Identification of Methylation-Regulated Differentially Expressed Genes and Related Pathways in Hepatocellular Carcinoma: A Study Based on TCGA Database and Bioinformatics Analysis

Identification of module genes and functional pathway analysis in septic shock subtypes by integrated bioinformatics analysis

BACKGROUND

The present study aimed to identify the module genes and key gene functions and biological pathways of septic shock (SS) through integrated bioinformatics analysis.

METHODS

In the study, we performed batch correction and principal component analysis on 282 SS samples and 79 normal control samples in three datasets, GSE26440, GSE95233 and GSE57065, to obtain a combined corrected gene expression matrix containing 21,654 transcripts. Patients with SS were then divided into three molecular subtypes according to sample subtyping analysis.

RESULTS

By analyzing the demographic characteristics of the different subtypes, we found no statistically significant differences in gender ratio and age composition among the three groups. Then, three subtypes of differentially expressed genes (DEGs) and specific upregulated DEGs (SDEGs) were identified by differential gene expression analysis. We found 7361 DEGs in the type I group, 5594 DEGs in the type II group, and 7159 DEGs in the type III group. There were 1698 SDEGs in the type I group, 2443 in the type II group, and 1831 in the type III group. In addition, we analyzed the correlation between the expression data of 5972 SDEGs in the three subtypes and the gender and age of 227 patients, constructed a weighted gene co-expression network, and identified 11 gene modules, among which the module with the highest correlation with gender ratio was MEgrey. The modules with the highest correlation with age composition were MEgrey60 and MElightyellow. Then, by analyzing the differences in module genes among different subgroups of SS, we obtained the differential expression of 11 module genes in four groups: type I, type II, type III and the control group. Finally, we analyzed the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment of all module DEGs, and the GO function and KEGG pathway enrichment of different module genes were different.

CONCLUSIONS

Our findings aim to identify the specific genes and intrinsic molecular functional pathways of SS subtypes, as well as further explore the genetic and molecular pathophysiological mechanisms of SS.

Identification of potential DNA methylation biomarkers related to diagnosis in patients with bladder cancer through integrated bioinformatic analysis

BACKGROUND

Bladder cancer (BLCA) is one of the most common malignancies among tumors worldwide. There are no validated biomarkers to facilitate such treatment diagnosis. DNA methylation modification plays important roles in epigenetics. Identifying methylated differentially expressed genes is a common method for the discovery of biomarkers.

METHODS

Bladder cancer data were obtained from Gene Expression Omnibus (GEO), including the gene expression microarrays GSE37817( 18 patients and 3 normal ), GSE52519 (9 patients and 3 normal) and the gene methylation microarray GSE37816 (18 patients and 3 normal). Aberrantly expressed genes were obtained by GEO2R. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were analyzed using the DAVID database and KOBAS. Protein-protein interactions (PPIs) and hub gene networks were constructed by STRING and Cytoscape software. The validation of the results which was confirmed through four online platforms, including Gene Expression Profiling Interactive Analysis (GEPIA), Gene Set Cancer Analysis (GSCA), cBioProtal and MEXPRESS.

RESULTS

In total, 253 and 298 upregulated genes and 674 and 454 downregulated genes were identified for GSE37817 and GSE52519, respectively. For the GSE37816 dataset, hypermethylated and hypomethylated genes involving 778 and 3420 genes, respectively, were observed. Seventeen hypermethylated and low expression genes were enriched in biological processes associated with different organ development and morphogenesis. For molecular function, these genes showed enrichment in extracellular matrix structural constituents. Pathway enrichment showed drug metabolic enzymes and several amino acids metabolism, PI3K-Akt, Hedgehog signaling pathway. The top 3 hub genes screened by Cytoscape software were EFEMP1, SPARCL1 and ABCA8. The research results were verified using the GEPIA, GSCA, cBioProtal and EXPRESS databases, and the hub hypermethylated low expression genes were validated.

CONCLUSION

This study screened possible aberrantly methylated expression hub genes in BLCA by integrated bioinformatics analysis. The results may provide possible methylation-based biomarkers for the precise diagnosis and treatment of BLCA in the future.

The coagulation-related genes for prognosis and tumor microenvironment in pancreatic ductal adenocarcinoma

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) is a malignancy characterized by challenging early diagnosis and poor prognosis. It is believed that coagulation has an impact on the tumor microenvironment of PDAC. The aim of this study is to further distinguish coagulation-related genes and investigate immune infiltration in PDAC.

