Multi-MicroRNA Analysis Can Improve the Diagnostic Performance of Mammography in Determining Breast Cancer Risk.

The objective of this study was to determine whether multi-microRNA analysis using a combination of four microRNA biomarkers (miR-1246, 202, 21, and 219B) could improve the diagnostic performance of mammography in determining breast cancer risk by age group (under 50 vs. over 50) and distinguish breast cancer from benign breast diseases and other cancers (thyroid, colon, stomach, lung, liver, and cervix cancers). To verify breast cancer classification performance of the four miRNA biomarkers and whether the model providing breast cancer risk score could distinguish between benign breast disease and other cancers, the model was verified using nonlinear support vector machine (SVM) and generalized linear model (GLM) and age and four miRNA qRT-PCR analysis values (dCt) were input to these models. Breast cancer risk scores for each Breast Imaging-Reporting and Data System (BI-RADS) category in multi-microRNA analysis were analyzed to examine the correlation between breast cancer risk scores and mammography categories. We generated two models using two classification algorithms, SVM and GLM, with a combination of four miRNA biomarkers showing high performance and sensitivities of 84.5% and 82.1%, a specificity of 85%, and areas under the curve (AUCs) of 0.967 and 0.965, respectively, which showed consistent performance across all stages of breast cancer and patient ages. The results of this study showed that this multi-microRNA analysis using the four miRNA biomarkers was effective in classifying breast cancer in patients under the age of 50, which is challenging to accurately diagnose. In addition, breast cancer and benign breast diseases can be classified, showing the possibility of helping with diagnosis by mammography. Verification of the performance of the four miRNA biomarkers confirmed that multi-microRNA analysis could be used as a new breast cancer screening aid to improve the accuracy of mammography. However, many factors must be considered for clinical use. Further validation with an appropriate screening population in large clinical trials is required. This trial is registered with (KNUCH 2022-04-036).

Stratification of Nuclear Homogeneous Patterns on HEp-2 Cells Based on Neutrophil Nuclear Staining.

Antinuclear antibody (ANA) testing is used to diagnose systemic autoimmune rheumatic disease (SARD). Nuclear homogeneous patterns on ANA-HEp-2 cells can result from anti-double-stranded DNA (dsDNA), anti-nucleosome, anti-histone, anti-Scl-70, or anti-dense fine speckles 70 (DFS70) antibodies (Abs). This study aimed to find a way to discriminate DFS70 Abs from others by way of assessing neutrophil nuclear staining on anti-neutrophil cytoplasmic antibody (ANCA) testing. Nuclear staining on ANCA-neutrophils was assessed to stratify nuclear homogeneous patterns on ANA-HEp-2 cells. Enrolled subjects included (1) young individuals with a dense fine speckled pattern on ANA testing (young non-SARD group, n=71) and patients with (2) systemic lupus erythematosus (SLE group, n=35); (3) rheumatoid arthritis possibly with histone, nucleosome Abs, and others (RA group, n=51); and (4) diffuse systemic sclerosis with Scl-70 Abs (diffuse SSc group, n=19). Negative rates (95% confidence interval) of neutrophil nuclear staining were 97.2% (90.2%-99.7%) in the young non-SARD group, 2.9% (0.1%-14.9%) in the SLE group, 3.9% (0.5%-13.5%) in the RA group, and 47.4% (24.5%-71.1%) in the diffuse SSc group. The negative rate of the young non-SARD group was significantly higher than those of the other groups (all p<0.05). In conclusion, this study suggests that the assessment of nuclear staining on ANCA-neutrophils can help to stratify nuclear homogeneous patterns on ANA-HEp-2 cells and thus to determine whether the ANA pattern is attributed to DFS70 Abs, which can be found in healthy individuals, especially in young individuals.

Evaluation of the Value of Multiplex MicroRNA Analysis as a Breast Cancer Screening in Korean Women under 50 Years of Age with a High Proportion of Dense Breasts.

This study was conducted to confirm the performance of the microRNA (miRNA) biomarker combination as a new breast cancer screening method in Korean women under the age of 50 with a high percentage of dense breasts. To determine the classification performance of a set of miRNA biomarkers (miR-1246, 202, 21, and 219B) useful for breast cancer screening, we determined whether there was a significant difference between the breast cancer and healthy control groups through box plots and the Mann-Whitney U-test, which was further examined in detail by age group. To verify the classification performance of the 4 miRNA biomarker set, 4 classification methods (logistic regression, random forest, XGBoost, and generalized linear model plus random forest) were applied, and 10-fold cross-validation was used as a validation method to improve performance stability. We confirmed that the best breast cancer detection performance was achievable in patients under 50 years of age when the set of 4 miRNAs were used. Under the age of 50, the 4 miRNA biomarkers showed the highest performance with a sensitivity of 85.29%, specificity of 93.33%, and area under the curve (AUC) of 0.961. Examining the results of 4 miRNA biomarkers was found to be an effective strategy for diagnosing breast cancer in Korean women under 50 years of age with dense breasts, and hence has the potential as a new breast cancer screening tool. Further validation in an appropriate screening population with large-scale clinical trials is required.