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Zou J, Qin Z, Li R, Yan X, Huang H, Yang B, Zhou F, Zhang L. iProPhos: A Web-Based Interactive Platform for Integrated Proteome and Phosphoproteome Analysis. Mol Cell Proteomics 2024; 23:100693. [PMID: 38097182 PMCID: PMC10828474 DOI: 10.1016/j.mcpro.2023.100693] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 11/06/2023] [Accepted: 12/11/2023] [Indexed: 01/29/2024] Open
Abstract
Large-scale omics studies have generated a wealth of mass spectrometry-based proteomics data, which provide additional insights into disease biology spanning genomic boundaries. However, there is a notable lack of web-based analysis and visualization tools that facilitate the reutilization of these data. Given this challenge, we present iProPhos, a user-friendly web server to deliver interactive and customizable functionalities. iProPhos incorporates a large number of samples, including 1444 tumor samples and 746 normal samples across 12 cancer types, sourced from the Clinical Proteomic Tumor Analysis Consortium. Additionally, users can also upload their own proteomics/phosphoproteomics data for analysis and visualization. In iProPhos, users can perform profiling plotting and differential expression, patient survival, clinical feature-related, and correlation analyses, including protein-protein, mRNA-protein, and kinase-substrate correlations. Furthermore, functional enrichment, protein-protein interaction network, and kinase-substrate enrichment analyses are accessible. iProPhos displays the analytical results in interactive figures and tables with various selectable parameters. It is freely accessible at http://longlab-zju.cn/iProPhos without login requirement. We present two case studies to demonstrate that iProPhos can identify potential drug targets and upstream kinases contributing to site-specific phosphorylation. Ultimately, iProPhos allows end-users to leverage the value of big data in cancer proteomics more effectively and accelerates the discovery of novel therapeutic targets.
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Affiliation(s)
- Jing Zou
- The Second Affiliated Hospital and Life Sciences Institute and School of Medicine, The MOE Key Laboratory of Biosystems Homeostasis and Protection and Zhejiang Provincial Key Laboratory for Cancer Molecular Cell Biology, Zhejiang University, Hangzhou, China
| | - Ziran Qin
- The Second Affiliated Hospital and Life Sciences Institute and School of Medicine, The MOE Key Laboratory of Biosystems Homeostasis and Protection and Zhejiang Provincial Key Laboratory for Cancer Molecular Cell Biology, Zhejiang University, Hangzhou, China
| | - Ran Li
- School of Medicine, Hangzhou City University, Hangzhou, China.
| | - Xiaohua Yan
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Nanchang University, Nanchang, China
| | - Huizhe Huang
- The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Bing Yang
- The Second Affiliated Hospital and Life Sciences Institute and School of Medicine, The MOE Key Laboratory of Biosystems Homeostasis and Protection and Zhejiang Provincial Key Laboratory for Cancer Molecular Cell Biology, Zhejiang University, Hangzhou, China; Department of Pharmaceutical Chemistry and the Cardiovascular Research Institute, University of California, San Francisco, California, USA
| | - Fangfang Zhou
- Institutes of Biology and Medical Sciences, Soochow University, Suzhou, China.
| | - Long Zhang
- The Second Affiliated Hospital and Life Sciences Institute and School of Medicine, The MOE Key Laboratory of Biosystems Homeostasis and Protection and Zhejiang Provincial Key Laboratory for Cancer Molecular Cell Biology, Zhejiang University, Hangzhou, China; Cancer Center, Zhejiang University, Hangzhou, China.
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