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Single-cell transcriptomic atlas of mouse cochlear aging.
Progressive functional deterioration in the cochlea is associated with age-related hearing loss (ARHL). However, the cellular and molecular basis underlying cochlear aging remains largely unknown. Here, we established a dynamic single-cell transcriptomic landscape of mouse cochlear aging, in which we characterized aging-associated transcriptomic changes in 27 different cochlear cell types across five different time points. Overall, our analysis pinpoints loss of proteostasis and elevated apoptosis as the hallmark features of cochlear aging, highlights unexpected age-related transcriptional fluctuations in intermediate cells localized in the stria vascularis (SV) and demonstrates that upregulation of endoplasmic reticulum (ER) chaperon protein HSP90AA1 mitigates ER stress-induced damages associated with aging. Our work suggests that targeting unfolded protein response pathways may help alleviate aging-related SV atrophy and hence delay the progression of ARHL.
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Construction of Prediction Model for Atrial Fibrillation with Valvular Heart Disease Based on Machine Learning.
BACKGROUND: Valvular heart disease (VHD) is a major precipitating factor of atrial fibrillation (AF) that contributes to decreased cardiac function, heart failure, and stroke. Stroke induced by VHD combined with atrial fibrillation (AF-VHD) is a much more serious condition in comparison to VHD alone. The aim of this study was to explore the molecular mechanism governing VHD progression and to provide candidate treatment targets for AF-VHD. METHODS: Four public mRNA microarray datasets were downloaded and differentially expressed genes (DEGs) screening was performed. Weighted gene correlation network analysis was carried out to detect key modules and explore their relationships and disease status. Candidate hub signature genes were then screened within the key module using machine learning methods. The receiver operating characteristic curve and nomogram model analysis were used to determine the potential clinical significance of the hub genes. Subsequently, target gene protein levels in independent human atrial tissue samples were detected using western blotting. Specific expression analysis of the hub genes in the tissue and cell samples was performed using single-cell sequencing analysis in the Human Protein Atlas tool. RESULTS: A total of 819 common DEGs in combined datasets were screened. Fourteen modules were identified using the cut tree dynamic function. The cyan and purple modules were considered the most clinically significant for AF-VHD. Then, 25 hub genes in the cyan and purple modules were selected for further analysis. The pathways related to dilated cardiomyopathy, hypertrophic cardiomyopathy, and heart contraction were concentrated in the purple and cyan modules of the AF-VHD. Genes of importance (CSRP3, MCOLN3, SLC25A5, and FIBP) were then identified based on machine learning. Of these, CSRP3 had a potential clinical significance and was specifically expressed in the heart tissue. CONCLUSIONS: The identified genes may play critical roles in the pathophysiological process of AF-VHD, providing new insights into VHD development to AF and helping to determine potential biomarkers and therapeutic targets for treating AF-VHD.
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PSME2 offers value as a biomarker of M1 macrophage infiltration in pan-cancer and inhibits osteosarcoma malignant phenotypes.
A growing number of studies have revealed an association between proteasome activator complex subunit 2 (PSME2) and the progression of various forms of cancer. However, the effect of PSME2 on osteosarcoma progression is unknown. Pan-cancer analyses focused on the immunological activity and prognostic relevance of PSME2 have yet to be conducted. The Cancer Genome Atlas and Genome-Tissue Expression databases were leveraged to evaluate PSME2 expression and activity across 33 cancer types. Significant PSME2 dysregulation was noted in a wide range of cancer types and this gene was found to offer significant diagnostic and prognostic utility in most analyzed cancers. From a mechanistic perspective, PSME2 expression levels were correlated with DNA methylation, DNA repair, genomic instability, and TME scores in multiple cancer types. PSME2 was subsequently established as a pan-cancer biomarker of M1 macrophage infiltration based on a combination of bulk, single-cell, and spatial transcriptomic data and confirmatory fluorescent staining results. In osteosarcoma cells, overexpressing PSME2 significantly suppressed tumor proliferative, migratory, and invasive activity. Screening efforts also successfully identified the PSME2-activating drug irinotecan, which can synergistically promote the death of osteosarcoma cells when combined with the chemotherapeutic drug paclitaxel. As a biomarker of M1 macrophage infiltration, PSME2 expression levels may offer insight into tumor development and progression for a wide range of cancers including osteosarcoma, emphasizing its potential utility as a prognostic and therapeutic target worthy of further study.
