Integrated bioinformatic analysis reveals YWHAB as a novel diagnostic biomarker for idiopathic pulmonary arterial hypertension

Identification and experimental verification of senescence-related gene signatures and molecular subtypes in idiopathic pulmonary arterial hypertension

Evidences illustrate that cell senescence contributes to the development of pulmonary arterial hypertension. However, the molecular mechanisms remain unclear. Since there may be different senescence subtypes between PAH patients, consistent senescence-related genes (SRGs) were utilized for consistent clustering by unsupervised clustering methods. Senescence is inextricably linked to the immune system, and the immune cells in each cluster were estimated by ssGSEA. To further screen out more important SRGs, machine learning algorithms were used for identification and their diagnostic value was assessed by ROC curves. The expression of hub genes were verified in vivo and in vitro. Transcriptome analysis was used to assess the effects of silence of hub gene on different pathways. Three senescence molecular subtypes were identified by consensus clustering. Compared with cluster A and B, most immune cells and checkpoint genes were higher in cluster C. Thus, we identified senescence cluster C as the immune subtype. The ROC curves of IGF1, HOXB7, and YWHAZ were remarkable in both datasets. The expression of these genes was increased in vitro. Western blot and immunohistochemical analyses revealed that YWHAZ expression was also increased. Our transcriptome analysis showed autophagy-related genes were significantly elevated after silence of YWHAZ. Our research provided several prospective SRGs and molecular subtypes. Silence of YWHAZ may contribute to the clearance of senescent endothelial cells by activating autophagy.

Identification of TFRC as a biomarker for pulmonary arterial hypertension based on bioinformatics and experimental verification

BACKGROUND

Pulmonary arterial hypertension (PAH) is a life-threatening chronic cardiopulmonary disease. However, there is a paucity of studies that reflect the available biomarkers from separate gene expression profiles in PAH.

METHODS

The GSE131793 and GSE113439 datasets were combined for subsequent analyses, and batch effects were removed. Bioinformatic analysis was then performed to identify differentially expressed genes (DEGs). Weighted gene co-expression network analysis (WGCNA) and a protein-protein interaction (PPI) network analysis were then used to further filter the hub genes. Functional enrichment analysis of the intersection genes was performed using Gene Ontology (GO), Disease Ontology (DO), Kyoto encyclopedia of genes and genomes (KEGG) and gene set enrichment analysis (GSEA). The expression level and diagnostic value of hub gene expression in pulmonary arterial hypertension (PAH) patients were also analyzed in the validation datasets GSE53408 and GSE22356. In addition, target gene expression was validated in the lungs of a monocrotaline (MCT)-induced pulmonary hypertension (PH) rat model and in the serum of PAH patients.

RESULTS

A total of 914 differentially expressed genes (DEGs) were identified, with 722 upregulated and 192 downregulated genes. The key module relevant to PAH was selected using WGCNA. By combining the DEGs and the key module of WGCNA, 807 genes were selected. Furthermore, protein-protein interaction (PPI) network analysis identified HSP90AA1, CD8A, HIF1A, CXCL8, EPRS1, POLR2B, TFRC, and PTGS2 as hub genes. The GSE53408 and GSE22356 datasets were used to evaluate the expression of TFRC, which also showed robust diagnostic value. According to GSEA enrichment analysis, PAH-relevant biological functions and pathways were enriched in patients with high TFRC levels. Furthermore, TFRC expression was found to be upregulated in the lung tissues of our experimental PH rat model compared to those of the controls, and the same conclusion was reached in the serum of the PAH patients.

CONCLUSIONS

According to our bioinformatics analysis, the observed increase of TFRC in the lung tissue of human PAH patients, as indicated by transcriptomic data, is consistent with the alterations observed in PAH patients and rodent models. These data suggest that TFRC may serve as a potential biomarker for PAH.

A novel complement C3 inhibitor CP40-KK protects against experimental pulmonary arterial hypertension via an inflammasome NLRP3 associated pathway

BACKGROUND

Pulmonary arterial hypertension (PAH) is a severe cardiopulmonary disease characterized by complement dependent and proinflammatory activation of macrophages. However, effective treatment for complement activation in PAH is lacking. We aimed to explore the effect and mechanism of CP40-KK (a newly identified analog of selective complement C3 inhibitor CP40) in the PAH model.

METHODS

We used western blotting, immunohistochemistry, and immunofluorescence staining of lung tissues from the monocrotaline (MCT)-induced rat PAH model to study macrophage infiltration, NLPR3 inflammasome activation, and proinflammatory cytokines (IL-1β and IL-18) release. Surface plasmon resonance (SPR), ELISA, and CH50 assays were used to test the affinity between CP40-KK and rat/human complement C3. CP40-KK group rats only received CP40-KK (2 mg/kg) by subcutaneous injection at day 15 to day 28 continuously.

RESULTS

C3a was significantly upregulated in the plasma of MCT-treated rats. SPR, ELISA, and CH50 assays revealed that CP40-KK displayed similar affinity binding to human and rat complement C3. Pharmacological inhibition of complement C3 cleavage (CP40-KK) could ameliorate MCT-induced NLRP3 inflammasome activity, pulmonary vascular remodeling, and right ventricular hypertrophy. Mechanistically, increased proliferation of pulmonary arterial smooth muscle cells is closely associated with macrophage infiltration, NLPR3 inflammasome activation, and proinflammatory cytokines (IL-1β and IL-18) release. Besides, C3a enhanced IL-1β activity in macrophages and promoted pulmonary arterial smooth muscle cell proliferation in vitro.

CONCLUSION

Our findings suggest that CP40-KK treatment was protective in the MCT-induced rat PAH model, which might serve as a therapeutic option for PAH.

Recent Advances of Using Exosomes as Diagnostic Markers and Targeting Carriers for Cardiovascular Disease

Cardiovascular diseases (CVDs) are the leading cause of human death worldwide. Exosomes act as endogenous biological vectors; they possess advantages of low immunogenicity and low safety risks, also providing tissue selectivity, including the inherent targeting the to heart. Therefore, exosomes not only have been applied as biomarkers for diagnosis and therapeutic outcome confirmation but also showed potential as drug carriers for cardiovascular targeting delivery. This review aims to summarize the progress and challenges of exosomes as novel biomarkers, especially many novel exosomal noncoding RNAs (ncRNAs), and also provides an overview of the improved targeting functions of exosomes by unique engineered approaches, the latest developed administration methods, and the therapeutic effects of exosomes used as the biocarriers of medications for cardiovascular disease treatment. Also, the possible therapeutic mechanisms and the potentials for transferring exosomes to the clinic for CVD treatment are discussed. The advances, in vivo and in vitro applications, modifications, mechanisms, and challenges summarized in this review will provide a general understanding of this promising strategy for CVD treatment.

