Identification of a transcription factor-microRNA network in esophageal adenocarcinoma through bioinformatics analysis and validation through qRT-PCR

Identification of the hub genes and transcription factor-miRNA axes involved in Helicobacter pylori-associated gastric cancer

It has been previously reported that transcription factor-microRNA (TF-miRNA) axes play a significant role in the carcinogenesis of several types of malignant tumor. However, there is a lack of research into the differences in the mechanism of Helicobacter pylori (HP)-positive [HP(+)] and HP-negative [HP(−)] gastric cancer. The aim of the present study was to identify the hub genes and TF-miRNA axes, and to determine the potential mechanisms involved in HP-associated gastric cancer. HP-associated mRNA and miRNA data, as well as the corresponding clinical information, was downloaded from The Cancer Genome Atlas database. Differentially expressed genes (DEGs) and DE miRNAs (DEMs) were then identified from the HP(+) and HP(−) cancer mRNA and miRNA datasets, respectively. Subsequently, gene set enrichment analysis and the protein-protein interaction (PPI) networks were investigated using the ClusterProfiler packages. Lastly, TF-miRNA-DEG networks were constructed using the miRWalk online tool. A total of 1,050 DEGs and 13 DEMs were identified from the normalized mRNA and miRNA expression datasets, respectively. In addition, 180 Gene Ontology terms and 30 Kyoto Encyclopedia of Genes and Genomes pathways were found to be enriched, while 6 hub genes were identified from the PPI analysis. Furthermore, 7 TF-miRNA interactions and 181 TF-miRNA-DEG axes were constructed using an integrated bioinformatics approach, while 2 TF-miRNA interactions (ZEB1-miRNA-144-3p and PAX2-miRNA-592) were confirmed using reverse transcription-quantitative PCR in samples from enrolled patients. Moreover, the ZEB1-miRNA-144-3p axis was further validated based on dual luciferase reporter assay results. In summary, an integrated bioinformatics approach was used to screen the significant molecular and regulatory axes, which may provide a novel direction to investigate the pathogenesis of gastric cancer associated with HP.

Bioinformatics-based identification of miRNAs, mRNA, and regulatory signaling pathways involved in esophageal squamous cell carcinoma

Aim

The current study analyzed the miRNA microarray dataset (GSE66274) and gene expression microarray dataset (GSE38129) with similar samples to achieve a better understanding of miRNA-mRNA interactions.

Background

The most common form of esophageal cancer is esophageal squamous cell carcinoma (ESCC). While, miRNAs are well recognized as having a critical regulatory role in human cancer, their responsibilities and mechanisms of miRNA-mRNA in ESCC are unknown.

Methods

Differentially expressed miRNAs (DEmiRNAs) and mRNAs (DEmRNAs) were identified using the LIMMA package in R. In total, 478 DEmRNA (224 upregulated and 254 downregulated) and 39 DEmiRNA (15 upregulated and 24 downregulated) were screened. The RNAInter database analyzed miRNA-mRNA interactions; then, the miRNA-mRNA network was visualized by Cytoscape software. ClusterProfiler packages were used to perform gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses for DEmRNA as targets of DEmiRNAs.

Results

KEGG pathway analysis indicated that the p53 signaling pathway, ECM-receptor interaction, and AGE-RAGE signaling pathway were significant. Cellular response to amino acid stimulus, negative regulation of apoptotic signaling pathway, and endoderm formation were most prevalent in the biological process category. Additionally, the collagen-containing extracellular matrix, actomyosin complex collagen trimers, basement membrane, and extracellular matrix structural constituent were more enriched.

Conclusion

Overall, the present survey provides evidence that could support the prognosis of esophageal tumors in the future.

CXCL10 and its related key genes as potential biomarkers for psoriasis: Evidence from bioinformatics and real-time quantitative polymerase chain reaction

Although several studies have attempted to investigate the etiology of and mechanism underlying psoriasis, the precise molecular mechanism remains unclear. Our study aimed to explore the molecular mechanism underlying psoriasis based on bioinformatics.GSE30999, GSE34248, GSE41662, and GSE50790 datasets were obtained from the Gene Expression Omnibus database. The Gene Expression Omnibus profiles were integrated to obtain differentially expressed genes in R software. Then a series of analyses was performed, such as Gene Ontology annotation, Kyoto Encyclopedia of Genes and Genomes pathway analysis, protein-protein interaction network analysis, among others. The key genes were obtained by CytoHubba, and validated by real-time quantitative polymerase chain reaction.A total of 359 differentially expressed genes were identified between 270 paired lesional and non-lesional skin groups. The common enriched pathways were nucleotide-binding and oligomerization domain-like receptor signaling pathway, and cytokine-cytokine receptor interaction. Seven key genes were identified, including CXCL1, ISG15, CXCL10, STAT1, OASL, IFIT1, and IFIT3. These key genes were validated as upregulated in the 4 datasets and M5-induced HaCaT cells.Our study identified 7 key genes, namely CXCL1, ISG15, CXCL10, STAT1, OASL, IFIT1, and IFIT3, and 2 mostly enriched pathways (nucleotide-binding and oligomerization domain-like receptor signaling pathway, and cytokine-cytokine receptor interaction) involved in psoriatic pathogenesis. More importantly, CXCL1, ISG15, STAT1, OASL, IFIT1, IFIT3, and especially CXCL10 may be potential biomarkers. Therefore, our findings may bring a new perspective to the molecular mechanism underlying psoriasis and suggest potential biomarkers.

