Bioinformatics Analysis Reveals the Potential Diagnostic Biomarkers for Abdominal Aortic Aneurysm

Revealing the roles of IL-7R in abdominal aortic aneurysm through integrated analysis of single-cell RNA-seq and bulk RNA-seq

Abdominal aortic aneurysm (AAA) is a common cardiovascular disease in the elderly, but there are still no therapeutic targets for this disease. In this study, we collected and analyzed bulk RNA sequencing (bulk RNA-seq) and single-cell RNA sequencing (scRNA-seq) data of AAA from the Gene Expression Omnibus (GEO) database. The immune infiltration-related genes were identified and categorized into various cell types, revealing potential key genes and pathways. Examination of three bulk RNA-seq datasets revealed a total of 4087 differentially expressed genes. The expression levels of the immune-related genes IL-7R were significantly elevated in AAA tissues across all three datasets. Furthermore, scRNA-Seq analysis revealed increased expression of IL-7R in CD4+ memory cells within AAA tissues. Immunofluorescence staining corroborated these findings, demonstrating increased expression of IL-7R in CD4+ T cells in AAA tissues. In vitro, activation of IL-7R elevated the activation of JAK/STAT pathway and phenotypic switching in SMCs, while inhibition of IL-7R abolished these effects and suppressed the secretion of IFN-γ. In conclusion, the activation of IL-7R in CD4+ T cells is a key contributor to the pathogenesis of AAA, as it can promote secretion of IFN-γ via JAK/STAT pathway and induce phenotypic switching of SMCs.

The Diagnostic and Prognostic Value of the Immune Checkpoint BGN in Thymoma

Thymoma is frequently correlated with various autoimmune diseases. However, unequivocal therapeutic targets for thymoma remain undefined, and the role of immune checkpoints in the development of thymoma-related autoimmune illnesses is unclear. We examined 39 thymoma samples and 44 normal control samples from the GEO database. Following batch correction, we identified 224 Differentially Expressed Genes (DEGs) using the Limma package. We employed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses to enrich for functional pathways of DEGs. We utilized a Protein-Protein Interaction (PPI) network to identify hub genes and determine their correlation with immune cell infiltration using CIBERSORT. Real-time quantitative polymerase chain reaction (RT-qPCR), western blot, and immunohistochemical staining were implemented to verify identified hub genes in vivo. Simultaneously, we evaluated the prognostic relevance of the hub gene using clinical data. We determined COL1A1, COL1A2, and BGN to be the central hub genes in thymoma. Validation via RT-qPCR, Western blot, and immunohistochemical staining established significant statistical divergence between thymoma tissue and the normal thymus for only BGN. Expression levels of BGN showed strong negative correlation with the infiltration level of B cells and CD4+ T cells, yet a significant positive correlation with the level of neutrophil infiltration. We found high immune infiltration levels of macrophages, NK cells, and Th1 cells in the thymoma microenvironment in patients with a high expression of BGN. Co-localization of BGN and macrophages within thymoma tissue was discerned via tissue staining. Clinical data dictated that thymoma patients exhibiting elevated BGN expression underwent longer hospital stays, longer lengths in intensive care units, greater hospitalization costs, and extended ventilator usage; our study, augmented by clinical information, recognized BGN as possessive of diagnostic and prognostic significance in thymoma through in silico and molecular verification experiments. Our findings offered an important objective for thymoma-treated autoimmune disease comprehension, supplemented by the strong association with immune infiltration.

