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biomarker revealed through bioinformatics analysis  \nPooya Jalali 1 | Amir Samii 2 | Malihe Rezaee 3 | Arvin Shahmoradi 4 | Fatemeh Pashizeh 5 | Zahra Salehi 6,7   \n1Basic and Molecular Epidemiology of Gastrointestinal Disorders Research Centre, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran  \n2Department of Hematology and Blood Transfusion, School of Allied Medical Sciences, Iran University of Medical Sciences,  \nTehran, Iran  \n3Department of Pharmacology, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran  \n4Department of Laboratory Medicine, Faculty of Paramedical, Kurdistan University of Medical Sciences, Sanandaj, Iran 5Department of Clinical Immunology, Shahid Sadoughi University of Medical Sciences, Yazd, Iran  \n6Hematology, Oncology and Stem Cell Transplantation Research Center, Tehran University of Medical Sciences, Tehran, Iran 7Research Institute for Oncology, Hematology and Cell Therapy, Tehran University of Medical Sciences, Tehran, Iran  \nCorrespondence  \nZahra Salehi, Hematology, Oncology and Stem Cell Transplantation Research Center, Tehran University of Medical Sciences, Shariati Hospital, Karegar Ave., Tehran 14114, Iran. Email: [zahra.salehi6463@yahoo.com](zahra.salehi6463@yahoo.com), [zsalehi@sina.tums.ac.ir](zsalehi@sina.tums.ac.ir)  \nAbstract  \nBackground: The diverse and complex attributes of cancer have made it a daunting challenge to overcome globally and remains to endanger human life. Detection of critical cancer-related gene alterations in solid tumor samples better defines patient diagnosis and prognosis, and indicates what targeted therapies must be administered to improve cancer patients' outcome.  \nMaterials and methods: To identify genes that have aberrant expression across different cancer types, differential expressed genes were detected within the TCGA datasets. Subsequently, the DEGs common to all pan cancers were determined. Furthermore, various methods were employed to gain genetic alterations, co-expression genes network and protein–protein interaction (PPI) network, pathway enrichment analysis of common genes. Finally, the gene regulatory network was constructed. Results: Intersectional analysis identified UBE2C as a common DEG between all 28 types of studied cancers. Upregulated UBE2C expression was significantly correlated with OS and DFS of 10 and 9 types of cancer patients. Also, UBE2C can be a diagnostic factor in CESC, CHOL, GBM, and UCS with AUC = 100% and diagnose 19 cancer types with AUC ≥90% . A ceRNA network constructed including UBE2C, 41 TFs, 10 shared miRNAs, and 21 circRNAs and 128 lncRNAs.  \nConclusion: In summary, UBE2C can be a theranostic gene, which may serve as a reliable biomarker in diagnosing cancers, improving treatment responses and increasing the overall survival of cancer patients and can be a promising gene to be target by cancer drugs in the future.  \nKEYWOR DS  \nbioinformatics analysis, cancer diagnosing, pan-cancer, TCGA, UBE2C  \n1 | INTRODUCTION  \nThe diverse and complex attributes of cancer have made it a daunting  \n  challenge to overcome globally and remains to endanger human life.  \nArvin Shahmoradi and Fatemeh Pashizeh contributed equally to this work. As a leading cause of mortality, each year new cases are added,  \nThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.  \n© 2024 The Authors. Cancer Reports published by Wiley Periodicals LLC.  \nCancer Reports. 2024;7:e2032 .  \n[https://doi.org/10.1002/cnr2.2032](https://doi.org/10.1002/cnr2.2032)  \n[wileyonlinelibrary.com/journal/cnr2](wileyonlinelibrary.com/journal/cnr2)  \n1 of 16  \nintensifying the burden of the disease. The annual prevalence of cancer was a","cbCaiuYlmZ2GqJa8","https://ap.wps.com/l/cbCaiuYlmZ2GqJa8","pdf",36191566,16,"English","# Abstract\n## Background\n## Materials and methods\n## Results\n## Conclusion\n# Introduction","[{\"question\":\"What data source and gene-selection strategy were used in this study?\",\"answer\":\"The study used TCGA datasets to detect differential expressed genes across multiple cancer types, then determined the DEGs common to all pan cancers.\"},{\"question\":\"Why is UBE2C highlighted as a key biomarker?\",\"answer\":\"Intersectional analysis identified UBE2C as a common DEG across 28 cancer types, and upregulated UBE2C expression was significantly associated with OS and DFS in multiple cancers.\"},{\"question\":\"What additional regulatory and interaction analyses support the findings?\",\"answer\":\"The workflow included co-expression analysis, protein–protein interaction (PPI) network construction, pathway enrichment analysis, gene regulatory network construction, and a ceRNA network combining UBE2C, transcription factors, miRNAs, circRNAs, and lncRNAs.\"}]","UBE2C: A pan-cancer diagnostic and prognostic biomarker revealed through bioinformatics analysis | PDF",1789989043]