Identification of genes in hepatocellular carcinoma induced by non-alcoholic fatty liver disease

RNA binding motif protein 25 is a negative prognostic biomarker and promotes cell proliferation via alternative splicing in hepatocellular carcinoma

BACKGROUND

The purpose of this study was to identify the role of RNA binding motif protein 25 (RBM25) in hepatocellular carcinoma (HCC).

METHODS

The expression of RBM25 was analyzed by public databases and IHC assay. The associations between RBM25 expression and clinicopathological characteristics of HCC patients were investigated. Cell proliferation and apoptosis were measured. RNA sequencing was utilized to analyze global transcription levels and alternative splicing (AS) events. Furthermore, GO and KEGG analyses based on differentially expressed genes were performed to predict underlying mechanisms.

RESULTS

The high and low expression rates of RBM25 were 55.6 %(133/239) and 44.4 %(106/239), respectively. Furthermore, high level of RBM25 was significantly associated with high level of AFP and poor differentiation. Moreover, liver cirrhosis, differentiation, and RBM25 expression were the independent risk factors related to overall survival (OS). Differentiation and MVI were the independent factors affecting recurrence-free survival (RFS). OS of the case with high RBM25 expression was shorter after hepatectomy than that of low RBM25 expression in liver cirrhosis(-), tumor size ≤ 5 cm, MVI(+) and TNM stage Ⅰ subgroup. Also, high expression of RBM25 was associated with a shorter RFS in tumor size ≤ 5 cm and MVI(+) subgroup. The deletion of RBM25 hindered cell proliferation. RBM25 was involved in AS of multiple genes, including HDAC1, ITGB3BP, RCC1, and TFDP1, which were associated with cell cycle and cell division.

CONCLUSION

RBM25 could be used as a candidate to evaluate the prognosis of HCC, which might be associated with the role of RBM25 in promoting cell proliferation.

Discovery of Genomic Targets and Therapeutic Candidates for Liver Cancer Using Single-Cell RNA Sequencing and Molecular Docking

Liver cancer is one of the most common malignancies and the second leading cause of cancer-related deaths worldwide, particularly in developing countries, where it poses a significant financial burden. Early detection and timely treatment remain challenging due to the complex mechanisms underlying the initiation and progression of liver cancer. This study aims to uncover key genomic features, analyze their functional roles, and propose potential therapeutic drugs identified through molecular docking, utilizing single-cell RNA sequencing (scRNA-seq) data from liver cancer studies. We applied two advanced hybrid methods known for their robust identification of differentially expressed genes (DEGs) regardless of sample size, along with four top-performing individual methods. These approaches were used to analyze four scRNA-seq datasets, leading to the identification of essential DEGs. Through a protein-protein-interaction (PPI) network, we identified 25 hub-of-hub genes (hHubGs) and 20 additional hHubGs from two naturally occurring gene clusters, ultimately validating a total of 36 hHubGs. Functional, pathway, and survival analyses revealed that these hHubGs are strongly linked to liver cancer. Based on molecular docking and binding-affinity scores with 36 receptor proteins, we proposed 10 potential therapeutic drugs, which we selected from a pool of 300 cancer meta-drugs. The choice of these drugs was further validated using 14 top-ranked published receptor proteins from a set of 42. The proposed candidates include Adozelesin, Tivozanib, NVP-BHG712, Nilotinib, Entrectinib, Irinotecan, Ponatinib, and YM201636. This study provides critical insights into the genomic landscape of liver cancer and identifies promising therapeutic candidates, serving as a valuable resource for advancing liver cancer research and treatment strategies.

The therapeutic effects of Paeoniae Radix Rubra on chronic hepatitis through network pharmacology and molecular docking

BACKGROUNDS

Chronic hepatitis (CH) refers to liver inflammation lasting at least 6 months caused by various factors, significantly impacting patients' daily lives. Paeoniae Radix Rubra (CS) is a classic blood-activating and stasis-dissolving herb known for its protective effects on the liver. This research seeks to investigate the underlying mechanisms by which CS treat CH, employing network pharmacology and molecular docking.

