Identification of key genes in calcific aortic valve disease by integrated bioinformatics analysis

Cathepsin D elevates the fibrocalcific activity in human aortic valve cells through the ERK1/2-Sox9 pathway

Background

Human Aortic valve interstitial cells (AVICs) from calcific aortic valve disease (CAVD)-affected valves exhibit elevated fibrocalcific activity although the underlying mechanism remains incompletely understood. This study aimed to identify endogenous factors that promote aortic valve fibrocalcification.

Methods and results

Proteomic analysis found increased cathepsin D levels in AVICs from CAVD-affected valves compared to AVICs from normal valves, and this finding was validated by immunoblotting. ELISA assay identified exacerbated release of cathepsin D by AVICs of diseased valves. Recombinant human cathepsin D upregulated the expression of runt-related transcription factor 2 (Runx2), alkaline phosphatase (ALP), collagen I and collagen IV in human AVICs, resulting in the deposition of calcium and collagen. Blocking of the ERK1/2-Sox9 signaling pathway markedly reduced the pro-fibrocalcific effect of cathepsin D. Moreover, normal AVICs express and release greater levels of cathepsin D when exposed to soluble matrilin 2. Knockdown of cathepsin D attenuated the fibrocalcific response induced by soluble matrilin 2.

Conclusion

AVICs of diseased aortic valves produce and release greater levels of cathepsin D that exerts a pro-fibrocalcific effect on AVICs through the ERK1/2-Sox9 pathway. Soluble matrilin 2 up-regulates cathepsin D to elevate AVIC fibrocalcific activity. Over-expression of cathepsin D in the aortic valve may enhance the pathobiological activities in AVICs.

Bioinformatics Analysis Identifies Key Genes in the Effect of Resistance Training on Female Skeletal Muscle Aging

Resistance training is used to combat skeletal muscle function decline in older adults. Few studies have been designed specific for females, resulting in very limited treatment options for skeletal muscle atrophy in aging women. Here, we analyzed the gene expression profiles of skeletal muscle samples from sedentary young women, sedentary older women, and resistance-trained older women, using microarray data from public database. A total of 45 genes that were differentially expressed during female muscle aging and reversed by resistance training were identified. Functional and pathway enrichment analysis, protein-protein interaction network analysis, and receiver operating characteristic analysis were performed to reveal the key genes and pathways involved in the effects of resistance training on female muscle aging. The collagen genes COL1A1, COL3A1, and COL4A1 were identified important regulators of female muscle aging and resistance training, by modulating multiple signaling pathways, such as PI3 kinase-Akt signaling, focal adhesions, extracellular matrix-receptor interactions, and relaxin signaling. Interestingly, the expression of CDKN1A and TP63 were increased during aging, and further upregulated by resistance training in older women, suggesting they may negatively affect resistance training outcomes. Our findings provide novel insights into the molecular mechanisms of resistance training on female muscle aging and identify potential biomarkers and targets for clinical intervention.

Construction of a diagnostic model for hepatitis B-related hepatocellular carcinoma using machine learning and artificial neural networks and revealing the correlation by immunoassay

HBV infection profoundly escalates hepatocellular carcinoma (HCC) susceptibility, responsible for a majority of HCC cases. HBV-driven immune-mediated hepatocyte impairment significantly fuels HCC progression. Regrettably, inconspicuous early HCC symptoms often culminate in belated diagnoses. Nevertheless, surgically treated early-stage HCC patients relish augmented five-year survival rates. In contrast, advanced HCC exhibits feeble responses to conventional interventions like radiotherapy, chemotherapy, and surgery, leading to diminished survival rates. This investigation endeavors to unearth diagnostic hallmark genes for HBV-HCC leveraging a bioinformatics framework, thus refining early HBV-HCC detection. Candidate genes were sieved via differential analysis and Weighted Gene Co-Expression Network Analysis (WGCNA). Employing three distinct machine learning algorithms unearthed three feature genes (HHIP, CXCL14, and CDHR2). Melding these genes yielded an innovative Artificial Neural Network (ANN) diagnostic blueprint, portending to alleviate patient encumbrance and elevate life quality. Immunoassay scrutiny unveiled accentuated immune damage in HBV-HCC patients relative to solitary HCC. Through consensus clustering, HBV-HCC was stratified into two subtypes (C1 and C2), the latter potentially indicating milder immune impairment. The diagnostic model grounded in these feature genes showcased robust and transferrable prognostic potentialities, introducing a novel outlook for early HBV-HCC diagnosis. This exhaustive immunological odyssey stands poised to expedite immunotherapeutic curatives' emergence for HBV-HCC.

