mRNAsi-related genes can effectively distinguish hepatocellular carcinoma into new molecular subtypes
Background: Recent studies have shown that the mRNA expression-based stemness index (mRNAsi) can accurately quantify the similarity of cancer cells to stem cells, and mRNAsi-related genes are used as biomarkers for cancer. However, mRNAsi-driven tumor heterogeneity is rarely investigated, especially...
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| Format: | Article |
| Language: | English |
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Elsevier
2022-01-01
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| Series: | Computational and Structural Biotechnology Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2001037022002252 |
| _version_ | 1797978224993501184 |
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| author | Canbiao Wang Shijie Qin Wanwan Pan Xuejia Shi Hanyu Gao Ping Jin Xinyi Xia Fei Ma |
| author_facet | Canbiao Wang Shijie Qin Wanwan Pan Xuejia Shi Hanyu Gao Ping Jin Xinyi Xia Fei Ma |
| author_sort | Canbiao Wang |
| collection | DOAJ |
| description | Background: Recent studies have shown that the mRNA expression-based stemness index (mRNAsi) can accurately quantify the similarity of cancer cells to stem cells, and mRNAsi-related genes are used as biomarkers for cancer. However, mRNAsi-driven tumor heterogeneity is rarely investigated, especially whether mRNAsi can distinguish hepatocellular carcinoma (HCC) into different molecular subtypes is still largely unknown. Methods: Using OCLR machine learning algorithm, weighted gene co-expression network analysis, consistent unsupervised clustering, survival analysis and multivariate cox regression etc. to identify biomarkers and molecular subtypes related to tumor stemness in HCC. Results: We firstly demonstrate that the high mRNAsi is significantly associated with the poor survival and high disease grades in HCC. Secondly, we identify 212 mRNAsi-related genes that can divide HCC into three molecular subtypes: low cancer stemness cell phenotype (CSCP-L), moderate cancer stemness cell phenotype (CSCP-M) and high cancer stemness cell phenotype (CSCP-H), especially over-activated ribosomes, spliceosomes and nucleotide metabolism lead to the worst prognosis for the CSCP-H subtype patients, while activated amino acids, fatty acids and complement systems result in the best prognosis for the CSCP-L subtype. Thirdly, we find that three CSCP subtypes have different mutation characteristics, immune microenvironment and immune checkpoint expression, which may cause the differential prognosis for three subtypes. Finally, we identify 10 robust mRNAsi-related biomarkers that can effectively predict the survival of HCC patients. Conclusions: These novel cancer stemness-related CSCP subtypes and biomarkers in this study will be of great clinical significance for the diagnosis, prognosis and targeted therapy of HCC patients. |
| first_indexed | 2024-04-11T05:19:35Z |
| format | Article |
| id | doaj.art-7c6f08acafa547eeab4c2c992f2c5780 |
| institution | Directory Open Access Journal |
| issn | 2001-0370 |
| language | English |
| last_indexed | 2024-04-11T05:19:35Z |
| publishDate | 2022-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Computational and Structural Biotechnology Journal |
| spelling | doaj.art-7c6f08acafa547eeab4c2c992f2c57802022-12-24T04:52:54ZengElsevierComputational and Structural Biotechnology Journal2001-03702022-01-012029282941mRNAsi-related genes can effectively distinguish hepatocellular carcinoma into new molecular subtypesCanbiao Wang0Shijie Qin1Wanwan Pan2Xuejia Shi3Hanyu Gao4Ping Jin5Xinyi Xia6Fei Ma7Laboratory for Comparative Genomics and Bioinformatics & Jiangsu Key Laboratory for Biodiversity and Biotechnology, College of Life Science, Nanjing Normal University, Nanjing 210046, ChinaLaboratory for Comparative Genomics and Bioinformatics & Jiangsu Key Laboratory for Biodiversity and Biotechnology, College of Life Science, Nanjing Normal University, Nanjing 210046, China; Institute of Laboratory Medicine, Jinling Hospital, Nanjing University School of Medicine, the First School of Clinical Medicine, Southern Medical University, Nanjing, Jiangsu 210002, ChinaLaboratory for Comparative Genomics and Bioinformatics & Jiangsu Key Laboratory for Biodiversity and