PANoptosis-based molecular subtyping and HPAN-index predicts therapeutic response and survival in hepatocellular carcinoma

Background

Hepatocellular carcinoma (HCC) is a highly prevalent and fatal cancer. The role of PANoptosis, a novel form of programmed cell death, in HCC is yet to be fully understood. This study focuses on identifying and analyzing PANoptosis-associated differentially expressed genes in HCC (HPAN_DEGs), aiming to enhance our understanding of HCC pathogenesis and potential treatment strategies.

Methods

We analyzed HCC differentially expressed genes from TCGA and IGCG databases and mapped them to the PANoptosis gene set, identifying 69 HPAN_DEGs. These genes underwent enrichment analyses, and consensus clustering analysis was used to determine three distinct HCC subgroups based on their expression profiles. The immune characteristics and mutation landscape of these subgroups were evaluated, and drug sensitivity was predicted using the HPAN-index and relevant databases.

Results

The HPAN_DEGs were mainly enriched in pathways associated with the cell cycle, DNA damage, Drug metabolism, Cytokines, and Immune receptors. We identified three HCC subtypes (Cluster_1, SFN+PDK4-; Cluster_2, SFN-PDK4+; Cluster_3, SFN/PDK4 intermediate expression) based on the expression profiles of the 69 HPAN_DEGs. These subtypes exhibited distinct clinical outcomes, immune characteristics, and mutation landscapes. The HPAN-index, generated by machine learning using the expression levels of 69 HPAN_DEGs, was identified as an independent prognostic factor for HCC. Moreover, the high HPAN-index group exhibited a high response to immunotherapy, while the low HPAN-index group showed sensitivity to small molecule targeted drugs. Notably, we observed that the YWHAB gene plays a significant role in Sorafenib resistance.

Conclusion

This study identified 69 HPAN_DEGs crucial to tumor growth, immune infiltration, and drug resistance in HCC. Additionally, we discovered three distinct HCC subtypes and constructed an HPAN-index to predict immunotherapeutic response and drug sensitivity. Our findings underscore the role of YWHAB in Sorafenib resistance, presenting valuable insights for personalized therapeutic strategy development in HCC.

PARM1 Drives Smooth Muscle Cell Proliferation in Pulmonary Arterial Hypertension via AKT/FOXO3A Axis

Pulmonary arterial hypertension (PAH) is a group of severe, progressive, and debilitating diseases with limited therapeutic options. This study aimed to explore novel therapeutic targets in PAH through bioinformatics and experiments. Weighted gene co-expression network analysis (WGCNA) was applied to detect gene modules related to PAH, based on the GSE15197, GSE113439, and GSE117261. GSE53408 was applied as validation set. Subsequently, the validated most differentially regulated hub gene was selected for further ex vivo and in vitro assays. PARM1, TSHZ2, and CCDC80 were analyzed as potential intervention targets for PAH. Consistently with the bioinformatic results, our ex vivo and in vitro data indicated that PARM1 expression increased significantly in the lung tissue and/or pulmonary artery of the MCT-induced PAH rats and hypoxia-induced PAH mice in comparison with the respective controls. Besides, a similar expression pattern of PARM1 was found in the hypoxia- and PDGF--treated isolated rat primary pulmonary arterial smooth muscle cells (PASMCs). In addition, hypoxia/PDGF--induced PARM1 protein expression could promote the elevation of phosphorylation of AKT, phosphorylation of FOXO3A and PCNA, and finally the proliferation of PASMCs in vitro, whereas PARM1 siRNA treatment inhibited it. Mechanistically, PARM1 promoted PAH via AKT/FOXO3A/PCNA signaling pathway-induced PASMC proliferation.

The identification and verification of hub genes associated with pulmonary arterial hypertension using weighted gene co-expression network analysis

BACKGROUND

Pulmonary arterial hypertension (PAH) is characterized by a progressive increase in pulmonary vascular resistance and pulmonary arterial pressure, with complex etiology, difficult treatment and poor prognosis. The objective of this study was to investigate the potential biomarkers for PAH based on bioinformatics analysis.

METHODS

The GSE117261 datasets were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were identified by screening PAH patients and controls. Then the DEGs were analyzed using a Weighted Gene Co-expression Network Analysis (WGCNA) and the key modules were determined, and to further explore their potential biological functions via Gene Ontology analysis (GO), Kyoto Encyclopedia of Genes and Genomes Pathway analysis (KEGG), and Gene Set Enrichment Analysis (GSEA). Moreover, Protein-protein interaction (PPI) networks were constructed to identify hub gene candidates in the key modules. Finally, real-time quantitative polymerase chain reaction was supplied to detect the expressions of hub genes in human pulmonary arterial smooth cells treated with cobalt chloride (COCl₂) which was used to mimic hypoxia.

RESULTS

There were 2299 DEGs identified. WGCNA indicated that yellow module was the key one correlated with PAH. GO and KEGG analysis demonstrated that genes in the yellow module were mainly enriched in 'Pathways in cancer'. GSEA revealed that 'HALLMARK_MYC_TARGETS_V1' was remarkably enriched in PAH. Based on the PPI network, vascular endothelial growth factor A, proto-oncogene receptor tyrosine kinase (KIT), PNN interacting serine and arginine rich protein (PNISR) and heterogeneous nuclear ribonucleoprotein H1 (HNRNPH1) were identified as the hub genes. Additionally, the PCR indicated that the elevated expressions of PNISR and HNRNPH1 were in line with the bioinformatics analysis. ROC analysis determined that PNISR and HNRNPH1 may be potential biomarkers to provide better diagnosis of PAH.

CONCLUSION

PNISR and HNRNPH1 were potential biomarkers to diagnosis PAH. In summary, the identified DEGs, modules, pathways, and hub genes provide clues and shed light on the potential molecular mechanisms of 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.

Identification of autophagy-related biomarkers in patients with pulmonary arterial hypertension based on bioinformatics analysis

Autophagy participates in the regulation of pulmonary arterial hypertension (PAH). However, the role of autophagy-related genes (ARGs) in the pathogenesis of the PAH is still unclear. This study aimed to identify the ARGs in PAH via bioinformatics analysis. A microarray dataset (GSE113439) was downloaded from the Gene Expression Omnibus database to identify differentially expressed ARGs (DEARGs). Protein–protein interactions network, gene ontology, and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were performed to screen hub genes and the underlying molecular mechanisms of PAH. Finally, the mRNA expression of the hub genes was validated using the GSE53408 dataset. Twenty-six DEARGs were identified, all of which were upregulated. Enrichment analyses revealed that these DEARGs were mainly enriched in the nucleotide-binding oligomerization domain (NOD)-like receptor signaling pathway, PI3K-Akt signaling pathway, response to hypoxia, response to nutrient levels, and autophagy. Among these hub genes, the mRNA expression levels of HSP90AA1, HIF1A, MET, IGF1, LRRK2, CLTC, DNM1L, MDM2, RICTOR, and ROCK2 were significantly upregulated in PAH patients than in healthy individuals. Ten hub DEARGs were identified and may participate in the pathogenesis of the PAH via the regulation of autophagy. The present study may provide novel therapeutic targets for PAH prevention and treatment and expand our understanding of PAH.

