Transcriptomic and Proteomic Profiling of Human Stable and Unstable Carotid Atherosclerotic Plaques

Recent advances in proteomic analysis to study carotid artery plaques

Objective

We sought to identify differentially expressed proteins in serum, plasma, and plaque samples of patients with carotid atherosclerotic lesions.

Methods

We performed a systematic review of the proteomic profile of serum, plasma, and plaque samples of patients with carotid artery disease. We included full-length peer-reviewed studies of adult humans and reported them using PRISMA guidelines. The quality of the design and content of the articles included in the review was assessed using the Newcastle-Ottawa scale.

Results

We included six peer-reviewed articles reporting protein expression in serum, plasma, or plaque samples from patients with carotid atherosclerosis. Three were single-center cross-sectional studies, two were single-center case-control studies, and one was a single-center cohort study. Thirty-six proteins were found to be expressed differentially when comparing samples from healthy subjects and individuals with diseased carotid vessels and between patients with symptomatic and asymptomatic carotid artery atherosclerotic lesions. Some of these were shown to be related to inflammatory or anti-inflammatory pathways in atherogenesis. CD5L and S100A12 were both found to be upregulated in patients with unstable plaque, the former owing to its anti-inflammatory properties and the latter for its pro-oxidant effects in atherosclerosis. ACTB is involved in cellular structure and integrity and was found to be downregulated in patients with ruptured carotid plaques.

Conclusions

Atherosclerotic carotid disease places the patient at increased risk of ischemic neurological events. Proteomics may help to understand their pathophysiological processes and can identify differential protein expression in blood samples from healthy subjects and patients with carotid artery plaques. This patient-centered approach will allow for the timely identification of individuals at higher risk of experiencing stroke.

miRNA and leptin signaling in metabolic diseases and at extreme environments

The burden of growing concern about the dysregulation of metabolic processes arises due to complex interplay between environment and nutrition that has great impact on genetics and epigenetics of an individual. Thereby, any abnormality at the level of food intake regulating hormones may contribute to the development of metabolic diseases in any age group due to malnutrition, overweight, changing lifestyle, and exposure to extreme environments such as heat stress (HS), cold stress, or high altitude (HA). Hormones such as leptin, adiponectin, ghrelin, and cholecystokinin regulate appetite and satiety to maintain energy homeostasis. Leptin, an adipokine and a pleiotropic hormone, play major role in regulating the food intake, energy gain and energy expenditure. Using in silico approach, we have identified the major genes (LEP, LEPR, JAK2, STAT3, NPY, POMC, IRS1, SOCS3) that play crucial role in leptin signaling pathway. Further, eight miRNAs (hsa-miR-204-5p, hsa-miR-211-5p, hsa-miR-30, hsa-miR-3163, hsa-miR-33a-3p, hsa-miR-548, hsa-miR-561-3p, hsa-miR-7856-5p) from TargetScan 8.0 database were screened out that commonly target these genes. The role of these miRNAs should be explored as they might play vital role in regulating the appetite, energy metabolism, metabolic diseases (obesity, type 2 diabetes, cardiovascular diseases, inflammation), and to combat extreme environments. The miRNAs regulating leptin signaling and appetite may be useful for developing novel therapeutics for metabolic diseases.

Real-time Elastography and Contrast-Enhanced Ultrasound for Evaluating Adventitia in the Early Diagnosis of Vulnerable Plaques: an Exploratory Study Based on Histopathology

Vulnerable plaque is closely related to the occurrence of ischemic stroke. Therefore, early accurate identification of plaque vulnerability is crucial in risk stratification. In the development of vulnerable plaques, the change of the adventitia is earlier than that of the intima. Currently, researchers focused on the ultrasound detection of intraplaque and intima, but adventitia was often ignored in the examination. Real-time elastography technology (RTE) provides an estimation of adventitia stiffness, and contrast-enhanced ultrasound (CEUS) provides the quantification of adventitial VV. Therefore, we aimed to evaluate the value of adventitia in the early diagnosis of plaque vulnerability by combining CEUS and RTE based on histopathology. Rabbit carotid atherosclerosis models were established, and CEUS and RTE were performed. Normalized maximal video-intensity enhancement (MVE) was calculated to quantify adventitial VV density, and strain values were acquired to evaluate the adventitial elasticity. After removal of the lesion lumen, histological analysis of each excised plaque and adventitia was performed, and vulnerable plaques (n = 32) and stable group (n = 13) were distinguished. Normalized MVE of the adventitial VV and adventitial strain values in the vulnerable group was significantly higher than those in the stable group. Normalized MVE and strain values had a positive linear correlation with histological findings. Normalized MVE of the adventitial VV combined with adventitial strain values could identify plaque vulnerability with the area under the curve of 0.913 (sensitivity 90% and specificity 97%). Accordingly, the multimodal ultrasound detection strategy of adventitia has a high diagnostic value for early plaque vulnerability.

