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Jiangzhou H, Zhang H, Sun R, Fahira A, Wang K, Li Z, Shi Y, Wang Z. Integrative omics analysis reveals effective stratification and potential prognosis markers of pan-gastrointestinal cancers. iScience 2021; 24:102824. [PMID: 34381964 PMCID: PMC8340129 DOI: 10.1016/j.isci.2021.102824] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Revised: 05/01/2021] [Accepted: 07/05/2021] [Indexed: 12/09/2022] Open
Abstract
Gastrointestinal (GI) tract cancers are the most common malignant cancers with high mortality rate. Pan-cancer multi-omics data fusion provides a powerful strategy to examine commonalities and differences among various cancer types and benefits for the identification of pan-cancer drug targets. Herein, we conducted an integrative omics analysis on The Cancer Genome Atlas pan-GI samples including six carcinomas and stratified into 9 clusters, i.e. 5 single-type-dominant clusters and 4 mixed clusters, the clustering reveals the molecular features of different subtypes, other than the organ and cell-of-origin classifications. Especially the mixed clusters revealed the homogeneity of pan-GI cancers. We demonstrated that the prognosis differences among pan-GI subtypes based on multi-omics integration are more significant than clustering by single-omics. The potential prognostic markers for pan-GI stratification were identified by proportional hazards model, such as PSCA (for colorectal and stomach cancer) and PPP1CB (for liver and pancreatic cancer), which have prominent prognostic power supported by high concordance index. Pan-cancer multi-omics strategy reveals homogeneity and heterogeneity of pan-GI cancers Identify 9 iclusters with significantly different survival and molecular features Potential prognostic markers have prominent power supported by concordance index
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Affiliation(s)
- Huiting Jiangzhou
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Collaborative Innovation Centre for Brain Science, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Hang Zhang
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Collaborative Innovation Centre for Brain Science, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Renliang Sun
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Collaborative Innovation Centre for Brain Science, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Aamir Fahira
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Collaborative Innovation Centre for Brain Science, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Ke Wang
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Collaborative Innovation Centre for Brain Science, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Zhiqiang Li
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Collaborative Innovation Centre for Brain Science, Shanghai Jiao Tong University, Shanghai 200030, China.,Affiliated Hospital of Qingdao University & Biomedical Sciences Institute of Qingdao University, Qingdao University, Qingdao 266003, China
| | - Yongyong Shi
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Collaborative Innovation Centre for Brain Science, Shanghai Jiao Tong University, Shanghai 200030, China.,Affiliated Hospital of Qingdao University & Biomedical Sciences Institute of Qingdao University, Qingdao University, Qingdao 266003, China
| | - Zhuo Wang
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Collaborative Innovation Centre for Brain Science, Shanghai Jiao Tong University, Shanghai 200030, China
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Lu J, Huang G, Li HP, Feng KY, Chen L, Zheng MY, Cai YD. Prediction of cancer drugs by chemical-chemical interactions. PLoS One 2014; 9:e87791. [PMID: 24498372 PMCID: PMC3912061 DOI: 10.1371/journal.pone.0087791] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2013] [Accepted: 12/31/2013] [Indexed: 11/19/2022] Open
Abstract
Cancer, which is a leading cause of death worldwide, places a big burden on health-care system. In this study, an order-prediction model was built to predict a series of cancer drug indications based on chemical-chemical interactions. According to the confidence scores of their interactions, the order from the most likely cancer to the least one was obtained for each query drug. The 1(st) order prediction accuracy of the training dataset was 55.93%, evaluated by Jackknife test, while it was 55.56% and 59.09% on a validation test dataset and an independent test dataset, respectively. The proposed method outperformed a popular method based on molecular descriptors. Moreover, it was verified that some drugs were effective to the 'wrong' predicted indications, indicating that some 'wrong' drug indications were actually correct indications. Encouraged by the promising results, the method may become a useful tool to the prediction of drugs indications.
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Affiliation(s)
- Jing Lu
- Department of Medicinal Chemistry, School of Pharmacy, Yantai University, Yantai, Shandong, People’s Republic of China
| | - Guohua Huang
- Institute of Systems Biology, Shanghai University, Shanghai, People’s Republic of China
- Department of Mathematics, Shaoyang University, Shaoyang, Hunan, People’s Republic of China
| | - Hai-Peng Li
- CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, People’s Republic of China
| | - Kai-Yan Feng
- Beijing Genomics Institute, Shenzhen Beishan Industrial zone, Shenzhen, People’s Republic of China
| | - Lei Chen
- College of Information Engineering, Shanghai Maritime University, Shanghai, People’s Republic of China
- * E-mail: (LC); (MYZ); (YDC)
| | - Ming-Yue Zheng
- State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Shanghai, People’s Republic of China
- * E-mail: (LC); (MYZ); (YDC)
| | - Yu-Dong Cai
- Institute of Systems Biology, Shanghai University, Shanghai, People’s Republic of China
- * E-mail: (LC); (MYZ); (YDC)
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Bano N, Najam R, Mateen A. Comparative Assessment of Skin and Subcutaneous Toxicity in Patients of Advanced Colorectal Carcinoma Treated with Different Schedules of FOLFOX. Asian Pac J Cancer Prev 2013; 14:1781-6. [DOI: 10.7314/apjcp.2013.14.3.1781] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
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