Single-Cell Sequencing Analysis and Multiple Machine Learning Methods Identified G0S2 and HPSE as Novel Biomarkers for Abdominal Aortic Aneurysm

Identifying biomarkers for abdominal aortic aneurysms (AAA) is key to understanding their pathogenesis, developing novel targeted therapeutics, and possibly improving patients outcomes and risk of rupture. Here, we identified AAA biomarkers from public databases using single-cell RNA-sequencing, weighted co-expression network (WGCNA), and differential expression analyses. Additionally, we used the multiple machine learning methods to identify biomarkers that differentiated large AAA from small AAA. Biomarkers were validated using GEO datasets. CIBERSORT was used to assess immune cell infiltration into AAA tissues and investigate the relationship between biomarkers and infiltrating immune cells. Therefore, 288 differentially expressed genes (DEGs) were screened for AAA and normal samples. The identified DEGs were mostly related to inflammatory responses, lipids, and atherosclerosis. For the large and small AAA samples, 17 DEGs, mostly related to necroptosis, were screened. As biomarkers for AAA, G0/G1 switch 2 (G0S2) (Area under the curve [AUC] = 0.861, 0.875, and 0.911, in GSE57691, GSE47472, and GSE7284, respectively) and for large AAA, heparinase (HPSE) (AUC = 0.669 and 0.754, in GSE57691 and GSE98278, respectively) were identified and further verified by qRT-PCR. Immune cell infiltration analysis revealed that the AAA process may be mediated by T follicular helper (Tfh) cells and the large AAA process may also be mediated by Tfh cells, M1, and M2 macrophages. Additionally, G0S2 expression was associated with neutrophils, activated and resting mast cells, M0 and M1 macrophages, regulatory T cells (Tregs), resting dendritic cells, and resting CD4 memory T cells. Moreover, HPSE expression was associated with M0 and M1 macrophages, activated and resting mast cells, Tregs, and resting CD4 memory T cells. Additional, G0S2 may be an effective diagnostic biomarker for AAA, whereas HPSE may be used to confer risk of rupture in large AAAs. Immune cells play a role in the onset and progression of AAA, which may improve its diagnosis and treatment.

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.

Screening of Potential Biomarkers in the Peripheral Serum for Steroid-Induced Osteonecrosis of the Femoral Head Based on WGCNA and Machine Learning Algorithms

Background. Steroid-induced osteonecrosis of the femoral head (SONFH) has produced a substantial burden of medical and social experience. However, the current diagnosis is still limited. Thus, this study is aimed at identifying potential biomarkers in the peripheral serum of patients with SONFH. Methods. The expression profile data of SONFH (number: GSE123568) was acquired from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) in SONFH were identified and used for weighted gene coexpression network analysis (WGCNA). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to investigate the biological functions. The protein-protein interaction (PPI) network and machine learning algorithms were employed to screen for potential biomarkers. Gene set enrichment analysis (GSEA), transcription factor (TF) enrichment analysis, and competing endogenous RNA (ceRNA) network were used to determine the biological functions and regulatory mechanisms of the potential biomarkers. Results. A total of 562 DEGs, including 318 upregulated and 244 downregulated genes, were identified between SONFH and control samples, and 94 target genes were screened based on WGCNA. Moreover, biological function analysis suggested that target genes were mainly involved in erythrocyte differentiation, homeostasis and development, and myeloid cell homeostasis and development. Furthermore, GYPA, TMCC2, and BPGM were identified as potential biomarkers in the peripheral serum of patients with SONFH based on machine learning algorithms and showed good diagnostic values. GSEA revealed that GYPA, TMCC2, and BPGM were mainly involved in immune-related biological processes (BPs) and signaling pathways. Finally, we found that GYPA might be regulated by hsa-miR-3137 and that BPGM might be regulated by hsa-miR-340-3p. Conclusion. GYPA, TMCC2, and BPGM are potential biomarkers in the peripheral serum of patients with SONFH and might affect SONFH by regulating erythrocytes and immunity.

Application of machine learning in diagnostic value of mRNAs for bipolar disorder

PURPOSE

Bipolar disorder (BD) is a type of severe mental illness with symptoms of mania or depression, it is necessary to find out effective diagnostic biomarkers for BD due to diagnosing BD is based on clinical interviews without objective indicators.

MATERIALS AND METHODS

The mRNA expression levels of genes included PIK3R1, FYN, TP53, PRKCZ, PRKCB, and YWHAB in the peripheral blood of 43 patients with bipolar disorder and 47 healthy controls were detected. Machine learning methods included Artificial Neural Networks, Extreme Gradient Boosting, Random Forest, and Support Vector Machine were adopted to fit different gene combinations to evaluate diagnostic value for bipolar disorder.

RESULTS

The combination 'PIK3R1 + FYN' in the SVM model showed the best diagnostic value, with AUC, sensitivity, and specificity values of 0.951, 0.928, and 0.937, respectively.

CONCLUSIONS

The diagnostic efficiency for bipolar disorder was significantly improved by fitting PIK3R1 and FYN through the Support Vector Machine model.

Detection of melanogenesis- and anti-apoptosis-associated melanoma factors: Array CGH and PPI mapping integrating study

BACKGROUND

We investigated melanogenesis- and anti-apoptosis-related melanoma factors in melanoma cells (TXM1, TXM18, A375P, and A375SM).

OBJECTIVE

To find melanoma associated hub factor, high-throughput screening-based techniques integrating with bioinformatics were investigated.

METHODS

Array CGH analysis was conducted with a commercial system. Total genomic DNAs prepared individually from each cell line with control DNA were properly labeled with Cy3-dCTP and Cy5-dCTP and hybridizations and subsequently performed data treatment by the log2 green (G; test) to red (R; reference) fluorescence ratios (G/R). Gain or loss of copy number was judged by spots with log2-transformed ratios. PPI mapping analysis of detected candidate genes based on the array CGH results was conducted using the human interactome in the STRING database. Energy minimization and a short molecular dynamics (MD) simulation using the implicit solvation model in CHARMM were performed to analyze the interacting residues between YWHAZ and YWHAB.

RESULTS

Three genes (BMP-4, BFGF, LEF-1) known to be involved in melanogenesis were found to lose chromosomal copy numbers, and Chr. 6q23.3 was lost in all tested cell lines. Ten hub genes (CTNNB1, PEX13, PEX14, PEX5, IFNG, EXOSC3, EXOSC1, EXOSC8, UBC, and PEX10) were predicted to be functional interaction factors in the network of the 6q23.3 locus. The apoptosis-associated genes E2F1, p50, BCL2L1, and BIRC7 gained, and FGF2 lost chromosomal copy numbers in the tested melanoma cell lines. YWHAB, which gained chromosomal copy numbers, was predicted to be the most important hub protein in melanoma cells. Molecular dynamics simulations for binding YWHAB and YWHAZ were conducted, and the complex was predicted to be energetically and structurally stable through its 3 hydrogen-bond patterns. The number of interacting residues is 27.

