Pre-diagnostic Demographic, Lifestyle, and Health History Factors in Association with Secreted Protein Acidic and Rich in Cysteine (SPARC) Expression in Colorectal Cancer Tissue
Bibliographic record
Abstract
Background: Demographic, health history, and lifestyle factors have been associated with prognosis of colorectal cancer (CRC), but mechanisms underlying these associations remain poorly understood. A compelling mechanism involves changes in expression of tumor markers that influence treatment outcomes, such as secreted protein acidic and rich in cysteine (SPARC), lower levels of which have previously been associated with poorer CRC prognosis. Objective: We explored the association of factors that have been previously associated with CRC prognosis with expression of SPARC in tumor tissues. Design: We conducted a prospective evaluation of 50 participants of a longitudinal cohort study that went on to develop CRC. Methods: Tumor and normal tissue cores were taken from formalin-fixed paraffin-embedded (FFPE) blocks of incident CRC cases and were used to create tissue microarrays (TMAs). Slides created from the TMAs were stained with SPARC antibodies and analyzed to calculate H-scores for both epithelial and non-epithelial components of tumor and normal tissues. H-scores were ln-transformed and analyzed in association with demographic, lifestyle, and health history factors assessed before cancer diagnosis using linear regression models. Results: In CRC tumor epithelium, smoking was associated with a 0.53-fold lower level of SPARC expression ( P = .054). Higher income was associated with a 1.33-fold greater level of SPARC expression in tumor non-epithelial tissue ( P = .041). Higher cancer stage was associated with a 0.74-fold lower level of non-epithelial tumor SPARC expression ( P = .040). In the epithelial component of normal colorectal tissues, higher fruit consumption was associated with a 2.74-fold greater SPARC H-score ( P = .002). Conclusions: The associations we observed for smoking, income, and cancer stage with SPARC in tumor tissue are consistent with previously established associations of these factors with CRC prognosis. Larger studies with prognostic data are needed, but our results suggest that differences in SPARC expression may contribute to previously observed impacts of various factors on CRC prognosis.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".