ECM2 and GLT8D2 in human pulmonary artery hypertension: fruits from weighted gene co-expression network analysis

Plasma Extracellular Matrix Protein 2 Level as a Predictive Biomarker for Rupture of Small Intracranial Aneurysms

As the neuroimaging technology improves, the detection rate of unruptured intracranial aneurysms (UIA) is gradually increasing. However, there is currently no effective means to evaluate and predict the risk of rupture for small intracranial aneurysm (sIA, diameter < 7 mm). We previously identified extracellular matrix protein 2 (ECM2) as a potential candidate biomarker for predicting intracranial aneurysm (IA) rupture through iTRAQ combined with LC-MS/MS protein quantification technology, so this study aimed to further validate the ability of plasma ECM2 expression levels to predict IA rupture. This prospective, observational, single-center cohort study enrolled 322 individuals with ruptured intracranial aneurysm (RIA, N = 123), UIA (N = 89), traumatic subarachnoid hemorrhage (tSAH, N = 55), or healthy controls (HC, N = 55). ECM2 plasma levels were quantified using enzyme-linked immunosorbent assay (ELISA). The Spearman rank correlation analysis was employed to examine the relationship between variables. Independent risk factors of sIA rupture were identified using logistic regression analysis. The ROC curve assessed the predictive capability for sIA rupture. Plasma ECM2 was notably higher in RIA patients than in UIA, tSAH, and HC groups. Plasma ECM2 levels showed no significant difference among the asymptomatic UIA, HC, and tSAH groups. There was also no significant difference in plasma ECM2 levels between symptomatic UIA patients and RIA patients. Furthermore, the plasma ECM2 level was closely related to hypertension history in sIA patients. ECM2 plasma level was an independent risk factor for sIA rupture. The plasma ECM2 cutoff level for predicting IA rupture was determined to be 1540.67 pg/ml. The combination of ECM2 levels and aneurysm location increased predictive accuracy (AUC = 0.828, sensitivity 87.0%, specificity 68.8%, accuracy 83.2%), surpassing the performance of PHASES and ELPASS scores. ECM2 could potentially act as an early warning biomarker for predicting the rupture of sIAs.

Dissecting the lung transcriptome of pulmonary fibrosis-associated pulmonary hypertension

Integrative multiomics can help elucidate the pathophysiology of pulmonary fibrosis (PF)-associated pulmonary hypertension (PH) (PF-PH). Weighted gene coexpression network analysis (WGCNA) was performed on a transcriptomic dataset of explanted lung tissue from 116 patients with PF. Patients were stratified by pulmonary vascular resistance (PVR), and differential gene expression analysis was conducted. Gene modules were correlated with hemodynamics at the time of transplantation and tested for enrichment in the lung transcriptomics signature of an independent pulmonary arterial hypertension (PAH) cohort. We found 1,250 differentially expressed genes between high and low PVR groups. WGCNA identified that black and yellowgreen modules negatively correlated with PVR, whereas the tan and darkgrey modules are positively correlated with PVR in PF-PH. In addition, the tan module showed the strongest enrichment for an independent PAH gene signature, suggesting shared gene expression patterns between PAH and PF-PH. Pharmacotranscriptomic analysis using the Connectivity Map implicated the tan and darkgrey modules as potentially pathogenic in PF-PH, given their combined module signature demonstrated a high negative connectivity score for treprostinil, a medication used in the treatment of PF-PH, and a high positive connectivity score for bone morphogenetic protein (BMP) loss of function. Pathway enrichment analysis revealed that inflammatory pathways and oxidative phosphorylation were downregulated, whereas epithelial-mesenchymal transition was upregulated in modules associated with increased PVR. Our integrative systems biology approach to the lung transcriptome of PF with and without PH identified several PH-associated coexpression modules and gene targets with shared molecular features with PAH warranting further investigation to uncover potential new therapies for PF-PH.NEW & NOTEWORTHY An integrative systems biology approach that included transcriptomic analysis of explanted lung tissue from patients with pulmonary fibrosis (PF) with and without pulmonary hypertension (PH) undergoing lung transplantation, combined with hemodynamic correlation and pharmacotranscriptomics, identified modules of genes associated with pulmonary vascular disease severity. Comparison with an independent pulmonary arterial hypertension (PAH) dataset identified shared gene expression patterns between PAH and PF-PH.

