Pattern recognition receptors (PRRs) play a crucial role in immune responses, acting as primary sensors for microbial and host-derived signals. PRRs, which include Toll-like receptors (TLRs), retinoic acid-inducible gene 1-like receptors, nucleotide-binding oligomerization domain-like receptors, C-type lectin receptors, and various cytoplasmic DNA sensors, are essential for initiating immune responses that regulate both inflammation and tumor immunity. Recent studies have highlighted their dual roles in cancer, where they can either suppress or promote tumor progression by influencing the tumor microenvironment and modulating responses to immunotherapy. In the context of cancer, PRRs not only activate immune cells but also contribute to immune evasion mechanisms within tumors. Therapeutically, targeting PRRs represents a promising approach for cancer treatment, with related drugs showing potential to enhance the efficacy of existing immunotherapies. Numerous PRR-based agents, particularly TLR agonists, are currently under clinical investigation for their ability to augment antitumor immunity and overcome resistance to immune checkpoint inhibitors. This review examines the molecular mechanisms by which PRRs influence cancer, with a focus on recent advancements in PRR-targeted therapies and their integration with contemporary immunotherapeutic strategies.

BACKGROUND: Immune checkpoint inhibitors (ICIs) therapy lacks viable biomarkers for response and prognosis prediction. This study aimed to investigate the correlation of peripheral blood laboratory test results combined with lymphocyte subset ratios to the response and prognosis of immunotherapy in advanced lung cancer. METHODS: Advanced lung cancer patients admitted to West China Hospital, Sichuan University from May 2021 to July 2023 were prospectively enrolled in this study. Clinical data and peripheral blood were collected before and after treatment and lymphocyte subset ratios were analyzed by flow cytometry. Logistic regression was used to identify factors correlated to ICIs treatment efficacy. Cox modeling was applied to explore the prognostic factors. RESULTS: Logistic regression showed that the baseline level of transcription factor T cell factor 1 (TCF1)+CD8+ T cell ratio and peripheral white blood cell (WBC) count, lymphocyte percentage, cytokeratin 19 fragment (CYFRA21-1) after 1 cycle of ICIs treatment were the potential predictors for ICIs response (P<0.05). Cox regression analysis showed that the baseline level of TCF1+CD8+ T cell ratio (P=0.020) and peripheral WBC count after 1 cycle of ICIs treatment (P<0.001) were prognostic factors. CONCLUSIONS Patients with high baseline TCF1+CD8+ T cell ratio combined with low WBC counts and low CYFRA21-1 level after 1 cycle of ICIs treatment are more likely to benefit from ICIs therapy.
Humans ; Lung Neoplasms/drug therapy* ; Carcinoma, Non-Small-Cell Lung/drug therapy* ; T Cell Transcription Factor 1/genetics* ; Prognosis ; CD8-Positive T-Lymphocytes ; Immunotherapy