Butyrate-Induced<i>In Vitro</i>Colonocyte Differentiation Network Model Identifies<i>ITGB1, SYK, CDKN2A, CHAF1A,</i>and<i>LRP1</i>as the Prognostic Markers for Colorectal Cancer Recurrence
Bibliographic record
Abstract
Numerous mechanisms are believed to contribute to the role of dietary fiber-derived butyrate in the protection against the development of colorectal cancers (CRCs). To identify the most crucial butyrate-regulated genes, we exploited whole genome microarray of HT-29 cells differentiated in vitro by butyrate treatment. Butyrate differentiates HT-29 cells by relaxing the perturbation, caused by mutations of Adenomatous polyposis coli (APC) and TP53 genes, the most frequent mutations observed in CRC. We constructed protein-protein interaction network (PPIN) with the differentially expressed genes after butyrate treatment and extracted the hub genes from the PPIN, which also participated in the APC-TP53 network. The idea behind this approach was that the expression of these hub genes also regulated cell differentiation, and subsequently CRC prognosis by evading the APC-TP53 mutational effect. We used mRNA expression profile of these critical hub genes from seven large CRC cohorts. Logistic Regression showed strong evidence for association of these common hubs with CRC recurrence. In this study, we exploited PPIN to reduce the dimension of microarray biologically and identified five prognostic markers for the CRC recurrence, which were validated across different datasets. Moreover, these five biomarkers we identified increase the predictive value of the TNM staging for CRC recurrence.
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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".