Integrative Analysis Identifies Cell-Type-Specific Genes Within Tumor Microenvironment as Prognostic Indicators in Hepatocellular Carcinoma
BackgroundHepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality worldwide, but effective early detection and prognostication methods are lacking. MethodsThe Cox regression model was built to stratify the HCC patients. The single-cell RNA sequencing data analysis and gene set...
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Frontiers Media S.A.
2022-05-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2022.878923/full |
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| author | Zi-Li Huang Zi-Li Huang Bin Xu Bin Xu Ting-Ting Li Yong-Hua Xu Xin-Yu Huang Xiu-Yan Huang |
| author_facet | Zi-Li Huang Zi-Li Huang Bin Xu Bin Xu Ting-Ting Li Yong-Hua Xu Xin-Yu Huang Xiu-Yan Huang |
| author_sort | Zi-Li Huang |
| collection | DOAJ |
| description | BackgroundHepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality worldwide, but effective early detection and prognostication methods are lacking. MethodsThe Cox regression model was built to stratify the HCC patients. The single-cell RNA sequencing data analysis and gene set enrichment analysis were employed to investigate the biological function of identified markers. PLCB1 gain- or loss-of-function experiments were performed, and obtained HCC samples were analyzed using quantitative real-time PCR and immunohistochemistry assay to validate the biological function of identified markers.ResultsIn this study, we developed a model using optimized markers for HCC recurrence prediction. Specifically, we screened out 8 genes through a series of data analyses, and built a multivariable Cox model based on their expression. The risk stratifications using the Eight-Gene Cox (EGC) model were closely associated with the recurrence-free survivals (RFS) in both training and three validation cohorts. We further demonstrated that this risk stratification could serve as an independent predictor in predicting HCC recurrence, and that the EGC model could outperform other models. Moreover, we also investigated the cell-type-specific expression patterns of the eight recurrence-related genes in tumor microenvironment using single-cell RNA sequencing data, and interpreted their functional roles from correlation and gene set enrichment analyses, in vitro and in vivo experiments. Particularly, PLCB1 and SLC22A7 were predominantly expressed in malignant cells, and they were predicted to promote angiogenesis and to help maintain normal metabolism in liver, respectively. In contrast, both FASLG and IL2RB were specifically expressed in T cells, and were highly correlated with T cell marker genes, suggesting that these two genes might assist in maintaining normal function of T cell-mediated immune response in tumor tissues.ConclusionIn conclusion, the EGC model and eight identified marker genes could not only facilitate the accurate prediction of HCC recurrence, but also improve our understanding of the mechanisms behind HCC recurrence. |
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| language | English |
| last_indexed | 2024-12-12T15:59:47Z |
| publishDate | 2022-05-01 |
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| series | Frontiers in Oncology |
| spelling | doaj.art-6f610b45604044fd9e504c8e38b8e2a52022-12-22T00:19:24ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2022-05-011210.3389/fonc.2022.878923878923Integrative Analysis Identifies Cell-Type-Specific Genes Within Tumor Microenvironment as Prognostic Indicators in Hepatocellular CarcinomaZi-Li Huang0Zi-Li Huang1Bin Xu2Bin Xu3Ting-Ting Li4Yong-Hua Xu5Xin-Yu Huang6Xiu-Yan Huang7Department of General Surgery, Shanghai Jiaotong University Affiliated Sixth People’s Hospital, Shanghai, ChinaDepartment of Radiology, Xuhui District Central Hospital of Zhongshan Hospital, Fudan University, Shanghai, ChinaDepartment of General Surgery, Shanghai Jiaotong University Affiliated Sixth People’s Hospital, Shanghai, ChinaDepartment of General Surgery, The Tenth People’s Hospital of Tongji University, Shanghai, ChinaDepartment of Infectious Disease, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, ChinaDepartment of Radiology, Xuhui District Central Hospital of Zhongshan Hospital, Fudan University, Shanghai, ChinaDepartment of General Surgery, Shanghai Jiaotong University Affiliated Sixth People’s Hospital, Shanghai, ChinaDepartment of General Surgery, Shanghai Jiaotong University Affiliated Sixth People’s Hospital, Shanghai, ChinaBackgroundHepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality worldwide, but effective early detection and prognostication methods are lacking. MethodsThe Cox regression model was built to stratify the HCC patients. The single-cell RNA sequencing data analysis and gene set enrichment analysis were employed to investigate the biological function of identified markers. PLCB1 gain- or loss-of-function experiments were performed, and obtained HCC samples were analyzed using quantitative real-time PCR and immunohistochemistry assay to validate the biological function of identified markers.ResultsIn this study, we developed a model using optimized markers for HCC recurrence prediction. Specifically, we screened out 8 genes through a series of data analyses, and built a multivariable Cox model based on their expression. The risk stratifications using the Eight-Gene Cox (EGC) model were closely associated with the recurrence-free survivals (RFS) in both training and three validation cohorts. We further demonstrated that this risk stratification could serve as an independent predictor in predicting HCC recurrence, and that the EGC model could outperform other models. Moreover, we also investigated the cell-type-specific expression patterns of the eight recurrence-related genes in tumor microenvironment using single-cell RNA sequencing data, and interpreted their functional roles from correlation and gene set enrichment analyses, in vitro and in vivo experiments. Particularly, PLCB1 and SLC22A7 were predominantly expressed in malignant cells, and they were predicted to promote angiogenesis and to help maintain normal metabolism in liver, respectively. In contrast, both FASLG and IL2RB were specifically expressed in T cells, and were highly correlated with T cell marker genes, suggesting that these two genes might assist in maintaining normal function of T cell-mediated immune response in tumor tissues.ConclusionIn conclusion, the EGC model and eight identified marker genes could not only facilitate the accurate prediction of HCC recurrence, but also improve our understanding of the mechanisms behind HCC recurrence.https://www.frontiersin.org/articles/10.3389/fonc.2022.878923/fullrecurrence-free survivalsingle-cell RNA sequencingtumor microenvironmentT cellimmune response |
| spellingShingle | Zi-Li Huang Zi-Li Huang Bin Xu Bin Xu Ting-Ting Li Yong-Hua Xu Xin-Yu Huang Xiu-Yan Huang Integrative Analysis Identifies Cell-Type-Specific Genes Within Tumor Microenvironment as Prognostic Indicators in Hepatocellular Carcinoma Frontiers in Oncology recurrence-free survival single-cell RNA sequencing tumor microenvironment T cell immune response |
| title | Integrative Analysis Identifies Cell-Type-Specific Genes Within Tumor Microenvironment as Prognostic Indicators in Hepatocellular Carcinoma |
| title_full | Integrative Analysis Identifies Cell-Type-Specific Genes Within Tumor Microenvironment as Prognostic Indicators in Hepatocellular Carcinoma |
| title_fullStr | Integrative Analysis Identifies Cell-Type-Specific Genes Within Tumor Microenvironment as Prognostic Indicators in Hepatocellular Carcinoma |
| title_full_unstemmed | Integrative Analysis Identifies Cell-Type-Specific Genes Within Tumor Microenvironment as Prognostic Indicators in Hepatocellular Carcinoma |
| title_short | Integrative Analysis Identifies Cell-Type-Specific Genes Within Tumor Microenvironment as Prognostic Indicators in Hepatocellular Carcinoma |
| title_sort | integrative analysis identifies cell type specific genes within tumor microenvironment as prognostic indicators in hepatocellular carcinoma |
| topic | recurrence-free survival single-cell RNA sequencing tumor microenvironment T cell immune response |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2022.878923/full |
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