CONCLUSION

Our study compares genome-wide screening interactomics predictions for melanoma factors and offers new information for understanding melanogenesis- and anti-apoptosis-associated mechanisms in melanoma. Especially, YWHAB was newly detected as a core factor in melanoma cells.

Screening of key biomarkers of tendinopathy based on bioinformatics and machine learning algorithms

Tendinopathy is a complex multifaceted tendinopathy often associated with overuse and with its high prevalence resulting in significant health care costs. At present, the pathogenesis and effective treatment of tendinopathy are still not sufficiently elucidated. The purpose of this research is to intensely explore the genes, functional pathways, and immune infiltration characteristics of the occurrence and development of tendinopathy. The gene expression profile of GSE106292, GSE26051 and GSE167226 are downloaded from GEO (NCBI comprehensive gene expression database) and analyzed by WGCNA software bag using R software, GSE26051, GSE167226 data set is combined to screen the differential gene analysis. We subsequently performed gene enrichment analysis of Gene Ontology (GO) and "Kyoto Encyclopedia of Genes and Genomes" (KEGG), and immune cell infiltration analysis. By constructing the LASSO regression model, Support vector machine (SVM-REF) and Gaussian mixture model (GMMs) algorithms are used to screen, to identify early diagnostic genes. We have obtained a total of 171 DEGs through WGCNA analysis and differentially expressed genes (DEGs) screening. By GO and KEGG enrichment analysis, it is found that these dysregulated genes were related to mTOR, HIF-1, MAPK, NF-κB and VEGF signaling pathways. Immune infiltration analysis showed that M1 macrophages, activated mast cells and activated NK cells had infiltration significance. After analysis of THE LASSO SVM-REF and GMMs algorithms, we found that the gene MACROD1 may be a gene for early diagnosis. We identified the potential of tendon disease early diagnosis way and immune gene regulation MACROD1 key infiltration characteristics based on comprehensive bioinformatics analysis. These hub genes and functional pathways may as early biomarkers of tendon injuries and molecular therapy level target is used to guide drug and basic research.

Identification of Underlying Hub Genes Associated with Hypertrophic Cardiomyopathy by Integrated Bioinformatics Analysis

Background

Considered as one of the major reasons of sudden cardiac death, hypertrophic cardiomyopathy (HCM) is a common inherited cardiovascular disease. However, effective treatment for HCM is still lacking. Identification of hub gene may be a powerful tool for discovering potential therapeutic targets and candidate biomarkers.

Methods

We analysed three gene expression datasets for HCM from the Gene Expression Omnibus. Two of them were merged by “sva” package. The merged dataset was used for analysis while the other dataset was used for validation. Following this, a weighted gene coexpression network analysis (WGCNA) was performed, and the key module most related to HCM was identified. Based on the intramodular connectivity, we identified the potential hub genes. Then, a receiver operating characteristic curve analysis was performed to verify the diagnostic values of hub genes. Finally, we validated changes of hub genes, for genetic transcription and protein expression levels, in datasets of HCM patients and myocardium of transverse aortic constriction (TAC) mice.

Results

In the merged dataset, a total of 455 differentially expressed genes (DEGs) were identified from normal and hypertrophic myocardium. In WGCNA, the blue module was identified as the key module and the genes in this module showed a high positive correlation with HCM. Functional enrichment analysis of DEGs and key module revealed that the extracellular matrix, fibrosis, and neurohormone pathways played important roles in HCM. FRZB, COL14A1, CRISPLD1, LUM, and sFRP4 were identified as hub genes in the key module. These genes showed a good predictive value for HCM and were significantly up-regulated in HCM patients and TAC mice. We also found protein expression of LUM and sFRP4 increased in myocardium of TAC mice.

Conclusion

This study revealed that five hub genes are involved in the occurrence and development of HCM, and they are potentially to be used as therapeutic targets and biomarkers for HCM.

Exploration and validation of hub genes and pathways in the progression of hypoplastic left heart syndrome via weighted gene co-expression network analysis

Background

Despite significant progress in surgical treatment of hypoplastic left heart syndrome (HLHS), its mortality and morbidity are still high. Little is known about the molecular abnormalities of the syndrome. In this study, we aimed to probe into hub genes and key pathways in the progression of the syndrome.

Methods

Differentially expressed genes (DEGs) were identified in left ventricle (LV) or right ventricle (RV) tissues between HLHS and controls using the GSE77798 dataset. Then, weighted gene co-expression network analysis (WGCNA) was performed and key modules were constructed for HLHS. Based on the genes in the key modules, protein–protein interaction networks were conducted, and hub genes and key pathways were screened. Finally, the GSE23959 dataset was used to validate hub genes between HLHS and controls.

Results

We identified 88 and 41 DEGs in LV and RV tissues between HLHS and controls, respectively. DEGs in LV tissues of HLHS were distinctly involved in heart development, apoptotic signaling pathway and ECM receptor interaction. DEGs in RV tissues of HLHS were mainly enriched in BMP signaling pathway, regulation of cell development and regulation of blood pressure. A total of 16 co-expression network were constructed. Among them, black module (r = 0.79 and p value = 2e−04) and pink module (r = 0.84 and p value = 4e−05) had the most significant correlation with HLHS, indicating that the two modules could be the most relevant for HLHS progression. We identified five hub genes in the black module (including Fbn1, Itga8, Itga11, Itgb5 and Thbs2), and five hub genes (including Cblb, Ccl2, Edn1, Itgb3 and Map2k1) in the pink module for HLHS. Their abnormal expression was verified in the GSE23959 dataset.

Conclusions

Our findings revealed hub genes and key pathways for HLHS through WGCNA, which could play key roles in the molecular mechanism of HLHS.

Integrated bioinformatic analysis reveals the underlying molecular mechanism of and potential drugs for pulmonary arterial hypertension

Pulmonary arterial hypertension (PAH) is a devastating cardiovascular disease without a clear mechanism or drugs for treatment. Therefore, it is crucial to reveal the underlying molecular mechanism and identify potential drugs for PAH. In this study, we first integrated three human lung tissue datasets (GSE113439, GSE53408, GSE117261) from GEO. A total of 151 differentially expressed genes (DEGs) were screened, followed by KEGG and GO enrichment analyses and PPI network construction. Five hub genes (CSF3R, NT5E, ANGPT2, FGF7, and CXCL9) were identified by Cytoscape (Cytohubba). GSEA and GSVA were performed for each hub gene to uncover the potential mechanism. Moreover, to repurpose known and therapeutic drugs, the CMap database was retrieved, and nine candidate compounds (lypressin, ruxolitinib, triclabendazole, L-BSO, tiaprofenic acid, AT-9283, QL-X-138, huperzine-a, and L-741742) with a high level of confidence were obtained. Then ruxolitinib was selected to perform molecular docking simulations with ANGPT2, FGF7, NT5E, CSF3R, JAK1, JAK2, JAK3, TYK2. A certain concentration of ruxolitinib could inhibit the proliferation and migration of rat pulmonary artery smooth muscle cells (rPASMCs) in vitro. Together, these analyses principally identified CSF3R, NT5E, ANGPT2, FGF7 and CXCL9 as candidate biomarkers of PAH, and ruxolitinib might exert promising therapeutic action for PAH.