Exploration and validation of the influence of angiogenesis-related factors in aortic valve calcification

Over the years, bioinformatics tools have been used to identify functional genes. In the present study, bioinformatics analyses were conducted to explore the underlying molecular mechanisms of angiogenic factors in calcific aortic valve disease (CAVD). The raw gene expression profiles were from datasets GSE153555, GSE83453, and GSE51472, and the angiogenesis-related gene set was from the Gene Set Enrichment Analysis database (GSEA). In this study, R was used to screen for differentially expressed genes (DEGs) and co-expressed genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG) Pathway enrichment analysis were performed on DEGs and validated in clinical samples. DEGs in CAVD were significantly enriched in numerous immune response pathways, inflammatory response pathways and angiogenesis-related pathways. Nine highly expressed angiogenesis-related genes were identified, of which secretogranin II (SCG2) was the most critical gene. MiRNA and transcription factors (TFs) networks were established centered on five DEGs, and zinc finger E-box binding homeobox 1 (ZEB1) was the most important transcription factor, verified by PCR, immunohistochemical staining and western blotting experiments. Overall, this study identified key genes and TFs that may be involved in the pathogenesis of CAVD and may have promising applications in the treatment of CAVD.

Effects of High-Fat Diet on Cardiovascular Protein Expression in Mice Based on Proteomics

PURPOSE

To investigate the effect of high-fat diet on protein expression in mouse heart and aorta using proteomic techniques.

METHODS

A high-fat diet was used to construct an obese mouse model, and body weight was checked regularly. After the experiment, serum lipid and oxidative stress levels were measured. Proteomic detection of cardiac and aortic protein expression. Cardiac and aortic common differentially expressed proteins (Co-DEPs) were screened based on proteomic results. Subsequently, functional enrichment analysis and screening of key proteins were performed.

RESULTS

A high-fat diet significantly increased body weight in mice. Obese mice had considerably higher levels of TC, TG, LDL-C, ROS, and MDA. In the heart and aorta, 17 Co-DEPs were discovered. The results of functional analysis of these proteins indicated that they were mainly related to lipid metabolism. Ech1, Decr1, Hsd17b4, Hsdl2 and Acadvl were screened as key proteins. In mice, a high-fat diet causes lipid metabolism to become disrupted, resulting in higher levels of oxidative stress and lipid peroxidation products.

CONCLUSION

Ech1, Decr1, Hsd17b4, Hsdl2 and Acadvl as cardiac and aortic Co-DEPs are closely related to lipid metabolism and may serve as potential diagnostic and therapeutic targets for obesity-induced cardiovascular disease.

Identification of Differentially Expressed Genes Particularly Associated with Immunity in Uremia Patients by Bioinformatic Analysis

Uremia is a common syndrome that happens to nearly all end-stage kidney diseases, which profound have changes in human gene expressions, but the related pathways are poorly understood. Gene Ontology categories and Kyoto Encyclopedia of Genes and Genomes pathways enriched in the differentially expressed genes (DEGs) were analyzed by using clusterProfiler, org.Hs.eg.db, and Pathview, and protein-protein interaction (PPI) network was built by Cytoscape. We identified 3432 DEGs (including 3368 down- and 64 up-regulated genes), of which there were 52 different molecular functions, and 178 genes were identified as immune genes controlled by the four transcription factors (POU domain class 6 transcription factor 1 (POU6F1), interferon regulator factor 7 [IRF7], forkhead box D3 (FOXD3), and interferon-stimulated response element [ISRE]). In the gender research, no significant difference was observed. The top 15 proteins of 178 immune-related genes were identified with the highest degree in PPI network. The DEG analysis of uremia patients was expected to provide fundamental information to relieve pain and add years to their life.

Integrated identification of key immune related genes and patterns of immune infiltration in calcified aortic valvular disease: A network based meta-analysis

Background: As the most prevalent valvular heart disease, calcific aortic valve disease (CAVD) has become a primary cause of aortic valve stenosis and insufficiency. We aim to illustrate the roles of immune related genes (IRGs) and immune cells infiltration in the occurrence of CAVD.

