Fig 1.
A flow chart illustrating the method used to identify six biomarkers associated with IA rupture.
The entries shown in the green box were completed in our previous study, while the entries shown in the red box are those that we intend to focus on in future studies.
Table 1.
Clinical characteristics of the patient groups who provided blood samples for qRT-PCR analysis.
Table 2.
Sequences of the qRT-PCR primers used in this study.
Fig 2.
Expression analysis of the GSE36791 dataset.
(A) Reliability of the data is shown with a box plot of the normalized data of GSE36791. (B) Sample clustering was performed to detect outliers. Three samples were excluded in this study (GSM901111, GSM901112, and GSM901161). (C) A clustering dendrogram of 58 samples and their associated clinical traits was generated based on gene expression. Color intensity is proportional to group (i.e., ruptured IA/reference groups) and gender. (D) The left and right panels show analyses of the scale-free fit index and mean connectivity for various soft-thresholding powers (β), respectively. (E) Modules associated with clinical information were identified and labeled with different colors.
Fig 3.
Identification of key modules.
(A) The distribution of average gene significance and errors are shown for the modules associated with IA rupture. The turquoise, blue, and brown modules were identified as key modules. (B) A heatmap was generated to show correlations between module eigengenes and various clinical characteristics (e.g., gender and group). Corresponding correlation coefficients and P-values are also provided. Associated P-values for the turquoise, blue, and brown modules are indicated in parentheses and significant values are presented in bold font.
Fig 4.
Detection and functional analysis of hub genes.
Scatterplots were generated to depict correlations between module membership and gene significance values for the turquoise (A), blue (B), and brown (C) modules. Highly significant correlations were observed between gene significance and module membership in each of these three modules. (D) GO and KEGG annotations of the hub genes were performed. GO terms and KEGG pathways with threshold values of FDR < 0.05 were considered significant and are labeled as follows: BP (biological process) in black; CC (cellular component) in red; MF (molecular function) in blue; and KEGG (Kyoto Encyclopedia of Genes and Genomes) in green. (E) A Venn diagram displays the six common genes among the hub genes, key genes, and DEGs. Thus, these six genes are considered candidate biomarkers.
Table 3.
The top 30 functional enrichment terms enriched for the hub genes identified in this study.
Fig 5.
Heat maps show the expression profiles of the six candidate biomarker genes in the GSE36791 (A) and GSE73378 (B) datasets. Orange and blue coloring indicates high and low expression values, respectively. In the GSE36791 dataset, there is a mixture of ruptured aneurysm samples in the reference group. However, a good cluster was still obtained for the ruptured IA group compared to the GSE73378 dataset. (C, D) Validation of gene expression levels for BASP1, CEBPB, SLC2A3, ECHDC2, GZMK, and KLHL3 in ruptured IA (or PaSAH) and reference samples. Data are presented as 2-ΔΔCT relative to GAPDH. *P < 0.05, **P < 0.01, ***P < 0.001, NS: not significant. (E) A representation of the biological events which occur during IA rupture through recovery, with gene regulation of the six relevant genes identified in the present study shown as well.
Table 4.
Comparison of six hub genes between two datasets.