Contains supporting figures and tables.

BackgroundRupture of abdominal aortic aneurysm (rAAA) is a fatal event in the elderly. Elevated blood pressure and weakening of vessel wall strength are major risk factors for this devastating event. This present study examined whether the expression profile of mechanosensitive genes correlates with the phenotype and outcome, thus, serving as a biomarker for AAA development.MethodsIn this study, we identified mechanosensitive genes involved in AAA development using general bioinformatics methods and machine learning with six human datasets publicly available from the GEO database. Differentially expressed mechanosensitive genes (DEMGs) in AAAs were identified by differential expression analysis. Molecular biological functions of genes were explored using functional clustering, Protein–protein interaction (PPI), and weighted gene co-expression network analysis (WGCNA). According to the datasets (GSE98278, GSE205071 and GSE165470), the changes of diameter and aortic wall strength of AAA induced by DEMGs were verified by consensus clustering analysis, machine learning models, and statistical analysis. In addition, a model for identifying AAA subtypes was built using machine learning methods.Results38 DEMGs clustered in pathways regulating ‘Smooth muscle cell biology’ and ‘Cell or Tissue connectivity’. By analyzing the GSE205071 and GSE165470 datasets, DEMGs were found to respond to differences in aneurysm diameter and vessel wall strength. Thus, in the merged datasets, we formally created subgroups of AAAs and found differences in immune characteristics between the subgroups. Finally, a model that accurately predicts the AAA subtype that is more likely to rupture was successfully developed.ConclusionWe identified 38 DEMGs that may be involved in AAA. This gene cluster is involved in regulating the maximum vessel diameter, degree of immunoinflammatory infiltration, and strength of the local vessel wall in AAA. The prognostic model we developed can accurately identify the AAA subtypes that tend to rupture.</div

Identification of DEGs between AAA subgroups.

(A) Expression level heatmap and hierarchical clustering of DEGs among subgroups. (B) Volcano plot of DEGs among subgroups.</p

Flowchart of this study.

DEMGs, differentially expressed mechanosensitive genes; RAW, Regional Aortic Weakness; SVM, support vector machine; ICS, inflammatory composite score.</p

Drug target genes among DEMGs (Common targets in bold).

Drug target genes among DEMGs (Common targets in bold).</p

WGCNA on AAA conditions.

(A) Based on the full gene expression of the AAA case, average linkage hierarchical clustering analysis revealed no outliers. (B) and (C) Soft-thresholding power analysis was used to obtain the scale-free fit index of network topology, and the optimum soft threshold is 10. (D) Hierarchical cluster analysis was conducted to detect co-expression clusters with corresponding color assignments. Each color represents a module in the constructed gene co-expression network. (E) Calculated correlation coefficients between the modules and subgroups. The magnitude of the correlation is indicated by the shade of the color. (F) Significant correlation existed in the module membership (MM) and gene significance (GS) of the turquoise module.</p

Enrichment analysis of DEMGs and validation and interaction analysis of hub genes.

(A, B) GO and KEGG enrichment analysis of DEMGs. € The Venn diagram shows the overlap of results generated by four different algorithms to obtain hub genes. (D) ROC curves for Triple cross-validation show the strong performance of the ‘rbf’ SVM model in GSE98278 (large- and intermediate-sized AAA). (E) Representative gene relationship network diagram of hub genes. Connections with a correlation of less than 0.3 are not shown. The shade of color indicates the absolute value of the correlation coefficient. (F) The transcription factor SRF regulates three hub genes.</p

Construction of Mechanical sensitivity score and nomogram.

(A) Venn diagram showing overlapping of three gene sets to obtain the signature genes. (B) ROC curves for Triple cross-validation show the strong performance of linear SVM (AAA samples in the merged dataset). (C) C1 had a higher mechanical sensitivity score than C2. (D) Use of hub gene shared with signature gene for the next step of analysis. (E) Construction of clinical diagnostic nomogram based on expression of CAV1, GJA1, and TAGLN. (F) Calibration curve showed the validity of the nomogram (P > 0.05).</p

Comparison of acrosome integrity of frozen-thawed spermatozoa among groups.

a<p>indicates a statistical difference when compared with Group A (P<0.05).</p

Establishment of AAA subgroups and comparison of differences between the subgroups for the merged dataset.

(A) Heatmap of the co-occurrence ratio matrix of AAA samples (k = 2). (B) Significant differences in Immune scores between AAA subgroups. (C) The violin plot shows the difference in the abundance of immune infiltrating cells between subgroups. (D) The violin plot shows the difference in HLA family gene expression between subgroups. (E) ICS was significantly different in the two subgroups. (F) The atherosclerosis score was similar in both subgroups. *P P P < 0.001, Blank: no significance.</p

Analysis for DEGs and correlation analysis between DEMGs.

(A) Expression level heatmap and hierarchical clustering of DEGs in the merged dataset. (B) Volcano plot of DEGs in the merged dataset. (C) The Venn diagram shows the common parts of DEGs and mechanosensitive genes as DEMGs. (D) Heatmap of correlation between DEMGs and scatter plot of highest correlation group. The color shades and circle sizes in the heatmap represent the magnitude of the absolute value of the correlation coefficient. Each point in the scatter plot represents a AAA sample, and the wave crest graph shows the distribution of gene expression data.</p