Oral Microbiome Modulates Tumor Micro-Environment Potentiating Oral Cancer Progression from Benign Stages- A Review
Bibliographic record
Abstract
Introduction/Background: Oral Squamous Cell Carcinomas (OSCC) are one of the common causes of death globally. Several etiological factors cause OSCC, including alcohol, tobacco use, poor oral hygiene, Human Papilloma Virus (HPV) infections, Yeast (Candida spp.) infections, etc. Oral microbial dysbiosis and inflammatory conditions like periodontitis have also been implicated recently. The host-microbiome interactions may further alter the homeostasis of the oral Tumor Microenvironment (TME). In this review, we aim to determine the role of resident microbiota in oral cancer progression and decipher how they interact with the TME. The article also discusses potential biomarkers and therapeutic targets to enhance the management of OSCC. Methods: PubMed, Medline, and Google Scholar were used as biomedical literature databases. The following keywords were looked up for academic literature using Boolean operators. Initially, a total of 273 articles were found as a result of the literature search. Results: Finally, 142 articles that met the exclusion and inclusion criteria were chosen for literature review, as depicted in the PRISMA flowchart. Discussion: Multiple oral bacterial species are linked to OSCC. Modulation immunotherapies that target the TME and reverse oral microbiota dysbiosis have been proposed in recent investigations. Changes in the oral microbiota have an impact on the oral microenvironment, leading to inflammatory cell infiltration, cytokine release, and chronic inflammation. All the data support an immunosuppressive TME. Antifungal and antibacterial medications are currently advised for the treatment. Conclusion: We conclude that the utilization of microbial biomarkers and generated products as potential anticancer targets could be one of the methods for mitigating oral cancer. Tumor immunotherapies are the newest method for treating cancer patients. However, there are still a lot of drawbacks. We attempted to outline the possible approaches to potential immunotherapies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".