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This study integrates gene-expression datasets to uncover shared molecular signatures among breast, colorectal, lung, and pancreatic cancers, emphasizing genes linked to cell-cycle regulation and the p53 signaling pathway. Differentially expressed genes were identified across GEO datasets and analyzed with WebGestalt, MSigDB/GSEA, and PPI network methods, followed by mRNA and protein validation via GEPIA2 and Human Protein Atlas. Survival association and drug-sensitivity gene interactions were further evaluated using TCGA-based analyses and GSCA drug response resources, yielding common DEGs and hub genes with prognostic and therapeutic 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":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/integrated-analysis-of-cell-cycle-and-p53-signaling-pathways-related-genes-in-breast-colorectal-lung-and-pancreatic-cancers-implications-for-prognosis-and-drug-sensitivity/331664/",{"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/integrated-analysis-of-cell-cycle-and-p53-signaling-pathways-related-genes-in-breast-colorectal-lung-and-pancreatic-cancers-implications-for-prognosis-and-drug-sensitivity/331664.png","ImageObject",300,407,{"name":92,"@type":93},"Theodora","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-29","2026-09-21",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},"Which cancer types were compared in the integrated analysis?","Question",{"text":112,"@type":113},"The study analyzed breast, colorectal, lung, and pancreatic cancers using corresponding gene-expression datasets.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How were common cell-cycle and p53-related genes identified?",{"text":117,"@type":113},"Differentially expressed genes were detected using GEO2R, functional/pathway analysis was performed with WebGestalt, and shared signatures were derived using MSigDB with GSEA, followed by intersection to obtain common genes.",{"name":119,"@type":110,"acceptedAnswer":120},"How were hub genes validated and what did the study conclude about them?",{"text":121,"@type":113},"Hub genes were validated at both mRNA and protein levels using GEPIA2 and Human Protein Atlas. The top hub genes showed significantly elevated expression across the studied cancers and were linked to survival outcomes.","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},331664,1790178219,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"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,"preview_page_count":4},687197207919,"https://ap-avatar.wpscdn.com/avatar/a000253d6f5f7c60be?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779446848396160552","Analysis  \nIntegrated analysis of cell cycle and p53 signaling pathways related genes in breast, colorectal, lung, and pancreatic cancers: implications for prognosis and drug sensitivity for therapeutic potential  \nJiyauddin Khan1 · Priyanjana Ghosh1 · Urmi Bajpai2 · Kountay Dwivedi3 · Daman Saluja1,4  \nReceived: 21 August 2024 / Accepted: 16 December 2024  \nPublished online: 23 December 2024 © The Author(s) 2024 OPEN  \nAbstract  \nCancer, a leading cause of death worldwide, is projected to increase by 76.6% in new cases and 89.7% in mortality by 2050 (WHO 2022). Among various types, lung cancer is the most prevalent with high morbidity, while breast, colorectal, and pancreatic cancers also show high mortality rates. Cancer progression often involves disruption in cell cycle regulation and signaling pathways, with mutations in genes like TP53, EGFR, and K-RAS playing significant roles. In this study, we analyzed gene expression datasets to identify common molecular signatures across breast, colorectal, lung and pancreatic cancers. Our focus was on genes related to cell cycle regulation and p53 signaling pathway, intending to discover potential biomarkers for improved diagnosis and treatment strategies. The study analyzed GEO datasets; GSE45827, GSE9348, GSE30219, and GSE62165 for breast, colorectal, lung, and pancreatic cancers respectively. Differentially