miRNA-Seq Tissue Diagnostic Signature: A Novel Model for NSCLC Subtyping

Abstract

Non-small cell lung cancer (NSCLC) encompasses distinct histopathological subtypes, namely adenocarcinoma (AC) and squamous cell lung carcinoma (SCC), which require precise differentiation for effective treatment strategies. In this study, we present a novel molecular diagnostic model that integrates tissue-specific expression profiles of microRNAs (miRNAs) obtained through next-generation sequencing (NGS) to discriminate between AC and SCC subtypes of NSCLC. This approach offers a more comprehensive and precise molecular characterization compared to conventional methods such as histopathology or immunohistochemistry. Firstly, we identified 31 miRNAs with significant differential expression between AC and SCC cases. Subsequently, we constructed a 17-miRNA signature through rigorous multistep analyses, including LASSO/elastic net regression. The signature includes both upregulated miRNAs (hsa-miR-326, hsa-miR-450a-5p, hsa-miR-1287-5p, hsa-miR-556-5p, hsa-miR-542-3p, hsa-miR-30b-5p, hsa-miR-4728-3p, hsa-miR-450a-1-3p, hsa-miR-375, hsa-miR-147b, hsa-miR-7705, and hsa-miR-653-3p) and downregulated miRNAs (hsa-miR-944, hsa-miR-205-5p, hsa-miR-205-3p, hsa-miR-149-5p, and hsa-miR-6510-3p). To assess the discriminative capability of the 17-miRNA signature, we performed receiver operating characteristic (ROC) curve analysis, which demonstrated an impressive area under the curve (AUC) value of 0.994. Our findings highlight the exceptional diagnostic performance of the miRNA signature as a stratifying biomarker for distinguishing between AC and SCC subtypes in lung cancer. The developed molecular diagnostic model holds promise for providing a more accurate and comprehensive molecular characterization of NSCLC, thereby guiding personalized treatment decisions and improving clinical management and prognosis for patients.