Mapping the landscape of predictive biomarkers for immune checkpoint inhibitors a bibliometric analysis.

This large-scale bibliometric analysis maps the global research landscape of predictive biomarkers for immune checkpoint inhibitors (ICIs) from 2011 to 2025. Leveraging 9,075 publications from the Web of Science Core Collection, we used co-citation, co-authorship, and keyword co-occurrence analyses to quantify publication dynamics, collaborative networks, and conceptual evolution. China produced the most publications (1,923, a country-level count reflecting multi-national co-authorship), while the United States led in influence as reflected by high-impact institutions (e.g., MD Anderson Cancer Center) and prolific authors (e.g., Kurzrock R, H-index 116). The strongest international collaboration was between the USA and China (276 co-authored publications). Thematic evolution revealed a paradigm shift from reliance on single biomarkers (e.g., PD-L1, tumor mutational burden [TMB]) toward integrated multi-omics signatures that incorporate tumor microenvironment features and advanced computational approaches. Keyword analysis highlighted artificial intelligence (n = 640), radiomics, and liquid biopsy as emerging frontiers. Notably, gastroesophageal junction cancers exhibited the strongest citation burst (strength = 11.53), highlighting unresolved tumor-specific controversies such as the predictive validity of PD-L1 in this setting. However, significant translational barriers remain: lack of biomarker assay standardization, high analytical variability (e.g., differing PD-L1 immunohistochemistry clones and inconsistent TMB cutoff thresholds), and insufficient clinical validation. This study provides an evidence-based overview to guide future research toward multi-omics integration, prospective validation, and cross-disciplinary collaboration, thereby advancing precision immuno-oncology.