Identification of Prognostic Model and Biomarkers for Cancer Stem Cell Characteristics in Glioblastoma by Network Analysis of Multi-Omics Data and Stemness Indices
The progression of most human cancers mainly involves the gradual accumulation of the loss of differentiated phenotypes and the sequential acquisition of progenitor and stem cell-like features. Glioblastoma multiforme (GBM) stem cells (GSCs), characterized by self-renewal and therapeutic resistance,...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2020-10-01
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| Series: | Frontiers in Cell and Developmental Biology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/article/10.3389/fcell.2020.558961/full |
| _version_ | 1819015655340376064 |
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| author | Jianyang Du Jianyang Du Xiuwei Yan Shan Mi Shan Mi Yuan Li Hang Ji Hang Ji Kuiyuan Hou Kuiyuan Hou Shuai Ma Yixu Ba Yixu Ba Peng Zhou Lei Chen Lei Chen Rui Xie Shaoshan Hu |
| author_facet | Jianyang Du Jianyang Du Xiuwei Yan Shan Mi Shan Mi Yuan Li Hang Ji Hang Ji Kuiyuan Hou Kuiyuan Hou Shuai Ma Yixu Ba Yixu Ba Peng Zhou Lei Chen Lei Chen Rui Xie Shaoshan Hu |
| author_sort | Jianyang Du |
| collection | DOAJ |
| description | The progression of most human cancers mainly involves the gradual accumulation of the loss of differentiated phenotypes and the sequential acquisition of progenitor and stem cell-like features. Glioblastoma multiforme (GBM) stem cells (GSCs), characterized by self-renewal and therapeutic resistance, play vital roles in GBM. However, a comprehensive understanding of GBM stemness remains elusive. Two stemness indices, mRNAsi and EREG-mRNAsi, were employed to comprehensively analyze GBM stemness. We observed that mRNAsi was significantly related to multi-omics parameters (such as mutant status, sample type, transcriptomics, and molecular subtype). Moreover, potential mechanisms and candidate compounds targeting the GBM stemness signature were illuminated. By combining weighted gene co-expression network analysis with differential analysis, we obtained 18 stemness-related genes, 10 of which were significantly related to survival. Moreover, we obtained a prediction model from both two independent cancer databases that was not only an independent clinical outcome predictor but could also accurately predict the clinical parameters of GBM. Survival analysis and experimental data confirmed that the five hub genes (CHI3L2, FSTL3, RPA3, RRM2, and YTHDF2) could be used as markers for poor prognosis of GBM. Mechanistically, the effect of inhibiting the proliferation of GSCs was attributed to the reduction of the ratio of CD133 and the suppression of the invasiveness of GSCs. The results based on an in vivo xenograft model are consistent with the finding that knockdown of the hub gene inhibits the growth of GSCs in vitro. Our approach could be applied to facilitate the development of objective diagnostic and targeted treatment tools to quantify cancer stemness in clinical tumors, and perhaps lead considerable benefits that could predict tumor prognosis, identify new stemness-related targets and targeted therapies, or improve targeted therapy sensitivity. The five genes identified in this study are expected to be the targets of GBM stem cell therapy. |
| first_indexed | 2024-12-21T02:35:11Z |
| format | Article |
| id | doaj.art-3d7604e3863f4269a1a3d59f4a6e2bf8 |
| institution | Directory Open Access Journal |
| issn | 2296-634X |
| language | English |
| last_indexed | 2024-12-21T02:35:11Z |
| publishDate | 2020-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Cell and Developmental Biology |
| spelling | doaj.art-3d7604e3863f4269a1a3d59f4a6e2bf82022-12-21T19:18:49ZengFrontiers Media S.A.Frontiers in Cell and Developmental Biology2296-634X2020-10-01810.3389/fcell.2020.558961558961Identification of Prognostic Model and Biomarkers for Cancer Stem Cell Characteristics in Glioblastoma by Network Analysis of Multi-Omics Data and Stemness IndicesJianyang Du0Jianyang Du1Xiuwei Yan2Shan Mi3Shan Mi4Yuan Li5Hang Ji6Hang Ji7Kuiyuan Hou8Kuiyuan Hou9Shuai Ma10Yixu Ba11Yixu Ba12Peng Zhou13Lei Chen14Lei Chen15Rui Xie16Shaoshan Hu17Department of Neurosurgery, The Second Affiliated Hospital of Harbin Medical University, Harbin, ChinaTranslational Medicine Research and Cooperation Center of Northern China, Heilongjiang Academy of Medical Sciences, Harbin, ChinaDepartment of Neurosurgery, The Second Affiliated Hospital of Harbin Medical University, Harbin, ChinaDepartment of Neurosurgery, The Second Affiliated Hospital of Harbin Medical University, Harbin, ChinaTranslational Medicine Research and Cooperation Center of Northern China, Heilongjiang Academy of Medical Sciences, Harbin, ChinaDepartment of Pharmacology (The State-Province Key Laboratories of Biomedicine-Pharmaceutics of China, Key Laboratory of Cardiovascular Research, Ministry of Education), College of Pharmacy, Harbin Medical University, Harbin, ChinaDepartment of Neurosurgery, The Second Affiliated Hospital of Harbin Medical University, Harbin, ChinaTranslational Medicine Research and Cooperation Center of Northern