Profiling of Small Nucleolar RNAs by Next Generation Sequencing: Potential New Players for Breast Cancer Prognosis
Bibliographic record
Abstract
One of the most abundant, yet least explored, classes of RNA is the small nucleolar RNAs (snoRNAs), which are well known for their involvement in post-transcriptional modifications of other RNAs. Although snoRNAs were only considered to perform housekeeping functions for a long time, recent studies have highlighted their importance as regulators of gene expression and as diagnostic/prognostic markers. However, the prognostic potential of these RNAs has not been interrogated for breast cancer (BC). The objective of the current study was to identify snoRNAs as prognostic markers for BC. Small RNA sequencing (Illumina Genome Analyzer IIx) was performed for 104 BC cases and 11 normal breast tissues. Partek Genomics Suite was used for analyzing the sequencing files. Two independent and proven approaches were used to identify prognostic markers: case-control (CC) and case-only (CO). For both approaches, snoRNAs significant in the permutation test, following univariate Cox proportional hazards regression model were used for constructing risk scores. Risk scores were subsequently adjusted for potential confounders in a multivariate Cox model. For both approaches, thirteen snoRNAs were associated with overall survival and/or recurrence free survival. Patients belonging to the high-risk group were associated with poor outcomes, and the risk score was significant after adjusting for confounders. Validation of representative snoRNAs (SNORD46 and SNORD89) using qRT-PCR confirmed the observations from sequencing experiments. We also observed 64 snoRNAs harboring piwi-interacting RNAs and/or microRNAs that were predicted to target genes (mRNAs) involved in tumorigenesis. Our results demonstrate the potential of snoRNAs to serve (i) as novel prognostic markers for BC and (ii) as indirect regulators of gene expression.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".