The heart pumps arterial blood through the aorta, then into its branches to supply the body in the abdominal section called the abdominal aorta. An abdominal aortic aneurysm is diagnosed when the diameter of the abdominal aorta enlarges by more than 50% of its diameter for various reasons (Chaikof et al., 2018). It is commonly understood that the abdominal aorta, which was originally straight, has an abnormal expansion, like blowing up a balloon. When the development reaches a certain extent, it may burst suddenly. Therefore, an abdominal aortic aneurysm is sometimes compared to a “bomb with no timing” (Sakalihasan et al., 2005).

Most patients with abdominal aortic aneurysms are asymptomatic. Occasionally, patients find a “pulsing lump” on their stomach by accident or during a visit for another medical condition. If an abdominal aortic aneurysm suddenly causes severe abdominal pain, it is often a sign that it has ruptured or has ruptured. In addition, enlarged aneurysms can compress other organs in the abdominal cavity, such as intestinal compression, causing nausea, vomiting, abdominal distension, and discomfort. Compression of the ureter can cause hydronephrosis and so on.

Abdominal aortic aneurysms (AAA) cause three main harms: 1) The enlarged abdominal aorta compresses the surrounding vital organs and tissues, affecting their physiological functions. 2) Thrombus is easy to form in the lumen, which blocks the lower limb blood vessels after the thrombus falls off, leading to acute ischemic necrosis of the limb, just like the sudden water cut or power cut in daily life. 3) Under the continuous impact of blood flow, the abdominal aorta gradually enlarges, and when it exceeds the maximum tolerance limit, the aneurysm will burst and cause sudden death (Sakalihasan et al., 2005). Although aneurysms and solid tumors are entirely different concepts, the risk of death is more significant once ruptured than any solid tumor (Wanhainen et al., 2019).

Vascular inflammation is the first occurrence of AAA (Marquez-Sanchez and Koltsova, 2022). In the early stages of AAA, immune cells infiltrate and aggregate in the blood vessels, leading to inflammatory responses in the vessel walls. Inflammatory cells stimulate smooth muscle cells to secrete matrix metalloproteinase, which degrades elastin and collagen, reduces the stability of the artery wall, and induces apoptosis of vascular smooth muscle cells, thus playing an essential role in the occurrence and development of AAA (Marquez-Sanchez and Koltsova, 2022). Further understanding the mechanism of regulating immune cell activation in AAA will provide important targets for therapeutic intervention.

As an important research direction of post-transcriptional gene regulation, N1 methyladenosine (m1A), 5-methylcytosine (m5C), N6-methyladenosine (m6A), and 7-methylguanosine (m7G) modified RNA is widely found in eukaryotic cells and plays a vital role in a variety of biological processes (Wu et al., 2021). They also function as a novel mechanism in cardiovascular diseases, including heart failure, coronary heart disease, and hypertension (He et al., 2019b; Wu et al., 2021). However, the specific roles of m1A/m5C/m6A/m7G regulation in AAA have not been fully elucidated.

In this regard, we developed an AAA-related model and provided new mechanism insights for immunity and m1A/m5C/m6A/m7G regulation in AAA using large-scale bioinformatics analysis.

In the era of big data, excavating novel biomarkers or models in predicting the pathogenic mechanisms of AAA using large-scale bioinformatics analysis has been increasingly attractive. A previous study revealed differential expression of transcripts in the peripheral blood of AAA, indicating functional roles in proteolysis, inflammation, and apoptotic processes based on microarray-based gene expression profiling (Butt et al., 2016). A miRNA signature in AAA suggested that miRNAs play a role in AAA pathogenesis (Pahl et al., 2012). Besides, a lncRNA signature in AAA demonstrated that lncRNA candidates are related to the pathogenesis of AAA (Yang et al., 2016). The characteristic of inflammatory infiltration in the perivascular adipose tissue (PAT) surrounding AAA may be represented by a cell network dominated by FOS made up of activated mast cells, plasma cells, and Tfh cells (Ding et al., 2022). However, these models were not validated by in vitro validation. Besides, most of the model applications and universality were not so good.

In our study, WGCNA, as a generally used algorithm for determining feature-related genes, was used to determine the AAA-related genes for a comprehensive exploration of the pathogenic characteristics of AAA. Ubiquitin-mediated proteolysis, phosphoric ester hydrolase activity, nuclear ubiquitin ligase complex, and interleukin-1 beta secretion were highly enriched based on the WGCNA-derived module genes. Notably, a previous study has proven that potential therapeutic usefulness exists in altering how the ubiquitin-proteasome system function in vascular disorders (Wang et al., 2020). So, the AAA-related genes by WGCNA were believed to be reliable.

G2/M transition of mitotic cell cycle and inflammatory response were positively enriched based on limma-based DEGs between AAA and normal samples. In contrast, regulation of autophagy, RNA metabolic process, histone methylation, glycosaminoglycan metabolic process, histone H2A monoubiquitination, interleukin-27-mediated signaling pathway, and regulation of response to DNA damage stimulus were negatively enriched based on limma-based DEGs between AAA and normal samples. It is thought that one of the primary molecular pathways underpinning AAA development is inflammation in the AAA wall (Stepien et al., 2022). Therefore, the limma-based DEGs were also believed to be reliable.

