Presentation Type
Poster
Discipline Track
Biomedical Science
Abstract Type
Research/Clinical
Abstract
Background: Pancreatic cancer, characterized by its high mortality rate, stands as one of the most aggressive cancer forms. The projected surge in pancreatic cancer-related deaths, making it the second leading cause in the United States by 2030, underscores the urgency for effective early screening tools. This study employs data mining methods to scrutinize bioinformatic data surrounding TRIP13. Examining differential expression across various cancers, correlating TRIP13 expression with pancreatic cancer stages, exploring associations with common cancer genes, and analyzing overall survival rates constitute the core investigations. Integrated with molecular biology techniques, the study further quantifies TRIP13 expression in progressive pancreatic cancer cell lines and human pancreatic tissues. The research unveils TRIP13's role at both transcriptional and translational levels, suggesting its potential as a specific biomarker for early pancreatic cancer detection, with implications for patient prognosis and targeted therapies in clinical settings.
Methods: Utilizing extensive transcriptomic data analysis, the study employs bioinformatics tools such as ConSurf, GTEx, GEPIA2, and LinkedOmics. Molecular biology techniques including qPCR, western blotting, and IHC are applied to validate and integrate bioinformatics findings.
Result: ConSurf analysis identifies highly conserved amino acids within the AAA+ ATPase domain of TRIP13. Increased TRIP13 expression correlates with lower disease-free survival in pancreatic cancer, displaying positive associations with CEACAM5 and S1004A. Isoform analysis reveals seven TRIP13 transcripts, with two coding transcripts. Multiple phosphorylation sites further characterize TRIP13. mRNA expression analysis in disease-free conditions indicates minimal TRIP13 expression, notably higher in pancreatic cancer than in normal tissues. Molecular biology techniques confirm elevated TRIP13 expression in moderately differentiated cell lines and tumor grades. Functional enrichment analysis links higher TRIP13 expression to modulation of crucial pathways such as DNA repair, cellular senescence, and viral carcinogenesis.
Conclusion: This study positions TRIP13 as a potential early diagnostic biomarker for pancreatic cancer, offering prospects for enhancing current biomarker panels. The integrated biology approach holds promise for identifying specific biomarkers not only for pancreatic cancer but also for other malignancies.
Academic/Professional Position
Post-doc
Mentor/PI Department
Immunology and Microbiology
Recommended Citation
Dhasmana, Swati; Dhasmana, Anupam; Rios, Stella; Enriquez-Perez, Iris A.; Khan, Sheema; Afaq, Farrukh; Manne, Upender; Yallapu, Murali M.; and Chauhan, Subhash, "TRIP13’s crucial role in pancreatic cancer progression" (2024). Research Symposium. 67.
https://scholarworks.utrgv.edu/somrs/2024/posters/67
Included in
Bioinformatics Commons, Cancer Biology Commons, Integrative Biology Commons, Molecular Biology Commons
TRIP13’s crucial role in pancreatic cancer progression
Background: Pancreatic cancer, characterized by its high mortality rate, stands as one of the most aggressive cancer forms. The projected surge in pancreatic cancer-related deaths, making it the second leading cause in the United States by 2030, underscores the urgency for effective early screening tools. This study employs data mining methods to scrutinize bioinformatic data surrounding TRIP13. Examining differential expression across various cancers, correlating TRIP13 expression with pancreatic cancer stages, exploring associations with common cancer genes, and analyzing overall survival rates constitute the core investigations. Integrated with molecular biology techniques, the study further quantifies TRIP13 expression in progressive pancreatic cancer cell lines and human pancreatic tissues. The research unveils TRIP13's role at both transcriptional and translational levels, suggesting its potential as a specific biomarker for early pancreatic cancer detection, with implications for patient prognosis and targeted therapies in clinical settings.
Methods: Utilizing extensive transcriptomic data analysis, the study employs bioinformatics tools such as ConSurf, GTEx, GEPIA2, and LinkedOmics. Molecular biology techniques including qPCR, western blotting, and IHC are applied to validate and integrate bioinformatics findings.
Result: ConSurf analysis identifies highly conserved amino acids within the AAA+ ATPase domain of TRIP13. Increased TRIP13 expression correlates with lower disease-free survival in pancreatic cancer, displaying positive associations with CEACAM5 and S1004A. Isoform analysis reveals seven TRIP13 transcripts, with two coding transcripts. Multiple phosphorylation sites further characterize TRIP13. mRNA expression analysis in disease-free conditions indicates minimal TRIP13 expression, notably higher in pancreatic cancer than in normal tissues. Molecular biology techniques confirm elevated TRIP13 expression in moderately differentiated cell lines and tumor grades. Functional enrichment analysis links higher TRIP13 expression to modulation of crucial pathways such as DNA repair, cellular senescence, and viral carcinogenesis.
Conclusion: This study positions TRIP13 as a potential early diagnostic biomarker for pancreatic cancer, offering prospects for enhancing current biomarker panels. The integrated biology approach holds promise for identifying specific biomarkers not only for pancreatic cancer but also for other malignancies.