Current landscape and emerging trends of PD-1/PD-L1 research in prostate cancer: A data-driven atlas from multidatabase integration.
Although programmed cell death protein 1/programmed death ligand 1 (PD-1/PD-L1) blockade has transformed treatment in several malignancies, its role in prostate cancer remains uncertain. Interest in combination therapy, tumor-microenvironment modulation, and biomarker-guided selection highlights the need to clarify research trends. To characterize the knowledge structure, hotspots, thematic evolution, and clinical-trial evidence on PD-1/PD-L1 in prostate cancer. Publications were retrieved from the Web of Science Core Collection and Scopus. CiteSpace, VOSviewer, R, and Python were used to analyze publication trends, collaboration networks, thematic evolution, and knowledge flow. Clinical-trial reports identified in PubMed were screened and summarized. A total of 2,641 publications were included, comprising 1,421 original articles and 1,220 reviews, involving 17,180 authors and 4,658 keywords. Annual publications increased after 2014 and reached a peak in 2022. Among the candidate models, the logistic model performed best (AIC = 116.4), with the fitted trajectory suggesting a possible slowing. The United States and China were the leading contributors, whereas Canada, Germany, and the United Kingdom showed higher proportions of multinational collaboration. Research increasingly emphasized mechanism-driven and precision-oriented strategies, while exploratory and pan-cancer studies remain part of its knowledge base. The 13 included clinical trials covered diverse combination, but evidence was heterogeneous and no standardized treatment paradigm has yet been established. This multidatabase delineates the knowledge structure, evolution, and emerging directions of PD-1/PD-L1 research in prostate cancer. Current evidence indicates limited activity in unselected populations, whereas potential benefit may be concentrated in biomarker-defined subgroups and selected combinations requiring prospective validation.
Authors
Zhou Zhou, Ju Ju, Wang Wang, Zhu Zhu, Yu Yu, Wu Wu, Fu Fu, Wu Wu, Song Song, Zhang Zhang
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