BACTERIAL MICROBIOTA DYSBIOSIS: A NOVEL APPROACH ELUCIDATING ORAL CANCER
Bibliographic record
Abstract
Introduction: The oral cavity contains a highly intricate microbial ecosystem that significantly contributes to the host's homeostasis. Emerging evidence suggests a critical role of the oral microbiome dysbiosis in Oral squamous cell carcinoma (OSCC) pathogenesis, with several oral pathogens being key prooncogenic bacteria. Objectives: Investigate the key differentially expressed genes (DEGs) and enriched pathways dysregulated in relation to Fusobacterium nucleatum and Streptococcus gordonii and their correlation with OSCCC using bioinformatics analysis. Material and Methods: Microarray dataset was processed and analyzed to obtain the DEGs in F. nucleatum and S. gordonii infected gingival cells, each compared to the control group. Enrichment pathway analysis of the identified DEGs from both analyses was done followed by their Protein- protein interaction network, identification of the hub genes of each analysis and their correlation with OSCC. Results: A total of 24 DEGs were identified in F. nucleatum infected keratinocytes with The most enriched KEGG terms include cancer pathways, Toll-like receptor signaling pathways and parathyroid hormone synthesis, secretion and action. The hub gene sequence is FOS, EGR1, NR4A2, GADD45B and FN1.Differential analysis of S. gordonii infected keratinocytes resulted in 30 DEGs with the most enriched KEGG terms involving MAPK signaling pathway and multiple cancers. The hub genes are EGR1, FOS, ATF3, MYC and RHOB. FOS and EGR1 were detected in common between the two analyses. These genes are strongly related to carcinogenesis and immune responses. Conclusion: Members of the oral microbiome may actively promote OSCC development and activate the expression of important factors linked to carcinogenesis.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".