An integrated multiomics analysis of rectal cancer patients identified <scp>POU2F3</scp> as a putative druggable target and entinostat as a cytotoxic enhancer of 5‐fluorouracil
Bibliographic record
Abstract
Rectal cancer (RC) accounts for one-third of colorectal cancers (CRC), and 40% of these are locally advanced rectal cancers (LARC). The use of neoadjuvant chemoradiotherapy (nCRT) significantly reduces the rate of local recurrence compared to adjuvant therapy or surgery alone. However, after nCRT, up to 40%-60% of patients show a poor pathological response, while only about 20% achieve a pathological complete response. In this scenario, the identification of novel predictors of tumor response to nCRT is urgently needed to reduce LARC mortality and to spare poorly responding patients from unnecessary treatments. Therefore, by combining gene and microRNA expression datasets with proteomic data from LARC patients, we developed an integrated network centered on seven hub-genes putatively involved in the response to nCRT. In an independent validation cohort of LARC patients, we confirmed that differential expression of NFKB1, TRAF6 and STAT3 is correlated with the response to nCRT. In addition, the functional enrichment analysis also revealed that these genes are strongly related to hallmarks of cancer and inflammation, whose dysfunction may causatively affect LARC patient's response to nCRT. Furthermore, by constructing the transcription factor-module network, we hypothesized a protective role of POU2F3 gene, which could be used as a new drug target in LARC patients. Finally, we identified and tested in vitro entinostat, a histone deacetylase inhibitor, as a chemical compound that could be combined with a classical therapeutic regimen in order to design more efficient therapeutic strategies in LARC management.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".