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Zinc transporter proteins in the SLC39A family mediate metal-ion transport and have been linked to multiple diseases, yet their molecular roles in CRC progression are unclear. By integrating 101 combinations of 10 machine-learning algorithms and thousands of CRC samples, a SLC39A family-related signature (SFRS) was built to predict prognosis and analyze tumor biology, immune microenvironment, mutation patterns, and immunotherapy response. Expression of SLC39A8 and SLC39A14 was validated using immunohistochemistry in an in-house cohort and supported by bioinformatics, associating them with CRC progression, biomarker value, and therapeutic targeting potential.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/unveiling-the-prognostic-and-therapeutic-landscape-of-the-zinc-transporter-protein-slc39a-family-in-colorectal-cancer-through-multiomics-and-machine-learning-approaches-research-abstract/362910/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/unveiling-the-prognostic-and-therapeutic-landscape-of-the-zinc-transporter-protein-slc39a-family-in-colorectal-cancer-through-multiomics-and-machine-learning-approaches-research-abstract/362910.png","ImageObject",300,407,{"name":92,"@type":93},"นรินทร์","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-30","2026-09-23",true,{"@type":102,"interactionType":103,"userInteractionCount":39},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What was the main goal of constructing the SFRS model in this study?","Question",{"text":112,"@type":113},"To predict colorectal cancer prognosis and to stratify patients by tumor characteristics, immune microenvironment composition, mutation features, and immunotherapy response.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How were SLC39A8 and SLC39A14 expression findings supported?",{"text":117,"@type":113},"Expression levels of SLC39A8 and SLC39A14 were assessed by immunohistochemistry in an in-house cohort, and the results were further supported by bioinformatic analyses.",{"name":119,"@type":110,"acceptedAnswer":120},"What data and methods were used to develop the SLC39A family-related signature (SFRS)?",{"text":121,"@type":113},"Thousands of CRC patient samples from large-scale public databases were analyzed, integrating 101 combinations of 10 machine learning algorithms to build the SFRS model.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},362910,1790598327,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":39,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145},2336475104957,"https://ap-avatar.wpscdn.com/avatar/22000c4c6bd8a5076e1?x-image-process=image/resize,m_fixed,w_180,h_180&k=1787554080175789136","Clinical and Experimental Medicine (2026) 26:202  \n[https://doi.org/10.1007/s10238-025-01996-2](https://doi.org/10.1007/s10238-025-01996-2)  \nRESEARCH  \nUnveiling the prognostic and therapeutic landscape of the zinc transporter protein SLC39A family in colorectal cancer through multiomics and machine learning approaches  \nHaizhen Chen1,2 · Chaozhao Chen1,2,4 · Jie Chen1,2 · Huang Zheng1,2 · Xinhao Zhu4 · Qianru Yu3,4 · Jiajie Zhou4 · Jiasheng Zong4 · Tingyan Lu4 · Jing Sun1,2 · Yanfei Shao1,2 · Minhua Zheng1,2  \nReceived: 13 July 2025 / Accepted: 2 December 2025 © The Author(s) 2025  \nAbstract  \nColorectal cancer (CRC) remains a significant global health challenge due to its high prevalence and mortality rates. Zinc transporter proteins of the SLC39A family are crucial mediators of metal ion transport and have been implicated in numerous diseases, including cancer. However, the specific molecular mechanisms underpinning their impact on CRC progression remain poorly understood. By analyzing thousands of CRC patient samples from large-scale public databases, we constructed a SLC39A family-related signature (SFRS) for prognostic prediction through the integration of 101 combinations of 10 machine learning algorithms. This model was utilized to explore tumor biology, immune microenvironment composition, mutation patterns, and responses to immunotherapy in CRC patients. To reinforce these findings, immunohistochemistry (IHC) analyses were conducted on our in-house cohort (RJ-TMA-Cohort) to examine expression levels of two key molecules, SLC39A8 and SLC39A14, and their associations with CRC progression. The SFRS model demonstrated excellent predictive performance, effectively stratifying CRC patients based on tumor characteristics, immune microenvironment, mutation features, and immunotherapy responses. Moreover, IHC and bioinformatic analyses revealed that SLC39A8 and SLC39A14 expression levels are closely associated with CRC progression, emphasizing their potential roles in tumor microenvironment regulation and their value as biomarkers and therapeutic targets. This study is the first to comprehensively investigate the functions of the SLC39A family in CRC, offering novel insights into their roles in tumor development, prognosis, and potential relevance to immunotherapy response. The SFRS model represents a powerful tool for clinical applications, while SLC39A8 and SLC39A14 present promising avenues for future research and therapeutic strategies.  \nHaizhen Chen, Chaozhao Chen and Jie Chen contributed equally to this work  \n􀀍 Jing Sun[sj11788@rjh.com.cn](sj11788@rjh.com.cn)  \n􀀍 Yanfei Shao [syf01p17@rjh.com.cn](syf01p17@rjh.com.cn)  \n􀀍 Minhua Zheng [zmhtiger@yeah.net](zmhtiger@yeah.net)  \n1 Department of General Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China  \n2 Shanghai Minimally Invasive Surgery Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China  \n3 Key Lab of Cell Differentiation and Apoptosis of Ministry of Education, Shanghai Jiao Tong University School of Medicine, Shanghai, China  \n4 Shanghai Jiao Tong University School of Medicine, Shanghai, China  \n1 3  \nGraphical Abstract  \nKeywords Colorectal cancer · SLC39A family · Machine learning · Prognostic predictive model · Tumor microenvironment · Immunotherapy  \nIntroduction  \nColorectal cancer remains one of the major deadly tumors worldwide, with its high incidence and mortality rates posing significant global health challenges [1, 2] . Over the past two decades, advancements in systemic chemotherapy, targeted therapies, and surgical interventions have led to a noticeable improvement in the mortality rate of colorectal cancer patients. However, the overall treatment efficacy and prognosis still remain suboptimal [3, 4]. Precision medicine, as a novel therapeutic concept, aims to achieve personalized treatments by combining individual data such as molecular markers, presenting irreplaceable ad","cbCaikp4gi7Uj8W6","https://ap.wps.com/l/cbCaikp4gi7Uj8W6","pdf",21874601,22,"English","# Abstract\n# Keywords\n# Introduction","[{\"question\":\"What was the main goal of constructing the SFRS model in this study?\",\"answer\":\"To predict colorectal cancer prognosis and to stratify patients by tumor characteristics, immune microenvironment composition, mutation features, and immunotherapy response.\"},{\"question\":\"How were SLC39A8 and SLC39A14 expression findings supported?\",\"answer\":\"Expression levels of SLC39A8 and SLC39A14 were assessed by immunohistochemistry in an in-house cohort, and the results were further supported by bioinformatic analyses.\"},{\"question\":\"What data and methods were used to develop the SLC39A family-related signature (SFRS)?\",\"answer\":\"Thousands of CRC patient samples from large-scale public databases were analyzed, integrating 101 combinations of 10 machine learning algorithms to build the SFRS model.\"}]","Unveiling the prognostic and therapeutic landscape of the zinc transporter protein SLC39A family in colorectal cancer through multiomics and machine learning approaches - Research abstract | PDF",1790149396,55]