Identification of a predictive marker signature for diagnosing HNSCC based on platelet RNAseq
Bibliographic record
Abstract
Introduction Liquid biopsy offers a way identifying cancer by examination of body fluids. The present study deals with the analyses of ‚tumor-educated platelets‘ (TEP), a recently discovered novel option of liquid biopsy. Previous research identified a tumor cell – platelet interaction in different tumor entities, resulting in a transfer of tumor derived RNA into platelets, named further TEP. Material and Methods Sequencing analysis of RNA derived from platelets of tumor patients and healthy donors was performed (n=5/5). Additionally, RNA from the corresponding tumor was sequenced. Bioinformatic tools were applied. Subsequently, quantitative RT-PCR was used for verification of differentially existing mRNA in platelets from tumor patients versus healthy donors in a second cohort (n=6/7). Results RNAseq data revealed 426 significantly differentially existing RNA. Among them, we identified RNA coding for 49 genes characteristically expressed in epithelial cells. Additionally, in tumor patient’s platelets we observed RNA coding for genes involved in tumor progression by contributing to proliferation, metastasis or angiogenesis. We identified 5 differentially existing mRNA as potentially liquid biopsy biomarkers in TEP. Conclusion Based on these promising results of this pilot study a prospective study including a larger cohort should be initiated in order to verify the here proposed predictive marker signature allowing the identification of HNSCC based on platelet RNAseq. Publication History Article published online: 24 May 2022 © 2022. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial-License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/). Georg Thieme Verlag Rüdigerstraße 14, 70469 Stuttgart, Germany
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".