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
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».