Carotid artery stenosis is the cause of 15% of strokes (1, 2). Based on early histopathologic studies, ischemic events are associated with intraplaque hemorrhage, ulceration, calcification, lipid-rich necrosis, plaque thrombus, macrophage infiltration, and high microvessel density (3–6).

Neutrophil extracellular traps (NETs) are specialized structures released by neutrophils. NETs were initially believed to form in response to stimuli such as infection and inflammation and contribute to the elimination of pathogens such as bacteria and viruses (7). Recently, they have been suggested to participate in the regulation of inflammatory responses, blood coagulation, and pathological conditions such as autoimmune diseases and thrombosis (8–10). Elevated peptidyl arginine deiminase 4 (PAD4) levels have been detected in blood samples collected during carotid artery stenting, suggesting the involvement of NETs in the pathogenesis of atherothrombotic stroke (11).

Microarray and ribonucleic acid sequencing (RNAseq) allow comprehensive analyzes of transcriptomes. Genome-wide transcriptome analysis is often required in addition to individual gene expression analyzes. There are already re-analysis reports of existing microarray data (12, 13). However, big data analysis requires knowledge of statistics, informatics, and data science, which can pose difficulties for general biologists, physicians, and surgeons (14, 15).

In the absence of a bioinformatics expert, this study analyzed whether correlations related to NETs could be detected using historical carotid plaque-derived complementary deoxyribonucleic acid (cDNA) microarray data.

Based on national ethical guidelines, this study did not originally fall under the category of research requiring written consent from study participants (16). This study was approved by the Institutional Review Board (number 23071016). The Gene Expression Omnibus¹ database was examined using the search terms human, carotid artery, and endarterectomy. Twelve data were found as of October 2023. GSE28829 and GSE43292 datasets, which appeared to compare plaque and normal to early atheromatous vessels, were selected for the present analysis (Table 1) (17, 18). The downloaded normalized data were converted to comma-separated value (CSV) matrix files using spreadsheet software. An outline of the strategy used for the GEO original data is provided in the Supplementary Files S1–S4. Analyzes of microarray data were conducted using integrated Differential Expression and Pathway analysis (iDEP) 1.1 (19).² The detailed methods and R session information are provided in the Supplementary File S5.

Existing cDNA microarray data on carotid plaques of human origin represent a valuable source of information as they can be repeatedly analyzed to reflect the latest research, depending on the researcher’s interest. Nai et al. reported a re-analysis of the GSE43292 dataset and explored novel genes and pathways of carotid atheroma (12). Gao et al. examined immune cell infiltration between early and advanced carotid atheromatous plaque using the GSE28829 dataset (13). The cooperation of bioinformatics experts is considered essential for the former consideration. On the other hand, the latter report uses a web tool and does not necessarily require an expert, which could be another option from the present study. Our study presented a method for uploading normalized CSV matrix files to the iDEP web platform and analyzing the data (see legends in Supplementary Files S1–S4). All analyzes were performed on a graphical user interface such that the character user interface was avoided. As the analysis is performed online, a computer with standard performance was sufficient. Some typical cDNA microarray and RNAseq analysis methods are available and free of charge for scientific use (15, 19). We adopted this method in the present study because it allows visualization and display of the NETs’ DEG information on the KEGG graph.

The organization of the controls was not consistent in the present study (Table 1). Data from GSE28829 compared advanced plaque with intimal thickening and intimal xanthoma, and advanced plaque and distant macroscopically intact tissues were compared in GSE43292. One possible reason for the discrepancies between the results of the two datasets in this study could be that the former detected mainly DEGs associated with plaque progression, while the latter detected mainly DEGs associated with plaque development. The lack of control samples compared to the number of validation samples in the GSE28829 data may have also affected the results. Conversely, it remains nearly impossible to obtain human-derived normal arterial tissue as control samples from an ethical viewpoint.

High PAD4 expression was not extracted as a DEG in our re-analysis of existing microarray data. This finding is inconsistent with that reported by Simonaga et al. (11). They collected blood samples from the luminal side, which could represent a different target from our study results, in which atheromas were analyzed. In other words, it is possible that different genes could be expressed in vascular endothelial cells and atheromas even though both contribute to a series of atherosclerotic processes. Therefore, we cannot exclude the possibility that NETs are involved in the development of carotid artery plaques and their rupture. Clinicopathological studies and single-cell comprehensive gene expression analyzes could be helpful for clarifying their pathogenesis.

Several limitations to this study warrant mention. Because of the inconsistency of the controls, whether they represented normal tissue may be debatable (Table 1). Next, microarrays are not chip-compatible, making integrated analysis extremely difficult. Then, although the results of analyzes of cDNA microarray and RNAseq data can suggest certain correlations, causal relationships cannot always be proven. Furthermore, scientists should consider the final biological interpretation as the results of big data and machine learning do not necessarily have biological relevance (15, 23). Finally, this research is an analysis that is only possible within the platform created by bioinformatics researchers. The need to rely on experts will continue to be necessary when detailed fine-tuning or new analysis methods are required.

The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author.

The studies involving humans were approved by the Nagasaki University Hospital Institutional Review Board. The studies were conducted in accordance with the local legislation and institutional requirements. The human samples used in this study were acquired from another research group. Written informed consent for participation was not required from the participants or the participants’ legal guardians/next of kin in accordance with the national legislation and institutional requirements.

RT: Conceptualization, Data curation, Formal analysis, Methodology, Writing – original draft, Writing – review & editing. KU: Conceptualization, Data curation, Formal analysis, Funding acquisition, Methodology, Project administration, Resources, Software, Writing – original draft, Writing – review & editing. TI: Conceptualization, Formal analysis, Funding acquisition, Investigation, Project administration, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing. AX: Data curation, Writing – review & editing. KO: Writing – review & editing. YM: Funding acquisition, Writing – review & editing. TM: Funding acquisition, Supervision, Writing – review & editing.