Identification of cell-to-cell interactions by ligand-receptor pairs in human fetal heart

Super-enhancer-driven LIF promotes the mesenchymal transition in glioblastoma by activating ITGB2 signaling feedback in microglia

BACKGROUND

The mesenchymal (MES) subtype of glioblastoma (GBM) is believed to be influenced by both cancer cell-intrinsic alterations and extrinsic cellular interactions, yet the underlying mechanisms remain unexplored.

METHODS

Identification of microglial heterogeneity by bioinformatics analysis. Transwell migration, invasion assays, and tumor models were used to determine gene function and the role of small molecule inhibitors. RNA sequencing, chromatin immunoprecipitation, and dual-luciferase reporter assays were performed to explore the underlying regulatory mechanisms.

RESULTS

We identified the inflammatory microglial subtype of tumor-associated microglia (TAM) and found that its specific gene integrin beta 2 (ITGB2) was highly expressed in TAM of MES GBM tissues. Mechanistically, the activation of ITGB2 in microglia promoted the interaction between the SH2 domain of STAT3 and the cytoplasmic domain of ITGB2, thereby stimulating the JAK1/STAT3/IL-6 signaling feedback to promote the MES transition of GBM cells. Additionally, microglia communicated with GBM cells through the interaction between the receptor ITGB2 on microglia and the ligand ICAM-1 on GBM cells, while an increased secretion of ICAM-1 was induced by the proinflammatory cytokine leukemia inhibitory factor (LIF). Further studies demonstrated that inhibition of cyclin-dependent kinase 7 substantially reduced the recruitment of SNW1 to the super-enhancer of LIF, resulting in transcriptional inhibition of LIF. We identified notoginsenoside R1 as a novel LIF inhibitor that exhibited synergistic effects in combination with temozolomide.

CONCLUSIONS

Our research reveals that the epigenetic-mediated interaction of GBM cells with TAM drives the MES transition of GBM and provides a novel therapeutic avenue for patients with MES GBM.

Examining the Development of Chronic Thromboembolic Pulmonary Hypertension at the Single-Cell Level

BACKGROUND

The mechanism of chronic thromboembolic pulmonary hypertension (CTEPH) is known to be multifactorial but remains incompletely understood.

METHODS

In this study, single-cell RNA sequencing, which facilitates the identification of molecular profiles of samples on an individual cell level, was applied to investigate individual cell types in pulmonary endarterectomized tissues from 5 patients with CTEPH. The order of single-cell types was then traced along the developmental trajectory of CTEPH by trajectory inference analysis, and intercellular communication was characterized by analysis of ligand-receptor pairs between cell types. Finally, comprehensive bioinformatics tools were used to analyze possible functions of branch-specific cell types and the underlying mechanisms.

RESULTS

Eleven cell types were identified, with immune-related cell types (T cells, natural killer cells, macrophages, and mast cells) distributed in the left (early) branch of the pseudotime tree, cancer stem cells, and CRISPLD2+ cells as intermediate cell types, and classic disease-related cell types (fibroblasts, smooth muscle cells, myofibroblasts, and endothelial cells) in the right (later) branch. Ligand-receptor interactions revealed close communication between macrophages and disease-related cell types as well as between smooth muscle cells and fibroblasts or endothelial cells. Moreover, the ligands and receptors were significantly enriched in key pathways such as the PI3K/Akt signaling pathway. Furthermore, highly expressed genes specific to the undefined cell type were significantly enriched in important functions associated with regulation of endoplasmic reticulum stress.

CONCLUSIONS

This single-cell RNA sequencing analysis revealed the order of single cells along a developmental trajectory in CTEPH as well as close communication between different cell types in CTEPH pathogenesis.

