miRNA as Biomarkers MicroRNAs (miRNAs) are small non-coding RNA molecules that regulate gene expression. In lung adenocarcinoma (LUAD), specific miRNAs are consistently dysregulated - either overproduced or suppressed - and are detectable in blood plasma, making them attractive candidates for non-invasive liquid biopsy diagnostics.
Why Bibliometric Analysis The field of miRNA-based liquid biopsy in LUAD has grown rapidly, generating thousands of publications. Bibliometric analysis applies quantitative methods to this scientific literature to map knowledge clusters, identify leading research themes, track temporal trends, and pinpoint gaps - providing a high-level map of where the field stands and where it is heading.
Study Scale Kartika and colleagues searched five major databases and ultimately analyzed 692 publications on miRNA liquid biopsy in LUAD, using visualization tools VOSviewer and Biblioshiny to transform citation and keyword data into interpretable research maps.
Multi-Database Search The authors searched five databases: Crossref (contributing 4,000 initial records), Google Scholar (2,500), Semantic Scholar (1,000), PubMed (587), and Scopus (419). After deduplication and quality screening, 692 articles met inclusion criteria for analysis.
VOSviewer Network Analysis VOSviewer generates co-occurrence networks where keywords that frequently appear together in the same publications are clustered together visually. The size of each keyword node reflects its frequency, while the thickness of connecting lines reflects the strength of co-occurrence.
Biblioshiny Time Analysis Biblioshiny (a Shiny interface for the R bibliometrix package) was used to analyze temporal trends - how the frequency of specific terms, authors, and journals changed over time, revealing which topics are emerging versus declining in the field.
Cluster 1 - miRNA Core The largest cluster (19 keywords) centered on the miRNA biomarker theme itself, including terms like biomarker, circulating miRNA, diagnosis, and specific miRNAs like miR-21. MiR-21 is one of the most studied oncomiRs in lung cancer, consistently upregulated and associated with poor prognosis.
Cluster 2 - Lung Adenocarcinoma Biology This 18-keyword cluster linked lung adenocarcinoma to prognosis, machine learning, and immunotherapy research, reflecting the growing integration of AI methods into miRNA analysis and the connections between miRNA biology and immune response.
Clusters 3 and 4 - Technology Platforms The liquid biopsy cluster (19 keywords) covered circulating tumor DNA (ctDNA), exosomes, and next-generation sequencing (NGS), while the bioinformatics cluster (9 keywords) included LncRNA interactions, TCGA database analyses, and systems biology approaches - reflecting the technical infrastructure supporting miRNA research.
Top Publishing Venues The journal Lung Cancer published the most articles in this space (31), followed by Oncology Letters (18) and Frontiers in Oncology (16). This concentration in lung-cancer-specific and oncology journals reflects that miRNA liquid biopsy research is primarily driven by clinical cancer research communities rather than basic science.
Geographic Contributions China and the United States dominated publication output, consistent with their overall leadership in cancer research. However, the geographic concentration suggests that findings may not fully capture population diversity, particularly across different ethnic groups with varying EGFR mutation frequencies.
Research Gaps Identified The bibliometric analysis identified underrepresentation of studies on miRNA detection standardization and multi-miRNA panel validation - critical gaps that must be addressed before miRNA liquid biopsy can move from research to clinical practice.
From Discovery to Standardization The bibliometric map reveals that most research has focused on discovering which miRNAs are dysregulated rather than standardizing detection methods. Future work must address pre-analytical variability (how blood is collected and processed) and analytical standardization (assay calibration across laboratories).
AI Integration Trend The machine learning cluster within the LUAD biology grouping signals a growing trend toward applying AI to optimize miRNA panel selection and interpretation. Multi-miRNA panels analyzed with machine learning models are likely to outperform any single miRNA as a diagnostic or prognostic biomarker.
Clinical Roadmap For miRNA liquid biopsy to achieve clinical validation, the field needs large prospective studies that test miRNA panels head-to-head against current standard-of-care diagnostics, with pre-registered endpoints and blinded analysis - elements that the current literature largely lacks.