Whole-miRNome profiling identifies prognostic serum miRNAs in esophageal adenocarcinoma: the influence of Helicobacter pylori infection status
Bibliographic record
Abstract
Cell free circulating microRNAs (cfmiRNAs) have been recognized as robust and stable biomarkers of cancers. However, little is known about the prognostic significance of cfmiRNAs in esophageal adenocarcinoma (EA). In this study, we explored whether specific cfmiRNA profiles could predict EA prognosis and whether Helicobacter pylori (HP) infection status could influence the association between cfmiRNAs and EA survival outcome. We profiled 1075 miRNAs in pooled serum samples from 30 EA patients and 30 healthy controls. The most relevant cfmiRNAs were then assessed for their associations with EA survival in an independent cohort of 82 patients, using Log-rank test and multivariate Cox regression models. Quantitative real-time PCR (qRT-PCR) was used for cfmiRNA profiling. HP infection status was determined by immunoblotting assay. We identified a panel of 18 cfmiRNAs that could distinguish EA patients from healthy subjects (P = 3.0E-12). In overall analysis and in HP-positive subtype patients, no cfmiRNA was significantly associated with EA prognosis. In HP-negative patients, however, 15 cfmiRNAs were significantly associated with overall survival (OS) (all P < 0.05). A combined 2-cfmiRNA (low miR-3935 and high miR-4286) risk score was constructed; that showed greater risk for worse OS (HR = 2.22, P = 0.0019) than individual cfmiRNA alone. Patients with high-risk score had >10-fold increased risk of death than patients with low risk score (P = 0.0302; HR = 10.91; P = 0.0094). Our findings suggest that dysregulated cfmiRNAs may contribute to EA survival outcome and HP infection status may modify the association between cfmiRNAs and EA survival.
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 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 | 0.000 | 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) | 0.000 | 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".