Matrix Metalloproteinase 11 Promotes Migration and Invasion of Colorectal Cancer by Elevating Slug Protein

Purpose: Matrix metalloproteinase-11 (MMP11), which belongs to the stromelysin subgroup, has been reported to play a role in the progression of colorectal cancer (CRC). However, the significance of MMP11 in the tumor microenvironment, immune/stromal cells, and its mechanism in CRC remain unclear. Methods: The impact of MMP11 knockdown using specific short hairpin RNAs (shRNAs) on the metastasis and invasion of colorectal cancer RKO and SW480 cells was investigated using western blot, quantitative real-time polymerase chain reaction (qRT-PCR), transwell assays, and immunohistochemistry. Results: MMP11 mRNA expression was significantly higher in CRC cells than in normal cells, and its expression was stimulated in CCD-18Co fibroblasts. Additionally, MMP11 expression was found to be higher in individuals aged ≤ 65 years, the T4/T3 group, and Stage III/IV patients. Overall survival (OS) and disease-free survival rates were significantly different between the high and low MMP11 groups. Furthermore, the receiver operating characteristic (ROC) curves for MMP11 at 1-, 3-, and 5-years were 0.450, 0.552, and 0.560, respectively. Moreover, MMP11 promoted the migration and invasion of CRC cells by elevating the expression of Slug protein. Most importantly, MMP11 was positively associated with M0-macrophages and negatively associated with M1-macrophages, NK cells activated, NK cells resting, T cells CD4 memory activated, and T cells follicular helper, indicating the remarkable interactions of MMP11 with tumor immunology. Conclusions: MMP11 plays an important role in colorectal cancer development, and its mechanism in CRC needs to be further explored in the future.

ASPORIN: A root of the matter in tumors and their host environment

Asporin (ASPN) has been identified as one of the members of the class I small leucine-rich proteoglycans (SLRPs) family in the extracellular matrix (ECM). It is involved in classic ensigns of cancers such as self-dependent growth, resistance to growth inhibitors, restricting apoptosis, cancer metastasis, and bone-related disorders. ASPN is different from other members of SLRPs, such as decorin (DCN) and biglycan (BGN), in a way that it contains a distinctive length of aspartate (D) residues in the amino (N) -terminal region. These D-repeats residues possess germline polymorphisms and are identified to be linked with cancer progression and osteoarthritis (OA). The polyaspartate stretch in the N-terminal region of the protein and its resemblance to DCN are the reasons it is called asporin. In this review, we comprehensively summarized and updated the dual role of ASPN in various malignancies, its structure in mice and humans, variants, mutations, cancer-associated signalings and functions, the relationship between ASPN and cancer-epithelial, stromal fibroblast crosstalk, immune cells and immunosuppression in cancer and other diseases. In cancer and other bone-related diseases, ASPN is identified to be regulating various signaling pathways such as TGFβ, Wnt/β-catenin, notch, hedgehog, EGFR, HER2, and CD44-mediated Rac1. These pathways promote cancer cell invasion, proliferation, and migration by mediating the epithelial-to-mesenchymal transition (EMT) process. Finally, we discussed mouse models mimicking ASPN in vivo function in cancers and the probability of therapeutic targeting of ASPN in cancer cells, fibrosis, and other bone-related diseases.

Study of molecular patterns associated with ferroptosis in Parkinson's disease and its immune signature

Parkinson's disease is the second most common neurodegenerative disease in the world. We downloaded data on Parkinson's disease and Ferroptosis-related genes from the GEO and FerrDb databases. We used WCGAN and Random Forest algorithm to screen out five Parkinson's disease ferroptosis-related hub genes. Two genes were identified for the first time as possibly playing a role in Braak staging progression. Unsupervised clustering analysis based on hub genes yielded ferroptosis isoforms, and immune infiltration analysis indicated that these isoforms are associated with immune cells and may represent different immune patterns. FRHGs scores were obtained to quantify the level of ferroptosis modifications in each individual. In addition, differences in interleukin expression were found between the two ferroptosis subtypes. The biological functions involved in the hub gene are analyzed. The ceRNA regulatory network of hub genes was mapped. The disease classification diagnosis model and risk prediction model were also constructed by applying hub genes based on logistic regression. Multiple external datasets validated the hub gene and classification diagnostic model with some accuracy. This study explored hub genes associated with ferroptosis in Parkinson's disease and their molecular patterns and immune signatures to provide new ideas for finding new targets for intervention and predictive biomarkers.

