Abstract
BackgroundPancreatic cancer is characterized by its unsatisfying early detection rate, rapid disease progression and poor prognosis. Further studies on molecular mechanism and novel predictive biomarkers for pancreatic cancer based on a large sample volume are required.MethodsMultiple bioinformatic analysis tools were utilized for identification and characterization of differentially expressed genes (DEGs) from a merged microarray data (100 pancreatic cancer samples and 62 normal samples). Data from the GEO and TCGA database was utilized to validate the diagnostic and prognostic value of the top 5 upregulated/downregulated DEGs. Immunohistochemical assay (46 paired pancreatic and para- cancerous samples) was utilized to validate the expression and prognostic value of COL11A1, GJB2 and CTRL from the identified DEGs.ResultsA total number of 300 DEGs were identified from the merged microarray data of 100 pancreatic cancer samples and 62 normal samples. These DEGs were closely correlated with the biological characteristics of pancreatic cancer. The top 5 upregulated/downregulated DEGs showed good individual diagnostic/prognostic value and better combined diagnostic/prognostic value. Validation of COL11A1, GJB2 and CTRL with immunohistochemical assay showed consistent expression level with bioinformatics analysis and promising prognostic value.ConclusionsMerged microarray data with bigger sample volume could reflect the biological characteristics of pancreatic cancer more effectively and accurately. COL11A1, GJB2 and CTRL are novel predictive biomarkers for pancreatic cancer.
Highlights
IntroductionFurther studies on molecular mechanism and novel predictive biomarkers for pancreatic cancer based on a large sample volume are required
Pancreatic cancer is characterized by its unsatisfying early detection rate, rapid disease progression and poor prognosis
Identification of differentially expressed genes from merged microarray dataset Three qualified Gene Expression Omnibus (GEO) datasets were merged into one dataset which contains microarray data of 100 pancreatic cancer tissue samples and 62 normal pancreas tissue samples
Summary
Further studies on molecular mechanism and novel predictive biomarkers for pancreatic cancer based on a large sample volume are required. Pancreatic cancer is characterized by its unsatisfying early detection rate, rapid disease progression and poor prognosis [1, 2]. Multiple aberrantly expressed genes and dysregulated signaling pathways have been reported to play critical roles in the development of pancreatic cancer [3,4,5,6,7]. The underlying molecular mechanism of pancreatic cancer is still not fully understood. Sun et al Cancer Cell Int (2018) 18:174 and biased. An integrated analysis of the exisiting whole genome microarray data is a more practical and cost effective way to overcome the aforementioned shortcomings [15,16,17]
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