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The cycling and aging mouse female reproductive tract at single-cell resolution.
The female reproductive tract (FRT) undergoes extensive remodeling during reproductive cycling. This recurrent remodeling and how it shapes organ-specific aging remains poorly explored. Using single-cell and spatial transcriptomics, we systematically characterized morphological and gene expression changes occurring in ovary, oviduct, uterus, cervix, and vagina at each phase of the mouse estrous cycle, during decidualization, and into aging. These analyses reveal that fibroblasts play central-and highly organ-specific-roles in FRT remodeling by orchestrating extracellular matrix (ECM) reorganization and inflammation. Our results suggest a model wherein recurrent FRT remodeling over reproductive lifespan drives the gradual, age-related development of fibrosis and chronic inflammation. This hypothesis was directly tested using chemical ablation of cycling, which reduced fibrotic accumulation during aging. Our atlas provides extensive detail into how estrus, pregnancy, and aging shape the organs of the female reproductive tract and reveals the unexpected cost of the recurrent remodeling required for reproduction.
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Biomarkers associated with cell-in-cell structure in kidney renal clear cell carcinoma based on transcriptome sequencing.
BACKGROUND: Kidney renal clear cell carcinoma (KIRC), the main histological subtype of renal cell carcinoma, has a high incidence globally. Cell-in-cell structures (CICs), as a cellular biological phenomenon, play pivotal roles in cell competition, immune evasion and tumor progression in the context of KIRC. METHODS: Data for this study were sourced from The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC), and Gene Expression Omnibus (GEO) databases. Differentially expressed genes (DEGs) were identified using the limma package. Enrichment analyses were performed using the clusterProfiler package. Support vector machine-recursive feature elimination (SVM-RFE) and Least Absolute Shrinkage and Selection Operator (LASSO) regression, implemented via the caret and glmnet packages in R, were used to select biomarkers. The accuracy of these biomarkers was verified by using the receiver operating characteristic (ROC) curve as well as in vitro experiments (CCK-8 assay, wound healing assay, Transwell assay, and quantitative real-time PCR). The CIBERSORT algorithm was applied to explore the association between immune infiltration and the biomarkers. Further analysis explored the association between these biomarkers and clinicopathological characteristics of KIRC. For single-cell data, the Seurat package is used to read the sample data, and the SCTransform function is employed for normalization. RESULTS: This study identified 1,256 DEGs which enriched in T-cell immune system regulation processes. Five hub genes (CDKN2A, VIM, TGFB1, CTSS, and CDC20) were biomarkers with area under the curve (AUC) values > 0.8, indicating high predictive performance. In vitro validation experiments demonstrated that the expressions of all five biomarkers in KIRC cells were elevated, and the knockdown of CTSS could inhibit the migration and invasion of KIRC cells. Immune infiltration analysis showed higher proportions of T-cells and macrophages in tumor tissues. CDKN2A and CDC20 expressions correlated significantly with stage and grade, while TGFB1, CDKN2A, and CDC20 were highly expressed in proliferative tumor cells. CONCLUSION: This study provides new biomarkers for KIRC, offering valuable insights into its developmental mechanisms for the research of CIC in this disease.
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Integrating bulk and single-cell RNA sequencing reveals cellular heterogeneity and immune infiltration in hepatocellular carcinoma.
Efficacy of immunotherapy in hepatocellular carcinoma (HCC) is blocked by its high degree of inter- and intra-tumor heterogeneity and immunosuppressive tumor microenvironment. However, the correlation between tumor heterogeneity and immunosuppressive microenvironment in HCC has not been well addressed. Here, we endeavored to dissect inter- and intra-tumor heterogeneity in HCC and uncover how they contribute to the immunosuppressive microenvironment. We performed consensus molecular subtyping with non-negative matrix factorization (NMF) clustering to stratify the inter-heterogeneity profile of HCC tumors. We grouped HCC tumors from the Cancer Genome Atlas (TCGA) patients into three subtypes (S1, S2 and S3), where S1 was characterized as a 'hot tumor' profile with high expression level of T cell genes and rate of immune scores. S2 was characterized as a 'cold tumor' profile with the highest tumor purity score, and S3 as an 'immunosuppressed tumor' profile with the poorest prognosis and a high expression level of immunosuppressive genes such as cytotoxic T-lymphocyte-associated protein-4, TIGIT, and PDCD1. Moreover, we combined weighted gene co-expression network analysis and single-cell regulatory network inference and clustering (SCENIC) in the single-cell dataset of the S3-like subtype (CS3) and identified a transcription factor, BATF, which could upregulate immunosuppressive genes. Finally, we identified a cell interaction network in which a myeloid-derived suppressor cell-like macrophage subtype could promote the formation of immunosuppressive T-cells.
