Identification of prognostic risk factors for pancreatic cancer using bioinformatics analysis

Abstract

Background Pancreatic cancer is one of the most common malignant cancers worldwide. Currently, the pathogenesis of pancreatic cancer remains unclear; thus, it is necessary to explore its precise molecular mechanisms. Methods To identify candidate genes involved in the tumorigenesis and proliferation of pancreatic cancer, the microarray datasets GSE32676, GSE15471 and GSE71989 were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between Pancreatic ductal adenocarcinoma (PDAC) and nonmalignant samples were screened by GEO2R. The Database for Annotation Visualization and Integrated Discovery (DAVID) online tool was used to obtain a synthetic set of functional annotation information for the DEGs. A PPI network of the DEGs was established using the Search Tool for the Retrieval of Interacting Genes (STRING) database, and a combination of more than 0.4 was considered statistically significant for the PPI. Subsequently, we visualized the PPI network using Cytoscape. Functional module analysis was then performed using Molecular Complex Detection (MCODE). Genes with a degree ≥10 were chosen as hub genes, and pathways of the hub genes were visualized using ClueGO and CluePedia. Additionally, GenCLiP 2.0 was used to explore interactions of hub genes. The Literature Mining Gene Networks module was applied to explore the cocitation of hub genes. The Cytoscape plugin iRegulon was employed to analyze transcription factors regulating the hub genes. Furthermore, the expression levels of the 13 hub genes in pancreatic cancer tissues and normal samples were validated using the Gene Expression Profiling Interactive Analysis (GEPIA) platform. Moreover, overall survival and disease-free survival analyses according to the expression of hub genes were performed using Kaplan-Meier curve analysis in the cBioPortal online platform. The relationship between expression level and tumor grade was analyzed using the online database Oncomine. Lastly, the eight snap-frozen tumorous and adjacent noncancerous adjacent tissues of pancreatic cancer patients used to detect the CDK1 and CEP55 protein levels by western blot. Conclusions Altogether, the DEGs and hub genes identified in this work can help uncover the molecular mechanisms underlying the tumorigenesis of pancreatic cancer and provide potential targets for the diagnosis and treatment of this disease.

ITGA2, LAMB3, and LAMC2 May Be the Potential Therapeutic Targets in Pancreatic Ductal Adenocarcinoma: an Integrated Bioinformatics Analysis

“…It is worth noting that DEGs in PDAC have already been demonstrated in several studies 6,7 . However, the results were not consistent, which could be due to the differences in the selection of datasets and statistical procedures.…”

“…The gene expression profiles from diverse microarray platforms are submitted to several public databases, including Gene Expression Omnibus (GEO: https://www.ncbi.nlm.nih.gov/gds/). Several previous studies used gene expression microarray technology to underpinning the DEGs of PDAC in recent years 6,7 . However, the results were inconsistent, and various aspects remain unclear due to sample heterogeneity.…”

Expression of Genomic Instability-Related Molecules: Cyclin F, RRM2 and SPDL1 and Their Prognostic Significance in Pancreatic Adenocarcinoma

“…Importantly, based on the analysis of the TCGA data, we identified RRM2 mRNA expression as an independent negative prognostic factor for OS in pancreatic adenocarcinoma patients. By in silico analysis of microarray-based gene expression data, Jin et al [ 32 ] have reported similar poor prognostic effect of RRM2 for PDAC, but with regard to disease-free survival (DFS; univariate Kaplan–Meier analysis). Correspondingly, in the cohort study by Itoi et al [ 28 ], high transcript expression of RRM2 in 31 fine-needle aspiration biopsy specimens of advanced stage pancreatic cancer significantly correlated with a poor patient survival and resistance to gemcitabine.…”

Gene expression of cytokinesis regulators PRC1, KIF14 and CIT has no prognostic role in colorectal and pancreatic cancer

“…Besides a few rather small-scale experimental studies in various cancer types, e.g., breast ( 21 ), ovarian ( 22 , 40 ), cervical ( 41 ) and prostate ( 25 ) carcinomas, Big Data mining, from the Gene Expression Omnibus (GEO) or the Cancer Genome Atlas (TCGA) databases, using various in silico bioinformatics have demonstrated that some cytokinesis regulators may have prognostic value alone or as a part of a specific gene sets ( 50 – 52 ). For example, KIF14 is one of 10 genes whose low transcript expression in the GSE62452 microarray dataset, composed of 69 PDAC tumors predicted significantly poorer OS time of patients with PDAC and this was also observed in the TCGA RNASeq dataset ( 50 ).…”

“…More recently, KIF14 was reported among 7 metastasis-related genes with prognostic potential based on OS time in PDAC based on 141 patients from the TCGA dataset validated with the GSE62452 dataset ( 51 ). Additionally, PRC1 is among 10 other genes whose signature predicts OS time and 12 genes predicting disease-free survival time of patients with PDAC based on in silico analysis of data of 77 patients with PDAC from 3 GEO datasets ( 52 ). High intra-tumoral CIT transcript level alone was recently identified as poor prognosis predictor (both OS and disease-free survival time) in PDAC through analysis of 178 patients from the TCGA dataset using the Gene Expression Profiling Interactive Analysis online tool ( 53 ).…”