DAMPs and PAMPs in the Perioperative Period: Danger Signaling, Immune Dysfunction, and Oncologic Implications
Hector Katifelis1,2, Theofania Lappa2, Sofia Poulopoulou1
1Department of Anaesthesiology, General Oncology Hospital of Athens "Saint Savvas" 171 Alexandras Ave., 11522 Athens, Greece.
Abstract:
Background and Objectives: The perioperative period is characterized by marked biological stress responses that extend beyond direct tissue injury. Innate immune activation during surgery is largely driven by molecular danger signals. Surgical trauma, ischemia-reperfusion injury, blood transfusions, mechanical ventilation, and perioperative infections are critical events that result in the release of danger signals: damage-associated molecular patterns (DAMPs) and pathogen-associated molecular patterns (PAMPs). This narrative review examines perioperative DAMP and PAMP sources, their molecular recognition pathways, and their clinical and oncological significance. Materials and Methods: A literature search was conducted across PubMed/MEDLINE, Scopus, and Web of Science databases for English-language articles published up to July 2026. Search terms included DAMPs, PAMPs, perioperative, surgical stress, cancer surgery, innate immunity, PRRs, TLRs, inflammasome, HMGB1, mtDNA, perioperative immunosuppression, and anesthesia. Results: Danger signals interact with pattern-recognition receptors, including Toll-like receptors and inflammasome pathways, driving sterile inflammation, immune dysregulation, and postoperative organ injury. Rather than initiating these cascades, anesthetics and opioids act as modulators. In cancer surgery, heightened danger signaling and temporary immunosuppression may compromise host defenses during a vulnerable window. Conclusions: Circulating DAMPs and potentially selected PAMP-related markers warrant further investigation as biomarkers for perioperative risk stratification. Overcoming translational barriers will require standardized assays, validation of biomarker signatures, and integration of artificial intelligence-driven molecular profiling to advance personalized onco-anesthesia strategies.
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