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Zhou X, Xia G, Liu Y, Xin X, Shi R, An L, Zhang J, Zhao R, Zhao Y, Wang H. Analysis of carcinogenic signaling networks in endometrial cancer identifies RAB17 as a potential target. J Cell Physiol 2020; 236:328-339. [PMID: 32529729 DOI: 10.1002/jcp.29845] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2020] [Revised: 05/07/2020] [Accepted: 05/26/2020] [Indexed: 02/06/2023]
Abstract
Endometrial cancer is one of the most common malignancies in postmenopausal women. Several potential therapeutic targets have been investigated in current research, but few have been used clinically. Therefore, further investigating the potential pathogenesis of endometrial cancer and new effective therapeutic targets for endometrial malignancies is still necessary. Our study used a The Cancer Genome Atlas dataset and two Gene Expression Omnibus datasets for weighted gene coexpression network analysis to identify important genes associated with the histological grades of endometrial cancer. In addition, we performed gene set enrichment analysis on the three datasets and found that abnormally activated signaling pathways and metabolic pathways are the main biological behaviors of endometrial cancer. Moreover, we further used different algorithms and identified the RAB17 gene as a potential study object. To further illustrate the potential role of the genes we analyzed in clinical and cellular aspects, we performed a clinical correlation analysis. Finally, we demonstrated the important roles and mechanisms of the RAB17 gene in the cell cycle, proliferation, and metastasis of endometrial cancer. Using repeated database analysis and cell-level assays, we propose RAB17 as a potential target gene for endometrial cancer for further study.
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Affiliation(s)
- Xing Zhou
- Department of Obstetrics and Gynecology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Geqing Xia
- Department of Obstetrics and Gynecology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Yan Liu
- Department of Obstetrics and Gynecology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Xiaoyan Xin
- Department of Obstetrics and Gynecology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Rui Shi
- Department of Obstetrics and Gynecology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Lanfen An
- Department of Obstetrics and Gynecology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Jun Zhang
- Department of Obstetrics and Gynecology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Rong Zhao
- Department of Obstetrics and Gynecology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Yingchao Zhao
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Hongbo Wang
- Department of Obstetrics and Gynecology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
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Chen KM, Tan J, Way GP, Doing G, Hogan DA, Greene CS. PathCORE-T: identifying and visualizing globally co-occurring pathways in large transcriptomic compendia. BioData Min 2018; 11:14. [PMID: 29988723 PMCID: PMC6029133 DOI: 10.1186/s13040-018-0175-7] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2018] [Accepted: 06/18/2018] [Indexed: 12/29/2022] Open
Abstract
Background Investigators often interpret genome-wide data by analyzing the expression levels of genes within pathways. While this within-pathway analysis is routine, the products of any one pathway can affect the activity of other pathways. Past efforts to identify relationships between biological processes have evaluated overlap in knowledge bases or evaluated changes that occur after specific treatments. Individual experiments can highlight condition-specific pathway-pathway relationships; however, constructing a complete network of such relationships across many conditions requires analyzing results from many studies. Results We developed PathCORE-T framework by implementing existing methods to identify pathway-pathway transcriptional relationships evident across a broad data compendium. PathCORE-T is applied to the output of feature construction algorithms; it identifies pairs of pathways observed in features more than expected by chance as functionally co-occurring. We demonstrate PathCORE-T by analyzing an existing eADAGE model of a microbial compendium and building and analyzing NMF features from the TCGA dataset of 33 cancer types. The PathCORE-T framework includes a demonstration web interface, with source code, that users can launch to (1) visualize the network and (2) review the expression levels of associated genes in the original data. PathCORE-T creates and displays the network of globally co-occurring pathways based on features observed in a machine learning analysis of gene expression data. Conclusions The PathCORE-T framework identifies transcriptionally co-occurring pathways from the results of unsupervised analysis of gene expression data and visualizes the relationships between pathways as a network. PathCORE-T recapitulated previously described pathway-pathway relationships and suggested experimentally testable additional hypotheses that remain to be explored. Electronic supplementary material The online version of this article (10.1186/s13040-018-0175-7) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Kathleen M Chen
- 1Department of Systems Pharmacology and Translational Therapeutics. Perelman School of Medicine, University of Pennsylvania, 3400 Civic Center Blvd., Philadelphia, PA 19104 USA
| | - Jie Tan
- 2Department of Molecular and Systems Biology, Geisel School of Medicine at Dartmouth, Hanover, NH 03755 USA
| | - Gregory P Way
- 1Department of Systems Pharmacology and Translational Therapeutics. Perelman School of Medicine, University of Pennsylvania, 3400 Civic Center Blvd., Philadelphia, PA 19104 USA
| | - Georgia Doing
- 3Department of Microbiology and Immunology, Geisel School of Medicine at Dartmouth, Hanover, NH 03755 USA
| | - Deborah A Hogan
- 3Department of Microbiology and Immunology, Geisel School of Medicine at Dartmouth, Hanover, NH 03755 USA
| | - Casey S Greene
- 1Department of Systems Pharmacology and Translational Therapeutics. Perelman School of Medicine, University of Pennsylvania, 3400 Civic Center Blvd., Philadelphia, PA 19104 USA
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