Identification of Hub mRNAs and lncRNAs in Atrial Fibrillation Using Weighted Co-expression Network Analysis With RNA-Seq Data

Atrial fibrillation (AF)/paroxysmal AF (PAF) is the main cause of cardiogenic embolism. In recent years, the progression from paroxysmal AF to persistent AF has attracted more and more attention. However, the molecular mechanism of the progression of AF is unclear. In this study, we performed RNA sequencing for normal samples, paroxysmal AF and persistent AF samples to identify differentially expressed gene (DEG) and explore the roles of these DEGs in AF. Totally, 272 differently expressed mRNAs (DEmRNAs) and 286 differentially expressed lncRNAs (DElncRNAs) were identified in paroxysmal AF compared to normal samples; 324 DEmRNAs and 258 DElncRNAs were found in persistent atrial fibrillation compared with normal samples; and 520 DEmRNAs and 414 DElncRNAs were identified in persistent AF compared to paroxysmal AF samples. Interestingly, among the DEGs, approximately 50% were coding genes and around 50% were non-coding RNAs, suggesting that lncRNAs may also have a crucial role in the progression of AF. Bioinformatics analysis demonstrated that these DEGs were significantly related to regulating multiple AF associated pathways, such as the regulation of vascular endothelial growth factor production and binding to the CXCR chemokine receptor. Furthermore, weighted gene co-expression network analysis (WGCNA) was conducted to identify key modules and hub RNAs and lncRNAs to determine their potential associations with AF. Five hub modules were identified in the progression of AF, including blue, brown, gray, turquoise and yellow modules. Interestingly, blue module and turquoise module were significantly negatively and positively correlated to the progression of AF respectively, indicating that they may have a more important role in the AF. Moreover, the hub protein-protein interaction (PPI) networks and lncRNA-mRNA regulatory network were constructed. Bioinformatics analysis on the hub PPI network in turquoise was involved in regulating immune response related signaling, such as leukocyte chemotaxis, macrophage activation, and positive regulation of α-β T cell activation. Our findings could clarify the underlying molecular changes associated fibrillation, and provide a useful resource for identifying AF marker.

Upregulation of miR-29c-3p Hinders Melanoma Progression by Inhibiting CDCA4 Expression

OBJECTIVE

To investigate the expression and regulation mechanism of miR-29c-3p and cell division cycle associated 4 (CDCA4) in melanoma (MM). Data and Methods. Fifty-nine patients with MM admitted to our hospital were enrolled as the MM group. They were followed up for 3 years to analyze the prognostic factors; meanwhile, 51 healthy subjects were allocated into a normal group. MM cell lines (M21 and C8161) were transfected with miR-29c-3p-mimics, miR-29c-3p-inhibitor, miR-NC, si-CDCA4, and sh-CDCA4. The expression of miR-29c-3p, CDCA4, Bax, Caspase3, Bcl-2, N-cadherin, vimentin, and E-cadherin was quantified, and cell proliferation, migration, invasion, and apoptosis, as well as epithelial-mesenchymal transition (EMT), were determined.

RESULTS

Serum miR-29c-3p was lowly expressed and CDCA4 was highly expressed in the MM group. The area under the curve (AUC) of both for diagnosing MM was greater than 0.9. miR-29c-3p and CDCA4 were related to regional lymph node staging (N staging), distant metastasis (M staging), tumor diameter, and pathological differentiation. Low miR-29c-3p and high CDCA4 were associated with poor prognosis of MM. Overexpression of miR-29c-3p and suppression of CDCA4 hindered cell proliferation, migration, invasion, and expression of Bax, Caspase3, N-cadherin, and vimentin, but cell apoptosis and expression of Bcl-2 and E-cadherin were enhanced. Dual-luciferase reporter (DLR) assay confirmed the targeted relationship between miR-29c-3p and CDCA4. After miR-29c-3p-mimics+sh-CDCA4 was transfected into M21 and C8161 cells, the proliferation, invasion, and apoptosis were not different from those in the miR-NC group transfected with unrelated sequences.

