Alternative Splicing Variant of Kallikrein-Related Peptidase 8 as an Independent Predictor of Unfavorable Prognosis in Lung Cancer
Bibliographic record
Abstract
BACKGROUND: A relatively unexplored area for biomarker identification is alternative splice variants. We undertook this study to evaluate the usefulness of mRNA isoforms encoded by the KLK8 (kallikrein-related peptidase 8) gene as prognostic markers for lung cancer. METHODS: Real-time reverse-transcription PCR was used to analyze the mRNAs encoded by KLK8 (particularly 2 mRNA splice variants, KLK8-T3 and KLK8-T4) in 60 non-small-cell lung cancer (NSCLC) tumors and in paired unaffected tissues. The ratios of these mRNAs to those encoded by the KLK5, KLK6, KLK7, KLK10, KLK11, KLK13, and KLK14 genes were also determined and analyzed for correlations with various clinicopathologic variables. RESULTS: KLK8-T3 and KLK8-T4 were the most abundant of the 6 mRNA isoforms identified in lung tissues. The overall expression of the KLK8 gene and the amounts of the KLK8-T3 and KLK8-T4 mRNAs were significantly increased in lung tumor tissue (P < 0.0001). Univariate survival analysis revealed significant relationships of the relative concentrations of mRNA splice variants KLK8 (P = 0.043), KLK8-T3 (P = 0.037), and KLK8-T4 (P = 0.009) with overall survival (OS). Cox multivariate analysis indicated that the amount of KLK8-T4 mRNA was an independent prognostic factor for OS (relative risk = 3.90; P = 0.016) and that high KLK8-T4/KLK7, KLK8-T4/KLK10, and KLK8-T4/KLK11 mRNA ratios in NSCLC indicated increased risk of death. The increase was approximately 5-fold for the KLK8-T4/KLK7 and KLK8-T4/KLK10 ratios (P = 0.006, and P = 0.011, respectively) and 8-fold for the KLK8-T4/KLK11 ratio (P = 0.001). CONCLUSIONS: The KLK8-T4 alternative splice variant, alone or in combination, may be a new independent marker of unfavorable prognosis in lung cancer.
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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 | 0.001 |
| 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) | 0.001 | 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".