Identification of key genes for atherosclerosis in different arterial beds

Atherosclerosis (AS) is the pathologic basis of various cardiovascular and cerebrovascular events, with a high degree of heterogeneity among different arterial beds. However, mechanistic differences between arterial beds remain unexplored. The aim of this study was to explore key genes and potential mechanistic differences between AS in different arterial beds through bioinformatics analysis. Carotid atherosclerosis (CAS), femoral atherosclerosis (FAS), infrapopliteal atherosclerosis (IPAS), abdominal aortic atherosclerosis (AAS), and AS-specific differentially expressed genes (DEGs) were screened from the GSE100927 and GSE57691 datasets. Immune infiltration analysis was used to identify AS immune cell infiltration differences. Unsupervised cluster analysis of AS samples from different regions based on macrophage polarization gene expression profiles. Weighted gene co-expression network analysis (WGCNA) was performed to identify the most relevant module genes with AS. Hub genes were then screened by LASSO regression, SVM-REF, and single-gene differential analysis, and a nomogram was constructed to predict the risk of AS development. The results showed that differential expression analysis identified 5, 4, 121, and 62 CAS, FAS, IPAS, AAS-specific DEGs, and 42 AS-common DEGs, respectively. Immune infiltration analysis demonstrated that the degree of macrophage and mast cell enrichment differed significantly in different regions of AS. The CAS, FAS, IPAS, and AAS could be distinguished into two different biologically functional and stable molecular clusters based on macrophage polarization gene expression profiles, especially for cardiomyopathy and glycolipid metabolic processes. Hub genes for 6 AS (ADAP2, CSF3R, FABP5, ITGAX, MYOC, and SPP1), 4 IPAS (CLECL1, DIO2, F2RL2, and GUCY1A2), and 3 AAS (RPL21, RPL26, and RPL10A) were obtained based on module gene, gender stratification, machine learning algorithms, and single-gene difference analysis, respectively, and these genes were effective in differentiating between different regions of AS. This study demonstrates that there are similarities and heterogeneities in the pathogenesis of AS between different arterial beds.

Immunogenic profiling of metastatic uveal melanoma discerns a potential signature related to prognosis

BACKGROUND

Uveal melanoma (UM) is an aggressive intraocular malignant tumor. The present study aimed to identify the key genes associated with UM metastasis and established a gene signature to analyze the relationship between the signature and prognosis and immune cell infiltration. Later, a predictive model combined with clinical variables was developed and validated.

METHODS

Two UM gene expression profile chip datasets were downloaded from TCGA and GEO databases. Immune-related genes (IRGs) were obtained from IMPORT database. First, these mRNAs were intersected with IRGs, and weighted gene co-expression network analysis (WGCNA) was used to identify the co-expression of genes primarily associated with metastasis of UM. Univariate Cox regression analysis screened the genes related to prognosis. LASSO-Cox established a risk score to distinguish high-risk group and low-risk group. Then the GSEA enrichment pathway and immune cell infiltration of the two groups were compared. And combined with clinical variables, a predictive model was constructed. The time-dependent receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) curve were used to verify the stability and accuracy of the final predictive model, and a nomogram was then drawn.

RESULTS

The MEblack, MEpurple, and MEblue modules were significantly associated with the metastasis of UM patients (P value < 0.001, = 0.001, = 0.022, respectively). Four genes (UBXN2B, OTUD3, KAT8, LAMTOR2) were obtained by Pearson correlation analysis, weighted gene correlation network analysis (WGCNA), univariate Cox, and LASSO-Cox. And a novel prognostic risk score was established. Immune-related prognostic signature can well classify UM patients into high-risk and low-risk groups. Kaplan-Meier curve showed that the OS of high-risk patients was worse than that of low-risk patients. In addition, the risk score played an important role in evaluating the signaling pathway and immune cell infiltration of UM patients in high-risk and low-risk groups. Both the training set and validation set of the model showed good predictive accuracy in the degree of differentiation and calibration (e.g., 1-year overall survival: AUC = 0.930 (0.857-1.003)). Finally, a nomogram was established to serve in clinical practice.

SIGNIFICANCE

UM key gene signature and prognosis predictive model might provide insights for further investigation of the pathogenesis and development of UM at the molecular level, and provide theoretical basis for determining new prognostic markers of UM and immunotherapy.

Screening of potential biomarkers for polycystic ovary syndrome and identification of expression and immune characteristics

BACKGROUND

Polycystic ovary syndrome (PCOS) seriously affects the fertility and health of women of childbearing age. We look forward to finding potential biomarkers for PCOS that can aid clinical diagnosis.

METHODS

We acquired PCOS and normal granulosa cell (GC) expression profiles from the Gene Expression Omnibus (GEO) database. After data preprocessing, differentially expressed genes (DEGs) were screened by limma package, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis and Gene Set Enrichment Analysis (GSEA) were performed. Recursive feature elimination (RFE) algorithm and the least absolute shrinkage and selection operator (LASSO) Cox regression analysis were used to acquire feature genes as potential biomarkers. Time-dependent receiver operator characteristic curve (ROC curve) and Confusion matrix were used to verify the classification performance of biomarkers. Then, the expression characteristics of biomarkers in PCOS and normal cells were analyzed, and the insulin resistance (IR) score of samples was computed by ssGSEA. Immune characterization of biomarkers was evaluated using MCP counter and single sample gene set enrichment analysis (ssGSEA). Finally, the correlation between biomarkers and the scores of each pathway was assessed.