Essential Genes and MiRNA-mRNA Network Contributing to the Pathogenesis of Idiopathic Pulmonary Arterial Hypertension

Background: Idiopathic pulmonary arterial hypertension (IPAH) is a life-threatening disease. Owing to its high fatality rate and narrow therapeutic options, identification of the pathogenic mechanisms of IPAH is becoming increasingly important. Methods: In our research, we utilized the robust rank aggregation (RRA) method to integrate four eligible pulmonary arterial hypertension (PAH) microarray datasets and identified the significant differentially expressed genes (DEGs) between IPAH and normal samples. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were performed to analyze their functions. The interaction network of protein-protein interaction (PPI) was constructed to explore the correlation between these DEGs. The functional modules and hub genes were further identified by the weighted gene coexpression network analysis (WGCNA). Moreover, a miRNA microarray dataset was involved and analyzed to filter differentially expressed miRNAs (DE-miRNAs). Potential target genes of screened DE-miRNAs were predicted and merged with DEGs to explore a miRNA-mRNA network in IPAH. Some hub genes were selected and validated by RT-PCR in lung tissues from the PAH animal model. Results: A total of 260 DEGs, consisting of 183 upregulated and 77 downregulated significant DEGs, were identified, and some of those genes were novel. Their molecular roles in the etiology of IPAH remained vague. The most crucial functional module involved in IPAH is mainly enriched in biological processes, including leukocyte migration, cell chemotaxis, and myeloid leukocyte migration. Construction and analysis of the PPI network showed that CXCL10, CXCL9, CCR1, CX3CR1, CX3CL1, CXCR2, CXCR1, PF4, CCL4L1, and ADORA3 were recognized as top 10 hub genes with high connectivity degrees. WGCNA further identified five main functional modules involved in the pathogenesis of IPAH. Twelve upregulated DE-miRNAs and nine downregulated DE-miRNAs were identified. Among them, four downregulated DEGs and eight upregulated DEGs were supposed to be negatively regulated by three upregulated DE-miRNAs and three downregulated DE-miRNAs, respectively. Conclusions: This study identifies some key and functional coexpression modules involved in IPAH, as well as a potential IPAH-related miRNA-mRNA regulated network. It provides deepening insights into the molecular mechanisms and provides vital clues in seeking novel therapeutic targets for IPAH.

Identification of Hub Genes and MicroRNAs Associated With Idiopathic Pulmonary Arterial Hypertension by Integrated Bioinformatics Analyses

Objective

The aim of this study is the identification of hub genes associated with idiopathic pulmonary arterial hypertension (IPAH).

Materials and Methods

GSE15197 gene expression data was downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified by screening IPAH patients and controls. The 5,000 genes with the greatest variances were analyzed using a weighted gene co-expression network analysis (WGCNA). Modules with the strongest correlation with IPAH were chosen, followed by a functional enrichment analysis. Protein–protein interaction (PPI) networks were constructed to identify hub gene candidates using calculated degrees. Real hub genes were found from the overlap of DEGs and candidate hub genes. microRNAs (miRNAs) targeting real hub genes were found by screening miRNet 2.0. The most important IPAH miRNAs were identified.

Results

There were 4,395 DEGs identified. WGCNA indicated that green and brown modules associated most strongly with IPAH. Functional enrichment analysis showed that green and brown module genes were mainly involved in protein digestion and absorption and proteoglycans in cancer, respectively. The top ten candidate hub genes in green and brown modules were identified, respectively. After overlapping with DEGs, 11 real hub genes were identified: EP300, MMP2, CDH2, CDK2, GNG10, ALB, SMC2, DHX15, CUL3, BTBD1, and LTN1. These genes were expressed with significant differences in IPAH versus controls, indicating a high diagnostic ability. The miRNA–gene network showed that hsa-mir-1-3p could associate with IPAH.

Conclusion

EP300, MMP2, CDH2, CDK2, GNG10, ALB, SMC2, DHX15, CUL3, BTBD1, and LTN1 may play essential roles in IPAH. Predicted miRNA hsa-mir-1-3p could regulate gene expression in IPAH. Such hub genes may contribute to the pathology and progression in IPAH, providing potential diagnostic and therapeutic opportunities for IPAH patients.

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

Background

Pulmonary artery hypertension (PAH) is an incurable disease with a high mortality rate. Current medications ameliorate symptoms but cannot target adverse vascular remodeling. New therapeutic strategies for PAH need to be established.

Methods

Using the weighted gene coexpression network analysis (WGCNA) algorithm, we constructed a coexpression network of dataset GSE117261 from the Gene Expression Omnibus (GEO) database. Key modules were identified by the relationship between module eigengenes and clinical traits. Hub genes were screened out based on gene significance (GS), module membership (MM), and mean pulmonary artery pressure (mPAP). External validations were conducted in GSE48149 and GSE113439. Functional enrichment and immune cell infiltration were analyzed using Metascape and CIBERSORTx.

Results

The WGCNA analysis revealed 13 coexpression modules. The pink module had the highest correlation with PAH in terms of module eigengene (r=0.79; P=2e-18) and module significance (MS =0.43). Functional enrichment indicated genes in the pink module contributed to the immune system process and extracellular matrix (ECM). In the pink module, ECM2 (GS =0.65, MM =0.86, ρ=0.407, P=0.0019) and GLT8D2 (GS =0.63, MM =0.85, ρ=0.443, P=0.006) were identified as hub genes. For immune cells infiltration in PAH lung tissue, hub genes were positively correlated with M2 macrophages and resting mast cells, and were negatively correlated with monocytes, neutrophils, and CD4-naïve T cells.

Conclusions

Our research identified 2 hub genes ECM2 and GLT8D2 related to PAH. The functions of these hub genes were involved in the immune process and ECM, indicating that they might serve as candidate therapeutic targets for PAH.

Molecular Mechanisms Underlying Intestinal Ischemia/Reperfusion Injury: Bioinformatics Analysis and In Vivo Validation

Background

Intestinal ischemia/reperfusion (I/R) injury is a serious clinical complication. This study aimed to explore the hub genes and pathways of intestinal I/R injury.

Material/Methods

GSE96733 from the GEO website was extracted to analyze the differentially expressed genes (DEGs) of intestinal I/R injured and sham-operated mice at 3 h and 6 h after surgery. The DAVID and STRING databases were used to construct functional enrichment analyses of DEGs and the protein–protein interaction (PPI) network. In Cytoscape software, cytoHubba was used to identify hub genes, and MCODE was used for module analysis. Testing by qRT-PCR detected the expression of hub genes in intestinal I/R injury. Western blot analysis detected the key proteins involved with the important pathways of intestinal I/R injury.

