Dataset Information

Prognostic Analysis and Biomarkers Identification of Immune Infiltration in Early and Late Stage Hepatocellular Carcinoma Based on TCGA Data.

ABSTRACT:

Background

Hepatocellular carcinoma (HCC) is a major cause of cancer death in the world. The aim of this study was to establish a new model to predict the prognosis of HCC.

Materials and methods

The mRNA, miRNA and lncRNA expression profiles of early (stage I-II) and late (stage III-IV) stage HCC patients were acquired from The Cancer Genome Atlas (TCGA) database. The differentially expressed mRNAs (DEmRNAs), miRNAs (DEmiRNAs) and lncRNAs (DElncRNAs) were identified between early and late stage HCC. Key molecules associated with the prognosis, and important immune cell types in HCC were identified. The nomogram based on incorporating age, gender, stage, and all important factors was constructed to predict the survival of HCC.

Results

A total of 1516 DEmRNAs, 97 DEmiRNAs and 87 DElncRNAs were identified. A DElncRNA-DEmiRNA-DEmRNA regulatory network including 78 mRNAs, 50 miRNAs and 1 lncRNA was established. Among the regulatory network, 11 molecules were significantly correlated with the prognosis of HCC based on Lasso regression analysis. Then, Preadipocytes and 3 survival-associated DEmRNAs were identified as crucial biomarkers. Subsequently, a nomogram with a differentiation degree of 0.758, including 1 immune cell, 11 mRNAs and 3 miRNAs, was generated.

Conclusion

Our study constructed a model by incorporating clinical information, significant biomarkers and immune cells to predict the survival of HCC, which achieved a good performance.

SUBMITTER: Jiang W

PROVIDER: S-EPMC10281275 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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Publications

Prognostic Analysis and Biomarkers Identification of Immune Infiltration in Early and Late Stage Hepatocellular Carcinoma Based on TCGA Data.

Jiang Wenying W Wang Yunxing Y Yu Changtao C Sui Deling D Du Gang G Li Youchun Y

International journal of general medicine 20230616

<h4>Background</h4>Hepatocellular carcinoma (HCC) is a major cause of cancer death in the world. The aim of this study was to establish a new model to predict the prognosis of HCC.<h4>Materials and methods</h4>The mRNA, miRNA and lncRNA expression profiles of early (stage I-II) and late (stage III-IV) stage HCC patients were acquired from The Cancer Genome Atlas (TCGA) database. The differentially expressed mRNAs (DEmRNAs), miRNAs (DEmiRNAs) and lncRNAs (DElncRNAs) were identified between early ...[more]

PMID: 37346812

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Project description:Background: Increasing evidence supports that competing endogenous RNAs (ceRNAs) and tumor immune infiltration act as pivotal players in tumor progression of hepatocellular carcinoma (HCC). Nonetheless, comprehensive analysis focusing on ceRNAs and immune infiltration in HCC is lacking. Methods: RNA and miRNA sequencing information, corresponding clinical annotation, and mutation data of HCC downloaded from The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) project were employed to identify significant differentially expressed mRNAs (DEMs), miRNAs (DEMis), and lncRNAs (DELs) to establish a ceRNA regulatory network. The Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene ontology (GO) enrichment pathways were analyzed to functionally annotate these DEMs. A multigene-based risk signature was developed utilizing least absolute shrinkage and selection operator method (LASSO) algorithm. Moreover, survival analysis and receiver operating characteristic (ROC) analysis were applied for prognostic value validation. Seven algorithms (TIMER, XCELL, MCPcounter, QUANTISEQ, CIBERSORT, EPIC, and CIBERSORT-ABS) were utilized to characterize tumor immune microenvironment (TIME). Finally, the mutation data were analyzed by employing "maftools" package. Results: In total, 136 DELs, 128 DEMis, and 2,028 DEMs were recognized in HCC. A specific lncRNA-miRNA-mRNA network consisting of 3 lncRNAs, 12 miRNAs, and 21 mRNAs was established. A ceRNA-based prognostic signature was established to classify samples into two risk subgroups, which presented excellent prognostic performance. In additional, prognostic risk-clinical nomogram was delineated to assess risk of individual sample quantitatively. Besides, risk score was significantly associated with contexture of TIME and immunotherapeutic targets. Finally, potential interaction between risk score with tumor mutation burden (TMB) was revealed. Conclusion: In this work, comprehensive analyses of ceRNAs coexpression network will facilitate prognostic prediction, delineate complexity of TIME, and contribute insight into precision therapy for HCC.