RESULTS

We acquired 93 DEGs, and the enrichment results indicated that most of DEGs in PCOS group were significantly enriched in immune-related biological pathways. Further screening results indicated that JDP2 and HMOX1 were potential biomarkers. The area under ROC curve (AUC) value and Confusion matrix of the two biomarkers were ideal when separated and combined. In the combination, the training set AUC = 0.929 and the test set AUC = 0.917 indicated good diagnostic performance of the two biomarkers. Both biomarkers were highly expressed in the PCOS group, and both biomarkers, which should be suppressed in the preovulation phase, were elevated in PCOS tissues. The IR score of PCOS group was higher, and the expression of JDP2 and HMOX1 showed a significant positive correlation with IR score. Most immune cell scores and immune infiltration results were significantly higher in PCOS. Comprehensive analysis indicated that the two biomarkers had strong correlation with immune-related pathways.

CONCLUSION

We acquired two potential biomarkers, JDP2 and HMOX1. We found that they were highly expressed in the PCOS and had a strong positive correlation with immune-related pathways.

Downregulation of FTO in the hippocampus is associated with mental disorders induced by fear stress during pregnancy

Mental disorders (MD), such as anxiety, depression, and cognitive impairment, are very common during pregnancy and predispose to adverse pregnancy outcomes; however, the underlying mechanisms are still under intense investigation. Although the most common RNA modification in epigenetics, N6-methyladenosine (m6A) has been widely studied, its role in MD has not been investigated. Here, we observed that fat mass and obesity-associated protein (FTO) are downregulated in the hippocampus of pregnant rats with MD induced by fear stress and demonstrated that FTO participates in and regulates MD induced by fear stress. In addition, we identified four genes with anomalous modifications and expression (double aberrant genes) that were directly regulated by FTO, namely Angpt2, Fgf10, Rpl21, and Adcy7. Furthermore, we found that these genes might induce MD by regulating the PI3K/Akt and Rap1 signaling pathways. It appears that FTO-mediated m6A modification is a key regulatory mechanism in MD caused by fear stress during pregnancy.

Predicting the Clinical Outcome of Triple-Negative Breast Cancer Based on the Gene Expression Characteristics of Necroptosis and Different Molecular Subtypes

Necroptosis, a kind of programmed necrotic cell apoptosis, is the gatekeeper for the host to defend against the invasion of pathogens. It helps to regulate different biological processes regarding human cancer. Nevertheless, studies that determine the impact of death on triple-negative breast cancer (TNBC) are scarce. Therefore, this paper has comprehensively examined the expression as well as clinical significance of necroptosis in TNBC. ConsensusClusterPlus was used to establish a stable molecular classification that used the expression regarding the necroptosis-linked genes. The clinical and immune characteristics of different subclasses were evaluated. Then, the weighted gene coexpression network analysis (WGCNA) assisted in determining key modules, and we selected the genes exhibiting obvious association with necroptosis prognosis through the relationship with prognosis. The univariate Cox regression analysis together with least absolute shrinkage and selection operator (LASSO) techniques served for the construction of the necroptosis-related prognostic risk score (NPRS) model, and the pathway characteristics of NPRS model grouping were further studied. Finally, the NPRS, taking into account the clinicopathological features, used the decision tree model for enhancing the prognostic model as well as the survival prediction. First, two stable molecular subtypes with different prognosis and immune characteristics were identified using necroptosis marker genes. Then, the key modules were identified, and 10 genes significantly related to the prognosis of necroptosis were selected. Then, the clinical prognostic model of NPRS was developed considering the prognosis-linked necroptosis genes. Finally, the NPRS model, taking into account the clinicopathological features, adopted the decision tree model for enhancing the prognostic model as well as the survival prediction. Herein, two new molecular subgroups considering necroptosis-linked genes are proposed, and an NPRS model composed of 10 genes is developed, which maybe assist in the personalized treatment and clinical treatment guidance of TNBC patients.

