Concentration of Secreted Frizzled-Related Proteins (SFRPs) in Non-Small Cell Lung Carcinoma Subtypes—A Preliminary Study
Bibliographic record
Abstract
Non-small cell lung carcinoma (NSCLC) is the most common lung cancer worldwide. Secreted frizzled-related proteins (SFRPs) are important tumour suppressors and antagonists of the Wnt signalling pathway, which is linked with cancer development. The aim of this study was to evaluate the concentrations of SFRP1, SFRP2, and SFRP5 proteins in tumour and non-tumour (NT) samples obtained from 65 patients with primary NSCLC. An enzyme-linked immunosorbent assay (ELISA) was used to measure the concentrations of SFRPs in the tissue homogenates. A significantly lower SFRP2 protein concentration was found in the total NSCLC tumour samples and the following NSCLC subtypes: squamous cell carcinoma (SCC) and adenocarcinoma (AC) (p > 0.05, p = 0.028 and p = 0.001, respectively). AC tumour samples had a higher SFRP1 level than NT samples (p = 0.022), while the highest SFRP1 concentration was found in NSCLC samples from patients with clinical stage T4 cancer. Increased concentrations of SFRP1 and SFRP5 were present in stage III NSCLC samples, while the tumour samples with high pleural invasion (PL2) had an increased level of SFRP2. The results from this study suggest that the tumour suppressor or oncogenic roles of SFRPs could be connected with the NSCLC subtype. The levels of SFRPs varied according to the clinicopathological parameters of NSCLC.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".