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Machine learning combined with single-cell analysis reveals predictive capacity and immunotherapy response of T cell exhaustion-associated lncRNAs in uterine corpus endometrial carcinoma.
BACKGROUND: The exhaustion of T-cells is a primary factor contributing to immune dysfunction in cancer. Long non-coding RNAs (lncRNAs) play a significant role in the advancement, survival, and treatment of Uterine Corpus Endometrial Carcinoma (UCEC). Nevertheless, there has been no investigation into the involvement of lncRNAs associated with T-cell exhaustion (TEXLs) in UCEC. The goal of this work is to establish predictive models for TEXLs in UCEC and study their related immune features. METHODS: Using transcriptome and single-cell sequencing data from The Cancer Genome Atlas and Gene Expression Omnibus databases, we employed co-expression analysis and univariate Cox regression to identify prognostic-associated TEXLs (pTEXLs). The prognostic model was developed using the Least Absolute Contraction and Selection Operator. The immunotherapy characteristics of the prognostic model risk score were studied. Then molecular subgroups were identified through non-negative Matrix Factorization based on pTEXLs. The identification of co-expressed genes was done using a weighted correlation network analysis. Subsequently, a diagnostic model for UCEC was created. In-depth investigations, both in vitro and in vivo, were carried out to elucidate the molecular mechanism of the key gene within the diagnostic model. RESULTS: Receiver operating characteristic curve, calibration curve, and decision curve analysis proved the validity of the predictive models established according to pTEXLs. The subgroup with lower risk scores in the prognostic model has better responses to blocking immune checkpoint therapy. Single-cell analysis suggests that the expression level of MIEN1 is relatively high in immune cells among diagnostic genes. Furthermore, the targeted suppression of MIEN1 via sh-MIEN1 diminishes the proliferative, migratory, and invasive capacities of UCEC cells, potentially associated with CD8+ T cell exhaustion. CONCLUSIONS: The association between TEXLs and UCEC was methodically elucidated by our investigation. A stable pTEXLs risk prediction model and a diagnosis model for UCEC were also established.
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High Expression of TACC3 Is Associated with the Poor Prognosis and Immune Infiltration in Lung Adenocarcinoma Patients.
BACKGROUND: Lung adenocarcinoma (LUAD) has been recognized as one of the commonest aggressive malignant tumors occurring in humans. The transforming acidic coiled-coil-containing protein 3 (TACC3) seems to be a probable prognostic marker and treatment target for non-small-cell lung cancer (NSCLC). Nevertheless, there exist no reports on the association between TACC3 and immunotherapy or other therapeutic interventions in LUAD. METHODS: Premised on the data accessed from The Cancer Genome Atlas- (TCGA-) LUAD, we carried out bioinformatics analysis. The TACC3 expression in LUAD was analyzed utilizing the GEPIA. A survival module was constructed to evaluate the effect of TACC3 on the survival of patients with LUAD. Logistic regression was undertaken to examine the relationship between TACC3 expression and clinical factors. Protein-protein interaction analysis was performed in the GeneMANIA database, and enrichment analysis and identification of predicted signaling pathways were performed using Gene Ontology and Kyoto Encyclopedia of Genes. Additionally, the Cox regression was used to assess the clinicopathologic features linked to the overall survival in TCGA patients. Lastly, we investigated the link between TACC3 and tumor-infiltrating immune cells (TIICs) through CIBERSORT and the "Correlation" module of GEPIA. The association between TACC3 gene expression and drug response was analyzed using the CellMiner database to predict drug sensitivity. RESULTS: The outcomes illustrated that TACC3 was upregulated and considerably correlated with dismal prognosis in LUAD patients. Moreover, the multivariate Cox regression analysis depicted TACC3 as an independent prognostic marker in LUAD patients. It was also revealed that the expression of TACC3 was related to clinical stage (P = 0.014), age (P = 0.002), and T classification (P ≤ 0.018). Moreover, we discovered that the expression of TACC3 was considerably linked to a wide range of TIICs, especially the T cells and NK cells. Single-cell results found that TACC3 was mainly expressed in the immune cells (especially tprolif cells) and malignant cells. TACC3 gene expression was positively correlated with TMB and MSI, and TACC3 may provide a prediction of the efficacy of immunotherapy. Moreover, the correlation analysis between TACC3 gene expression and immune checkpoint gene expression revealed that TACC3 may coordinate the activities of these ICP genes in different signal transduction pathways. TACC3 is related to biological progress (BP), cellular component (CC), and molecular function (MF). The pathways involved in the interaction network involving TACC3 include nonhomologous end-joining, RNA transport, pantothenate and CoA biosynthesis, homologous recombination, and nucleotide excision repair. Furthermore, we investigated the association between the expression of TACC3 and the use of antitumor drugs, and TACC3 was positively correlated with response to most drugs. CONCLUSION: The findings from this research offer robust proof that the expression of TACC3 could be a prognostic marker correlated with TIICs in LUAD. TACC3 can also provide new ideas for immunotherapy as a potential therapeutic target.