Results

IL-6, IL-10, CXCL1, CXCL2, and IL-1β were identified as critical upregulated genes, while IRF7, IFIT3, IFIT1, Herc6, and Oasl2 were identified as hub genes among the downregulated genes. The qRT-PCR testing showed the expression of critical upregulated genes was significantly increased in intestinal I/R injury (P<0.05), while the expression of hub downregulated genes was notably reduced (P<0.05). The proteins of CXCL1 and CXCR2 were upregulated following intestinal I/R injury (P<0.05) and the CXCL1/CXCR2 axis was involved with intestinal I/R injury.

Conclusions

The results of the present study identified IL-6, IL-10, CXCL1, CXCL2, IL-1β, IRF7, IFIT3, IFIT1, Herc6, and Oasl2 as hub genes in intestinal I/R injury and identified the involvement of the CXCL1/CXCR2 axis in intestinal I/R injury.

Weighted gene co-expression network analysis identified underlying hub genes and mechanisms in the occurrence and development of viral myocarditis

Background

Myocarditis is an inflammatory myocardial disease, which may lead to heart failure and sudden death. Despite extensive research into the pathogenesis of myocarditis, effective treatments for this condition remain elusive. This study aimed to explore the potential pathogenesis and hub genes for viral myocarditis.

Methods

A weighted gene co-expression network analysis (WGCNA) was performed based on the gene expression profiles derived from mouse models at different stages of viral myocarditis (GSE35182). Functional annotation was executed within the key modules. Potential hub genes were predicted based on the intramodular connectivity (IC). Finally, potential microRNAs that regulate gene expression were predicted by miRNet analysis.

Results

Three gene co-expression modules showed the strongest correlation with the acute or chronic disease stage. A significant positive correlation was detected between the acute disease stage and the turquoise module, the genes of which were mainly enriched in antiviral response and immune-inflammatory activation. Furthermore, a significant positive correlation and a negative correlation were identified between the chronic disease stage and the brown and yellow modules, respectively. These modules were mainly associated with the cytoskeleton, phosphorylation, cellular catabolic process, and autophagy. Subsequently, we predicted the underlying hub genes and microRNAs in the three modules.

Conclusions

This study revealed the main biological processes in different stages of viral myocarditis and predicted hub genes in both the acute and chronic disease stages. Our results may be helpful for developing new therapeutic targets for viral myocarditis in future research.

Identification and Validation of Novel Biomarkers for Diagnosis and Prognosis of Hepatocellular Carcinoma

Introduction: Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related deaths worldwide due to poor survival outcome. Thus, there is an urgent need to identify effective biomarkers for early diagnosis and prognosis prediction.

Methods: A total of 389 differentially expressed genes (DEGs) between HCC samples and normal were selected based on the Robust Rank Aggregation (RRA) method. We combined DEGs expression and clinical traits to construct a gene co-expression network through WGCNA. Forty hub genes were selected from the key module. Among them, YWHAB, PPAT, NOL10 were eventually identified as prognostic biomarkers using multivariate Cox regression model. Biomarkers expression pattern was investigated by informatic analysis and verified by RNA-seq of 32 patients with HCC. DiseaseMeth 2.0, MEXPRESS, and Tumor Immune Estimation Resource (TIMER) were used to assess the methylation and immune status of biomarkers. GSVA, CCK8, colony formation assay, Edu imaging kit, wound-healing assay, and xenograft tumor model were utilized to investigate the effects of biomarkers on proliferation, metastasis of HCC cells in vitro, and in vivo. The Kaplan–Meier (KM) plotter and ROC curves were used to validate the prognostic and diagnostic value of biomarker expression.

Results: All the selected biomarkers were upregulated in HCC samples and higher expression levels were associated with advanced tumor stages and T grades. The regulation of YWHAB, PPAT, NOL10 promoter methylation varied in tumors, and precancerous normal tissues. Immune infiltration analysis suggested that the abnormal regulations of these biomarkers were likely attributed to B cells and dendritic cells. GSVA for these biomarkers showed their great contributions to proliferation of HCC. Specific inhibition of their expression had strong effects on tumorigenesis in vitro and in vivo. ROC and KM curves confirmed their usefulness of diagnosis and prognosis of HCC.

Conclusions: These findings identified YWHAB, PPAT, and NOL10 as novel biomarkers and validated their diagnostic and prognostic value for HCC.

Comprehensive Analyses of miRNA-mRNA Network and Potential Drugs in Idiopathic Pulmonary Arterial Hypertension

Introduction

Idiopathic pulmonary arterial hypertension (IPAH) is a severe cardiopulmonary disease with a relatively low survival rate. Moreover, the pathogenesis of IPAH has not been fully recognized. Thus, comprehensive analyses of miRNA-mRNA network and potential drugs in IPAH are urgent requirements.

Methods

Microarray datasets of mRNA and microRNA (miRNA) in IPAH were searched and downloaded from Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) and differentially expressed miRNAs (DEMIs) were identified. Then, the DEMI-DEG network was conducted with associated comprehensive analyses including Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, and protein-protein interaction (PPI) network analysis, while potential drugs targeting hub genes were investigated using L1000 platform.

Results

30 DEGs and 6 DEMIs were identified in the lung tissue of IPAH. GO and KEGG pathway analyses revealed that these DEGs were mostly enriched in antimicrobial humoral response and African trypanosomiasis, respectively. The DEMI-DEG network was conducted subsequently with 4 DEMIs (hsa-miR-34b-5p, hsa-miR-26b-5p, hsa-miR-205-5p, and hsa-miR-199a-3p) and 16 DEGs, among which 5 DEGs (AQP9, SPP1, END1, VCAM1, and SAA1) were included in the top 10 hub genes of the PPI network. Nimodipine was identified with the highest CMap connectivity score in L1000 platform.

Conclusion

Our study conducted a miRNA-mRNA network and identified 4 miRNAs as well as 5 mRNAs which may play important roles in the pathogenesis of IPAH. Moreover, we provided a new insight for future therapies by predicting potential drugs targeting hub genes.

Screening and Identification of Therapeutic Targets for Pulmonary Arterial Hypertension Through Microarray Technology

Pulmonary arterial hypertension (PAH) is a rare but fatal disease characterized by vascular cell proliferation; the pathogenesis of PAH has yet to be fully elucidated. Publicly available genetic data were downloaded from the Gene Expression Omnibus (GEO) database, and gene set enrichment analysis (GSEA) was used to determine significant differences in gene expression between tissues with PAH and healthy lung tissues. Differentially expressed genes (DEGs) were identified using the online tool, GEO2R, and functional annotation of DEGs was performed using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Next, the construction and module analysis of the protein–protein interaction (PPI) network and verification of the expression level of hub genes was performed. Finally, prediction and enrichment analysis of microRNAs associated with the hub genes was carried out. A total of 110 DEGs were detected by screening PAH and healthy lung samples. The expression of nine genes [polo-like kinase 4 (PLK4), centromere protein U, kinesin family member 20B, structural maintenance of chromosome 2 (SMC2), abnormal spindle microtubule assembly, Fanconi Anemia complementation group I, kinesin family member 18A, spindle apparatus coiled-coil protein 1, and MIS18 binding protein 1] was elevated in PAH; this was statistically significant compared with their expression in healthy lung tissue, and they were identified as hub genes. GO and KEGG analysis showed that the variations in DEGs were abundant in DNA-templated transcription, sister chromatid cohesion, mitotic nuclear division, cell proliferation, and regulation of the actin cytoskeleton. In conclusion, this study has successfully identified hub genes and key pathways of PAH, with a total of 110 DEGs and nine hub genes related to PAH, especially the PLK4 and SMC2 genes, thus providing important clues for the in-depth understanding of the molecular mechanism of PAH and providing potential therapeutic targets.