Identification of crucial genes related to heart failure based on GEO database

BACKGROUND

The molecular biological mechanisms underlying heart failure (HF) remain poorly understood. Therefore, it is imperative to use innovative approaches, such as high-throughput sequencing and artificial intelligence, to investigate the pathogenesis, diagnosis, and potential treatment of HF.

METHODS

First, we initially screened Two data sets (GSE3586 and GSE5406) from the GEO database containing HF and control samples from the GEO database to establish the Train group, and selected another dataset (GSE57345) to construct the Test group for verification. Next, we identified the genes with significantly different expression levels in patients with or without HF and performed functional and pathway enrichment analyses. HF-specific genes were identified, and an artificial neural network was constructed by Random Forest. The ROC curve was used to evaluate the accuracy and reliability of the constructed model in the Train and Test groups. Finally, immune cell infiltration was analyzed to determine the role of the inflammatory response and the immunological microenvironment in the pathogenesis of HF.

RESULTS

In the Train group, 153 significant differentially expressed genes (DEGs) associated with HF were found to be abnormal, including 81 down-regulated genes and 72 up-regulated genes. GO and KEGG enrichment analyses revealed that the down-regulated genes were primarily enriched in organic anion transport, neutrophil activation, and the PI3K-Akt signaling pathway. The upregulated genes were mainly enriched in neutrophil activation and the calcium signaling. DEGs were identified using Random Forest, and finally, 16 HF-specific genes were obtained. In the ROC validation and evaluation, the area under the curve (AUC) of the Train and Test groups were 0.996 and 0.863, respectively.

CONCLUSIONS

Our research revealed the potential functions and pathways implicated in the progression of HF, and designed an RNA diagnostic model for HF tissues using machine learning and artificial neural networks. Sensitivity, specificity, and stability were confirmed by ROC curves in the two different cohorts.

Cancer-associated fibroblasts refine the classifications of gastric cancer with distinct prognosis and tumor microenvironment characteristics

Background

Cancer-associated fibroblasts (CAFs) are essential tumoral components of gastric cancer (GC), contributing to the development, therapeutic resistance and immune-suppressive tumor microenvironment (TME) of GC. This study aimed to explore the factors related to matrix CAFs and establish a CAF model to evaluate the prognosis and therapeutic effect of GC.

Methods

Sample information from the multiply public databases were retrieved. Weighted gene co-expression network analysis was used to identify CAF-related genes. EPIC algorithm was used to construct and verify the model. Machine-learning methods characterized CAF risk. Gene set enrichment analysis was employed to elucidate the underlying mechanism of CAF in the development of GC.

Results

A three-gene (GLT8D2, SPARC and VCAN) prognostic CAF model was established, and patients were markedly divided according to the riskscore of CAF model. The high-risk CAF clusters had significantly worse prognoses and less significant responses to immunotherapy than the low-risk group. Additionally, the CAF risk score was positively associated with CAF infiltration in GC. Moreover, the expression of the three model biomarkers were significantly associated with the CAF infiltration. GSEA revealed significant enrichment of cell adhesion molecules, extracellular matrix receptors and focal adhesions in patients at a high risk of CAF.

Conclusion

The CAF signature refines the classifications of GC with distinct prognosis and clinicopathological indicators. The three-gene model could effectively aid in determining the prognosis, drug resistance and immunotherapy efficacy of GC. Thus, this model has promising clinical significance for guiding precise GC anti-CAF therapy combined with immunotherapy.

High ECM2 Expression Predicts Poor Clinical Outcome and Promotes the Proliferation, Migration, and Invasiveness of Glioma

OBJECTIVE

Glioma is the most prevalent and fatal intracranial malignant tumor. Extracellular matrix protein 2 (ECM2) has rarely been studied in gliomas. Therefore, we explored the role of ECM2 in lower-grade gliomas (LGGs).

METHODS

The RNA-seq and clinicopathology data were obtained from the TCGA database. The immunohistochemical (IHC) staining was used to verify the expression of ECM2. Functional enrichment analyses, immune-related analyses, drug sensitivity, and mutation profile analyses were further conducted. Cox regression and Kaplan-Meier curves were utilized for survival analyses, while four external datasets were used to validate the prognostic role of ECM2. Furthermore, qRT-PCR, CCK-8, wound healing, and transwell assays were performed to confirm the function of ECM2 in gliomas.

