ArticleDiscover oncology2025
Research trends and highlights in PD-1/PD-L1 inhibitor immunotherapy in lung cancer: a bibliometric analysis.
Article in Discover oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Global trends of peripheral immune tolerance research: a bibliometric and visualization analysis.Frontiers in immunology · 2026Pooled it
- Advancing the understanding of the immune escape in lung cancer immunotherapy: Global trends, collaborations, and future directions.Human vaccines & immunotherapeutics · 2026Article
- Charting the blueprint: a bibliometric analysis reveals future strategies in lung cancer targeted therapy (2003-2025).Journal of thoracic disease · 2026Article
- Breaching the immune-cold barrier in pMMR/MSS metastatic colorectal cancer: emerging strategies beyond standard care.Frontiers in immunology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundLung cancer is one of the most common malignant tumors worldwide. This article aims to review the current research status and trends in PD-1/PD-L1 inhibitor immunotherapy.
methodOn the basis of the Web of Science Core Collection database, literature on PD-1/PD-L1 inhibitor immunotherapy in lung cancer patients was searched and analyzed for all years up to August 5, 2023. Bibliometric techniques were employed, including CiteSpace (6.1.R6), VOSviewer, and the Bibliometrix package in R, to examine publication counts, countries, institutions, authors, journals, cited literature, keywords, and research trends.
resultsA total of 1,252 documents were included following the screening process. The analysis revealed that China had the highest number of publications (512), whereas the institution with the most publications was the UDICE French Association of Research Universities Union (193). The journal with the most articles was the Journal for Immunotherapy of Cancer (48), and the most prolific author was Zhou Caixun from Tongji University in China (20). Co-citation analysis revealed that Borghaei H's 2015 article in the New England Journal of Medicine had the highest citation frequency. The clustering results indicated that the most frequently referenced keywords included predictors, treatment monitoring, and hyperprogressive diseases. There is a growing trend toward combination therapies, such as dual immune checkpoint inhibitors, and research into molecular mechanisms within the tumor microenvironment, aimed at enhancing the efficacy of immunotherapy and reducing adverse effects.
conclusionBibliometric analysis indicates that PD-1/PD-L1 inhibitors are pivotal in lung cancer immunotherapy. Research in this domain focuses on identifying biomarkers within the tumor microenvironment, addressing immune evasion and resistance to maximize efficacy, and mitigating adverse effects.
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Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.