Antonina Aleksandrovna Ilyushchenko

BACKGROUND: Asymptomatic development and untimely diagnostics of lung cancer are the main reasons of high mortality caused by this disease. The development of tests based on “fluid biopsy”, which analyze circulating nucleic acids in blood, including the microRNA (miRNA) derived from the microvesicles, can be implemented as an effective screening test for lung cancer assuring the timely detection of the disease. AIM: To analyze the relative expression of 17 miRNAs in the microvesicles fraction from blood plasma samples from patients with non-small-cell lung cancer (NSCLC) and healthy donors. METHODS: The blood plasma microvesicles were isolated by aggregation-precipitation. miRNA was then isolated from the obtained blood microvesicles fraction using the glass fiber filters. Using the method of reverse transcription polymerаse chain reaction, an evaluation was carried out for the diagnostic efficiency of previously proposed miRNA pairs. This analysis was also used to select the most effective diagnostic pairs and panels. RESULTS: The obtained data have confirmed the diagnostic efficiency of four miRNA pairs (-31/-125b; -133b/-374a; -133b/-425; -133b/-222). The sensitivity and the specificity of distinguishing NSCLC patients from donors were 79% and 100%, respectively. Further analysis using paired normalization, compiling the regression models and multiple sample generations yielded three independent minimal panels of miRNA pairs, capable of diagnosing NSCLC with 100% accuracy: (1) -19b/-425, -125b/-378a, -205/-660; (2) -324/-374a, -30e/-92a, -125b/-378a; (3) -324/-374a, -125b/-378a, -205/-660. CONCLUSION: The diagnostic performance of the proposed miRNA panel across independent cohorts, along with the observed inter-cohort variability, supports its promising potential for NSCLC screening test development. These data demonstrate the necessity to identify additional multi-purpose miRNA markers and to validate the panel in further independent patient groups. Such research is essential to establish a reliable and timely miRNA-based screening tool for NSCLC.

Lung cancer (LC) remains a leading cause of global cancer mortality, driving the need for novel timely detection strategies, i.e., stages I–II detection when tumor curation is efficient. Circulating microRNA (miRNAs), with their unique stability in biofluids, offer a powerful approach for non-invasive detection. This review compiles validated miRNAs implicated in the early stages of non-small-cell lung cancer (NSCLC), elucidating their roles in key oncogenic pathways such as epithelial-mesenchymal transition (EMT), PI3K/AKT/mTOR, and JAK-STAT, which regulate proliferation, apoptosis, and metastasis. Furthermore, we critically evaluate developed miRNA panels with a specific focus on advanced quantification and normalization strategies, including exogenous spike-in controls and data-driven methods like pairwise normalization, to enhance diagnostic accuracy. Consequently, we identify and rank the most viable miRNA candidates according to key analytical and clinical metrics, providing a clear roadmap for translating these biomarkers into effective panels for the timely detection of NSCLC.

The stability of miRNAs in biological fluids and their association with pathological conditions make miRNAs promising biomarkers for non-invasive disease diagnostics, including non-small cell lung cancer (NSCLC). However, variability in miRNA expression and technical aspects of quantitative reverse transcription PCR (RT-qPCR) necessitate effective normalization methods to ensure accurate evaluation of miRNA levels and identification of biological differences. In this study, we performed a comparative analysis of several miRNA normalization approaches applied to extracellular vesicles isolated from plasma of NSCLC patients, including pairwise normalization, “Tres” and “Quadro” normalization strategies, normalization to the mean, and normalization considering miRNA functional groups. Method effectiveness was evaluated using quality metrics of diagnostic models. The most robust results were observed with normalization methods utilizing miRNA pairs, triplets, and quadruplets, which provided high accuracy, model stability, and minimal overfitting. In contrast, normalization to the general or exclusive mean and functional group-based normalization showed lower efficiency in terms of classification performance and feature selection stability. These findings highlight the critical importance of normalization strategy choice to improve the accuracy and interpretability of RT-qPCR-based diagnostic models, particularly in the development of biomarker panels for NSCLC diagnostics. Pairwise normalization, combining computational simplicity and high efficiency, appears optimal for practical applications.

Lung cancer represents a heterogeneous group of malignant neoplasms, among which two main forms can be distinguished — the non-small-cell and the small-cell lung cancer. These subtypes significantly differ by the histological, the molecular-genetic and the clinical characteristics, which defines the necessity of precise differential diagnostics for selecting the optimal treatment tactics. The review highlights the modern methods of diagnostics for the non-small-cell and the small-cell lung cancer, including the instrumental diagnostics, the histological and immunohistochemical examinations. Special attention was paid to the pros and cons of the promising non- and minimally invasive approaches, such as the analysis of circulating tumor cells, of the extracellular DNA, of the miRNA, of the marker proteins, of the volatile organic compounds and of the modern medical visualization (radiomics). Despite the significant progress in developing new diagnostic approaches, the problems remain that are related to the heterogeneity of tumors, the limited accessibility of the materials of small-cell lung cancer and the necessity of standardizing the new methods. The promising direction seems to the integration of multimodal approaches, combining the fluid biopsy, radiomics and the algorithms of machine learning, which can increase the precision of diagnostics and optimize the personalized treatment of the patients with various subtypes of lung cancer.