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Single-cell transcriptome atlas revealed bronchoalveolar immune features related to disease severity in pediatric Mycoplasma pneumoniae pneumonia.
The mechanisms underlying protective immunity in mild Mycoplasma pneumoniae pneumonia (MPP) and the pathogenesis of severe MPP, characterized by dysregulated immune responses, remain unclear. Here, we performed single-cell RNA sequencing (scRNA-seq) to profile bronchoalveolar lavage fluid (BALF) samples from 13 healthy donors and 24 hospitalized pediatric patients with MPP, covering both mild and severe cases. Severe MPP patients exhibited high levels of exhausted T cells and M1-like macrophages, with the exhaustion of T cells attributed to persistent type I interferon signaling and inadequate assistance from CD4+ T cells. Significant cell-cell interactions between exhausted T cells and programmed death-ligand 1+ (PD-L1+) macrophages were detected in severe patients, potentially mediated through inhibitor molecules (e.g., PD1) and their receptors (e.g., PD-L1), as well as human leukocyte antigen class I molecules and their receptors (e.g., KLRC1/D2), resulting in the dysfunction of anti-MP immune responses. Mild MPP patients were featured by an increased abundance of neutrophils, coupled with enhanced activation, contributing to protective immunity. Together, our study provides a detailed characterization of the BALF immune landscape in MPP patients, revealing distinct immune characteristics between mild and severe cases, which offers a valuable resource for understanding MPP immunopathogenesis and formulating effective therapeutic strategies.
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Cardiac endothelial cells and cardiomyocytes alter their communication properties in diabetic mice.
OBJECTIVE: We aimed to explore the heterogeneities and communication properties of cardiac CMs and ECs in diabetes. METHODS: GSE213337 dataset was retrieved from NCBI Gene Expression Omnibus, containing the single-cell RNA sequencing data of hearts from the control and streptozotocin-induced diabetic mice. Cell cluster analysis was performed to identify the cell atlas. Data of CMs and ECs were extracted individually for re-cluster analysis, functional enrichment analysis and trajectory analysis. Cell communication analysis was conducted to explore the altered signals and significant ligand-receptor interactions. RESULTS: Eleven cell types were identified in the heart tissue. CMs were re-clustered into four subclusters, and cluster 4 was dominant in diabetic condition and enriched in cellular energy metabolism processes. ECs were re-clustered into six subclusters, and clusters 2, 4 and 5 were dominant in the diabetic condition and mainly enriched in cellular energy metabolism and lipid transport processes. The cellular communication network was altered in the diabetic heart. ECs dominated the overall signaling and notably increased the ANGPTL and SEMA4 signals in the diabetic heart. Four significant ligand-receptor pairs implicating the two signals contributed to the communication between ECs and other cell types, including Angptl1-(Itga1 + Itgb1), Angptl4-Cdh5, Angptl4-Sdc3, and Sema4a-(Nrp + Plxna2). The ligand Angptl4 engaged in ECs-CMs communication in a paracrine manner. CONCLUSION: Single-cell sequencing analysis revealed heterogeneities of ECs and CMs in diabetes, Angptl4-Cdh5 and Angptl4-Sdc3 were involved in the communication between ECs and CMs in diabetes.
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Predicting immunotherapy prognosis and targeted therapy sensitivity of colon cancer based on a CAF-related molecular signature.
The role of cancer-associated fibroblasts (CAFs) in modulating the tumor microenvironment (TME) is gaining attention, yet their impact on prognosis and therapeutic response in colon cancer remains unclear. Here, we identified genes associated with CAF infiltration via weighted gene co-expression network analysis (WGCNA) utilizing data from The Cancer Genome Atlas (TCGA) and GSE39582 cohorts. Univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses were used to construct CAF molecular signatures (CAFscore). Patients were categorized into high and low CAFscore groups to analyze clinicopathological traits, somatic mutations, immune evasion, and treatment responses. In this study, a total of 244 genes were correlated with CAF infiltration, with 11 linked to overall survival. Notably, FSTL3, CRIP2, and SLC2A3 were selected for the CAFscore. A higher CAFscore was associated with poorer prognoses, increased malignancy, and therapeutic resistance, particularly among patients with high tumor mutation burden and microsatellite instability. Furthermore, elevated FSTL3 expression was associated with reduced CD8+ T cell infiltration, indicating a suppressive TME. Mechanistically, CAFs may promote immune evasion via NAMPT ligand-receptor interactions based on single-cell RNA sequencing data. Thus, the CAFscore is crucial for personalizing treatment strategies and identifying patients who require more aggressive management.