CONCLUSION

Overexpression of miR-29c-3p suppresses CDCA4 expression and decreases proliferation, migration, invasion, apoptosis, and EMT of MM cells, thus hindering MM progression.

CircITGA7 Suppresses Gastric Cancer Progression Through miR-1471/MTDH Axis

In recent years, there have been reports about the involvement of circular RNAs (circRNAs) in the pathogenesis of gastric cancer (GC), but the molecular mechanism in cell proliferation, invasion, and migration is still unclear. Based on The Cancer Genome Atlas (TCGA) database, we analyzed differentially expressed circRNAs between GC and non-tumor tissues. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis were used to clarify the functional role in GC. Here, we showed that circITGA7 was lowly expressed in GC tissues based on the TCGA database. In vitro, silencing the expression of circITGA7 increased cell proliferation and metastasis, whereas overexpression did the opposite. Mechanistically, miR-1471 has circITGA7 as a sponge, and miR-1471 has metadherin (MTDH) as a target gene. Consequently, functional analysis showed that the tumor suppressor effect of circITGA7 was the result of regulating the miR-1471/MTDH axis. Overall, the circITGA7/miR-1471/MTDH signaling pathway may play a crucial role in GC, providing a new potential mechanism involved in GC progression.

Circular RNA circCCDC66 promotes glioma proliferation by acting as a ceRNA for miR-320a to regulate FOXM1 expression

BACKGROUND

In this study, we determine the potential roles and uncover the regulatory mechanisms of circCCDC66 in regulating cell growth and cell metastasis of glioma.

METHODS

qRT-PCR was used to detect the expressions of circCCDC66 in gliomas and tissues. The biological function of circCCDC66 in glioma cell lines was elucidated by functional experiments. Cell counting kit-8 and transwell were used to detect the effect of circCCDC66 on the proliferation, migration and invasion of glioma cells. Bioinformatics analysis was applied to reveal the targets of circCCDC66.

RESULTS

The results showed circCCDC66 was overexpressed in glioma and acted as an oncogene. CircCCDC66 knockdown suppressed the proliferation, migration, and invasion of glioma cells. We constructed a circCCDC66 regulating miRNA network and revealed miR-320a was a potential target of circCCDC66, which was down-regulated in high-grade gliomas compared to low-grade gliomas. Bioinformatics analysis showed circCCDC66-miR-320a/b axis was involved in regulating multiple cancer-related pathways. Furthermore, we identified FOXM1 as a key target of circCCDC66, which was involved in regulating DNA damage response pathways. In mechanism study, circCCDC66 could sponge miR-320a, thereby increasing the expression of FOXM1.

CONCLUSIONS

CircCCDC66 could facilitate glioma cells proliferation, invasion and migration by down-regulating miR-320a and up-regulating FOXM1.

Increased Expression of PDK4 Was Displayed in Gastric Cancer and Exhibited an Association With Glucose Metabolism

Previous studies reported that pyruvate dehydrogenase kinase 4 (PDK4) is closely related to diabetes, heart disease, and carcinomas. Nevertheless, the role of PDK4 in gastric cancer (GC) occurrence and development is yet poorly understood. Our experiments were taken to evaluate PDK4's function in GC. The Cancer Genome Atlas tumor genome map database was employed to validate the levels of PDK family in different grades and stages of GC. The survival ratio of PDK families in GC was detected by the Kaplan-Meier plotter database. The links existing in the expression of PDK family and the level of tumor-infiltrating immune cells were investigated by tumor immunity assessment resource (TIMER). PDK4-associated signal pathways in GC were analyzed by the Kyoto Encyclopedia of Genes and Genomes pathway analysis. PDK4 mRNA level in the GC cells was measured by qRT-PCR. Cell counting kit-8 and Transwell assays were separately carried out to evaluate PDK4-induced influence on GC cell proliferation, migration, and invasion. Our data suggested that GC cells highly expressed PDK4, and PDK4 expression presented a significant relation with the staging, grade, and survival rate of GC. PDK4 expression presented a positive correlation with the types of different infiltrating immune cells, comprising B cells, CD4+ T cells, and dendritic cells. Meanwhile, PDK4 expression exhibited a strong association with macrophages. Survival analysis revealed that the expression of PDK4 displayed a relationship with the prognosis of patients. Therefore, PDK4 was liable to be a biomarker for prognosis. Our results further displayed that PDK4 might modulate the glycolysis level in GC cells, and its expression was associated with GC cell proliferation, migration, and invasion. These data may provide insights into designing a new treatment strategy for GC.

