Single-Cell Transcriptomics-Based Study of Transcriptional Regulatory Features in the Mouse Brain Vasculature

Identifying PTAFR as a hub gene in atherosclerosis: implications for NETosis and disease progression

BACKGROUND

Atherosclerosis (AS) is a major cause of cardiovascular diseases and neutrophil extracellular traps (NETs) may be actively involved in the development of atherosclerosis. Identifying key biomarkers in this process is essential for developing targeted treatments for AS.

METHODS

We performed bioinformatics analysis using a NETosis-related gene (NRGs) set and three AS datasets (GSE100927, GSE21545, and GSE159677). Differential expression analysis and machine learning techniques (random forest and SVM-RFE) were used to screen for key NRGs. Functional enrichment analysis was conducted using GO and KEGG pathways. The expression and role of PTAFR and NETs in the mouse AS model were validated through histology, immunofluorescence, flow cytometry, and Western blot analysis. The regulatory relationship between PTAFR and NETs was confirmed by siRNA and antagonist intervention targeting PTAFR.

RESULTS

We identified 24 differentially expressed NRGs in AS. Random Forest and SVM-RFE analyses highlighted PTAFR as a key gene. Prognostic analysis revealed PTAFR significantly impacts ischemic events in AS patients. WB and immunofluorescence confirmed increased levels of NETs and PTAFR in the mouse AS model. Single-cell analysis, flow cytometry, and immunofluorescence revealed that PTAFR is primarily distributed in macrophages and neutrophils. Cellular experiments further confirmed that PTAFR regulates NETs formation.

CONCLUSION

PTAFR is an important regulatory factor for NET formation in AS, influencing the progression and prognosis of atherosclerosis. Targeting PTAFR may provide new therapeutic strategies for AS.

Cross-disease transcriptomic analysis reveals DOK3 and PAPOLA as therapeutic targets for neuroinflammatory and tumorigenic processes

Objective

Subarachnoid hemorrhage (SAH) and tumorigenesis share numerous biological complexities; nevertheless, the specific gene expression profiles and underlying mechanisms remain poorly understood. This study aims to identify differentially expressed genes (DEGs) that could serve as biomarkers for diagnosis and prognosis.

Methods

Gene expression datasets (GSE122063, GSE13353, GSE161870) were analyzed using machine learning algorithms and logistic regression to identify DEGs associated with both SAH and tumorigenesis. Lasso regression and receiver operating characteristic (ROC) curve analysis were employed to evaluate the classification accuracy of these genes. Validation of critical DEGs was performed through pan-cancer analysis and experimental studies, focusing on the role of DOK3 in modulating inflammation and oxidative stress in U251MG glioblastoma and BV2 microglia cells.

Results

Fifteen common DEGs were identified, with DOK3 and PAPOLA highlighted as crucial genes implicated in SAH and neurodegenerative processes. Experimental validation demonstrated that DOK3 overexpression significantly reduced pro-inflammatory cytokine levels and oxidative stress markers while enhancing antioxidant enzyme activity. Additionally, DOK3 influenced tumorigenic processes such as apoptosis, cell cycle regulation, and proliferation, effectively mitigating LPS-induced cytotoxicity and inflammation in BV2 microglial cells.

Conclusions

DOK3 and PAPOLA play critical roles in both SAH and related neurodegeneration, presenting themselves as potential prognostic biomarkers and therapeutic targets. Notably, DOK3 exhibits potential as an antitumor agent with anti-inflammatory and antioxidative properties, offering therapeutic benefits for both cancer and neuroinflammatory conditions.

m6A methylation profiling as a prognostic marker in nasopharyngeal carcinoma: insights from MeRIP-Seq and RNA-Seq

Background

Nasopharyngeal carcinoma (NPC) is a type of malignant tumors commonly found in Southeast Asia and China, with insidious onset and clinical symptoms. N6-methyladenosine (m6A) modification significantly contributes to tumorigenesis and progression by altering RNA secondary structure and influencing RNA-protein binding at the transcriptome level. However, the mechanism and role of abnormal m6A modification in nasopharyngeal carcinoma remain unclear.

Methods

Nasopharyngeal Carcinoma tissues from 3 patients and non-cancerous nasopharyngeal tissues from 3 individuals, all from Fujian Cancer Hospital, were sequenced for m6A methylation. These were combined with transcriptome sequencing data from 192 nasopharyngeal cancer tissues. Genes linked to prognosis were discovered using differential analysis and univariate Cox regression. Subsequently, a prognostic model associated with m6A was developed through the application of LASSO regression analysis. The model's accuracy was verified using both internal transcriptome databases and external databases. An extensive evaluation of the tumor's immune microenvironment and signaling pathways was performed, analyzing both transcriptomic and single-cell data.