METHODS

We gathered two subtypes of coagulation-related genes from the KEGG database, and acquired transcriptome sequencing data and clinical information on PDAC from The Cancer Genome Atlas (TCGA) database. Using an unsupervised clustering method, we categorized patients into distinct clusters. We investigated the mutation frequency to explore genomic features and performed enrichment analysis, utilizing Gene Ontology (GO) and Kyoto Encyclopedia of Genes (KEGG) to explore pathways. CIBERSORT was used to analyze the relationship between tumor immune infiltration and the two clusters. A prognostic model was created for risk stratification, and a nomogram was established to assist in determining the risk score. The response to immunotherapy was assessed using the IMvigor210 cohort. Finally, PDAC patients were recruited, and experimental samples were collected to validate the infiltration of neutrophils using immunohistochemistry. In addition, and identify the ITGA2 expression and function were identified by analyzing single cell sequencing data.

RESULTS

Two coagulation-related clusters were established based on the coagulation pathways present in PDAC patients. Functional enrichment analysis revealed different pathways in the two clusters. Approximately 49.4% of PDAC patients experienced DNA mutation in coagulation-related genes. Patients in the two clusters displayed significant differences in terms of immune cell infiltration, immune checkpoint, tumor microenvironment and TMB. We developed a 4-gene prognostic stratified model through LASSO analysis. Based on the risk score, the nomogram can accurately predict the prognosis in PDAC patients. We identified ITGA2 as a hub gene, which linked to poor overall survival (OS) and short disease-free survival (DFS). Single-cell sequencing analysis demonstrated that ITGA2 was expressed by ductal cells in PDAC.

CONCLUSIONS

Our study demonstrated the correlation between coagulation-related genes and the tumor immune microenvironment. The stratified model can predict the prognosis and calculate the benefits of drug therapy, thus providing the recommendations for clinical personalized treatment.

Targeted DNA demethylation of the ZNF334 promoter inhibits colorectal cancer growth

Colorectal cancer (CRC) is a leading cause of cancer deaths worldwide. Aberrant regulation of DNA methylation in promoters of tumor suppressor genes or proto-oncogenes is one of the fundamental processes driving the initiation and progression of CRC. Zinc-finger proteins (ZNFs) are one of the most abundant groups of proteins and function in many important biological processes related to tumorigenesis. Herein, we detected abnormal hypermethylation of the ZNF334 gene in CRC tissues compared with normal tissues, and this modification downregulated the expression of ZNF334. Furthermore, ten-eleven translocation 1 (TET1) was identified to be involved in regulating the methylation level of ZNF334. Next, a dCas9-multiGCN4/scFv-TET1CD-sgZNF334-targeted demethylation system was constructed to reverse the expression of ZNF334 through sgRNA targeting the ZNF334 promoter. Both in vitro and in vivo experiments demonstrated the targeted demethylation system upregulated ZNF334 expression and inhibited CRC growth. Collectively, targeted DNA demethylation of the ZNF334 promoter sheds light on the precise treatment of CRC.

Bioinformatics Analysis Identifies ASCL1 as the Key Transcription Factor in Hepatocellular Carcinoma Progression

Methods

Differentially transcription factors (DETFs) were identified from differentially expressed genes (DEGs) in GSE62232 and transcription factors. Then, they were analyzed by regulatory networks, prognostic risk model, and overall survival analyses to identify the key DETF. Combined with the regulatory networks and binding site analysis, the target mRNA of key DETF was determined, and its prognostic value in HCC was evaluated by survival, clinical characteristics analyses, and experiments. Finally, the expressions and functions of the key DETF on the DEmRNAs were investigated in HCC cells.

Results

Through multiple bioinformatics analyses, ASCL1 was identified as the key DETF, and SLC6A13 was predicted to be its target mRNA with the common binding site of CCAGCAACTGGCC, both downregulated in HCC. In survival analysis, high SLC6A13 was related to better HCC prognosis, and SLC6A13 was differentially expressed in HCC patients with clinical characteristics. Furthermore, cell experiments showed the mRNA expressions of ASCL1 and SLC6A13 were both reduced in HCC, and their overexpressions suppressed the growth, invasion, and migration of HCC cells. Besides, over-ASCL1 could upregulate SLC6A13 expression in HCC cells.

Conclusion

This study identifies two suppressor genes in HCC progression, ASCL1 and SLC6A13, and the key transcription factor ASCL1 suppresses HCC progression by targeting SLC6A13 mRNA. They are both potential treatment targets and prognostic biomarkers for HCC patients, which provides new clues for HCC research.

