Dongyan Ding1,2, Tingyuan Lang3,4,5, Dongling Zou2, Jiawei Tan6, Jia Chen6, Lei Zhou7,8,9, Dong Wang2, Rong Li2, Yunzhe Li1,2, Jingshu Liu1,2, Cui Ma10, Qi Zhou11,12,13. 1. Key Laboratory of Biorheological Science and Technology (Chongqing University), Ministry of Education, Bioengineering College, Chongqing University, Chongqing, 400044, People's Republic of China. 2. Department of Gynecologic Oncology, School of Medicine, Chongqing University Cancer Hospital, , Chongqing University, Chongqing, 400030, People's Republic of China. 3. Key Laboratory of Biorheological Science and Technology (Chongqing University), Ministry of Education, Bioengineering College, Chongqing University, Chongqing, 400044, People's Republic of China. michaellang2009@163.com. 4. Department of Gynecologic Oncology, School of Medicine, Chongqing University Cancer Hospital, , Chongqing University, Chongqing, 400030, People's Republic of China. michaellang2009@163.com. 5. Chongqing Key Laboratory of Translational Research for Cancer Metastasis and Individualized Treatment, School of Medicine, Chongqing University Cancer Hospital, Chongqing University, Chongqing, 400030, People's Republic of China. michaellang2009@163.com. 6. School of Mathematics and Statistics, Changchun University of Technology, Changchun, 130012, People's Republic of China. 7. Singapore Eye Research Institute, The academia, 20 College Road, Discovery Tower Level 6, Singapore, 169856, Singapore. 8. Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore. 9. Duke-NUS Medical School, Ophthalmology and Visual Sciences Academic Clinical Research Program, National University of Singapore, Singapore, Singapore. 10. Department of Pediatric Hematology, First Hospital of Jilin University, Changchun, 130023, Jilin, People's Republic of China. 11. Key Laboratory of Biorheological Science and Technology (Chongqing University), Ministry of Education, Bioengineering College, Chongqing University, Chongqing, 400044, People's Republic of China. cqzl_zq@163.com. 12. Department of Gynecologic Oncology, School of Medicine, Chongqing University Cancer Hospital, , Chongqing University, Chongqing, 400030, People's Republic of China. cqzl_zq@163.com. 13. Chongqing Key Laboratory of Translational Research for Cancer Metastasis and Individualized Treatment, School of Medicine, Chongqing University Cancer Hospital, Chongqing University, Chongqing, 400030, People's Republic of China. cqzl_zq@163.com.
Abstract
BACKGROUND: Accurately forecasting the prognosis could improve cervical cancer management, however, the currently used clinical features are difficult to provide enough information. The aim of this study is to improve forecasting capability by developing a miRNAs-based machine learning survival prediction model. RESULTS: The expression characteristics of miRNAs were chosen as features for model development. The cervical cancer miRNA expression data was obtained from The Cancer Genome Atlas database. Preprocessing, including unquantified data removal, missing value imputation, samples normalization, log transformation, and feature scaling, was performed. In total, 42 survival-related miRNAs were identified by Cox Proportional-Hazards analysis. The patients were optimally clustered into four groups with three different 5-years survival outcome (≥ 90%, ≈ 65%, ≤ 40%) by K-means clustering algorithm base on top 10 survival-related miRNAs. According to the K-means clustering result, a prediction model with high performance was established. The pathways analysis indicated that the miRNAs used play roles involved in the regulation of cancer stem cells. CONCLUSION: A miRNAs-based machine learning cervical cancer survival prediction model was developed that robustly stratifies cervical cancer patients into high survival rate (5-years survival rate ≥ 90%), moderate survival rate (5-years survival rate ≈ 65%), and low survival rate (5-years survival rate ≤ 40%).
BACKGROUND: Accurately forecasting the prognosis could improve cervical cancer management, however, the currently used clinical features are difficult to provide enough information. The aim of this study is to improve forecasting capability by developing a miRNAs-based machine learning survival prediction model. RESULTS: The expression characteristics of miRNAs were chosen as features for model development. The cervical cancer miRNA expression data was obtained from The Cancer Genome Atlas database. Preprocessing, including unquantified data removal, missing value imputation, samples normalization, log transformation, and feature scaling, was performed. In total, 42 survival-related miRNAs were identified by Cox Proportional-Hazards analysis. The patients were optimally clustered into four groups with three different 5-years survival outcome (≥ 90%, ≈ 65%, ≤ 40%) by K-means clustering algorithm base on top 10 survival-related miRNAs. According to the K-means clustering result, a prediction model with high performance was established. The pathways analysis indicated that the miRNAs used play roles involved in the regulation of cancer stem cells. CONCLUSION: A miRNAs-based machine learning cervical cancer survival prediction model was developed that robustly stratifies cervical cancerpatients into high survival rate (5-years survival rate ≥ 90%), moderate survival rate (5-years survival rate ≈ 65%), and low survival rate (5-years survival rate ≤ 40%).
Authors: Umesh Kumar Lilhore; M Poongodi; Amandeep Kaur; Sarita Simaiya; Abeer D Algarni; Hela Elmannai; V Vijayakumar; Godwin Brown Tunze; Mounir Hamdi Journal: Comput Math Methods Med Date: 2022-05-04 Impact factor: 2.809