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Cellular and molecular atlas of the placenta from a COVID-19 pregnant woman infected at midgestation highlights the defective impacts on foetal health.
OBJECTIVES: The impacts of the current COVID-19 pandemic on maternal and foetal health are enormous and of serious concern. However, the influence of SARS-CoV-2 infection at early-to-mid gestation on maternal and foetal health remains unclear. MATERIALS AND METHODS: Here, we report the follow-up study of a pregnant woman of her whole infective course of SARS-CoV-2, from asymptomatic infection at gestational week 20 to mild and then severe illness state, and finally cured at Week 24. Following caesarean section due to incomplete uterine rupture at Week 28, histological examinations on the placenta and foetal tissues as well as single-cell RNA sequencing (scRNA-seq) for the placenta were performed. RESULTS: Compared with the gestational age-matched control placentas, the placenta from this COVID-19 case exhibited more syncytial knots and lowered expression of syncytiotrophoblast-related genes. The scRNA-seq analysis demonstrated impaired trophoblast differentiation, activation of antiviral and inflammatory CD8 T cells, as well as the tight association of increased inflammatory responses in the placenta with complement over-activation in macrophages. In addition, levels of several inflammatory factors increased in the placenta and foetal blood. CONCLUSION: These findings illustrate a systematic cellular and molecular signature of placental insufficiency and immune activation at the maternal-foetal interface that may be attributed to SARS-CoV-2 infection at the midgestation stage, which highly suggests the extensive care for maternal and foetal outcomes in pregnant women suffering from COVID-19.
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Analysis of Nucleoporin 107 Overexpression and Its Association with Prognosis and Immune Infiltration in Lung Adenocarcinoma by Bioinformatics Methods.
BACKGROUND: Lung adenocarcinoma (LUAD) has high morbidity and mortality. Current studies indicate nucleoporin 107 (NUP107) is involved in the construction of nuclear pore complex, and NUP107 overexpression contributes to the growth and development in most types of cancers, but its effect in LUAD has not been elucidated. METHODS: Differences in NUP107 expression were investigated using the Cancer Genome Atlas (TCGA) and multiple Gene Expression Omnibus (GEO) data sets. Enrichment analysis were implemented to probe the NUP107 function. The association of NUP107 with the degree of immune cell infiltration was investigated by the TIMER database, single-sample gene set enrichment analysis (ssGSEA), and ESTIMATE. The association of NUP107 expression with tumor mutation burden (TMB), TP53, and immune checkpoint was analyzed. Single-cell RNA sequencing data were used to detect NUP107 expression in different cell clusters. Finally, we performed real-time quantitative polymerase chain reaction (RT-qPCR) and immunohistochemistry (IHC) to prove the difference of NUP107 expression. RESULTS: NUP107 was overexpressed in LUAD and mainly expressed in cancer stem cell (CSC). Overexpression of NUP107 in LUAD suggested a poorer prognosis. Functional enrichment analysis pointed out that NUP107 was mainly linked to the regulation of cell cycle. Both immune cell infiltration and TMB were found to be in connection with NUP107. Cases in the group with high NUP107 expression had poorer immune infiltration, but had higher expression of immune checkpoints, TMB, and proportion of TP53 mutations. CONCLUSION: NUP107 is a sensitive diagnostic and prognostic factor for LUAD and may be involved in tumor progression through its effects on cell cycle and immune infiltration.
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Exploring the role of CDCA4 in liver hepatocellular carcinoma using bioinformatics analysis and experiments.
Liver hepatocellular carcinoma (LIHC) encompasses diverse therapeutic approaches, among which targeted therapy has gained significant prominence in recent years. The identification of numerous targets and the increasing clinical application of targeted drugs have greatly improved LIHC treatment. However, the precise role of CDCA4 (Cell Division Cycle Associated 4), as well as its underlying mechanisms and prognostic implications in LIHC, remains unclear. CDCA4 expression levels in LIHC were analyzed using multiple databases including the cancer genome atlas (TCGA), gene expression profiling interactive analysis (GEPIA), and ULCAN, as well as the datasets E_TABM_36, GSE144269, GSE14520, and GSE54236. The prognostic value of CDCA4 was then evaluated. Subsequently, the association between CDCA4 and immune cells was investigated. Enrichment analysis (GSEA) was utilized to investigate the functional roles and pathways linked to CDCA4. Additionally, the methylation patterns and drug sensitivity of CDCA4 were examined. A predictive model incorporating immune genes related to CDCA4 was developed. The TISCH dataset was used to investigate the single-cell expression patterns of CDCA4. Finally, validation of CDCA4 expression levels was conducted through RT-PCR, Western blotting, and immunohistochemistry. CDCA4 exhibited significant overexpression in LIHC and demonstrated significant correlations with clinical features. High expression of CDCA4 is associated with a poorer prognosis. Analysis of immune infiltration and enrichment revealed its association with the immune microenvironment. Furthermore, its expression is correlated with methylation and mutation patterns. CDCA4 is associated with 19 drugs. Prognostic models utilizing CDCA4 demonstrate favorable effectiveness. T cell subtypes were found to be associated with CDCA4 through single-cell analysis. The conclusive experiment provided evidence of significant upregulation of CDCA4 in LIHC. The high expression of CDCA4 in LIHC is associated with prognostic significance and is highly expressed in T cell subtypes, providing a new therapeutic target and potential therapeutic strategy for LIHC.
