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A gene-panel blood test for the detection of colorectal adenomas and cancer.

2025· article· en· W4410804167 on OpenAlexafffundabout
Anouska Agarwal, Fatim Diaby, Sheen Dube, K Sareen, Sahil Mittal, Vimi Sunil Mutalik, Harminder Singh, Anuraag Shrivastav

Bibliographic record

VenueJournal of Clinical Oncology · 2025
Typearticle
Languageen
FieldMedicine
TopicGenetic factors in colorectal cancer
Canadian institutionsUniversity of ManitobaUniversity of Winnipeg
FundersResearch Manitoba
KeywordsMedicineColorectal cancerOncologyInternal medicineBlood testCancerCancer research

Abstract

fetched live from OpenAlex

e15728 Background: Colorectal cancer (CRC) is among the most treatable cancers, yet it remains the second leading cause of cancer deaths in men and third in women. Early detection of CRC precursors and early-stage CRC could significantly reduce mortality rates. The existing screening tests, including stool-based tests and the invasive colonoscopy with its challenging bowel preparation, often face low compliance. Therefore, a more convenient, accurate, and cost-effective screening test for CRC could greatly improve patient quality of life and most importantly, increase survival rates. The protein, N-myristoyltransferase-2 (NMT2), was discovered to be overexpressed in the peripheral blood mononuclear cells (PBMC) of CRC patients and individuals with adenomatous polyps (AP). In a proof of concept (POC) study, we demonstrated that an NMT2-based immunohistochemical (IHC) test is highly effective in detecting precancerous colorectal polyps and CRC with high sensitivity (91%) and specificity (81%) and surpasses other FDA approved tests, including the fecal immunochemical test (FIT), that has a sensitivity of 73%. A study involving a larger cohort would validate NMT2 as an effective biomarker that can be used for a commercial blood test to screen for adenomas and CRC and triage patients for colonoscopies. We are developing a quantitative real-time polymerase chain reaction (qRT-PCR)-based blood test to analyze the expression pattern of the NMT2 gene, its paralog, NMT1, and upstream target MetAP2 in the PBMC of CRC or AP cases and subjects with no evidence of disease (NED). By comparing the results of the subjects' colonoscopy procedure to those of the qPCR and IHC tests, we aim to use NMT2 as a biomarker to discriminate between CRC, AP, and NED. Methods: qRT-PCR was used to validate endogenous control genes and analyze the expression pattern of NMT2, NMT1, and MetAP2 . DNA libraries were prepared for next-generation sequencing according to the manufacturer's protocol [Illumina] and sequencing was conducted at The Centre for Applied Genomics in Toronto, Ontario. Results: We assessed the expression stability of candidate genes in CRC, AP, and NED PBMC samples and validated two endogenous control genes, RPS17 and RPL37A , for a qRT-PCR-based screening assay, using robust statistical algorithms such as the geNorm and NormFinder tools. Upon validation, we assessed the relative expression of NMT2 , NMT1 , and MetAP2 across sample types and observed differential expression, which aligns with previous IHC results. In parallel, we used NGS to identify two novel synonymous mutations in NMT2 [ID:rs137889266] and NMT1 [ID:rs1132898] within the CRC samples, providing further insights into tumorigenic mechanisms. Conclusions: Our findings demonstrate that RPS17 and RPL37A are reliable control genes for qRT-PCR assays, the potential of NMT2 as a biomarker, and highlights the potential of NGS to uncover clinically relevant mutations.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.003

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.

Opus teacher head0.108
GPT teacher head0.449
Teacher spread0.341 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes3
Has abstractyes

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