Results

The m6A methylation sequencing analysis revealed 194 genes with varying expression levels, many of which are predominantly associated with immune pathways. By integrating transcriptome sequencing data, 19 m6A-modified genes were found to be upregulated in tumor tissues, leading to the development of a three-gene (EME1, WNT4, SHISA2) risk prognosis model. The group with lower risk exhibited notable enrichment in pathways related to immunity, displaying traits like enhanced survival rates, stronger immune profiles, and increased responsiveness to immunotherapy when compared to the higher-risk group. Single-cell analysis revealed that malignant cells exhibited the highest risk score levels compared to immune cells, with a high-risk score indicating worse biological behavior. The three hub genes demonstrated significant correlation with m6A modification regulators, and MeRIP-RT-PCR confirmed the occurrence of m6A methylation in these genes within nasopharyngeal carcinoma cells.

Conclusions

A prognostic model for nasopharyngeal carcinoma risk based on m6A modification genes was developed, and its prognostic value was confirmed through self-assessment data. The study highlighted the crucial impact of m6A modification on the immune landscape of nasopharyngeal cancer.

The causal impact of complement C3d receptor 2 on head and neck cancer microenvironment and its implications for immunotherapy response prediction

This research dives into the intricate immune landscape of head and neck cancer (HNC), with a keen focus on the roles of specific immune cell subpopulations and their linked genes. We used tumour RNA-seq (in-house cohort: n = 192, TCGA-HNSC: n = 546) and Mendelian randomization to pinpoint key SNPs in immune cells that have a causal connection to HNC. Our discoveries unveil a spectrum of tumour immune phenotypes that either offer protection against or increase the risk of HNC. We underscore the therapeutic promise of Complement C3d Receptor 2 (CR2), a gene closely tied to immune cells, with its increased expression in tumour tissues linked to a more favourable prognosis. This is correlated with heightened immune pathway activity, stronger resistance to radiochemotherapy, and improved immunotherapy responses. Our research emphasises the pivotal role of CR2 in immune regulation and the significance of immune cells in tumour progression, highlighting the potential of CR2-targeted therapeutic interventions.

Insulin-like growth factor binding protein 2: a core biomarker of left ventricular dysfunction in dilated cardiomyopathy

BACKGROUND

RNA modifications, especially N6-methyladenosine, N1-methyladenosine and 5-methylcytosine, play an important role in the progression of cardiovascular disease. However, its regulatory function in dilated cardiomyopathy (DCM) remains to be undefined.

METHODS

In the study, key RNA modification regulators (RMRs) were screened by three machine learning models. Subsequently, a risk prediction model for DCM was developed and validated based on these important genes, and the diagnostic efficiency of these genes was assessed. Meanwhile, the relevance of these genes to clinical traits was explored. In both animal models and human subjects, the gene with the strongest connection was confirmed. The expression patterns of important genes were investigated using single-cell analysis.

RESULTS

A total of 4 key RMRs were identified. The risk prediction models were constructed basing on these genes which showed a good accuracy and sensitivity in both the training and test set. Correlation analysis showed that insulin-like growth factor binding protein 2 (IGFBP2) had the highest correlation with left ventricular ejection fraction (LVEF) (R = -0.49, P = 0.00039). Further validation expression level of IGFBP2 indicated that this gene was significantly upregulated in DCM animal models and patients, and correlation analysis validation showed a significant negative correlation between IGFBP2 and LVEF (R = -0.87; P = 6*10-5). Single-cell analysis revealed that this gene was mainly expressed in endothelial cells.

CONCLUSION

In conclusion, IGFBP2 is an important biomarker of left ventricular dysfunction in DCM. Future clinical applications could possibly use it as a possible therapeutic target.

An immune-related prognostic model predicts neoplasm-immunity interactions for metastatic nasopharyngeal carcinoma

Background

The prognosis of nasopharyngeal carcinoma (NPC) has been recognized to improve immensely owing to radiotherapy combined with chemotherapy. However, patients with metastatic NPC have a poor prognosis. Immunotherapy has dramatically prolonged the survival of patients with NPC. Hence, further research on immune-related biomarkers is imperative to establish the prognosis of metastatic NPC.

Methods

10 NPC RNA expression profiles were generated from patients with or without distant metastasis after chemoradiotherapy from the Fujian Cancer Hospital. The differential immune-related genes were identified and validated by immunohistochemistry analysis. The method of least absolute shrinkage and selection operator (LASSO)was used to further establish the immune-related prognostic model in an external GEO database (GSE102349, n=88). The immune microenvironment and signal pathways were evaluated in multiple dimensions at the transcriptome and single-cell levels.

Results

1328 differential genes were identified, out of which 520 were upregulated and 808 were downregulated. Notably, most of the immune genes and pathways were down-regulated in the metastasis group. A prognostic immune model involving nine hub genes. Patients in low-risk group were characterized by survival advantage, hot immune phenotype and benefit from immunotherapy. Compared with immune cells, malignant cell exhibited the most active levels of risk score by ssGSEA. Accordingly, intercellular communications including LT, CD70, CD40 and SPP1, and the like, between high-risk and low-risk were explored by the R package “Cellchat”.

