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Found: 4 939
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Single-cell RNA sequencing reveals pro-invasive cancer-associated fibroblasts in hypopharyngeal squamous cell carcinoma.
BACKGROUND: Hypopharyngeal squamous cell carcinoma (HPSCC) has the worst prognosis among all head-and-neck cancers, and treatment options are limited. Tumor microenvironment (TME) analysis can help identify new therapeutic targets and combined treatment strategies. METHODS: Six primary HPSCC tissues and two adjacent normal mucosae from six treatment-naïve patients with HPSCC were analyzed using scRNA-seq. Cell types were curated in detail, ecosystemic landscapes were mapped, and cell-cell interactions were inferred. Key results were validated with The Cancer Genome Atlas and cell biology experiments. RESULTS: Malignant HPSCC epithelial cells showed significant intratumor heterogeneity. Different subtypes exhibited distinct histological features, biological behaviors, and spatial localization, all affecting treatment selection and prognosis. Extracellular matrix cancer-associated fibroblasts (mCAFs) expressing fibroblast activation protein were the dominant CAFs in HPSCC tumors. mCAFs, constituting an aggressive CAF subset, promoted tumor cell invasion, activated endothelial cells to trigger angiogenesis, and synergized with SPP1+ tumor associated macrophages to induce tumor progression, ultimately decreasing the overall survival of patients with HPSCC. Moreover, the LAMP3+ dendritic cell subset was identified in HPSCC and formed an immunosuppressive TME by recruiting Tregs and suppressing CD8+ T cell function. CONCLUSIONS: mCAFs, acting as the communication center of the HPSCC TME, enhance the invasion ability of HPSCC cells, mobilizing surrounding cells to construct a tumor-favorable microenvironment. Inhibiting mCAF activation offers a new anti-HPSCC therapeutic strategy. Video Abstract.
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Integrated single-cell genomics, transcriptomics, and pathomics to identify potential biomarkers in muscle-invasive bladder cancer.
BACKGROUND: The integration of multi-omics approaches provides a powerful strategy for understanding cancer. By combining these methods, researchers gain insights into tumor diversity, gene activity, and the tumor microenvironment, which are essential for advancing cancer biology, improving early detection, refining prognostic tools, and developing targeted treatments. This study aims to explore key biomarkers in muscle-invasive bladder cancer (MIBC) and to develop a predictive model for better understanding disease progression and therapeutic responses. METHODS: Single-cell analysis of MIBC samples from public datasets identified basal-related genes. Using Cox analysis of The Cancer Genome Atlas (TCGA)-bladder urothelial carcinoma (BLCA) clinical data and the expression matrix, and combining it with weighted gene co-expression network analysis (WGCNA) of another MIBC dataset, a disintegrin and metalloprotease protein 17 (ADAM17) was identified as a key target gene. We collected BLCA patient samples from TCGA, Shanghai Tenth People's Hospital (STPH), and Guangdong Second Provincial General Hospital (GD2H) to develop the pathomics model for predicting ADAM17 expression. Single-cell validation of ADAM17 expression was performed using GD2H MIBC samples. RESULTS: ADAM17 was highly expressed in MIBC and associated with poor prognosis. Its expression correlated with clinical features such as non-papillary subtype, advanced pathological stage, and higher tissue grade. ADAM17 overexpression was linked to immune reprogramming and drug resistance. The pathomics model effectively predicted ADAM17 expression in BLCA samples. Single-cell analysis of GD2H MIBC samples confirmed ADAM17 overexpression in epithelial cells and identified key pathways in cell communication, matrix remodeling, tumor invasion, and immune regulation. CONCLUSIONS: Multi-omics approaches effectively identify biomarkers for MIBC, with ADAM17 emerging as a key biomarker. Further research is needed to clarify its role in MIBC progression.
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A Bioinformatics Analysis on the Subtypes of Hepatocellular Carcinoma Related to RNA Processing Genes to Reveal Prognosis and Immune Microenvironment Features.
