Integrating OMICS-based platforms and analytical tools for diagnosis and management of pancreatic cancer: a review

• Type: Another Publication in an International Scientific Journal
• Date: 2024

Authors

Sousa, P (Author) Other; Silva, L (Author) Other; Camara, JS (Author) Other; de Pinho, PG (Author) FFUP; Perestrelo, R (Author) Other

Journal

The Journal is awaiting validation by the Administrative Services.

Title: MOLECULAR OMICS

ISSN: 2515-4184

Publisher: ROYAL SOC CHEMISTRY

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 0 Citations

Other information

Authenticus ID: P-017-RG0

DOI: 10.1039/d4mo00187g

Abstract (EN): Cancer remains the second leading cause of death worldwide, surpassed only by cardiovascular disease. From the different types of cancer, pancreatic cancer (PaC) has one of the lowest survival rates, with a survival rate of about 20% after the first year of diagnosis and about 8% after 5 years. The lack of highly sensitive and specific biomarkers, together with the absence of symptoms in the early stages, determines a late diagnosis, which is associated with a decrease in the effectiveness of medical intervention, regardless of its nature - surgery and/or chemotherapy. This review provides an updated overview of recent studies combining multi-OMICs approaches (e.g., proteomics, metabolomics) with analytical tools, highlighting the synergy between high-throughput molecular data generation and precise analytical tools such as LC-MS, GC-MS and MALDI-TOF MS. This combination significantly improves the detection, quantification and identification of biomolecules in complex biological systems and represents the latest advances in understanding PaC management and the search for effective diagnostic tools. Large-scale data analysis coupled with bioinformatics tools enables the identification of specific genetic mutations, gene expression patterns, pathways, networks, protein modifications and metabolic signatures associated with PaC pathogenesis, progression and treatment response through the integration of multi-OMICs data.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 14