Identification of oncofetal PIWI-interacting RNAs as potential prognostic biomarkers in non-small cell lung cancer
Bibliographic record
Abstract
Lung cancer is the leading cause of cancer-related deaths worldwide, with non-small cell lung cancer (NSCLC) accounting for the majority of these cases. Despite advancements in targeted therapies, early detection remains a significant challenge, highlighting the need for novel biomarkers. This study investigates the role of PIWI-interacting RNAs (piRNAs) in lung cancer, specifically focusing on their potential as oncofetal biomarkers in lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), the two most common histological subtypes of NSCLC. We hypothesize that piRNAs exhibit oncofetal expression patterns and may contribute to lung cancer development. Through bioinformatics analysis, we identified distinct piRNA profiles in non-neoplastic, malignant, and fetal lung tissues. Among these, 37 piRNAs in LUAD and 46 piRNAs in LUSC displayed oncofetal expression, meaning they were present in tumor tissues but absent in adjacent normal lung tissue. These oncofetal piRNAs showed significant prognostic value in both LUAD and LUSC cohorts, with a specific signature of eight oncofetal piRNAs predicting high-risk patients in LUAD. We validated the robustness of this signature in a separate in-house cohort, which underscores its potential as a prognostic biomarker. Our findings suggest that oncofetal piRNAs could offer new diagnostic and therapeutic opportunities, particularly for early detection.
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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 | 0.001 | 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".