METHODS

The active constituents of CS for CH treatment were identified through the TCMSP database. Targets associated with CH were gathered from GeneCards, the Therapeutic Target Database, and OMIM databases. The intersecting genes between these targets and the components of CS were considered potential therapeutic targets. Protein-protein interaction analysis was performed with the use of the STRING database and Cytoscape software, leading to the identification of core targets. These core targets underwent KEGG and GO enrichment analysis, and the top 10 pathways were chosen for building a drug-compound-target-pathway-disease' network. Finally, molecular docking was utilized to evaluate the binding affinities between the compounds and the core targets.

RESULTS

From the TCMSP database, 29 compounds were screened, and 101 potential intersection targets of CS for treating CH were identified. The protein-protein interaction network analysis revealed that the core targets included EGFR, HSP90AA1, SRC, TNF, ALB, ESR1, CASP3, PTGS2, ERBB2, and FGF2. Pathway analysis indicated that CS's treatment of CH is mainly associated with the pathway in cancer. Molecular docking results indicated that Paeoniflorin and Baicalin exhibited strong binding affinity with EGFR and HSP90AA1.

CONCLUSION

This research uncovers the possible mechanisms of CS in CH treatment, offering new avenues for future studies.

Shared Genes and Molecular Mechanisms between Nonalcoholic Fatty Liver Disease and Hepatocellular Carcinoma Established by WGCNA Analysis

Background  Hepatocellular carcinoma (HCC) is one of the leading causes of death from cancer worldwide. The histopathological features, risk factors, and prognosis of HCC caused by nonalcoholic fatty liver disease (NAFLD) appear to be significantly different from those of HCC caused by other etiologies of liver disease. Objective  This article explores the shared gene and molecular mechanism between NAFLD and HCC through bioinformatics technologies such as weighted gene co-expression network analysis (WGCNA), so as to provide a reference for comprehensive understanding and treatment of HCC caused by NAFLD. Methods  NAFLD complementary deoxyribonucleic acid microarrays (GSE185051) from the Gene Expression Omnibus database and HCC ribonucleic acid (RNA)-sequencing data (RNA-seq data) from The Cancer Genome Atlas database were used to analyze the differentially expressed genes (DEGs) between NAFLD and HCC. Then, the clinical traits and DEGs in the two disease data sets were analyzed by WGCNA to obtain W-DEGs, and cross-W-DEGs were obtained by their intersection. We performed subsequent Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG) enrichment analyses of the cross-W-DEGs and established protein-protein interaction networks. Then, we identified the hub genes in them by Cytoscape and screened out the final candidate genes. Finally, we validated candidate genes by gene expression, survival, and immunohistochemical analyses. Results  The GO analysis of 79 cross-W-DEGs showed they were related mainly to RNA polymerase II (RNAP II) and its upstream transcription factors. KEGG analysis revealed that they were enriched predominantly in inflammation-related pathways (tumor necrosis factor and interleukin-17). Four candidate genes (JUNB, DUSP1, NR4A1, and FOSB) were finally screened out from the cross-W-DEGs. Conclusion  JUNB, DUSP1, NR4A1, and FOSB inhibit NAFLD and HCC development and progression. Thus, they can serve as potential useful biomarkers for predicting and treating NAFLD progression to HCC.

Integrating Network Pharmacology and Transcriptomic Strategies to Explore the Pharmacological Mechanism of Hydroxysafflor Yellow A in Delaying Liver Aging

Aging affects the structure and function of the liver. Hydroxysafflor yellow A (HSYA) effectively improves liver aging (LA) in mice, but the potential mechanisms require further exploration. In this study, an integrated approach combining network pharmacology and transcriptomics was used to elucidate the potential mechanisms of HSYA delay of LA. The targets of HSYA were predicted using the PharmMapper, SwissTargetPrediction, and CTD databases, and the targets of LA were collected from the GeneCards database. An ontology (GO) analysis and a Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway annotation of genes related to HSYA delay of LA were performed using the DAVID database, and Cytoscape software was used to construct an HSYA target pathway network. The BMKCloud platform was used to sequence mRNA from mouse liver tissue, screen differentially expressed genes (DEGs) that were altered by HSYA, and enrich their biological functions and signaling pathways through the OmicShare database. The results of the network pharmacology and transcriptomic analyses were combined. Then, quantitative real-time PCR (qRT-PCR) and Western blot experiments were used to further verify the prediction results. Finally, the interactions between HSYA and key targets were assessed by molecular docking. The results showed that 199 potentially targeted genes according to network pharmacology and 480 DEGs according to transcriptomics were involved in the effects of HSYA against LA. An integrated analysis revealed that four key targets, including HSP90AA1, ATP2A1, NOS1 and CRAT, as well as their three related pathways (the calcium signaling pathway, estrogen signaling pathway and cGMP-PKG signaling pathway), were closely related to the therapeutic effects of HSYA. A gene and protein expression analysis revealed that HSYA significantly inhibited the expressions of HSP90AA1, ATP2A1 and NOS1 in the liver tissue of aging mice. The molecular docking results showed that HSYA had high affinities with the HSP90AA1, ATP2A1 and NOS1 targets. Our data demonstrate that HSYA may delay LA in mice by inhibiting the expressions of HSP90AA1, ATP2A1 and NOS1 and regulating the calcium signaling pathway, the estrogen signaling pathway, and the cGMP-PKG signaling pathway.

