Integrating single cell analysis and machine learning methods reveals stem cell-related gene S100A10 as an important target for prediction of liver cancer diagnosis and immunotherapy
BackgroundHepatocellular carcinoma (LIHC) poses a significant health challenge worldwide, primarily due to late-stage diagnosis and the limited effectiveness of current therapies. Cancer stem cells are known to play a role in tumor development, metastasis, and resistance to treatment. A thorough und...
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Immunology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fimmu.2024.1534723/full |
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| author | Shenjun Huang Tingting Tu |
| author_facet | Shenjun Huang Tingting Tu |
| author_sort | Shenjun Huang |
| collection | DOAJ |
| description | BackgroundHepatocellular carcinoma (LIHC) poses a significant health challenge worldwide, primarily due to late-stage diagnosis and the limited effectiveness of current therapies. Cancer stem cells are known to play a role in tumor development, metastasis, and resistance to treatment. A thorough understanding of genes associated with stem cells is crucial for improving the diagnostic precision of LIHC and for the advancement of effective immunotherapy approaches.MethodThis research combines single-cell RNA sequencing with machine learning techniques to identify vital stem cell-associated genes that could act as prognostic biomarkers and therapeutic targets for LIHC. We analyzed various datasets, applying negative matrix factorization alongside machine learning algorithms to reveal gene expression patterns and construct diagnostic models. The XGBoost algorithm was specifically utilized to identify key regulatory genes related to stem cells in LIHC, and the expression levels and prognostic significance of these genes were validated experimentally.ResultsOur single-cell analysis identified 16 differential prognostic genes associated with liver cancer stem cells. Cluster analysis and diagnostic models constructed using various machine learning techniques confirmed the significance of these 16 genes in the diagnosis and immunotherapy of LIHC. Notably, the XGBoost algorithm identified S100A10 as the stem cell-related gene most relevant to the prognosis of LIHC patients. Experimental validation further supports S100A10 as a potential prognostic marker for this cancer type. Additionally, S100A10 shows a positive correlation with the stem cell marker POU5F1.ConclusionThe results of this study highlight S100A10 as an essential predictor for liver cancer diagnosis and treatment response, particularly regarding immunotherapy. This research offers valuable insights into the molecular mechanisms underlying LIHC and suggests S100A10 as a promising target for enhancing treatment outcomes in liver cancer patients. |
| format | Article |
| id | doaj-art-a1ca6859e2194d9a9eb71d84f506f50d |
| institution | Kabale University |
| issn | 1664-3224 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Immunology |
| spelling | doaj-art-a1ca6859e2194d9a9eb71d84f506f50d2025-01-07T06:51:15ZengFrontiers Media S.A.Frontiers in Immunology1664-32242025-01-011510.3389/fimmu.2024.15347231534723Integrating single cell analysis and machine learning methods reveals stem cell-related gene S100A10 as an important target for prediction of liver cancer diagnosis and immunotherapyShenjun Huang0Tingting Tu1Department of Oncology, Nantong Tumur Hospital (Affiliated Tumur Hospital of Nantong University), Nantong, ChinaDepartment of Radiation Oncology, Lianyungang Second People’s Hospital (Lianyungang Tumur Hospital), Lianyungang, ChinaBackgroundHepatocellular carcinoma (LIHC) poses a significant health challenge worldwide, primarily due to late-stage diagnosis and the limited effectiveness of current therapies. Cancer stem cells are known to play a role in tumor development, metastasis, and resistance to treatment. A thorough understanding of genes associated with stem cells is crucial for improving the diagnostic precision of LIHC and for the advancement of effective immunotherapy approaches.MethodThis research combines single-cell RNA sequencing with machine learning techniques to identify vital stem cell-associated genes that could act as prognostic biomarkers and therapeutic targets for LIHC. We analyzed various datasets, applying negative matrix factorization alongside machine learning algorithms to reveal gene expression patterns and construct diagnostic models. The XGBoost algorithm was specifically utilized to identify key regulatory genes related to stem cells in LIHC, and the expression levels and prognostic significance of these genes were validated experimentally.ResultsOur single-cell analysis identified 16 differential prognostic genes associated with liver cancer stem cells. Cluster analysis and diagnostic models constructed using various machine learning techniques confirmed the significance of these 16 genes in the diagnosis and immunotherapy of LIHC. Notably, the XGBoost algorithm identified S100A10 as the stem cell-related gene most relevant to the prognosis of LIHC patients. Experimental validation further supports S100A10 as a potential prognostic marker for this cancer type. Additionally, S100A10 shows a positive correlation with the stem cell marker POU5F1.ConclusionThe results of this study highlight S100A10 as an essential predictor for liver cancer diagnosis and treatment response, particularly regarding immunotherapy. This research offers valuable insights into the molecular mechanisms underlying LIHC and suggests S100A10 as a promising target for enhancing treatment outcomes in liver cancer patients.https://www.frontiersin.org/articles/10.3389/fimmu.2024.1534723/fullcancer stem cellhepatocellular carcinomasingle cell analysismachine learningS100A10 |
| spellingShingle | Shenjun Huang Tingting Tu Integrating single cell analysis and machine learning methods reveals stem cell-related gene S100A10 as an important target for prediction of liver cancer diagnosis and immunotherapy Frontiers in Immunology cancer stem cell hepatocellular carcinoma single cell analysis machine learning S100A10 |
| title | Integrating single cell analysis and machine learning methods reveals stem cell-related gene S100A10 as an important target for prediction of liver cancer diagnosis and immunotherapy |
| title_full | Integrating single cell analysis and machine learning methods reveals stem cell-related gene S100A10 as an important target for prediction of liver cancer diagnosis and immunotherapy |
| title_fullStr | Integrating single cell analysis and machine learning methods reveals stem cell-related gene S100A10 as an important target for prediction of liver cancer diagnosis and immunotherapy |
| title_full_unstemmed | Integrating single cell analysis and machine learning methods reveals stem cell-related gene S100A10 as an important target for prediction of liver cancer diagnosis and immunotherapy |
| title_short | Integrating single cell analysis and machine learning methods reveals stem cell-related gene S100A10 as an important target for prediction of liver cancer diagnosis and immunotherapy |
| title_sort | integrating single cell analysis and machine learning methods reveals stem cell related gene s100a10 as an important target for prediction of liver cancer diagnosis and immunotherapy |
| topic | cancer stem cell hepatocellular carcinoma single cell analysis machine learning S100A10 |
| url | https://www.frontiersin.org/articles/10.3389/fimmu.2024.1534723/full |
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