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Fibroblast reprogramming in the dura mater of NTG-induced migraine-related chronic hypersensitivity model drives monocyte infiltration via Angptl1-dependent stromal signaling.
BACKGROUND: Migraine, characterized by recurrent episodes of severe headache, remains mechanistically enigmatic. While traditional theories emphasize trigeminovascular activation, the role of meningeal stromal-immune crosstalk in disease chronicity is poorly understood. METHODS: A migraine-related chronic hypersensitivity model was utilized via intermittent intraperitoneal nitroglycerin (NTG, 10 mg/kg, every other day for 9 days) and peripheral mechanical hypersensitivity was assessed using von Frey filaments. Single-cell RNA sequencing (scRNA-seq) was performed on dura tissues to construct a cellular atlas of NTG-induced remodeling. These data were then integrated with migraine genome-wide association study (GWAS) risk genes, cell-cell interaction networks, and transcriptional regulation analysis to dissect NTG-driven meningeal remodeling. RESULTS: The NTG-induced migraine-related chronic hypersensitivity model demonstrated sustained mechanical allodynia, as evidenced by significantly decreased paw withdrawal thresholds (p < 0.0001). Single-cell profiling of the dura mater revealed a 2.4-fold expansion of a pro-inflammatory fibroblast subpopulation (Fibro_c5: 1.9% in Vehicle vs. 4.6% in NTG group), which exhibited marked activation of TNF-α/NF-κB signaling pathways (normalized enrichment score [NES] = 1.83). Concomitantly, we observed an 82% increase in meningeal monocytes (5.7-10.4%) that showed preferential interaction with Fibro_c5 fibroblasts through Angptl1-mediated stromal-immune crosstalk (log2 fold change = 1.41). Regulatory network analysis identified Mafk as the upstream transcriptional regulator orchestrating Angptl1 expression in this pathological communication axis. CONCLUSION: Our study reveals that NTG reprograms meningeal fibroblasts to expand a pro-inflammatory fibroblast subtype, which drives migraine-related chronic hypersensitivity through TNF-α/NF-κB signaling and Angptl1-mediated monocyte crosstalk. The identified Mafk-Angptl1 axis presents a potential therapeutic target, though human validation remains essential.
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Illustrating the biological functions and diagnostic value of transmembrane protein family members in glioma.
BACKGROUND: It is well-established that patients with glioma have a poor prognosis. Although the past few decades have witnessed unprecedented medical advances, the 5-year survival remains dismally low. OBJECTIVE: This study aims to investigate the role of transmembrane protein-related genes in the development and prognosis of glioma and provide new insights into the pathogenesis of the disease. METHODS: The datasets of glioma patients, including RNA sequencing data and relative clinical information, were obtained from The Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA) and Gene Expression Omnibus (GEO) databases. Prognostic transmembrane protein-related genes were identified by univariate Cox analysis. New disease subtypes were recognized based on the consensus clustering method, and their biological uniqueness was verified via various algorithms. The prognosis signature was constructed using the LASSO-Cox regression model, and its predictive power was validated in external datasets by receiver operating characteristic (ROC) curve analysis. An independent prognostic analysis was conducted to evaluate whether the signature could be considered a prognostic factor independent of other variables. A nomogram was constructed in conjunction with traditional clinical variables. The concordance index (C-index) and Decision Curve Analysis (DCA) were used to assess the net clinical benefit of the signature over traditional clinical variables. Seven different softwares were used to compare the differences in immune infiltration between the high- and low-risk groups to explore potential mechanisms of glioma development and prognosis. Hub genes were found using the random forest method, and their expression was based on multiple single-cell datasets. RESULTS: Four molecular subtypes were identified, among which the C1 group had the worst prognosis. Principal Component Analysis (PCA) results and heatmaps indicated that prognosis-related transmembrane protein genes exhibited differential expression in all four groups. Besides, the microenvironment of the four groups exhibited significant heterogeneity. The 6 gene-based signatures could predict the 1-, 2-, and 3-year overall survival (OS) of glioma patients. The signature could be used as an independent prognosis factor of glioma OS and was superior to traditional clinical variables. More immune cells were infiltrated in the high-risk group, suggesting immune escape. According to our signature, many genes were associated with the content of immune cells, which revealed that transmembrane protein-related genes might influence the development and prognosis of glioma by regulating the immune microenvironment. TMEM158 was identified as the most important gene using the random forest method. The single-cell datasets consistently showed that TMEM158 was expressed in multiple malignant cells. CONCLUSION: The expression of transmembrane protein-related genes is closely related to the immune status and prognosis of glioma patients by regulating tumor progression in various ways. The interaction between transmembrane protein-related genes and immunity during glioma development lays the groundwork for future studies on the molecular mechanism and targeted therapy of glioma.
