Presentation Type
Poster
Discipline Track
Biomedical ENGR/Technology/Computation
Abstract Type
Research/Clinical
Abstract
Background: With the rise in pancreatic cancer (PanCa) prevalence and mortality rate, by 2030 it will secure second position among leading causes of cancer-related deaths. Due to poor prognosis of PanCa only 11% of PanCa patients have a 5-year survival rate, resulting in an equal mortality rate and incidence rate. 85% of PanCa are Pancreatic ductal adenocarcinoma (PDAC). The main clinical challenge with PanCa is poor treatment outcomes due the late diagnosis. Currently, there are traditional biomarkers panels available for diagnosis, however, 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 PanCa by using integrative big data mining and transcriptomic approaches.
Methods: The current study used big transcriptomic data analysis. MUC13 structure was elucidated using SPARKS-X and ConSurf server followed by GTEx server to analyze protein expression coverage & tissue specific gene expression. PDAC patient’s gene data was downloaded from TCGA dataset for DEG analysis and R packages “DEseq2 package” was used for the count data normalization and visualization. Furthermore, ONCOMINE and GEPIA2 were used for analyzing and predicting CNV, pathological staging, disease-free survival plot, MUC13 isoforms and phosphorylation sites. Lastly, LinkedOmics was employed for exploring the genes that exhibited disparity in association with MUC13 in Pancreatic Cancer.
Results: We have modeled the structure of MUC13 to visualize its various domains, exposed and functional residues, as its crystal structure is unavailable in public domain. Interestingly, we identified approximately 63 highly conserved, exposed and functionally active residues. It was observed via DEGseq2 of TCGA-PAAD data set that MUC13 had a better expression profile (∼ 3.73-fold) as compared to MUC1 (∼ 2.52-fold) in PanCa condition which suggests better specificity of MUC13 over MUC1. The higher expression of MUC13 correlated to a lower diseasefree survival in PanCa. Isoform analysis suggested that MUC13 has 5 transcripts, among which only 2 transcripts (ENST00000616727.4 & ENST00000478191.1) of MUC13 are coding. Interestingly, ENST00000616727.4 transcript which encodes for long form of MUC13 (L-MUC13 & 512 residues), is tumorigenic (tMUC13). While ENST00000478191.1 transcript encodes for the short form of the MUC13 (s-MUC13 &184 residues) and has shown less expression in tumors. Socio-behavioral & demographic studies on MUC13 show that ethnicity, age, and gender are important factors for higher expression of MUC13 in PanCa. Our analysis suggests that AfroAmerican and Asian PanCa patients express relatively higher MUC13 as compared to Caucasian. The higher expression of MUC13 leads to modulation of several important pathways like chemical carcinogenesis, maturity onset diabetes of the young, pancreatic-bile secretion and glucose and lipid metabolism.
Conclusion: This investigation sheds light on MUC13 as a potential 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.
Academic/Professional Position
Staff
Academic/Professional Position (Other)
Assistant Research Scientist
Mentor/PI Department
Immunology and Microbiology
Recommended Citation
Dhasmana, Anupam; Dhasmana, Swati; Khan, Sheema; Jaggi, Meena; Yallapu, Murali M.; and Chauhan, Subhash C., "Update on the Role of MUC13 in Pancreatic Cancer: A Promising Early Detection Biomarker" (2024). Research Symposium. 71.
https://scholarworks.utrgv.edu/somrs/2024/posters/71
Included in
Update on the Role of MUC13 in Pancreatic Cancer: A Promising Early Detection Biomarker
Background: With the rise in pancreatic cancer (PanCa) prevalence and mortality rate, by 2030 it will secure second position among leading causes of cancer-related deaths. Due to poor prognosis of PanCa only 11% of PanCa patients have a 5-year survival rate, resulting in an equal mortality rate and incidence rate. 85% of PanCa are Pancreatic ductal adenocarcinoma (PDAC). The main clinical challenge with PanCa is poor treatment outcomes due the late diagnosis. Currently, there are traditional biomarkers panels available for diagnosis, however, 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 PanCa by using integrative big data mining and transcriptomic approaches.
Methods: The current study used big transcriptomic data analysis. MUC13 structure was elucidated using SPARKS-X and ConSurf server followed by GTEx server to analyze protein expression coverage & tissue specific gene expression. PDAC patient’s gene data was downloaded from TCGA dataset for DEG analysis and R packages “DEseq2 package” was used for the count data normalization and visualization. Furthermore, ONCOMINE and GEPIA2 were used for analyzing and predicting CNV, pathological staging, disease-free survival plot, MUC13 isoforms and phosphorylation sites. Lastly, LinkedOmics was employed for exploring the genes that exhibited disparity in association with MUC13 in Pancreatic Cancer.
Results: We have modeled the structure of MUC13 to visualize its various domains, exposed and functional residues, as its crystal structure is unavailable in public domain. Interestingly, we identified approximately 63 highly conserved, exposed and functionally active residues. It was observed via DEGseq2 of TCGA-PAAD data set that MUC13 had a better expression profile (∼ 3.73-fold) as compared to MUC1 (∼ 2.52-fold) in PanCa condition which suggests better specificity of MUC13 over MUC1. The higher expression of MUC13 correlated to a lower diseasefree survival in PanCa. Isoform analysis suggested that MUC13 has 5 transcripts, among which only 2 transcripts (ENST00000616727.4 & ENST00000478191.1) of MUC13 are coding. Interestingly, ENST00000616727.4 transcript which encodes for long form of MUC13 (L-MUC13 & 512 residues), is tumorigenic (tMUC13). While ENST00000478191.1 transcript encodes for the short form of the MUC13 (s-MUC13 &184 residues) and has shown less expression in tumors. Socio-behavioral & demographic studies on MUC13 show that ethnicity, age, and gender are important factors for higher expression of MUC13 in PanCa. Our analysis suggests that AfroAmerican and Asian PanCa patients express relatively higher MUC13 as compared to Caucasian. The higher expression of MUC13 leads to modulation of several important pathways like chemical carcinogenesis, maturity onset diabetes of the young, pancreatic-bile secretion and glucose and lipid metabolism.
Conclusion: This investigation sheds light on MUC13 as a potential 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.