Molecular subtypes, clinical significance, and tumor immune landscape of angiogenesis-related genes in ovarian cancer

Angiogenesis is a physiological process, where new blood vessels are formed from pre-existing vessels through the mechanism called sprouting. It plays a significant role in supporting tumor growth and is expected to provide novel therapeutic ideas for treating tumors that are resistant to conventional therapies. We investigated the expression pattern of angiogenesis-related genes (ARGs) in ovarian cancer (OV) from public databases, in which the patients could be classified into two differential ARG clusters. It was observed that patients in ARGcluster B would have a better prognosis but lower immune cell infiltration levels in the tumor microenvironment. Then ARG score was computed based on differentially expressed genes via cox analysis, which exhibited a strong correlation to copy number variation, immunophenoscore, tumor mutation load, and chemosensitivity. In addition, according to the median risk score, patients were separated into two risk subgroups, of which the low-risk group had a better prognosis, increased immunogenicity, and stronger immunotherapy efficacy. Furthermore, we constructed a prognostic nomogram and demonstrated its predictive value. These findings help us better understand the role of ARGs in OV and offer new perspectives for clinical prognosis and personalized treatment.

lncRNA-AC079061.1/VIPR1 axis may suppress the development of hepatocellular carcinoma: a bioinformatics analysis and experimental validation

Background

Hepatocellular carcinoma (HCC) is one of the most malignant tumors to threaten human life, and the survival rate remains low due to delayed diagnosis. Meanwhile, lncRNAs have great potential for application in tumor prognosis, therefore relevant research in hepatocellular carcinoma is indispensable.

Methods

Based on the EZH2 expression, the differentially expressed lncRNAs DElncRNAs), miRNAs (DEmiRNAs), and mRNAs (DEmRNAs) were identified in hepatocellular carcinoma by using the TCGA database. Bioinformatics technology was utilized to determine the effect of key genes in HCC progression. The methylation and immune infiltration analyses were performed to explore the underlying function of hub genes. Finally, cellular function experiments were performed to investigate the association between identified genes and biological phenotypes in HCC.

Results

lncRNA-AC079061.1, hsa-miR-765, and VIPR1 were identified as independent factors that affect the prognosis of hepatocellular carcinoma. The immune infiltration analyses revealed that lncRNA-AC079061.1 can alter the immune microenvironment and thus inhibit the development of HCC by regulating the expression of an immune-related gene (VIPR1). Methylation analyses demonstrated that VIPR1 expression is negatively related to the methylation level in HCC. Experimental results suggested that lncRNA-AC079061.1 and VIPR1 were frequently downregulated in HCC cells, while hsa-miR-765 was significantly upregulated. Moreover, the lncRNA-AC079061.1/VIPR1 axis suppressed the proliferation and invasion of HCC cells.

Conclusion

The present study identified the lncRNA-AC079061.1/VIPR1 axis as a novel biomarker that inhibited the proliferation and invasion of hepatocellular carcinoma, affecting the ultimate disease outcome.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-022-03573-7.

Crosstalk of Redox-Related Subtypes, Establishment of a Prognostic Model and Immune Responses in Endometrial Carcinoma

Simple Summary

In order to explore the role of redox as a prognostic indicator in endometrial carcinoma (EC), we detected the expression patterns of 55 redox-related genes (RRGs) in EC cohorts from public databases. Performing consensus cluster algorithm, we determined four molecular subclusters based on RRGs which had significant differences in overall survival (OS) and immune activities of EC patients. Furthermore, we developed a prognostic risk model on the basis of the redox-related subtype by stepwise Cox regression analyses. All EC patients were divided into high-risk and low-risk groups according to the median value of risk score. Our proposed model could accurately assess the clinical outcome and had favorable independent ability in EC cases. Moreover, our signature can serve as a predictor for immune status and chemotherapy sensitivity.

Abstract

Redox plays a central part in the pathogeneses and development of tumors. We comprehensively determined the expression patterns of redox-related genes (RRGs) in endometrial carcinoma (EC) cohorts from public databases and identified four different RRG-related clusters. The prognosis and the characteristics of TME cell infiltration of RRGcluster C patients were worse than those of other RRG clusters. When it comes to the gene cluster, there were great differences in clinicopathology traits and immunocyte infiltration. The RRG score was calculated by Cox analyses, and an RRG-based signature was developed. The risk score performed well in the EC cohort. Samples were separated into two risk subgroups with the standard of the value of the median risk score. Low-risk patients had a better prognosis and higher immunogenicity. In addition, RRG score was closely associated with immunophenoscore, microsatellite instability, tumor mutation burden, tumor stem cell index, copy number variation and chemotherapy sensitivity. The nomogram accurately predicted the prognosis of patients, and our model showed better performance than other published models. In conclusion, we built a prognostic model of RRGs which can help to evaluate clinical outcomes and guide more effective treatment.