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Comprehensive characterization of NK cell-related genes in cutaneous melanoma identified a novel prognostic signature for predicting the prognosis, immunotherapy, and chemotherapy efficacy.
BACKGROUND: Natural killer (NK) cells, as key effectors of the innate immune system, play a crucial role in cancer prognosis and immunotherapy. This study aimed to develop a predictive model based on NK cell-associated immune genes (NKIGs) for patients with cutaneous melanoma (CM) and to assess its utility in predicting prognosis and guiding personalized treatment. METHODS: RNA-sequencing data and clinical information of CM patients were retrieved from The Cancer Genome Atlas (TCGA) database, while expression profiles from normal skin specimens were obtained from the Genotype-Tissue Expression (GTEx) database. Single-cell transcriptomes from CM were analyzed in conjunction with immune genes to identify NKIGs. These genes were then utilized to construct a prognostic model using univariate Cox regression and LASSO-Cox analysis. The model's accuracy and efficacy were evaluated through various statistical methods. Immunological characteristics, as well as the effectiveness of immunotherapy and chemotherapy in groups defined by the NKIGs signature, were explored. Additionally, the expression levels of NKIGs were validated via RT-qPCR in vitro experiments. RESULTS: The overall survival (OS) of patients in the low-risk group, as determined by the NKIGs signature, was significantly better than the high-risk group. The low-risk group also showed higher immune cell infiltration, particularly of CD8 T cells and activated CD4 memory T cells. CONCLUSIONS: This study has established a novel NKIGs signature with significant potential for predicting prognosis and guiding personalized therapeutic approaches in CM patients.
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Carboxypeptidase Q(CPQ) promotes glioma progression by inducing M2 macrophage polarization and immunosuppression.
BACKGROUND: The tumor microenvironment (TME) is immunosuppressive due to the high presence of tumor-associated macrophages (TAMs). Among the different macrophage phenotypes, M2 macrophages, which are typically associated with tissue repair and immune suppression, are often the predominant form within the TME. This environment is essential for the growth and advancement of glioma. In this study, we explored whether Carboxypeptidase Q (CPQ) plays a crucial role in immune regulation of glioma by driving polarization and recruitment of M2 macrophages. METHODS: Molecular and clinical data were derived from The Cancer Genome Altas (TCGA) and Chinese Glioma Genome Atlas (CGGA) databases. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were utilized to predict the potential biological functions of CPQ. Single-cell analysis was utilized to identify the specific cell types expressing CPQ. The GSVA algorithm was employed to calculate the activity of immune suppression signaling pathways, while the CIBERSORT algorithm was used to quantify the infiltration of immune cells. The GSEA algorithm was utilized to analyze signal pathways between CPQ high- and low-expressing groups. Human glioma specimens were collected and CPQ expression was evaluated using immunofluorescence analysis. RESULTS: CPQ was linked to immune suppression in glioma. Elevated expression of CPQ was correlated with the increased expression of immunosuppressive checkpoints, such as PD-1, TIM-3, and CTLA-4. CPQ served as an effective gene for M2 macrophage polarization and recruitment. Furthermore, it could increase radiation resistance and operate as an independent marker of poor prognosis for glioma. CONCLUSION: CPQ was implicated in immunosuppressive activities, and its elevated expression was associated with increased radiation resistance. Furthermore, CPQ served as an independent adverse prognostic biomarker for patients with glioma.
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Decoding the spatial chromatin organization and dynamic epigenetic landscapes of macrophage cells during differentiation and immune activation.