BACKGROUND: Alterations in RNA processing or anomalies in gene expression and function can contribute to the development of cancer. This work aims to investigate the stratification and prognostic value of RNA processing-related genes (RPRGs). METHODS: We collected single-cell and transcriptomic data from hepatocellular carcinoma (HCC) samples from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA), as well as RPRGs from the Molecular Signatures Database (MSigDB). The bioinformatic analysis was undertaken to reveal differentially expressed RPRGs (DE-RPRGs) at the cellular level. Different subtypes based on RPRGs were obtained based on AUcell scores. The RPRG-related prognostic model was created through regression analysis and was validated in different datasets. Prognostic feature genes were screened, with their expression patterns observed at the single-cell level. The single-sample gene set enrichment analysis (ssGSEA) was carried out for immune infiltration analysis. Potential therapeutic approaches for different risk groups were investigated. High-sensitive drugs were identified by analyzing drug information. RESULTS: Based on the AUcell scoring on DE-RPRGs, two HCC molecular subtypes with distinct RPRG characteristics were identified. Based on the regression analysis of DE-RPRGs, 8 prognostic feature genes with varying expression levels in high-risk (HR) and low-risk (LR) groups as well as distinct cell clusters were identified, including LGALS3, THOC2, TYW3, SFPQ, GTPBP4, SMAD2, DTWD1, and ZC3H13. A predictive model that exhibited robust performance across the training and validation datasets was established. Two risk groups with distinct immune characteristics and survival rates were obtained. The HR group with poorer survival exhibited a greater enrichment of Treg cells linked with immune suppression, while the LR group with better survival had a higher accumulation of anti-tumor immune cells. CONCLUSION: The work highlights the stratification and prognostic value of RPRGs in HCC patients and reveals immune and mutational characteristics of RPRGs, which may hold great implications for patient management and targeted therapy.
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Discovery and Validation of a Metastasis-Related Prognostic and Diagnostic Biomarker for Melanoma Based on Single Cell and Gene Expression Datasets.
BACKGROUND: Single cell sequencing can provide comprehensive information about gene expression in individual tumor cells, which can allow exploration of heterogeneity of malignant melanoma cells and identification of new anticancer therapeutic targets. METHODS: Single cell sequencing of 31 melanoma patients in GSE115978 was downloaded from the Gene Expression Omniniub (GEO) database. First, the limma package in R software was used to identify the differentially expressed metastasis related genes (MRGs). Next, we developed a prognostic MRGs biomarker in the cancer genome atlas (TCGA) by combining univariate cox analysis and the least absolute shrinkage and selection operator (LASSO) method and was further validated in another two independent datasets. The efficiency of MRGs biomarker in diagnosis of melanoma was also evaluated in multiple datasets. The pattern of somatic tumor mutation, immune infiltration, and underlying pathways were further explored. Furthermore, nomograms were constructed and decision curve analyses were also performed to evaluate the clinical usefulness of the nomograms. RESULTS: In total, 41 MRGs were screened out from 1958 malignant melanoma cell samples in GSE115978. Next, a 5-MRGs prognostic marker was constructed and validated, which show more effective performance for the diagnosis and prognosis of melanoma patients. The nomogram showed good accuracies in predicting 3 and 5 years survival, and the decision curve of nomogram model manifested a higher net benefit than tumor stage and clark level. In addition, melanoma patients can be divided into high and low risk subgroups, which owned differential mutation, immune infiltration, and clinical features. The low risk subgroup suffered from a higher tumor mutation burden (TMB), and higher levels of T cells infiltrating have a significantly longer survival time than the high risk subgroup. Gene Set Enrichment Analysis (GSEA) revealed that the extracellular matrix (ECM) receptor interaction and epithelial mesenchymal transition (EMT) were the most significant upregulated pathways in the high risk group. CONCLUSIONS: We identified a robust MRGs marker based on single cell sequencing and validated in multiple independent cohort studies. Our finding provides a new clinical application for prognostic and diagnostic prediction and finds some potential targets against metastasis of melanoma.
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Single-Cell Profiling Reveals Altered Endometrial Cellular Features Across the Menstrual Cycle in Endometriosis Patients.
Endometriosis is a chronic, estrogen-dependent condition affecting over 190 million women globally, characterized by the ectopic presence of endometrial-like tissue that leads to inflammation, pain, and infertility. Despite its prevalence, the pathogenesis of endometriosis remains poorly understood. Here, we present a comprehensive single-cell transcriptomic atlas comprising 228,000 cells derived from 43 eutopic endometrial biopsies from patients with endometriosis, fibroid controls and healthy controls, sampled across the menstrual cycle. This analysis reveals previously uncharacterized subpopulations of endometrial fibroblasts and epithelial cells undergoing epithelial-mesenchymal transition, alongside disrupted immune cell communication networks. Comparative gene expression profiling implicates oxidative stress, aberrant cell migration, and dysregulated apoptosis as central features of the disease state. These findings suggest that endometriosis alters eutopic endometrial homeostasis, with potential consequences for fertility, regeneration, and disease progression. Our dataset provides a valuable resource for biomarker discovery and identifies candidate therapeutic targets aimed at restoring endometrial function and alleviating symptoms in affected individuals.
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Identification and validation of a prognostic anoikis-related gene signature in papillary thyroid carcinoma by integrated analysis of single-cell and bulk RNA-sequencing.
