Integrated Analysis of Multi-Omics Data to Identify Prognostic Genes for Pancreatic Cancer

Evaluation of tumor targets selected from public genomic databases for imaging of pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive malignancy with a 5-year survival rate of approximately 5-7%, and complete surgical resection remains the only curative treatment but is often unfeasible. Fluorescence-guided surgery (FGS) using tumor-targeted probes may improve tumor visualization and facilitate complete resection. This study aimed to identify and validate tumor targets for FGS during PDAC resection procedures. RNA expression data from over 4000 cell surface genes, obtained from public genomic databases, were analyzed to identify genes encoding PDAC-associated proteins. Eleven potential tumor targets were identified, including CEACAM5, TMPRSS4, COL17A1, CLDN18, and AQP5. Protein expression was evaluated by immunohistochemistry (IHC) in tissues from 44 PDAC and 7 chronic pancreatitis (CP) patients. All targets, except COL17A1, showed significantly higher expression in PDAC tissue compared to healthy pancreatic, CP, and duodenal tissue (p < 0.001), as well as in tumor-positive versus tumor-negative lymph nodes. Especially CEACAM5, TMPRSS4, and AQP5 were identified as the most promising targets for distinguishing PDAC from healthy tissues and detecting lymph node metastasis during FGS. The development of probes targeting multiple markers, such as AQP5 with CEACAM5 and/or TMPRSS4, may help overcome interpatient variability and enhance detection across patients.

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

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.

The Role of Krüppel-like Factors in Pancreatic Physiology and Pathophysiology

Krüppel-like factors (KLFs) belong to the family of transcription factors with three highly conserved zinc finger domains in the C-terminus. They regulate homeostasis, development, and disease progression in many tissues. It has been shown that KLFs play an essential role in the endocrine and exocrine compartments of the pancreas. They are necessary to maintain glucose homeostasis and have been implicated in the development of diabetes. Furthermore, they can be a vital tool in enabling pancreas regeneration and disease modeling. Finally, the KLF family contains proteins that act as tumor suppressors and oncogenes. A subset of members has a biphasic function, being upregulated in the early stages of oncogenesis and stimulating its progression and downregulated in the late stages to allow for tumor dissemination. Here, we describe KLFs' function in pancreatic physiology and pathophysiology.

Multi-Omics Integrative Approach of Extracellular Vesicles: A Future Challenging Milestone

In the era of multi-omic sciences, dogma on singular cause-effect in physio-pathological processes is overcome and system biology approaches have been providing new perspectives to see through. In this context, extracellular vesicles (EVs) are offering a new level of complexity, given their role in cellular communication and their activity as mediators of specific signals to target cells or tissues. Indeed, their heterogeneity in terms of content, function, origin and potentiality contribute to the cross-interaction of almost every molecular process occurring in a complex system. Such features make EVs proper biological systems being, therefore, optimal targets of omic sciences. Currently, most studies focus on dissecting EVs content in order to either characterize it or to explore its role in various pathogenic processes at transcriptomic, proteomic, metabolomic, lipidomic and genomic levels. Despite valuable results being provided by individual omic studies, the categorization of EVs biological data might represent a limit to be overcome. For this reason, a multi-omic integrative approach might contribute to explore EVs function, their tissue-specific origin and their potentiality. This review summarizes the state-of-the-art of EVs omic studies, addressing recent research on the integration of EVs multi-level biological data and challenging developments in EVs origin.