ABSP: an automated R tool to efficiently analyze region-specific CpG methylation from bisulfite sequencing PCR

A noninvasive method for predicting clinically significant prostate cancer using magnetic resonance imaging combined with PRKY promoter methylation level: a machine learning study

BACKGROUND

Traditional process for clinically significant prostate cancer (csPCA) diagnosis relies on invasive biopsy and may bring pain and complications. Radiomic features of magnetic resonance imaging MRI and methylation of the PRKY promoter were found to be associated with prostate cancer.

METHODS

Fifty-four Patients who underwent prostate biopsy or photoselective vaporization of the prostate (PVP) from 2022 to 2023 were selected for this study, and their clinical data, blood samples and MRI images were obtained before the operation. Methylation level of two PRKY promoter sites, cg05618150 and cg05163709, were tested through bisulfite sequencing PCR (BSP). The PI-RADS score of each patient was estimated and the region of interest (ROI) was delineated by 2 experienced radiologists. After being extracted by a plug-in of 3D-slicer, radiomic features were selected through LASSCO regression and t-test. Selected radiomic features, methylation levels and clinical data were used for model construction through the random forest (RF) algorithm, and the predictive efficiency was analyzed by the area under the receiver operation characteristic (ROC) curve (AUC).

RESULTS

Methylation level of the site, cg05618150, was observed to be associated with prostate cancer, for which the AUC was 0.74. The AUC of T2WI in csPCA prediction was 0.84, which was higher than that of the apparent diffusion coefficient ADC (AUC = 0.81). The model combined with T2WI and clinical data reached an AUC of 0.94. The AUC of the T2WI-clinic-methylation-combined model was 0.97, which was greater than that of the model combined with the PI-RADS score, clinical data and PRKY promoter methylation levels (AUC = 0.86).

CONCLUSIONS

The model combining with radiomic features, clinical data and PRKY promoter methylation levels based on machine learning had high predictive efficiency in csPCA diagnosis.

The predictive value of BTG1 for the response of newly diagnosed acute myeloid leukemia to decitabine

BACKGROUND

Decitabine has been widely used to treat acute myeloid leukemia (AML); however as AML is a heterogeneous disease, not all patients benefit from decitabine. This study aimed to identify markers for predicting the response to decitabine.

METHODS

An intersection of in vitro experiments and bioinformatics was performed using a combination of epigenetic and transcriptomic analysis. A tumor-suppressor gene associated with methylation and the response to decitabine was screened. Then the sensitivity and specificity of this marker in predicting the response to decitabine was confirmed in 54 samples from newly diagnosed AML patients treated with decitabine plus IA regimen in a clinical trial (ChiCTR2000037928).

RESULTS

In vitro experiments showed that decitabine caused hypomethylation and upregulation of BTG1, while downregulation of BTG1 attenuated the inhibitory effect of decitabine. In newly diagnosed AML patients who received decitabine plus IA regimen, the predictive value of BTG1 to predict complete remission (CR) was assigned with a sensitivity of 86.7% and a specificity of 100.0% when BTG1 expression was < 0.292 (determined using real-time quantitative PCR), with area under the curve (AUC) = 0.933, P = 0.021. The predictive value of BTG1 to predict measurable residual disease (MRD) negativity was assigned with a sensitivity of 100.0% and a specificity of 80.0% when BTG1 expression was < 0.292 (AUC = 0.892, P = 0.012). Patients were divided into low and high BTG1 expression groups according to a cutoff of 0.292, and the CR rate of the low-expression group was significantly higher than that of the high-expression group (97.5% vs. 50%, P < 0.001).

CONCLUSIONS

Low expression of BTG1 was associated with CR and MRD negativity in newly diagnosed AML patients treated with a decitabine-containing regimen, suggesting that BTG1 is a potential marker for predicting the response to decitabine in newly diagnosed AML.

CLINICAL TRIAL REGISTRATION

ChiCTR2000037928.

CpG DNA methylation changes during epididymal sperm maturation in bulls

BACKGROUND

During epididymal transit spermatozoa acquire specific morphological features which enhance their ability to swim in a progressive manner and interact with the oocytes. At the same time, sperm cells undergo specific molecular rearrangements essential for the fertilizing sperm to drive a correct embryo development. To assess epigenetic sperm changes during epididymal maturation, the caput, corpus and cauda epididymis sperm tracts were isolated from eight bulls and characterized for different sperm quality parameters and for CpG DNA methylation using Reduced Representation Bisulfite Sequencing (RRBS) able to identify differentially methylated regions (DMRs) in higher CpG density regions.

RESULTS

Caput sperm showed significant variation in motility and sperm kinetics variables, whereas spermatozoa collected from the corpus presented morphology variation and significant alterations in variables related to acrosome integrity. A total of 57,583 methylated regions were identified across the eight bulls, showing a significantly diverse distribution for sperm collected in the three epididymal regions. Differential methylation was observed between caput vs corpus (n = 11,434), corpus vs cauda (n = 12,372) and caput vs cauda (n = 2790). During epididymal transit a high proportion of the epigenome was remodeled, showing several regions in which methylation decreases from caput to corpus and increases from corpus to cauda.

CONCLUSIONS

Specific CpG DNA methylation changes in sperm isolated from the caput, corpus, and cauda epididymis tracts are likely to refine the sperm epigenome during sperm maturation, potentially impacting sperm fertilization ability and spatial organization of the genome during early embryo development.