Correlations between 14-gene RNA-level assay and clinical and molecular features in resectable non-squamous non-small cell lung cancer: a cross-sectional study
Bibliographic record
Abstract
Background: Non-small cell lung cancer (NSCLC) is the leading cause of cancer-related death worldwide. Accurate risk stratification is essential for optimizing treatment strategies. A 14-gene RNA-level assay of lung cancer, which involves quantitative reverse transcription polymerase chain reaction (qRT-PCR) analysis of formalin-fixed paraffin-embedded (FFPE) tissue samples, offers a promising approach. The aim of our study was to assess the relationships between risk stratification, as determined by a 14-gene RNA-level assay, and various clinical and molecular characteristics. Methods: We retrospectively collected the preoperative clinical information and molecular testing information from 102 resectable non-squamous NSCLC patients. The 14-gene RNA-level assay was performed by extracting RNA from FFPE samples, followed by reverse transcription and quantification via quantitative polymerase chain reaction (qPCR) to assess the expression levels of 11 cancer-associated genes and three housekeeping genes. These gene expression levels were used to calculate a risk score, enabling patient stratification into distinct risk groups. Based on the 14-gene risk stratification, we analyzed the correlations between the clinical and molecular characteristics across the high-, medium-, and low-risk groups. Results: A total of 102 patients were included in the study. The mean age was 55.19 years, 67 (65.7%) patients were female, and 18 (17.6%) had a smoking history. The 14-gene risk stratification classified patients into low-risk (n=63), intermediate-risk (n=25), and high-risk (n=14) groups. No significant differences were observed in baseline demographics between the three risk groups. High-risk patients had significantly higher mean computed tomography (CT) value (P=0.01) and enhanced CT value (P=0.02) compared to low-risk patients. Genomic profiling of 89 patients revealed specific mutations that were significantly associated with the higher-risk groups. Tumor mutational burden (TMB) was higher in higher-risk groups (P=0.007). In clinically low-risk patients (n=85) as recognized by the NCCN guidelines, the 14-gene risk stratification model reclassified 30 out from the 85 clinically low-risk patients, with 19 placed in the medium-risk group and 11 in the high-risk group, while the remaining samples were still classified as low-risk. Additionally, we found that three patients who were not recommended for adjuvant therapy by the Multiple-gene INdex to Evaluate the Relative benefit of Various Adjuvant therapies (MINERVA) model were classified as high risk and 13 as intermediate risk. Conclusions: . These insights provide a stronger foundation for integrating molecular risk assessment with clinical and imaging data, offering more comprehensive information to guide more targeted and effective adjuvant therapy strategies in the future management of lung cancer.
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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 | 0.002 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".