HMPA: a pioneering framework for the noncanonical peptidome from discovery to functional insights

Advancements in peptidomics have revealed numerous small open reading frames with coding potential and revealed that some of these micropeptides are closely related to human cancer. However, the systematic analysis and integration from sequence to structure and function remains largely undeveloped. Here, as a solution, we built a workflow for the collection and analysis of proteomic data, transcriptomic data, and clinical outcomes for cancer-associated micropeptides using publicly available datasets from large cohorts. We initially identified 19 586 novel micropeptides by reanalyzing proteomic profile data from 3753 samples across 8 cancer types. Further quantitative analysis of these micropeptides, along with associated clinical data, identified 3065 that were dysregulated in cancer, with 370 of them showing a strong association with prognosis. Moreover, we employed a deep learning framework to construct a micropeptide-protein interaction network for further bioinformatics analysis, revealing that micropeptides are involved in multiple biological processes as bioactive molecules. Taken together, our atlas provides a benchmark for high-throughput prediction and functional exploration of micropeptides, providing new insights into their biological mechanisms in cancer. The HMPA is freely available at http://hmpa.zju.edu.cn.

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Publication Details

Journal
Briefings in Bioinformatics
Published
2024-09-23
DOI
https://doi.org/10.1093/bib/bbae510
Citations
1
Primary Topic
RNA and protein synthesis mechanisms
Type
article
Field-Weighted Citation Impact
0.23

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article

HMPA: a pioneering framework for the noncanonical peptidome from discovery to functional insights

Zhaoyuan Fang, Manman Tan, Chengyu Shi, Qiang Sun et al.
1 citations
Briefings in Bioinformatics
RNA and protein synthesis mechanisms
0.23
article

HMPA: a pioneering framework for the noncanonical peptidome from discovery to functional insights

Zhaoyuan Fang, Manman Tan, Chengyu Shi, Qiang Sun, Aifu Lin, Weiqiang Lin, Xinwan Su, Jian Liu, Fangzhou Liu, Yu Meng, Tianhua Zhou, Linyu Zhu, Yu Chen, Yang Liu, Xinyi Wang, Ying Wang
article en
1 citations

Abstract

Advancements in peptidomics have revealed numerous small open reading frames with coding potential and revealed that some of these micropeptides are closely related to human cancer. However, the systematic analysis and integration from sequence to structure and function remains largely undeveloped. Here, as a solution, we built a workflow for the collection and analysis of proteomic data, transcriptomic data, and clinical outcomes for cancer-associated micropeptides using publicly available datasets from large cohorts. We initially identified 19 586 novel micropeptides by reanalyzing proteomic profile data from 3753 samples across 8 cancer types. Further quantitative analysis of these micropeptides, along with associated clinical data, identified 3065 that were dysregulated in cancer, with 370 of them showing a strong association with prognosis. Moreover, we employed a deep learning framework to construct a micropeptide-protein interaction network for further bioinformatics analysis, revealing that micropeptides are involved in multiple biological processes as bioactive molecules. Taken together, our atlas provides a benchmark for high-throughput prediction and functional exploration of micropeptides, providing new insights into their biological mechanisms in cancer. The HMPA is freely available at http://hmpa.zju.edu.cn.

Briefings in BioinformaticsVol. 25(6)
Ministry of Education of the People's Republic of China (CN), University of Toronto (CA), Canada Research Chairs (CA), Zhejiang Cancer Hospital (CN), Yangtze River Delta Physics Research Center (China) (CN), Second Affiliated Hospital of Zhejiang University (CN), Zhejiang University-University of Edinburgh Institute (CN), Zhejiang University (CN)
National Natural Science Foundation of China, Chinese Academy of Sciences, Natural Science Foundation for Young Scientists of Shanxi Province, Young Scientists Fund, National Key Research and Development Program of China, Fundamental Research Funds for the Central Universities, National Science Fund for Distinguished Young Scholars
Quality Education
Openalex Percentile: Top 42%
RNA and protein synthesis mechanisms
0.23
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