Investigation of the Pathogenic Mechanism of Ciprofloxacin in Aortic Aneurysm and Dissection by an Integrated Proteomics and Network Pharmacology Strategy

Aortic aneurysm and dissection (AAD) is a life-threatening disease worldwide. Recently, fluoroquinolones have been reported to significantly increase the risk of AAD. This study aimed to investigate the potential functional mechanism and molecular targets of fluoroquinolones in relation to AAD by an integrated proteomic and network pharmacology strategy. A total of 1351 differentially expressed proteins were identified in human aortic vascular smooth muscle cells (VSMCs) after ciprofloxacin (CIP) stimulation. The functional analysis emphasized the important roles of metabolism, extracellular matrix homeostasis, mitochondrial damage, focal adhesion, and apoptosis in CIP-stimulated VSMCs. CIP targets were predicted with online databases and verified by molecular docking. Protein-protein interaction (PPI) analysis and module construction of the 34 potential CIP targets and 37 selected hub molecules after CIP stimulation identified four critical target proteins in the module: PARP1, RAC1, IGF1R and MKI67. Functional analysis of the PPI module showed that the MAPK signalling pathway, focal adhesion, apoptosis, regulation of actin cytoskeleton, and PI3K-Akt signalling pathway were significantly enriched. Our results will provide novel insights into the pathogenic mechanism of fluoroquinolones in aortic diseases.

Identification of biomarkers and analysis of infiltrated immune cells in stable and ruptured abdominal aortic aneurysms

Objectives

The mortality rate of abdominal aortic aneurysm (AAA) is extremely high in the older population. This study aimed to identify potential biomarkers of AAA and aortic rupture and analyze infiltration of immune cells in stable and ruptured AAA samples.

Methods

Raw data of GSE47472, GSE57691, and GSE98278 were downloaded. After data processing, the co-expression gene networks were constructed. Gene Ontology and pathway enrichment analysis of AAA- and aortic rupture-related gene modules were conducted using the Database for Annotation, Visualization, and Integrated Discovery. Gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) were used for further enrichment analysis. The CIBERSORT tool was used to analyze the relative abundance of immune cells in samples. Differentially expressed immune-related genes were analyzed between different samples. Predictive models were constructed via extreme gradient boosting, and hub genes were identified according to feature importance.

Results

Blue and yellow modules were significantly related to AAA, and genes in these modules were associated with the aortic wall and immune response, respectively. In terms of aortic rupture, the most relevant module was significantly enriched in the inflammatory response. The results of GSEA and GSVA suggested that immune cells and the inflammatory response were involved in the development of AAA and aortic rupture. There were significant differences in the infiltration of immune cells and expression levels of immune-related genes among different samples. NFKB1 might be an important transcription factor mediating the inflammatory response of AAA and aortic rupture. After the construction of a predictive model, CD19, SELL, and CCR7 were selected as hub genes for AAA whereas OAS3, IFIT1, and IFI44L were identified as hub genes for aortic rupture.

Conclusion

Weakening of the aortic wall and the immune response both contributed to the development of AAA, and the inflammatory response was closely associated with aortic rupture. The infiltration of immune cells was significantly different between different samples. NFKB1 might be an important transcription factor in AAA and aortic rupture. CD19, SELL, and CCR7 had potential diagnostic value for AAA. OAS3, IFIT1, and IFI44L might be predictive factors for aortic rupture.

Celecoxib alleviates denervation-induced muscle atrophy by suppressing inflammation and oxidative stress and improving microcirculation

The molecular mechanism underlying denervation-induced muscle atrophy is complex and incompletely understood. Our previous results suggested that inflammation may play an important role in the early stages of muscle atrophy. Celecoxib is reported to exert anti-inflammatory effects. Here, we explored the effect of celecoxib on denervation-induced muscle atrophy and sought to identify the mechanism involved. We found that celecoxib treatment significantly increased the wet weight ratio and CSA of the tibialisanteriormuscle. Additionally, celecoxib downregulated the levels of COX-2, inflammatory factors and reduced inflammatory cell infiltration. GO and KEGG pathway enrichment analysis indicated that after 3 days of celecoxib treatment in vivo, the differentially expressed genes (DEGs) were mainly associated with the regulation of immune responses related to complement activation; after 14 days, the DEGs were mainly involved in the regulation of oxidative stress and inflammation-related responses. Celecoxib administration reduced the levels of ROS and oxidative stress-related proteins. Furthermore, we found that celecoxib treatment inhibited the denervation-induced up-regulation of the ubiquitin-proteasome and autophagy-lysosomal systems related proteins; decreased mitophagy in target muscles; and increased levels of MHC. Finally, celecoxib also attenuated microvascular damage in denervated skeletal muscle. Combined, our findings demonstrated that celecoxib inhibits inflammation and oxidative stress in denervated skeletal muscle, thereby suppressing mitophagy and proteolysis, improving blood flow in target muscles, and, ultimately, alleviating denervation-induced muscle atrophy. Our results confirmed that inflammatory responses play a key role in denervation-induced muscle atrophy and highlight a novel strategy for the prevention and treatment of this condition.

