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Screening and identification of angiogenesis-related genes as potential novel prognostic biomarkers of hepatocellular carcinoma through bioinformatics analysis.

ABSTRACT: Hepatocellular carcinoma (HCC) is a malignant tumor with high morbidity and mortality, which makes the prognostic prediction challenging. Angiogenesis appears to be of critical importance in the progression and metastasis of HCC. Some of the angiogenesis-related genes promote this process, while other anti-angiogenesis genes suppress tumor growth and metastasis. Therefore, the comprehensive prognostic value of multiple angiogenesis-related genes in HCC needs to be further clarified. In this study, the mRNA expression profile of HCC patients and the corresponding clinical data were acquired from multiple public databases. Univariate Cox regression analysis was utilized to screen out differentially expressed angiogenesis-related genes with prognostic value. A multigene signature was established with the least absolute shrinkage and selection operator Cox regression in the Cancer Genome Atlas cohort, and validated through an independent cohort. The results suggested that a total of 16 differentially expressed genes (DEGs) were associated with overall survival (OS) and a 7-gene signature was constructed. The risk score of each patient was calculated using this signature, the median value of which was used to divide these patients into a high-risk group and a low-risk group. Compared with the low-risk group, the patients in the high-risk group had a poor prognosis. The risk score was an independent predictor for OS through multivariate Cox regression analysis. Then, unsupervised learning was used to verify the validity of this 7-gene signature. A nomogram by further integrating clinical information and the prognostic signature was utilized to predict prognostic risk and individual OS. Functional enrichment analyses demonstrated that these DEGs were enriched in the pathways of cell proliferation and mitosis, and the immune cell infiltration was significantly different between the two risk groups. In summary, a novel angiogenesis-related genes signature could be used to predict the prognosis of HCC and for targeted therapy.

SUBMITTER: Zhen Z

PROVIDER: S-EPMC8312452 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Objective: Adrenocortical carcinoma (ACC) is a rare but aggressive malignant cancer that has been attracting growing attention over recent decades. This study aims to integrate protein interaction networks with gene expression profiles to identify potential biomarkers with prognostic value in silico. Methods: Three microarray data sets were downloaded from the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs) according to the normalization annotation information. Enrichment analyses were utilized to describe biological functions. A protein-protein interaction network (PPI) of the DEGs was developed, and the modules were analyzed using STRING and Cytoscape. LASSO Cox regression was used to identify independent prognostic factors. The Kaplan-Meier method for the integrated expression score was applied to analyze survival outcomes. A receiver operating characteristic (ROC) curve was constructed with area under curve (AUC) analysis to determine the diagnostic ability of the candidate biomarkers. Results: A total of 150 DEGs and 24 significant hub genes with functional enrichment were identified as candidate prognostic biomarkers. LASSO Cox regression suggested that ZWINT, PRC1, CDKN3, CDK1 and CCNA2 were independent prognostic factors in ACC. In multivariate Cox analysis, the integrated expression scores of the modules showed statistical significance in predicting disease-free survival (DFS, P = 0.019) and overall survival (OS, P < 0.001). Meanwhile, ROC curves were generated to validate the ability of the Cox model to predict prognosis. The AUC index for the integrated genes scores was 0.861 (P < 0.0001). Conclusion: In conclusion, the present study identifies DEGs and hub genes that may be involved in poor prognosis and early recurrence of ACC. The expression levels of ZWINT, PRC1, CDKN3, CDK1 and CCNA2 are of high prognostic value, and may help us understand better the underlying carcinogenesis or progression of ACC. Further studies are required to elucidate molecular pathogenesis and alteration in signaling pathways for these genes in ACC.

Project description:BackgroundRenal cell carcinoma (RCC) is one of the ten most prevalent cancers in the world and its incidence has been rising over the past decade. However, effective biomarkers to predict the prognosis of patients remains absent, and the exact molecular mechanism of the disease remains unclear. Therefore, the identification of key genes and their biological pathways are of great significance to identify the differential expressed genes associated with the prognosis for patients with RCC, and to further explore their potential protein-protein interactions (PPIs) in tumorigenesis.MethodsThe gene expression microarray data for GSE15641 and GSE40435 were extracted from the Gene Expression Omnibus (GEO) database, including 150 primary tumors and their matched adjacent non-tumor tissues. Afterwards, gene expression for fold changes (FCs) and P value for tumor and non-tumor tissues were analyzed using online tool GEO2R. Gene expression with logFCs of greater than two combined with P value of lower than 0.01 were considered as candidate targets for treatment of RCC. The survival analysis of candidate genes was performed by online software OncoLnc. The PPI network was implemented with Search Tool for the Retrieval of Interacting Genes (STRING).ResultsIn total, there were 625 differentially expressed genes (DEGs) in GSE15641, including 415 increased and 210 decreased genes. A total of 343 DEGs were identified in the GSE40435 with 101 upregulated and 242 downregulated genes, the 20 genes with highest FC in high or low expression in each database were summarized. Five candidate genes were overlapped genes in the two GEO datasets. However, aldolase, fructose-bisphosphate B (ALDOB) was found to be the only gene affecting the prognosis. A number of critical genes were identified behind the mechanism, of which they interacted with ALDOB. Among them, phosphofructokinase, platelet (PFKP), phosphofructokinase, muscle (PFKM), pyruvate kinase L/R (PKLR), and fructose-bisphosphatase 1 (FBP1) showed a better prognosis, whereas only glyceraldehyde-3-phosphate dehydrogenase (GAPDH) rendered a bleak outcome.ConclusionsFive genes were found to be overlappingly expressed in the top 20 greatest FC in two human GEO datasets. This is of great value in the treatment and prognosis of RCC.

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Screening and identification of angiogenesis-related genes as potential novel prognostic biomarkers of hepatocellular carcinoma through bioinformatics analysis.

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