1
|
Chen Y, Wen Y, Xie C, Chen X, He S, Bo X, Zhang Z. MOCSS: Multi-omics data clustering and cancer subtyping via shared and specific representation learning. iScience 2023; 26:107378. [PMID: 37559907 PMCID: PMC10407241 DOI: 10.1016/j.isci.2023.107378] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 05/23/2023] [Accepted: 07/07/2023] [Indexed: 08/11/2023] Open
Abstract
Cancer is an extremely complex disease and each type of cancer usually has several different subtypes. Multi-omics data can provide more comprehensive biological information for identifying and discovering cancer subtypes. However, existing unsupervised cancer subtyping methods cannot effectively learn comprehensive shared and specific information of multi-omics data. Therefore, a novel method is proposed based on shared and specific representation learning. For each omics data, two autoencoders are applied to extract shared and specific information, respectively. To reduce redundancy and mutual interference, orthogonality constraint is introduced to separate shared and specific information. In addition, contrastive learning is applied to align the shared information and strengthen their consistency. Finally, the obtained shared and specific information for all samples are used for clustering tasks to achieve cancer subtyping. Experimental results demonstrate that the proposed method can effectively capture shared and specific information of multi-omics data and outperform other state-of-the-art methods on cancer subtyping.
Collapse
Affiliation(s)
- Yuxin Chen
- School of Informatics, Xiamen University, Xiamen 361005, China
| | - Yuqi Wen
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Chenyang Xie
- School of Informatics, Xiamen University, Xiamen 361005, China
| | - Xinjian Chen
- School of Informatics, Xiamen University, Xiamen 361005, China
| | - Song He
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Xiaochen Bo
- Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Zhongnan Zhang
- School of Informatics, Xiamen University, Xiamen 361005, China
| |
Collapse
|
2
|
Xie B, Chen X, Deng Q, Shi K, Xiao J, Zou Y, Yang B, Guan A, Yang S, Dai Z, Xie H, He S, Chen Q. Development and Validation of a Prognostic Nomogram for Lung Adenocarcinoma: A Population-Based Study. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:5698582. [PMID: 36536690 PMCID: PMC9759395 DOI: 10.1155/2022/5698582] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Revised: 11/16/2022] [Accepted: 11/17/2022] [Indexed: 01/22/2024]
Abstract
PURPOSE To establish an effective and accurate prognostic nomogram for lung adenocarcinoma (LUAD). Patients and Methods. 62,355 LUAD patients from 1975 to 2016 enrolled in the Surveillance, Epidemiology, and End Results (SEER) database were randomly and equally divided into the training cohort (n = 31,179) and the validation cohort (n = 31,176). Univariate and multivariate Cox regression analyses screened the predictive effects of each variable on survival. The concordance index (C-index), calibration curves, receiver operating characteristic (ROC) curve, and area under the ROC curve (AUC) were used to examine and validate the predictive accuracy of the nomogram. Kaplan-Meier curves were used to estimate overall survival (OS). RESULTS 10 prognostic factors associated with OS were identified, including age, sex, race, marital status, American Joint Committee on Cancer (AJCC) TNM stage, tumor size, grade, and primary site. A nomogram was established based on these results. C-indexes of the nomogram model reached 0.777 (95% confidence interval (CI), 0.773 to 0.781) and 0.779 (95% CI, 0.775 to 0.783) in the training and validation cohorts, respectively. The calibration curves were well-fitted for both cohorts. The AUC for the 3- and 5-year OS presented great prognostic accuracy in the training cohort (AUC = 0.832 and 0.827, respectively) and validation cohort (AUC = 0.835 and 0.828, respectively). The Kaplan-Meier curves presented significant differences in OS among the groups. CONCLUSION The nomogram allows accurate and comprehensive prognostic prediction for patients with LUAD.
Collapse
Affiliation(s)
- Bin Xie
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha 410008, China
- Department of Geriatrics,Respiratory Medicine, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Xi Chen
- Department of Respiratory Medicine, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Qi Deng
- Department of Neurology, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Ke Shi
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha 410008, China
- Department of Geriatrics, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Jian Xiao
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha 410008, China
- Department of Geriatrics,Respiratory Medicine, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Yong Zou
- Department of Emergency Medicine, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Baishuang Yang
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha 410008, China
- Department of Geriatrics,Respiratory Medicine, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Anqi Guan
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha 410008, China
- Department of Geriatrics,Respiratory Medicine, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Shasha Yang
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha 410008, China
- Department of Geriatrics,Respiratory Medicine, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Ziyu Dai
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha 410008, China
- Department of Geriatrics,Respiratory Medicine, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Huayan Xie
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha 410008, China
- Department of Geriatrics,Respiratory Medicine, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Shuya He
- Institute of Biochemistry and Molecular Biology, Hengyang Medical College, University of South China, Hengyang 421001, China
| | - Qiong Chen
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha 410008, China
- Department of Geriatrics,Respiratory Medicine, Xiangya Hospital, Central South University, Changsha 410008, China
| |
Collapse
|