Conclusion

We have constructed a model based on immunity of metastatic NPC and determined its prognostic value. The model identified the level of immune cell infiltration, cell-cell communication, along with potential immunotherapy for metastatic NPC.

High-fiber-diet-related metabolites improve neurodegenerative symptoms in patients with obesity with diabetes mellitus by modulating the hippocampal-hypothalamic endocrine axis

Objective

Through transcriptomic and metabolomic analyses, this study examined the role of high-fiber diet in obesity complicated by diabetes and neurodegenerative symptoms.

Method

The expression matrix of high-fiber-diet-related metabolites, blood methylation profile associated with pre-symptomatic dementia in elderly patients with type 2 diabetes mellitus (T2DM), and high-throughput single-cell sequencing data of hippocampal samples from patients with Alzheimer's disease (AD) were retrieved from the Gene Expression Omnibus (GEO) database and through a literature search. Data were analyzed using principal component analysis (PCA) after quality control and data filtering to identify different cell clusters and candidate markers. A protein-protein interaction network was mapped using the STRING database. To further investigate the interaction among high-fiber-diet-related metabolites, methylation-related DEGs related to T2DM, and single-cell marker genes related to AD, AutoDock was used for semi-flexible molecular docking.

Result

Based on GEO database data and previous studies, 24 marker genes associated with high-fiber diet, T2DM, and AD were identified. Top 10 core genes include SYNE1, ANK2, SPEG, PDZD2, KALRN, PTPRM, PTPRK, BIN1, DOCK9, and NPNT, and their functions are primarily related to autophagy. According to molecular docking analysis, acetamidobenzoic acid, the most substantially altered metabolic marker associated with a high-fiber diet, had the strongest binding affinity for SPEG.

Conclusion

By targeting the SPEG protein in the hippocampus, acetamidobenzoic acid, a metabolite associated with high-fiber diet, may improve diabetic and neurodegenerative diseases in obese people.

Single-Cell Transcriptomics Unveils the Dedifferentiation Mechanism of Lung Adenocarcinoma Stem Cells

Lung adenocarcinoma (LUAD) is a major subtype of lung cancer, and its prognosis is still poor due to therapy resistance, metastasis, and recurrence. In recent years, increasing evidence has shown that the existence of lung cancer stem cells is responsible for the propagation, metastasis, therapy resistance, and recurrence of the tumor. During their transition to cancer stem cells, tumor cells need to inhibit cell differentiation and acquire invasive characteristics. However, our understanding of the property and role of such lung cancer stem cells is still limited. In this study, lung adenocarcinoma cancer stem cells (LCSCs) were enriched from the PC-9 cell line in a serum-free condition. PC-9 cells grew into spheres and showed higher survival rates when exposed to gefitinib: the drug used for the treatment of LUAD. Additionally, we found that the canonical stemness marker protein CD44 was significantly increased in the enriched LCSCs. Then, LCSCs were inoculated into the groin of nude mice for 1.5 months, and tumors were detected in the animals, indicating the strong stemness of the cells. After that, we performed single-cell RNA sequencing (scRNA-seq) on 7320 LCSCs and explored the changes in their transcriptomic signatures. We identified cell populations with a heterogeneous expression of cancer stem marker genes in LCSCs and subsets with different degrees of differentiation. Further analyses revealed that the activation of the FOXM1 (oncoprotein) transcription factor is a key factor in cell dedifferentiation, which enables tumor cells to acquire an epithelial-mesenchymal transition phenotype and increases the LCSC surface marker CD44. Moreover, we found that the combination of CD44, ABCG2, and ALCAM was a specific marker for LCSCs. In summary, this study identified the potential factors and molecular mechanisms underlying the stemness properties of LUAD cancer cells; it could also provide insight into developing novel and effective therapeutic approaches.

Analysis of co-expression gene network associated with intracranial aneurysm and type 2 diabetes mellitus

To screen for common target genes in intracranial aneurysms (IA) and type 2 diabetes mellitus (T2DM), construct a common transcriptional regulatory network to predict clusters of candidate genes involved in the pathogenesis of T2DM and IA, and identify the common neurovascular markers and pathways in T2DM causing IA. Microarray datasets (GSE55650, GSE25462, GSE26969, GSE75436, and GSE13353) from the GEO database were analyzed in this research. Screening of the IA and the T2DM datasets yielded a total of 126 DEGs, among which 78 were upregulated and 138 were downregulated. Functional enrichment analysis revealed that these DEGs were enriched for a total of 68 GO pathways, including extracellular matrix composition, coagulation regulation, hemostasis regulation, and collagen fiber composition pathways. We also constructed transcriptional regulatory networks, and identified key transcription factors involved in both the conditions. Univariate logistic regression analysis showed that ARNTL2 and STAT1 were significantly associated with the development of T2DM and IA, acting as the common neurovascular markers for both the diseases. In cellular experiments, hyperglycemic microenvironments exhibited upregulated STAT1 expression. STAT1 may be involved in the pathogenesis of IA in T2DM patients. Being the common neurovascular markers, STAT1 may acts as novel therapeutic targets for the treatment of IA and T2DM.

