[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121663-en":3,"doc-seo-121663-105":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},121663,1099513958762,"Logic","https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253",7,"Healthcare","Identification of AKI signatures and classification patterns in ccRCC based on machine learning - Original Research","Acute kidney injury (AKI) requires early detection to enable timely mitigation, yet existing biomarkers show limitations for accurate prediction. This study integrates multiple public gene expression datasets and applies machine learning to identify seven robust AKI-related biomarkers. The work also explores the relationship between AKI and clear cell renal cell carcinoma (ccRCC), defining distinct ccRCC molecular subtypes with different prognoses and immune landscapes. A predictive nomogram supports reliable AKI risk stratification.","TYPE Original Research PUBLISHED 24 May 2023  \nDOI 10.3389/fmed.2023.1195678  \nOPEN ACCESS  \nEDITED BY  \nZheng Wang,  \nShanghai Jiao Tong University, China  \nREVIEWED BY  \nYasha Wang,  \nPeking University, China Chao Chen,  \nChongqing University, China  \n*CORRESPONDENCE  \nZhi Guo Mao  \n [maozhiguo518@126.com](maozhiguo518@126.com)[ ](maozhiguo518@126.com)Lin Li  \n [lilin_616@163.com](lilin_616@163.com)  \n†These authors have contributed equally to this work  \nRECEIVED 28 March 2023  \nACCEPTED 03 May 2023  \nPUBLISHED 24 May 2023  \nCITATION  \nWang L, Peng F, Li ZH, Deng YF, Ruan MN, Mao ZG and Li L (2023) Identification of AKI signatures and classification patterns in ccRCC based on machine learning.  \nFront. Med. 10:1195678 .  \ndoi: 10.3389/fmed.2023.1195678  \nCOPYRIGHT  \n© 2023 Wang, Peng, Li, Deng, Ruan, Mao and Li. 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.  \nIdentification of AKI signaturesand classification patterns inccRCC based on machine learning  \nLi Wang 1†, Fei Peng 2†, Zhen Hua Li3, Yu Fei Deng 1, Meng Na Ruan 1, Zhi Guo Mao 1*and Lin Li 1*  \n1 Department of Nephrology, Changzheng Hospital, Naval Medical University, Shanghai, China,  \n2 Department of Cardiology, Jinshan Hospital of Fudan University, Shanghai, China, 3 Department of Cardiology, Changzheng Hospital, Naval Medical University, Shanghai, China  \nBackground: Acute kidney injury can be mitigated if detected early. There are limited biomarkers for predicting acute kidney injury (AKI) . In this study, we used public databases with machine learning algorithms to identify novel biomarkers to predict AKI. In addition, the interaction between AKI and clear cell renal cell carcinoma (ccRCC) remain elusive.  \nMethods: Four public AKI datasets (GSE126805, GSE139061, GSE30718, and GSE90861) treated as discovery datasets and one (GSE43974) treated asa validation dataset were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between AKI and normal kidney tissues were identified using the R package limma. Four machine learning algorithms were used to identify the novel AKI biomarkers. The correlations between the seven biomarkers and immune cells or their components were calculated using the R package ggcor. Furthermore, two distinct ccRCC subtypes with different prognoses and immune characteristics were identified and verified using seven novel biomarkers.  \nResults: Seven robust AKI signatures were identified using the four machine learning methods. The immune infiltration analysis revealed that the numbers of activated CD4 T cells, CD56dim natural killer cells, eosinophils, mast cells, memory B cells, natural killer T cells, neutrophils, T follicular helper cells, and type 1T helper cells were significantly higher in the AKI cluster. The nomogram for prediction of AKI risk demonstrated satisfactory discrimination with an Area Under the Curve (AUC) of 0.919 in the training set and 0.945 in the testing set. In addition, the calibration plot demonstrated few errors between the predicted and actual values. In a separate analysis, the immune components and cellular differences between the two ccRCC subtypes based on their AKI signatures were compared. Patients in the CS1 had better overall survival, progression-free survival, drug sensitivity, and survival probability.  \nConclusion: Our study identified seven distinct AKI-related biomarkers based on four machine learning methods and proposed a nomogram for stratified AKI risk prediction. We also confirmed that AKI signatures were valuable for predicting ccRCC prognosis. The cu","cbCaina3oE6W3835","https://ap.wps.com/l/cbCaina3oE6W3835","pdf",14360779,1,14,"English","en",105,"# Introduction\n## Background and clinical significance of AKI\n## Machine learning and biomarker discovery\n## AKI and renal cancer relationship\n# Methods\n## Datasets and study design\n## Differential gene expression analysis\n## Machine learning biomarker identification\n## Immune correlation analyses and ccRCC subtype verification\n# Results\n## Seven AKI signatures and validation\n## Immune infiltration differences in AKI cluster\n## Nomogram performance and calibration\n## ccRCC subtype comparison using AKI signatures\n# Conclusion","[{\"question\":\"What problem does the study address regarding acute kidney injury (AKI)?\",\"answer\":\"The study targets the need for early AKI detection and the limited predictive biomarkers available for AKI.\"},{\"question\":\"How were AKI biomarkers identified in this research?\",\"answer\":\"Four discovery AKI datasets and one validation dataset were analyzed to find differentially expressed genes, then four machine learning algorithms were used to derive novel AKI biomarkers.\"},{\"question\":\"What did the study find about the relationship between AKI and clear cell renal cell carcinoma (ccRCC)?\",\"answer\":\"The authors identified distinct ccRCC subtypes using seven AKI-related biomarkers, showing different prognoses and immune characteristics, and they confirmed that AKI signatures help predict ccRCC outcomes.\"}]","Identification of AKI signatures and classification patterns in ccRCC based on machine learning - Original Research | PDF",1785806060,35,{"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},"identification-of-aki-signatures-and-classification-patterns-in-ccrcc-based-on-machine-learning-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/healthcare/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/identification-of-aki-signatures-and-classification-patterns-in-ccrcc-based-on-machine-learning-original-research/121663/",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},"What problem does the study address regarding acute kidney injury (AKI)?","Question",{"text":75,"@type":76},"The study targets the need for early AKI detection and the limited predictive biomarkers available for AKI.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How were AKI biomarkers identified in this research?",{"text":80,"@type":76},"Four discovery AKI datasets and one validation dataset were analyzed to find differentially expressed genes, then four machine learning algorithms were used to derive novel AKI biomarkers.",{"name":82,"@type":73,"acceptedAnswer":83},"What did the study find about the relationship between AKI and clear cell renal cell carcinoma (ccRCC)?",{"text":84,"@type":76},"The authors identified distinct ccRCC subtypes using seven AKI-related biomarkers, showing different prognoses and immune characteristics, and they confirmed that AKI signatures help predict ccRCC outcomes.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,118,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":116,"slug":117},40,"healthcare",{"id":119,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":121,"slug":122},8,"Research & Report",30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]