expressed genes (DEGs) were identified using GEO2R, and functional annotation and pathway analysis were performed using WebGestalt. Common cell cycle and p53 signaling genes were acquired from MSigDB using GSEA. A protein–protein interaction network was constructed using STRING and Cytoscape, identifying top hub genes. Validation of hub genes at mRNA and protein levels was done via GEPIA2 and Human Protein Atlas. Survival analysis was conducted using TCGA data by GEPIA2 and LASSO, and drug sensitivity was analyzed with the GSCA drug bank database, highlighting potential therapeutic targets. The study identified 411 common DEGs among these four cancers. Pathway and functional enrichment revealed key biological processes and pathways like p53 signaling, and cell cycle. The intersection of these DEGs with genes involved in cell cycle and p53 signaling, identified 23 common genes that were used for constructing a PPI network. The top 10 hub genes were validated both for mRNA and protein expression, revealing they are significantly overexpressed in all studied cancers. Prognostic relevance showed that MCM4, MCM6, CCNA2, CDC20, and CHEK1 are associated with survival. Additionally, drug sensitivity analysis highlighted key gene-drug interactions, suggesting potential targets for therapeutic intervention.  \nKeywords Cell cycle · p53 signaling · Breast cancer · Lung cancer · Colorectal cancer · Pancreatic cancer  \nSupplementary Information The online version contains supplementary material available at [https://doi.org/10.1007/s12672-024-](https://doi.org/10.1007/s12672-024-)[ ](https://doi.org/10.1007/s12672-024-)[01712-8](01712-8.)[.](01712-8.)  \n* Daman Saluja, [dsalujach59@gmail.com |](dsalujach59@gmail.com |1Dr B R Ambedkar Center)[1](dsalujach59@gmail.com |1Dr B R Ambedkar Center)[Dr B R Ambedkar Center](dsalujach59@gmail.com |1Dr B R Ambedkar Center) for Biomedical Research, University of Delhi, Delhi 110007, India. 2Department of Biomedical Sciences, Acharya Narendra Dev College University of Delhi, University of Delhi, New Delhi 110019, India. 3Center for Clinical Research, University Clinic of Dentistry, Medical University of Vienna, Vienna, Austria. 4Department of Allied and Basic Sciences, Shri Guru Gobind Singh Tricentenary University, Gurugram 122505, Haryana, India.  \nDiscover Oncology (2024) 15:832 | [https://doi.org/10.1007/s12672-024-01712-8](https://doi.org/10.1007/s12672-024-01712-8)  \nAbbreviations  \nBRC  \nLUC CRC  \nPAC  \nWHO  \nEGFR  \nKRAS  \nHER  \nATP  \nEGFR-TK  \nNSCLC  \nPTEN  \nINK4  \nARF DNA DEG GEO logFC GO KEGG  \nWebGestalt ORA  \nBP  \nMF  \nCRC  \nFDR  \nMSigDBGSE","cbCaimLVd2PkDC4O","https://ap.wps.com/l/cbCaimLVd2PkDC4O","pdf",5590455,27,"English","# Abstract\n# Methods and Data Sources\n## Differential Expression and Enrichment\n## PPI Network Construction and Validation\n## Survival and Drug Sensitivity Analyses\n# Results\n## Common Differentially Expressed Genes\n## Hub Genes and Prognostic Associations\n## Gene–Drug Interactions","[{\"question\":\"Which cancer types were compared in the integrated analysis?\",\"answer\":\"The study analyzed breast, colorectal, lung, and pancreatic cancers using corresponding gene-expression datasets.\"},{\"question\":\"How were common cell-cycle and p53-related genes identified?\",\"answer\":\"Differentially expressed genes were detected using GEO2R, functional/pathway analysis was performed with WebGestalt, and shared signatures were derived using MSigDB with GSEA, followed by intersection to obtain common genes.\"},{\"question\":\"How were hub genes validated and what did the study conclude about them?\",\"answer\":\"Hub genes were validated at both mRNA and protein levels using GEPIA2 and Human Protein Atlas. The top hub genes showed significantly elevated expression across the studied cancers and were linked to survival outcomes.\"}]","Integrated analysis of cell cycle and p53 signaling pathways related genes in breast, colorectal, lung, and pancreatic cancers - implications for prognosis and drug sensitivity | PDF",1789994500,68]