Altered Gene Expression in the Testis of Infertile Patients with Nonobstructive Azoospermia

Background

The molecular mechanism of nonobstructive azoospermia (NOA) remains unclear. The aim of this study was to identify gene expression changes in NOA patients and to explore potential biomarkers and therapeutic targets.

Methods

The gene expression profiles of GSE45885 and GSE145467 were collected from the Gene Expression Omnibus (GEO) database, and the differences between NOA and normal spermatogenesis were analyzed. Enrichment analysis was performed to explore biological functions for common differentially expressed genes (DEGs) in GSE45885 and GSE145467. Coexpression analysis of DEGs in GSE45885 was performed, and two modules with the highest correlation with NOA were screened. Key genes were then screened from the intersection genes of the two modules and common DEGs and PPI network. The expression of key genes was validated by quantitative real-time polymerase chain reaction (qRT-PCR) experiments. Finally, through miRTarBase, miRDB, and RAID, the miRNAs were predicted to regulate key genes, respectively.

Results

A total of 345 common DEGs were identified and they were mainly related to spermatogenesis, insulin signaling pathway. Coexpression analysis of DEGs in GSE45885 yielded eight modules; MEblack and MEturquoise had the highest correlation with NOA. Six genes in MEturquoise and RNF141 in MEblack were identified as key genes. qRT-PCR experiments validated the differential expression of key genes between NOA and control. Furthermore, RNF141 was regulated by the largest number of miRNAs.

Conclusion

Our findings suggest that the significant change expression of key genes may be potential markers and therapeutic targets of NOA and may have some impact on the development of NOA.

Upregulation Mitochondrial Carrier 1 (MTCH1) Is Associated with Cell Proliferation, Invasion, and Migration of Liver Hepatocellular Carcinoma

Among the primary causes of cancer-associated death in the world, liver hepatocellular carcinoma (LIHC) ranks the third. LIHC is defined as the sixth most frequently diagnosed carcinoma. The gene mitochondrial carrier 1 (MTCH1) is a protein-coding gene. Recent research suggests that MTCH1 may be associated with some diseases. Here, our study attempts to explore the role and implication of MTCH1 in LIHC. Kaplan Meier Plotter and GEPIA (Gene Expression Profiling Interactive Analysis) databases were employed to determine the expression of MTCH1 and its correlation with prognostic status in LIHC patients. For the first time, our results suggested that MTCH1 was aberrantly expressed in human pan-cancer and highly expressed in LIHC. Its high expression was closely associated with metastasis of tumor, stage of cancer, and poor survival of patients. Then, through enrichment analysis, MTCH1 was found to be closely related to RNA splicing in LIHC. Subsequently, we conducted a series of functional experiments. PCR data showed that LIHC cell lines and samples are highly expressed MTCH1. CCK-8 (Cell Counting Kit-8) assays and Transwell assays indicated that silencing MTCH1 certainly suppressed cell proliferation, migration, and invasion. These findings shed the clue that MTCH1 could be regarded as the potential prognosis biomarker of LIHC and a promising therapeutic target for LIHC.

Marker Genes Change of Synovial Fibroblasts in Rheumatoid Arthritis Patients

Background

Rheumatoid arthritis (RA) is a chronic condition that manifests as inflammation of synovial joints, leading to joint destruction and deformity.

