Transformative insights from transcriptome analysis of colorectal cancer patient tissues: identification of four key prognostic genes
Bibliographic record
Abstract
Colorectal cancer (CRC) is a leading cause of cancer-related deaths worldwide, necessitating accurate and robust predictive approaches to assist oncologists with prognosis prediction and therapeutic decision-making in clinical practice. Here, we aimed to identify key genes involved in colorectal cancer pathology and develop a model for prognosis prediction and guide therapeutic decisions in CRC patients. We profiled 49 matched tumour and normal formalin-fixed paraffin-embedded (FFPE) samples using Affymetrix HGU133-X3P arrays and identified 845 differentially expressed genes (FDR ≤ 0.001, fold change ≥2), predominantly enriched in the extracellular matrix (ECM)-receptor interaction pathway. The integrative analysis of our data with publicly available mRNA and miRNA datasets, including their differentially expressed gene analyses, identified four overexpressed genes in the ECM-receptor interaction pathway as key regulators of human CRC development and progression. These four genes were independently validated for their differential expression and association with prognosis in a newly collected CRC cohort and publicly available datasets. A prognostic risk score was developed using these genes, with patient stages weighted by multivariate Cox regression coefficients to stratify patients into low-risk and high-risk groups, showing significantly poorer overall survival (OS) in the high-risk group. In conclusion, our risk assessment model exhibits strong potential for predicting poor survival and unfavorable clinicopathological features in CRC patients, offering valuable insights for personalised management strategies.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".