Identification of novel diagnostic and prognostic microRNAs in sarcoma on TCGA dataset: bioinformatics and machine learning approach
Abstract The discovery of unique microRNA (miR) patterns and their corresponding genes in sarcoma patients indicates their involvement in cancer development and suggests their potential use in medical management. MiRs were identified from The Cancer Genome Atlas (TCGA) dataset, with a Deep Neural Ne...
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Nature Portfolio
2025-03-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-91007-x |
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| author | Rahem Rahmati Fatemeh Zarimeidani Farnaz Ahmadi Hannaneh Yousefi-Koma Abdolreza Mohammadnia Maryam Hajimoradi Shadi Shafaghi Elham Nazari |
| author_facet | Rahem Rahmati Fatemeh Zarimeidani Farnaz Ahmadi Hannaneh Yousefi-Koma Abdolreza Mohammadnia Maryam Hajimoradi Shadi Shafaghi Elham Nazari |
| author_sort | Rahem Rahmati |
| collection | DOAJ |
| description | Abstract The discovery of unique microRNA (miR) patterns and their corresponding genes in sarcoma patients indicates their involvement in cancer development and suggests their potential use in medical management. MiRs were identified from The Cancer Genome Atlas (TCGA) dataset, with a Deep Neural Network (DNN) employed for novel miR identification. MiRDB facilitated target predictions. Functional enrichment analysis, identify critical pathways, protein-protein interaction network, and diseases/clinical data correlations were explored. COX regression, Kaplan-Meier analyses, and CombioROC was also utilized. The population consisted of 119 females and 142 males, and 1046 miRs were uncovered. Ten miRs was selected for further analysis using DNN. Upon analyzing for gene ontology, it was found that these genes showed enrichment in various activities. We identified a significant association between the overall survival rate of sarcoma patients and miRs levels. The combination of miR.3688 and miR.3936 achieved the greatest diagnostic standing. MiRs have the capability to screen sarcoma patients to identify undetected tumors, predict prognosis, and pinpoint prospective targets for treatment. Further large clinical trials are required to validate our findings. |
| format | Article |
| id | doaj-art-e3296f69b4e941e9a2fbf8b871a8e568 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-e3296f69b4e941e9a2fbf8b871a8e5682025-08-20T03:06:00ZengNature PortfolioScientific Reports2045-23222025-03-0115111410.1038/s41598-025-91007-xIdentification of novel diagnostic and prognostic microRNAs in sarcoma on TCGA dataset: bioinformatics and machine learning approachRahem Rahmati0Fatemeh Zarimeidani1Farnaz Ahmadi2Hannaneh Yousefi-Koma3Abdolreza Mohammadnia4Maryam Hajimoradi5Shadi Shafaghi6Elham Nazari7Students Research Committee, Shahrekord University of Medical SciencesStudents Research Committee, Shahrekord University of Medical SciencesLung Transplantation Research Center, National Research Institute of Tuberculosis and Lung Diseases (NRITLD), Shahid Beheshti University of Medical SciencesLung Transplantation Research Center, National Research Institute of Tuberculosis and Lung Diseases (NRITLD), Shahid Beheshti University of Medical SciencesChronic Respiratory Diseases Research Center, National Research Institute of Tuberculosis and Lung Diseases (NRITLD), Shahid Beheshti University of Medical SciencesLung Transplantation Research Center, National Research Institute of Tuberculosis and Lung Diseases (NRITLD), Shahid Beheshti University of Medical SciencesLung Transplantation Research Center, National Research Institute of Tuberculosis and Lung Diseases (NRITLD), Shahid Beheshti University of Medical SciencesProteomics Research Center, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical SciencesAbstract The discovery of unique microRNA (miR) patterns and their corresponding genes in sarcoma patients indicates their involvement in cancer development and suggests their potential use in medical management. MiRs were identified from The Cancer Genome Atlas (TCGA) dataset, with a Deep Neural Network (DNN) employed for novel miR identification. MiRDB facilitated target predictions. Functional enrichment analysis, identify critical pathways, protein-protein interaction network, and diseases/clinical data correlations were explored. COX regression, Kaplan-Meier analyses, and CombioROC was also utilized. The population consisted of 119 females and 142 males, and 1046 miRs were uncovered. Ten miRs was selected for further analysis using DNN. Upon analyzing for gene ontology, it was found that these genes showed enrichment in various activities. We identified a significant association between the overall survival rate of sarcoma patients and miRs levels. The combination of miR.3688 and miR.3936 achieved the greatest diagnostic standing. MiRs have the capability to screen sarcoma patients to identify undetected tumors, predict prognosis, and pinpoint prospective targets for treatment. Further large clinical trials are required to validate our findings.https://doi.org/10.1038/s41598-025-91007-xArtificial intelligenceBiomarkerCancerNoncoding RNAPrognosis |
| spellingShingle | Rahem Rahmati Fatemeh Zarimeidani Farnaz Ahmadi Hannaneh Yousefi-Koma Abdolreza Mohammadnia Maryam Hajimoradi Shadi Shafaghi Elham Nazari Identification of novel diagnostic and prognostic microRNAs in sarcoma on TCGA dataset: bioinformatics and machine learning approach Scientific Reports Artificial intelligence Biomarker Cancer Noncoding RNA Prognosis |
| title | Identification of novel diagnostic and prognostic microRNAs in sarcoma on TCGA dataset: bioinformatics and machine learning approach |
| title_full | Identification of novel diagnostic and prognostic microRNAs in sarcoma on TCGA dataset: bioinformatics and machine learning approach |
| title_fullStr | Identification of novel diagnostic and prognostic microRNAs in sarcoma on TCGA dataset: bioinformatics and machine learning approach |
| title_full_unstemmed | Identification of novel diagnostic and prognostic microRNAs in sarcoma on TCGA dataset: bioinformatics and machine learning approach |
| title_short | Identification of novel diagnostic and prognostic microRNAs in sarcoma on TCGA dataset: bioinformatics and machine learning approach |
| title_sort | identification of novel diagnostic and prognostic micrornas in sarcoma on tcga dataset bioinformatics and machine learning approach |
| topic | Artificial intelligence Biomarker Cancer Noncoding RNA Prognosis |
| url | https://doi.org/10.1038/s41598-025-91007-x |
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