Methods: Integrative meta-analysis of expression data (INMEX) was adopted to incorporate multiple gene expression datasets of CAVD from Gene Expression Omnibus (GEO) database. By matching the differentially expressed genes (DEGs) to IRGs from “ImmPort” database, differentially expressed immune related genes (DEIRGs) were screened out. We performed enrichment analysis and found that DEIRGs in CAVD were closely related to inflammatory response and immune cells infiltration. We also constructed protein–protein interaction (PPI) network of DEIRGs and identified 5 key DEIRGs in CAVD according to the mixed character calculation results. Moreover, CIBERSORT algorithm was used to explore the profile of infiltrating immune cells in CAVD. Based on Spearman’s rank correlation method, correlation analysis between key DEIRGs and infiltrating immune cells was performed.

Results: A total of 220 DEIRGs were identified and the enrichment analysis of DEIRGs showed that they were significantly enriched in inflammatory responses. PPI network was constructed and PTPN11, GRB2, SYK, PTPN6 and SHC1 were identified as key DEIRGs. Compared with normal aortic valve tissue samples, the proportion of neutrophils, T cells CD4 memory activated and macrophages M0 was elevated in calcified aortic valves tissue samples, as well as reduced infiltration of macrophages M2 and NK cells activated. Furthermore, key DEIRGs identified in the present study, including PTPN11, GRB2, PTPN6, SYK, and SHC1, were all significantly correlated with infiltration of various immune cells.

Conclusion: This meta-analysis suggested that PTPN11, GRB2, PTPN6, SYK, and SHC1 might be key DEIRGs associated with immune cells infiltration, which play a pivotal role in pathogenesis of CAVD.

Integrated bioinformatics analysis reveals marker genes and immune infiltration for pulmonary arterial hypertension

Pulmonary arterial hypertension (PAH) is a chronic cardiopulmonary syndrome with high pulmonary vascular load and eventually causing RV heart failure even death. However, the mechanism of pulmonary hypertension remains unclear. The purpose of this research is to detect the underlying key genes and potential mechanism of PAH using several bioinformatic methods. The microarrays GSE22356, GSE131793 and GSE168905 were acquired from the GEO. Subsequently, a host of bioinformatics techniques such as DAVID, STRING, R language and Cytoscape were utilized to investigate DEGs between PAH and healthy controls and conduct GO annotation, KEGG enrichment analysis and PPI network construction etc. Additionally, we predicted the transcription factors regulating DEGs through iRegulon plugin of Cytoscape and CIBERSORT was used to conduct immune infiltration analysis. One thousand two hundred and seventy-seven DEGs (403 up-regulated and 874 down-regulated) were identified from peripheral blood samples of 32 PAH patients and 29 controls, among which SLC4A1, AHSP, ALAS2, CA1, HBD, SNCA, HBM, SELENBP1, SERPINE1 and ITGA2B were detected as hub genes. The functional enrichment changes of DEGs were mainly enriched in protein binding, extracellular exosome, extracellular space, extracellular region and integral component of plasma membrane. The hub genes are chiefly enriched at extracellular exosome, hemoglobin complex, blood microparticle, oxygen transporter activity. Among TF-DEGs network, 42 target DEGs and 6 TFs were predicted with an NES > 4 (TEAD4, TGIF2LY, GATA5, GATA1, GATA2, FOS). Immune infiltration analysis showed that monocytes occupied the largest proportion of immune cells. The trend analysis results of infiltration immune cells illustrated that PAH patients had higher infiltration of NK cell activation, monocyte, T cell CD4 memory activation, and mast cell than healthy controls and lower infiltration of T cell CD4 naive. We detected SLC4A1, AHSP, ALAS2, CA1, HBD, SNCA, HBM, SELENBP1, SERPINE1 and ITGA2B as the most significant markers of PAH. The PAH patients had higher infiltration of NK cell activation, monocyte, T cell CD4 memory activation, and mast cell than healthy controls and lower infiltration of T cell CD4 naive. These identified genes and these immune cells probably have precise regulatory relationships in the development of PAH.