Methods

We identified single-cell RNA-seq data of synovial fibroblasts from RA and osteoarthritis (OA) patients in GSE109449 dataset. RA- and OA-specific cellular subpopulations were identified, and enrichment analysis was performed. Further, key genes for RA and OA were obtained by combined analysis with differentially expressed genes (DEGs) between RA and OA in GSE56409 dataset. The diagnostic role of key genes for RA was predicted using receiver operating characteristic (ROC) curve. Finally, we identified differences in immune cell infiltration between RA and OA patients, and utilized flow cytometry, qRT-PCR, and Western blot were used to examine the immune cell and key genes in RA patients.

Results

The cluster 0 matched OA and cluster 3 matched RA and significantly enriched for neutrophil-mediated immunity and ECM receptor interaction, respectively. We identified 478 DEGs. In the top 20 degrees of connection in the PPI network, the key genes for RA were obtained by comparing with the gene markers of cluster 0 and cluster 3, respectively. ROC curve showed that CCL2 and MMP13 might be diagnostic markers for RA. We found aberrant levels of CD8+T, neutrophil, and B cells in RA fibroblasts, which were validated in clinical samples. Importantly, we also validated the differential expression of key genes between RA and OA.

Conclusion

High expression of CCL2 and MMP13 in RA may be a diagnostic and therapeutic target.

PD-L1 and Immune Infiltration of m6A RNA Methylation Regulators and Its miRNA Regulators in Hepatocellular Carcinoma

BACKGROUND

The aim of this study was to systematically evaluate the relationship between the expression of m6A RNA methylation regulators and prognosis in HCC.

METHODS

We compared the expression of m6A methylation modulators and PD-L1 between HCC and normal in TCGA database. HCC samples were divided into two subtypes by consensus clustering of data from m6A RNA methylation regulators. The differences in PD-L1, immune infiltration, and prognosis between the two subtypes were further compared. The LASSO regression was used to build a risk score for m6A modulators. In addition, we identified miRNAs that regulate m6A regulators.

RESULTS

We found that fourteen m6A regulatory genes were significantly differentially expressed between HCC and normal. HCC samples were divided into two clusters. Of these, there are higher PD-L1 expression and poorer overall survival (OS) in cluster 1. There was a significant difference in immune cell infiltration between cluster 1 and cluster 2. Through the LASSO model, we obtained 12 m6A methylation regulators to construct a prognostic risk score. Compared with patients with a high-risk score, patients with a low-risk score had upregulated PD-L1 expression and worse prognosis. There was a significant correlation between risk score and tumor-infiltrating immune cells. Finally, we found that miR-142 may be the important regulator for m6A RNA methylation in HCC.

CONCLUSION

Our results suggest that m6A RNA methylation modulators may affect the prognosis through PD-L1 and immune cell infiltration in HCC patients. In addition, the two clusters may be beneficial for prognostic stratification and improving immunotherapeutic efficacy.

Identification of Molecular Regulatory Features and Markers for Acute Type A Aortic Dissection

BACKGROUND

Acute type A aortic dissection (ATAAD) is one of the most lethal cardiovascular diseases, and its molecular mechanism remains unclear.

METHODS

Differentially expressed genes (DEGs) between ATAAD and control were detected by limma R package in GSE52093, GSE153434, GSE98770, and GSE84827, respectively. The coexpression network of DEGs was identified by the WGCNA package. Enrichment analysis was performed for module genes that were positively correlated with ATAAD using clusterProfiler R package. In addition, differentially methylated markers between aortic dissection and control were identified by ChAMP package. After comparing with ATAAD-related genes, a protein-protein interaction (PPI) network was established based on the STRING database. The genes with the highest connectivity were identified as hub genes. Finally, differential immune cell infiltration between ATAAD and control was identified by ssGSEA.