Delving into the Heterogeneity of Different Breast Cancer Subtypes and the Prognostic Models Utilizing scRNA-Seq and Bulk RNA-Seq

Background: Breast cancer (BC) is the most common malignancy in women with high heterogeneity. The heterogeneity of cancer cells from different BC subtypes has not been thoroughly characterized and there is still no valid biomarker for predicting the prognosis of BC patients in clinical practice. Methods: Cancer cells were identified by calculating single cell copy number variation using the inferCNV algorithm. SCENIC was utilized to infer gene regulatory networks. CellPhoneDB software was used to analyze the intercellular communications in different cell types. Survival analysis, univariate Cox, least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox analysis were used to construct subtype specific prognostic models. Results: Triple-negative breast cancer (TNBC) has a higher proportion of cancer cells than subtypes of HER2+ BC and luminal BC, and the specifically upregulated genes of the TNBC subtype are associated with antioxidant and chemical stress resistance. Key transcription factors (TFs) of tumor cells for three subtypes varied, and most of the TF-target genes are specifically upregulated in corresponding BC subtypes. The intercellular communications mediated by different receptor–ligand pairs lead to an inflammatory response with different degrees in the three BC subtypes. We establish a prognostic model containing 10 genes (risk genes: ATP6AP1, RNF139, BASP1, ESR1 and TSKU; protective genes: RPL31, PAK1, STARD10, TFPI2 and SIAH2) for luminal BC, seven genes (risk genes: ACTR6 and C2orf76; protective genes: DIO2, DCXR, NDUFA8, SULT1A2 and AQP3) for HER2+ BC, and seven genes (risk genes: HPGD, CDC42 and PGK1; protective genes: SMYD3, LMO4, FABP7 and PRKRA) for TNBC. Three prognostic models can distinguish high-risk patients from low-risk patients and accurately predict patient prognosis. Conclusions: Comparative analysis of the three BC subtypes based on cancer cell heterogeneity in this study will be of great clinical significance for the diagnosis, prognosis and targeted therapy for BC patients.

Exploring the Potential Mechanisms of Melilotus officinalis (L.) Pall. in Chronic Muscle Repair Patterns Using Single Cell Receptor-Ligand Marker Analysis and Molecular Dynamics Simulations

Information regarding the function of Melilotus officinalis (L.) Pall. in skeletal muscles is still unknown. In this study, we explored the possible regulatory targets of M. (L.) Pall. that affects the repair patterns in chronic muscle injury. We analyzed the potential target genes and chemical composition of M. (L.) Pall. and constructed a "drug-component-disease target genes" network analysis. Five active ingredients and 87 corresponding targets were obtained. Muscle-tendon junction (MTJ) cells were used to perform receptor-ligand marker analysis using the CellphoneDB algorithm. Targets of M. (L.) Pall. were screened further for the cellular ligand-receptor protein action on MTJs. Enrichment analysis suggests that those protein-associated ligand receptors may be associated with a range of intercellular signaling pathways. Molecular docking validation was then performed. Five proteins (CCL2, VEGFA, MMP2, MET, and EGFR) may be regulated by the active ingredient luteolin and scoparone. Finally, molecular dynamics simulations revealed that luteolin can stably target binding to MMP2. M. (L.) Pall. influences skeletal muscle repair patterns by affecting the fibroblast interactions in the muscle-tendon junctions through the active ingredients luteolin and scoparone.

Potential Mechanism Underlying Exercise Upregulated Circulating Blood Exosome miR-215-5p to Prevent Necroptosis of Neuronal Cells and a Model for Early Diagnosis of Alzheimer's Disease

Exercise is crucial for preventing Alzheimer's disease (AD), although the exact underlying mechanism remains unclear. The construction of an accurate AD risk prediction model is beneficial as it can provide a theoretical basis for preventive exercise prescription. In recent years, necroptosis has been confirmed as an important manifestation of AD, and exercise is known to inhibit necroptosis of neuronal cells. In this study, we extracted 67 necroptosis-related genes and 32 necroptosis-related lncRNAs and screened for key predictive AD risk genes through a random forest analysis. Based on the neural network Prediction model, we constructed a new logistic regression-based AD risk prediction model in order to provide a visual basis for the formulation of exercise prescription. The prediction model had an area under the curve (AUC) value of 0.979, indicative of strong predictive power and a robust clinical application prospect. In the exercise group, the expression of exosomal miR-215-5p was found to be upregulated; miR-215-5p could potentially inhibit the expressions of IDH1, BCL2L11, and SIRT1. The single-cell SCENIC assay was used to identify key transcriptional regulators in skeletal muscle. Among them, CEBPB and GATA6 were identified as putative transcriptional regulators of miR-215. After "skeletal muscle removal of load," the expressions of CEBPB and GATA6 increased substantially, which in turn led to the elevation of miR-215 expression, thereby suggesting a putative mechanism for negative feedback regulation of exosomal homeostasis.

