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Fig. 1 | BMC Cancer

Fig. 1

From: The identification of gene signatures in patients with extranodal NK/T-cell lymphoma from a pair of twins

Fig. 1

Flow diagram of the procedure. First, from the peripheral blood of a pair of twins, we applied a whole-genome shotgun (WGS, Beijing Boao Biological Co., Ltd) for sequencing to identify unique variant genes. One individual is diagnosed with ENKTL, while the other is healthy. Next, we downloaded a training dataset (GSE 80632) and testing database (GSE 19067) from Gene Expression Omnibus (GEO) (https://www.ncbi.nlm.nih.gov/gds/). Subsequently, we conducted the Robust Multi-array Analysis (RMA) and z score normalization to preprocess the data. To understand the biological function of unique mutated genes, GO (Gene Ontology) enrichment analysis and KEGG (Kyoto Encyclopedia of Genes and Genomes) enrichment analysis were performed in DAVID (https://david.ncifcrf.gov/). R package goplot was used for visualization. Then, to select unique genes, we used an elastic net to fit a generalized linear model by the R package glmnet and analyzed the training dataset by using the elastic net. Simultaneously, we used another algorithm called Support Vector Machine-Recursive Feature Elimination (SVM-RFE) to identify unique genes. Nest, to explore pathway gene sets of selected markers, we conducted GSEA and GSVA of the training set data. We performed GSEA with GSEA V4.1.0 software. Correspondingly, GSVA relied on R package “GSVA”. We conducted single-sample gene set enrichment analysis (ssGSEA) to achieve enrichment scores of immune-filtrating cells by calculating enrichment scores. Also, we performed Spearman correlation tests to assess correlation and used R package pheatmap for visualization. Fianally, to validate the reliability and accuracy of unique genes, the validation set was used to verify the expression of the screened markers. For differential genes, we used Boxvolin plots to demonstrate their expression levels. Created with BioRender.com

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