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Deciphering the interaction between the expression of LRP2 served as a mitochondrial metabolism-related gene and prognosis in colon cancer integrating multi-omics analysis.
BACKGROUND: Colon adenocarcinoma (COAD) is increasingly prevalent among patients under 50 years old, and the 5-year survival rate for patients with metastasis is less than 20%. Identifying significant biomarkers and therapeutic targets is crucial. We investigated the expression of LRP2 in COAD and its prognostic value utilizing single-cell sequencing and transcriptomics datasets, which was conducted preliminary validation at the patient samples and cellular levels as well. METHODS: Based on differential gene expression of tumor samples and normal tissues in The Cancer Genome Atlas (TCGA), we performed consensus clustering, univariate and multivariate Cox regression analysis applying 1,234 mitochondrial metabolism-related genes (MMRGs) to identify some essential genes associated with poor prognosis in COAD patients. We validated survival outcome and biological function of the target gene leveraging single-cell sequencing and transcriptomics datasets from Gene Expression Omnibus (GEO), and evaluated the value of the target gene in the clinical pathology stage of COAD patients. Simultaneously, the expression levels of critical gene were detected in the diverse tissues of COAD by immunohistochemistry (IHC) staining. Transcriptomics data was continuously implemented to compare the discrepancy between the expression levels of the target gene and somatic mutation burden, inspecting the key pathways of the target gene by gene set enrichment analysis (GSEA) and examining its drug sensitivity synthetically in the CellMiner databases. The proliferative capacity augmented in LRP2-overexpressed colon cancer cells was determined employing cell counting kit-8 (CCK-8) and flow cytometry assays. RESULTS: LRP2 served as a key mitochondrial metabolism-related gene was assessed clinical prognosis in COAD patients according to the TCGA database. High expression of LRP2 was prominently associated with poor prognosis in COAD patients (P < 0.05), which was validated by GEO databases, and the expression levels of LRP2 were positively related to clinical pathological stage simultaneously (P < 0.05). Some specific cell types were clustered and proliferation pathways were immensely enriched, which were correlated with LRP2 in two single-cell sequencing datasets. The mutation profiles displayed remarkable differences in two levels of LRP2, we also observed high expressions of LRP2 were immensely correlated with high tumor mutation burden (TMB) and unfavorable prognosis in these patients (P < 0.05). LRP2 was significantly enriched in multiple cancer proliferation-related pathways, and the noteworthy correlation between LRP2 and the sensitivity to various drugs was identified (P < 0.05). The expression levels of LRP2 were multifarious in different COAD patients based on IHC staining. LRP2-overpression could stimulate the proliferation capability of HCT116 and SW480 cell lines markedly (P < 0.05). CONCLUSION: The expression levels of LRP2 were intimately correlated with gene mutations, prognosis, pathological stage and the sensitivity to anticancer drugs in COAD. Augmented levels of LRP2 would manifest poor prognosis, which furnished novel insights for clinical diagnosis and treatment in COAD. LRP2 could extensively facilitate the proliferation ability of colon cell lines.
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Thyroid differentiation score-related genes and prognostic model for thyroid cancer.
