Critical appraisal of quantitative PCR results in colorectal cancer research: Can we rely on published qPCR results?
Bibliographic record
Abstract
The use of real-time quantitative polymerase chain reaction (qPCR) in cancer research has become ubiquitous. The relative simplicity of qPCR experiments, which deliver fast and cost-effective results, means that each year an increasing number of papers utilizing this technique are being published. But how reliable are the published results? Since the validity of gene expression data is greatly dependent on appropriate normalisation to compensate for sample-to-sample and run-to-run variation, we have evaluated the adequacy of normalisation procedures in qPCR-based experiments. Consequently, we assessed all colorectal cancer publications that made use of qPCR from 2006 until August 2013 for the number of reference genes used and whether they had been validated. Using even these minimal evaluation criteria, the validity of only three percent (6/179) of the publications can be adequately assessed. We describe common errors, and conclude that the current state of reporting on qPCR in colorectal cancer research is disquieting. Extrapolated to the study of cancer in general, it is clear that the majority of studies using qPCR cannot be reliably assessed and that at best, the results of these studies may or may not be valid and at worst, pervasive incorrect normalisation is resulting in the wholesale publication of incorrect conclusions. This survey demonstrates that the existence of guidelines, such as MIQE, is necessary but not sufficient to address this problem and suggests that the scientific community should examine its responsibility and be aware of the implications of these findings for current and future research.
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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 | 0.573 | 0.873 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.022 | 0.013 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.017 | 0.014 |
| Open science | 0.008 | 0.006 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier 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".