Altered Cellular Immunity and Differentially Expressed Immune-Related Genes in Patients With Systemic Sclerosis-Associated Pulmonary Arterial Hypertension

Mmu_circ_0005373 and hsa_circ_0136255 participate in the pulmonary fibrosis of systemic sclerosis

The pathogenesis of SSc pulmonary fibrosis is complex and prognosis is poor. In order to find biomarkers to provide assistance in the diagnosis and treatment of systemic sclerosis (SSc), this study explored the role of SSc-related differentially expressed circRNAs in the fibrosis process. This study explored whether circular RNA (circRNA) mediated the mTOR signaling pathway by interacting with the eukaryotic translation initiation factor eIF4E-binding protein 1 (4E-BP1), participated in a competing endogenous RNA (ceRNA) network, and regulated the mechanism of pulmonary fibrosis in systemic sclerosis (SSc). The results showed that the expression of mmu_circ_0005373 was reduced, and mmu_circ_0005373 may regulate the mTOR signaling pathway by inhibiting the interacting with 4E-BP1 protein in the lung of SSc mice, and promote fibrosis in SSc. Hsa_circ_0136255, which is homologous to mmu_circ_0005373, is also reduced in SSc peripheral blood mononuclear cells, and predicted to interact with 4E-BP1 protein. Hsa_circ_0136255/hsa-miR-330-3p/TNFAIP3 ceRNA network had biological significance in SSc, and correlated with clinical data, including high-resolution CT, average expiratory flow at 25% vital capacity, neutrophil count, lymphocyte percentage, standard deviation of red blood cell distribution width, coefficient of variation of red blood cell distribution width, platelet distribution width, glutamic transaminase, γ-glutamyl transpeptidase, lymphocyte percentage, basophils percentage, red blood cell, plateletcrit, cholinesterase, and mean corpuscular hemoglobin concentration. Hsa_circ_0136255, hsa-miR-330-3p, and TNFAIP3 may be used as biomarkers for clinical diagnosis and treatment of SSc.

Relationships between systemic sclerosis and atherosclerosis: screening for mitochondria-related biomarkers

Background

Patients with systemic sclerosis (SSc) are known to have higher incidence of atherosclerosis (AS). Mitochondrial injuries in SSc can cause endothelial dysfunction, leading to AS; thus, mitochondria appear to be hubs linking SSc to AS. This study aimed to identify the mitochondria-related biomarkers of SSc and AS.

Methods

We identified common differentially expressed genes (DEGs) in the SSc (GSE58095) and AS (GSE100927) datasets of the Gene Expression Omnibus (GEO) database. Considering the intersection between genes with identical expression trends and mitochondrial genes, we used the least absolute shrinkage and selection operator (LASSO) as well as random forest (RF) algorithms to identify four mitochondria-related hub genes. Diagnostic nomograms were then constructed to predict the likelihood of SSc and AS. Next, we used the CIBERSORT algorithm to evaluate immune infiltration in both disorders, predicted the transcription factors for the hub genes, and validated these genes for the two datasets.

Results

A total of 112 genes and 13 mitochondria-related genes were identified; these genes were then significantly enriched for macrophage differentiation, collagen-containing extracellular matrix, collagen binding, antigen processing and presentation, leukocyte transendothelial migration, and apoptosis. Four mitochondria-related hub DEGs (IFI6, FSCN1, GAL, and SGCA) were also identified. The nomograms showed good diagnostic values for GSE58095 (area under the curve (AUC) = 0.903) and GSE100927 (AUC = 0.904). Further, memory B cells, γδT cells, M0 macrophages, and activated mast cells were significantly higher in AS, while the resting memory CD4+ T cells were lower and M1 macrophages were higher in SSc; all of these were closely linked to multiple immune cells. Gene set enrichment analysis (GSEA) showed that IFI6 and FSCN1 were involved in immune-related pathways in both AS and SSc; GAL and SGCA are related to mitochondrial metabolism pathways in both SSc and AS. Twenty transcription factors (TFs) were predicted, where two TFs, namely BRCA1 and PPARγ, were highly expressed in both SSc and AS.

Conclusion

Four mitochondria-related biomarkers were identified in both SSc and AS, which have high diagnostic value and are associated with immune cell infiltration in both disorders. Hence, this study provides new insights into the pathological mechanisms underlying SSc and AS. The specific roles and action mechanisms of these genes require further clinical validation in SSc patients with AS.