Immunocytes dynamically reprogram their gene expression profiles during differentiation and immunoresponse. However, the underlying mechanism remains elusive. Here, we develop a single-cell Hi-C method and systematically delineate the 3D genome and dynamic epigenetic atlas of macrophages during these processes. We propose "degree of disorder" to measure genome organizational patterns inside topologically-associated domains, which is correlated with the chromatin epigenetic states, gene expression, and chromatin structure variability in individual cells. Furthermore, we identify that NF-κB initiates systematic chromatin conformation reorganization upon Mycobacterium tuberculosis infection. The integrated Hi-C, eQTL, and GWAS analysis depicts the atlas of the long-range target genes of mycobacterial disease susceptible loci. Among these, the SNP rs1873613 is located in the anchor of a dynamic chromatin loop with LRRK2, whose inhibitor AdoCbl could be an anti-tuberculosis drug candidate. Our study provides comprehensive resources for the 3D genome structure of immunocytes and sheds insights into the order of genome organization and the coordinated gene transcription during immunoresponse.
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CCNA2 orchestrates the PI3K/AKT signaling axis to propel prostate cancer metastasis.
BACKGROUND: Prostate cancer (PCa) remains one of the most common malignancies in men, posing a persistent global burden in terms of both public health and socioeconomic costs. Although early detection is essential for improving patient outcomes, existing clinical tools, including prostate-specific antigen (PSA) screening, digital rectal examination, and transrectal ultrasound-guided biopsy, are hampered by suboptimal specificity and positive predictive value, resulting in frequent overdiagnosis and overtreatment of indolent lesions while missing a subset of aggressive tumors at an early stage. In this context, the rapid advancement of high-throughput omics technologies, coupled with sophisticated machine learning (ML) algorithms, provides a powerful computational framework to dissect high-dimensional genomic data, uncover latent gene expression signatures, and identify candidate biomarkers with superior discriminative performance over conventional clinicopathological parameters. Therefore, in this study, we sought to screen for crucial ML-based biomarkers associated with PCa, with a particular focus on systematically assessing the diagnostic and prognostic value of CCNA2. Leveraging large-scale transcriptomic cohorts from public repositories, we employed an ensemble of ML approaches to prioritize candidate genes and subsequently evaluated the diagnostic performance of CCNA2 through receiver operating characteristic curve analysis, as well as its prognostic utility via Kaplan-Meier survival estimation and multivariate Cox proportional hazards modeling. Our findings are anticipated to elucidate the molecular landscape of PCa and offer a promising biomarker candidate for early detection and risk stratification. METHODS: This study integrated single-cell RNA sequencing, bulk transcriptomic data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) repositories, immunofluorescence, and multiple ML algorithms with in vitro functional assays to evaluate CCNA2 expression, clinical relevance, and biological behavior in PCa. RESULTS: CCNA2 was linked to metastasis and poor prognosis. High CCNA2 expression significantly correlated with adverse survival outcomes, and knockdown of CCNA2 suppressed proliferation, migration, and invasion in PCa cell lines. Mechanistically, CCNA2 modulated the PI3K/AKT signaling pathway. An ML-based diagnostic model incorporating CCNA2 demonstrated high predictive accuracy across multiple validation cohorts. CONCLUSIONS: CCNA2 serves as a promising prognostic biomarker and therapeutic target in prostate adenocarcinoma, driving tumor progression potentially via the PI3K/AKT axis.
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Comprehensive pan-cancer analysis of YBX family reveals YBX2 as a potential biomarker in liver cancer.
BACKGROUND: The Y-box-binding proteins (YBX) act as a multifunctional role in tumor progression, metastasis, drug resistance by regulating the transcription and translation process. Nevertheless, their functions in a pan-cancer setting remain unclear. METHODS: This study examined the clinical features expression, prognostic value, mutations, along with methylation patterns of three genes from the YBX family (YBX1, YBX2, and YBX3) in 28 different types of cancer. Data used for analysis were obtained from Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases. A novel YBXs score was created using the ssGSEA algorithm for the single sample gene set enrichment analysis. Additionally, we explored the YBXs score's association with the tumor microenvironment (TME), response to various treatments, and drug resistance. RESULTS: Our analysis revealed that YBX family genes contribute to tumor progression and are indicative of prognosis in diverse cancer types. We determined that the YBXs score correlates significantly with numerous malignant pathways in pan-cancer. Moreover, this score is also linked with multiple immune-related characteristics. The YBXs score proved to be an effective predictor for the efficacy of a range of treatments in various cancers, particularly immunotherapy. To summarize, the involvement of YBX family genes is vital in pan-cancer and exhibits a significant association with TME. An elevated YBXs score indicates an immune-activated TME and responsiveness to diverse therapies, highlighting its potential as a biomarker in individuals with tumors. Finally, experimental validations were conducted to explore that YBX2 might be a potential biomarker in liver cancer. CONCLUSION: The creation of YBXs score in our study offered new insights into further studies. Besides, YBX2 was found as a potential therapeutic target, significantly contributing to the improvement of HCC diagnosis and treatment strategies.