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- #PROTEIN SCAFFOLD ROC CURVES HOW TO#
- #PROTEIN SCAFFOLD ROC CURVES SKIN#
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Despite recent advances in understanding CM biology and genetics, but the prognosis of patients with advanced CM is still poor due to the high potential of CM invasion and metastasis. Early CM may be cured by surgery, but advanced disease remains a challenge. It is estimated that there will be 91,270 patients were diagnosed with CM, and approximately 9,320 individuals died from the condition in 2018 in the United States. Its incidence is rising worldwide, especially in people with light complexion. It is reported that CM is the sixth leading cause of cancer-associated mortality worldwide. It is characterized by rapid progression, metastasis to regional lymph nodes and distant organs, and limited response to treatment.
#PROTEIN SCAFFOLD ROC CURVES SKIN#
The proposed nomogram may provide information for individualized treatment in CM patients.Ĭutaneous melanoma (CM) is the most lethal form of skin malignancy. We established a novel three lncRNA-based risk score model and nomogram to predict overall survival of CM. The prognostic accuracy of the risk model was confirmed by the discrimination and calibration plots in both training set and validation set. Furthermore, the nomogram for predicting 3-, 5-, and 10-year OS was established based on lncRNA-based risk score and clinicopathologic factors. The ROC curve showed good performance in survival prediction in both sets. The proposed risk score model could divide patients into high-risk and low-risk groups with significantly different survival in both training set and validation set. After univariate analysis, LASSO penalized regression analysis, and multivariate analysis, 3 lncRNAs were used to construct risk score model. Results:Ī total of 212 lncRNAs were identified to be differentially expressed in CM. The accuracy of the model was evaluated by the discrimination and calibration plots. Then, a prognostic nomogram combining the lncRNA-based risk score and clinicopathological characteristics was developed in training set, and assessed in the validation set. The established prognostic model was evaluated, and validated in the validation set. A lncRNA-based prognostic model was established in training set. We obtained lncRNAs expression profiles and clinicopathological data from the Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) databases. The aim of this study was to screen survival-related lncRNAs and to construct a lncRNA-based prognostic model in patients with cutaneous melanoma (CM). Plenty of evidence has suggested that long non-coding RNAs (lncRNAs) have played a vital part may act as prognostic biomarkers in a variety of cancers. The work cannot be used commercially without permission from the journal.
#PROTEIN SCAFFOLD ROC CURVES LICENSE#
This is an open access article distributed under the terms of the Creative Commons Attribution-Non Commercial License 4.0 (CCBY-NC), where it is permissible to download, share, remix, transform, and buildup the work provided it is properly cited. The authors have no funding and conflicts of interest to disclose. The authors declare no conflict of interest in this study. The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. A novel RNA sequencing-based prognostic nomogram to predict survival for patients with cutaneous melanoma: clinical trial/experimental study.
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#PROTEIN SCAFFOLD ROC CURVES HOW TO#
How to cite this article: Tian J, Yang Y, Li My, Zhang Y. 256 West Youyi Road, Beilin District, Xi’an 710068, China (e-mail: ).Ībbreviations: AIC = Akaike information criterion, AJCC = the American Joint Committee on Cancer, CM = cutaneous melanoma, DEGs = differentially expressed genes, DElncRNAs = differentially expressed lncRNAs, FC = fold change, FDR = false discovery rate, GO = Gene Ontology, GTE = the Genotype-Tissue Expression, KEGG = Kyoto Encyclopedia of Gene and Genomes, LASSO = the least absolute shrinkage and selection operator, lncRNA = long non-coding RNAs, OS = overall survival, ROC = receiver operating characteristic, TCGA = the Cancer Genome Atlas, TNM = tumor-node-metastasis, UCSC = the University of California Santa Cruz. ∗Correspondence: Yuan Zhang, Department of Oncology, Shanxi Provincial People's Hospital, No. ADepartment of Dermatology, Shanxi Provincial People's Hospital, Xi’anīDepartment of Dermatology, 63600 Hospital of PLA, LanzhouĬDepartment of Hepatobiliary Surgery, The Fourth Medical Center, Chinese PAL General Hospital, BeijingĭDepartment of Oncology, Shanxi Provincial People's Hospital, Xi’an, China.