Increased expression of PDGFA and RAF1 in Tumor-derived exosomes in humancolorectal cancer
Bibliographic record
Abstract
The overexpression of tumor markers within Extracellular Vesicles (EVs), particularly in tumor-derived exosomes (TDEs), plays a pivotal role in metastasis in the context of colorectal cancer (CRC). Nonetheless, the precise role of EV content in CRC diagnosis and prognosis necessitates extensive validation through bioinformatics and clinical investigations. We explored molecular markers shared between TDEs and circulating tumor cells (CTCs) in the blood of cancer patients to identify candidate genes involved in metastasis. Common markers were analyzed in gene expression profiles of two studies (GSE31023 and GSE72577). The expression of candidate genes was assessed by RT-PCR in CTC, TDEs, and microvesicles (MVs), and was correlated with clinicopathological features. To further confirm, the expression of candidate genes was investigated in exosomes derived from the parental HT-29 colorectal cancer cell line (HT-29-EXOs), and cancer stem cells (CSCs) -enriched spheroids (CSC-EXOs) derived thereof. Gene ontology (GO) analysis suggested platelet-derived growth factor A (PDGFA) and proto-oncogene, Serine/Threonine kinase Raf-1 (RAF1) as new CRC candidate markers in CTCs and TDEs. Expression of PDGFA (P=0.0086) and RAF1 (P=0.048) were upregulated in TDEs but significantly decreased (P=0.0001) in MVs. Furthermore, expression in CSC-EXOs (P=0.0004) was increased compared to HT-29-EXOs. PDGFA and RAF1 mRNA are higher in CSC-EXOs than in HT-29-EXOs, which correlates with higher expression in CSC than in the primary tumor. Notably, as no increase was observed in MVs, PDGFA and RAF1 mRNA appear to be actively recruited into TDE.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".