Exercise Modifies the Transcriptional Regulatory Features of Monocytes in Alzheimer's Patients: A Multi-Omics Integration Analysis Based on Single Cell Technology

Monocytes have been reported to be important mediators of the protective effect of exercise against the development of Alzheimer's disease (AD). This study aims explored the mechanism by which monocytes achieve this. Using single cell transcriptome analysis, results showed that CD14 + and CD16 + monocytes interacted with other cells in the circulating blood. TNF, CCR1, APP, and AREG, the key ligand-receptor-related genes, were found to be differentially expressed between exercise-treated and AD patients. The SCENIC analysis was performed to identify individual clusters of the key transcription factors (TFs). Nine clusters (M1-M9) were obtained from the co-expression network. Among the identified TFs, MAFB, HES4, and FOSL1 were found to be differentially expressed in AD. Moreover, the M4 cluster to which MAFB, HES4, and FOSL1 belonged was defined as the signature cluster for AD phenotype. Differential analysis by bulkRNA-seq revealed that the expression of TNF, CCR1, and APP were all upregulated after exercise (p < 0.05). And ATF3, MAFB, HES4, and KLF4 that were identified in M4 clusters may be the TFs that regulate TNF, CCR1, and APP in exercise prescription. After that, APP, CCR1, TNF, ATF3, KLF4, HES4, and MAFB formed a regulatory network in the ERADMT gene set, and all of them were mechanistically linked. The ERADMT gene set has been found to be a potential risk marker for the development of AD and can be used as an indicator of compliance to exercise therapy in AD patients. Using single-cell integration analysis, a network of exercise-regulating TFs in monocytes was constructed for AD disease. The constructed network reveals the mechanism by which exercise regulated monocytes to confer therapeutic benefits against AD and its complications. However, this study, as a bioinformatic research, requires further experimental validation.

Long Non Coding RNA Based Regulation of Cerebrovascular Endothelium

The rapid and high throughput discovery of long non coding RNAs (lncRNAs) has far outstripped the functional annotation of these novel transcripts in their respective cellular contexts. The cells of the blood brain barrier (BBB), especially the cerebrovascular endothelial cells (CVECs), are strictly regulated to maintain a controlled state of homeostasis for undisrupted brain function. Several key pathways are understood in CVEC function that lead to the development and maintenance of their barrier properties, the dysregulation of which leads to BBB breakdown and neuronal injury. Endothelial lncRNAs have been discovered and functionally validated in the past decade, spanning a wide variety of regulatory mechanisms in health and disease. We summarize here the lncRNA-mediated regulation of established pathways that maintain or disrupt the barrier property of CVECs, including in conditions such as ischemic stroke and glioma. These lncRNAs namely regulate the tight junction assembly/disassembly, angiogenesis, autophagy, apoptosis, and so on. The identification of these lncRNAs suggests a less understood mechanistic layer, calling for further studies in appropriate models of the blood brain barrier to shed light on the lncRNA-mediated regulation of CVEC function. Finally, we gather various approaches for validating lncRNAs in BBB function in human organoids and animal models and discuss the therapeutic potential of CVEC lncRNAs along with the current limitations.

hsa-miR-518-5p/hsa-miR-3135b Regulates the REL/SOD2 Pathway in Ischemic Cerebral Infarction

Objectives

Ischemic cerebral infarction (ICI) is a fatal neurovascular disorder. A bioinformatics approach based on single-cell and bulk RNA-seq analyses was applied to investigate the pathways and genes involved in ICI and study the expression profile of these genes.

Methods

First, the aberrantly regulated “small-molecule ribonucleic acids” [microRNA (miRNAs)] and messenger RNAs (mRNAs) were analyzed using transcriptome data from the ischemic brain infarction dataset of the Gene Expression Omnibus (GEO) database. In mouse cerebrovascular monocytes, the single-cell regulatory network inference and clustering (SCENIC) workflow was used to identify key transcription factors (TFs). Then, the two miRNA-TF-mRNA interaction networks were constructed. Moreover, the molecular complex detection (MCODE) extracted the core sub-networks and identified the important TFs within these sub-networks. Finally, whole blood samples were collected for validation of the expression of critical molecules in ICI.

Results

We identified four cell types and 266 regulons in mouse cerebrovascular monocytes using SCENIC analysis. Moreover, 112 differently expressed miRNAs and 3,780 differentially expressed mRNAs were identified. We discovered potential biomarkers in ICI by building a miRNA-TF-mRNA interaction network. The hsa-miR-518-5p/hsa-miR-3135b/REL/SOD2 was found to play a potential role in ICI progression. The expression of REL and superoxide dismutase 2 (SOD2) was significantly elevated in the ICI group in the clinical cohort (P &lt; 0.05). Furthermore, a REL expression was elevated in endothelial cells and fibroblasts at the single-cell level, indicating that REL is a cell-specific regulon. Functional enrichment analyses revealed that REL is primarily engaged in neurotransmitter activity and oxidative phosphorylation.

