An investigation for phylogenetic characterization of human pancreatic cancer microbiome by 16S rDNA sequencing and bioinformatics techniques
Bibliographic record
Abstract
Pancreatic cancer is a significant public health concern, with increasing incidence rates and limited treatment options. Recent studies have highlighted the role of the human microbiome, particularly the gut microbiota, in the development and progression of this disease. Microbial dysbiosis, characterized by alterations in the composition and function of the gut microbiota, has been implicated in pancreatic carcinogenesis through mechanisms involving chronic inflammation, immune dysregulation, and metabolic disturbances. Researchers have identified specific microbial signatures associated with pancreatic cancer, offering potential biomarkers for early detection and prognostication. By leveraging advanced sequencing and bioinformatics tools, scientists have delineated differences in the gut microbiota between pancreatic cancer patients and healthy individuals, providing insights into disease pathogenesis and potential diagnostic strategies. Moreover, the microbiome holds promise as a therapeutic target in pancreatic cancer treatment. Interventions aimed at modulating the microbiome, such as probiotics, prebiotics, and fecal microbiota transplantation, have demonstrated potential in enhancing the efficacy of existing cancer therapies, including chemotherapy and immunotherapy. These approaches can influence immune responses, alter tumor microenvironments, and sensitize tumors to treatment, offering new avenues for improving patient outcomes and overcoming therapeutic resistance. Overall, understanding the complex interplay between the microbiome and pancreatic cancer is crucial for advancing our knowledge of disease mechanisms and identifying innovative therapeutic strategies. Here we report phylogenetic analysis of the 16S rDNA microbial sequences of the pancreatic cancer mice microbiome and corresponding age matched healthy mice microbiome. We successfully identified differentially abundant microbiota in pancreatic cancer.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".