Expression Profiles of circRNAs and Identification of hsa_circ_0007608 and hsa_circ_0064656 as Potential Biomarkers for COPD-PH Patients

Introduction

Pulmonary hypertension (PH) is a common complication of chronic obstructive pulmonary disease (COPD), which can worsen the prognosis and increase the mortality of COPD patients. Circular RNA (circRNA) has been discovered to participate in the occurrence and progression of PH in COPD and may have significant prospects for advanced diagnostics and prognosis evaluation. However, the expression profile of circRNAs in human lung tissues with definite diagnosis of COPD-PH remains to be further explored and validated.

Methods

Twelve human lung tissue samples (6 each from COPD-PH and control groups) were collected and subjected to high-throughput sequencing. QRT-PCR was performed to validate the differential expression levels of the top 10 dysregulated circRNAs in patients' plasma samples, HPAECs and HPASMCs. Functional and pathway enrichment analysis on target genes was performed to explore the potential functions and pathways of those circRNAs. Hub genes obtained after conducting bioinformatics analysis on the predicted target mRNAs were verified by qRT-PCR in HPAECs and HPASMCs, and then we selected VCAN as a potential key gene involved in the pathogenesis of COPD-PH for immunohistochemistry validation in lung tissue.

Results

A total of 136 circRNAs (39 up-regulated and 97 down-regulated) were differentially expressed between the two groups. Following qRT-PCR validation, two circRNAs (hsa_circ_0007608 and hsa_circ_0064656) were believed to be involved in the pathogenesis. GO and KEGG pathway analysis suggested that these two DECs were mainly related to the celluar proliferation, migration and EndMT. PPI network revealed 11 pairs of key mRNAs. VCAM1, VCAN and THBS1, three hub mRNAs with the highest reliability among all, were validated and proven to be up-regulated in COPD-PH. We innovatively found that VCAN may be involved in COPD-PH.

Conclusion

This study identified the functional circRNAs, providing insights into the molecular mechanisms and predictions of COPD-PH, and may provide potential diagnostic biomarkers or therapeutic targets for COPD-PH.

Identification of lncRNA-miRNA-mRNA networks in circulating exosomes as potential biomarkers for systemic sclerosis

Objective

This study aimed to analyze potential biomarkers for systemic sclerosis (SSc) by constructing lncRNA-miRNA-mRNA networks in circulating exosomes (cirexos).

Materials and methods

Differentially expressed mRNAs (DEmRNAs) and lncRNAs (DElncRNAs) in SSc cirexos were screened using high-throughput sequencing and detected with real-time quantitative PCR (RT-qPCR). Differentially expressed genes (DEGs) were analyzed using the DisGeNET, GeneCards, GSEA4.2.3, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. Receiver operating characteristic (ROC) curves, correlation analyses, and a double-luciferase reporter gene detection assay were used to analyze competing endogenous RNA (ceRNA) networks and clinical data.

Results

In this study, 286 DEmRNAs and 192 DElncRNAs were screened, of which 18 DEGs were the same as the SSc-related genes. The main SSc-related pathways included extracellular matrix (ECM) receptor interaction, local adhesion, platelet activation, and IgA production by the intestinal immune network. A hub gene, COL1A1, was obtained by a protein-protein interaction (PPI) network. Four ceRNA networks were predicted through Cytoscape. The relative expression levels of COL1A1, ENST0000313807, and NON-HSAT194388.1 were significantly higher in SSc, while the relative expression levels of hsa-miR-29a-3p, hsa-miR-29b-3p, and hsa-miR-29c-3p were significantly lower in SSc (P < 0.05). The ROC curve showed that the ENST00000313807-hsa-miR-29a-3p-COL1A1 network as a combined biomarker of SSc is more valuable than independent diagnosis, and that it is correlated with high-resolution CT (HRCT), Scl-70, C-reactive protein (CRP), Ro-52, IL-10, IgM, lymphocyte percentage, neutrophil percentage, albumin divided by globulin, urea, and RDW-SD (P < 0.05). Double-luciferase reporter gene detection showed that ENST00000313807 interacts with hsa-miR-29a-3p, which interacts with COL1A1.

Conclusion

The ENST00000313807-hsa-miR-29a-3p-COL1A1 network in plasma cirexos represents a potential combined biomarker for the clinical diagnosis and treatment of SSc.

