Integrated bioinformatics analyses of key genes involved in hepatocellular carcinoma immunosuppression

Circular RNAs (circRNAs) are increasingly recognized as having a role in cancer development. Their expression is modified in numerous cancers, including hepatocellular carcinoma (HCC); however, little is known about the mechanisms of their regulation. The aim of this study was to identify regulators of circRNAome expression in HCC. Using publicly available datasets, we identified RNA binding proteins (RBPs) with enriched motifs around the splice sites of differentially expressed circRNAs in HCC. We confirmed the binding of some of the candidate RBPs using ChIP-seq and eCLIP datasets in the ENCODE database. Several of the identified RBPs were found to be differentially expressed in HCC and/or correlated with the overall survival of HCC patients. According to our bioinformatics analyses and published evidence, we propose that NONO, PCPB2, PCPB1, ESRP2, and HNRNPK are candidate regulators of circRNA expression in HCC. We confirmed that the knocking down the epithelial splicing regulatory protein 2 (ESRP2), known to be involved in the maintenance of the adult liver phenotype, significantly changed the expression of candidate circRNAs in a model HCC cell line. By understanding the systemic changes in transcriptome splicing, we can identify new proteins involved in the molecular pathways leading to HCC development and progression.

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The Ribosomal Protein L28 Gene Is Involved in Sorafenib Resistance in Hepatocellular Carcinoma

10.21203/rs.3.rs-259580/v1 ◽

2021 ◽

Author(s):

Yi Shi ◽

Xiaojiang Wang ◽

Qiong Zhu ◽

Gang Chen

Keyword(s):

Hepatocellular Carcinoma ◽

Drug Resistance ◽

Hepg2 Cells ◽

Cell Model ◽

Resistant Cell ◽

Drug Resistant ◽

Resistant Gene ◽

Key Genes ◽

Resistant Cells

Abstract Background: Sorafenib is the first molecular-targeted drug for the treatment of advanced hepatocellular carcinoma (HCC). However, its treatment efficiency decreases after a short period of time because of the development of drug resistance. This study investigates the role of key genes in regulating sorafenib-resistance in hepatocellular carcinoma and elucidates the mechanism of drug resistance. Methods: The HCC HepG2 cells were used to generate a sorafenib-resistant cell model by culturing the cells in gradually increasing concentration of sorafenib. RNA microarray was applied to profile gene expression and screen key genes associated with sorafenib resistance. Specific targets were knockdown in sorafenib-resistant HepG2 cells for functional studies. The HCC model was established in ACI rats using Morris hepatoma3924A cells to validate selected genes associated with sorafenib resistance in vivo. Results: The HepG2 sorafenib-resistant cell model was successfully established. The IC50 of sorafenib was 9.988mM in HepG2 sorafenib-resistant cells. A total of 35 up-regulated genes were detected by expression profile chip. High-content screening technology was used and a potential drug-resistant gene RPL28 was filtered out. After knocking down of RPL28 in HepG2 sorafenib-resistant cells, the results of cell proliferation and apoptosis illustrated that RPL28 is the key drug-resistant gene in the cells. Furthermore, it was found that both RNA and protein expression of RPL28 increased in HepG2 sorafenib-resistant specimens of Morris Hepatoma rats. In addition, the expression of functional proteins Ki-67 increased in sorafenib-resistant cells. Conclusion: Our study suggested that RPL28 was a key gene for sorafenib resistance in HCC both in vitro and in vivo.

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Identification of Key Genes and Pathways Associated With Paclitaxel Resistance in Esophageal Squamous Cell Carcinoma Based on Bioinformatics Analysis

Frontiers in Genetics ◽

10.3389/fgene.2021.671639 ◽

2021 ◽

Vol 12 ◽

Author(s):

Zhimin Shen ◽

Mingduan Chen ◽

Fei Luo ◽

Hui Xu ◽

Peipei Zhang ◽

...

Keyword(s):