High expression of RASAL1, a hub gene in the progression of liver cancer, suggests a poor prognostis

OBJECTIVE

Liver cancer (LC) is a frequently occurring lethal malignancy worldwide, yet the molecular mechanisms of carcinogenesis and their development remain uncharacterized. In this study, bioinformatics methods were used to find candidate hub genes for prognosis assessment and clinical treatment of LC.

METHODS

Differential analysis was carried out based on the evidence of gene expression profiling in LC on The Cancer Genome Atlas (TCGA). The differentially expressed genes (DEGs) were constructed into co-expression networks and divided into modules by virtue of weighted gene co-expression network analysis (WGCNA). Based on the Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG), the module genes were subjected to functional enrichment analysis. The LC microarray (GSE105130) in the Gene Expression Omnibus was selected to verify the hub genes' expression profiles. The validity of the hub genes was verified via survival analysis, as well as expression correlation with the clinicopathological features. Thereafter, gene set variation analysis (GSVA) and single-sample gene set enrichment analysis (GSEA) were applied to investigate the possible biological functions of the hub genes.

RESULTS

In total, 3780 DEGs and 17 co-expression modules were obtained. The blue module had the strongest correlation with the tumour stage and the module genes were principally enriched in tumour-associated GO terms, as well as pathways such as Ras protein signal transduction, ERK1/2 cascade, Ras signal pathway, and ECM-receptor interaction. RASAL1, which is highly expressed in LC, was identified as a hub gene for LC progression. Its high expression suggested unfavorable patient prognosis and was correlated with T stage, gender and tumour stage. Further analysis identified that the overexpression of RASAL1 was substantially enriched in cancer-associated gene sets.

CONCLUSION

RASAL1 is a hub gene that influences LC progression, constituting a novel biomarker and molecular target in the future diagnosis and therapy of LC.

Degenerative changes of the aortic valve during left ventricular assist device support

AIMS

Donor heart shortage leads to increasing use of left ventricular assist device (LVAD) as bridge-to-transplant or destination therapy. Prolonged LVAD support is associated with aortic valve insufficiency, representing a relevant clinical problem in LVAD patients. Nevertheless, the impact of LVAD support on inflammation, remodelling, and chondro-osteogenic differentiation of the aortic valve is still not clearly understood. The aim of the study is to evaluate the impact of LVAD support on structural and molecular alterations of the aortic valve.

METHODS AND RESULTS

During heart transplantation, aortic valves of 63 heart failure patients without (n = 22) and with LVAD support (n = 41) were collected and used for analysis. Data on clinical course as well as echocardiographic data were analysed. Calcification and markers of remodelling, chondro-osteogenic differentiation, and inflammation were evaluated by computed tomography, by mRNA analysis and by histology and immunohistochemistry. Expression of inflammation markers of the LVAD group was analysed with regard to levels of C-reactive protein and driveline infections. Calcium accumulation and mRNA expression of determined markers were correlated with duration of LVAD support. Data were also analysed relating to aortic valve opening and aortic valve insufficiency. There was no difference in the frequency of cardiovascular risk factors or comorbidities between the patient groups. Expression of matrix metalloproteinase-9 (P = 0.007), alpha-smooth muscle actin (P = 0.045), and osteopontin (P = 0.003) were up-regulated in aortic valves of LVAD patients. Histological appearance of the aortic valve was similar in patients with or without LVAD, and computed tomography-based analysis not yet revealed significant difference in tissue calcification. Expression of interferon gamma (P = 0.004), interleukin-1 beta (P < 0.0001), and tumour necrosis factor alpha (P = 0.04) was up-regulated in aortic valves of LVAD patients without concomitant inflammatory cell infiltration and independent from unspecific inflammation. Expression of matrix metalloproteinase-2 (P = 0.038) and transforming growth factor beta (P = 0.0504) correlated negatively with duration of LVAD support. Presence of aortic valve insufficiency led to a significantly higher expression of interferon gamma (P = 0.007) in LVAD patients. There was no alteration in the determined markers in relation to aortic valve opening in LVAD patients.

CONCLUSIONS

Left ventricular assist device support leads to signs of early aortic valve degeneration independent of support duration. Thus, the aortic valve of patients with LVAD support should be closely monitored, particularly in patients receiving destination therapy as well as in the prospect of using aortic valves of LVAD patients as homografts in case of bridge-to-transplant therapy.