A signal recognition particle-related joint model of LASSO regression, SVM-RFE and artificial neural network for the diagnosis of systemic sclerosis-associated pulmonary hypertension

Background: Systemic sclerosis-associated pulmonary hypertension (SSc-PH) is one of the most common causes of death in patients with systemic sclerosis (SSc). The complexity of SSc-PH and the heterogeneity of clinical features in SSc-PH patients contribute to the difficulty of diagnosis. Therefore, there is a pressing need to develop and optimize models for the diagnosis of SSc-PH. Signal recognition particle (SRP) deficiency has been found to promote the progression of multiple cancers, but the relationship between SRP and SSc-PH has not been explored. Methods: First, we obtained the GSE19617 and GSE33463 datasets from the Gene Expression Omnibus (GEO) database as the training set, GSE22356 as the test set, and the SRP-related gene set from the MSigDB database. Next, we identified differentially expressed SRP-related genes (DE-SRPGs) and performed unsupervised clustering and gene enrichment analyses. Then, we used least absolute shrinkage and selection operator (LASSO) regression and support vector machine-recursive feature elimination (SVM-RFE) to identify SRP-related diagnostic genes (SRP-DGs). We constructed an SRP scoring system and a nomogram model based on the SRP-DGs and established an artificial neural network (ANN) for diagnosis. We used receiver operating characteristic (ROC) curves to identify the SRP-related signature in the training and test sets. Finally, we analyzed immune features, signaling pathways, and drugs associated with SRP and investigated SRP-DGs' functions using single gene batch correlation analysis-based GSEA. Results: We obtained 30 DE-SRPGs and found that they were enriched in functions and pathways such as "protein targeting to ER," "cytosolic ribosome," and "coronavirus disease-COVID-19". Subsequently, we identified seven SRP-DGs whose expression levels and diagnostic efficacy were validated in the test set. As one signature, the area under the ROC curve (AUC) values for seven SRP-DGs were 0.769 and 1.000 in the training and test sets, respectively. Predictions made using the nomogram model are likely beneficial for SSc-PH patients. The AUC values of the ANN were 0.999 and 0.860 in the training and test sets, respectively. Finally, we discovered that some immune cells and pathways, such as activated dendritic cells, complement activation, and heme metabolism, were significantly associated with SRP-DGs and identified ten drugs targeting SRP-DGs. Conclusion: We constructed a reliable SRP-related ANN model for the diagnosis of SSc-PH and investigated the possible role of SRP in the etiopathogenesis of SSc-PH by bioinformatics methods to provide a basis for precision and personalized medicine.

Comprehensive analysis and validation of novel immune and vascular remodeling related genes signature associated with drug interactions in pulmonary arterial hypertension

Background: Previous studies revealed that the gene signatures are associated with the modulation and pathogenesis of pulmonary arterial hypertension (PAH). However, identifying critical transcriptional signatures in the blood of PAH patients remains lacking. Methods: The differentially expressed transcriptional signatures in the blood of PAH patients were identified by a meta-analysis from four microarray datasets. Then we investigated the enrichment of gene ontology and KEGG pathways and identified top hub genes. Besides, we investigated the correlation of crucial hub genes with immune infiltrations, hallmark gene sets, and blood vessel remodeling genes. Furthermore, we investigated the diagnostic efficacy of essential hub genes and their expression validation in an independent cohort of PAH, and we validate the expression level of hub genes in monocrotaline (MCT) induced PAH rats' model. Finally, we have identified the FDA-approved drugs that target the hub genes and their molecular docking. Results: We found 1,216 differentially expressed genes (DEGs), including 521 up-regulated and 695 down-regulated genes, in the blood of the PAH patients. The up-regulated DEGs are significantly associated with the enrichment of KEGG pathways mainly involved with immune regulation, cellular signaling, and metabolisms. We identified 13 master transcriptional regulators targeting the dysregulated genes in PAH. The STRING-based investigation identified the function of hub genes associated with multiple immune-related pathways in PAH. The expression levels of RPS27A, MAPK1, STAT1, RPS6, FBL, RPS3, RPS2, and GART are positively correlated with ssGSEA scores of various immune cells as positively correlated with the hallmark of oxidative stress. Besides, we found that these hub genes also regulate the vascular remodeling in PAH. Furthermore, the expression levels of identified hub genes showed good diagnostic efficacy in the blood of PAH, and we validated most of the hub genes are consistently dysregulated in an independent PAH cohort. Validation of hub genes expression level in the monocrotaline (MCT)-induced lung tissue of rats with PAH revealed that 5 screened hub genes (MAPK1, STAT1, TLR4, TLR2, GART) are significantly highly expressed in PAH rats, and 4 screened hub genes (RPS6, FBL, RPS3, and RPS2) are substantially lowly expressed in rats with PAH. Finally, we analyzed the interaction of hub proteins and FDA-approved drugs and revealed their molecular docking, and the results showed that MAPK1, TLR4, and GART interact with various drugs with appropriate binding affinity. Conclusion: The identified blood-derived key transcriptional signatures significantly correlate with immune infiltrations, hypoxia, glycolysis, and blood vessel remodeling genes. These findings may provide new insight into the diagnosis and treatment of PAH patients.