Conclusions

Our research uncovered novel biomarkers for ICI of neurovascular disease. The hsa-miR-518-5p/hsa-miR-3135b may regulate the REL/SOD2 pathway in ICI progression.

Prognostic and Functional Analysis of NPY6R in Uveal Melanoma Using Bioinformatics

Neuropeptides can mediate tumor cell proliferation and differentiation through autocrine, paracrine, neurosecretory, and endocrine mechanisms. This study investigated the expression and prognostic significance of neuropeptide Y receptor Y6 (NPY6R) in uveal melanoma (UVM) and preliminarily investigated the biological function of NPY6R in UVM. NPY6R was poorly expressed in most tumors and was associated with better prognosis in UVM. Among the clinicopathological features of UVM, NPY6R expression was lower in male patients. The area under the curve (AUC) value of NPY6R for the diagnosis of UVM was 0.676 (95% CI: 0.556–0.795). A nomogram including four clinical predictors was constructed. NPY6R expression was significantly associated with features of the UVM immune microenvironment. ESTIMATE and CIBERSORT algorithms were used to calculate the fraction of immune cells and the percentage of infiltration in each patient, respectively. NPY6R expression-related gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set enrichment analyses were performed. GO and KEGG enrichment analyses revealed that NPY6R-related genes are mainly enriched in pathways and functions related to visual light perception. Gene set enrichment analysis suggested that NPY6R is associated with tumor progression in UVM. NPY6R is involved in the tumor progression of UVM and has a good predictive value as a prognostic marker of UVM.

The Neuronal Transcription Factor Creb3l1 Potential Upregulates Ntrk2 in the Hypertensive Microenvironment to Promote Vascular Smooth Muscle Cell-Neuron Interaction and Prevent Neurons from Ferroptosis: A Bioinformatic Research of scRNA-seq Data

Background

There is still a lack of knowledge regarding the association between hypertension and ferroptosis. A single-cell approach was used to study the changes in neuropeptide expression as they might contribute to the mechanisms leading to ferroptosis in a hypertensive microenvironment.

Methods

We analyzed 11798 cells from the SHR group and 12589 cells from the WKY group of mouse arterial cells. CellPhoneDB was used for cell communication analysis, and the SCENIC method was used to identify key transcription factors in neurons. The correlation between Ntrk2 and ferroptosis-related genes was further analyzed and validated via quantitative polymerase chain reaction.

Results

The arterial cells were clustered into six cell types. Ligand-receptor analysis suggested that Ngf, Ntf3, Cxcr4, and Ntrk2 were key neuropeptide-related genes involved in the communication between vascular smooth muscle cells and neural cells. In the hypertensive microenvironment, the neuronal transcription factor Creb3l1 appears to play a key role in the upregulation of Ntrk2 to promote the interaction between neurons and vascular smooth muscle cells. An association between Ntrk2 and the ferroptosis death inhibitor Gpx4 was suggested. RT-qPCR experiments confirmed that Ntrk2 downregulation in neural cells was followed by downregulated expression of Gpx4.

Conclusions

Creb3l1, a key transcription factor in vascular neurons, may upregulate Ntrk2 to promote vascular smooth muscle cell-neuron interaction and thereby potentially prevent ferroptosis in neurons.

Identification of Synovial Fibroblast-Associated Neuropeptide Genes and m6A Factors in Rheumatoid Arthritis Using Single-Cell Analysis and Machine Learning

Objectives

Synovial fibroblasts (SFs) play an important role in the development and progression of rheumatoid arthritis (RA). However, the pathogenic mechanism of SFs remains unclear. The objective of this study was to investigate how neuropeptides and N6-methyladenosine (m6A) played an important role in the underlying pathogenic processes of SFs that contribute to the development of RA.

Methods

Single-cell RNA sequencing data were examined using single-cell analysis and machine learning. SF subgroups were identified based on the clustering and annotation results of the single-cell analysis. Moreover, cell–cell communication was used to analyse neuropeptide-related receptor and ligand pairs on the surface of SF cell membranes. Machine learning was used to explore the m6A factors acting on these neuropeptide genes.

Results

NPR3, GHR, BDKRB2, and CALCRL, four neuropeptide genes, were shown to be differently expressed among SF subgroups. Further investigation of receptor–ligand interactions found that NPR3 (in conjunction with NPPC, OSTN, NPPB, and NPPA) and GHR (in conjunction with GH1 and GH2) may have a role in SF interactions. As predicted by machine learning, IGFBP2 and METTL3 were identified as key factors regulating m6A of NPR3 and GHR. The expression levels and enrichment pathways of METTL3 and IGFBP2 were different among SF subgroups.

