Identification of key candidate genes for pancreatic cancer by bioinformatics analysis

Identification of potential biomarkers for pancreatic ductal adenocarcinoma: a bioinformatics analysis

PDA is an aggressive cancer with a 5-year survival rate, which is very low. There is no effective prognosis or therapy for PDA because of the lack of target biomarkers. The objective of this article is to identify the target biomarkers for PDA using a bioinformatics approach. In this work, we have analysed the three microarray datasets from the NCBI GEO database. We used the Geo2R tool to analyse the microarray data with the Benjamini and Hochberg false discovery rate method, and the significance level cut-off was set to 0.05. We have identified 659 DEGs from the datasets. There are a total of 15 hub genes that were selected from the PPI network constructed using the STRING application. Furthermore, these 15 genes were evaluated on PDA patients using TCGA and GTEx databases in (GEPIA). The online tool DAVID was used to analyse the functional annotation information for the DEGs. The functional pathway enrichment was performed on the GO and KEGG. The hub genes were mainly enriched for cell division, chromosome segregation, protein binding and microtubule binding. Further, the gene alteration study was performed using the cBioportal tool and screened out six hub genes (ASPM, CENPF, BIRC5, TTK, DLGAP5, and TOP2A) with a high alteration rate in PDA samples. Furthermore, Kaplan-Meier survival analysis was performed on the six hub genes and identified poor-survival outcomes that may be involved in tumorigenesis and PDA development. So, this study concludes that, these six hub genes may be potential prognostic biomarkers for PDA.

Gene Expression Characteristics of Tumor and Adjacent Non-Tumor Tissues of Pancreatic Ductal Adenocarcinoma (PDAC) In-Silico

BACKGROUND

One of the deadliest and most prevalent cancer is pancreatic ductal adenocarcinoma (PDAC). Microarray has become an important tool in the research of PDAC genes and target therapeutic drugs.

OBJECTIVES

This study intends to clarify the promising prognostic and biomarker targets in PDAC using GSE78229 and GSE62452 datasets, publicly accessible at the Gene Expression Omnibus database.

MATERIALS AND METHODS

Utilizing GEOquery, Bio base, gplots, and ggplot2 packages in the R program, this study detects 428 differentially expressed genes that are further applied to build a co-expression network by the weighted correlation network analysis (WGCNA). The turquoise module presented a higher correlation with PDAC progression. 79 candidate genes were selected based on the co-expression and protein-protein interaction (PPI) networks. In addition, the functional enrichment analysis was studied.

RESULTS

Five significant KEGG pathways linked to PDAC were detected, in which the endoplasmic reticulum protein processing pathway was remarked to be vital. The resulting 19 hub genes as HSPA4, PABPC1, HSP90B1, PPP1CC, USP9X, EIF2S3, MSN, RAB10, BMPR2, P4HB, UBC, B2M, SLC25A5, MMP7, SPTBN1, RALB, DNAJB1, CENPE, and PDIA6 were identified by the Network Analyst web tool founded on PPI network by the STRING. These were identified as the most connected hub proteins. The quantification of the expression of levels and survival probabilities were analyzed overall survival (OS) of the real hub genes and were investigated by Kaplan-Meier (KM) plotter through The Cancer Genome Atlas Program (TCGA) database.

CONCLUSIONS

The protein-protein interactions and KEGG pathway enrichment by DAVID indicated that some pathways were involved in PDAC, such as "pathways in cancer (hsa05200)", "protein processing in the endoplasmic reticulum (hsa04141)", "antigen processing and presentation (hsa04612)", "dopaminergic synapse (hsa04728)", and "measles (hsa05162)"; in which these pathways, the "protein processing in endoplasmic reticulum (hsa04141)", was further studied because of its closely relationship with PDAC. The rest of the hub genes reviewed throughout the study might be promising targets for diagnosing and treating PDAC and relevant diseases.

Hypoxia-Related Gene FUT11 Promotes Pancreatic Cancer Progression by Maintaining the Stability of PDK1

Background

Hypoxia is associated with the development of pancreatic cancer (PC). However, genes associated with hypoxia response and their regulatory mechanism in PC cells were unclear. The current study aims to investigate the role of the hypoxia associated gene fucosyltransferase 11 (FUT11) in the progression of PC.

