High-Speed Videoendoscopy Enhances the Objective Assessment of Glottic Organic Lesions: A Case-Control Study with Multivariable Data-Mining Model Development.

The aim of the study was to utilize a quantitative assessment of the vibratory characteristics of vocal folds in diagnosing benign and malignant lesions of the glottis using high-speed videolaryngoscopy (HSV). METHODS: Case-control study including 100 patients with unilateral vocal fold lesions in comparison to 38 normophonic subjects. Quantitative assessment with the determination of vocal fold oscillation parameters was performed based on HSV kymography. Machine-learning predictive models were developed and validated. RESULTS: All calculated parameters differed significantly between healthy subjects and patients with organic lesions. The first predictive model distinguishing any organic lesion patients from healthy subjects reached an area under the curve (AUC) equal to 0.983 and presented with 89.3% accuracy, 97.0% sensitivity, and 71.4% specificity on the testing set. The second model identifying malignancy among organic lesions reached an AUC equal to 0.85 and presented with 80.6% accuracy, 100% sensitivity, and 71.1% specificity on the training set. Important predictive factors for the models were frequency perturbation measures. CONCLUSIONS: The standard protocol for distinguishing between benign and malignant lesions continues to be clinical evaluation by an experienced ENT specialist and confirmed by histopathological examination. Our findings did suggest that advanced machine learning models, which consider the complex interactions present in HSV data, could potentially indicate a heightened risk of malignancy. Therefore, this technology could prove pivotal in aiding in early cancer detection, thereby emphasizing the need for further investigation and validation.

MicroRNA profiling in a case-control study of African American women with uterine serous carcinoma.

OBJECTIVE: Uterine serous carcinoma (USC) is an aggressive subtype of endometrial cancer associated with worse survival outcomes in African American (AA) patients. This study evaluated tumor miRNA expression by race, clinical and tumor characteristics, and survival outcomes. METHODS: FFPE tumor tissue from hysterectomy specimens was identified for 29 AA cases. Case matching was performed by computer-based random assignment in a 1:1 ratio with Caucasian controls based on age, stage and histologic subtype (pure vs. mixed). RNA was extracted from 77 specimens (54 tumors and 23 matched normal endometrium). MicroRNA array profiling was performed by microRNA Hi-Power Labeling (Hy3/Hy5) and hybridization to miRCURY LNA microRNA Array 7th Gen. RESULTS: Clinical and treatment characteristics were similar for cases and controls, although use of adjuvant radiation was less common in African Americans (p = 0.03). Of 968 miRNAs analyzed, 649 were differentially expressed in normal endometrium vs. tumor. When compared by race, histologic subtype, stage or presence of LVI, no differentially expressed miRNAs were identified. In patients with disease recurrence at 3 years, the three most upregulated miRNAs were miR-1, miR-21-5p and miR-223. Of these, increased miR-223 expression (>median) was associated with worse OS (p = 0.0496) in an independent dataset (TCGA dataset) comprising of 140 patients with USC (mixed or pure serous). After adjusting for age, ethnicity and BMI, upregulation of miR-223 remained risk factor for death (adjusted HR 2.87, 95% CI 1.00-8.27). CONCLUSIONS: MiRNA profiling did not identify biological differences between AA and Caucasian patients with USC. Upregulation of miR-223 may be a prognostic factor in USC.

Expression-based decision tree model reveals distinct microRNA expression pattern in pediatric neuronal and mixed neuronal-glial tumors.

BACKGROUND: The understanding of the molecular biology of pediatric neuronal and mixed neuronal-glial brain tumors is still insufficient due to low frequency and heterogeneity of those lesions which comprise several subtypes presenting neuronal and/or neuronal-glial differentiation. Important is that the most frequent ganglioglioma (GG) and dysembryoplastic neuroepithelial tumor (DNET) showed limited number of detectable molecular alterations. In such cases analyses of additional genomic mechanisms seem to be the most promising. The aim of the study was to evaluate microRNA (miRNA) profiles in GGs, DNETs and pilocytic asytrocytomas (PA) and test the hypothesis of plausible miRNA connection with histopathological subtypes of particular pediatric glial and mixed glioneronal tumors. METHODS: The study was designed as the two-stage analysis. Microarray testing was performed with the use of the miRCURY LNA microRNA Array technology in 51 cases. Validation set comprised 107 samples used during confirmation of the profiling results by qPCR bioinformatic analysis. RESULTS: Microarray data was compared between the groups using an analysis of variance with the Benjamini-Hochberg procedure used to estimate false discovery rates. After filtration 782 miRNAs were eligible for further analysis. Based on the results of 10 × 10-fold cross-validation J48 algorithm was identified as the most resilient to overfitting. Pairwise comparison showed the DNETs to be the most divergent with the largest number of miRNAs differing from either of the two comparative groups. Validation of array analysis was performed for miRNAs used in the classification model: miR-155-5p, miR-4754, miR-4530, miR-628-3p, let-7b-3p, miR-4758-3p, miRPlus-A1086 and miR-891a-5p. Model developed on their expression measured by qPCR showed weighted AUC of 0.97 (95% CI for all classes ranging from 0.91 to 1.00). A computational analysis was used to identify mRNA targets for final set of selected miRNAs using miRWalk database. Among genomic targets of selected molecules ZBTB20, LCOR, PFKFB2, SYNJ2BP and TPD52 genes were noted. CONCLUSIONS: Our data showed the existence of miRNAs which expression is specific for different histological types of tumors. miRNA expression analysis may be useful in in-depth molecular diagnostic process of the tumors and could elucidate their origins and molecular background.