Bibliometric analysis and visualization of Connexin 43 in the field of solid tumor research(2000-2024)
Bibliographic record
Abstract
Background: Connexin 43 (Cx43) plays a pivotal role in tumor growth, metastasis, and disease progression. This study employs bibliometric analysis to identify key research trends and emerging hotspots in Cx43-related solid tumor research. Methods: In December 2024, the Web of Science Core Collection (WoSCC) database was searched for publications on Cx43 in solid tumor research from 2000 to 2024. Bibliometric analysis and data visualization were primarily conducted using CiteSpace, VOSviewer, and Bibliometrix, with a focus on visualizing aspects such as countries, institutions, journals, authors, references, and keywords in the field. Results: A total of 1,666 publications were retrieved, with the annual number of articles and citations continuing to grow. The United States and China had the highest number of publications, while the University of Western Ontario in Canada was the leading institution, with the most publications by Christian C.G. Nau. Lampe, P.D. was the most cited author. The International Journal of Molecular Sciences was the most frequently published journal, and the Journal of Biological Chemistry was the most frequently co-cited journal. High-frequency keywords included phosphorylation, breast cancer, gastric cancer, prognostic markers, anti-tumor immune response, and drug resistance. Conclusion: Contemporary research focuses on the role of Cx43 phosphorylation in tumorigenesis, progression, and metastasis, its potential as a prognostic biomarker, and its critical role as an immunotherapeutic target and in tumor drug resistance. These studies provide a comprehensive analysis for a deeper understanding of the role of Cx43 in solid tumors and help to promote further research in this area.
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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 | 0.007 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.154 | 0.221 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".