Exploration of differentially-expressed exosomal mRNAs, lncRNAs and circRNAs from serum samples of gallbladder cancer and xantho-granulomatous cholecystitis patients
Bibliographic record
Abstract
Gallbladder cancer (GBC) is the most common biliary tract malignancy worldwide. Although a growing number of studies have explored the mechanism of GBC, thus far, few molecules have been discovered that can be utilized as specific biomarkers for the early diagnosis and therapeutic treatment of GBC. Recent studies have shown that exosomes not only participate in the progression of tumors, but also carry specific information that can define multiple cancer types. The present study investigated the expression profiles of coding (or messenger) ribonucleic acids (mRNAs) and non-coding RNAs (ncRNAs, including long non-coding RNAs [lncRNAs] and circular RNAs [circRNAs]) in plasma-derived exosomes from GBC patients. Using high-throughput RNA sequencing and subsequent bioinformatic analysis, a number of differentially expressed (DE) mRNAs, lncRNAs, and circRNAs were identified in GBC exosomes, compared to their expressions in xantho-granulomatous cholecystitis (XGC) exosomes. Gene Ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) analyses were then conducted to investigate the potential functions of these DE RNAs. Furthermore, the interaction networks and competing endogenous RNA networks of these DE RNAs and their target genes were investigated, revealing a complex regulatory network among mRNAs and ncRNAs. In summary, this study demonstrates the diagnostic value of plasma-derived exosomes in GBC and provides a new perspective on the mechanism of GBC.
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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 | 0.001 |
| 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".