Pancreatic Cancer

Pancreatic cancer, particularly pancreatic ductal adenocarcinoma (PDA), is an aggressive malignancy associated with a low 5-year survival rate. Its occult nature and the lack of non-invasive sensitive biomarkers result in diagnosis often after the tumor has advanced locally to the point of being nonresectable or metastasized to distant sites. Identification of novel molecular contributors involved in PDA onset and progression will pave the way to improved strategies for disease prevention and therapeutic targeting.

Bioinformatic and computational approaches have been utilized to screen key candidate genes for PDA  and to research their potential functional, pathway mechanisms associated with PDA progression. It may help to understand the role of associated genes in the development and progression of PDA and identify relevant molecular markers with value for early diagnosis and targeted therapy.

Data Sources

• Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/)
• The Database for Annotation, Visualization and Integrated Discovery (DAVID, https://david.ncifcrf.gov/)
• Kyoto Encyclopedia of Genes and Genomes (KEGG, http://www.kegg.jp)
• Search Tool for the Retrieval of Interacting Genes (STRING, https://string-db.org/)

Computational and Experimental Approaches

• Analyze the microarray datasets from the GEO database.
• Identify differentially expressed genes (DEGs).
• Use the DAVID database for Gene Ontology (GO) and KEGG pathway analyses.
• Construct protein-protein interaction (PPI) networks using STRING. Perform module analysis using Cytoscape.
• Use Gene Expression Profiling Interactive Analysis (GEPIA) to evaluate the differential expression of hub genes in patients with PDA.
• Verify the expression of these genes in PDA cell lines and normal pancreatic epithelial cells.

The identified DEGs and hub genes not only contribute to a better understanding of the molecular mechanisms underlying the carcinogenesis and progression of PDA but may also serve as potential new biomarkers and targets for PDA.

CD ComputaBio utilizes microarray, deep sequencing platforms, advanced biostatistical and computational analyses methods to detect biological signals in highly dimensional and often noisy genomic data. We are also interested in how the machine learning-based integration of multi-omic datasets can aid in the discovery of new cancer subgroups and biomarkers.

Moreover, CD ComputaBio has multiple resources including academic research and preclinical works in the identification of a suitable disease target and its corresponding hit. Contact us for more service details.

Reference:

1. Shi H, Xu H, Chai C, Qin Z, Zhou W. Integrated bioinformatics analysis of potential biomarkers for pancreatic cancer. J Clin Lab Anal. 2022 May;36(5):e24381.