Blood RNA biomarker panel detects both left- and right-sided colorectal neoplasms: a case-control study
Bibliographic record
Abstract
BACKGROUND: Colonoscopy is widely regarded to be the gold standard for colorectal cancer (CRC) detection. Recent studies, however, suggest that the effectiveness of colonoscopy is mostly confined to tumors on the left side of the colon (descending, sigmoid, rectum), and that the technology has poor tumor detection for right-sided (cecum, ascending, transverse) lesions. A minimally invasive test that can detect both left-sided and right-sided lesions could increase the effectiveness of screening colonoscopy by revealing the potential presence of neoplasms in the right-sided "blind spot". METHODS: We previously reported on a seven-gene, blood-based biomarker panel that effectively stratifies a patient's risk of having CRC. For the current study, we assessed the effectiveness of the seven-gene panel for the detection of left- and right-sided CRC lesions. Results were evaluated for 314 patients with CRC (left-sided: TNM I, 65; TNM II, 57; TNM III, 60; TNM IV, 17; unknown, 9. right-sided: TNM I, 28; TNM II, 29; TNM III, 38; TNM IV, 12; unknown, 1 and including two samples with both left and right lesions) and 328 control samples. Blood samples were obtained prior to clinical staging and therapy. Most CRC subjects had localized disease (stages I and II, 58%); regional (stage III) and systemic (stage IV) disease represented 32% and 9%, respectively, of the study population. RESULTS: The panel detected left-sided (74%, 154/208) and right-sided (85%, 92/108) lesions with an overall sensitivity of 78% (215/316) at a specificity of 66% (215/328). Treatable cancer (stages I to III) was detected with left-sided lesion sensitivity of 76% (138/182) and right-sided sensitivity of 84% (80/95). CONCLUSION: This seven-gene biomarker panel detected right-sided CRC lesions across all cancer stages with a sensitivity that is at least equal to that for left-sided lesions. This study supports the use of this panel as the basis for a patient-friendly, blood-based test that can be easily incorporated into a routine physical examination in advance of colonoscopy to provide a convenient companion diagnostic and a pre-screening alert, ultimately leading to enhanced CRC screening effectiveness.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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; 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".