Meta-analysis of Colorectal Cancer Gene Expression Profiling Studies Identifies Consistently Reported Candidate Biomarkers
Bibliographic record
Abstract
PURPOSE: Elucidation of candidate colorectal cancer biomarkers often begins by comparing the expression profiles of cancerous and normal tissue by performing gene expression profiling. Although many such studies have been done, the resulting lists of differentially expressed genes tend to be inconsistent with each other, suggesting that there are some false positives and false negatives. One solution is to take the intersection of the lists from independent studies. However, often times, the statistical significance of the observed intersection are not assessed. METHODS: Recently, we developed a meta-analysis method that ranked differentially expressed genes in thyroid cancer based on the intersection among studies, total sample sizes, average fold change, and direction of differential expression. We applied an improved version of the method to 25 independent colorectal cancer profiling studies that compared cancer versus normal, adenoma versus normal, and cancer versus adenoma to highlight genes that were consistently reported as differentially expressed at a statistically significant frequency. RESULTS: We observed that some genes were consistently reported as differentially expressed with a statistically significant frequency (P < 0.05) in cancer versus normal and adenoma versus normal comparisons but not in the cancer versus adenoma comparison. CONCLUSION: Our meta-analysis method identified genes that were consistently reported as differentially expressed. A review of some of the candidates revealed genes described previously as having diagnostic and/or prognostic value as well as novel candidate biomarkers. The genes presented here will aid in the identification of highly sensitive and specific biomarkers in colorectal cancer.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.009 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 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; both teacher heads agree on what is shown here.
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".