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Identification and Validation of Three Core Genes in P53 Signaling

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Identification and Validation of Three Core Genes in P53 Signaling Yu et al. World Journal of Surgical Oncology (2021) 19:66 https://doi.org/10.1186/s12957-021-02174-w RESEARCH Open Access Identification and validation of three core genes in p53 signaling pathway in hepatitis B virus-related hepatocellular carcinoma Mingxue Yu1†, Wenli Xu1†, Yusheng Jie1, Jiahui Pang1, Siqi Huang1, Jing Cao1, Jiao Gong2, Xinhua Li1* and Yutian Chong1* Abstract Background: Hepatocellular carcinoma (HCC) is a common cancer and the leading cause is persistent hepatitis B virus (HBV) infection. We aimed to identify some core genes and pathways for HBV-related HCC. Methods: Gene expression profiles of GSE62232, GSE121248, and GSE94660 were available from Gene Expression Omnibus (GEO). The GSE62232 and GSE121248 profiles were the analysis datasets and GSE94660 was the validation dataset. The GEO2R online tool and Venn diagram software were applied to analyze commonly differentially expressed genes between HBV-related HCC tissues and normal tissues. Then, functional enrichment analysis using Gene Ontology (GO) and the Kyoto Encyclopedia of Gene and Genome (KEGG) as well as the protein-protein interaction (PPI) network was conducted. The overall survival rates and the expression levels were detected by Kaplan-Meier plotter and Gene Expression Profiling Interactive Analysis (GEPIA). Next, gene set enrichment analysis (GSEA) was performed to verify the KEGG pathway analysis. Furthermore, quantitative reverse transcriptase polymerase chain reaction (qRT-PCR) was performed to validate the levels of these three core genes in tumor tissues and adjacent non-tumor liver tissues from 12 HBV related HCC patients, HBV-associated liver cancer cell lines and normal liver cell lines, and HepG2 with p53 knockdown or deletion, respectively. Results: Fifteen highly expressed genes associated with significantly worse prognoses were selected and CCNB1, CDK1, and RRM2 in the p53 signaling pathway were identified as core genes. GSEA results showed that samples highly expressing three core genes were all enriched in the p53 signaling pathway in a validation dataset (P <0.0001).The expression of these three core genes in tumor tissue samples was higher than that in relevant adjacent non-tumor liver tissues (P < 0.0001). Furthermore, we also found that the above genes were highly expressed in liver cancer cell lines compared with normal liver cells. In addition, we found that the expression of these three core genes in p53 knockdown or knockout HCC cell lines was lower than that in negative control HCC cell lines (P <0.05). Conclusions: CCNB1, CDK1, and RRM2 were enriched in the p53 signaling pathway and could be potential biomarkers and therapeutic targets for HBV-related HCC. Keywords: Hepatocellular carcinoma (HCC), Hepatitis B virus (HBV), Biomarker, Bioinformatical analysis, p53 signaling pathway * Correspondence: [email protected]; [email protected] †Mingxue Yu and Wenli Xu contributed equally to this work. 1Department of Infectious Diseases and Key Laboratory of Liver Disease of Guangdong Province, Third Affiliated Hospital of Sun Yat-sen University, Guangzhou 510630, Guangdong Province, China Full list of author information is available at the end of the article © The Author(s). 2021 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Yu et al. World Journal of Surgical Oncology (2021) 19:66 Page 2 of 13 Introduction using quantitative reverse transcriptase polymerase chain Hepatocellular carcinoma (HCC) is a common cancer reaction (qRT-PCR). and the third leading cause of cancer death in the world [1, 2]. It has been reported that Chinese liver cancer pa- Materials and methods tients account for more than 50% of all cases worldwide Microarray data [3]. The major risk factors for HCC include chronic GSE62232, GSE121248, and GSE94660 gene expression hepatitis B or C virus infection, alcoholic liver disease, profiles were downloaded from the Gene Expression and liver cirrhosis. The leading cause of HCC is persist- Omnibus [20] (GEO, https://www.ncbi.nlm.nih.gov/geo), ent hepatitis B virus (HBV) infection, which happens in which included 10 HBV-related HCC tissues and 10 more than half of HCC case s[4, 5]. To date, many