Identification and Validation of Immune-Related Genes Diagnostic for Progression of Atherosclerosis and Diabetes

Background Atherosclerosis and type 2 diabetes mellitus contribute to a large part of cardiovascular events, but the underlying mechanism remains unclear. In this study, we focused on identifying the linking genes of the diagnostic biomarkers and effective therapeutic targets associated with these two diseases. Methods The transcriptomic datasets of atherosclerosis and type 2 diabetes mellitus were obtained from the GEO database. Differentially expressed genes analysis was performed by R studio software, and differential analysis including functional enrichment, therapeutic small molecular agents prediction, and protein–protein interaction analysis were applied to the common shared differentially expressed genes. Hub genes were identified and further validated using an independent dataset and clinical samples. Furthermore, we measured the expression correlations, immune cell infiltration, and diagnostic capability of the three key genes. Results We screened out 28 up-regulated and six down-regulated common shared differentially expressed genes. Functional enrichment analysis showed that cytokines and immune activation were involved in the development of these two diseases. Six small molecules with the highest absolute enrichment value were identified. Three critical genes (CD4, PLEK, and THY1) were further validated both in validation sets and clinical samples. The gene correlation analysis showed that CD4 was strongly positively correlated with PLEK, and ROC curves confirmed the good discriminatory capacity of CD4 and PLEK in two diseases. We have established the co-expression network between atherosclerosis lesions progressions and type 2 diabetes mellitus, and identified CD4 and PLEK as key genes in the two diseases, which may facilitate both development of diagnosis and therapeutic strategies.

[1]  Yue Cao,et al.  Targeting the Microenvironment of Vulnerable Atherosclerotic Plaques: An Emerging Diagnosis and Therapy Strategy for Atherosclerosis , 2022, Advanced materials.

[2]  G. Burke,et al.  Lifetime Cardiovascular Disease Risk by Coronary Artery Calcium Score in Individuals With and Without Diabetes: An Analysis From the Multi-Ethnic Study of Atherosclerosis. , 2022, Diabetes care.

[3]  A. García-Hernández,et al.  CD4+ T-cell activation of bone marrow causes bone fragility and insulin resistance in type 2 diabetes. , 2021, Bone.

[4]  C. Monaco,et al.  Therapeutic strategies targeting inflammation and immunity in atherosclerosis: how to proceed? , 2022, Nature Reviews Cardiology.

[5]  W. Rathmann,et al.  Leukocyte Counts and T-Cell Frequencies Differ Between Novel Subgroups of Diabetes and Are Associated With Metabolic Parameters and Biomarkers of Inflammation , 2021, Diabetes.

[6]  R. Eckel,et al.  Cardiovascular disease in diabetes, beyond glucose. , 2021, Cell metabolism.

[7]  S. S. Reddy,et al.  Macrophage p47phox Regulates Pressure Overload-Induced Left Ventricular Remodelling by Modulating IL-4/STAT6/PPARγ Signaling. , 2021, Free radical biology & medicine.

[8]  Q. Ning,et al.  Immunological Characteristics in Type 2 Diabetes Mellitus Among COVID-19 Patients , 2021, Frontiers in Endocrinology.

[9]  C. Cannon,et al.  Cardiovascular Outcomes with Ertugliflozin in Type 2 Diabetes. , 2020, The New England journal of medicine.

[10]  Xiufeng Xu,et al.  Identification of Four Potential Biomarkers Associated With Coronary Artery Disease in Non-diabetic Patients by Gene Co-expression Network Analysis , 2020, Frontiers in Genetics.

[11]  Merlin C. Thomas,et al.  Transient Intermittent Hyperglycemia Accelerates Atherosclerosis by Promoting Myelopoiesis , 2020, Circulation research.

[12]  R. Virmani,et al.  Vulnerable plaques and patients: state-of-the-art. , 2020, European heart journal.

[13]  K. Rahimi,et al.  Global and regional prevalence, burden, and risk factors for carotid atherosclerosis: a systematic review, meta-analysis, and modelling study. , 2020, The Lancet. Global health.

[14]  Alexander R. Pico,et al.  Transcriptional profiling and therapeutic targeting of oxidative stress in neuroinflammation , 2020, Nature Immunology.

[15]  K. Ley,et al.  T cell subsets and functions in atherosclerosis , 2020, Nature Reviews Cardiology.

[16]  J. Olefsky,et al.  The role of macrophages in obesity-associated islet inflammation and β-cell abnormalities , 2019, Nature Reviews Endocrinology.

[17]  C. Kahn,et al.  Homozygous receptors for insulin and not IGF-1 accelerate intimal hyperplasia in insulin resistance and diabetes , 2019, Nature Communications.

