A case-controlled validation study of a blood-based seven-gene biomarker panel for colorectal cancer in Malaysia
Bibliographic record
Abstract
BACKGROUND: Colorectal cancer (CRC) screening is key to CRC prevention and mortality reduction, but patient compliance with CRC screening is low. We previously reported a blood-based test for CRC that utilizes a seven-gene panel of biomarkers. The test is currently utilized clinically in North America for CRC risk stratification in the average-risk North American population in order to improve screening compliance and to enhance clinical decision making. METHODS: In this study, conducted in Malaysia, we evaluated the seven-gene biomarker panel validated in a North American population using blood samples collected from local patients. The panel employs quantitative RT-PCR (qRT-PCR) to analyze gene expression of the seven biomarkers (ANXA3, CLEC4D, TNFAIP6, LMNB1, PRRG4, VNN1 and IL2RB) that are differentially expressed in CRC patients as compared with controls. Blood samples from 210 patients (99 CRC and 111 controls) were collected, and total blood RNA was isolated and subjected to quantitative RT-PCR and data analysis. RESULTS: The logistic regression analysis of seven-gene panel has an area under the curve (AUC) of 0.76 (95% confidence interval: 0.70 to 0.82), 77% specificity, 61% sensitivity and 70% accuracy, comparable to the data obtained from the North American investigation of the same biomarker panel. CONCLUSIONS: Our results independently confirm the results of the study conducted in North America and demonstrate the ability of the seven biomarker panel to discriminate CRC from controls in blood samples drawn from a Malaysian population.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".