Identification and Validation Model for Informative Liquid Biopsy-Based microRNA Biomarkers: Insights from Germ Cell Tumor In Vitro, In Vivo and Patient-Derived Data
Bibliographic record
Abstract
Liquid biopsy-based biomarkers, such as microRNAs, represent valuable tools for patient management, but often do not make it to integration in the clinic. We aim to explore issues impeding this transition, in the setting of germ cell tumors, for which novel biomarkers are needed. We describe a model for identifying and validating clinically relevant microRNAs for germ cell tumor patients, using both in vitro, in vivo (mouse model) and patient-derived data. Initial wide screening of candidate microRNAs is performed, followed by targeted profiling of potentially relevant biomarkers. We demonstrate the relevance of appropriate (negative) controls, experimental conditions (proliferation), and issues related to sample origin (serum, plasma, cerebral spinal fluid) and pre-analytical variables (hemolysis, contaminants, temperature), all of which could interfere with liquid biopsy-based studies and their conclusions. Finally, we show the value of our identification model in a specific scenario, contradicting the presumed role of miR-375 as marker of teratoma histology in liquid biopsy setting. Our findings indicate other putative microRNAs (miR-885-5p, miR-448 and miR-197-3p) fulfilling this clinical need. The identification model is informative to identify the best candidate microRNAs to pursue in a clinical setting.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".