Comparative Analysis of ncRNA Biomarkers in NSCLC: Exploring Diagnostic Potential and Addressing Gaps in Current Literature

Abstract

This paper will provide a detailed systematic review of non-coding RNA (ncRNA) biomarkers, such as microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and circular RNAs (circRNAs) in the early detection of non-small cell lung cancer (NSCLC). Based on the information obtained in more than 70 peer-reviewed articles published in the period between 2005 and 2025, the diagnostic accuracy, reproducibility, and translational potential of the most important ncRNAs were critically compared. miRNA-21 was the most diagnostically effective of all biomarkers with pooled sensitivity and specificity of over 85 percent, and area under the curve (AUC) values of over 0.90 in multi-ethnic cohorts. Its strength, stability in plasma and serum and mechanistic significance in oncogenic pathways including PTEN/PI3K/AKT highlight its clinical feasibility. Other miRNAs such as miRNA-155, miRNA-20a, lncRNAs MALAT1 and HOTAIR, and circRNAs circRNA-100876, CDR1as and hsa-circ-0013958 had moderate to high AUCs (0.80-0.88) but were not consistently validated and standardized on a large scale. Relative evaluation showed that miRNAs are closest to clinical translation because of cost-effective and reproducible quantification by qRT-PCR, lncRNAs and circRNAs have long-term prospects of being integrated into multi-biomarker diagnostic panels. The discussion identifies significant barriers to translation, including biological variability, inconsistency in detection, and the absence of cross-population validation, but argues that combinatorial ncRNA panels, which are enabled by artificial intelligence and multi-omics integration, have the potential to enhance diagnostic accuracy to AUCs exceeding 0.95. In conclusion, miRNA-21 is the most promising ncRNA to be used in the nearest future, and multi-centre trials, standardisation of the analysis processes, and the use of diverse populations are the key factors that will convert the ncRNA research into clinically usable NSCLC diagnostics. © (2025), (Central University of Finance and Economics). All rights reserved.