Identification of a Transcription Factor-microRNA-Gene Coregulation Network in Meningioma through a Bioinformatic Analysis

Background

Meningioma is a prevalent type of brain tumor. However, the initiation and progression mechanisms involved in the meningioma are mostly unknown. This study aimed at exploring the potential transcription factors/micro(mi)RNAs/genes and biological pathways associated with meningioma.

Methods

mRNA expressions from GSE88720, GSE43290, and GSE54934 datasets, containing data from 83 meningioma samples and eight control samples, along with miRNA expression dataset GSE88721, which had 14 meningioma samples and one control sample, were integrated analyzed. The bioinformatics approaches were used for identifying differentially expressed genes and miRNAs, as well as predicting transcription factor targets related to the differentially expressed genes. The approaches were also used for gene ontology term analysis and biological pathway enrichment analysis, construction, and analysis of protein-protein interaction network, and transcription factor-miRNA-gene coregulation network construction.

Results

Fifty-six upregulated and 179 downregulated genes were identified. Thirty transcription factors able to target the differentially expressed genes were predicted and selected based on public databases. One hundred seventeen overlapping genes were identified from the differentially expressed genes and the miRNAs predicted by miRWalk. Furthermore, NF-κB/IL6, PTGS2, MYC/hsa-miR-574-5p, hsa-miR-26b-5p, hsa-miR-335-5p, and hsa-miR-98-5p, which are involved in the transcription factor-miRNA-mRNA coregulation network, were found to be associated with meningioma.

Conclusion

The bioinformatics analysis identified several potential molecules and relevant pathways that may represent critical mechanisms involved in the progression and development of meningioma. This work provides new insights into meningioma pathogenesis and treatments.

Upregulation of miR-34c after silencing E2F transcription factor 1 inhibits paclitaxel combined with cisplatin resistance in gastric cancer cells

BACKGROUND

MicroRNA 34c (miR-34c) has been reported to be associated with malignant types of cancer, however, it remains unknown whether miR-34c is involved in chemoresistance in gastric cancer (GC).

AIM

To investigate the effect of miR-34c and its upstream transcription factor E2F1 on paclitaxel combined with cisplatin resistance in GC cells.

METHODS

Paired GC tissues and adjacent normal tissues were randomly sampled from 74 GC patients. miR-34c and E2F1 were detected by real-time quantitative PCR (qPCR) and Western blot. In addition, the drug resistance of GC cells to paclitaxel and cisplatin was induced by concentration gradient increasing methods, and changes in miR-34c and E2F1 during this process were measured. Furthermore, E2F1 and miR-34c overexpression or underexpression vectors were constructed and transfected into drug-resistant GC cells. MTT was employed to test the sensitivity of cells to paclitaxel combined with cisplatin, qPCR was adopted to detect the expression of miR-34c, Western blot was applied to detect the expression levels of E2F1, drug resistance-related proteins and apoptosis-related proteins, and flow cytometry was used for the determination of cell apoptosis and cell cycle status.

RESULTS

E2F1 was overexpressed while miR-34c was underexpressed in GC. After inducing GC cells to be resistant to paclitaxel and cisplatin, E2F1 expression increased while miR-34c expression decreased. Both silencing E2F1 and over-expressing miR-34c could increase the sensitivity of drug-resistant GC cells to paclitaxel combined with cisplatin, promote cell apoptosis and inhibit cell proliferation. Among which, silencing E2F1 could reduce the expression of drug resistance-related proteins and apoptosis-related proteins, while over-expression of miR-34c could upregulate the expression of apoptosis-related proteins without affecting the expression of MDR-1, MRP and other drug resistance-related proteins. Rescue experiments demonstrated that inhibiting miR-34c could significantly weaken the sensitization of drug resistant cells, and Si E2F1 to paclitaxel combined with cisplatin.

CONCLUSION

E2F1 inhibits miR-34c to promote the proliferation of GC cells and enhance the resistance to paclitaxel combined with cisplatin, and silencing E2F1 is conducive to improving the efficacy of paclitaxel combined with cisplatin in GC cells.