Identification of a Six-lncRNA Prognosis Model for Predicting Progression-Free Survival in Patients with Thyroid Cancer
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Abstract Background: Thyroid cancer is the most common malignant tumor of the endocrine system. Long non-coding RNAs (lncRNAs) have been demonstrated as novel biomarkers for cancer prognosis. Methods: In this study, we performed differential expression analysis of lncRNA expression profiles in GEO datasets. LASSO regression analysis was conducted to identify lncRNA-based prognosis model that can predict progression-free survival in thyroid cancer patients from The Cancer Genome Atlas (TCGA). Further functional analysis revealed the potential biological functions of the lncRNAs. Results: A risk score model based on six lncRNA biomarkers were established after LASSO Cox regression analysis. The prognostic value of the 6-lncRNA prognosis model was successfully validated and ROC curves was analysed. Patients were classified into high- and low-risk groups using the 6-lncRNA signature-based risk score. Patients in the low-risk group had significantly better progression-free survival than high-risk group. The result of multivariate analysis showed that the six-lncRNA signature was independent from clinical features such as age, gender and stage. GO and KEGG enrichment analysis and estimation of immune infiltration suggested that the lncRNAs might closely associated with tumorigenesis. Conclusion: Our study has constructed a novel six-lncRNA prognosis model to improve progression-free survival prediction in patients with thyroid cancer.Receiver operating characteristic (ROC) curves have been used increasingly to evaluate radiographic imaging systems in terms of observer detection performance. In this study, we evaluate radiographic screed-film combinations by d'e and the maximum image information content (I_) derived from ROC curves.
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What is the ROC Curve?Sensitivity and specificity, which are defined as the number of true positive decisions/the number of actually positive cases and the number of true negative decisions/the number of actually negative cases, respectively, constitute the basic measures of performance of diagnostic tests (Table 1).When the results of a test fall into one of two obviously defined categories, such as either the presence or absence of a disease, then the test has only one pair of sensitivity and specificity values.However, in many diagnostic situations, making a decision in a binary mode is both difficult and impractical.Image findings may not be obvious or clean-cut.There may be a considerable variation in the diagnostic confidence levels between the radiologists who interpret the findings.As a result, a single pair of sensitivity and specificity values is
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This study reviewed the concepts and properties of the receiver operating characteristic (ROC) curve and precision recall (PR) curve, and made suggestions on the application of two curves based on the prevalence in combination with the results of simulation data. This study demonstrated that the ROC curve and PR curve had different properties, which could reflect the performance of diagnostic methods from various aspects. These two curves should be selected with a consideration of prevalence and clinical scenarios. When the prevalence was less than 20%, especially less than 5%, the PR curve could be adopted.本研究对受试者特征工作（ROC）曲线和查准率-查全（PR）曲线的概念和性质进行概括回顾，结合模拟数据结果，基于患病率对ROC曲线和PR曲线的应用做出建议。研究显示，ROC曲线和PR曲线具有不同的性质，可以从不同的侧面反映诊断方法的性能，应结合患病率和临床场景进行选择。当患病率小于20%，尤其是小于5%时，应重视PR曲线的应用。.
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Survival curves are a popular tool for representing the association between a binary marker and the risk of an event. The separation between the survival curves in patients with a positive marker (high-risk group) and a negative marker (low-risk group) reflects the prognostic ability of the marker. In this article, we propose an alternative graphical approach to represent the discriminative capacity of the marker—a receiver operating characteristic (ROC) curve, tentatively named prognostic ROC curve—obtained by plotting 1 minus the survival in the high-risk group against 1 minus the survival in the low-risk group. The area under the curve corresponds to the probability that a patient in the low-risk group has a longer lifetime than a patient in the high-risk group. The prognostic ROC curve provides complementary information compared with survival curves. However, when the survival functions do not reach 0, the prognostic ROC curve is incomplete. We show how a range of possible values for the area under the curve can be derived in this situation. A simulation study is performed to analyze the accuracy of this methodology, which is also illustrated by applications to the survival of patients with brain metastases and survival of kidney transplant recipients.
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질병에 이환된 개체로부터 이환되지 않은 개체를 구분하기 위해 사용되는 대부분의 진단검사는 판별의 기준점 (cut-off value)을 필요로 한다. ROC (receiver operating characteristic) 곡선은 이러한 목적으로 흔히 사용되고 있으며 진단의 기준점을 다양하게 변화시킬 때 진단검사의 정확도 (민감도와 특이도)를 제시해주는 지표로 활용되고 있다. 저자들은 수의학관련 연구자들이 이 방법을 효과적으로 사용할 수 있도록 EXCEL에 내장된 비쥬얼 베이직으로 binormal ROC 곡선의 최대우도비를 계산해주는 프로그램을 작성하였다. 방사선 분야의 자료와 미생물학 자료를 예제로 들어 이 프로그램의 활용성을 높이고자 하였고 이 분야에 관심이 있는 연구자는 저자에게 연락하여 이 프로그램을 얻을 수 있다. 【Diagnostic tests often require the determination of cut-off values that discriminate uninfected from infected individuals. The receiver operating characteristic (ROC) curve has been frequently used to attain this purpose and gives a representation of diagnostic accuracy (sensitivity and specificity) of a prediction model when varying the cut-point of a decision rule on a whole spectrum. We have written and tested a visual basic application program in EXCEL for maximum likelihood estimation of a binormal ROC curve, which also computes univariate statistics of a diagnostic test employed. Examples applying for computed tomographic images in radiology and methicillin-resistant Staphylococcus aureus research are given to illustrate this approach. This stand-alone module is available from the first author on request.】
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The area under the receiver operating characteristic (ROC) curve is a measure of discrimination ability used in diagnostic and prognostic research. The ROC plot is usually represented without additional information about decision thresholds used to generate the graph. In our article, we show that adding at least one or more informative cutoff points on the ROC graph facilitates the characterization of the test and the evaluation of the discriminatory capacities, which can result in more informed medical decisions. We use the rocreg and rocregplot commands.
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The ROC curve is mainly used to evaluate the discrimination power of a continuous variable for a binary outcome. Recently, time dependent ROC curves have been used to assess the predictive power of diagnostic markers for time dependent disease outcomes, thus to analyze censored survival data. Among the various methods of estimating the time dependent ROC curves, the Kaplan-Meier method is based on Bayes’ theorem and Kaplan-Meier survival function. It is easy to understand, implement and use. In this paper we implement the Kaplan-Meier estimate of time dependent ROC curves in SAS. Using the data of a clinical study, we demonstrate how time dependent ROC curves and area under the curve can be used to select predictive covariates and build better survival models.
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The receiver operating characteristic (ROC) curve was applied to observer performances in a multiple-alternative decision task. It was shown that the probability of correct classification, a performance criterion often maximized in multiple-classification procedures, corresponds to the area under an appropriately constructed ROC curve. Degrees of confidence in the observer's judgment of 0, 1, ... , 10 were used for both classification and ROC rating. To demonstrate the validity of the method, 1,190 photofluorograms were examined by experi enced staff radiologists to identify four cardiovascular conditions distinguishable on the basis of images of structural elements of the contours of the heart and great vessels. The clas sification matrices for three radiologists who achieved high, medium, and low performance ratings in this experiment are reported. The ROC curves are symmetric, with their points located around the off-diagonal. Differences between the overall probability of correct clas sification and the ROC curve index calculated from the same evaluator's data were very small, 0.004 to 0.011. Key words: receiver operating characteristic (ROC) curve; multiple classification; medical decision making; cardiovascular diagnostics; thorax, radiography. (Med Decis Making 7:234-237, 1987)
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