Integrated Analysis of Microarray and RNA-Seq Data for the Identification of Hub Genes and Networks Involved in the Pancreatic Cancer.

Mehak Rafiq,Iqra Arshad,Sidra Adil,Zamir Hussain,Rehan Zafar Paracha,Maryum Nisar,Rumeza Hanif,Sabaoon Zeb

doi:10.3389/fgene.2021.663787

Mehak Rafiq, Iqra Arshad + Show 6 more

Open Access

https://doi.org/10.3389/fgene.2021.663787

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Abstract

Pancreatic cancer (PaCa) is the seventh most fatal malignancy, with more than 90% mortality rate within the first year of diagnosis. Its treatment can be improved the identification of specific therapeutic targets and their relevant pathways. Therefore, the objective of this study is to identify cancer specific biomarkers, therapeutic targets, and their associated pathways involved in the PaCa progression. RNA-seq and microarray datasets were obtained from public repositories such as the European Bioinformatics Institute (EBI) and Gene Expression Omnibus (GEO) databases. Differential gene expression (DE) analysis of data was performed to identify significant differentially expressed genes (DEGs) in PaCa cells in comparison to the normal cells. Gene co-expression network analysis was performed to identify the modules co-expressed genes, which are strongly associated with PaCa and as well as the identification of hub genes in the modules. The key underlaying pathways were obtained from the enrichment analysis of hub genes and studied in the context of PaCa progression. The significant pathways, hub genes, and their expression profile were validated against The Cancer Genome Atlas (TCGA) data, and key biomarkers and therapeutic targets with hub genes were determined. Important hub genes identified included ITGA1, ITGA2, ITGB1, ITGB3, MET, LAMB1, VEGFA, PTK2, and TGFβ1. Enrichment analysis characterizes the involvement of hub genes in multiple pathways. Important ones that are determined are ECM–receptor interaction and focal adhesion pathways. The interaction of overexpressed surface proteins of these pathways with extracellular molecules initiates multiple signaling cascades including stress fiber and lamellipodia formation, PI3K-Akt, MAPK, JAK/STAT, and Wnt signaling pathways. Identified biomarkers may have a strong influence on the PaCa early stage development and progression. Further, analysis of these pathways and hub genes can help in the identification of putative therapeutic targets and development of effective therapies for PaCa.

Highlights

Pancreatic cancer (PaCa) is the 11th most common and the seventh most fatal malignancy worldwide with 459,000 new cases and 432,000 deaths in the year 2018 (Bray et al, 2018)
In majority of the patients, PaCa leads to the metastasis and pancreatic exocrine insufficiency (PEI), which eventually results in the development of metabolic abnormalities (Li et al, 2004)
Differential expression analysis of microarray and RNAseq datasets generate the lists of significant differentially expressed genes (DEGs) for every data, fulfilling the selection criteria of Log2FC and p-value

Summary

Introduction

Pancreatic cancer (PaCa) is the 11th most common and the seventh most fatal malignancy worldwide with 459,000 new cases and 432,000 deaths in the year 2018 (Bray et al, 2018). This is because usually patients exhibit symptoms in advanced stages. Pancreatic adenocarcinoma known as pancreatic ductal adenocarcinoma (PDAC) develops in the ductal tissues of the pancreas exocrine gland (De La Cruz et al, 2014; Hidalgo et al, 2015) It is the most prevalent type of PaCa with more than 95% cases, very poor early diagnosis, and high mortality rate. Stage III is localized, advanced, and unresectable phase of PaCa, while stage IV is metastatic phase (De La Cruz et al, 2014)

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