Identification of Common Hub Genes and Key Molecular Pathways between Multiple Sclerosis and Urological Disorders
Bibliographic record
Abstract
Introduction: The immune system plays a vital role in affording protection for the body against a wide variety of diseases and infections. On occasion, the system malfunctions and attacks intact cells, tissues, and organs influencing any part of the body, tapering off bodily function, and leading to life-threatening. multiple sclerosis (MS) is characterized by inflammatory demyelination with a diverse range of urologic indications.Methods: To extract the overlapped genes and single-nucleotide polymorphisms (SNPs; until November 2022) between MS and several urological disorders, we searched the DisGeNET database. Furthermore, to identify significant Gene Ontology (GO) terms and the Kyoto Encyclopedia of Genes and Genome (KEGG) pathway, the Enrichr assessment was used. Additionally, in the case of overlapped genes, the maximum level of linkage hub genes was investigated by the protein-protein interaction (PPI) network construction via cytoHubba.Results: 1362 common genes between MS and urological disease were recognized, of which 154 genes have SNPs linked with MS susceptibility. Three DisGeNET-indexed MS-associated SNPs, including rs653178, rs10936599, and rs4976646 were shared between MS and urological disorders. TNF, AKT1, IL1B, IL6, VEGFA, INS, C-C CCL5, TP53), RELA proto-oncogene, STAT3, and EGFR were detected as hub genes overrepresented in the identified pathways.Conclusion: Of 1362 common genes, 11 key genes, and 3 SNPs were shared between MS and urology-related diseases. These identified features might serve as potential therapeutic targets in both disorders, with a probable role in the management of urological complications in MS patients.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".