Incidence of microvascular dysfunction is increased in hyperlipidemic mice, reducing cerebral blood flow and impairing remote memory

Introduction

The development of cognitive dysfunction is not necessarily associated with diet-induced obesity. We hypothesized that cognitive dysfunction might require additional vascular damage, for example, in atherosclerotic mice.

Methods

We induced atherosclerosis in male C57BL/6N mice by injecting AAV-PCSK9DY (2x1011 VG) and feeding them a cholesterol-rich Western diet. After 3 months, mice were examined for cognition using Barnes maze procedure and for cerebral blood flow. Cerebral vascular morphology was examined by immunehistology.

Results

In AAV-PCSK9DY-treated mice, plaque burden, plasma cholesterol, and triglycerides are elevated. RNAseq analyses followed by KEGG annotation show increased expression of genes linked to inflammatory processes in the aortas of these mice. In AAV-PCSK9DY-treated mice learning was delayed and long-term memory impaired. Blood flow was reduced in the cingulate cortex (-17%), caudate putamen (-15%), and hippocampus (-10%). Immunohistological studies also show an increased incidence of string vessels and pericytes (CD31/Col IV staining) in the hippocampus accompanied by patchy blood-brain barrier leaks (IgG staining) and increased macrophage infiltrations (CD68 staining).

Discussion

We conclude that the hyperlipidemic PCSK9DY mouse model can serve as an appropriate approach to induce microvascular dysfunction that leads to reduced blood flow in the hippocampus, which could explain the cognitive dysfunction in these mice.

Proteomic analysis of the extracellular matrix of human atherosclerotic plaques shows marked changes between plaque types

Cardiovascular disease is the leading cause of death, with atherosclerosis the major underlying cause. While often asymptomatic for decades, atherosclerotic plaque destabilization and rupture can arise suddenly and cause acute arterial occlusion or peripheral embolization resulting in myocardial infarction, stroke and lower limb ischaemia. As extracellular matrix (ECM) remodelling is associated with plaque instability, we hypothesized that the ECM composition would differ between plaques. We analyzed atherosclerotic plaques obtained from 21 patients who underwent carotid surgery following recent symptomatic carotid artery stenosis. Plaques were solubilized using a new efficient, single-step approach. Solubilized proteins were digested to peptides, and analyzed by liquid chromatography-mass spectrometry using data-independent acquisition. Identification and quantification of 4498 plaque proteins was achieved, including 354 ECM proteins, with unprecedented coverage and high reproducibility. Multidimensional scaling analysis and hierarchical clustering indicate two distinct clusters, which correlate with macroscopic plaque morphology (soft/unstable versus hard/stable), ultrasound classification (echolucent versus echogenic) and the presence of hemorrhage/ulceration. We identified 714 proteins with differential abundances between these groups. Soft/unstable plaques were enriched in proteins involved in inflammation, ECM remodelling, and protein degradation (e.g. matrix metalloproteinases, cathepsins). In contrast, hard/stable plaques contained higher levels of ECM structural proteins (e.g. collagens, versican, nidogens, biglycan, lumican, proteoglycan 4, mineralization proteins). These data indicate that a single-step proteomics method can provide unique mechanistic insights into ECM remodelling and inflammatory mechanisms within plaques that correlate with clinical parameters, and help rationalize plaque destabilization. These data also provide an approach towards identifying biomarkers for individualized risk profiling of atherosclerosis.

