Logistic regression and other statistical tools in diagnostic biomarker studies.

A biomarker is a measured indicator of a variety of processes, and is often used as a clinical tool for the diagnosis of diseases. While the developmental process of biomarkers from lab to clinic is complex, initial exploratory stages often focus on characterizing the potential of biomarkers through utilizing various statistical methods that can be used to assess their discriminatory performance, establish an appropriate cut-off that transforms continuous data to apt binary responses of confirming or excluding a diagnosis, or establish a robust association when tested against confounders. This review aims to provide a gentle introduction to the most common tools found in diagnostic biomarker studies used to assess the performance of biomarkers with an emphasis on logistic regression.

The lncRNAs Gas5, MALAT1 and SNHG8 as diagnostic biomarkers for epithelial malignant pleural mesothelioma in Egyptian patients.

Long noncoding RNAs have been shown to be involved in a myriad of physiological and pathological pathways. To date, malignant pleural mesothelioma (MPM) is considered an extremely aggressive cancer. One reason for this is the late diagnosis of the disease, which can occur within 30-40 years of asbestos exposure. There is an immense need for the development of new, sensitive, inexpensive and easy methods for the early detection of this disease other than invasive methods such as biopsy. The aim of this study was to determine the expression of circulating lncRNAs in mesothelioma patient plasma to identify potential biomarkers. Ten previously identified lncRNAs that were shown to be aberrantly expressed in mesothelioma tissues were selected as candidates for subsequent validation. The expression of the ten selected candidate lncRNAs was verified via quantitative PCR (qPCR) in human plasma samples from mesothelioma patients versus healthy controls. The expression levels of circulating GAS5, SNHG8 and MALAT1 were significantly greater in plasma samples from patients than in those from controls. The ROC analysis of both MALAT1 and SNHG8 revealed 88.89% sensitivity and 66.67% specificity. The sensitivity of these markers was greater than that of GAS5 (sensitivity 72.22% and specificity 66.67%). The regression model for GAS5 was statistically significant, while that for SNHG8 and MALAT1 was not significant due to the small sample size. The area under the curve (AUC) of the three ROC curves was acceptable and significant: 0.7519 for GAS5, 0.7352 for SNHG8 and 0.7185 for MALAT1. This finding confirmed their ability to be used as markers. The three lncRNAs were not affected by age, sex or smoking status. The three lncRNAs showed great potential as independent predictive diagnostic biomarkers. Although the prediction model for MALAT1 did not significantly differ, MALAT1 was significantly expressed in patients more than in controls (p = 0.0266), and the recorded sensitivity and specificity were greater than those of GAS5.