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Abstract Aim Autophagy plays essential roles in abdominal aortic aneurysm (AAA) development and progression. The objective of this study was to verify the autophagy-related genes (ARGs) underlying AAA empirically and using bioinformatics analysis. Methods Two gene expression profile datasets GSE98278 and GSE57691 were downloaded from the Gene Expression Omnibus (GEO) database, and principal component analysis was performed. Following, the R software (version 4.0.0) was employed to analyze potentially differentially expressed genes related with AAA and autophagy. Subsequently, the candidate genes were screened using protein–protein interaction (PPI), gene ontology (GO) enrichment analysis, and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Finally, quantitative real-time polymerase chain reaction (RT-qPCR) was performed to detect the RNA expression levels of the top five selected abnormal ARGs in clinical samples obtained from the normal and AAA patients. Results According to the information contained (97 AAA patients and 10 healthy controls) in the two datasets, a total of 44 differentially expressed autophagy-related genes (6 up-regulated genes and 38 down-regulated genes) were screened. GO enrichment analysis of differentially expressed autophagy-related genes (DEARGs) demonstrated that some enrichment items were associated with inflammation, and PPI analysis indicated interaction between these genes. RT-qPCR results presented that the expression levels of IL6, PPARG, SOD1, and MAP1LC3B were in accordance with the bioinformatics prediction results acquired from the mRNA chip. Conclusion Bioinformatics analysis identified 44 potential autophagy-related differentially expressed genes in AAA. Further verification by RT- qPCR presented that IL6, PPARG, SOD1, and MAP1LC3B may affect the development of AAA by regulating autophagy. These findings might help explain the pathogenesis of AAA and be helpful in its diagnosis and treatment.

Abdominal aortic aneurysm (AAA) is a common and serious disease with a high mortality rate, but its genetic determinants have not been fully identified. In this feasibility study, we aimed to elucidate the transcriptome profile of AAA and further reveal its molecular mechanisms through the Oxford Nanopore Technologies (ONT) MinION platform. Overall, 9574 novel transcripts and 781 genes were identified by comparing and analysing the redundant-removed transcripts of all samples with known reference genome annotations. We characterized the alternative splicing, alternative polyadenylation events and simple sequence repeat (SSR) loci information based on full-length transcriptome data, which would help us further understand the genome annotation and gene structure of AAA. Moreover, we proved that ONT methods were suitable for the identification of lncRNAs via identifying the comprehensive expression profile of lncRNAs in AAA. The results of differentially expressed transcript (DET) analysis showed that a total of 7044 transcripts were differentially expressed, of which 4278 were upregulated and 2766 were downregulated among two groups. In the KEGG analysis, 4071 annotated DETs were involved in human diseases, organismal systems and environmental information processing. These pilot findings might provide novel insights into the pathogenesis of AAA and provide new ideas for the optimization of personalized treatment of AAA, which is worthy of further study in subsequent studies.

Common obstacles to successful retrieval of retrievable inferior vena cave filter include embedded filter hook and severe tilt of the filter. We described a modified wire-loop snare technique using self-made fishhook-like pigtail catheter and 11-F-long sheath to retrieve a severe tilted filter with embedded hook successfully. The modified wire-loop snare technique is simple and effective requiring only standard equipment and single venous access. This technique may be suitable for some types of retrievable filter with embedded hook.