Emerging applications of single-cell profiling in precision medicine of atherosclerosis

Atherosclerosis is a chronic, progressive, inflammatory disease that occurs in the arterial wall. Despite recent advancements in treatment aimed at improving efficacy and prolonging survival, atherosclerosis remains largely incurable. In this review, we discuss emerging single-cell sequencing techniques and their novel insights into atherosclerosis. We provide examples of single-cell profiling studies that reveal phenotypic characteristics of atherosclerosis plaques, blood, liver, and the intestinal tract. Additionally, we highlight the potential clinical applications of single-cell analysis and propose that combining this approach with other techniques can facilitate early diagnosis and treatment, leading to more accurate medical interventions.

Transcriptome analysis unveils the mechanisms of lipid metabolism response to grayanotoxin I stress in Spodoptera litura

Background

Spodoptera litura (tobacco caterpillar, S. litura) is a pest of great economic importance due to being a polyphagous and world-distributed agricultural pest. However, agricultural practices involving chemical pesticides have caused resistance, resurgence, and residue problems, highlighting the need for new, environmentally friendly methods to control the spread of S. litura.

Aim

This study aimed to investigate the gut poisoning of grayanotoxin I, an active compound found in Pieris japonica, on S. litura, and to explore the underlying mechanisms of these effects.

Methods

S. litura was cultivated in a laboratory setting, and their survival rate, growth and development, and pupation time were recorded after grayanotoxin I treatment. RNA-Seq was utilized to screen for differentially expressed genes (DEGs). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted to determine the functions of these DEGs. ELISA was employed to analyze the levels of lipase, 3-hydroxyacyl-CoA dehydrogenase (HOAD), and acetyl-CoA carboxylase (ACC). Hematoxylin and Eosin (H & E) staining was used to detect the development of the fat body.

Results

Grayanotoxin I treatment significantly suppressed the survival rate, growth and development, and pupation of S. litura. RNA-Seq analysis revealed 285 DEGs after grayanotoxin I exposure, with over 16 genes related to lipid metabolism. These 285 DEGs were enriched in the categories of cuticle development, larvae longevity, fat digestion and absorption. Grayanotoxin I treatment also inhibited the levels of FFA, lipase, and HOAD in the hemolymph of S. litura.

Conclusion

The results of this study demonstrated that grayanotoxin I inhibited the growth and development of S. litura. The mechanisms might, at least partly, be related to the interference of lipid synthesis, lipolysis, and fat body development. These findings provide valuable insights into a new, environmentally-friendly plant-derived insecticide, grayanotoxin I, to control the spread of S. litura.

Immune-associated biomarkers identification for diagnosing carotid plaque progression with uremia through systematical bioinformatics and machine learning analysis

BACKGROUND

Uremia is one of the most challenging problems in medicine and an increasing public health issue worldwide. Patients with uremia suffer from accelerated atherosclerosis, and atherosclerosis progression may trigger plaque instability and clinical events. As a result, cardiovascular and cerebrovascular complications are more likely to occur. This study aimed to identify diagnostic biomarkers in uremic patients with unstable carotid plaques (USCPs).

METHODS

Four microarray datasets (GSE37171, GSE41571, GSE163154, and GSE28829) were downloaded from the NCBI Gene Expression Omnibus database. The Limma package was used to identify differentially expressed genes (DEGs) in uremia and USCP. Weighted gene co-expression network analysis (WGCNA) was used to determine the respective significant module genes associated with uremia and USCP. Moreover, a protein-protein interaction (PPI) network and three machine learning algorithms were applied to detect potential diagnostic genes. Subsequently, a nomogram and a receiver operating characteristic curve (ROC) were plotted to diagnose USCP with uremia. Finally, immune cell infiltrations were further analyzed.

RESULTS

Using the Limma package and WGCNA, the intersection of 2795 uremia-related DEGs and 1127 USCP-related DEGs yielded 99 uremia-related DEGs in USCP. 20 genes were selected as candidate hub genes via PPI network construction. Based on the intersection of genes from the three machine learning algorithms, three hub genes (FGR, LCP1, and C5AR1) were identified and used to establish a nomogram that displayed a high diagnostic performance (AUC: 0.989, 95% CI 0.971-1.000). Dysregulated immune cell infiltrations were observed in USCP, showing positive correlations with the three hub genes.

CONCLUSION

The current study systematically identified three candidate hub genes (FGR, LCP1, and C5AR1) and established a nomogram to assist in diagnosing USCP with uremia using various bioinformatic analyses and machine learning algorithms. Herein, the findings provide a foothold for future studies on potential diagnostic candidate genes for USCP in uremic patients. Additionally, immune cell infiltration analysis revealed that the dysregulated immune cell proportions were identified, and macrophages could have a critical role in USCP pathogenesis.