By intersecting WGCNA-derived module genes and limma-based DEGs, the intersected genes had a solid potential to represent AAA. In this study, based on the WGCNA-derived module genes and limma-based DEGs between AAA and normal samples, the AAA-related score was developed using the random forest and LASSO machine learning algorithms. Artificial neural networks are a machine learning method that deep learning, a branch of artificial intelligence, employs to mine patterns and forecast outcomes from massive data sets. Research into the utility of deep learning in understanding the complicated biology of the disease has been boosted by the growing usage of deep learning across healthcare domains and the availability of extensively defined disease datasets (Tran et al., 2021). Although preliminary findings seem encouraging, this is a rapidly developing field with fresh insights into disease biology and deep learning. As machine learning has been widely proven with robust ability in model development, the AAA-related disease model was thought to be reliable.

Among the four model genes, UBE2K, TMEM230, VAMP7, and PUM2 were widely reported in human diseases. UBE2K could promote the progression of hepatocellular carcinoma via the regulation of c-Myc (Lei et al., 2022). TMEM230 was revealed to be a marker in Parkinson’s disease (Wang et al., 2021). VAMP7-dependent secretion of RTN3 was proven to regulate neurite growth (Wojnacki et al., 2020). PUM2 was a hazardous marker for mitochondrial dynamics and mitophagy during aging (D'Amico et al., 2019). Our study showed that UBE2K had significantly higher expression in AAA than in normal samples. In contrast, TMEM230, VAMP7, and PUM2 had significantly lower expression in AAA compared with normal samples. In other words, UBE2K was a hazardous marker in AAA, while TMEM230, VAMP7, and PUM were favorable markers in AAA. The clinical meaning and predictive value of UBE2K, TMEM230, VAMP7, and PUM2 were promising.

AAA-related score positively correlated with regulatory T cells (Tregs), plasma cells, activated dendritic cells, naïve B cells, activated CD4 memory T cells, and CD8 T cells. Double-strand break repair via homologous recombination, protein phosphorylation, interferon-gamma-mediated signaling pathway, immune response, cellular response to DNA damage stimulus, T cell costimulation, T cell activation, innate immune response, and T cell receptor signaling pathway were positively enriched in high AAA-related score AAA samples. Studies have shown the presence of multiple inflammatory cell types in AAA, such as macrophages, CD4⁺ T cells, and B cells, which play an essential role in the diseased aortic wall through phenotypic regulation (Okrzeja et al., 2022). In addition, recent evidence suggests that toll-like receptors, chemokine receptors, and complements in the innate immune system are involved in the progression of AAA (Li et al., 2018).

In our study, AAA-related scores positively correlated with most m1A/m5C/m6A/m7G-related genes. The AAA-related score was significantly associated with m5C-related genes DNMT1, NSUN2, NSUN5, and YBX1. The AAA-related score was significantly associated with m6A-related genes FTO, HNRNPC, METTL3, and RBM15. The AAA-related score was significantly associated with m7G-related genes CYFIP1, EIF3D, EIF4E3, NSUN2, and NUDT11. M1A, m5C, m6A, and m7G are new types of RNA methylation. M1A, m5C, m6A, and m7G are widely involved in regulating cardiovascular diseases, including heart failure, cardiac hypertrophy, aneurysm, and vascular calcification. Studies have also shown the expression pattern and functional significance of m6A-related genes in AAA (Zhang et al., 2021). m6A-related genes were found to play non-negligible roles in the occurrence of AAA ruptured (rAAA) (Fu et al., 2022). Besides, increased methylation levels of YTHDF3, FTO, and METTL14 were revealed to be associated with the progression of AAA (He et al., 2019b). METTL3 modulates m6A-dependent primary miR34a processing to induce AAA development and progression (Zhong et al., 2020). M6A could influence the circRNA-miRNA-mRNA network in hypoxia-mediated pulmonary hypertension. Moreover, the METTL3/YTHDF2/PTEN axis was proven to promote hypoxia-induced pulmonary artery hypertension (Zhou et al., 2021). YTHDF1 was reported to regulate pulmonary hypertension through the control of MAGED1 (Hu et al., 2021). NSUN2 influences autotaxin expression and T cell recruitment to control aneurysm development (Miao et al., 2021). In AAA, RBM15 knockdown decreased CASP3 expression in an m6A-dependent way (Fu et al., 2022). In AAA, YBX1 was discovered to be a direct target of GAS5, and it also collaborated with GAS5 to control the downstream target p21 through a positive feedback loop (He et al., 2019a). However, the specific mechanisms of other m1A/m5C/m6A/m7G epigenetic regulation (DNMT1, NSUN5, FTO, HNRNPC, CYFIP1, EIF3D, EIF4E3, NUDT11) in AAA have not been fully explored.

Our study developed a reliable AAA-related disease model for predicting immunity and m1A/m5C/m6A/m7G epigenetic regulation. The pathogenic roles of four model genes, UBE2K (hazardous), TMEM230 (favorable), VAMP7 (favorable), and PUM2 (favorable), in AAA, need further validation by in vitro and in vivo experiments.