Project description:BackgroundTransforming growth factor-beta (TGF-β) signaling is essential in initialization and progression of hepatocellular carcinoma (HCC). Therefore, a treatment targeting TGF-β pathway may be a promising option for HCC control.MethodsFirst, publicly available RNA-seq datasets and clinical characteristics of 374 HCC patients in The Cancer Genome Atlas (TCGA) database were downloaded. Then, Cox regression analysis and LASSO analysis were used to construct a prognostic model for TGF-β family genes. The area under the curve (AUC) of the risk signature was calculated to evaluate the predictive power of the model. Cox regression analysis was applied to predict whether TGF-β1 can be an independent prognosis factor for HCC. Next, hazard ratio and survival analyses were performed to investigate the correlation between TGF-β1 expression and survival time. Furthermore, differential expression level of TGF-β1 in HCC tissues and cells was determined. In addition, Gene Set Enrichment Analysis (GSEA) identified the top significantly activated and inhibited signal pathways related to high expression of TGF-β1. Finally, the CIBERSORT tool was adopted to correlate the tumor-infiltrating immune cells (TICs) with TGF-β1 expression in HCC cohorts.ResultsCox regression analysis and LASSO analysis revealed that seven TGF-β family members (including TGF-β1) could be used as prognostic factors for HCC. Interestingly, TGF-β1 was demonstrated to be an independent prognostic factor of HCC. RT-qPCR and immunofluorescence staining confirmed the high expression of TGF-β1 in HCC cell lines and tissues, which is significantly related to pathological classifications, poor prognosis, and short survival time. Finally, GSEA and CIBERSORT analyses suggested that TGF-β1 may interact with various immune cells and influence the prognosis of HCC patients through Tregs and γδ T cells.ConclusionWe established a novel prognostic prediction method to predict the risk scores of TGF-β genes in HCC prognosis. TGF-β1 is highly expressed in HCC cell lines and tissues, correlates to poor prognosis, and thus can be used as a potential biomarker to predict HCC prognosis. We showed that TGF-β1 may play its roles in HCC prognosis by modulating the immune microenvironment of tumor cells. Our data may shed more light on better understanding the role of TGF-β1 in HCC prognosis.

Project description:Background - Hepatocellular carcinomas (HCCs) are heterogeneous tumors with respect to etiology, cell of origin and biology. The course of the disease is unpredictable and is in part dependent on the tumor microenvironment. One of the microenvironmental factors is hypoxia, which is known to promote aggressiveness in other malignant tumors. We hypothesized that certain regions in HCC exist with chronic hypoxia and a characteristic gene expression pattern. Moreover, during the development of HCC there is an important contribution of this chronic hypoxia on prognosis via this gene expression. Until now, most research has been performed in acute hypoxic models (< 24 hours). Methods – Human hepatoblastoma cells HepG2 were cultured in either normoxic (20% O2) or hypoxic (2% O2) conditions for 72 hrs, the time it takes to adapt to chronic hypoxia. After 3 days the cells were harvested and analyzed by microarray technology. The highly significant differentially expressed genes were selected and used to assess the clinical value of our in vitro chronic hypoxia gene signature in four published patient studies. Three of these independent microarray studies on HCC patients were used as training sets to determine a minimal prognostic gene set and one study was used for validation. Gene expression analysis and correlation with clinical outcome was assessed with the bioinfomatic method of Goeman et al (). Results – In the HepG2 cells, 3592 genes were differentially expressed in cells cultured at 2% oxygen for 72 hrs. Out of these, 265 showed a high significant change (2-fold change and p=0.0001). The level of gene expression after 72 hrs was different from the acute hypoxic response (during the first 24 hours) and represented chronicity. Using computational methods we identified 7 out of the 265 highly significant genes that showed correlation with prognosis in all three different training sets and this was independently validated in a 4th dataset. With our approach we could include the largest number of HCC patients in one single study. Conclusion – We identified a 7-gene signature, which is associated with chronic hypoxia and predicts prognosis in patients with HCC. In the future this signature could be used as a diagnostic tool. In addition, chronic hypoxia gene expression information can be used in the search for new therapeutic targets. Two conditions were compared and each sample has a biological replicate. Samples are hybridized in dye-swap, resulting in 4 hybridizations.

Project description:Background - Hepatocellular carcinomas (HCCs) are heterogeneous tumors with respect to etiology, cell of origin and biology. The course of the disease is unpredictable and is in part dependent on the tumor microenvironment. One of the microenvironmental factors is hypoxia, which is known to promote aggressiveness in other malignant tumors. We hypothesized that certain regions in HCC exist with chronic hypoxia and a characteristic gene expression pattern. Moreover, during the development of HCC there is an important contribution of this chronic hypoxia on prognosis via this gene expression. Until now, most research has been performed in acute hypoxic models (< 24 hours). Methods – Human hepatoblastoma cells HepG2 were cultured in either normoxic (20% O2) or hypoxic (2% O2) conditions for 72 hrs, the time it takes to adapt to chronic hypoxia. After 3 days the cells were harvested and analyzed by microarray technology. The highly significant differentially expressed genes were selected and used to assess the clinical value of our in vitro chronic hypoxia gene signature in four published patient studies. Three of these independent microarray studies on HCC patients were used as training sets to determine a minimal prognostic gene set and one study was used for validation. Gene expression analysis and correlation with clinical outcome was assessed with the bioinfomatic method of Goeman et al (). Results – In the HepG2 cells, 3592 genes were differentially expressed in cells cultured at 2% oxygen for 72 hrs. Out of these, 265 showed a high significant change (2-fold change and p=0.0001). The level of gene expression after 72 hrs was different from the acute hypoxic response (during the first 24 hours) and represented chronicity. Using computational methods we identified 7 out of the 265 highly significant genes that showed correlation with prognosis in all three different training sets and this was independently validated in a 4th dataset. With our approach we could include the largest number of HCC patients in one single study. Conclusion – We identified a 7-gene signature, which is associated with chronic hypoxia and predicts prognosis in patients with HCC. In the future this signature could be used as a diagnostic tool. In addition, chronic hypoxia gene expression information can be used in the search for new therapeutic targets.

Dataset Information

High TGF-?1 expression predicts poor disease prognosis in hepatocellular carcinoma patients.

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High TGF-β1 expression predicts poor disease prognosis in hepatocellular carcinoma patients.

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