The clinicopathological implications of serum IL-33 and sST2 as cancer biomarkers: A narrative review
Bibliographic record
Abstract
Background: Interleukin (IL)-33 and its receptor, soluble suppression of tumorigenicity 2 (sST2), are key players in the immune response and cancer biology. IL-33 can promote tumorigenesis by enhancing cancer cell proliferation and modulating the immune environment to support tumor growth. Conversely, it can also bolster anti-tumor immunity by recruiting and activating immune effector cells. IL-33 plays a role in multiple aspects of cancer biology, such as promoting immune evasion, tumor growth, and metastasis. Objective: This study intends to assess the prognostic significance of serum IL-33 and sST2 in cancer and their association with clinicopathologic characteristics (CPC). Material & methods: Scopus, PubMed electronic databases and other sources were searched and analysed from 2008-2025. The quality of the study was assessed using the Newcastle-Ottawa Quality Assessment Scale. Results: A total of forty-four studies meeting the inclusion criteria were analyzed. These studies primarily employed an observational and analytical designs, with the majority conducted in the Southeast Asian region, particularly in China. Among the studies investigating serum IL-33 levels in cancer, 68% (26/38) reported elevated serum IL-33 levels, with the majority focusing on hepatocellular carcinoma (HCC) and non-small cell lung cancer (NSCLC), followed by breast (BC) and colon rectal cancer (CRC). Additionally, 85% (22/26) of the reports found a significant association between serum IL-33 expression in cancer and CPC. For regulating the availability and activity of IL-33, sST2, a decoy receptor that binds to IL-33, is crucial. Of the studies assessing sST2 in cancer, 55% (12/22) showed elevated sST2 levels, with most focusing on HCC, followed by BC and CRC. Furthermore, 54% (7/13) of these studies identified a significant correlation between sST2 levels and CPC. Conclusion: The detection of increased serum IL-33 across various malignancies highlights its potential as an emerging biomarker for cancer detection and prognosis. Similarly, elevated sST2 levels have been observed in different cancers and are linked to poor prognosis, further highlighting its potential as a biomarker for tumor progression. The IL-33/ST2 signaling pathway could offer new cancer treatment strategies by enhancing immune responses while mitigating tumor-promoting effects. This study explores the roles of IL-33 and sST2 as biomarkers, their relevance in cancer diagnostics and therapeutics, and their correlation with clinical outcomes across different cancer types.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".