MicroRNA signature in patients with hepatocellular carcinoma associated with type 2 diabetes
Bibliographic record
Abstract
BACKGROUND: Nonalcoholic steatohepatitis-related cirrhosis is one of the liver complications in type 2 diabetes mellitus (T2DM) and reported to be a risk factor for developing hepatocellular carcinoma (HCC). A reliable screening biomarker of liver cirrhosis (LC) and HCC among T2DM patients is important to reduce the morbidity and mortality of this disease. MicroRNA (miRNA) is considered a key player in HCC and T2DM, and it might be a hidden culprit in diabetes-associated HCC, making it a promising reliable prognostic tool. AIM: To investigate the signature of serum miRNAs as early biomarkers for the screening of HCC among diabetic patients. METHODS: Expression profiles of miRNAs in serum samples of diabetic LC and diabetic HCC patients were assessed using Illumina sequencing; then, RT-qPCR was used to validate significantly altered miRNAs between the two groups. Candidate miRNAs were tested in serum samples of 200 T2DM patients, 270 LC patients, 200 HCC patients, and 225 healthy control subjects. Additionally, receiver operating characteristic (ROC) analysis, with area under the curve (AUC), was performed to assess the diagnostic performance of the screened miRNAs for discriminating HCC from LC and nonmalignant patients (LC + T2DM). RESULTS: LC-positive T2DM patients. Two miRNAs (miR-34a, miR-221) were significantly up-regulated and five miRNAs (miR-16, miR-23-3p, miR-122-5p, miR-198, miR-199a-3p) were significantly down-regulated in HCC compared to LC patients. Analysis of ROC curve demonstrated that the combination of these seven miRNAs can be used as a reliable biomarker for detection of HCC in diabetic patients, as it could identify HCC with high diagnostic accuracy in diabetic LC patients (AUC = 0.993) and in diabetic nonmalignant patients (AUC = 0.961). CONCLUSION: the miRNA regulatory pathway.
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 | 0.001 | 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".