Project description:BackgroundMetabolic reprogramming has been identified as a core hallmark of cancer. Solute carrier family 2 is a major glucose carrier family. It consists of 14 members, and we mainly study solute carrier family 2 member 1 (SLC2A1) and solute carrier family 2 member 2 (SLC2A2) here. SLC2A1, mainly existing in human erythrocytes, brain endothelial cells, and normal placenta, was found to be increased in hepatocellular carcinoma (HCC), while SLC2A2, the major transporter of the normal liver, was decreased in HCC.AimTo identify if SLC2A1 and SLC2A2 were associated with immune infiltration in addition to participating in the metabolic reprogramming in HCC.MethodsThe expression levels of SLC2A1 and SLC2A2 were tested in HepG2 cells, HepG215 cells, and multiple databases. The clinical characteristics and survival data of SLC2A1 and SLC2A2 were examined by multiple databases. The correlation between SLC2A1 and SLC2A2 was analyzed by multiple databases. The functions and pathways in which SLC2A1, SLC2A2, and frequently altered neighbor genes were involved were discussed in String. Immune infiltration levels and immune marker genes associated with SLC2A1 and SLC2A2 were discussed from multiple databases.ResultsThe expression level of SLC2A1 was up-regulated, but the expression level of SLC2A2 was down-regulated in HepG2 cells, HepG215 cells, and liver cancer patients. The expression levels of SLC2A1 and SLC2A2 were related to tumor volume, grade, and stage in HCC. Interestingly, the expression levels of SLC2A1 and SLC2A2 were negatively correlated. Further, high SLC2A1 expression and low SLC2A2 expression were linked to poor overall survival and relapse-free survival. SLC2A1, SLC2A2, and frequently altered neighbor genes played a major role in the occurrence and development of tumors. Notably, SLC2A1 was positively correlated with tumor immune infiltration, while SLC2A2 was negatively correlated with tumor immune infiltration. Particularly, SLC2A2 methylation was positively correlated with lymphocytes.ConclusionSLC2A1 and SLC2A2 are independent therapeutic targets for HCC, and they are quintessential marker molecules for predicting and regulating the number and status of immune cells in HCC.

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Project description:The poliovirus receptor (PVR) is a member of the immunoglobulin superfamily (Ig SF) and is essential for the promotion of cancer cell proliferation and invasion. However, the correlation between PVR expression and prognosis as well as immune infiltration in hepatocellular carcinoma (HCC) remains unclear. The expression level of PVR was quantified using the Tumor and Tumor Immunity Evaluation Resource (TIMER) and Sangerbox. The Gene Expression Omnibus (GEO) database was used to validate the PVR expression. The receiver operating characteristic (ROC) curve was used to evaluate the feasibility of using PVR as a differentiating factor according to the area under curve (AUC) score. A PVR binding protein network was built using the STRING tool. An enrichment analysis using the R package clusterProfiler was used to explore the potential function of PVR. Immune infiltration analysis was calculated with ESTIMATE algorithms. We also assessed the correlation between PVR expression and immune infiltration by the single-sample Gene Set Enrichment Analysis (ssGSEA) method from the R package GSVA and TIMER database. The results showed that PVR was commonly overexpressed in multiple types of tumors including HCC. The data of GSE64041 confirmed the same result. The ROC curve suggested that PVR could be a potential diagnostic biomarker. Additionally, high mRNA expression of PVR in HCC was significantly correlated with poor overall survival (OS) and relapse free survival (RFS). Results also indicated correlations between PVR mRNA expression with the level of infiltration immune cells including B cells, CD8+ T cells, cytotoxic cells, DCs, CD56dim NK cells, pDCs, and Th2 cells. Furthermore, the PVR level was significantly correlated with immune markers for immunosuppressive cells in HCC. In conclusion, PVR might be an important regulator of tumor immune cell infiltration and a valuable prognostic biomarker in HCC. However, additional work is needed to fully elucidate the underlying mechanisms.

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Dataset Information

Prognostic Analysis and Biomarkers Identification of Immune Infiltration in Early and Late Stage Hepatocellular Carcinoma Based on TCGA Data.

Background

Materials and methods

Results

Conclusion

Publications

Prognostic Analysis and Biomarkers Identification of Immune Infiltration in Early and Late Stage Hepatocellular Carcinoma Based on TCGA Data.

Similar Datasets

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