Circular RNA Expression Signatures Provide Promising Diagnostic and Therapeutic Biomarkers for Chronic Lymphocytic Leukemia

Abstract

Chronic lymphocytic leukemia (CLL) is a known hematologic malignancy associated with a growing incidence and post-treatment relapse. Hence, finding a reliable diagnostic biomarker for CLL is crucial. Circular RNAs (circRNAs) represent a new class of RNA involved in many biological processes and diseases. This study aimed to define a circRNA-based panel for the early diagnosis of CLL. To this point, the list of the most deregulated circRNAs in CLL cell models was retrieved using bioinformatic algorithms and applied to the verified CLL patients' online datasets as the training cohort (n = 100). The diagnostic performance of potential biomarkers represented in individual and discriminating panels, was then analyzed between CLL Binet stages and validated in individual sample sets I (n = 220) and II (n = 251). We also estimated the 5-year overall survival (OS), introduced the cancer-related signaling pathways regulated by the announced circRNAs, and provided a list of possible therapeutic compounds to control the CLL. These findings show that the detected circRNA biomarkers exhibit better predictive performance compared to current validated clinical risk scales, and are applicable for the early detection and treatment of CLL.

Keywords: cancer; chronic lymphocytic leukemia; circular RNA; diagnosis; drug sensitivity prediction; prognosis.

Figure 1

Bioinformatics analysis of circRNAs in CLL samples. (A) Heatmap demonstration of 52 differentially expressed circRNAs between CLL lymphocytes (red bar, right) and normal B-cells (blue bar, left). (B) Volcano plotting of deregulated circRNAs in CLL samples. (C) Interaction analysis between candidate circRNAs and other signaling mediators. (D) Gene ontology (GO) biological process and (E) WikiPathways enrichment analysis of gene networks. (F) Drug sensitivity prediction of gene networks in CLL.

Figure 2

Receiver operating characteristics (ROC) curve analysis of the log it model with the circKAT6A/circLNPEP/circMDM2/circMYH9 panel in the training set. The study group consisted of 100 CLLs and healthy B-lymphocytes. Using the optimal cutoff value of 0.7262, the diagnostic performance of the circRNA panel for discriminating CLLs with (A) all Binet stages, (B) Binet stage A, (C) Binet stage B, and (D) Binet stage C from healthy samples were examined. Log it (p) of the model was 0.7262 + 0.7690 × CircKAT6A + 0.1786 × CircLNPEP − 0.2246 × CircMDM2 + 0.9938 × CircMYH9.

Figure 3

Receiver operating characteristics (ROC) curve analysis of the log it model with the circKAT6A/circLNPEP/circMDM2/circMYH9 panel in the validation set I. The study group consisted of 220 CLLs and healthy B-lymphocytes. Using the optimal cutoff value of 0.7262, the diagnostic performance of the circRNA panel for discriminating CLLs with (A) all Binet stages, (B) Binet stage A, (C) Binet stage B, and (D) Binet stage C from healthy samples were examined. Log it (p) of the model was 0.7262 + 0.7690 × CircKAT6A + 0.1786 × CircLNPEP − 0.2246 × CircMDM2 + 0.9938 × CircMYH9.

Figure 4

Receiver operating characteristics (ROC) curve analysis of the log it model with the circKAT6A/circLNPEP/circMDM2/circMYH9 panel in the validation set II. The study group consisted of 251 CLLs and healthy B-lymphocytes. Using the optimal cutoff value as 0.7262, the diagnostic performance of circRNA panel for discriminating CLLs with (A) all Binet stages, (B) Binet stage A, (C) Binet stage B, and (D) Binet stage C from healthy samples were examined. Log it (p) of the model was 0.7262 + 0.7690 × CircKAT6A + 0.1786 × CircLNPEP − 0.2246 × CircMDM2 + 0.9938 × CircMYH9.

Figure 5

Correlation between the abnormal expression of candidate circRNAs and overall survival (OS) of CLL in the training set (n = 100). (A–D) Survival impact of CircKAT6A, CircLNPEP, CircMDM2, and CircMYH9 as individual biomarkers, and (E) in the form of a panel in CLL. Kaplan-Meier analysis indicated a reverse correlation between the high level of combined circRNAs and the poor survival of CLL patients in training set I.

Figure 6

Correlation between the abnormal expression of candidate circRNAs and overall survival (OS) of CLL in validation set I (n = 220). (A–D) Survival impact of CircKAT6A, CircLNPEP, CircMDM2, and CircMYH9 as individual biomarkers, and (E) in the form of a panel in CLL. A Kaplan-Meier analysis indicated a reverse correlation between the high level of circRNAs individually and combined, and the worse survival of CLL patients in validation set I.

Figure 7

Correlation between the abnormal expression of candidate circRNAs and overall survival (OS) of CLL in validation set II (n = 251). (A–D) Survival impact of CircKAT6A, CircLNPEP, CircMDM2, and CircMYH9 as individual biomarkers, and (E) in the form of a panel in CLL. A Kaplan-Meier analysis indicated a reverse correlation between the high level of circRNAs individually and combined, and the worse survival of CLL patients in the validation set II.