Abstract
Big data analysis holds a considerable influence on several aspects of biomedical health science. It permits healthcare providers to gain insights from large and complex datasets, leading to improvements in the understanding, diagnosis, medication, and restraint of pathological conditions including cancer. The incidences of pancreatic cancer (PanCa) are sharply rising, and it will become the second leading cause of cancer related deaths by 2030. Various traditional biomarkers are currently in use but are not optimal in sensitivity and specificity. Herein, we determine the role of a new transmembrane glycoprotein, MUC13, as a potential biomarker of pancreatic ductal adenocarcinoma (PDAC) by using integrative big data mining and transcriptomic approaches. This study is helpful to identify and appropriately segment the data related to MUC13, which are scattered in various data sets. The assembling of the meaningful data, representation strategy was used to investigate the MUC13 associated information for the better understanding regarding its structural, expression profiling, genomic variants, phosphorylation motifs, and functional enrichment pathways. For further in-depth investigation, we have adopted several popular transcriptomic methods like DEGseq2, coding and non-coding transcript, single cell seq analysis, and functional enrichment analysis. All these analyzes suggest the presence of three nonsense MUC13 genomic transcripts, two protein transcripts, short MUC13 (s-MUC13, non-tumorigenic or ntMUC13), and long MUC13 (L-MUC13, tumorigenic or tMUC13), several important phosphorylation sites in tMUC13. Altogether, this data confirms that importance of tMUC13 as a potential biomarker, therapeutic target of PanCa, and its significance in pancreatic pathobiology.