Global trends and research progress on immunotherapy for EGFR-mutant non-small cell lung cancer: a bibliometric analysis

全球EGFR突变型非小细胞肺癌免疫治疗趋势及研究进展:文献计量分析

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Abstract

BACKGROUND: Immunotherapy for epidermal growth factor receptor (EGFR)-mutant non-small cell lung cancer (NSCLC) has emerged as a promising treatment strategy, yet global research trends remain uncharacterized. This study aims to provide a comprehensive bibliometric analysis of the field's evolution and current state. METHODS: The Web of Science Core Collection was searched to identify relevant articles published from 1995 to 2024. Using a combination of bibliometric tools including VOSviewer, CiteSpace, and "bibliometrix" package of R, we analyzed publication trends, citation patterns, collaborative networks, and keyword frequencies. RESULTS: The analysis encompassed 1,537 publications from 352 sources, involving 12,131 authors. China led in publication volume, while the USA demonstrated the highest citation impact and strongest international collaboration network. Harvard University topped institutional output, but Dana Farber Cancer Institute showed the highest citation impact. The Journal of Clinical Oncology emerged as the most influential journal in this field, with the highest impact factor and citation count. Kazuhiko Nakagawa led with the highest H-index and G-index, Yi-Long Wu published the most articles, and Caicun Zhou showed the strongest collaboration. Keyword analysis revealed a shift from molecular mechanisms to targeted therapies and immunotherapy approaches, with "chemotherapy", "gefitinib", and "mutations" emerging as the most frequent keywords. CONCLUSIONS: This study's bibliometric analysis of EGFR-mutant NSCLC immunotherapy reveals rapid growth but identifies key gaps: optimal immunotherapy-EGFR-tyrosine kinase inhibitors (TKIs) sequencing, predictive biomarkers, and resistance mechanisms beyond T790M linked to the tumor immune microenvironment. These insights guide future research to enhance therapeutic strategies in this evolving field.

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