MicroRNA Profiling of Red Blood Cells for Lung Cancer Diagnosis

Abstract

Background: Despite extensive endeavors to establish cell-free circulating biomarkers for lung cancer diagnosis, clinical adoption remains elusive. Noteworthy, emergent evidence suggests the pivotal roles of red blood cells (RBCs) and their derivatives in tumorigenesis, illuminating potential avenues for diagnostic advancements using blood cell-derived microRNAs (miRNAs).

Methods: We executed microarray analyses on three principal blood cell types-RBCs, peripheral blood mononuclear cells (PBMCs), and neutrophils-encompassing 26 lung cancer patients and 26 healthy controls. Validation was performed using droplet digital PCR within an additional cohort comprising 42 lung cancer and 39 control cases.

Results: Our investigation unearthed distinct miRNA profiles associated with lung cancer across all examined blood cell types. Intriguingly, RBC-miRNAs emerged as potential novel biomarkers for lung cancer, an observation yet to be documented. Importantly, integrating miRNAs from disparate blood cell types yielded a superior diagnostic accuracy for lung cancer over individual cell-type miRNAs. Subsequently, we formulated three diagnostic panels, adeptly discerning non-small cell lung cancer, adenocarcinoma, and squamous cell carcinoma, maintaining consistency across various disease stages.

Conclusion: RBC-derived molecules introduce novel cancer biomarkers, and exploiting miRNA profiles across varied blood cell types unveils a promising frontier for lung cancer's early detection and histological classification.

Keywords: biomarkers; diagnosis; lung cancer; miRNAs; red blood cells.

Figure 1

A heatmap showcasing the differential expression of the 50 miRNAs with the most significant standard deviation across three distinct cell populations from the cancer-free smokers. The color gradient moves from green (denoting down-regulation) to black (indicating no change) and culminates in red (highlighting up-regulation). The results of normalized dCq values were analyzed using the Kruskal–Wallis test. A False Discovery Rate (FDR)-adjusted p-value of less than 0.05 was considered significant. Each row corresponds to a specific miRNA, while each column signifies a unique sample.

Figure 2

Heatmaps displaying unique miRNA profiles for each cell population, comparing lung cancer patients to cancer-free controls. The color gradient transitions from green (down-regulation) to black (no change) and culminates in red (up-regulation). Expression data rely on normalized dCq values. The Mann–Whitney U test was used for statistical analysis, with significance set at an FDR-adjusted p-value of <0.05. Within the heatmap, each row signifies a unique miRNA, while columns denote individual samples. Labels (A–C) indicate miRNA changes in RBCs, neutrophils (Neu), and PBMCs, respectively, across the 26 cancer-free smokers and 26 lung cancer patients.

Figure 3

Receiver Operating Characteristic (ROC) curve analysis for panels of miRNAs in differentiating NSCLC patients from cancer-free smokers. The ROC curve showcases the diagnostic performance of these biomarker panels, with the Area Under the Curve (AUC) indicating their overall diagnostic accuracy. (A) A panel of RBC miRNA biomarkers for detecting NSCLC. (B) A panel of RBC miRNA biomarkers for diagnosing AC. (C) A panel of RBC miRNA biomarkers for diagnosing SCC. (D) A panel of PBMC miRNA biomarkers for diagnosing NSCLC. (E) A PBMC miRNA biomarker (miR-29b-3p) for diagnosing AC. (F) A panel of PBMC miRNA biomarkers for diagnosing SCC. (G) A panel of neutrophil biomarkers for diagnosing NSCLC. (H) A neutrophil miR-26a-2-3p for diagnosing AC. (I) Integrated panel of biomarkers for diagnosing NSCLC. (J) Integrated panel of biomarkers for diagnosing AC. (K) Integrated panel of biomarkers for diagnosing SCC.