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Transcriptome data and radiomics features were extracted from public resources, followed by WGCNA and machine-learning gene screening using RF, LASSO-logistic regression, and SVM-RFE, with immune associations evaluated by CIBERSORT and ESTIMATE. Validation used ROC, PR, calibration, and DCA, and functional tests employed RT-qPCR, transwell, and CCK8 assays. A four-gene diagnostic set highlighted TEDC2, and a TEDC2-based radiomics model improved performance, while TEDC2 knockdown suppressed LUAD cell proliferation, migration, and invasion.",{"@graph":69,"@context":123},[70,84,106],{"@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":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/comprehensive-analysis-of-transcriptomics-and-radiomics-revealed-the-potential-of-tedc2-as-a-diagnostic-marker-for-lung-adenocarcinoma/331533/",{"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/comprehensive-analysis-of-transcriptomics-and-radiomics-revealed-the-potential-of-tedc2-as-a-diagnostic-marker-for-lung-adenocarcinoma/331533.png","ImageObject",300,407,{"name":92,"@type":93},"Quinn","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-03","2026-09-21",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",12,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What is the study’s main goal for LUAD?","Question",{"text":113,"@type":114},"To identify diagnostic markers for lung adenocarcinoma by integrating transcriptomics and radiomics, with emphasis on evaluating TEDC2 as a diagnostic marker.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"Which methods were used to screen diagnostic genes?",{"text":118,"@type":114},"The study extracted transcriptome data and radiomics features and used WGCNA plus machine-learning approaches including Random Forest, LASSO logistic regression, and SVM-RFE to筛选 diagnostic genes.",{"name":120,"@type":111,"acceptedAnswer":121},"How was TEDC2 functionally validated in LUAD cells?",{"text":122,"@type":114},"The study measured gene expression and assessed cellular behaviors using RT-qPCR, transwell assays, and CCK8, and found that TEDC2 knockdown slowed LUAD cell proliferation, migration, and invasion.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},331533,1790009070,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":145,"read_time":146,"preview_page_count":4},962075114765,"https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd","Submitted 25 March 2024 Accepted 24 September 2024 Published 14 November 2024  \nCorresponding authors  \nChao Tao, [taocc1986@sina.cn](taocc1986@sina.cn)[ ](taocc1986@sina.cn)Zongyang Yu, [yuzy527@sina.com](yuzy527@sina.com)  \nAcademic editor Fanglin Guan  \nAdditional Information and Declarations can be found on page 19  \nDOI 10.7717/peerj.18310  \n Copyright 2024 Huang et al.  \nDistributed under  \nCreative Commons CC-BY 4.0  \nComprehensive analysis of transcriptomicsand radiomics revealed the potential of TEDC2 as a diagnostic marker for lung adenocarcinoma  \nQian Huang 1, *, Peng Zhang2, *, Zhixu Guo3, Min Li4, Chao Tao2 and Zongyang Yu5  \n1 Department of Hepatobiliary Disease, The Third People’s Hospital of Fujian University of Traditional Chinese Medicine, Fuzhou, China  \n2 Radiodiagnostic Department, The 900 Hospital of the Joint Service Support Force of the People’s Liberation Army of China, Fuzhou, China  \n3 Information Department, The 900 Hospital of the Joint Service Support Force of the People’s Liberation Army of China, Fuzhou, China  \n4 Pathology Department, The 900 Hospital of the Joint Service Support Force of the People’s Liberation Army of China, Fuzhou, China  \n5 Respiratory Department, The 900 Hospital of the Joint Service Support Force of the People’s Liberation Army of China, Fuzhou, China  \n* These authors contributed equally to this work.  \nABSTRACT  \nBackground: Lung adenocarcinoma (LUAD) is a widely occurring cancer with a high death rate. Radiomics, as a high-throughput method, has a wide range of applications in different aspects of the management of multiple cancers. However, the molecular mechanism ofLUAD by combining transcriptomics and radiomics in order to probe LUAD remains unclear.  \nMethods: The transcriptome data and radiomics features of LUAD were extracted from the public database. Subsequently, we used weighted gene co-expression network analysis (WGCNA) and a series of machine learning algorithms including Random Forest (RF), Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression, and Support Vector Machines Recursive Feature Elimination (SVM-RFE) to proceed with the screening of diagnostic genes for LUAD. In addition, the CIBERSORT and ESTIMATE algorithms were utilized to assess the association of these genes with immune profiles. The LASSO algorithm further identified the features most relevant to the expression levels of LUAD diagnostic genes and validated the model based on receiver operating characteristic (ROC),  \nprecision-recall (PR), calibration curves and decision curve analysis (DCA) curves. Finally, RT-qPCR, transwell and cell counting kit-8 (CCK8) based assays were performed to assess the expression levels and potential functions of the screened genes in LUAD cell lines.  \nResults: We screened a total of 214 modular genes with the highest correlation with LUAD samples based on WGCNA, of which 192 genes were shown to be highly expressed in LUAD patients. Subsequently, three machine learning algorithmsidentified a total of four genes, including UBE2T, TEDC2, RCC1, and FAM136A, as diagnostic molecules for LUAD, and the ROC curves showed that these diagnostic molecules had good diagnostic performance (AUC values of 0.989, 0.989, 989, and  \nHow to cite this article Huang Q, Zhang P, Guo Z, Li M, Tao C, Yu Z. 2024. Comprehensive analysis of transcriptomics and radiomics revealed the potential of TEDC2 as a diagnostic marker for lung adenocarcinoma. PeerJ 12:e18310 DOI 10.7717/peerj.18310  \n0.987, respectively) . The expression of these diagnostic molecules was significantly higher in tumor samples than in normal para-cancerous tissue samples and also correlated significantly and negatively with stromal and immune scores. Specifically, we also constructed a model based on TEDC2 expression consisting of seven radiomic features. Among them, the ROC and PR curves showed that the model had an AUC value of up to 0.96, respectively. Knockdown of TEDC2 slowed down the prolifer","cbCaibSt6kFesv0u","https://ap.wps.com/l/cbCaibSt6kFesv0u","pdf",9437009,23,"English","# Abstract\n## Background\n## Methods\n## Results\n## Conclusion\n# Introduction","[{\"question\":\"What is the study’s main goal for LUAD?\",\"answer\":\"To identify diagnostic markers for lung adenocarcinoma by integrating transcriptomics and radiomics, with emphasis on evaluating TEDC2 as a diagnostic marker.\"},{\"question\":\"Which methods were used to screen diagnostic genes?\",\"answer\":\"The study extracted transcriptome data and radiomics features and used WGCNA plus machine-learning approaches including Random Forest, LASSO logistic regression, and SVM-RFE to筛选 diagnostic genes.\"},{\"question\":\"How was TEDC2 functionally validated in LUAD cells?\",\"answer\":\"The study measured gene expression and assessed cellular behaviors using RT-qPCR, transwell assays, and CCK8, and found that TEDC2 knockdown slowed LUAD cell proliferation, migration, and invasion.\"}]","Comprehensive analysis of transcriptomics and radiomics revealed the potential of TEDC2 as a diagnostic marker for lung adenocarcinoma | PDF",1789993777,58]