re- normal tissues, 70 HBV-related HCC tissues and 37 nor- searchers have contributed to the identification of the mal issues, and 21 pairs of tumor and non-neoplastic underlying molecular mechanisms. However, the mo- liver tissues of HBV-HCC patients, respectively. The lecular pathogenesis and mechanisms of the hepatic car- GSE62232 and GSE121248 profiles were the analysis cinogenesis triggered by HBV is extremely complex, datasets and GSE94660 was the validation dataset. involving gene aberrations, mRNA expression, and the genome [6–8]. Data processing of differentially expressed genes (DEGs) Various studies have shown that the occurrence and The identification of DEGs between HCC specimens and progression of liver cancer are closely related to the over- normal specimens was performed in GEO2R (https:// expression of oncogenes and the inactivation of tumor www.ncbi.nlm.nih.gov/geo/geo2r/)[20]. The DEGs were suppressor genes. Villanueva et al. [9] discovered many screened according to adjusted P values < 0.05 and genes that are aberrantly methylated in HCC and observed |logFC| > 2. Then, the common DEGS expressed jointly that some signaling pathways are clearly deregulated by in the two data files were detected with Venn software. DNA methylation in HCC. Khemlina et al. also reported The DEGs with log FC < 0 were considered downregu- that genetic alterations in HCC include mutations in the lated genes, while the DEGs with log FC > 0 were consid- TERT promoter [10]. Furthermore, Jiang et al. [11]found ered upregulated genes. two genetic susceptibility loci for hepatitis B virus-related HCC. The Janus Kinase (JAK)/STAT pathway and Wnt/ Gene ontology (GO) and Kyoto Encyclopedia of Genes β-catenin pathway may play roles in HCC acting as two and Genomes (KEGG) pathway enrichment analysis major oncogenic drivers, which might serve as potential The Database for Annotation, Visualization, and Inte- treatment targets [12, 13]. grated Discovery (DAVID Version 6.8, https://david. With the rapid development of high-throughput DNA ncifcrf.gov/)[21]andKOBAS3.0(http://kobas.cbi.pku. microarray technology, gene expression microarray ana- ed-u.cndate)[22], which are online bioinformatic tools lysis has emerged as a promising and efficient tool to with a threshold of FDR < 0.05 and P < 0.05, were used help with an understanding of the precise underlying to analyze the DEGs of GO [23], enrichment of BP (bio- mechanisms in cancer. Biomarkers have been identified logical process), MF (molecular function), and CC (cellu- and confirmed to potentially improve the diagnosis and lar component) and KEGG pathways [24]. Data therapy of HCC [14, 15]. Recently, some gene expression processing and the creation of graphics were performed profiling microarrays have been used to identify various with “ggplot2” R package in R software (Version 3.4.0, differentially expressed genes (DEGs) in HCC [16–19]. https://www.r-project.org/). However, few reliable biomarkers have been identified in hepatitis B virus (HBV)-related hepatocellular carcin- Construction of protein-protein interaction (PPI) network oma. It is critical to explore more effective biomarkers STRING online database (http://string-db.org )[25] was for the development and recurrence of HBV related applied to detect core candidate genes. Cytoscape soft- HCC. We took the initiative to combine integrated bio- ware [26] (Version 3.4.0, http://www.cytoscape.org/) was informatics methods and validation experiments to in- used to construct a PPI relationship network. In vestigate some core genes and underlying molecular addition, the plugin MCODE in Cytoscape was utilized mechanisms. to identify crucial genes of highly intraconnected nodes In this study, the common DEGs in datasets (degree cutoff ≥ 2, node score cutoff ≥ 0.2, Kcore ≥ 2, GSE62232 and GSE121248 were used in the analysis. and max depth = 100). Core genes were identified based on bioinformatic ana- lysis. Then, gene set enrichment analysis (GSEA) was Survival analysis and expression level of core genes performed in the validation dataset GSE94660. Subse- Kaplan Meier-plotter [27](http://www.kmplot.com) was quently, we validated the expression of core genes in applied to perform a survival analysis of the core genes. liver cancer tissue samples and liver cancer cell lines The hazard ratio (HR) with 95% confidence intervals Yu
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