Abstract
BACKGROUND: Immunotherapy has revolutionized the therapeutic paradigm for lung cancer, yet its clinical efficacy exhibits significant interpatient heterogeneity. The implementation of personalized immunotherapy critically depends on the precise identification of predictive biomarkers. However, this field is currently characterized by rapid expansion of research outputs alongside marked fragmentation of knowledge domains, while a systematic evaluation of scientific advancements and emerging frontiers remains conspicuously absent. This study aims to synthesize global research achievements to methodologically delineate the evolutionary trajectory of immunotherapy biomarkers in lung cancer, identify research hotspots, and forecast developmental trends. METHODS: A bibliometric analysis was conducted on 6,180 publications (2001-2025) from the Web of Science Core Collection. Advanced tools (Bibliometrix, VOSviewer, CiteSpace) were employed to analyze publication trends, country/institutional contributions, journal influence, author networks, keyword evolution, and citation dynamics. RESULTS: The field progressed through incubation (2003-2014), rapid expansion (2015-2022), and maturation (2023-2025) phases, with a 28.87% annual growth rate. China led in productivity (9,394 publications), while the U.S. dominated academic impact (93,888 citations). Harvard University Harvard University emerged as the predominant contributor (532 publications). WANG Y was ranked first in the top 10 most prolific authors (97 publications) while RAMALINGAM SS (5,033 citations) was the most cited authors. CANCERS (319 papers; Q1) was the most published journal, while JOURNAL FOR IMMUNOTHERAPY OF CANCER (8,511 citations; Q1) was the most cited journal. Emerging frontiers encompassed genomics, gut microbiome, soluble PD-L1, and immune infiltration patterns, driven by multi-omics integration and artificial intelligence. CONCLUSION: This study represents the first bibliometric analysis of biomarkers in lung cancer immunotherapy. Through scientometric visualization tools, our analysis delineates a paradigm shift from reductionist biomarker discovery to multidimensional integration and translational validation. Prospective advancements necessitate leveraging technological innovations, fostering international collaborative networks, and promoting multi-omics convergence. These efforts aim to facilitate a critical transition from population-based therapeutic strategies to precision-driven stratification, ultimately optimizing clinical outcomes and survival benefits for heterogeneous patient populations.