A Quantitative Proteomic Approach of the Different Stages of Colorectal Cancer Establishes OLFM4 as a New Nonmetastatic Tumor Marker
Bibliographic record
Abstract
Expression profiles represent new molecular tools that are useful to characterize the successive steps of tumor progression and the prediction of recurrence or chemotherapy response. In this study, we have used quantitative proteomic analysis to compare different stages of colorectal cancer. A combination of laser microdissection, OFFGEL separation, iTRAQ labeling, and MALDI-TOF/TOF MS was used to explore the proteome of 28 colorectal cancer tissues. Two software packages were used for identification and quantification of differentially expressed proteins: Protein Pilot and iQuantitator. Based on ∼1,190,702 MS/MS spectra, a total of 3138 proteins were identified, which represents the largest database of colorectal cancer realized to date and demonstrates the value of our quantitative proteomic approach. In this way, individual protein expression and variation have been identified for each patient and for each colorectal dysplasia and cancer stage (stages I-IV). A total of 555 proteins presenting a significant fold change were quantified in the different stages, and this differential expression correlated with immunohistochemistry results reported in the Human Protein Atlas database. To identify a candidate biomarker of the early stages of colorectal cancer, we focused our study on secreted proteins. In this way, we identified olfactomedin-4, which was overexpressed in adenomas and in early stages of colorectal tumors. This early stage overexpression was confirmed by immunohistochemistry in 126 paraffin-embedded tissues. Our results also indicate that OLFM4 is regulated by the Ras-NF-κB2 pathway, one of the main oncogenic pathways deregulated in colorectal tumors.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".