Identification of Four Potential Biomarkers Associated With Coronary Artery Disease in Non-diabetic Patients by Gene Co-expression Network Analysis

Background

Coronary artery disease (CAD) is a type of cardiovascular disease that greatly hurts the health of human beings. Diabetic status is one of the largest clinical factors affecting CAD-associated gene expression changes. Most of the studies focus on diabetic patients, whereas few have been done for non-diabetic patients. Since the pathophysiological processes may vary among these patients, we cannot simply follow the standard based on the data from diabetic patients. Therefore, the prognostic and predictive diagnostic biomarkers for CAD in non-diabetic patient need to be fully recognized.

Materials and Methods

To screen out candidate genes associated with CAD in non-diabetic patients, weighted gene co-expression network analysis (WGCNA) was constructed to conduct an analysis of microarray expression profiling in patients with CAD. First, the microarray data GSE20680 and GSE20681 were downloaded from NCBI. We constructed co-expression modules via WGCNA after excluding the diabetic patients. As a result, 18 co-expression modules were screened out, including 1,225 differentially expressed genes (DEGs) that were obtained from 152 patients (luminal stenosis ≥50% in at least one major vessel) and 170 patients (stenosis of <50%). Subsequently, a Pearson's correlation analysis was conducted between the modules and clinical traits. Then, a functional enrichment analysis was conducted, and we used gene network analysis to reveal hub genes. Last, we validated the hub genes with peripheral blood samples in an independent patient cohort using RT-qPCR.

Results

The results showed that the midnight blue module and the yellow module played vital roles in the pathogenesis of CAD in non-diabetic patients. Additionally, CD40, F11R, TNRC18, and calcium/calmodulin-dependent protein kinase type II gamma (CAMK2G) were screened out and validated using enzyme-linked immunosorbent assay (ELISA) in an independent patient cohort and immunohistochemical (IHC) staining in an atherosclerosis mouse model.

Conclusion

Our findings demonstrate that hub genes, CD40, F11R, TNRC18, and CAMK2G, are surrogate diagnostic biomarkers and/or therapeutic targets for CAD in non-diabetic patients and require deeper validation.

Identification of Biomarkers Related to Systemic Sclerosis With or Without Pulmonary Hypertension Using Co-expression Analysis

Systemic sclerosis (SSc), also known as scleroderma, is an autoimmune disease with multiple system involvement, and pulmonary complications, including pulmonary hypertension (PH), are leading causes of death. This study aimed to develop early biomarkers to distinguish SSc with or without PH from normal population using bioinformatics approaches. The gene expression profile GSE22356, which contains 10 SSc samples with PH, 10 SSc samples without PH, and 10 normal samples, was obtained from the Gene Expression Omnibus database. First, we constructed co-expression networks and identified critical gene modules using the weighted gene co-expression network analysis. Then, functional enrichment analysis of significant modules was performed. Finally, the "real" hub gene was screened out by intramodule analysis and protein-protein interaction networks, and the receiver operating characteristic analysis was conducted. A total of 5046 genes were screened out to construct co-expression networks, and 18 modules were identified. Of these modules, the turquoise module had a strong correlation with SSc only, whereas the midnightblue module showed an obvious positive correlation with SSc with PH. Functional enrichment analysis indicated that the turquoise module was mainly enriched in transcription of DNA template and its regulation and protein ubiquitination and involved in apoptosis and pyrimidine metabolism pathway. The midnightblue module was significantly associated with inflammatory and immune response and pathways in Staphylococcus aureus infection and Chagas disease. The "real" hub genes in the turquoise module were WDR36, POLR1B, and SRSF1, and those in midnightblue were TLR2 and TNFAIP6.

Identification of gene modules associated with survival of diffuse large B-cell lymphoma treated with CHOP-based chemotherapy

Diffuse Large B-cell Lymphoma (DLBCL), a heterogeneous disease, is influenced by complex network of gene interactions. Most previous studies focused on individual genes, but ignored the importance of intergenic correlations. In current study, we aimed to explore the association between gene networks and overall survival (OS) of DLBCL patients treated with CHOP-based chemotherapy (cyclophosphamide combination with doxorubicin, vincristine and prednisone). Weighted gene co-expression network analysis was conducted to obtain insights into the molecular characteristics of DLBCL. Ten co-expression gene networks (modules) were identified in training dataset (n = 470), and their associations with patients' OS after chemotherapy were tested. The results were validated in four independent datasets (n = 802). Gene ontology (GO) biological function enrichment analysis was conducted with Metascape. Three modules (purple, brown and red), which were enriched in T-cell immune, cell-cell adhesion and extracellular matrix (ECM), respectively, were found to be related to longer OS. Higher expression of several hub genes within these three co-expression modules, for example, LCP2 (HR = 0.77, p = 5.40 × 10^-2), CD2 (HR = 0.87, p = 6.31 × 10^-2), CD3D (HR = 0.83, p = 6.94 × 10^-3), FYB (HR = 0.82, p = 1.40 × 10^-2), GZMK (HR = 0.92, p = 1.19 × 10^-1), FN1 (HR = 0.88, p = 7.06 × 10^-2), SPARC (HR = 0.82, p = 2.06 × 10^-2), were found to be associated with favourable survival. Moreover, the associations of the modules and hub genes with OS in different molecular subtypes and different chemotherapy groups were also revealed. In general, our research revealed the key gene modules and several hub genes were upregulated correlated with good survival of DLBCL patients, which might provide potential therapeutic targets for future clinical research.

Epigenetic Inheritance Underlying Pulmonary Arterial Hypertension

In pulmonary arterial hypertension (PAH), the Warburg effect (glycolytic shift) and mitochondrial fission are determinants of phenotype alterations characteristic of the disease, such as proliferation, apoptosis resistance, migration, endothelial-mesenchymal transition, and extracellular matrix stiffness. Current therapies, focusing largely on vasodilation and antithrombotic protection, do not restore these aberrant phenotypes suggesting that additional pathways need be targeted. The multifactorial nature of PAH suggests epigenetic changes as potential determinants of vascular remodeling. Transgenerational epigenetic changes induced by hypoxia can result in permanent changes early in fetal development increasing PAH risk in adulthood. Unlike genetic mutations, epigenetic changes are pharmacologically reversible, making them an attractive target as therapeutic strategies for PAH. This review offers a landscape of the most current clinical, epigenetic-sensitive changes contributing to PAH vascular remodeling both in early and later life, with a focus on a network medicine strategy. Furthermore, we discuss the importance of the application (from morphogenesis to disease onset) of molecular network-based algorithms to dissect PAH molecular pathobiology. Additionally, we suggest an integrated network-based program for clinical disease gene discovery that may reveal novel biomarkers and novel disease targets, thus offering a truly innovative path toward redefining and treating PAH, as well as facilitating the trajectory of a comprehensive precision medicine approach to PAH.

