Emerging trends and hotspots of tRNA-derived small RNAs in tumours: a bibliometric analysis via VOSviewer and CiteSpace
Bibliographic record
Abstract
INTRODUCTION: TRNA-derived small RNAs(tsRNAs) play an important role in many biological processes, and their dysregulation is closely related to the progression of cancer, but the research trend and future direction are not clear. This study aims to identify the leading contributors, collaboration networks, and emerging research trends in tsRNAs and their role in oncology, providing a more comprehensive and intuitive reference for researchers in this field. MATERIALS AND METHODS: Related publications related to tsRNA in the field of oncology from 1990 to 2022 were collected from the Science Citation Index Expanded through the Web of Science Core Collection (WOSCC) database on 6 December 2022. RESULTS: There were 2,108 publications related to tsRNAs in oncology. The articles came from 69 countries/regions, 2,218 institutions, 11,340 authors, and 200 journals, and included 9,530 keywords. The annual total number of papers and total global citation score increased steadily every year over the study period. Among the articles related to tsRNAs in oncology, the United States had the highest number of publications with 732 articles, and the United States, China, Japan, Canada, and South Korea had the highest number of collaborations. Seoul National University Sun and the journal Nucleic Acids Research had the most publications at 81 and 63 articles, respectively, and the keyword "tRF" was a hotspot. CONCLUSION: This study provides an in-depth analysis of the research status and development trends of tsRNAs in the field of cancer from a bibliometric perspective. Offering possible guidance for researchers to explore hot topics and frontiers, select suitable journals, and partners in this field.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.154 | 0.158 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".