Statistical and machine learning based platform-independent key genes identification for hepatocellular carcinoma.
Hepatocellular carcinoma (HCC) is the most prevalent and deadly form of liver cancer, and its mortality rate is gradually increasing worldwide. Existing studies used genetic datasets, taken from various platforms, but focused only on common differentially expressed genes (DEGs) across platforms. Con...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0318215 |
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| author | Md Al Mehedi Hasan Md Maniruzzaman Jie Huang Jungpil Shin |
| author_facet | Md Al Mehedi Hasan Md Maniruzzaman Jie Huang Jungpil Shin |
| author_sort | Md Al Mehedi Hasan |
| collection | DOAJ |
| description | Hepatocellular carcinoma (HCC) is the most prevalent and deadly form of liver cancer, and its mortality rate is gradually increasing worldwide. Existing studies used genetic datasets, taken from various platforms, but focused only on common differentially expressed genes (DEGs) across platforms. Consequently, these studies may missed some important genes in the investigation of HCC. To solve these problems, we have taken datasets from multiple platforms and designed a statistical and machine learning-based system to determine platform-independent key genes (KGs) for HCC patients. DEGs were determined from each dataset using limma. Individual combined DEGs (icDEGs) were identified from each platform and then determined grand combined DEGs (gcDEGs) from icDEGs of all platforms. Differentially expressed discriminative genes (DEDGs) was determined based on the classification accuracy using Support vector machine. We constructed PPI network on DEDGs and identified hub genes using MCC. This study determined the optimal modules using the MCODE scores of the PPI network and selected their gene combinations. We combined all genes, obtained from previous studies to form metadata, known as meta-hub genes. Finally, six KGs (CDC20, TOP2A, CENPF, DLGAP5, UBE2C, and RACGAP1) were selected by intersecting the overlapping hub genes, meta-hub genes, and hub module genes. The discriminative power of six KGs and their prognostic potentiality were evaluated using AUC and survival analysis. |
| format | Article |
| id | doaj-art-a72da38189c842058fe7ed62536fcd6e |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-a72da38189c842058fe7ed62536fcd6e2025-02-10T05:30:38ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01202e031821510.1371/journal.pone.0318215Statistical and machine learning based platform-independent key genes identification for hepatocellular carcinoma.Md Al Mehedi HasanMd ManiruzzamanJie HuangJungpil ShinHepatocellular carcinoma (HCC) is the most prevalent and deadly form of liver cancer, and its mortality rate is gradually increasing worldwide. Existing studies used genetic datasets, taken from various platforms, but focused only on common differentially expressed genes (DEGs) across platforms. Consequently, these studies may missed some important genes in the investigation of HCC. To solve these problems, we have taken datasets from multiple platforms and designed a statistical and machine learning-based system to determine platform-independent key genes (KGs) for HCC patients. DEGs were determined from each dataset using limma. Individual combined DEGs (icDEGs) were identified from each platform and then determined grand combined DEGs (gcDEGs) from icDEGs of all platforms. Differentially expressed discriminative genes (DEDGs) was determined based on the classification accuracy using Support vector machine. We constructed PPI network on DEDGs and identified hub genes using MCC. This study determined the optimal modules using the MCODE scores of the PPI network and selected their gene combinations. We combined all genes, obtained from previous studies to form metadata, known as meta-hub genes. Finally, six KGs (CDC20, TOP2A, CENPF, DLGAP5, UBE2C, and RACGAP1) were selected by intersecting the overlapping hub genes, meta-hub genes, and hub module genes. The discriminative power of six KGs and their prognostic potentiality were evaluated using AUC and survival analysis.https://doi.org/10.1371/journal.pone.0318215 |
| spellingShingle | Md Al Mehedi Hasan Md Maniruzzaman Jie Huang Jungpil Shin Statistical and machine learning based platform-independent key genes identification for hepatocellular carcinoma. PLoS ONE |
| title | Statistical and machine learning based platform-independent key genes identification for hepatocellular carcinoma. |
| title_full | Statistical and machine learning based platform-independent key genes identification for hepatocellular carcinoma. |
| title_fullStr | Statistical and machine learning based platform-independent key genes identification for hepatocellular carcinoma. |
| title_full_unstemmed | Statistical and machine learning based platform-independent key genes identification for hepatocellular carcinoma. |
| title_short | Statistical and machine learning based platform-independent key genes identification for hepatocellular carcinoma. |
| title_sort | statistical and machine learning based platform independent key genes identification for hepatocellular carcinoma |
| url | https://doi.org/10.1371/journal.pone.0318215 |
| work_keys_str_mv | AT mdalmehedihasan statisticalandmachinelearningbasedplatformindependentkeygenesidentificationforhepatocellularcarcinoma AT mdmaniruzzaman statisticalandmachinelearningbasedplatformindependentkeygenesidentificationforhepatocellularcarcinoma AT jiehuang statisticalandmachinelearningbasedplatformindependentkeygenesidentificationforhepatocellularcarcinoma AT jungpilshin statisticalandmachinelearningbasedplatformindependentkeygenesidentificationforhepatocellularcarcinoma |