Muqi Li1, Minni Liang2, Tian Lan3, Xiwen Wu1, Wenxuan Xie1, Tielong Wang4,5,6, Zhitao Chen4,5,6, Shunli Shen1, Baogang Peng1. 1. Department of Liver Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China. 2. Center of Surgery and Anaesthiology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China. 3. Department of Pancreatobiliary Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China. 4. Organ Transplant Center, First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China. 5. Guangdong Provincial Key Laboratory of Organ Donation and Transplant Immunology, Guangzhou, China. 6. Guangdong Provincial International Cooperation Base of Science and Technology (Organ Transplantation), Guangzhou, China.
Abstract
BACKGROUND: Long non-coding RNA (LncRNA) plays an important role in the occurrence and development of hepatocellular carcinoma (HCC). This study aims to establish an immune-related LncRNA model for risk assessment and prognosis prediction in HCC patients. METHODS: Hepatocellular carcinoma patient samples with complete clinical data and corresponding whole transcriptome expression were obtained from the Cancer Genome Atlas (TCGA). Immune-related genes were acquired from the Gene Set Enrichment Analysis (GSEA) website and matched with LncRNA in the TCGA to get immune-related LncRNA. Least Absolute Shrinkage and Selection Operator (LASSO) regression was used for screening the candidate LncRNAs and calculating the risk coefficient to establish the prognosis model. Patients were divided into a high-risk group and a low-risk group depending on the median risk score. The reliability of the prediction was evaluated in the validation cohort and the whole cohort. GSEA and principal component analysis were used for function evaluation. RESULTS: A total of 319 samples met the screening criteria and were randomly distributed across the training cohort and the validation cohort. After comparison with the IMMUNE_RESPONSE gene set and the IMMUNE_SYSTEM_PROCESS gene set, a total of 3094 immune-related LncRNAs were screened. Ultimately, four immune-related LncRNAs were used to construct a formula using LASSO regression. According to the formula, the low-risk group showed a higher survival rate than the high-risk group in the validation cohort and the whole cohort. The receiver operating characteristic curves data demonstrated that the risk score was more specific than other traditional clinical characteristics in predicting the 5-year survival rate for HCC. CONCLUSION: The four-immune-related-LncRNA model can be used for survival prediction in HCC and guide clinical therapy.
BACKGROUND: Long non-coding RNA (LncRNA) plays an important role in the occurrence and development of hepatocellular carcinoma (HCC). This study aims to establish an immune-related LncRNA model for risk assessment and prognosis prediction in HCC patients. METHODS: Hepatocellular carcinoma patient samples with complete clinical data and corresponding whole transcriptome expression were obtained from the Cancer Genome Atlas (TCGA). Immune-related genes were acquired from the Gene Set Enrichment Analysis (GSEA) website and matched with LncRNA in the TCGA to get immune-related LncRNA. Least Absolute Shrinkage and Selection Operator (LASSO) regression was used for screening the candidate LncRNAs and calculating the risk coefficient to establish the prognosis model. Patients were divided into a high-risk group and a low-risk group depending on the median risk score. The reliability of the prediction was evaluated in the validation cohort and the whole cohort. GSEA and principal component analysis were used for function evaluation. RESULTS: A total of 319 samples met the screening criteria and were randomly distributed across the training cohort and the validation cohort. After comparison with the IMMUNE_RESPONSE gene set and the IMMUNE_SYSTEM_PROCESS gene set, a total of 3094 immune-related LncRNAs were screened. Ultimately, four immune-related LncRNAs were used to construct a formula using LASSO regression. According to the formula, the low-risk group showed a higher survival rate than the high-risk group in the validation cohort and the whole cohort. The receiver operating characteristic curves data demonstrated that the risk score was more specific than other traditional clinical characteristics in predicting the 5-year survival rate for HCC. CONCLUSION: The four-immune-related-LncRNA model can be used for survival prediction in HCC and guide clinical therapy.
Authors: Yang Wang; Huiling Zhong; Xiaodan Xie; Crystal Y Chen; Dan Huang; Ling Shen; Hui Zhang; Zheng W Chen; Gucheng Zeng Journal: Proc Natl Acad Sci U S A Date: 2015-07-06 Impact factor: 11.205
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