Integrative analysis identifies key genes related to metastasis and a robust gene-based prognostic signature in uveal melanoma

PURPOSE

Uveal melanoma (UM) is an aggressive intraocular malignancy, leading to systemic metastasis in half of the patients. However, the mechanism of the high metastatic rate remains unclear. This study aimed to identify key genes related to metastasis and construct a gene-based signature for better prognosis prediction of UM patients.

METHODS

Weighted gene co-expression network analysis (WGCNA) was used to identify the co-expression of genes primarily associated with metastasis of UM. Univariate, Lasso-penalized and multivariate Cox regression analyses were performed to establish a prognostic signature for UM patients.

RESULTS

The tan and greenyellow modules were significantly associated with the metastasis of UM patients. Significant genes related to the overall survival (OS) in these two modules were then identified. Additionally, an OS-predicting signature was established. The UM patients were divided into a low- or high-risk group. The Kaplan-Meier curve indicated that high-risk patients had poorer OS than low-risk patients. The receiver operating curve (ROC) was used to validate the stability and accuracy of the final five-gene signature. Based on the signature and clinical traits of UM patients, a nomogram was established to serve in clinical practice.

CONCLUSIONS

We identified key genes involved in the metastasis of UM. A robust five-gene-based prognostic signature was constructed and validated. In addition, the gene signature-based nomogram was created that can optimize the prognosis prediction and identify possible factors causing the poor prognosis of high-risk UM patients.

Biomarkers in EndoVascular Aneurysm Repair (EVAR) and Abdominal Aortic Aneurysm: Pathophysiology and Clinical Implications

Circulating biomarkers have been recently investigated among patients undergoing endovascular aortic aneurysm repair (EVAR) for abdominal aortic aneurysm (AAA). Considering the plethora of small descriptive studies reporting potential associations between biomarkers and clinical outcomes, this review aims to summarize the current literature considering both the treated disease (post EVAR) and the untreated disease (AAA before EVAR). All studies describing outcomes of tissue biomarkers in patients undergoing EVAR and in patients with AAA were included, and references were checked for additional sources. In the EVAR scenario, circulating interleukin-6 (IL-6) is a marker of inflammatory reaction which might predict postoperative morbidity; cystatin C is a promising early marker of post-procedural acute kidney injury; plasma matrix metalloproteinase-9 (MMP-9) concentration after 3 months from EVAR might help in detecting post-procedural endoleak. This review also summarizes the current gaps in knowledge and future direction of this field of research. Among markers used in patients with AAA, galectin and granzyme appear to be promising and should be carefully investigated even in the EVAR setting. Larger prospective trials are required to establish and evaluate prognostic models with highest values with these markers.

POU class 2 homeobox associating factor 1 (POU2AF1) participates in abdominal aortic aneurysm enlargement based on integrated bioinformatics analysis

Abdominal aortic aneurysm (AAA) is life-threatening, its natural course is progressively sac expansion and rupture. Elegant studies have been conducted to investigate the molecular markers associated with AAA growth and expansion, this topic however, still needs to be further elucidated. This study aimed to identify potential genes for AAA growth and expansion based on comprehensive bioinformatics approaches. Firstly, 29 up-regulated genes were identified through DEGs analysis between large AAA and small AAA in GSE57691. Secondly, signed WGCNA analysis was conducted based on GSE57691 and the green module was found to exhibit the topmost correlation with large AAA as well as AAA, 133 WGCNA hub genes were further identified. Merged gene set including 29 up-regulated DEGs and 858 green module genes was subjected to constructing a PPI network where 195 PPI hub genes were identified. Subsequently, 4 crucial genes including POU2AF1, FCRLA, CD79B, HLA-DOB were recognized by Venn plot. In addition, by using GSE7084 and GSE98278 for verification, POU2AF1 showed potential diagnostic value between AAA and normal groups, and exhibited a significant higher expression level in large AAA samples compared with small AAA samples. Furthermore, immunohistochemistry results indicated up-regulation of POU2AF1 in large AAA samples than small AAA samples, which implies POU2AF1 may be a key regulator in AAA enlargement and growth. In summary, this study indicates that POU2AF1 has great predictive value for the expansion of AAA, and may contribute to the further exploration of pathogenesis and progression of AAA.