Key therapeutic targets implicated at the early stage of hepatocellular carcinoma identified through machine-learning approaches
Abstract Hepatocellular carcinoma (HCC) is the most frequent type of primary liver cancer. Early-stage detection plays an essential role in making treatment decisions and identifying dominant molecular mechanisms. We utilized machine learning algorithms to find significant mRNAs and microRNAs (miRNA...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-03-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-30720-x |
| _version_ | 1827984427249041408 |
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| author | Seyed Mahdi Hosseiniyan Khatibi Farima Najjarian Hamed Homaei Rad Mohammadreza Ardalan Mohammad Teshnehlab Sepideh Zununi Vahed Saeed Pirmoradi |
| author_facet | Seyed Mahdi Hosseiniyan Khatibi Farima Najjarian Hamed Homaei Rad Mohammadreza Ardalan Mohammad Teshnehlab Sepideh Zununi Vahed Saeed Pirmoradi |
| author_sort | Seyed Mahdi Hosseiniyan Khatibi |
| collection | DOAJ |
| description | Abstract Hepatocellular carcinoma (HCC) is the most frequent type of primary liver cancer. Early-stage detection plays an essential role in making treatment decisions and identifying dominant molecular mechanisms. We utilized machine learning algorithms to find significant mRNAs and microRNAs (miRNAs) at the early and late stages of HCC. First, pre-processing approaches, including organization, nested cross-validation, cleaning, and normalization were applied. Next, the t-test/ANOVA methods and binary particle swarm optimization were used as a filter and wrapper method in the feature selection step, respectively. Then, classifiers, based on machine learning and deep learning algorithms were utilized to evaluate the discrimination power of selected features (mRNAs and miRNAs) in the classification step. Finally, the association rule mining algorithm was applied to selected features for identifying key mRNAs and miRNAs that can help decode dominant molecular mechanisms in HCC stages. The applied methods could identify key genes associated with the early (e.g., Vitronectin, thrombin-activatable fibrinolysis inhibitor, lactate dehydrogenase D (LDHD), miR-590) and late-stage (e.g., SPRY domain containing 4, regucalcin, miR-3199-1, miR-194-2, miR-4999) of HCC. This research could establish a clear picture of putative candidate genes, which could be the main actors at the early and late stages of HCC. |
| first_indexed | 2024-04-09T23:00:02Z |
| format | Article |
| id | doaj.art-e4527b015d1143c48925835e3c4d1be5 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-09T23:00:02Z |
| publishDate | 2023-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-e4527b015d1143c48925835e3c4d1be52023-03-22T11:04:28ZengNature PortfolioScientific Reports2045-23222023-03-0113112010.1038/s41598-023-30720-xKey therapeutic targets implicated at the early stage of hepatocellular carcinoma identified through machine-learning approachesSeyed Mahdi Hosseiniyan Khatibi0Farima Najjarian1Hamed Homaei Rad2Mohammadreza Ardalan3Mohammad Teshnehlab4Sepideh Zununi Vahed5Saeed Pirmoradi6Kidney Research Center, Tabriz University of Medical SciencesFaculty of Medicine, Tabriz University of Medical SciencesRahat Breath and Sleep Research Center, Tabriz University of Medical ScienceKidney Research Center, Tabriz University of Medical SciencesDepartment of Electric and Computer Engineering, K.N. Toosi University of TechnologyKidney Research Center, Tabriz University of Medical SciencesClinical Research Development Unit of Tabriz Valiasr Hospital, Tabriz University of Medical SciencesAbstract Hepatocellular carcinoma (HCC) is the most frequent type of primary liver cancer. Early-stage detection plays an essential role in making treatment decisions and identifying dominant molecular mechanisms. We utilized machine learning algorithms to find significant mRNAs and microRNAs (miRNAs) at the early and late stages of HCC. First, pre-processing approaches, including organization, nested cross-validation, cleaning, and normalization were applied. Next, the t-test/ANOVA methods and binary particle swarm optimization were used as a filter and wrapper method in the feature selection step, respectively. Then, classifiers, based on machine learning and deep learning algorithms were utilized to evaluate the discrimination power of selected features (mRNAs and miRNAs) in the classification step. Finally, the association rule mining algorithm was applied to selected features for identifying key mRNAs and miRNAs that can help decode dominant molecular mechanisms in HCC stages. The applied methods could identify key genes associated with the early (e.g., Vitronectin, thrombin-activatable fibrinolysis inhibitor, lactate dehydrogenase D (LDHD), miR-590) and late-stage (e.g., SPRY domain containing 4, regucalcin, miR-3199-1, miR-194-2, miR-4999) of HCC. This research could establish a clear picture of putative candidate genes, which could be the main actors at the early and late stages of HCC.https://doi.org/10.1038/s41598-023-30720-x |
| spellingShingle | Seyed Mahdi Hosseiniyan Khatibi Farima Najjarian Hamed Homaei Rad Mohammadreza Ardalan Mohammad Teshnehlab Sepideh Zununi Vahed Saeed Pirmoradi Key therapeutic targets implicated at the early stage of hepatocellular carcinoma identified through machine-learning approaches Scientific Reports |
| title | Key therapeutic targets implicated at the early stage of hepatocellular carcinoma identified through machine-learning approaches |
| title_full | Key therapeutic targets implicated at the early stage of hepatocellular carcinoma identified through machine-learning approaches |
| title_fullStr | Key therapeutic targets implicated at the early stage of hepatocellular carcinoma identified through machine-learning approaches |
| title_full_unstemmed | Key therapeutic targets implicated at the early stage of hepatocellular carcinoma identified through machine-learning approaches |
| title_short | Key therapeutic targets implicated at the early stage of hepatocellular carcinoma identified through machine-learning approaches |
| title_sort | key therapeutic targets implicated at the early stage of hepatocellular carcinoma identified through machine learning approaches |
| url | https://doi.org/10.1038/s41598-023-30720-x |
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