Squamous Cell Carcinoma ◽

Esophageal Squamous Cell Carcinoma ◽

Cell Carcinoma ◽

Squamous Cell ◽

Differential Expression Analysis ◽

Biological Significance ◽

The Cancer Genome Atlas ◽

Paclitaxel Resistance ◽

Bioinformatics Analyses ◽

Key Genes

Esophageal squamous cell carcinoma (ESCC) ranks as the fourth leading cause of cancer-related death in China. Although paclitaxel has been shown to be effective in treating ESCC, the prolonged use of this chemical will lead to paclitaxel resistance. In order to uncover genes and pathways driving paclitaxel resistance in the progression of ESCC, bioinformatics analyses were performed based on The Cancer Genome Atlas (TCGA) database and the Gene Expression Omnibus (GEO) database including GSE86099 and GSE161533. Differential expression analysis was performed in TCGA data and two GEO datasets to obtain differentially expressed genes (DEGs). Based on GSE161533, weighted gene co-expression network analysis (WGCNA) was conducted to identify the key modules associated with ESCC tumor status. The DEGs common to the two GEO datasets and the genes in the key modules were intersected to obtain the paclitaxel resistance-specific or non-paclitaxel resistance-specific genes, which were subjected to subsequent least absolute shrinkage and selection operator (LASSO) feature selection, whereby paclitaxel resistance-specific or non-paclitaxel resistance-specific key genes were selected. Ten machine learning models were used to validate the biological significance of these key genes; the potential therapeutic drugs for paclitaxel resistance-specific genes were also predicted. As a result, we identified 24 paclitaxel resistance-specific genes and 18 non-paclitaxel resistance-specific genes. The ESCC machine classifiers based on the key genes achieved a relatively high AUC value in the cross-validation and in an independent test set, GSE164158. A total of 207 drugs (such as bevacizumab) were predicted to be alternative therapeutics for ESCC patients with paclitaxel resistance. These results might shed light on the in-depth research of paclitaxel resistance in the context of ESCC progression.

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Identification of Hypoxia-Related Differentially Expressed Genes and Construction of the Clinical Prognostic Predictor in Hepatocellular Carcinoma by Bioinformatic Analysis

BioMed Research International ◽

10.1155/2021/7928051 ◽

2021 ◽

Vol 2021 ◽

pp. 1-15

Author(s):

Hao Guo ◽

Jing Zhou ◽

Yanjun Zhang ◽

Zhi Wang ◽

Likun Liu ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Regression Model ◽

Molecular Mechanisms ◽

Cox Regression ◽

The Cancer Genome Atlas ◽

Functional Enrichment ◽

Differentially Expressed ◽

Clinical Prognosis ◽

Prognostic Predictor ◽

Key Genes

Background. Hypoxia closely relates to malignant progression and appears to be prognostic for outcome in hepatocellular carcinoma (HCC). Our research is aimed at mining the hypoxic-related genes (HRGs) and constructing a prognostic predictor (PP) model on clinical prognosis in HCC patients. Methods. RNA-sequencing data about HRGs and clinical data of patients with HCC were obtained from The Cancer Genome Atlas (TCGA) database portal. Differentially expressed HRGs between HCC and para-carcinoma tissue samples were obtained by applying the Wilcox analysis in R statistical software. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes were used for gene functional enrichment analyses. Then, the patients who were asked to follow up for at least one month were enrolled in the following study. Cox proportional risk regression model was applied to obtain key HRGs which related to overall survival (OS) in HCC. PP was constructed and defined, and the accuracy of PP was validated by constructing the signature in a training set and validation set. Connectivity map (CMap) was used to find potential drugs, and gene set cancer analysis (GSCA) was also performed to explore the underlying molecular mechanisms. Results. Thirty-seven differentially expressed HRGs were obtained. It contained 28 upregulated and 9 downregulated genes. After the univariate Cox regression model analysis, we obtained 27 prognosis-related HRGs. Of these, 25 genes were risk factors for cancer, and 2 genes were protective factors. The PP was composed by 12 key genes (HDLBP, SAP30, PFKP, DPYSL4, SLC2A1, HMOX1, PGK1, ERO1A, LDHA, ENO2, SLC6A6, and TPI1). GSCA results showed the overall activity of these 12 key genes in 10 cancer-related pathways. Besides, CMap identified deferoxamine, crotamiton, talampicillin, and lycorine might have effects with HCC. Conclusions. This study firstly reported 12 prognostic HRGs and constructed the model of the PP. This comprehensive research of multiple databases helps us gain insight into the biological properties of HCC and provides deferoxamine, crotamiton, talampicillin, and lycorine as potential drugs to fight against HCC.

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Epigenetic silencing of CDKN1A and CDKN2B by SNHG1 promotes the cell cycle, migration and epithelial-mesenchymal transition progression of hepatocellular carcinoma

Cell Death and Disease ◽

10.1038/s41419-020-03031-6 ◽

2020 ◽

Vol 11 (10) ◽

Author(s):

Bei Li ◽

Ang Li ◽

Zhen You ◽

Jingchang Xu ◽

Sha Zhu

Keyword(s):

