Article Figures & Data
Figures
- Figure 1
- Differentially expressed genes (DEGs) between the 2 groups of samples and functional enrichment analyses of them. A) Clustering heatmap of the top 100 DEGs in the training group (red represents relative upregulation, and blue represents relative downregulation of gene expression). B) Volcano plot of DEGs in SCLC tissues with normal lung tissues in the training group (red dots for upregulated genes and green dots for downregulated genes with an adjusted p<0.05 and |log fold change| >2). C) Gene ontology enrichment analysis of functions in the training group. D) Kyoto encyclopedia of genes and genomes enrichment analysis of pathways in the training group. E) Disease ontology enrichment analysis of the training group.
- Figure 2
- Gene set enrichment analysis (GSEA) of functions and pathways in the training group. The top 5 functions of GSEA in: A) normal lung tissues and B) small cell lung cancer (SCLC) tissues, and the top 5 pathways of GSEA in: C) normal lung tissues and D) SCLC tissues.
- Figure 3
- Identification of candidate diagnostic genes. A) Least absolute shrinkage and selection operator regression plot (the X-axis is logλ, and the Y-axis is the cross-validation error). B) Support vector machine-recursive feature elimination algorithm (the X-axis represents a change in the number of genes, and the Y-axis represents a cross-validation error). C) Venn diagram (intersection of genes using 2 machine learning methods). LASSO: least absolute shrinkage and selection operator, SVM-RFE: support vector machine-recursive feature elimination, RMSE: root mean squared error
- Figure 4
- Evaluation of the candidate diagnostic genes. A&B) Box plots revealing the expression of ZWINT and NRCAM between small cell lung cancer tissues (treat) and normal lung tissues (con) in the validation group A (p<0.05 represents a significant difference). C&D) The receiver operating characteristic (ROC) curves of ZWINT and NRCAM in the training group. E&F) The ROC curves of ZWINT and NRCAM in the validation group A. AUC: area under the curve, CI: confidence interval
- Figure 5
- Further validation of the candidate diagnostic genes and relative expression of them. A) The differential expression of ZWINT and B) NRCAM between small cell lung cancer cell lines (treat) and normal lung cell lines (con) in the validation group B. C) Relative expression of ZWINT and D) NRCAM by quantitative real-time polymerase chain reaction in different cell lines. *P<0.05, **p<0.01, ***p<0.001, ****p<0.0001
- Figure 6
- Analysis of immunocyte infiltration. A) The relative percentage of different immunocytes infiltration in each sample. B) Violin plot revealing the differences in each infiltrating immunocyte type between small cell lung cancer (SCLC) tissues and normal lung tissues. C) Correlation analysis between different immune cell infiltration levels. Correlation analysis of D) ZWINT and E) NRCAM with different immune infiltrating cells (the X-axis represents the correlation coefficient, and the Y-axis represents the immunocyte names). Con represents normal lung tissue, and treat represents SCLC tissue. A p-value of <0.05 indicates a significant difference.
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