Macrophage Phenotyping in Atherosclerosis by Proteomics

Macrophages are heterogeneous and plastic cells, able to adapt their phenotype and functions to changes in the microenvironment. They are involved in several homeostatic processes and also in many human diseases, including atherosclerosis, where they participate in all the stages of the disease. For these reasons, macrophages have been studied extensively using different approaches, including proteomics. Proteomics, indeed, may be a powerful tool to better understand the behavior of these cells, and a careful analysis of the proteome of different macrophage phenotypes can help to better characterize the role of these phenotypes in atherosclerosis and provide a broad view of proteins that might potentially affect the course of the disease. In this review, we discuss the different proteomic techniques that have been used to delineate the proteomic profile of macrophage phenotypes and summarize some results that can help to elucidate the roles of macrophages and develop new strategies to counteract the progression of atherosclerosis and/or promote regression.

Defining atherosclerotic plaque biology by mass spectrometry-based omics approaches

Atherosclerosis is the principal cause of vascular diseases and one of the leading causes of worldwide death. Even though several insights into its natural course, risk factors and interventions have been identified, it is still an ongoing global pandemic. Since the structure and biochemical composition of the plaques show high heterogeneity, a comprehensive understanding of the intraplaque composition, its microenvironment, and the mechanisms of the progression and instability across different vascular beds at their progression stages is crucial for better risk stratification and treatment modalities. Even though several cell-based studies, animal studies, and extensive multicentric population studies have been conducted concerning cardiovascular diseases for assessing the risk factors and plaque biology, the studies on human clinical samples are very limited. New novel approaches utilize samples from percutaneous coronary interventions, which could possibly gain more access to clinical samples at different stages of the diseases without complex invasive resections. As an emerging technological platform in disease discovery research, mass spectrometry-based omics technologies offer capabilities for a comprehensive understanding of the mechanisms linked to several vascular diseases. Here, we discuss the cellular and molecular processes of atherosclerosis, different mass spectrometry-based omics approaches, and the studies mostly done on clinical samples of atheroma plaque using mass spectrometry-based proteomics, metabolomics and lipidomics approaches.

Identification of Monocyte-Associated Genes Related to the Instability of Atherosclerosis Plaque

Background. Atherosclerotic plaque instability is a common cause of stroke and ischemic infarction, and identification of monocyte-associated genes has become a prominent feature in cardiovascular research as a contributing/predictive marker. Methods. Whole genome sequencing data were downloaded from GSE159677, GSE41571, GSE120521, and GSE118481. Single-cell sequencing data analysis was conducted to cluster molecular subtypes of atherosclerotic plaques and identify specific genes. Differentially expressed genes (DEGs) between normal subjects and patients with unstable atheromatous plaques were screened. Weighted gene coexpression network analysis (WGCNA) was performed to find key module genes. In addition, GO and KEGG enrichment analyses explored potential biological signaling pathways to generate protein interaction (PPI) networks. GSEA and GSVA demonstrated activations in plaque instability subtypes. Results. 239 monocyte-associated genes were identified based on bulk and single-cell RNA-sequencing, followed by the recognition of 1221 atherosclerotic plaque-associated DEGs from the pooled matrix. GO and KEGG analyses suggested that DEGs might be related to inflammation response and the PI3K-Akt signaling pathway. Eight no-grey modules were obtained through WGCNA analysis, and the turquoise module has the highest correlation with unstable plaque ( $R^2=0.40$ ), which contained 1323 module genes. After fetching the intersecting genes, CXCL3, FPR1, GK, and LST1 were obtained that were significantly associated with plaque instability, which had an intense specific interaction. Monocyte-associated genes associated with atherosclerotic plaque instability have certain diagnostic significance and are generally overexpressed in this patient population. In addition, 11 overlapping coexpressed genes (CEG) might also activated multiple pathways regulating inflammatory responses, platelet activation, and hypoxia-inducible factors. GSVA showed that the corresponding pathways were significantly activated in high expression samples. Conclusions. Overexpression of CXCL3, GK, FPR1, and LST1 was advanced recognition and intervention factors for unstable plaques, which might become targets for atherosclerosis rupture prevention. We also analyzed the potential mechanisms of CEG from inflammatory and oxidative stress pathways.