CEP192 is a novel prognostic marker and correlates with the immune microenvironment in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) responds poorly to standard chemotherapy or targeted therapy; hence, exploration for novel therapeutic targets is urgently needed. CEP192 protein is indispensable for centrosome amplification, which has been extensively characterized in both hematological malignancies and solid tumors. Here, we combined bioinformatics and experimental approaches to assess the potential of CEP192 as a prognostic and therapeutic target in HCC. CEP192 expression increased with tumor stage and was associated with poor clinicopathologic features, frequent recurrence, and higher mortality. Upon single-cell RNA sequencing, CEP192 was found to be involved in the proliferation and self-renewal of hepatic progenitor-like cells. This observation was further evidenced using CEP192 silencing, which prevented tumor cell proliferation and self-renewal by arresting cells in the G0/G1 phase of the cell cycle. Notably, CEP192 was highly correlated with multiple tumor-associated cytokine ligand–receptor axes, including IL11–IL11RA, IL6–IL6R, and IL13–IL13RA1, which could promote interactions between hepatic progenitor-like cells, PLVAP+ endothelial cells, tumor-associated macrophages, and CD4+ T cells. Consequently, CEP192 expression was closely associated with an immunosuppressive tumor microenvironment and low immunophenoscores, making it a potential predictor of response to immune checkpoint inhibitors. Taken together, our results unravel a novel onco-immunological role of CEP192 in establishing the immunosuppressive tumor microenvironment and provide a novel biomarker, as well as a potential target for therapeutic intervention of HCC.

Investigation of the Effect of Curcumin on Protein Targets in NAFLD Using Bioinformatic Analysis

BACKGROUND: Non-alcoholic fatty liver disease (NAFLD) is a prevalent metabolic disorder. Defects in function/expression of genes/proteins are critical in initiation/progression of NAFLD. Natural products may modulate these genes/proteins. Curcumin improves steatosis, inflammation, and fibrosis progression. Here, bioinformatic tools, gene−drug and gene-disease databases were utilized to explore targets, interactions, and pathways through which curcumin could impact NAFLD. METHODS: Significant curcumin−protein interaction was identified (high-confidence:0.7) in the STITCH database. Identified proteins were investigated to determine association with NAFLD. gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were analyzed for significantly involved targets (p < 0.01). Specificity of obtained targets with NAFLD was estimated and investigated in Tissue/Cells−gene associations (PanglaoDB Augmented 2021, Mouse Gene Atlas) and Disease−gene association-based EnrichR algorithms (Jensen DISEASES, DisGeNET). RESULTS: Two collections were constructed: 227 protein−curcumin interactions and 95 NAFLD-associated genes. By Venn diagram, 14 significant targets were identified, and their biological pathways evaluated. Based on gene ontology, most targets involved stress and lipid metabolism. KEGG revealed chemical carcinogenesis, the AGE-RAGE signaling pathway in diabetic complications and NAFLD as the most common significant pathways. Specificity to diseases database (EnrichR algorithm) revealed specificity for steatosis/steatohepatitis. CONCLUSION: Curcumin may improve, or inhibit, progression of NAFLD through activation/inhibition of NAFLD-related genes.

