Proteomic meta-analysis unveils new frontiers for biomarkers research in pancreatic carcinoma.

Pancreatic carcinoma (PC) is the sixth leading cause of cancer death in both sexes in 2022, responsible for almost 5% of all cancer deaths worldwide; it is characterized by a poor prognosis since most patients present with an unresectable and metastatic tumor. To date, the decreasing trend in mortality rates related to the most common cancers has contributed to making pancreatic cancer a serious public health problem. In the last few years, scientific research has led to many advances in diagnostic approaches, perioperative management, radiotherapy techniques, and systemic therapies for advanced disease, but only with modest incremental progress in PC patient outcomes. Most of the causes of this high mortality are, unfortunately, late diagnosis and an important therapeutic resistance; for this reason, the most recent high-throughput proteomics technologies focus on the identification of novel biomarkers and molecular profiling to generate new insights in the study of PC, to improve diagnosis and prognosis and to monitor the therapies progress. In this work, we present and discuss the integration of results from different revised studies on protein biomarkers in a global proteomic meta-analysis to understand which path to pursue scientific research. In particular, cancer signaling, inflammatory response, and cell migration and signaling have emerged as the main pathways described in PC, as well as scavenging of free radicals and metabolic alteration concurrently highlighted new research insights on this disease. Interestingly, from the study of upstream regulators, some were found to be shared by collecting data relating to both biological fluid and tissue biomarkers, side by side: specifically, TNF, LPS, p38-MAPK, AGT, miR-323-5p, and miR-34a-5p. By integrating many biological components with their interactions and environmental relationships, it's possible to achieve an in-depth description of the pathological condition in PC and define correlations between concomitant symptoms and tumor genesis and progression. In conclusion, our work may represent a strategy to combine the results from different studies on various biological samples in a more comprehensive way.