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Background <p>Non-invasive biomarkers offer potential to improve risk stratification and early diagnosis of lung cancer, complementing low-dose computed tomography (LDCT) screening. This study employed bibliometric analysis to identify global research trends, collaborative networks, and future directions in lung cancer biomarker research. Publications on lung cancer biomarkers for screening were retrieved from the Web of Science Core Collection (WoSCC). Data processing and visualisation were performed using Citespace, VOSviewer, KH Coder, Latent Dirichlet Allocation (LDA) topic modelling, and the online bibliometric analysis platform. Burst detection analysis was performed to predict emerging research trends.</p> Results <p>Analysis of 3636 publications revealed exponential growth in research output since 2014. International collaboration demonstrated a dual-core structure centred on China and the United States, with Chinese institutions showing high publication volumes and American institutions demonstrating greater citation influence. Journal citation mapping revealed three evolutionary phases: basic mechanisms-clinical translation-intelligent integration. LDA topic modelling identified 22 topics grouped into five core research directions: imaging and pathological diagnostic techniques; molecular and omics marker research; liquid biopsy and new detection technologies; clinical and translational medicine research; and tumour biology and treatment mechanisms. Burst detection analysis predicted future four priority areas: epigenetic studies centred on <i>DNA methylation</i> for risk prediction; treatment <i>resistance</i> and <i>invasion</i> mechanisms; liquid biopsy technology development; and targeted therapy clinical trials.</p> Conclusions <p>Lung cancer biomarker research has evolved towards multimodal, intelligent screening approaches. Future research priorities include DNA methylation-based markers, circulating microRNA signatures, and artificial intelligence-assisted diagnostic platforms to improve early detection accuracy and complement LDCT screening.</p>

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A visual analysis of the research dynamics of biomarkers for lung cancer screening

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月26日

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  • Huange Zhu,
  • Burong Li,
  • Ranran Kong,
  • Jun Yang,
  • Zeqi Guo,
  • Jiafeng Yin

摘要

Background

Non-invasive biomarkers offer potential to improve risk stratification and early diagnosis of lung cancer, complementing low-dose computed tomography (LDCT) screening. This study employed bibliometric analysis to identify global research trends, collaborative networks, and future directions in lung cancer biomarker research. Publications on lung cancer biomarkers for screening were retrieved from the Web of Science Core Collection (WoSCC). Data processing and visualisation were performed using Citespace, VOSviewer, KH Coder, Latent Dirichlet Allocation (LDA) topic modelling, and the online bibliometric analysis platform. Burst detection analysis was performed to predict emerging research trends.

Results

Analysis of 3636 publications revealed exponential growth in research output since 2014. International collaboration demonstrated a dual-core structure centred on China and the United States, with Chinese institutions showing high publication volumes and American institutions demonstrating greater citation influence. Journal citation mapping revealed three evolutionary phases: basic mechanisms-clinical translation-intelligent integration. LDA topic modelling identified 22 topics grouped into five core research directions: imaging and pathological diagnostic techniques; molecular and omics marker research; liquid biopsy and new detection technologies; clinical and translational medicine research; and tumour biology and treatment mechanisms. Burst detection analysis predicted future four priority areas: epigenetic studies centred on DNA methylation for risk prediction; treatment resistance and invasion mechanisms; liquid biopsy technology development; and targeted therapy clinical trials.

Conclusions

Lung cancer biomarker research has evolved towards multimodal, intelligent screening approaches. Future research priorities include DNA methylation-based markers, circulating microRNA signatures, and artificial intelligence-assisted diagnostic platforms to improve early detection accuracy and complement LDCT screening.