Wen Yin1, Hecheng Zhu2, Jun Tan1, Zhaoqi Xin1, Quanwei Zhou1, Yudong Cao1, Zhaoping Wu1, Lei Wang3, Ming Zhao2, Xingjun Jiang4, Caiping Ren5, Guihua Tang6. 1. Department of Neurosurgery, Xiangya Hospital of Central South University, Changsha, Hunan Province, 410008, China. 2. Changsha Kexin Cancer Hospital, Changsha, Hunan, 410205, China. 3. Cancer Research Institute, Collaborative Innovation Center for Cancer Medicine, The Key Laboratory for Carcinogenesis of Chinese Ministry of Health and the Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, School of Basic Medical Science, Central South University, Changsha, Hunan, People's Republic of China. 4. Department of Neurosurgery, Xiangya Hospital of Central South University, Changsha, Hunan Province, 410008, China. jiangxj@csu.edu.cn. 5. Cancer Research Institute, Collaborative Innovation Center for Cancer Medicine, The Key Laboratory for Carcinogenesis of Chinese Ministry of Health and the Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, School of Basic Medical Science, Central South University, Changsha, Hunan, People's Republic of China. rencaiping@csu.edu.cn. 6. Department of Clinical Laboratory, Hunan Provincial People's Hospital (The first affiliated hospital of Hunan Normal University, The college of clinical medicine of Human Normal University), Changsha, Hunan Province, 410005, China. guihuatang0827@163.com.
Abstract
BACKGROUND: Gliomas account for the majority of fatal primary brain tumors, and there is much room for research in the underlying pathogenesis, the multistep progression of glioma, and how to improve survival. In our study, we aimed to identify potential biomarkers or therapeutic targets of glioma and study the mechanism underlying the tumor progression. METHODS: We downloaded the microarray datasets (GSE43378 and GSE7696) from the Gene Expression Omnibus (GEO) database. Then, we used weighted gene co-expression network analysis (WGCNA) to screen potential biomarkers or therapeutic targets related to the tumor progression. ESTIMATE (Estimation of STromal and Immune cells in MAlignant Tumors using Expression data) algorithm and TIMER (Tumor Immune Estimation Resource) database were used to analyze the correlation between the selected genes and the tumor microenvironment. Real-time reverse transcription polymerase chain reaction was used to measure the selected gene. Transwell and wound healing assays were used to measure the cell migration and invasion capacity. Western blotting was used to test the expression of epithelial-mesenchymal transition (EMT) related markers. RESULTS: We identified specific module genes that were positively correlated with the WHO grade but negatively correlated with OS of glioma. Importantly, we identified that 6 collagen genes (COL1A1, COL1A2, COL3A1, COL4A1, COL4A2, and COL5A2) could regulate the immunosuppressive microenvironment of glioma. Moreover, we found that these collagen genes were significantly involved in the EMT process of glioma. Finally, taking COL3A1 as a further research object, the results showed that knockdown of COL3A1 significantly inhibited the migration, invasion, and EMT process of SHG44 and A172 cells. CONCLUSIONS: In summary, our study demonstrated that collagen genes play an important role in regulating the immunosuppressive microenvironment and EMT process of glioma and could serve as potential therapeutic targets for glioma management.
BACKGROUND:Gliomas account for the majority of fatal primary brain tumors, and there is much room for research in the underlying pathogenesis, the multistep progression of glioma, and how to improve survival. In our study, we aimed to identify potential biomarkers or therapeutic targets of glioma and study the mechanism underlying the tumor progression. METHODS: We downloaded the microarray datasets (GSE43378 and GSE7696) from the Gene Expression Omnibus (GEO) database. Then, we used weighted gene co-expression network analysis (WGCNA) to screen potential biomarkers or therapeutic targets related to the tumor progression. ESTIMATE (Estimation of STromal and Immune cells in MAlignant Tumors using Expression data) algorithm and TIMER (Tumor Immune Estimation Resource) database were used to analyze the correlation between the selected genes and the tumor microenvironment. Real-time reverse transcription polymerase chain reaction was used to measure the selected gene. Transwell and wound healing assays were used to measure the cell migration and invasion capacity. Western blotting was used to test the expression of epithelial-mesenchymal transition (EMT) related markers. RESULTS: We identified specific module genes that were positively correlated with the WHO grade but negatively correlated with OS of glioma. Importantly, we identified that 6 collagen genes (COL1A1, COL1A2, COL3A1, COL4A1, COL4A2, and COL5A2) could regulate the immunosuppressive microenvironment of glioma. Moreover, we found that these collagen genes were significantly involved in the EMT process of glioma. Finally, taking COL3A1 as a further research object, the results showed that knockdown of COL3A1 significantly inhibited the migration, invasion, and EMT process of SHG44 and A172 cells. CONCLUSIONS: In summary, our study demonstrated that collagen genes play an important role in regulating the immunosuppressive microenvironment and EMT process of glioma and could serve as potential therapeutic targets for glioma management.
Authors: Anastasia Murat; Eugenia Migliavacca; Thierry Gorlia; Wanyu L Lambiv; Tal Shay; Marie-France Hamou; Nicolas de Tribolet; Luca Regli; Wolfgang Wick; Mathilde C M Kouwenhoven; Johannes A Hainfellner; Frank L Heppner; Pierre-Yves Dietrich; Yitzhak Zimmer; J Gregory Cairncross; Robert-Charles Janzer; Eytan Domany; Mauro Delorenzi; Roger Stupp; Monika E Hegi Journal: J Clin Oncol Date: 2008-06-20 Impact factor: 44.544