[18]  P. Kovanen.  Mast Cells as Potential Accelerators of Human Atherosclerosis—From Early to Late Lesions , 2019, International journal of molecular sciences.

[19]  Shuyang Zhang,et al.  Identification and validation of four hub genes involved in the plaque deterioration of atherosclerosis , 2019, Aging.

[20]  Xiaodong Xu,et al.  Identification of core genes and pathways in type 2 diabetes mellitus by bioinformatics analysis , 2019, Molecular medicine reports.

[21]  P. Lu,et al.  CD90 highly expressed population harbors a stemness signature and creates an immunosuppressive niche in pancreatic cancer. , 2019, Cancer letters.

[22]  Yilong Wu,et al.  Identification of key genes involved in type 2 diabetic islet dysfunction: a bioinformatics study , 2019, Bioscience reports.

[23]  D. Burks,et al.  Changes in CDKN2A/2B expression associate with T‐cell phenotype modulation in atherosclerosis and type 2 diabetes mellitus , 2019, Translational research : the journal of laboratory and clinical medicine.

[24]  G. Mauri,et al.  MTGO: PPI Network Analysis Via Topological and Functional Module Identification , 2018, Scientific Reports.

[25]  R. Testa,et al.  Inflammageing and metaflammation: The yin and yang of type 2 diabetes , 2018, Ageing Research Reviews.

[26]  Lin Li,et al.  Screening and identification of key biomarkers in hepatocellular carcinoma: Evidence from bioinformatic analysis , 2017, Oncology reports.

[27]  Angela N. Brooks,et al.  A Next Generation Connectivity Map: L1000 Platform and the First 1,000,000 Profiles , 2017, Cell.

[28]  R. Virmani,et al.  Pathology of Human Coronary and Carotid Artery Atherosclerosis and Vascular Calcification in Diabetes Mellitus. , 2017, Arteriosclerosis, thrombosis, and vascular biology.

[29]  A. Zernecke,et al.  Protective and pathogenic roles of CD8+ T cells in atherosclerosis , 2016, Basic Research in Cardiology.

[30]  J. Lundeberg,et al.  Transcriptome analysis reveals mucin 4 to be highly associated with periodontitis and identifies pleckstrin as a link to systemic diseases , 2015, Scientific Reports.

[31]  Jung Jae Shin,et al.  Damage-associated molecular patterns and their pathological relevance in diabetes mellitus , 2015, Ageing Research Reviews.

[32]  Ash A. Alizadeh,et al.  Robust enumeration of cell subsets from tissue expression profiles , 2015, Nature Methods.

[33]  Davide Heller,et al.  STRING v10: protein–protein interaction networks, integrated over the tree of life , 2014, Nucleic Acids Res..

[34]  Chung-Yen Lin,et al.  cytoHubba: identifying hub objects and sub-networks from complex interactome , 2014, BMC Systems Biology.

[35]  Sean R. Davis,et al.  NCBI GEO: archive for functional genomics data sets—update , 2012, Nucleic Acids Res..

[36]  C. Kahn,et al.  Loss of insulin signaling in vascular endothelial cells accelerates atherosclerosis in apolipoprotein E null mice. , 2010, Cell metabolism.

[37]  I. Tabas.  Macrophage death and defective inflammation resolution in atherosclerosis , 2010, Nature Reviews Immunology.

[38]  K. Hirata,et al.  Pioglitazone reduces the necrotic-core component in coronary plaque in association with enhanced plasma adiponectin in patients with type 2 diabetes mellitus. , 2009, Circulation journal : official journal of the Japanese Circulation Society.

[39]  E. Tuzcu,et al.  Comparison of pioglitazone vs glimepiride on progression of coronary atherosclerosis in patients with type 2 diabetes: the PERISCOPE randomized controlled trial. , 2008, JAMA.

[40]  H. Ellingsgaard,et al.  Increased Number of Islet-Associated Macrophages in Type 2 Diabetes , 2007, Diabetes.

[41]  J. A. Badwey,et al.  Phosphorylation of Pleckstrin Increases Proinflammatory Cytokine Secretion by Mononuclear Phagocytes in Diabetes Mellitus1 , 2007, The Journal of Immunology.

[42]  Vincent J Carey,et al.  Bioconductor: an open source framework for bioinformatics and computational biology. , 2006, Methods in enzymology.

[43]  Rafael A Irizarry,et al.  Exploration, normalization, and summaries of high density oligonucleotide array probe level data. , 2003, Biostatistics.

[44]  D. Gailani,et al.  P47 phosphoprotein of blood platelets (pleckstrin) is a major target for phorbol ester‐induced protein phosphorylation in intact platelets, granulocytes, lymphocytes, monocytes and cultured leukaemic cells: absence of P47 in non‐haematopoietic cells , 1990, British journal of haematology.