RESULTS

The study found a significant upregulation of ECM2 expression with increasing glioma grades and a significant association between ECM2 expression and tumor immune infiltration. Cox regression verified the prognostic role of ECM2 in LGG patients (HR = 1.656, 95%CI = 1.055-2.600, p = 0.028). High ECM2 expression was significantly associated with poor outcome (p < 0.001). Four external datasets validated its prognostic value. After the knockdown of ECM2, the functional experiments showed a significant decrease in proliferation, migration, and invasion in glioma cell lines.

CONCLUSION

The study suggested the potential of ECM2 as a novel immune-associated prognostic biomarker and therapeutic target for glioma patients.

Bioinformatic analysis and machine learning to identify the diagnostic biomarkers and immune infiltration in adenomyosis

Background: Adenomyosis is a hormone-dependent benign gynecological disease characterized by the invasion of the endometrium into the myometrium. Women with adenomyosis can suffer from abnormal uterine bleeding, severe pelvic pain, and subfertility or infertility, which can interfere with their quality of life. However, effective diagnostic biomarkers for adenomyosis are currently lacking. The aim of this study is to explore the mechanism of adenomyosis by identifying biomarkers and potential therapeutic targets for adenomyosis and analyzing their correlation with immune infiltration in adenomyosis. Methods: Two datasets, GSE78851 and GSE68870, were downloaded and merged for differential expression analysis and functional enrichment analysis using R software. Weighted gene co-expression network analysis (WGCNA), the least absolute shrinkage and selection operator (LASSO), and support vector machine-recursive feature elimination (SVE-RFE) were combined to explore candidate genes. Quantitative reverse transcriptase PCR (qRT-PCR) was conducted to verify the biomarkers and receiver operating characteristic curve analysis was used to assess the diagnostic value of each biomarker. Single-sample Gene Set Enrichment Analysis (ssGSEA) and CIBERSORT were used to explore immune cell infiltration in adenomyosis and the correlation between diagnostic biomarkers and immune cells. Results: A total of 318 genes were differentially expressed. Through the analysis of differentially expressed genes and WGCNA, we obtained 189 adenomyosis-related genes. After utilizing the LASSO and SVM-RFE algorithms, four hub genes, namely, six-transmembrane epithelial antigen of the prostate-1 (STEAP1), translocase of outer mitochondrial membrane 20 (TOMM20), glycosyltransferase eight domain-containing 2 (GLT8D2), and NME/NM23 family member 5 (NME5) expressed in nucleoside-diphosphate kinase, were identified and verified by qRT-PCR. Immune infiltration analysis indicated that T helper 17 cells, CD56dim natural killer cells, monocytes, and memory B-cell may be associated with the occurrence of adenomyosis. There were significant correlations between the diagnostic biomarkers and immune cells. Conclusion: STEAP1, TOMM20, GLT8D2, and NME5 were identified as potential biomarkers and therapeutic targets for adenomyosis. Immune infiltration may contribute to the onset and progression of adenomyosis.

Weighted gene co-expression network analysis identifies dysregulated B-cell receptor signaling pathway and novel genes in pulmonary arterial hypertension

Background

Pulmonary arterial hypertension (PAH) is a devastating cardio-pulmonary vascular disease in which chronic elevated pulmonary arterial pressure and pulmonary vascular remodeling lead to right ventricular failure and premature death. However, the exact molecular mechanism causing PAH remains unclear.

Methods

RNA sequencing was used to analyze the transcriptional profiling of controls and rats treated with monocrotaline (MCT) for 1, 2, 3, and 4 weeks. Weighted gene co-expression network analysis (WGCNA) was employed to identify the key modules associated with the severity of PAH. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to explore the potential biological processes and pathways of key modules. Real-time PCR and western blot analysis were used to validate the gene expression. The hub genes were validated by an independent dataset obtained from the Gene Expression Omnibus database.

Results

A total of 26 gene modules were identified by WGCNA. Of these modules, two modules showed the highest correlation with the severity of PAH and were recognized as the key modules. GO analysis of key modules showed the dysregulated inflammation and immunity, particularly B-cell-mediated humoral immunity in MCT-induced PAH. KEGG pathway analysis showed the significant enrichment of the B-cell receptor signaling pathway in the key modules. Pathview analysis revealed the dysregulation of the B-cell receptor signaling pathway in detail. Moreover, a series of humoral immune response-associated genes, such as BTK, BAFFR, and TNFSF4, were found to be differentially expressed in PAH. Additionally, five genes, including BANK1, FOXF1, TLE1, CLEC4A1, and CLEC4A3, were identified and validated as the hub genes.

