4.7 Review

Current State of Omics Biomarkers in Pancreatic Cancer

Journal

JOURNAL OF PERSONALIZED MEDICINE
Volume 11, Issue 2, Pages -

Publisher

MDPI
DOI: 10.3390/jpm11020127

Keywords

pancreatic cancer; systems biology; omics; biomarker; genomics; transcriptomics; proteomics; metabolomics; glycomics; metagenomics; personalized medicine

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Pancreatic cancer poses challenges in early diagnosis and personalized treatment, and the use of multi-omics approaches shows promise in discovering new biomarkers for this disease.
Pancreatic cancer is one of the most fatal malignancies and the seventh leading cause of cancer-related deaths related to late diagnosis, poor survival rates, and high incidence of metastasis. Unfortunately, pancreatic cancer is predicted to become the third leading cause of cancer deaths in the future. Therefore, diagnosis at the early stages of pancreatic cancer for initial diagnosis or postoperative recurrence is a great challenge, as well as predicting prognosis precisely in the context of biomarker discovery. From the personalized medicine perspective, the lack of molecular biomarkers for patient selection confines tailored therapy options, including selecting drugs and their doses or even diet. Currently, there is no standardized pancreatic cancer screening strategy using molecular biomarkers, but CA19-9 is the most well known marker for the detection of pancreatic cancer. In contrast, recent innovations in high-throughput techniques have enabled the discovery of specific biomarkers of cancers using genomics, transcriptomics, proteomics, metabolomics, glycomics, and metagenomics. Panels combining CA19-9 with other novel biomarkers from different omics levels might represent an ideal strategy for the early detection of pancreatic cancer. The systems biology approach may shed a light on biomarker identification of pancreatic cancer by integrating multi-omics approaches. In this review, we provide background information on the current state of pancreatic cancer biomarkers from multi-omics stages. Furthermore, we conclude this review on how multi-omics data may reveal new biomarkers to be used for personalized medicine in the future.

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