Immune escape mechanisms critically restrict the efficacy of immunotherapy in lung cancer (LC), with only 20%-30% of patients responding to checkpoint inhibitors. To systematically map global trends and identify emerging hotspots, this study analyzed 2813 publications related to immune escape in LC immunotherapy from 1995 to 2025 (data retrieved August 9, 2025; analysis of annual trends uses complete year data through 2024) sourced from the Web of Science Core Collection database. Bibliometric visualization was performed using VOSviewer (version 1.6.19), CiteSpace (version 6.2.R3), and the biblioshiny R package. The dataset encompassed 75 countries, 3421 institutions, and 18,023 researchers across 693 journals. Publication trends delineated three distinct phases: an initial phase (1995-2010), an acceleration phase (2011-2018), and a maturation phase (2019-2024). China dominates in publication volume (1,382 papers) and total citations (45,657), whereas the University of Texas MD Anderson Cancer Center emerged as the most influential institution (n = 87). The Frontiers in Immunology recorded the highest number of publications (n = 155), while Cancer Research received the most co-citations (6,149). Li Zhang ranks as the most prolific author with 19 publications and 1251 citations, while R.S. Herbst holds the highest number of co-citations (n = 628). Current research clusters focus primarily on "therapeutic strategies and treatment integration," "tumor microenvironment characterization and immune cell dynamics," and "translational biomarkers and precision immunotherapy." Future research emphasizes "spatial microenvironment architecture," "computational predictive modeling," "alternative cell death pathways," "precision biomarker development," "hypoxia-immune interactions," and "stemness-EMT immune interfaces." Overall, the field's exponential growth and geographic disparities provide a structural foundation for developing strategies to overcome immune escape mechanisms by integrating spatial microenvironment characterization with advanced computational modeling.
Li et al. (Mon,) studied this question.