School of Medicine Publications and Presentations

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  • Integrative computational biology techniques are holistic approaches in cancer discovery by exploring multiple levels of convolution in biological systems.

  • Pancreatic cancer is expected that by 2030, it will become the 2nd leading cause of cancer related deaths in the United States alone.•

  • For the first time, the role and patholgenic events of TRIP13 in PanCa have been discussed.

  • This study elucidates the role of TRIP13 on molecular level, as a specific signal for early events of pancreatic cancer, enhancing patient prognosis, and boosting targeted therapies in clinical settings.

  • This study has potential to enrich the existing biomarker panel for the early detection of pancreatic cancer.


Pancreatic cancer (PanCa) is one of the most aggressive forms of cancer and its incidence rate is continuously increasing every year. It is expected that by 2030, PanCa will become the 2nd leading cause of cancer-related deaths in the United States due to the lack of early diagnosis and extremely poor survival. Despite great advancements in biomedical research, there are very limited early diagnostic modalities available for the early detection of PanCa. Thus, understanding of disease biology and identification of newer diagnostic and therapeutic modalities are high priority. Herein, we have utilized high dimensional omics data along with some wet laboratory experiments to decipher the expression level of hormone receptor interactor 13 (TRIP13) in various pathological staging including functional enrichment analysis. The functional enrichment analyses specifically suggest that TRIP13 and its related oncogenic network genes are involved in very important patho-physiological pathways. These analyses are supported by qPCR, immunoblotting and IHC analysis. Based on our study we proposed TRIP13 as a novel molecular target for PanCa diagnosis and therapeutic interventions. Overall, we have demonstrated a crucial role of TRIP13 in pathogenic events and progression of PanCa through applied integrated computational biology approaches.


Under a Creative Commons license

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Publication Title

Computational and Structural Biotechnology Journal



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Mentor/PI Department

Immunology and Microbiology



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