MUC13 a potential early cancer detection biomarker of Pancreatic Cancer
Introduction
The rate of pancreatic cancer (PanCa) prevalence and mortality are increasing day by day and by 2030 it will secure the spot as second leading cause of cancer-related deaths. PanCa has a very poor prognosis as only 11% of PanCa patients have a 5-year survival rate, and as a result, the mortality rate of PanCa is almost equal to the incidence rate. Pancreatic ductal adenocarcinoma (PDAC) is a major form of PanCa (approximately 85%). The main clinical challenge with PanCa is poor treatment outcomes due the late diagnosis. While there is availability of traditional biomarkers panels, these biomarkers do not have optimal sensitivity and specificity for PanCa. Considering this alarming unmet clinic need, our team has identified a novel transmembrane glycoprotein, MUC13, as a potential biomarker of pancreatic PanCa by using integrative big data mining and transcriptomic approaches.
Methods
The current study was conducted by using big transcriptomic data analysis. Here we have utilized various bioinformatics tools like SPARKS-X and ConSurf server for the MUC13 protein structure elucidation. GTEx server was applied for the protein expression coverage, tissue specific gene expression analysis. For DEG analysis, the PDAC patients were downloaded from TCGA dataset. R packages “DEseq2 package” was used for the count data normalization and visualization. ONCOMINE and GEPIA2 were used for the CNV, pathological staging, disease-free survival plot, MUC13 isoforms and phosphorylation sites. LinkedOmics employed for functional enrichment.
Results
MUC13 doesn’t have available crystal structure in public domain, in that case we have modeled it to visualize its various domains, exposed and functional residues. Approximately 63 highly conserved, exposed and functionally active residues are identified in MUC13 through this analysis. In the PanCa condition expression level (via DEGseq2 of TCGA-PAAD data set) of both proteins was cross validated; interestingly, a better expression profile of MUC13 (∼ 3.73-fold) was observed in PanCa patients as compared to MUC1 (∼ 2.52-fold). This differential expression profile suggests better specificity of MUC13 in PanCa with respect to MUC1. The higher expression of MUC13 has also shown a lower disease-free survival in PanCa. According to isoform analysis MUC13 has total of 5 transcripts, among them, 2 transcripts (ENST00000616727.4 & ENST00000478191.1) of MUC13 are coding transcripts. Interestingly, ENST00000616727.4 transcript which is coding for the long form of MUC13 (L-MUC13 & 512 residues), considered it as tumorigenic MUC13 (tMUC13). While ENST00000478191.1 transcript encodes for the short form of the MUC13 (s-MUC13 &184 residues), which has shown less expression in tumors. The MUC13 cytoplasmic domain contains a total of 8 phosphorylation sites, among these 2 are the most important tyrosine phosphorylation sites (501 & 512). In socio-behavioral & demographic studies on MUC13, it was observed that ethnicity, age, and gender are important factors for higher expression of MUC13 in PanCa. Our analysis suggests that Afro-American and Asian PanCa patients expressed relatively higher MUC13 as compared to Caucasian. Finally, the functional enrichment analysis was performed, and it was elucidated that the higher expression of MUC13 leads to modulation of several important pathways like upregulation of chemical carcinogenesis, maturity onset diabetes of the young, pancreatic-bile secretion and several glucoses, and lipid metabolism associated pathways.
Conclusion
This investigation exhibits that MUC13 can be a new early diagnostic biomarker for PanCa, and it also has prospective to upgrade the effectiveness of the current biomarker panel. This kind of methodology will enhance the conception of the role of MUC13 in PanCa. Additionally, the big data analysis methodology is releasing a significant opportunity for the discoveries of specific and significant biomarkers not only for PanCa but also for other malignancies.