Tissue-resident microbiota impacts colorectal cancer progression and prognosis
Bibliographic record
Abstract
To deepen the understanding of tissue-resident microbiota in colorectal cancer (CRC), we analyzed whole-genome and transcriptome data from 937 patients. We identified 249 genera and 361 species commonly present in both tumors and adjacent normal tissues (NATs). Distinct microbial signatures were associated with anatomical location, tumor stages, hypermutation status, mutations in CRC driver and DNA damage repair genes, as well as consensus molecular subtypes (CMSs). Notably, the presence of the pks island and elevated abundance of Enterobacteriaceae were linked to poor prognosis specifically in CMS2 tumors. Finally, microbial risk scores derived from taxa present in tumor or NATs predicted patient prognosis independently of established clinico-molecular factors. Prognostic taxa were strongly associated with tumor transcriptomic pathways related to hypoxia, immune response, and metabolic status. These findings revealed the heterogeneity of tissue-resident microbiota and their critical role in CRC progression, highlighting potential avenues for targeted intervention. Here, the authors show that colorectal tumors harbor diverse tissue-resident microbes whose compositions vary by tumor location and host genomics, and identify specific microbial signatures that predict patient prognosis beyond established clinical factors.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".