total of 5878 patients diagnosed as ccRCC from four independent patient cohorts were recruited. The least absolute shrinkage and selection operator analysis was implemented to identify optimal clinicopathological characteristics and calculate each coefficient to construct the prognosis model. In addition, weighted gene co-expression network and gene enrichment analysis associated with risk score were also carried out. Three clinicopathologic features were selected for the construction of the prognosis risk score model as the prognostic factors of ccRCC, including tumor size, tumor grade, and tumor stage. In the CPTAC (Clinical Proteomic Tumor Analysis Consortium) cohort, the General cohort, the SEER (Surveillance, Epidemiology, and End Results) cohort, and the Huashan cohort, patients with high-risk score had worse clinical outcomes than patients with low-risk score (hazard ratio 5.15, 4.64, 3.96, and 5.15, respectively). Further functional enrichment analysis demonstrated that our machine learning-based risk score was significantly connected with some cell proliferation-related pathways, consisting of DNA repair, cell division, and cell cycle. In summary, we developed and validated a machine learning-based prognosis prediction model, which might contribute to clinical decision-making for patients with ccRCC.http://www.sciencedirect.com/science/article/pii/S2405844022018667Clear cell renal cell carcinomaMachine learningMulti-center studyPrognosisClinicopathology |
| spellingShingle | Siteng Chen Tuanjie Guo Encheng Zhang Tao Wang Guangliang Jiang Yishuo Wu Xiang Wang Rong Na Ning Zhang Machine learning-based prognosis signature for survival prediction of patients with clear cell renal cell carcinoma Heliyon Clear cell renal cell carcinoma Machine learning Multi-center study Prognosis Clinicopathology |
| title | Machine learning-based prognosis signature for survival prediction of patients with clear cell renal cell carcinoma |
| title_full | Machine learning-based prognosis signature for survival prediction of patients with clear cell renal cell carcinoma |
| title_fullStr | Machine learning-based prognosis signature for survival prediction of patients with clear cell renal cell carcinoma |
| title_full_unstemmed | Machine learning-based prognosis signature for survival prediction of patients with clear cell renal cell carcinoma |
| title_short | Machine learning-based prognosis signature for survival prediction of patients with clear cell renal cell carcinoma |
| title_sort | machine learning based prognosis signature for survival prediction of patients with clear cell renal cell carcinoma |
| topic | Clear cell renal cell carcinoma Machine learning Multi-center study Prognosis Clinicopathology |
| url | http://www.sciencedirect.com/science/article/pii/S2405844022018667 |
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