Changes in gut microbiota and plasma inflammatory factors across the stages of colorectal tumorigenesis: a case-control study
Bibliographic record
Abstract
BACKGROUND: Colorectal cancer (CRC) is a common malignant gastrointestinal tumor. In China, CRC is the 5th most commonly diagnosed cancer. The vast majority of CRC cases are sporadic and evolve with the adenoma-carcinoma sequence. There is mounting evidence indicating that gut microbiota and inflammation play important roles in the development of CRC although study results are not entirely consistent. In the current study, we investigated the changes in the CRC-associated bacteria and plasma inflammatory factors and their relationships based on data from a case-control study of Han Chinese. We included 130 initially diagnosed CRC patients, 88 advanced colorectal adenoma patients (A-CRA), 62 patients with benign intestinal polyps and 130 controls. RESULTS: Fecal microbiota composition was obtained using 16S ribosomal DNA (16S rDNA) sequencing. PCOA analysis showed structural differences in microbiota among the four study groups (P = 0.001, Unweighted Unifrac). Twenty-four CRC-associated bacteria were selected by a two-step statistical method and significant correlations were observed within these microbes. CRC-associated bacteria were found to change with the degree of malignancy. Plasma C-reactive protein (CRP) and soluble tumor necrosis factor II (sTNFR-II) displayed significant differences among the four study groups and increased with adenoma-carcinoma sequence. The correlations of CRP and sTNFR-II with several CRC-associated microbes were also explored. CONCLUSIONS: CRC-associated species and plasma inflammatory factors tended to change along the adenoma-carcinoma sequence. Several CRC-associated bacteria were correlated with CRP and sTNFR-II. It is likely that gut microbiome and inflammation gradually form a microenvironment that is associated with CRC development.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".