Conclusion

This study identified the dysregulated B-cell receptor signaling pathway, as well as novel genes associated with humoral immune response in MCT-induced PAH, thereby providing a novel insight into the molecular mechanisms underlying inflammation and immunity and therapeutic targets for PAH.

FAM171B as a Novel Biomarker Mediates Tissue Immune Microenvironment in Pulmonary Arterial Hypertension

The purpose of this study was to uncover potential diagnostic indicators of pulmonary arterial hypertension (PAH), evaluate the function of immune cells in the pathogenesis of the disease, and find innovative treatment targets and medicines with the potential to enhance prognosis. Gene Expression Omnibus was utilized to acquire the PAH datasets. We recognized differentially expressed genes (DEGs) and investigated their functions utilizing R software. Weighted gene coexpression network analysis, least absolute shrinkage and selection operators, and support vector machines were used to identify biomarkers. The extent of immune cell infiltration in the normal and PAH tissues was determined using CIBERSORT. Additionally, the association between diagnostic markers and immune cells was analyzed. In this study, 258DEGs were used to analyze the disease ontology. Most DEGs were linked with atherosclerosis, arteriosclerotic cardiovascular disease, and lung disease, including obstructive lung disease. Gene set enrichment analysis revealed that compared to normal samples, results from PAH patients were mostly associated with ECM-receptor interaction, arrhythmogenic right ventricular cardiomyopathy, the Wnt signaling pathway, and focal adhesion. FAM171B was identified as a biomarker for PAH (area under the curve = 0.873). The mechanism underlying PAH may be mediated by nave CD4 T cells, resting memory CD4 T cells, resting NK cells, monocytes, activated dendritic cells, resting mast cells, and neutrophils, according to an investigation of immune cell infiltration. FAM171B expression was also associated with resting mast cells, monocytes, and CD8 T cells. The results suggest that PAH may be closely related to FAM171B with high diagnostic performance and associated with immune cell infiltration, suggesting that FAM171B may promote the progression of PAH by stimulating immune infiltration and immune response. This study provides valuable insights into the pathogenesis and treatment of PAH.

Connections for Matters of the Heart: Network Medicine in Cardiovascular Diseases

Cardiovascular diseases (CVD) are diverse disorders affecting the heart and vasculature in millions of people worldwide. Like other fields, CVD research has benefitted from the deluge of multiomics biomedical data. Current CVD research focuses on disease etiologies and mechanisms, identifying disease biomarkers, developing appropriate therapies and drugs, and stratifying patients into correct disease endotypes. Systems biology offers an alternative to traditional reductionist approaches and provides impetus for a comprehensive outlook toward diseases. As a focus area, network medicine specifically aids the translational aspect of in silico research. This review discusses the approach of network medicine and its application to CVD research.

Identification of Key Signaling Pathways and Genes in Eosinophilic Asthma and Neutrophilic Asthma by Weighted Gene Co-Expression Network Analysis

Background: Asthma is a heterogeneous disease with different subtypes including eosinophilic asthma (EA) and neutrophilic asthma (NA). However, the mechanisms underlying the difference between the two subtypes are not fully understood.

Methods: Microarray datasets (GSE45111 and GSE137268) were acquired from Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) in induced sputum between EA (n = 24) and NA (n = 15) were identified by “Limma” package. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses and Gene set enrichment analysis (GSEA) were used to explore potential signaling pathways. Weighted gene co-expression network analysis (WGCNA) were performed to identify the key genes that were strongly associated with EA and NA.

Results: A total of 282 DEGs were identified in induced sputum of NA patients compared with EA patients. In GO and KEGG pathway analyses, DEGs were enriched in positive regulation of cytokine production, and cytokine-cytokine receptor interaction. The results of GSEA showed that ribosome, Parkinson’s disease, and oxidative phosphorylation were positively correlated with EA while toll-like receptor signaling pathway, primary immunodeficiency, and NOD-like receptor signaling pathway were positively correlated with NA. Using WGCNA analysis, we identified a set of genes significantly associated NA including IRFG, IRF1, STAT1, IFIH1, IFIT3, GBP1, GBP5, IFIT2, CXCL9, and CXCL11.

Conclusion: We identified potential signaling pathways and key genes involved in the pathogenesis of the asthma subsets, especially in neutrophilic asthma.