2023 World Conference on Lung Cancer (Posters)-P1.28. EVs-miRNAs-Based Models in Predicting Radiosensitive Patients with Local Advanced-NSCLC Who Can Benefit from Adaptive Radiotherapy

P1.28. EVs-miRNAs-Based Models in Predicting Radiosensitive Patients with Local Advanced-NSCLC Who Can Benefit from Adaptive Radiotherapy - PDF(Slides)

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Researchers in China have developed models based on extracellular vesicle-microRNA (EVs-miRNA) analysis to predict which patients with locally advanced non-small cell lung cancer (LA-NSCLC) will benefit from adaptive radiotherapy (ART). The study aimed to establish models that could identify radiotherapy-sensitive (RS) patients who would gain more benefits from treatment compared to radiotherapy-resistant (RR) patients. The researchers used next-generation sequencing (NGS) to analyze the differential expression profile of EVs-miRNA in the plasma of LA-NSCLC patients before radiotherapy. They then validated the results using RT-PCR and grouped patients based on their radiosenstivity and tumor regression. Logistic regression was used to establish models based on the identified miRNAs, and bootstrap validation was used to verify the stability of the models. The models were evaluated using ROC curve analysis. Metascape was used to perform functional enrichment analysis of the target genes of the identified miRNAs. The study found that the RR and RS groups had no significant differences in clinical features when grouped by radiosenstivity criteria. A total of 142 differentially expressed EVs-miRNAs associated with radiosensitivity were identified. EVs-miR-657, -4514, and -4797 were confirmed in the validation cohort, with EVs-miR-657 significantly downregulated in RR patients and EVs-miR-4514 and EVs-miR-4797 upregulated in RR patients. Radiosensitive predictive models based on these miRNAs were established, with AUC values of 0.929, 0.877, and 0.856 for different criteria. Metascape analysis showed that the target genes of these miRNAs were mainly enriched in cellular response to DNA damage stimulus and cell cycle pathways. Cell tests confirmed that miR-657 was significantly lower in NSCLC cells compared to normal lung cells. The researchers concluded that their EVs-miRNA-based models can effectively predict which RS patients will benefit from ART, assisting in guiding its clinical application and supporting the exploration of radiosensitizers.

Asset Subtitle

Haihua Yang

Meta Tag

Speaker Haihua Yang

Topic Local-Regional NSCLC

Keywords

extracellular vesicle-microRNA analysis

locally advanced non-small cell lung cancer

adaptive radiotherapy

radiotherapy-sensitive patients

radiotherapy-resistant patients

next-generation sequencing

differential expression profile

logistic regression

functional enrichment analysis

radiosensitive predictive models