China, Heilongjiang Academy of Medical Sciences, Harbin, ChinaDepartment of Neurosurgery, The Second Affiliated Hospital of Harbin Medical University, Harbin, ChinaTranslational Medicine Research and Cooperation Center of Northern China, Heilongjiang Academy of Medical Sciences, Harbin, ChinaDepartment of Neurosurgery, The Second Affiliated Hospital of Harbin Medical University, Harbin, ChinaDepartment of Neurosurgery, The Second Affiliated Hospital of Harbin Medical University, Harbin, ChinaTranslational Medicine Research and Cooperation Center of Northern China, Heilongjiang Academy of Medical Sciences, Harbin, ChinaDepartment of Neurosurgery, The Second Affiliated Hospital of Harbin Medical University, Harbin, ChinaDepartment of Neurosurgery, The Second Affiliated Hospital of Harbin Medical University, Harbin, ChinaDepartment of Neurosurgery, The First Affiliated Hospital of Harbin, Harbin, ChinaDepartment of Digestive Internal Medicine, Harbin Medical University Cancer Hospital, Harbin, ChinaDepartment of Neurosurgery, The Second Affiliated Hospital of Harbin Medical University, Harbin, ChinaThe progression of most human cancers mainly involves the gradual accumulation of the loss of differentiated phenotypes and the sequential acquisition of progenitor and stem cell-like features. Glioblastoma multiforme (GBM) stem cells (GSCs), characterized by self-renewal and therapeutic resistance, play vital roles in GBM. However, a comprehensive understanding of GBM stemness remains elusive. Two stemness indices, mRNAsi and EREG-mRNAsi, were employed to comprehensively analyze GBM stemness. We observed that mRNAsi was significantly related to multi-omics parameters (such as mutant status, sample type, transcriptomics, and molecular subtype). Moreover, potential mechanisms and candidate compounds targeting the GBM stemness signature were illuminated. By combining weighted gene co-expression network analysis with differential analysis, we obtained 18 stemness-related genes, 10 of which were significantly related to survival. Moreover, we obtained a prediction model from both two independent cancer databases that was not only an independent clinical outcome predictor but could also accurately predict the clinical parameters of GBM. Survival analysis and experimental data confirmed that the five hub genes (CHI3L2, FSTL3, RPA3, RRM2, and YTHDF2) could be used as markers for poor prognosis of GBM. Mechanistically, the effect of inhibiting the proliferation of GSCs was attributed to the reduction of the ratio of CD133 and the suppression of the invasiveness of GSCs. The results based on an in vivo xenograft model are consistent with the finding that knockdown of the hub gene inhibits the growth of GSCs in vitro. Our approach could be applied to facilitate the development of objective diagnostic and targeted treatment tools to quantify cancer stemness in clinical tumors, and perhaps lead considerable benefits that could predict tumor prognosis, identify new stemness-related targets and targeted therapies, or improve targeted therapy sensitivity. The five genes identified in this study are expected to be the targets of GBM stem cell therapy.https://www.frontiersin.org/article/10.3389/fcell.2020.558961/fullconnectivity mapmachine learning methodsglioblastomaprognostic modelstemnesstumor immune environment |
| spellingShingle | Jianyang Du Jianyang Du Xiuwei Yan Shan Mi Shan Mi Yuan Li Hang Ji Hang Ji Kuiyuan Hou Kuiyuan Hou Shuai Ma Yixu Ba Yixu Ba Peng Zhou Lei Chen Lei Chen Rui Xie Shaoshan Hu Identification of Prognostic Model and Biomarkers for Cancer Stem Cell Characteristics in Glioblastoma by Network Analysis of Multi-Omics Data and Stemness Indices Frontiers in Cell and Developmental Biology connectivity map machine learning methods glioblastoma prognostic model stemness tumor immune environment |
| title | Identification of Prognostic Model and Biomarkers for Cancer Stem Cell Characteristics in Glioblastoma by Network Analysis of Multi-Omics Data and Stemness Indices |
| title_full | Identification of Prognostic Model and Biomarkers for Cancer Stem Cell Characteristics in Glioblastoma by Network Analysis of Multi-Omics Data and Stemness Indices |
| title_fullStr | Identification of Prognostic Model and Biomarkers for Cancer Stem Cell Characteristics in Glioblastoma by Network Analysis of Multi-Omics Data and Stemness Indices |
| title_full_unstemmed | Identification of Prognostic Model and Biomarkers for Cancer Stem Cell Characteristics in Glioblastoma by Network Analysis of Multi-Omics Data and Stemness Indices |
| title_short | Identification of Prognostic Model and Biomarkers for Cancer Stem Cell Characteristics in Glioblastoma by Network Analysis of Multi-Omics Data and Stemness Indices |
| title_sort | identification of prognostic model and biomarkers for cancer stem cell characteristics in glioblastoma by network analysis of multi omics data and stemness indices |
| topic | connectivity map machine learning methods glioblastoma prognostic model stemness tumor immune environment |
| url | https://www.frontiersin.org/article/10.3389/fcell.2020.558961/full |
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