RESULTS

From GSE52093 and GSE153434, 268 module genes were obtained with consistent direction of differential expression and high correlation with ATAAD. They were significantly enriched in T cell activation, HIF-1 signaling pathway, and cell cycle. In addition, 2060 differentially methylated markers were obtained from GSE84827. Among them, 77 methylation markers were ATAAD-related DEGs. Using the PPI network, we identified MYC, ITGA2, RND3, BCL2, and PHLPP2 as hub genes. Finally, we identified significantly differentially infiltrated immune cells in ATAAD.

CONCLUSION

The hub genes we identified may be regulated by methylation and participate in the development of ATAAD through immune inflammation and oxidative stress response. The findings may provide new insights into the molecular mechanisms and therapeutic targets for ATAAD.

Comprehensive Analysis of the Prognostic Role and Mutational Characteristics of m6A-Related Genes in Lung Squamous Cell Carcinoma

BACKGROUND

There have been limited treatment therapies for lung squamous cell carcinoma (LUSC). M6A-related genes may be the next therapeutic targets for LUSC. In this study, we explored the prognostic role and mutational characteristics of m6A-related genes in LUSC.

METHODS

LUSC gene expression data, mutational data, and corresponding clinical information were extracted from The Cancer Genome Atlas database. Differentially expressed genes (DEGs) were identified, and the mutation characteristics of LUSC patients were explored. Then, m6A-related genes were extracted and the correlations among the genes were detected. Finally, the prognostic roles of the genes were investigated and the nomogram model was developed. Besides, the protein-protein interaction (PPI) network was used to explore the potential interactions among the genes.

RESULTS

In total, there are 551 LUSC samples enrolled in our study, containing 502 LUSC tumor samples and 49 adjacent normal LUSC samples, respectively. There were 2970 upregulated DEGs and 1806 downregulated DEGs were further explored. IGF2BP1 and RBM15 had significant co-occurrence frequency (p < 0.05). Besides, METTL14 and ZC3H13 or YTHDF3 also had significant co-occurrence frequency (p < 0.05). All the m6A-related genes represent the positive correlation. WTAP was identified as a prognostic gene in the TCGA database while YTHDC1 and YTHDF1 were identified as prognostic genes. In multivariate Cox analysis, YTHDF1, age, pN stage, pTNM stage, and smoking were all identified as significant prognostic factors for OS.

CONCLUSION

We investigated the expression patterns and mutational characteristics of LUSC patients and identified three potential independent prognostic m6A-related genes (WTAP, YTHDC1, and YTHDF1) for OS in LUSC patients.

Integrated WGCNA and PPI Network to Screen Hub Genes Signatures for Infantile Hemangioma

BACKGROUND

Infantile hemangioma (IH) is characterized by proliferation and regression.

METHODS

Based on the GSE127487 dataset, the differentially expressed genes (DEGs) between 6, 12, or 24 months and normal samples were screened, respectively. STEM software was used to screen the continued up-regulated or down-regulated in common genes. The modules were assessed by weighted gene co-expression network analysis (WGCNA). The enrichment analysis was performed to identified the biological function of important module genes. The area under curve (AUC) value and protein-protein interaction (PPI) network were used to identify hub genes. The differential expression of hub genes in IH and normal tissues was detected by qPCR.

RESULTS

There were 5,785, 4,712, and 2,149 DEGs between 6, 12, and 24 months and normal tissues. We found 1,218 DEGs were up-regulated or down-regulated expression simultaneously in common genes. They were identified as 10 co-expression modules. Module 3 and module 4 were positively or negatively correlated with the development of IH, respectively. These two module genes were significantly involved in immunity, cell cycle arrest and mTOR signaling pathway. The two module genes with AUC greater than 0.8 at different stages of IH were put into PPI network, and five genes with the highest degree were identified as hub genes. The differential expression of these genes was also verified by qRTPCR.

CONCLUSION

Five hub genes may distinguish for proliferative and regressive IH lesions. The WGCNA and PPI network analyses may help to clarify the molecular mechanism of IH at different stages.