ArticleFrontiers in genetics2021
Integrated Analysis of Microarray and RNA-Seq Data for the Identification of Hub Genes and Networks Involved in the Pancreatic Cancer.
Article in Frontiers in genetics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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16 citing papers in PubMed, 21 citations in OpenAlex.
- Comparative transcriptomic analysis identifies conserved abiotic stress-responsive hub genes in Sorghum bicolor.BMC plant biology · 2026Article
- Integrative multi-transcriptomic analysis uncovers core genes and potential defense mechanisms in rice-Magnoporthe oryzae interaction.Plant cell reports · 2025Article
- Weighted gene co-expression network analysis reveals key stromal prognostic markers in pancreatic cancer.Scientific reports · 2024Article
- Deciphering early responsive signature genes in rice blast disease: an integrated temporal transcriptomic study.Journal of applied genetics · 2024Article
- Network Analysis of Brain and Bone Tissue Transcripts Reveals Shared Molecular Mechanisms Underlying Alzheimer's Disease and Related Dementias and Osteoporosis.The journals of gerontology. Series A, Biological sciences and medical sciences · 2024Article
- Insights into the Gene Expression Profile of Classical Hodgkin Lymphoma: A Study towards Discovery of Novel Therapeutic Targets.Molecules (Basel, Switzerland) · 2024Article
- A novel feature selection algorithm for identifying hub genes in lung cancer.Scientific reports · 2023Article
- Isoform switching leads to downregulation of cytokine producing genes in estrogen receptor positive breast cancer.Frontiers in genetics · 2023Article
- A self-training subspace clustering algorithm based on adaptive confidence for gene expression data.Frontiers in genetics · 2023Article
- GeneCompete: an integrative tool of a novel union algorithm with various ranking techniques for multiple gene expression data.PeerJ. Computer science · 2023Article
- Article
- HGF/c-Met pathway facilitates the perineural invasion of pancreatic cancer by activating the mTOR/NGF axis.Cell death & disease · 2022Article
- Transplantation of umbilical cord-derived mesenchymal stem cells promotes the recovery of thin endometrium in rats.Scientific reports · 2022Article
- Identification of potential core genes at single-cell level contributing to pathogenesis of pancreatic ductal adenocarcinoma through bioinformatics analysis.Cancer biomarkers : section A of Disease markers · 2022Article
- Interaction Analysis of Adenovirus L5 Protein With Pancreatic Cancer Cell Surface Receptor to Analyze Its Affinity for Oncolytic Virus Therapy.Frontiers in oncology · 2022Article
- An Extensive Review on Preclinical and Clinical Trials of Oncolytic Viruses Therapy for Pancreatic Cancer.Frontiers in oncology · 2022Review
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.
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