Hepatocellular Carcinoma ◽

Cell Cycle ◽

Epithelial Mesenchymal Transition ◽

Critical Role ◽

Luciferase Assay ◽

Bioinformatics Analyses ◽

Mesenchymal Transition ◽

Rna Immunoprecipitation

Abstract Enhanced SNHG1 (small nucleolar RNA host gene 1) expression has been found to play a critical role in the initiation and progression of hepatocellular carcinoma (HCC) with its detailed mechanism largely unknown. In this study, we show that SNHG1 promotes the HCC progression through epigenetically silencing CDKN1A and CDKN2B in the nucleus, and competing with CDK4 mRNA for binding miR-140-5p in the cytoplasm. Using bioinformatics analyses, we found hepatocarcinogenesis is particularly associated with dysregulated expression of SNHG1 and activation of the cell cycle pathway. SNHG1 was upregulated in HCC tissues and cells, and its knockdown significantly inhibited HCC cell cycle, growth, metastasis, and epithelial–mesenchymal transition (EMT) both in vitro and in vivo. Chromatin immunoprecipitation and RNA immunoprecipitation assays demonstrate that SNHG1 inhibit the transcription of CDKN1A and CDKN2B through enhancing EZH2 mediated-H3K27me3 in the promoter of CDKN1A and CDKN2B, thus resulting in the de-repression of the cell cycle. Dual-luciferase assay and RNA pulldown revealed that SNHG1 promotes the expression of CDK4 by competitively binding to miR-140-5p. In conclusion, we propose that SNHG1 formed a regulatory network to confer an oncogenic function in HCC and SNHG1 may serve as a potential target for HCC diagnosis and treatment.

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Bioinformatics-based screening of key genes for transformation of liver cirrhosis to hepatocellular carcinoma

Journal of Translational Medicine ◽

10.1186/s12967-020-02229-8 ◽

2020 ◽

Vol 18 (1) ◽

Author(s):

Chen Hao Jiang ◽

Xin Yuan ◽

Jiang Fen Li ◽

Yu Fang Xie ◽

An Zhi Zhang ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Liver Cirrhosis ◽

Key Genes

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Examining the key genes and pathways in hepatocellular carcinoma development from hepatitis B virus‑positive cirrhosis

Molecular Medicine Reports ◽

10.3892/mmr.2018.9494 ◽

2018 ◽

Cited By ~ 3

Author(s):

Qi‑Feng Chen ◽

Jin‑Guo Xia ◽

Wang Li ◽

Lu‑Jun Shen ◽

Tao Huang ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Hepatitis B Virus ◽

Hepatitis B ◽

B Virus ◽

Key Genes

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Identification of Key Genes and Prognostic Value Analysis in Hepatocellular Carcinoma by Integrated Bioinformatics Analysis

International Journal of Genomics ◽

10.1155/2019/3518378 ◽

2019 ◽

Vol 2019 ◽

pp. 1-21 ◽

Cited By ~ 1

Author(s):

Meng Wang ◽

Licheng Wang ◽

Shusheng Wu ◽

Dongsheng Zhou ◽

Xianming Wang

Keyword(s):

Hepatocellular Carcinoma ◽

Overall Survival ◽

Expression Profiles ◽

Cyclin B1 ◽

Ppi Network ◽

Hub Genes ◽

Altered Expression ◽

Value Analysis ◽

Kaplan Meier ◽

Key Genes

Emerging evidence indicates that various functional genes with altered expression are involved in the tumor progression of human cancers. This study is aimed at identifying novel key genes that may be used for hepatocellular carcinoma (HCC) diagnosis, prognosis, and targeted therapy. This study included 3 expression profiles (GSE45267, GSE74656, and GSE84402), which were obtained from the Gene Expression Omnibus (GEO). GEO2R was used to analyze the differentially expressed genes (DEGs) between HCC and normal samples. The functional and pathway enrichment analysis was performed by the Database for Annotation, Visualization and Integrated Discovery. A protein-protein interaction (PPI) network of the identified DEGs was constructed using the Search Tool for the Retrieval of Interacting Gene, and hub genes were identified. ONCOMINE and CCLE databases were used to verify the expression of the hub genes in HCC tissues and cells. Kaplan-Meier plotter was used to assess the effects of the hub genes on the overall survival of HCC patients. A total of 99 DEGs were identified from the 3 expression profiles. These DEGs were enriched with functional processes and pathways related to HCC pathogenesis. From the PPI network, 5 hub genes were identified. The expression of the 5 hub genes was all upregulated in HCC tissues and cells compared with the control tissues and cells. Kaplan-Meier survival curves indicated that high expression of cyclin-dependent kinase (CDK1), cyclin B1 (CCNB1), cyclin B2 (CCNB2), MAD2 mitotic arrest deficient-like 1 (MAD2L1), and topoisomerase IIα (TOP2A) predicted poor overall survival in HCC patients (all log-rank P<0.01). These results revealed that the DEGs may serve as candidate key genes during HCC pathogenesis. The 5 hub genes, including CDK1, CCNB1, CCNB2, MAD2L1, and TOP2A, may serve as promising prognostic biomarkers in HCC.

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