Thioredoxin-1 and Correlations of the Plasma Cytokines Regarding Aortic Valve Stenosis Severity

Aortic valve stenosis (AS) develops not only with a pronounced local inflammatory response, but also oxidative stress is involved. The aim of this study was to evaluate the plasma levels of thioredoxin-1 (TRX1), myeloperoxidase (MPO), chemerin, growth differentiation factor 15 (GDF-15), angiopoietin-2 (Ang-2), vascular endothelial growth factor A (VEGF-A), fibroblast growth factor 2 (FGF-2), fibroblast growth factor 21 (FGF-21), and metalloproteinase (MMP)-1, -3, and -9 in acquired AS patients as well as to clarify the correlations of TXR1 and the plasma inflammatory biomarkers regarding AS severity. AS patients were classified into three groups: 16 patients with mild AS stenosis, 19 with moderate and 11 with severe AS, and 30 subjects without AS were selected as a control group. AS patients had significantly higher plasma levels of TRX1 compared to controls, but the highest difference was found in mild AS patients compared to the controls. We conclude that AS is associated with significantly increased plasma TRX1 levels, and TRX1 might serve as a specific and sensitive biomarker of AS. TRX1 and also chemerin, GDF-15, VEGF-A, FGF-2 and FGF-21 significantly correlate with AS severity degrees. TRX1 also showed positive association with FGF-2, VEGF-A, and MMP-3 in all AS patients.

Identification of key genes in calcific aortic valve disease via weighted gene co-expression network analysis

BACKGROUND

Calcific aortic valve disease (CAVD) is the most common subclass of valve heart disease in the elderly population and a primary cause of aortic valve stenosis. However, the underlying mechanisms remain unclear.

METHODS

The gene expression profiles of GSE83453, GSE51472, and GSE12644 were analyzed by 'limma' and 'weighted gene co-expression network analysis (WGCNA)' package in R to identify differentially expressed genes (DEGs) and key modules associated with CAVD, respectively. Then, enrichment analysis was performed based on Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, DisGeNET, and TRRUST database. Protein-protein interaction network was constructed using the overlapped genes of DEGs and key modules, and we identified the top 5 hub genes by mixed character calculation.

RESULTS

We identified the blue and yellow modules as the key modules. Enrichment analysis showed that leukocyte migration, extracellular matrix, and extracellular matrix structural constituent were significantly enriched. SPP1, TNC, SCG2, FAM20A, and CD52 were identified as hub genes, and their expression levels in calcified or normal aortic valve samples were illustrated, respectively.

CONCLUSIONS

This study suggested that SPP1, TNC, SCG2, FAM20A, and CD52 might be hub genes associated with CAVD. Further studies are required to elucidate the underlying mechanisms and provide potential therapeutic targets.

Multi-Omics Approaches to Define Calcific Aortic Valve Disease Pathogenesis

Calcific aortic valve disease sits at the confluence of multiple world-wide epidemics of aging, obesity, diabetes, and renal dysfunction, and its prevalence is expected to nearly triple over the next 3 decades. This is of particularly dire clinical relevance, as calcific aortic valve disease can progress rapidly to aortic stenosis, heart failure, and eventually premature death. Unlike in atherosclerosis, and despite the heavy clinical toll, to date, no pharmacotherapy has proven effective to halt calcific aortic valve disease progression, with invasive and costly aortic valve replacement representing the only treatment option currently available. This substantial gap in care is largely because of our still-limited understanding of both normal aortic valve biology and the key regulatory mechanisms that drive disease initiation and progression. Drug discovery is further hampered by the inherent intricacy of the valvular microenvironment: a unique anatomic structure, a complex mixture of dynamic biomechanical forces, and diverse and multipotent cell populations collectively contributing to this currently intractable problem. One promising and rapidly evolving tactic is the application of multiomics approaches to fully define disease pathogenesis. Herein, we summarize the application of (epi)genomics, transcriptomics, proteomics, and metabolomics to the study of valvular heart disease. We also discuss recent forays toward the omics-based characterization of valvular (patho)biology at single-cell resolution; these efforts promise to shed new light on cellular heterogeneity in healthy and diseased valvular tissues and represent the potential to efficaciously target and treat key cell subpopulations. Last, we discuss systems biology- and network medicine-based strategies to extract meaning, mechanisms, and prioritized drug targets from multiomics datasets.