Different HCV Exposure Drives Specific miRNA Profile in PBMCs of HIV Patients

Micro RNAs (miRNAs) are essential players in HIV and HCV infections, as both viruses modulate cellular miRNAs and interact with the miRNA-mediated host response. We aim to analyze the miRNA profile of HIV patients with different exposure to HCV to explore specific signatures in the miRNA profile of PBMCs for each type of infection. We massively sequenced small RNAs of PBMCs from 117 HIV+ infected patients: 45 HIV+ patients chronically infected with HCV (HIV/HCV+), 36 HIV+ that spontaneously clarified HCV after acute infection (HIV/HCV-) and 36 HIV+ patients without previous HCV infection (HIV). Thirty-two healthy patients were used as healthy controls (HC). Differential expression analysis showed significantly differentially expressed (SDE) miRNAs in HIV/HCV+ (n = 153), HIV/HCV- (n = 169) and HIV (n = 153) patients. We found putative dysregulated pathways, such as infectious-related and PI3K signaling pathways, common in all contrasts. Specifically, putatively targeted genes involved in antifolate resistance (HIV/HV+), cancer-related pathways (HIV/HCV-) and HIF-signaling (HIV) were identified, among others. Our findings revealed that HCV strongly influences the expression profile of PBMCs from HIV patients through the disruption of its miRNome. Thus, different HCV exposure can be identified by specific miRNA signatures in PBMCs.

Identification of Key Genes and Immune Infiltrate in Nonalcoholic Steatohepatitis: A Bioinformatic Analysis

BACKGROUND

Nonalcoholic steatohepatitis (NASH) can progress to cirrhosis and hepatic carcinoma and is closely associated with changes in the neurological environment. The discovery of new biomarkers would aid in the treatment of NASH.

METHODS

Data GSE89632 were downloaded from the Gene Expression Omnibus (GEO) database, and R package "limma" was used to identify differentially expressed genes (DEGs) for NASH vs. normal tissues. The STRING database was used to construct a protein-protein interaction (PPI) network, and the Cytoscape software program (Version 3.80) was used to visualize the PPI network and identify key genes. The immune infiltration of NASH was determined using the R package "CIBERSORT".

RESULTS

We screened 41 DEGs. GO and KEGG enrichment analyses of the DEGs revealed the enrichment of pathways related to NAFLD steatosis and inflammation. A PPI network analysis was also performed on the DEGs, and seven genes (MYC, CXCL8, FOS, SOCS1, SOCS3, IL6, and PTGS2) were identified as hub genes. An immune infiltration assessment revealed that macrophages M2, memory resting CD4+ T cells, and γΔ T cells play important roles in the immune microenvironment of NASH, which may be mediated by the seven identified hub genes.

Identification and Validation of a Prognostic Model Based on Three Autophagy-Related Genes in Hepatocellular Carcinoma

Background

Accumulating studies have demonstrated that autophagy plays an important role in hepatocellular carcinoma (HCC). We aimed to construct a prognostic model based on autophagy-related genes (ARGs) to predict the survival of HCC patients.

Methods

Differentially expressed ARGs were identified based on the expression data from The Cancer Genome Atlas and ARGs of the Human Autophagy Database. Univariate Cox regression analysis was used to identify the prognosis-related ARGs. Multivariate Cox regression analysis was performed to construct the prognostic model. Receiver operating characteristic (ROC), Kaplan-Meier curve, and multivariate Cox regression analyses were performed to test the prognostic value of the model. The prognostic value of the model was further confirmed by an independent data cohort obtained from the International Cancer Genome Consortium (ICGC) database.

Results

A total of 34 prognosis-related ARGs were selected from 62 differentially expressed ARGs identified in HCC compared with noncancer tissues. After analysis, a novel prognostic model based on ARGs (PRKCD, BIRC5, and ATIC) was constructed. The risk score divided patients into high- or low-risk groups, which had significantly different survival rates. Multivariate Cox analysis indicated that the risk score was an independent risk factor for survival of HCC after adjusting for other conventional clinical parameters. ROC analysis showed that the predictive value of this model was better than that of other conventional clinical parameters. Moreover, the prognostic value of the model was further confirmed in an independent cohort from ICGC patients.

Conclusion

The prognosis-related ARGs could provide new perspectives on HCC, and the model should be helpful for predicting the prognosis of HCC patients.