Methods

In the preliminary study, bioinformatics analysis predicted FUT11 as a key hypoxia associated gene in PC. The expression of FUT11 in PC was evaluated using quantitative real-time PCR (qRT-PCR), Western blot and immunohistochemistry. The effects of FUT11 on PC cells proliferation and migration under normoxia and hypoxia were evaluated using Cell Counting Kit 8, 5-ethynyl-2'-deoxyuridine (EDU) assay, colony formation assay and transwell assay. The effects of FUT11 in vivo was examined in mouse tumor models of liver metastasis and subcutaneous xenograft. Furthermore, Western blot, luciferase assay and immunoprecipitation were performed to explore the regulatory relationship among FUT11, hypoxia-inducible factor 1α (HIF1α) and pyruvate dehydrogenase kinase 1 (PDK1) in PC.

Results

FUT11 was markedly increased of PC cells with hypoxia, upregulated in the PC clinical tissues, and predicted a poor outcome of PC patients. Inhibition of FUT11 reduced PC cell growth and migratory ability of PC cells under normoxia and hypoxia conditions in vitro, and growth and tumor cell metastasis in vivo. FUT11 bound to PDK1 and regulated the expression PDK1 under normoxia and hypoxia. FUT11 interacted with PDK1 and decreased the ubiquitination of PDK1, lead to the activation of AKT/mTOR signaling pathway. FUT11 knockdown significantly increased the degradation of PDK1 under hypoxia, while treatment with MG132 can relieve the degradation of PDK1 induced by FUT11 knockdown. Overexpression of PDK1 in PC cells under hypoxia conditions reversed the suppressive impacts of FUT11 knockdown on PC cell growth and migration. In addition, HIF1α bound to the promoter of FUT11 and increased its expression, as well as co-expressed with FUT11 in PC tissues. Furthermore, overexpression of FUT11 partially rescued the suppressive effects of HIF1α knockdown on PC cell growth and migration in hypoxia condition.

Conclusion

Our data implicate that hypoxia-induced FUT11 contributes to proliferation and metastasis of PC by maintaining the stability of PDK1, thus mediating activation of AKT/mTOR signaling pathway, and suggest that FUT11 could be a novel and effective target for the treatment of pancreatic cancer.

Identification of key differentially expressed mRNAs and microRNAs in non-small cell lung cancer using bioinformatics analysis

Non-small cell lung cancer (NSCLC) is a leading cause of mortality worldwide. However, the pathogenesis of NSCLC remains to be fully elucidated. Therefore, the present study aimed to explore the differential expression of mRNAs and microRNAs (miRNAs/miRs) in NSCLC and to determine how these RNA molecules interact with one another to affect disease progression. Differentially expressed genes (DEGs) and differentially expressed miRNAs (DEMs) were identified from the GSE18842, GSE32863 and GSE29250 datasets downloaded from the Gene Expression Omnibus (GEO database). Functional and pathway enrichment analysis were performed based on Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. STRING, Cytoscape and MCODE were applied to construct a protein-protein interaction (PPI) network and to screen hub genes. The interactions between miRNAs and mRNAs were predicted using miRWalk 3.0 and a miRNA-mRNA regulatory network was constructed. The prognostic value of the identified hub genes was then evaluated via Kaplan-Meier survival analyses using datasets from The Cancer Genome Atlas. A total of 782 DEGs and 46 DEMs were identified from the 3 GEO datasets. The enriched pathways and functions of the DEGs and target genes of the DEMs included osteoclast differentiation, cell adhesion, response to a drug, plasma membrane, extracellular exosome and protein binding. A subnetwork composed of 11 genes was extracted from the PPI network and the genes in this subnetwork were mainly involved in the cell cycle, cell division and DNA replication. A miRNA-gene regulatory network was constructed with 247 miRNA-gene pairs based on 6 DEMs and 210 DEGs. Kaplan-Meier survival analysis indicated that the expression of ubiquitin E2 ligase C, cell division cycle protein 20, DNA topoisomerase IIα, aurora kinase A and B, cyclin B2, maternal embryonic leucine zipper kinase, slit guidance ligand 3, phosphoglucomutase 5, endomucin, cysteine dioxygenase type 1, dihydropyrimidinase-like 2, miR-130b, miR-1181 and miR-127 was significantly associated with overall survival of patients with lung adenocarcinoma. In the present study, a miRNA-mRNA regulatory network in NSCLC was established, which may provide future avenues for scientific exploration and therapeutic targeting of NSCLC.