Biotechnology, College of Life Science, Nanjing Normal University, Nanjing 210046, ChinaLaboratory for Comparative Genomics and Bioinformatics & Jiangsu Key Laboratory for Biodiversity and Biotechnology, College of Life Science, Nanjing Normal University, Nanjing 210046, ChinaLaboratory for Comparative Genomics and Bioinformatics & Jiangsu Key Laboratory for Biodiversity and Biotechnology, College of Life Science, Nanjing Normal University, Nanjing 210046, ChinaLaboratory for Comparative Genomics and Bioinformatics & Jiangsu Key Laboratory for Biodiversity and Biotechnology, College of Life Science, Nanjing Normal University, Nanjing 210046, China; Corresponding authors.Institute of Laboratory Medicine, Jinling Hospital, Nanjing University School of Medicine, the First School of Clinical Medicine, Southern Medical University, Nanjing, Jiangsu 210002, China; Corresponding authors.Laboratory for Comparative Genomics and Bioinformatics & Jiangsu Key Laboratory for Biodiversity and Biotechnology, College of Life Science, Nanjing Normal University, Nanjing 210046, China; Corresponding authors.Background: Recent studies have shown that the mRNA expression-based stemness index (mRNAsi) can accurately quantify the similarity of cancer cells to stem cells, and mRNAsi-related genes are used as biomarkers for cancer. However, mRNAsi-driven tumor heterogeneity is rarely investigated, especially whether mRNAsi can distinguish hepatocellular carcinoma (HCC) into different molecular subtypes is still largely unknown. Methods: Using OCLR machine learning algorithm, weighted gene co-expression network analysis, consistent unsupervised clustering, survival analysis and multivariate cox regression etc. to identify biomarkers and molecular subtypes related to tumor stemness in HCC. Results: We firstly demonstrate that the high mRNAsi is significantly associated with the poor survival and high disease grades in HCC. Secondly, we identify 212 mRNAsi-related genes that can divide HCC into three molecular subtypes: low cancer stemness cell phenotype (CSCP-L), moderate cancer stemness cell phenotype (CSCP-M) and high cancer stemness cell phenotype (CSCP-H), especially over-activated ribosomes, spliceosomes and nucleotide metabolism lead to the worst prognosis for the CSCP-H subtype patients, while activated amino acids, fatty acids and complement systems result in the best prognosis for the CSCP-L subtype. Thirdly, we find that three CSCP subtypes have different mutation characteristics, immune microenvironment and immune checkpoint expression, which may cause the differential prognosis for three subtypes. Finally, we identify 10 robust mRNAsi-related biomarkers that can effectively predict the survival of HCC patients. Conclusions: These novel cancer stemness-related CSCP subtypes and biomarkers in this study will be of great clinical significance for the diagnosis, prognosis and targeted therapy of HCC patients.http://www.sciencedirect.com/science/article/pii/S2001037022002252Hepatocellular CarcinomamRNAsiCancer stem cellMolecular subtypePrognosis |
| spellingShingle | Canbiao Wang Shijie Qin Wanwan Pan Xuejia Shi Hanyu Gao Ping Jin Xinyi Xia Fei Ma mRNAsi-related genes can effectively distinguish hepatocellular carcinoma into new molecular subtypes Computational and Structural Biotechnology Journal Hepatocellular Carcinoma mRNAsi Cancer stem cell Molecular subtype Prognosis |
| title | mRNAsi-related genes can effectively distinguish hepatocellular carcinoma into new molecular subtypes |
| title_full | mRNAsi-related genes can effectively distinguish hepatocellular carcinoma into new molecular subtypes |
| title_fullStr | mRNAsi-related genes can effectively distinguish hepatocellular carcinoma into new molecular subtypes |
| title_full_unstemmed | mRNAsi-related genes can effectively distinguish hepatocellular carcinoma into new molecular subtypes |
| title_short | mRNAsi-related genes can effectively distinguish hepatocellular carcinoma into new molecular subtypes |
| title_sort | mrnasi related genes can effectively distinguish hepatocellular carcinoma into new molecular subtypes |
| topic | Hepatocellular Carcinoma mRNAsi Cancer stem cell Molecular subtype Prognosis |
| url | http://www.sciencedirect.com/science/article/pii/S2001037022002252 |
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