Altered expression of cytokines, chemokines, growth factors, and soluble receptors in patients with colorectal cancer, and correlation with treatment outcome
Bibliographic record
Abstract
Insofar as they play an important role in the pathogenesis of colorectal cancer (CRC), this study analyzes the serum profile of cytokines, chemokines, growth factors, and soluble receptors in patients with CRC and cancer-free controls as possible CRC signatures. Serum levels of 65 analytes were measured in patients with CRC and age- and sex-matched cancer-free controls using the ProcartaPlex Human Immune Monitoring 65-Plex Panel. Of the 65 tested analytes, 8 cytokines (CSF-3, IFN-γ, IL-12p70, IL-18, IL-20, MIF, TNF-α and TSLP), 8 chemokines (fractalkine, MIP-1β, BLC, Eotaxin-1, Eotaxin-2, IP-10, MIP-1a, MIP-3a), 2 growth factors (FGF-2, MMP-1), and 4 soluble receptors (APRIL, CD30, TNFRII, and TWEAK), were differentially expressed in CRC. ROC analysis confirmed the high association of TNF-α, BLC, Eotaxin-1, APRIL, and Tweak with AUC > 0.70, suggesting theranostic application. The expression of IFN-γ, IL-18, MIF, BLC, Eotaxin-1, Eotaxin-2, IP-10, and MMP1 was lower in metastatic compared to non-metastatic CRC; only AUC of MIF and MIP-1β were > 0.7. Moreover, MDC, IL-7, MIF, IL-21, and TNF-α are positively associated with tolerance to CRC chemotherapy (CT) (AUC > 0.7), whereas IL-31, Fractalkine, Eotaxin-1, and Eotaxin-2 were positively associated with resistance to CT. TNF-α, BLC, Eotaxin-1, APRIL, and Tweak may be used as first-line early detection of CRC. The variable levels of MIF and MIP-1β between metastatic and non-metastatic cases assign prognostic nature to these factors in CRC progression. Regarding tolerance to CT, MDC, IL-7, MIF, IL-21, and TNF-α are key when down-regulated or resistant to treatment is observed.
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 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) | 0.000 | 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 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".