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Developmental and sexual dimorphic atlas of the prenatal mouse external genitalia at the single-cell level.
Birth defects of the external genitalia are among the most common in the world. Proper formation of the external genitalia requires a highly orchestrated process that involves special cell populations and sexually dimorphic hormone signaling. It is clear what the end result of the sexually dimorphic development is (a penis in the male versus clitoris in the female); however, the cell populations involved in the process remain poorly defined. Here, we used single-cell messenger RNA sequencing in mouse embryos to uncover the dynamic changes in cell populations in the external genitalia during the critical morphogenetic window. We found that overall, male and female external genitalia are largely composed of the same core cellular components. At the bipotential stage of development (embryonic day or E14.5), few differences in cell populational composition exist between male and female. Although similar in cell population composition, genetic differences in key sexual differentiation developmental pathways arise between males and females by the early (E16.5) and late (E18.5) differentiation stages. These differences include discrete cell populations with distinct responsiveness to androgen and estrogen. By late sexual differentiation (E18.5), unique cell populations in both male and female genitalia become apparent and are enriched with androgen- and estrogen-responsive genes, respectively. These data provide insights into the morphogenesis of the external genitalia that could be used to understand diseases associated with defects in the external genitalia.
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Tumour-infiltrating immune cells as a novel prognostic model for bladder cancer.
Bladder cancer (BLCA) is the tenth most commonly diagnosed cancer and poses a significant challenge due to its complexity and associated high morbidity and mortality rates in the absence of optimal treatment. The tumor microenvironment (TME) is recognized as a critical factor in tumor initiation, progression and therapeutic response, and offers numerous potential targets for intervention. A comprehensive understanding of immune infiltration patterns in BLCA is essential for the development of effective prevention and treatment strategies. In this study, bioinformatics analysis was used to identify differentially expressed genes (DEGs) and tumor-infiltrating immune cells (TIICs) between BLCA tissues and adjacent normal tissues. Weighted gene co-expression network analysis (WGCNA) and protein-protein interaction (PPI) analysis were used to identify the top 10 hub genes with the most significant co-expression effects, and their potential relationship with patient prognosis was then predicted. The random survival forest (RSF) model was used to further identify six variables among the hub genes and establish a novel scoring system, defined as the tumor-infiltrating immune score (TIIS) to predict the prognosis of BLCA patients. In addition, the correlation analysis between TIIS and drug sensitivity was investigated using the Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Therapeutics Response Portal (CTRP) databases. Patients with high TIIS were found to have a poor prognosis but may be more sensitive to Cisplatin and certain novel agents. This study provided a systematic analysis of immune cell infiltration in BLCA and established TIIS to predict patient prognosis and the efficacy of specific drugs in the treatment of BLCA.
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Spatial Transcriptomics Reveals Injured Cells, Signature Genes, and Communication Patterns in the Cyst Microenvironment of Polycystic Kidney Disease.