Exploration of Ziziphi Spinosae Semen in Treating Insomnia Based on Network Pharmacology Strategy

Ziziphi Spinosae Semen (ZSS) is a common natural medicine used to treat insomnia, and to show clearly its method of action, we managed and did an in-depth discussion. Network pharmacology research is very suitable for the analysis of multiple components, multiple targets, and multiple pathways of Traditional Chinese Medicine (TCM). According to the relevant theory, we first carefully collected and screened the active ingredients in ZSS and received 11 active ingredients that may work. The targets going along with these active components were also strongly related to insomnia targets, 108 common genes were identified, and drug-compound-gene symbol-disease visualization network and protein-protein interaction network were constructed. Forty-eight core genes were identified by PPI analysis and subjected to GO functional analysis with KEGG pathway analysis. The results of GO analysis pointed that there were 998 gene ontology items for the treatment of insomnia, including terms of 892 biological processes, 47 cellular components, and 59 molecular functions. It mainly shows the coupling effect and transport mode of some proteins in the biological pathways of ZSS in the treatment of insomnia and explains the mechanism of action through the connection between the target and the cell biomembrane. KEGG enrichment analyzed 19 signaling pathways, which were collectively classified into seven categories. We have identified the potential pathways of ZSS against insomnia and obtained the regulatory relationship between core genes and pathways and know that the same target can be regulated by multiple components at the same time. The results of molecular docking also prove this conclusion. We sought to provide a new analytical approach to explore TCM treatments for diseases using network pharmacology analysis tools.

Epigenetic Changes Affecting the Development of Hepatocellular Carcinoma

Simple Summary

Hepatocellular carcinoma is a life-threatening disease. Despite many efforts to understand the exact pathogenesis and the signaling pathways involved in its formation, treatment remains unsatisfactory. Currently, an important function in the development of neoplastic diseases and treatment effects is attributed to changes taking place at the epigenetic level. Epigenetic studies revealed modified methylation patterns in HCC, dysfunction of enzymes engaged in the DNA methylation process, the aberrant function of non-coding RNAs, and a set of histone modifications that influence gene expression. The aim of this review is to summarize the current knowledge on the role of epigenetics in the formation of hepatocellular carcinoma.

Abstract

Hepatocellular carcinoma (HCC) remains a serious oncologic issue with still a dismal prognosis. So far, no key molecular mechanism that underlies its pathogenesis has been identified. Recently, by specific molecular approaches, many genetic and epigenetic changes arising during HCC pathogenesis were detected. Epigenetic studies revealed modified methylation patterns in HCC tumors, dysfunction of enzymes engaged in the DNA methylation process, and a set of histone modifications that influence gene expression. HCC cells are also influenced by the disrupted function of non-coding RNAs, such as micro RNAs and long non-coding RNAs. Moreover, a role of liver cancer stem cells in HCC development is becoming evident. The reversibility of epigenetic changes offers the possibility of influencing them and regulating their undesirable effects. All these data can be used not only to identify new therapeutic targets but also to predict treatment response. This review focuses on epigenetic changes in hepatocellular carcinoma and their possible implications in HCC therapy.

PROZ May Serve as a Prognostic Biomarker for Early Hepatocellular Carcinoma

Objective

The occurrence and development of hepatocellular carcinoma (HCC) remain unclear. This study aimed to investigate potential diagnostic or prognostic markers for early HCC by applying bioinformatic analysis.

Methods

The gene expression profiles of early HCC and normal tissues from a TCGA dataset were used to identify differentially expressed genes (DEGs) and then analysed by weighted gene coexpression network analysis. The integrated genes were selected to construct the protein–protein interaction (PPI) network and determine the hub genes. The prognostic impact of the hub genes was then analysed.

Results

A total of 508 integrated genes were selected from the 615 DEGs and 8956 genes in the turquoise module. A PPI network was constructed, and the top 20 hub genes, including apolipoprotein A-IV (APOA4), fibrinogen gamma chain (FGG), vitamin K-dependent protein Z (PROZ), secreted phosphoprotein 24 (SPP2) and fetuin-B (FETUB), were identified. Only PROZ was significantly associated with the prognosis of early HCC.

Conclusion

In this study, we demonstrated that the expression of PROZ was decreased in early HCC compared with normal liver controls, and low PROZ expression might result in poor overall survival of early HCC.