[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124405-en":3,"doc-seo-124405-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124405,1374391974564,"Clementine","https://ap-avatar.wpscdn.com/avatar/14000253aa45c000a9e?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779874745381141002",8,"Research & Report","Machine learning-based integration develops an immune-derived signature for diagnosing high-altitude pulmonary hypertension - Original Research","High-altitude pulmonary hypertension (HAPH) requires early, accurate diagnosis, yet gold-standard procedures remain difficult to deploy in high-altitude settings. A retrospective study integrates single-cell RNA sequencing, bulk RNA sequencing, and proteomic profiling to map immune microenvironment remodeling and train a machine learning diagnostic model. HAPH-associated signatures are validated with quantitative PCR, and a six-gene random forest model shows strong performance across training and external validation cohorts.","OPEN ACCESS  \nEDITED BY  \nDawei Yang,  \nFudan University, China  \nREVIEWED BY  \nMayur Doke,  \nUniversity of Miami, United States Yunhuan Liu,  \nTongji University, China  \n*CORRESPONDENCE  \nWu Li  \n [goodli002@163.com](goodli002@163.com)[ ](goodli002@163.com)Dongfeng Yin  \n [ydf1112@163.com](ydf1112@163.com)  \n†These authors have contributed equally to this work  \nRECEIVED 15 April 2025  \nACCEPTED 15 August 2025  \nPUBLISHED 02 September 2025  \nCITATION  \nYang D, Li Q, Yang F, Wang R, Jiang P, Wu J, Yang X, Huang Y, Liu Y, Wang S, Gou J, Sun Z, Ma J, Qin Y, Li W and Yin D (2025) Machine learning-based integration develops an immune-derived signature for diagnosing high-altitude pulmonary hypertension.  \nFront. Med. 12:1603140 .  \ndoi: 10.3389/fmed.2025.1603140  \nCOPYRIGHT  \n© 2025 Yang, Li, Yang, Wang, Jiang, Wu, Yang, Huang, Liu, Wang, Gou, Sun, Ma, Qin, Li and Yin. 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) and the copyright owner(s) 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.  \nTYPE Original Research PUBLISHED 02 September 2025 DOI 10.3389/fmed.2025.1603140  \nMachine learning-based integration develops an immune-derived signature for diagnosing high-altitude pulmonary hypertension  \nDan Yang 1,2†, Qian Li 1,2†, Feng Yang 1†, Rui Wang 1, Peng Jiang 1, Jialin Wu 1, Xi Yang 1, Yixuan Huang 2, Yuqiang Liu3,  \nShishang Wang 1, Junqiang Gou 1, Zhangfeng Sun 2, Junjie Ma 1, Yanhui Qin 2, Wu Li 1* and Dongfeng Yin 1,2,4*  \n1General Hospital of Xinjiang Military Command, Urumqi, China, 2Xinjiang Medical University, Urumqi, China, 3 No.951 Hospital of PLA, Korla, China, 4Shihezi University, Shihezi, China  \nBackground: High-altitude pulmonary hypertension (HAPH) is a common disease in high-altitude regions where implementation of gold-standard diagnostic methods remains logistically challenging.  \nMethods: In the retrospective analysis, we employed an integrative multi-omics approach combining single-cell RNA sequencing (scRNA-seq, n = 10), bulk RNA sequencing (RNA-seq, n = 126), and proteomic profiling (n = 42) to characterize immune microenvironment remodeling in HAPH. Subsequently, we established a machine learning-based diagnostic model. The HAPH-associated signatures were finally validated by Quantitative PCR.  \nResults: Through scRNA-seq analysis utilizing Ro/e and contribution scoring analysis, we first demonstrated the pivotal role of myeloid lineages in HAPHpathogenesis. Pseudotime trajectory analysis of the myeloid subsets further revealed 2,615 differentially expressed genes (DEGs) associated with HAPH progression. We also identified 144 and 77 DEGs from bulk RNA-seq and proteomic data between HAPH and control groups, respectively. Finally, 22 candidate biomarkers were screened by muti-omics analysis. These genes were further refined through ensemble machine learning algorithms. Evaluation of 113 algorithm combinations revealed that a six-gene random forest (RF) model (HEMGN, HBG2, MYL9, ANK1, UBE2O, RBPMS2) achieved optimal diagnostic accuracy, with an area under the curve (AUC) of 0.995 in the training cohort (n = 55) and 0.773 in external validation cohorts (n = 71) . Quantitative PCR validated significant overexpression of these biomarkers in HAPH compared to controls (p \u003C 0.05) .  \nConclusion: Our findings propose the minimally invasive blood-derived  \nimmune signature for HAPH diagnosis, providing a practical framework for early detection in resource-constrained high-altitude populations.  \nKEYWORDS  \nhigh-altitude pulmonary hypertension, single-cell RNA sequencing, multi-omics integration, machine learning, non-invasive diagnosis  \nFrontiers in Medicine 01 [frontiersin.org](frontiersin.or","cbCaijflgjyZovkN","https://ap.wps.com/l/cbCaijflgjyZovkN","pdf",6781522,1,10,"English","en",105,"# Introduction\n## Background and diagnostic challenges\n## Current diagnostic standards and unmet needs\n# Methods\n## Integrative multi-omics cohort and retrospective design\n## Machine learning model construction and validation\n# Results\n## Immune lineage roles and gene discovery\n## Differential expression and biomarker screening\n## Model performance and quantitative PCR validation\n# Conclusion\n## Blood-derived immune signature for early detection","[{\"question\":\"Why is early diagnosis of high-altitude pulmonary hypertension difficult in high-altitude regions?\",\"answer\":\"Gold-standard diagnosis relies on right heart catheterization and specialized infrastructure, and initial clinical symptoms are often nonspecific, causing delays and reduced therapeutic effectiveness.\"},{\"question\":\"What data types were integrated to build the diagnostic model?\",\"answer\":\"The study used single-cell RNA sequencing, bulk RNA sequencing, and proteomic profiling, then combined them through an integrative multi-omics workflow.\"},{\"question\":\"How was the immune-derived signature validated and what model achieved the best performance?\",\"answer\":\"HAPH-associated biomarkers were refined via ensemble machine learning and validated by quantitative PCR; a six-gene random forest model achieved high diagnostic accuracy with an AUC of 0.995 in training and 0.773 in external validation.\"}]","Machine learning-based integration develops an immune-derived signature for diagnosing high-altitude pulmonary hypertension - Original Research | PDF",1785822036,25,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-based-integration-develops-an-immune-derived-signature-for-diagnosing-high-altitude-pulmonary-hypertension-original-research","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-based-integration-develops-an-immune-derived-signature-for-diagnosing-high-altitude-pulmonary-hypertension-original-research/124405/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is early diagnosis of high-altitude pulmonary hypertension difficult in high-altitude regions?","Question",{"text":75,"@type":76},"Gold-standard diagnosis relies on right heart catheterization and specialized infrastructure, and initial clinical symptoms are often nonspecific, causing delays and reduced therapeutic effectiveness.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What data types were integrated to build the diagnostic model?",{"text":80,"@type":76},"The study used single-cell RNA sequencing, bulk RNA sequencing, and proteomic profiling, then combined them through an integrative multi-omics workflow.",{"name":82,"@type":73,"acceptedAnswer":83},"How was the immune-derived signature validated and what model achieved the best performance?",{"text":84,"@type":76},"HAPH-associated biomarkers were refined via ensemble machine learning and validated by quantitative PCR; 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