Inflammation and Oxidative Stress Role of S100A12 as a Potential Diagnostic and Therapeutic Biomarker in Acute Myocardial Infarction

Acute myocardial infarction (AMI) is one of the most serious cardiovascular diseases with high morbidity and mortality. Numerous studies have indicated that S100A12 may has an essential role in the occurrence and development of AMI, and in-depth studies are currently lacking. The purpose of this study is to investigate the effect of S100A12 on inflammation and oxidative stress and to determine its clinical applicability in AMI. Here, AMI datasets used to explore the expression pattern of S100A12 in AMI were derived from the Gene Expression Omnibus (GEO) database. The pooled standard average deviation (SMD) was calculated to further determine S100A12 expression. The overlapping differentially expressed genes (DEGs) contained in all included datasets were recognized by the GEO2R tool. Then, functional enrichment analyses, including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses, were carried out to determine the molecular function of overlapping DEGs. Gene set enrichment analysis (GSEA) was conducted to determine unrevealed mechanisms of S100A12. Summary receiver operating characteristic (SROC) curve analysis and receiver operating characteristic (ROC) curve analysis were carried out to identify the diagnostic capabilities of S100A12. Moreover, we screened miRNAs targeting S100A12 using three online databases (miRWalk, TargetScan, and miRDB). In addition, by comprehensively using enzyme-linked immunosorbent assay (ELISA), real-time quantitative PCR (RT-qPCR), Western blotting (WB) methods, etc., we used the AC16 cells to validate the expression and underlying mechanism of S100A12. In our study, five datasets related to AMI, GSE24519, GSE60993, GSE66360, GSE97320, and GSE48060 were included; 412 overlapping DEGs were identified. Protein-protein interaction (PPI) network and functional analyses showed that S100A12 was a pivotal gene related to inflammation and oxidative stress. Then, S100A12 overexpression was identified based on the included datasets. The pooled standard average deviation (SMD) also showed that S100A12 was upregulated in AMI (SMD = 1.36, 95% CI: 0.70-2.03, p = 0.024). The SROC curve analysis result suggested that S100A12 had remarkable diagnostic ability in AMI (AUC = 0.90, 95% CI: 0.87-0.92). And nine miRNAs targeting S100A12 were also identified. Additionally, the overexpression of S100A12 was further confirmed that it maybe promote inflammation and oxidative stress in AMI through comprehensive in vitro experiments. In summary, our study suggests that overexpressed S100A12 may be a latent diagnostic biomarker and therapeutic target of AMI that induces excessive inflammation and oxidative stress. Nine miRNAs targeting S100A12 may play a crucial role in AMI, but further studies are still needed. Our work provides a positive inspiration for the in-depth study of S100A12 in AMI.

Clinical Parameters and Epigenetic Biomarkers of Plaque Vulnerability in Patients with Carotid Stenosis

Atheromatous disease is the first cause of death and dependency in developed countries and carotid artery atherosclerosis is one of the main causes of severe ischaemic strokes. Current management strategies are mainly based on the degree of stenosis and patient selection has limited accuracy. This information could be complemented by the identification of biomarkers of plaque vulnerability, which would permit patients at greater and lesser risk of stroke to be distinguished, thus enabling a better selection of patients for surgical or intensive medical treatment. Although several circulating protein-based biomarkers with significance for both the diagnosis of carotid artery disease and its prognosis have been identified, at present, none have been clinically implemented. This review focuses especially on the most relevant clinical parameters to take into account in routine clinical practice and summarises the most up-to-date data on epigenetic biomarkers of carotid atherosclerosis and plaque vulnerability.

Next-Generation and Single-Cell Sequencing Approaches to Study Atherosclerosis and Vascular Inflammation Pathophysiology: A Systematic Review

Background and Aims

Atherosclerosis is a chronic inflammatory disease that remains the leading cause of morbidity and mortality worldwide. Despite decades of research into the development and progression of this disease, current management and treatment approaches remain unsatisfactory and further studies are required to understand the exact pathophysiology. This review aims to provide a comprehensive assessment of currently published data utilizing single-cell and next-generation sequencing techniques to identify key cellular and molecular contributions to atherosclerosis and vascular inflammation.

