Incidence and detection of high microsatellite instability in colorectal cancer in a Chinese population: a meta-analysis
Bibliographic record
Abstract
Background: The 4 most common types of DNA mutations in tumors are single-nucleotide variations, insertion-deletion, fusion, and copy number variations. This is followed by microsatellite instability (MSI), which is known to trigger the development of MSI-high (MSI-H) cancer and is responsible for 300,000 new cases of cancer per year in China. We aim to conduct a meta-analysis based on a comparison between the positive rates of the National Cancer Institute (NCI) panel (also known as 2B3D NCI panel) and mononucleotide panels for the diagnosis of MSI in the Chinese population. Methods: In the present meta-analysis, we searched the PubMed, Embase, Web of Science, CNKI, Wanfang, CQVIP, and CBM databases. MSI diagnosis studies by PCR and capillary electrophoresis were included to compare the incidence of MSI-H in colorectal cancer obtained from panels with different microsatellite markers. Egger’s bias test was used to assess risk of bias. Results: Seventeen articles were included, which used the Newcastle-Ottawa Scale (NOS) scale for quality evaluation. The NOS scores of the included documents were ≥7 points, and the quality of the documents met the requirements. The incidence of MSI-H detected by the 2B3D NCI panel was 13.5% [95% confidence interval (CI): 10.8–16.4, I2=52.321%, P=0.026, n=10 studies including 2,681 participants], the incidence of MSI-H detected by the mononucleotide panels was 10.6% (95% CI: 7.1–14.7, I2=81.147%, P=0.000, n=7 studies including 3,249 participants). This indicates that, in the Chinese population, the 2B3D NCI panel can detect 27.4% more MSI-H cancers than the mononucleotide panels, 54.7% more MSI-H cancers than the panel of 6 mononucleotides, and its sensitivity is comparable to that of Promega. Conclusions: The findings of the meta-analysis demonstrated that, using the 2B3D NCI panel for MSI detection can avoid the underestimation of the incidence MSI-H in colorectal cancer and can be considered the most suitable panel for MSI detection in the Chinese population. The inclusion of only published data might be a potential source of publication bias.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".