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Comprehensive Analysis of VCAN Expression Profiles and Prognostic Values in HCC.

ABSTRACT: Background: Hepatocellular carcinoma (HCC) is the world's most common cause of cancer death. Therefore, more molecular mechanisms need to be clarified to meet the urgent need to develop new detection and treatment strategies. Methods: We used TCGAportal, Kaplan-Meier Plotter, the Cistrome DB Toolkit Database, MExpress, GEPIA2, and other databases to discuss the expression profiles, possible biological function, and potential prognostic value of versican (VCAN) in HCC. We conducted cell experiments such as Transwell migration and invasion assays, wound healing assay, and CCK8 experiment to explore the function of VCAN in HCC. Result: We selected three HCC transcriptome databases GSE124535, GSE136247, and GSE144269 and analyzed the overexpressed genes contained in them. The overlapping genes were found by the Venn map, and two interacting network modules were found by Mcode. Module 1 was mainly related to mitosis and cell cycle, and module 2 was mainly related to EMT, angiogenesis, glycolysis, and so on. We found that the seed gene in module 2 is VCAN. Data from TCGAportal showed that compared with normal tissues, the expression of VCAN was up-regulated in HCC tissues. The patients with high expression of VCAN had shorter distant recurrence-free survival and overall survival. Multiple possible VCAN interactions had also been identified. These results revealed that the level of VCAN was higher in the subtypes of HCC with higher malignant degree and was connected to the poor prognosis. In addition, the treatment of VCAN with DNA methyltransferase inhibitors and transcription factor inhibitors may improve the prognosis of patients with HCC. Conclusion: Our findings systematically elucidated the expression profile and different prognostic values of VCAN in HCC, which may provide new therapeutic targets and potential prognostic biomarkers for HCC patients.

SUBMITTER: Sun G

PROVIDER: S-EPMC9263583 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:AbstractGrowing evidence supports that the tumor microenvironment plays a key role in the development and progression of tumors. But immune microenvironment of hepatocellular carcinoma (HCC) has not yet been fully explored. In the present investigation, the clinical value and prognostic significance of immune-related genes in HCC were investigated.The immune and stromal scores of HCC were calculated through the application of Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data Algorithm based on the Cancer Genome Atlas database. Differentially expressed genes were identified using the "edgeR" package of the R software. Functional annotation and pathway enrichment were performed using "ggplots2" and "clusterProfiler" packages in R software. Protein-protein interaction network was constructed using STRING, and the hub genes were identified through the Cytoscape. Survival analysis was performed using Kaplan-Meier methods. Tumor Immune Estimation Resource algorithm was used to view the immune landscape of the microenvironment in HCC.Firstly, the immune and stromal scores of HCC were calculated and we found that the immune and stromal scores of HCC were closely related to the patients' prognosis. Then the differentially expressed genes were identified respectively stratified by the median value of the immune and stromal scores, and the immune-related genes that related to the prognosis in HCC patients were further identified. Functional enrichment analysis and protein-protein interaction networks further showed that these genes mainly participated in immune-related biological process. In addition, dendritic cells were found to be the most abundant in the microenvironment of HCC through Tumor Immune Estimation Resource algorithm and were significantly associated with the patients' prognosis. To robust the results, the immune-related genes were validated in an independent dataset from the Gene Expression Omnibus database.We arrived at a more comprehensive understanding of the microenvironment of HCC and extracted 7 immune-related genes that were significantly associated with the recurrence survival of HCC.

Project description:BackgroundAccumulating studies have demonstrated the abnormal expressions and prognostic values of certain members of the tripartite motif (TRIM) family in diverse cancers. However, comprehensive prognostic values of the TRIM family in hepatocellular carcinoma (HCC) are yet to be clearly defined.MethodsThe prognostic values of the TRIM family were evaluated by survival analysis and univariate Cox regression analysis based on gene expression data and clinical data of HCC from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The expression profiles, protein-protein interaction among the TRIM family, prediction of transcription factors (TFs) or miRNAs, genetic alterations, correlations with the hallmarks of cancer and immune infiltrates, and pathway enrichment analysis were explored by multiple public databases. Further, a TRIM family gene-based signature for predicting overall survival (OS) in HCC was built by using the least absolute shrinkage and selection operator (LASSO) regression. TCGA-Liver Hepatocellular Carcinoma (LIHC) cohort was used as the training set, and GSE76427 was used for external validation. Time-dependent receiver operating characteristic (ROC) and survival analysis were used to estimate the signature. Finally, a nomogram combining the TRIM family risk score and clinical parameters was established.ResultsHigh expressions of TRIM family members including TRIM3, TRIM5, MID1, TRIM21, TRIM27, TRIM32, TRIM44, TRIM47, and TRIM72 were significantly associated with HCC patients' poor OS. A novel TRIM family gene-based signature (including TRIM5, MID1, TRIM21, TRIM32, TRIM44, and TRIM47) was built for OS prediction in HCC. ROC curves suggested the signature's good performance in OS prediction. HCC patients in the high-risk group had poorer OS than the low-risk patients based on the signature. A nomogram integrating the TRIM family risk score, age, and TNM stage was established. The ROC curves suggested that the signature presented better discrimination than the similar model without the TRIM family risk score.ConclusionOur study identified the potential application values of the TRIM family for outcome prediction in HCC.

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Comprehensive Analysis of VCAN Expression Profiles and Prognostic Values in HCC.

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