Analysis and validation of genes joint expression in Crohn's disease and cervical cancer based on GEO database.
Bibliographic record
Abstract
Abstract Background: In recent years, numerous studies have demonstrated an increased incidence of cervical cancer in women with Crohn's disease (CD). This paper aims to delve into the underlying mechanism of this association. Methods: Gene expression profiles for Crohn's disease (GSE95095 and GSE186582) and cervical cancer (GSE63514 and GSE63678) were obtained from the GEO database. Heterozygotes (DEGs) were searched for in experimental and control groups for both diseases. Gene modules for Crohn's disease and cervical cancer were also analyzed using the WGCNA method. Machine learning (LASSO logistic regression algorithm & RF method) was applied to screen the characterized genes in the two diseases. And the transcription factors related to the characterized genes were predicted. Finally, it was validated by Western Blot (WB) and immunohistochemistry experiments. Results: From the pool of differential genes in both disease groups, we identified a total of 60 co-expressed genes. Using the WGCNA method, we found 11 key modular genes that were common to both diseases. Machine learning screening allowed us to identify a shared biomarker for both diseases: CXCR4. Furthermore, we predicted MYC as its transcriptional regulator. Finally, to validate our findings, we conducted immunohistochemistry and protein immunoblotting experiments, which confirmed that CXCR4 exhibits a higher expression level in cervical cancer. Conclusion: This study screened a gene co-expressed in Crohn's disease and cervical cancer based on machine learning: CXCR4, which is expected to be a potential biomarker for both diseases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".