[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-seo-333549-105":3,"detail-sidebar-cat-0-en-105":81,"doc-detail-333549-en":130},{"code":4,"msg":5,"data":6},0,"ok",{"site_id":7,"language":8,"slug":9,"title":10,"keywords":11,"description":12,"schema_data":13,"social_meta":74,"head_meta":76,"extra_data":78,"updated_unix":80},105,"en","establishing-a-model-composed-of-immune-related-gene-modules-to-predict-tumor-immunotherapy-response","Establishing a model composed of immune-related gene-modules to predict tumor immunotherapy response","","Tumor immunotherapy is widely used but predicting treatment efficacy remains insufficient. This study constructs a prediction model using a modified WGCNA algorithm to judge anti-tumor immune responses. Differentially expressed genes are screened in a murine colon cancer model, irDEGs are analyzed with GSEA, and 7 gene-modules are identified. Four modules are selected and 34 vital irDEGs are derived from module intersections. Correlations between modules and immunotherapy response yield AUC values of 0.727 and 0.705, validated across additional clinical cancer datasets.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & Report",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/establishing-a-model-composed-of-immune-related-gene-modules-to-predict-tumor-immunotherapy-response/333549/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/establishing-a-model-composed-of-immune-related-gene-modules-to-predict-tumor-immunotherapy-response/333549.png","ImageObject",300,407,{"name":42,"@type":43},"Maeve","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-10-29","2026-09-21",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",6,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"What problem does this study address in tumor immunotherapy?","Question",{"text":63,"@type":64},"It addresses the lack of a significant breakthrough in accurately predicting treatment efficacy for tumor immunotherapy.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"How is the prediction model constructed?",{"text":68,"@type":64},"The model is built using a modified WGCNA algorithm, mapping immune-related gene-modules and calculating correlation coefficients between four selected modules and immunotherapy response.",{"name":70,"@type":61,"acceptedAnswer":71},"How well does the model perform and how is it validated?",{"text":72,"@type":64},"Model scores produce an AUC of 0.727, and classification based AUC reaches 0.705. Prediction accuracy is further validated in clinical datasets of other cancers.","https://schema.org",{"og:url":32,"og:type":75,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":77,"canonical":32},"index,follow",{"doc_id":79,"site_id":7},333549,1790334211,{"code":4,"msg":82,"data":83},"success",[84,88,92,96,101,105,110,114,119,122,126],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":85,"show_sort_weight":86,"slug":87},"Story & Novel",90,"story-novel",{"id":26,"doc_module":4,"doc_module_name":25,"category_name":89,"show_sort_weight":90,"slug":91},"Literature",80,"literature",{"id":33,"doc_module":4,"doc_module_name":25,"category_name":93,"show_sort_weight":94,"slug":95},"Exam",70,"exam",{"id":97,"doc_module":4,"doc_module_name":25,"category_name":98,"show_sort_weight":99,"slug":100},5,"Comic",60,"comic",{"id":55,"doc_module":4,"doc_module_name":25,"category_name":102,"show_sort_weight":103,"slug":104},"Technology",50,"technology",{"id":106,"doc_module":4,"doc_module_name":25,"category_name":107,"show_sort_weight":108,"slug":109},7,"Healthcare",40,"healthcare",{"id":111,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":112,"slug":113},8,30,"research-report",{"id":115,"doc_module":4,"doc_module_name":25,"category_name":116,"show_sort_weight":117,"slug":118},9,"Religion & Spirituality",20,"religion-spirituality",{"id":117,"doc_module":4,"doc_module_name":25,"category_name":120,"show_sort_weight":117,"slug":121},"World Cup","world-cup",{"id":123,"doc_module":4,"doc_module_name":25,"category_name":124,"show_sort_weight":123,"slug":125},10,"Lifestyle","lifestyle",{"id":127,"doc_module":4,"doc_module_name":25,"category_name":128,"show_sort_weight":97,"slug":129},19,"General","general",{"code":4,"msg":82,"data":131},{"doc_id":79,"user_id":132,"nickname":42,"user_avatar":133,"doc_module":4,"category_id":111,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":55,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":139,"language":140,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":12,"update_tm":144,"read_time":145,"preview_page_count":4},5909877438554,"https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272","[www. nature.com/scientificreports](www. nature.com/scientificreports)  \nOPEN  \nEstablishing a model composed of immune‑related gene‑modules to predict tumor immunotherapy response  \nDeqiang Fu1,4, Xiaoyuan Weng2,3,4, Yunxia Su1, Binhuang Hong1, Aiyue zhao1* & Jianqing Lin2*  \nAt present, tumor immunotherapy has been widely applied to treat various cancers. However, the accuracy of predicting treatment efficacy has not yet achieved a significant breakthrough. This study aimed to construct a prediction model based on the modified WGCNA algorithm to precisely judge the anti‑tumor immune response. First, we used a murine colon cancer model to screen corresponding DEGs according to different groups. GSEA was used to analyze the potential mechanisms ofthe immune‑related DEGs (irDEGs) in each group. Subsequently, the intersection of the irDEGs in  \nevery group was acquired, and 7 gene‑modules were mapped. Finally, 4 gene‑modules including cogenes, antiPD‑1 immu‑genes, chemo immu‑genes and comb immu‑genes, were selected for subsequent study. Furthermore, a clinical dataset of gastric cancer patients receiving immunotherapy was enrolled, and the irDEGs were identified. A total of 34 vital irDEGs were obtained from the intersections ofthe vital irDEGs and the four gene‑modules. Next, the vital irDEGs were analyzed by the modified WGCNA algorithm, and the correlation coefficients between the 4 gene‑modules and the response status to immunotherapy were calculated. Thus, a prediction model based on correlation coefficients was built, and the corresponding model scores were acquired. TheAUC calculated according to the model score was 0.727, which was non‑inferior to that of the ESTIMATE score and the TIDE score. Meanwhile, theAUC calculated according to the classification of the model scores was 0.705, which was non‑inferior to that of the ESTIMATE classification and the TIDE classification. The prediction accuracy of the model was validated in clinical datasets of other cancers.  \nTo date, millions of cancer patients have acquired great survival benefits since tumor immunotherapy was applied in the field of malignant diseases, such as melanoma1, lung cancer2, hepatocellular carcinoma3, gastric cancer4 and endometrial cancer5, etc. Representative tumor immunotherapy drugs are immune checkpoint inhibitors (ICIs), such as anti-PD-1 antibody or anti-CTLA-4 antibody. In a word, tumor immunotherapy has been regarded as one of the primary means of anti-tumor treatment. Excitingly, an increasing number of new ICIs have entered clinical trials. We believe that tumor immunotherapy will exhibit better prospects in the near future.  \nHowever, there has been no breakthrough in the prediction of treatment efficacy before tumor immunotherapy in recent years6. Many patients harboring high PD-L1 expression or a high tumor mutation burden (TMB-H) can not achieve a treatment response from anti-PD-1 therapy. Conversely, some cancer patients with low PD-L1 expression or a low TMB exhibit great tumor regression and long-term survival after anti-PD-1 therapy. This deviation may depend on the trials and the patient populations; more importantly, the mechanisms underlying resistance or lack of response to ICIs have still been investigated7. Thus, scientists have been trying to identify amore powerful method to accurately predict the response to ICIs.  \nCurrently, for clinicians, the response status to ICIs is primarily predicted based on PD-L1 expression8, TMB9 and microsatellite instability (MSI)10 of tumor tissue. However, the prediction accuracy is not satisfactory. In recent years, many research teams have tried their best to look for the optimal bioinformatics algorithms  \n1Department of Oncology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China. 2Thyroid and Breast Surgery, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China. 3Quanzhou Medical College, Quanzhou, China. 4These authors contributed equally: ","cbCair0XQyWc0e0W","https://ap.wps.com/l/cbCair0XQyWc0e0W","pdf",8951046,14,"English","# Results\n## Different outcomes and TIMEs based on different treatment regimens in a murine colon cancer model","[{\"question\":\"What problem does this study address in tumor immunotherapy?\",\"answer\":\"It addresses the lack of a significant breakthrough in accurately predicting treatment efficacy for tumor immunotherapy.\"},{\"question\":\"How is the prediction model constructed?\",\"answer\":\"The model is built using a modified WGCNA algorithm, mapping immune-related gene-modules and calculating correlation coefficients between four selected modules and immunotherapy response.\"},{\"question\":\"How well does the model perform and how is it validated?\",\"answer\":\"Model scores produce an AUC of 0.727, and classification based AUC reaches 0.705. Prediction accuracy is further validated in clinical datasets of other cancers.\"}]","Establishing a model composed of immune-related gene-modules to predict tumor immunotherapy response | PDF",1790002944,35]