高齢と前立腺がんにおける免疫機能：慢性炎症に焦点を当てた文献学的洞察

Prostate cancer (PCa) is a leading malignancy among men worldwide, with its progression strongly influenced by aging-associated changes in immune function and chronic inflammation. Chronic inflammation acts as a critical mechanism linking aging and immune dysfunction to tumor initiation, progression, and resistance to therapy. This bibliometric analysis aims to comprehensively evaluate the research landscape on aging, immune function, and chronic inflammation in PCa, identifying key trends, contributors, and emerging strategies for addressing these challenges.

A total of 1,556 publications, spanning January 2015 to October 2025, were retrieved from the Web of Science Core Collection (WoSCC). Bibliometric tools, including VOSviewer, CiteSpace, and Bibliometrix, were employed to analyze publication trends, co-authorship networks, institutional collaborations, and keyword co-occurrences. Special focus was placed on senescence-associated secretory phenotype (SASP) factors, inflammatory biomarkers, and immune dysfunction as aging-related drivers of PCa.

Research in this domain has grown significantly over the past two decades, with the United States, China, and Italy emerging as leading contributors. Key themes include the role of SASP factors, oxidative stress, and immune evasion in aging-related PCa progression. Biomarkers such as interleukin-6 (IL-6) and tumor mutational burden (TMB) are increasingly explored for their potential to guide personalized interventions. Emerging therapeutic strategies involve SASP-targeting interventions, immunotherapies like CAR-T cells, and combination approaches to reprogram the immunosuppressive tumor microenvironment.

This bibliometric analysis provides a comprehensive overview of research trends at the intersection of aging, immune function, and chronic inflammation in PCa. Rather than establishing mechanistic causality, the findings highlight chronic inflammation as a central and evolving research focus within the field. The results offer a data-driven framework for understanding current research priorities and may inform future mechanistic and translational studies aimed at improving therapeutic strategies for aging-associated PCa.