Dataset Information

Diagnostic and Prognostic Indications of Nasopharyngeal Carcinoma.

ABSTRACT: Nasopharyngeal carcinoma (NPC) is a disease that is highly associated with the latent infection of Epstein-Barr virus. The absence of obvious clinical signs at the early stage of the disease has made early diagnosis practically impossible, thereby promoting the establishment and progression of the disease. To enhance the stride for a reliable and less invasive tool for the diagnosis and prognosis of NPC, we synopsize biomarkers belonging to the two most implicated biological domains (oncogenes and tumor suppressors) in NPC disease. Since no single biomarker is sufficient for diagnosis and prognosis, coupled with the fact that the known established methods such as methylation-specific polymerase chain reaction (PCR), multiplex methylation-specific PCR, microarray assays, etc., can only accommodate a few biomarkers, we propose a 10-biomarker panel (KIT, LMP1, PIKC3A, miR-141, and miR-18a/b (oncogenic) and p16, RASSF1A, DAP-kinase, miR-9, and miR-26a (tumor suppressors)) based on their diagnostic and prognostic values. This marker set could be explored in a multilevel or single unified assay for the diagnosis and prognosis of NPC. If carefully harnessed and standardized, it is hoped that the proposed marker set would help transform the diagnostic and prognostic realm of NPC, and ultimately, help prevent the life-threatening late-stage NPC disease.

SUBMITTER: E A R ENS

PROVIDER: S-EPMC7554987 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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Diagnostic and Prognostic Indications of Nasopharyngeal Carcinoma.

E A R Engku Nur Syafirah ENS Irekeola Ahmad Adebayo AA Yean Yean Chan C

Diagnostics (Basel, Switzerland) 20200819 9

Nasopharyngeal carcinoma (NPC) is a disease that is highly associated with the latent infection of Epstein-Barr virus. The absence of obvious clinical signs at the early stage of the disease has made early diagnosis practically impossible, thereby promoting the establishment and progression of the disease. To enhance the stride for a reliable and less invasive tool for the diagnosis and prognosis of NPC, we synopsize biomarkers belonging to the two most implicated biological domains (oncogenes a ...[more]

PMID: 32825179

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Project description:BackgroundDiagnostic models based on gene signatures of nasopharyngeal carcinoma (NPC) were constructed by random forest (RF) and artificial neural network (ANN) algorithms. Least absolute shrinkage and selection operator (Lasso)-Cox regression was used to select and build prognostic models based on gene signatures. This study contributes to the early diagnosis and treatment, prognosis, and molecular mechanisms associated with NPC.MethodsTwo gene expression datasets were downloaded from the Gene Expression Omnibus (GEO) database, and differentially expressed genes (DEGs) associated with NPC were identified by gene expression differential analysis. Subsequently, significant DEGs were identified by a RF algorithm. ANN were used to construct a diagnostic model for NPC. The performance of the diagnostic model was evaluated by area under the curve (AUC) values using a validation set. Lasso-Cox regression examined gene signatures associated with prognosis. Overall survival (OS) and disease-free survival (DFS) prediction models were constructed and validated from The Cancer Genome Atlas (TCGA) database and the International Cancer Genome Consortium (ICGC) database.ResultsA total of 582 DEGs associated with NPC were identified, and 14 significant genes were identified by the RF algorithm. A diagnostic model for NPC was successfully constructed using ANN, and the validity of the model was confirmed on the training set AUC =0.947 [95% confidence interval (CI): 0.911-0.969] and the validation set AUC =0.864 (95% CI: 0.828-0.901). The 24-gene signatures associated with prognosis were identified by Lasso-Cox regression, and prediction models for OS and DFS of NPC were constructed on the training set. Finally, the ability of the model was validated on the validation set.ConclusionsSeveral potential gene signatures associated with NPC were identified, and a high-performance predictive model for early diagnosis of NPC and a prognostic prediction model with robust performance were successfully developed. The results of this study provide valuable references for early diagnosis, screening, treatment and molecular mechanism research of NPC in the future.

Dataset Information

Diagnostic and Prognostic Indications of Nasopharyngeal Carcinoma.

Publications

Diagnostic and Prognostic Indications of Nasopharyngeal Carcinoma.

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