Conclusions

Single-cell analysis and machine learning efficiently identified neuropeptide genes and m6A factors that perform important regulatory functions in RA. Our strategy may provide a basis for future studies to identify pathogenic cell subpopulations and molecular mechanisms in RA and other diseases.

A Hyperglycemic Microenvironment Inhibits Tendon-to-Bone Healing through the let-7b-5p/CFTR Pathway

Background

Tendon-to-bone healing is a difficult process in treatment of rotator cuff tear (RCT). In addition, diabetes is an important risk factor for poor tendon-to-bone healing. Therefore, we investigated the specific mechanisms through which diabetes affects tendon-to-bone healing by regulating the Cystic Fibrosis Transmembrane Conductance Regulator (CFTR).

Methods

Tendon-derived stem cells (TDSCs) were extracted from rats after which their proliferative capacities were evaluated by the MTT assay. The expression levels of CFTR and tendon-related markers were determined by qRT-PCR. Then, bioinformatics analyses and dual luciferase reporter gene assays were used to identify miRNAs with the ability to bind CFTR mRNA. Finally, CFTR was overexpressed in TDSCs to validate the specific mechanisms through which the high glucose microenvironment inhibits tendon-to-bone healing.

Results

The high glucose microenvironment downregulated mRNA expression levels of tendon-related markers and CFTR in TDSCs cultured with different glucose concentrations. Additionally, bioinformatics analyses revealed that let-7b-5p may be regulated by the high glucose microenvironment and can regulate CFTR levels. Moreover, a dual luciferase reporter gene assay was used to confirm that let-7b-5p targets and binds CFTR mRNA. Additional experiments also confirmed that overexpressed CFTR effectively reversed the negative effects of the hyperglycaemic microenvironment and upregulation of let-7b-5p on TDSC proliferation and differentiation. These findings imply that the hyperglycemic microenvironment inhibits CFTR transcription and, consequently, proliferation and differentiation of TDSCs in vitro by upregulating let-7b-5p.

Conclusions

A hyperglycemic microenvironment inhibits TDSC proliferation in vitro via the let-7b-5p/CFTR pathway, and this is a potential mechanism in diabetes-induced poor tendon-to-bone healing.

Transcription Factors Leading to High Expression of Neuropeptide L1CAM in Brain Metastases from Lung Adenocarcinoma and Clinical Prognostic Analysis

Background

There is a lack of understanding of the development of metastasis in lung adenocarcinoma (LUAD). This study is aimed at exploring the upstream regulatory transcription factors of L1 cell adhesion molecule (L1CAM) and to construct a prognostic model to predict the risk of brain metastasis in LUAD.

Methods

Differences in gene expression between LUAD and brain metastatic LUAD were analyzed using the Wilcoxon rank-sum test. The GRNdb (http://www.grndb.com) was used to reveal the upstream regulatory transcription factors of L1CAM in LUAD. Single-cell expression profile data (GSE131907) were obtained from the transcriptome data of 10 metastatic brain tissue samples. LUAD prognostic nomogram prediction models were constructed based on the identified significant transcription factors and L1CAM.

Results

Survival analysis suggested that high L1CAM expression was negatively significantly associated with overall survival, disease-specific survival, and prognosis in the progression-free interval (p < 0.05). The box plot indicates that high expression of L1CAM was associated with distant metastases in LUAD, while ROC curves suggested that high expression of L1CAM was associated with poor prognosis. FOSL2, HOXA9, IRF4, IKZF1, STAT1, FLI1, ETS1, E2F7, and ADARB1 are potential upstream transcriptional regulators of L1CAM. Single-cell data analysis revealed that the expression of L1CAM was found significantly and positively correlated with the expression of ETS1, FOSL2, and STAT1 in brain metastases. L1CAM, ETS1, FOSL2, and STAT1 were used to construct the LUAD prognostic nomogram prediction model, and the ROC curves suggest that the constructed nomogram possesses good predictive power.

Conclusion

By bioinformatics methods, ETS1, FOSL2, and STAT1 were identified as potential transcriptional regulators of L1CAM in this study. This will help to facilitate the early identification of patients at high risk of metastasis.

Role of Calcium Signaling Pathway-Related Gene Regulatory Networks in Ischemic Stroke Based on Multiple WGCNA and Single-Cell Analysis

Background

This study is aimed at investigating the changes in relevant pathways and the differential expression of related gene expression after ischemic stroke (IS) at the single-cell level using multiple weighted gene coexpression network analysis (WGCNA) and single-cell analysis.

Methods

The transcriptome expression datasets of IS samples and single-cell RNA sequencing (scRNA-seq) profiles of cerebrovascular tissues were obtained by searching the Gene Expression Omnibus (GEO) database. First, gene pathway scoring was calculated via gene set variation analysis (GSVA) and was imported into multiple WGCNA to acquire key pathways and pathway-related hub genes. Furthermore, SCENIC was used to identify transcription factors (TFs) regulating these core genes using scRNA-seq data. Finally, the pseudotemporal trajectory analysis was used to analyse the role of these TFs on various cell types under hypoxic and normoxic conditions.