KEY POINTS: Myofibroblasts and injury repair-related cell types were exclusively observed in polycystic kidney disease and enriched within the cyst microenvironment. Cyst-associated gene signature of 45 genes with decreased expression further away from the cysts was largely related to inflammation. Communication in low-inflamed cystic microdomains related to cellular signaling, morphogenesis, and inflammation in polycystic kidney disease. BACKGROUND: Changes in the cyst microenvironment in polycystic kidney disease (PKD) may drive progressive cyst formation. Bulk-cell and single-cell RNA sequencing have advanced our understanding of altered signaling; however, the lack of spatial information has limited our insights into local gene expression and cellular communication near cysts. METHODS: We used wild-type and Pkd1 -deficient mouse kidneys to generate 10× Genomics Visium Spatial Gene Expression datasets. Using our single-cell mouse kidney atlas and single-cell sequencing data for spot deconvolution, we enhanced resolution and estimated enriched cell types. We analyzed spatial gene expression patterns and used a cyst-centered analysis to identify cyst-associated gene signature. Cell communication near cysts was investigated, identifying key ligand-receptors. Prioritized key factors were validated in tissues. RESULTS: We observed enrichment of fibroblasts, injury repair-related cell types, and diverse immune populations in PKD. Injury repair-related cells were exclusively observed in PKD, predominantly localized within immune cell-dense regions near cysts. These cells collectively contributed to the altered gene expression profile in PKD, including cyst-associated signature genes related to inflammatory processes. Analysis of cellular communication in less-inflamed regions around cysts revealed the involvement of multiple cell types. Key ligand-receptor interactions were associated with cytokine signaling, fibrosis, cellular development, and repair. These included Angpt2 , C3 , Csf1 , Cxcl12 , Il34 , Gas6 , Il16 , Mdk , Mif , Ptn , Sfrp2 , Spp1 , Sdc1 , Tnc , Tnfsf12 , and Wnt5a . In addition, extracellular matrix (ECM) proteins implicated in immune response, ECM remodeling, cell adhesion, and cell signaling were identified, such as Adam9 , Adam10 , Col1a1, Col3a1, Col4a2, Lamb2, Lamc1, Efnb1 , Efnb2 , Thbs1, Thbs2 , and Vcam1 . Immunohistochemistry confirmed expression of Syndecan-1-Collagen IV, Midkine-Integrin β 1, CSF-1, Pleiotrophin, and Tenascin-C in cystic kidneys. CONCLUSIONS: Spatial transcriptomics in PKD revealed enrichment of (myo)fibroblasts, immune, and injury repair-related cells near cysts, creating a (pro)inflammatory and (pro)fibrotic niche. Key ligand-receptor and ECM interactions were identified and validated.
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Spatial Proteomics for Further Exploration of Missing Proteins: A Case Study of the Ovary.
In the quest for "missing proteins" (MPs), the proteins encoded by the human genome still lacking evidence of existence at the protein level, novel approaches are needed to detect this challenging group of proteins. The current count stands at 1,343 MPs, and it is likely that many of these proteins are expressed at low levels, in rare cell or tissue types, or the cells in which they are expressed may only represent a small minority of the tissue. Here, we used an integrated omics approach to identify and explore MPs in human ovaries. By taking advantage of publicly available transcriptomics and antibody-based proteomics data in the Human Protein Atlas (HPA), we selected 18 candidates for further immunohistochemical analysis using an exclusive collection of ovarian tissues from women and patients of reproductive age. The results were compared with data from single-cell mRNA sequencing, and seven proteins (CTXN1, MRO, RERGL, TTLL3, TRIM61, TRIM73, and ZNF793) could be validated at the single-cell type level with both methods. We present for the first time the cell type-specific spatial localization of 18 MPs in human ovarian follicles, thereby showcasing the utility of the HPA database as an important resource for identification of MPs suitable for exploration in specialized tissue samples. The results constitute a starting point for further quantitative and qualitative analysis of the human ovaries, and the novel data for the seven proteins that were validated with both methods should be considered as evidence of existence of these proteins in human ovary.
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Intratumoral tertiary lymphoid structure (TLS) maturation is influenced by draining lymph nodes of lung cancer.
BACKGROUND: Tertiary lymphoid structure (TLS) is an organized infiltration of immune cells, showing features of germinal center (GC) commonly seen in secondary lymphoid organs. However, its relationship with tumor-draining lymph nodes (TDLNs) has not been studied and we hypothesized that TDLN may influence maturation of intratumoral TLS in non-small cell lung cancer (NSCLC). METHODS: Tissue slides of 616 patients that had undergone surgeries were examined. Cox proportional hazard regression model was used to assess risk factors of patients' survival, and logistic regression model was used for their relationship with TLS. Single-cell RNA-sequencing (scRNA-seq) was employed to explore transcriptomic features of TDLNs. Immunohistochemistry, multiplex immunofluorescence and flow cytometry were performed to analyze cellular composition. Cellular components of NSCLC samples from The Cancer Genome Atlas database were inferred with Microenvironment Cell Populations-counter (MCP-counter) method. Murine NSCLC models were used to dissect underlying mechanisms for relationship between TDLN and TLS maturation. RESULTS: While GC+ TLS was associated with better prognosis, GC- TLS was not. TDLN metastasis reduced the prognostic relevance of TLS, and was associated with less GC formation. Primary tumor sites showed reduced B cell infiltration in TDLN-positive patients, and scRNA-seq revealed diminished memory B cell formation in tumor-invaded TDLNs, together with an emphasis on weakened interferon (IFN)-γ response. Murine NSCLC models revealed that IFN-γ signaling is involved in memory B cell differentiation in TDLNs and GC formation in primary tumors. CONCLUSIONS: Our research emphasizes the influence of TDLN on intratumoral TLS maturation and suggests a role of memory B cells and IFN-γ signaling in this communication.