Potentially Critical Roles of NDUFB5, TIMMDC1, and VDAC3 in the Progression of Septic Cardiomyopathy Through Integrated Bioinformatics Analysis

Septic cardiomyopathy (SC) is a rare and harmful cardiovascular disease with decreased left ventricular (LV) output and multiple organ failure, which poses a serious threat to human life. Despite the advances in SC, its diagnostic basis and treatment methods are limited, and the specific diagnostic biomarkers and its candidate regulatory targets have not yet been fully established. In this study, the GSE79962 gene expression profile was retrieved, with 20 patients with SC and 11 healthy donors as control. Weighted gene coexpression network analysis (WGCNA) was employed to investigate gene modules that were strongly correlated with clinical phenotypes. Blue module was found to be most significantly related to SC. Moreover, Gene Ontology (GO) functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed on the coexpression genes in blue module and showed that it was associated with metabolic pathways, oxidative phosphorylation, and cardiac muscle contraction. Furthermore, a total of 10 hub genes NDUFB5, TIMMDC1, VDAC3, COQ10A, MRPL16 (mitochondrial ribosomal protein L16), C3orf43, TMEM182, DLAT, NDUFA8, and PDHB (pyruvate dehydrogenase E1 beta subunit) in the blue module were identified at transcriptional level and further validated at translational level in myocardium of an lipopolysaccharide-induced septic cardiac dysfunction mouse model. Overall, the results of quantitative real-time polymerase chain reaction were consistent with most of the microarray analysis results. Intriguingly, we observed that the highest change was NDUFB5, TIMMDC1, and VDAC3. These identified and validated genes provided references that would advance the understanding of molecular mechanisms of SC. Taken together, using WGCNA, the hub genes NDUFB5, TIMMDC1, and VDAC3 might serve as potential biomarkers for diagnosis and/or therapeutic targets for precise treatment of SC in the future.

Identification of differentially expressed genes in the endothelial precursor cells of patients with type 2 diabetes mellitus by bioinformatics analysis

Type 2 diabetes mellitus (DM) is a metabolic disease with worldwide prevalence that is associated with a decrease in the number and function of endothelial progenitor cells (EPCs). The aim of the present study was to explore the potential hub genes of EPCs in patients with type 2 DM. Differentially expressed genes (DEGs) were screened from a public microarray dataset (accession no. GSE43950). Pathway and functional enrichment analyses were performed using the Database for Annotation, Visualization and Integrated Discovery. The protein-protein interaction (PPI) network was visualized. The most significantly clustered modules and hub genes were identified using Cytoscape. Furthermore, hub genes were validated by quantitative PCR analysis of EPCs isolated from diabetic and normal subjects. Subsequently, weighted gene co-expression network analysis (WGCNA) was performed to identify the modules incorporating the genes exhibiting the most significant variance. A total of 970 DEGs were obtained and they were mainly accumulated in inflammation-associated pathways. A total of 9 hub genes were extracted from the PPI network and the highest differential expression was determined for the interleukin 8 (IL8) and CXC chemokine ligand 1 (CXCL1) genes. In the WGCNA performed to determine the modules associated with type 2 DM, one module incorporated IL8 and CXCL1. Finally, pathway enrichment of 10% genes in the pink module ordered by intramodular connectivity (IC) was associated with the IL17 and the chemokine signaling pathways. The present results revealed that the expression of IL8 and CXCL1 may serve important roles in the pathophysiology of EPCs during type 2 DM and inflammatory response may be critical for the reduced number and hypofunction of EPCs isolated from patients with diabetes.

Investigation of the underlying hub genes and mechanisms of reperfusion injury in patients undergoing coronary artery bypass graft surgery by integrated bioinformatic analyses

Background

Although coronary artery bypass graft (CABG) surgery is the main method to revascularize the occluded coronary vessels in coronary artery diseases, the full benefits of the operation are mitigated by ischemia-reperfusion (IR) injury. Although many studies have been devoted to reducing IR injury in animal models, the translation of this research into the clinical field has been disappointing. Our study aimed to explore the underlying hub genes and mechanisms of IR injury.

Methods

A weighted gene co-expression network analysis (WGCNA) was executed based on the expression profiles in patients undergoing CABG surgery (GSE29396). Functional annotation and protein-protein interaction (PPI) network construction were executed within the modules of interest. Potential hub genes were predicted, combining both intramodular connectivity (IC) and degrees. Meanwhile, potential transcription factors (TFs) and microRNAs (miRNAs) were predicted by corresponding bioinformatics tools.

Results

A total of 336 differentially expressed genes (DEGs) were identified. DEGs were mainly enriched in neutrophil activity and immune response. Within the modules of interest, 5 upregulated hub genes (IL-6, CXCL8, IL-1β, MYC, PTGS-2) and 6 downregulated hub genes (C3, TIMP1, VSIG4, SERPING1, CD163, and HP) were predicted. Predicted miRNAs (hsa-miR-333-5p, hsa-miR-26b-5p, hsa-miR-124-3p, hsa-miR-16-5p, hsa-miR-98-5p, hsa-miR-17-5p, hsa-miR-93-5p) and TF (STAT1) might have regulated gene expression in the most positively related module, while hsa-miR-333-5p and HSF-1 were predicted to regulate the genes within the most negatively related module.

Conclusions

Our study illustrates an overview of gene expression changes in human atrial samples from patients undergoing CABG surgery and might help translate future research into clinical work.

Network-based analysis reveals novel gene signatures in the peripheral blood of patients with sporadic nonsyndromic thoracic aortic aneurysm

Thoracic aortic aneurysm (TAA), a serious cardiovascular disease that causes morbidity and mortality worldwide. At present, few biomarkers can accurately diagnose the appearance of TAA before dissection or rupture. Our research has the intention to investigate the developing applicable biomarkers for TAA promising clinically diagnostic biomarkers or probable regulatory targets for TAA. In our research, we built correlation networks utilizing the expression profile of peripheral blood mononuclear cell obtained from a public microarray data set (GSE9106). Furthermore, we chose the turquoise module, which has the strongest significance with TAA and was further analyzed. Fourteen genes that overlapped with differentially expressed proteins in the medial aortic layer were obtained. Subsequently, we verified the results applying quantitative polymerase chain reaction (Q-PCR) to our clinical specimen. In general, the Q-PCR results coincide with the majority of the expression profile. Fascinatingly, a notable change occurred in CLU, DES, MYH10, and FBLN5. In summary, using weighted gene coexpression analysis, our study indicates that CLU, DES, MYH10, and FBLN5 were identified and validated to be related to TAA and might be candidate biomarkers or therapeutic targets for TAA.