Combined Non-Invasive Prediction and New Biomarkers of Oral and Fecal Microbiota in Patients With Gastric and Colorectal Cancer
Bibliographic record
Abstract
Background There is no information on the commonality and specificity of oral and fecal microbiota in patients with gastric cancer (GC) and colorectal cancer (CRC). Methods The high-throughput 16S rRNA gene V4 region sequencing was used to perform bioinformatics analysis of oral, fecal, and tissue microbiota in GC (76 subjects), CRC (53), and healthy controls (HC, 70). Furthermore, we determined the microbial characteristics of each part, constructed and verified three classifiers for GC and CRC, and evaluated curves of receiver operating characteristic and precision–recall with probability of disease. Results Compared to HC, the microbial richness and diversity of GC and CRC decreased in oral cavity and increased in stool; additionally, these indexes in GC tissue were higher than those in CRC tissue. In GC and CRC patients, Haemophilus , Neisseria , Faecalibacterium , and Romboutsia were significantly reduced compared to the relative abundance value of oral or fecal bacterial genera in the HC group, while the Streptococcus , Gemella , Escherichia-Shigella , and Fusobacterium were significantly increased. The oral and tissue microbiota have similar and abundant shared bacterial networks. The single and combined microbial detection have good AUC values based on POD indices for predicting GC, CRC, and gastrointestinal (GI) cancers (GC and CRC). Conclusion This study is the first to examine the characteristics of oral, fecal, and tumor microbiota in GC and CRC patients, and the similarities and differences in their microbial changes are reported. These oral or fecal bacteria ( Haemophilus , Neisseria , Faecalibacterium , Romboutsia , Streptococcus , Gemella , Escherichia-Shigella , and Fusobacterium ) may be involved in tumor evolution as potentially characteristic genera. In addition, both oral and fecal microbial detection may provide a solid theoretical foundation for the non-invasive prediction of these cancers.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".