Methods

Electronic searches of Cochrane Central Register of Controlled Trials, MEDLINE, and EMBASE databases were undertaken from inception until February 2022. A narrative synthesis of all included studies was performed for all included studies. Quality assessment and risk of bias analysis was evaluated using the ARRIVE and SYRCLE checklist tools.

Results

Thirty-four studies were eligible for narrative synthesis, with 16 articles utilizing single-cell exclusively, 10 utilizing next-generation sequencing and 8 using a combination of these approaches. Studies investigated numerous targets, ranging from exploratory tissue and plaque analysis, cell phenotype investigation and physiological/hemodynamic contributions to disease progression at both the single-cell and whole genome level. A significant area of focus was placed on smooth muscle cell, macrophage, and stem/progenitor contributions to disease, with little focus placed on contributions of other cell types including lymphocytes and endothelial cells. A significant level of heterogeneity exists in the outcomes from single-cell sequencing of similar samples, leading to inter-sample and inter-study variation.

Conclusions

Single-cell and next-generation sequencing methodologies offer novel means of elucidating atherosclerosis with significantly higher resolution than previous methodologies. These approaches also show significant potential for translatability into other vascular disease states, by facilitating cell-specific gene expression profiles between disease states. Implementation of these technologies may offer novel approaches to understanding the disease pathophysiology and improving disease prevention, management, and treatment.Systematic Review Registration: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021229960, identifier: CRD42021229960.

Identification of potential biomarkers and pathways associated with carotid atherosclerotic plaques in type 2 diabetes mellitus: A transcriptomics study

Type 2 diabetes mellitus (T2DM) affects the formation of carotid atherosclerotic plaques (CAPs) and patients are prone to plaque instability. It is crucial to clarify transcriptomics profiles and identify biomarkers related to the progression of T2DM complicated by CAPs. Ten human CAP samples were obtained, and whole transcriptome sequencing (RNA-seq) was performed. Samples were divided into two groups: diabetes mellitus (DM) versus non-DM groups and unstable versus stable groups. The Limma package in R was used to identify lncRNAs, circRNAs, and mRNAs. Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses, protein-protein interaction (PPI) network creation, and module generation were performed for differentially expressed mRNAs. Cytoscape was used to create a transcription factor (TF)-mRNA regulatory network, lncRNA/circRNA-mRNA co-expression network, and a competitive endogenous RNA (ceRNA) network. The GSE118481 dataset and RT-qPCR were used to verify potential mRNAs.The regulatory network was constructed based on the verified core genes and the relationships were extracted from the above network. In total, 180 differentially expressed lncRNAs, 343 circRNAs, and 1092 mRNAs were identified in the DM versus non-DM group; 240 differentially expressed lncRNAs, 390 circRNAs, and 677 mRNAs were identified in the unstable versus stable group. Five circRNAs, 14 lncRNAs, and 171 mRNAs that were common among all four groups changed in the same direction. GO/KEGG functional enrichment analysis showed that 171 mRNAs were mainly related to biological processes, such as immune responses, inflammatory responses, and cell adhesion. Five circRNAs, 14 lncRNAs, 46 miRNAs, and 54 mRNAs in the ceRNA network formed a regulatory relationship. C22orf34-hsa-miR-6785-5p-RAB37, hsacirc_013887-hsa-miR-6785-5p/hsa-miR-4763-5p/hsa-miR-30b-3p-RAB37, MIR4435-1HG-hsa-miR-30b-3p-RAB37, and GAS5-hsa-miR-30b-3p-RAB37 may be potential RNA regulatory pathways. Seven upregulated mRNAs were verified using the GSE118481 dataset and RT-qPCR. The regulatory network included seven mRNAs, five circRNAs, six lncRNAs, and 14 TFs. We propose five circRNAs (hsacirc_028744, hsacirc_037219, hsacirc_006308, hsacirc_013887, and hsacirc_045622), six lncRNAs (EPB41L4A-AS1, LINC00969, GAS5, MIR4435-1HG, MIR503HG, and SNHG16), and seven mRNAs (RAB37, CCR7, CD3D, TRAT1, VWF, ICAM2, and TMEM244) as potential biomarkers related to the progression of T2DM complicated with CAP. The constructed ceRNA network has important implications for potential RNA regulatory pathways.