BACKGROUND: Thyroid differentiation score (TDS) reflects the differentiation degree of thyroid cancer (THCA). This study aimed to construct a TDS-related prognostic risk model for THCA and explore the potential biomarkers. METHODS: Using The Cancer Genome Atlas (TCGA)-THCA dataset, overlapping differentially expressed genes (DEGs) between THCA-DEGs and TDS-DEGs were identified for functional enrichment analyses to determine their biological functions. Least absolute shrinkage and selection operator (Lasso) and Cox regression analyses were applied to construct a prognostic model. The model's predictive performance was validated through Kaplan-Meier curves, receiver operating characteristic curves, and decision curve analyses. Gene set enrichment analysis (GSEA) was performed to explore the functional pathways. Single-cell RNA sequencing analysis was performed to further explore the role of risk genes. RESULTS: A four-gene risk model, including ATPase secretory pathway Ca2+ transporting 2 (ATP2C2), mast cell expressed membrane protein 1 (MCEMP1), FAM111 trypsin-like peptidase B (FAM111B), and uronyl 2-sulfotransferase (UST), was established, with significant predictive value for overall survival. High expression of ATP2C2 and MCEMP1 correlated with poorer prognosis, while FAM111B and UST were protective factors. GSEA revealed the involvement of apoptosis and p53 signaling pathways with four risk genes. Additionally, UST was linked to p53 signaling pathways in CD4+ memory cells, suggesting its critical role in THCA progression. CONCLUSIONS: The TDS-related gene risk model demonstrates strong prognostic utility in THCA. UST may inhibit the p53 signaling pathway to activate CD4+ memory cells in THCA, highlighting its potential as a therapeutic target.
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SCRIPT: Predicting Single-Cell Long-Range Cis-Regulation Based on Pretrained Graph Attention Networks.
Single-cell cis-regulatory relationships (CRRs) are essential for deciphering transcriptional regulation and understanding the pathogenic mechanisms of disease-associated non-coding variants. Existing computational methods struggle to accurately predict single-cell CRRs due to inadequately integrating causal biological principles and large-scale single-cell data. Here, SCRIPT (Single-cell Cis-regulatory Relationship Identifier based on Pre-Trained graph attention networks) is presented for inferring single-cell CRRs from transcriptomic and chromatin accessibility data. SCRIPT incorporates two key innovations: graph causal attention networks supported by empirical CRR evidence, and representation learning enhanced through pretraining on atlas-scale single-cell chromatin accessibility data. Validation using cell-type-specific chromatin contact and CRISPR perturbation data demonstrates that SCRIPT achieves a mean AUC of 0.89, significantly outperforming state-of-the-art methods (AUC: 0.7). Notably, SCRIPT obtains an over twofold improvement in predicting long-range CRRs (>100 Kb) compared to existing methods. By applying SCRIPT to Alzheimer's disease and schizophrenia, a framework is established for prioritizing disease-causing variants and elucidating their functional effects in a cell-type-specific manner. By uncovering molecular genetic mechanisms undetected by existing computational methods, SCRIPT provides a roadmap for advancing genetic diagnosis and target discovery.
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USP35 is a Potential Immunosuppressive Factor in Skin Cutaneous Melanoma.
BACKGROUND: As one of the most immunogenic malignancies, skin cutaneous melanoma (SKCM) is mainly characterized by a high prevalence in immune-compromised patients and a brisk lymphocyte infiltration in the tumor microenvironment (TME). However, to date, studies on deubiquitination in SKCM are still very limited. METHODS: Public data with regard to this study in SKCM patients were acquired from The Cancer Genome Atlas (TCGA) and the Gene-Expression Omnibus (GEO) databases. We stratified TCGA-SKCM cases using consensus clustering and identified independent prognostic factors in deubiquitinating enzymes encoding genes (DECGs) by LASSO-Cox analysis. USP35 transcriptome level was examined using public data and validated by Immunohistochemical (IHC) staining at the protein level. Enrichment analysis was used to explore the potential functions of USP35, and the TISCH database, providing further evidence at the single-cell level. The CIBERSORT algorithm was used to assess the relationship between USP35 and the immune microenvironment, and IHC was used to further evaluate the relationship between USP35 and immunotherapy response. Finally, we used the cBioPortal and the Methsurv database to analyze the significance of genomic alterations of USP35 in melanoma. RESULTS: Our results showed that DECGs can be effectively used to stratify SKCM patients, suggesting their potential significance in the development of SKCM. Furthermore, USP35 overexpression was significantly associated with an unfavorable prognosis. We further revealed that USP35 may be involved in the activation of TORC1 signaling. Most importantly, USP35 was found to be significantly associated with an immunosuppressive TME, both in terms of negative correlation with the abundance of infiltrating CD8+ T cells and in terms of the fact that patients with high USP35 expression may benefit less from immunotherapy than those with low USP35 expression. CONCLUSION: Deubiquitinating enzymes are of great importance in the diagnosis and treatment of SKCM, and USP35 is an extremely promising target for immunotherapy.