Results

The scores of 186 KEGG pathways were obtained via GSVA using microarray expression profiles of 40 specimens. WGCNA of the KEGG pathways revealed the two following pathways: calcium signaling pathway and neuroactive ligand-receptor interaction pathways. Subsequently, WGCNA of the gene expression matrix of the samples revealed the calcium signaling pathway-related genes (AC079305.10, BCL10, BCL2A1, BRE-AS1, DYNLL2, EREG, and PTGS2) that were identified as core genes via correlation analysis. Furthermore, SCENIC and pseudotemporal analysis revealed JUN, IRF9, ETV5, and PPARA score gene-related TFs. Jun was found to be associated with hypoxia in endothelial cells, whereas Irf9 and Etv5 were identified as astrocyte-specific TFs associated with oxygen concentration in the mouse cerebral cortex.

Conclusions

Calcium signaling pathway-related genes (AC079305.10, BCL10, BCL2A1, BRE-AS1, DYNLL2, EREG, and PTGS2) and TFs (JUN, IRF9, ETV5, and PPARA) were identified to play a key role in IS. This study provides a new perspective and basis for investigating the pathogenesis of IS and developing new therapeutic approaches.

Identification of KLF6/PSGs and NPY-Related USF2/CEACAM Transcriptional Regulatory Networks via Spinal Cord Bulk and Single-Cell RNA-Seq Analysis

Background

To further understand the development of the spinal cord, an exploration of the patterns and transcriptional features of spinal cord development in newborn mice at the cellular transcriptome level was carried out.

Methods

The mouse single-cell sequencing (scRNA-seq) dataset was downloaded from the GSE108788 dataset. Single-cell RNA-Seq (scRNA-Seq) was conducted on cervical and lumbar spinal V2a interneurons from 2 P0 neonates. Single-cell analysis using the Seurat package was completed, and marker mRNAs were identified for each cluster. Then, pseudotemporal analysis was used to analyze the transcription changes of marker mRNAs in different clusters over time. Finally, the functions of these marker mRNAs were assessed by enrichment analysis and protein-protein interaction (PPI) networks. A transcriptional regulatory network was then constructed using the TRRUST dataset.

Results

A total of 949 cells were screened. Single-cell analysis was conducted based on marker mRNAs of each cluster, which revealed the heterogeneity of neonatal mouse spinal cord neuronal cells. Functional analysis of pseudotemporal trajectory-related marker mRNAs suggested that pregnancy-specific glycoproteins (PSGs) and carcinoembryonic antigen cell adhesion molecules (CEACAMs) were the core mRNAs in cluster 3. GSVA analysis then demonstrated that the different clusters had differences in pathway activity. By constructing a transcriptional regulatory network, USF2 was identified to be a transcriptional regulator of CEACAM1 and CEACAM5, while KLF6 was identified to be a transcriptional regulator of PSG3 and PSG5. This conclusion was then validated using the Genotype-Tissue Expression (GTEx) spinal cord transcriptome dataset.

Conclusions

This study completed an integrated analysis of a single-cell dataset with the utilization of marker mRNAs. USF2/CEACAM1&5 and KLF6/PSG3&5 transcriptional regulatory networks were identified by spinal cord single-cell analysis.

Integrative Bioinformatics Analysis Reveals That Infarct-Mediated Overexpression of Potential miR-662/CREB1 Pathway-Induced Neuropeptide VIP Is Associated with the Risk of Atrial Fibrillation: A Correlation Analysis between Myocardial Electrophysiology and Neuroendocrine

Background

Neuropeptide levels are closely associated with the development and maintenance of atrial fibrillation (AF) after myocardial infarction (MI). This study was aimed at investigating the regulatory network that affects neuropeptide expression through transcription factor modulation.

Methods

We downloaded three datasets from the GEO database, and after performing differential and crosstabulation analyses, we screened out differentially expressed (DE) miRNAs and DEmRNAs coexpressed in AF and MI and performed DEmiRNA–DEmRNA pairing prediction; from which, we constructed a regulatory network. Subsequently, the hsa-miR-662-CREB1-VIP axis was obtained, and the role of CREB1 and VIP in the development of AF after MI was further revealed by single-cell analysis and prediction model construction.

Results

In this study, eight DEmRNAs and four miRNAs were screened. hsa-miR-662 was identified by database integration analysis to regulate the transcription factor CREB1, a potential transcriptional regulator in VIP. CREB1 and VIP are mainly enriched in pathways of energy metabolism, ion channels, and myocardial contraction. CREB1 and VIP were identified as biomarkers of the onset and prognosis of MI and AF.

Conclusions

In this study, the miR-662/CREB1/VIP regulatory pathway was constructed through integrated analysis of datasets, thus providing new ideas to study the mechanisms of AF development.