ORIGINAL RESEARCH article

Front. Genet.

Sec. Computational Genomics

Volume 16 - 2025 | doi: 10.3389/fgene.2025.1587854

This article is part of the Research TopicDeep Machine Learning and Big Data Resources for Transcriptional Regulation Analysis, Volume IIView all 4 articles

Shared genetic features inference among hypoxia-ischemia diseases in the presence of heterogenous omics data based on a novel risk assessment method

Provisionally accepted
Yifan  ZhangYifan Zhang1,2,3,4Jianfeng  LiuJianfeng Liu5Qianxun  YangQianxun Yang2,6Hongce  ChenHongce Chen2,7Shuo  ChenShuo Chen2,8Shaogang  LiShaogang Li9Changgui  LeiChanggui Lei10Mingyan  FangMingyan Fang10,11Huanhuan  LiuHuanhuan Liu1Xin  JinXin Jin1,11,2,4*Yingying  WangYingying Wang2,4*
  • 1BGI Research, Chongqing, China
  • 2BGI Research, Shenzhen, China
  • 3University of Chinese Academy of Sciences, Beijing, Beijing, China
  • 4Shenzhen Key Laboratory of Transomics Biotechnologies, Shenzhen, China
  • 5Department of Neurology, The Eighth Affiliated Hospital, Sun Yat-Sen University, Shenzhen, China
  • 6Shenzhen University, Shenzhen, Guangdong Province, China
  • 7Lanzhou University, Lanzhou, Gansu Province, China
  • 8Hunan Agricultural University, Changsha, Hunan, China
  • 9South China University of Technology, Guangzhou, Guangdong Province, China
  • 10BGI Research, Wuhan, Hebei Province, China
  • 11State Key Laboratory of Genome and Multi-omics Technologies, Shenzhen, China

The final, formatted version of the article will be published soon.

The hypoxia-ischemia (H-I) diseases share some common mechanisms which may help to delay the diseases' processing. However, the shared features are still unclear due to the lack of large scale high-quality multi -omics data that specifically target the same disease, population, and tissues/cells. In this study, we developed a novel risk assessment method to analyze four H-I diseases including eclampsia/preeclampsia (PE), pulmonary arterial hypertension (PAH), high-altitude polycythemia (HAPC), and ischemic stroke (IS). A combined new evaluation score was designed to integrate evaluation information from genomics, transcriptomics, proteomics, and metabolomics in previous researches. Genes were then divided into different groups according to their risk assessment score. The most significant group (direct biomarkers) contained genes with direct evidence of association to H-I disease: PIEZO2 and HPGD (shared), TSIX and SAA1 (PAH -specific), GSTM1, DNTT, and IGKC (HAPC -specific), LEP, SERPINA3, and ARHGEF4 (PE -specific), CD3D, ITK, and RPL18A (IS -specific). The groups 'Intermediate crucial biomarkers' contained genes played important roles in H-I disease related biological processes: CXCL8 (shared), HBG2, GRIN2A, and FGFBP1 (PAHspecific), FAM111B (HAPC -specific), C12orf39 and SLAMF1 (PE -specific). The genes lacking disease-association evidence but with similar characteristics with the above two groups were considered as 'potential minor-effect biomarkers': are SRRM2 -AS1 (shared), ATP8A1 (PAHspecific), RXFP1 and HJURP (HAPC -specific), HIST1H1T (PE -specific). With the development of biological experiments, these intermediate crucial and potential minor-effect biomarkers may be proved to be direct biomarkers in the future. Therefore, these biomarkers may serve as an entry point for subsequent research and are of great significance.

Keywords: Risk Assessment, hypoxia-ischemia, shared features, Disease profile, omics

Received: 05 Mar 2025; Accepted: 14 Apr 2025.

Copyright: © 2025 Zhang, Liu, Yang, Chen, Chen, Li, Lei, Fang, Liu, Jin and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence:
Xin Jin, BGI Research, Chongqing, China
Yingying Wang, BGI Research, Shenzhen, China

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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