Papillary thyroid carcinoma (PTC) prognosis may be deteriorated due to the metastases, and anoikis palys an essential role in the tumor metastasis. However, the potential effect of anoikis-related genes on the prognosis of PTC was unclear. The mRNA and clinical information were obtained from the cancer genome atlas database. Hub genes were identified and risk model was constructed using Cox regression analysis. Kaplan-Meier (K-M) curve was applied for the survival analysis. Immune infiltration and immune therapy response were calculated using CIBERSORT and TIDE. The identification of cell types and cell interaction was performed by Seurat, SingleR and CellChat packages. GO, KEGG, and GSVA were applied for the enrichment analysis. Protein-protein interaction network was constructed in STRING and Cytoscape. Drug sensitivity was assessed in GSCA. Based on bulk RNA data, we identified 4 anoikis-related risk signatures, which were oncogenes, and constructed a risk model. The enrichment analysis found high risk group was enriched in some immune-related pathways. High risk group had higher infiltration of Tregs, higher TIDE score and lower levels of monocytes and CD8 T cells. Based on scRNA data, we found that 4 hub genes were mainly expressed in monocytes and macrophages, and they interacted with T cells. Hub genes were significantly related to immune escape-related genes. Drug sensitivity analysis suggested that cyclin dependent kinase inhibitor 2A may be a better chemotherapy target. We constructed a risk model which could effectively and steadily predict the prognosis of PTC. We inferred that the immune escape may be involved in the development of PTC.
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Insights gained from single-cell analysis of immune cells in tofacitinib treatment of Vogt-Koyanagi-Harada disease.
Vogt-Koyanagi-Harada disease (VKH) is an important refractory uveitis mediated by pathological T cells (TCs). Tofacitinib (TOFA) is a JAK- targeted therapy for several autoimmune diseases. However, the specific pathogenesis and targeted therapeutics for VKH remain largely unknown. Based on single-cell RNA sequencing and mass cytometry, we present what we believe is the first multimodal, high-dimensional analysis to generate a comprehensive human immune atlas regarding subset composition, gene signatures, enriched pathways, and intercellular interactions of VKH patients undergoing TOFA therapy. Patients with VKH are characterized by TCs' polarization from naive to effector and memory subsets, together with accrued monocytes and upregulated cytokines and JAK/STAT signaling pathways. In vitro, TOFA reversed Th17/Treg imbalance and inhibited IL-2-induced STAT1/3 phosphorylation. TOFA alleviated VKH symptoms by restoring pathological TCs' polarization and functional marker expression and downregulating cytokine signaling and lymphocyte function. Remarkably, inflammation-related responses and intercellular interactions decreased after TOFA treatment, particularly in monocytes. Notably, we identified 2 inflammation- and JAK-associated monocyte subpopulations that were strongly implicated in VKH pathogenesis and mechanisms involved in TOFA treatment. Here, we provide a potentially novel JAK-targeted therapy for VKH and elaborate on the possible therapeutic mechanisms of TOFA, expanding our knowledge of VKH pathological patterns.
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Time-series single-cell transcriptomic profiling of luteal-phase endometrium uncovers dynamic characteristics and its dysregulation in recurrent implantation failures.
Understanding human endometrial dynamics in the establishment of endometrial receptivity remains a challenge, which limits early diagnosis and treatment of endometrial-factor infertility. Here, we decode the endometrial dynamics of fertile women across the window of implantation and characterize the endometrial deficiency in women with recurrent implantation failure. A computational model capable of both temporal prediction and pattern discovery is used to analyze single-cell transcriptomic data from over 220,000 endometrial cells. The time-series atlas highlights a two-stage stromal decidualization process and a gradual transitional process of the luminal epithelial cells across the window of implantation. In addition, a time-varying gene set regulating epithelium receptivity is identified, based on which the recurrent implantation failure endometria are stratified into two classes of deficiencies. Further investigation uncovers a hyper-inflammatory microenvironment for the dysfunctional endometrial epithelial cells of recurrent implantation failure. The holistic characterization of the physiological and pathophysiological window of implantation and a computational tool trained on this temporal atlas provide a platform for future therapeutic developments.
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Single-cell Immune Landscape of Human Recurrent Miscarriage.
Successful pregnancy in placental mammals substantially depends on the establishment of maternal immune tolerance to the semi-allogenic fetus. Disorders in this process are tightly associated with adverse pregnancy outcomes including recurrent miscarriage (RM). However, an in-depth understanding of the systematic and decidual immune environment in RM remains largely lacking. In this study, we utilized single-cell RNA-sequencing (scRNA-seq) to comparably analyze the cellular and molecular signatures of decidual and peripheral leukocytes in normal and unexplained RM pregnancies at the early stage of gestation. Integrative analysis identifies 22 distinct cell clusters in total, and a dramatic difference in leukocyte subsets and molecular properties in RM cases is revealed. Specifically, the cytotoxic properties of CD8+ effector T cells, nature killer (NK), and mucosal-associated invariant T (MAIT) cells in peripheral blood indicates apparently enhanced pro-inflammatory status, and the population proportions and ligand-receptor interactions of the decidual leukocyte subsets demonstrate preferential immune activation in RM patients. The molecular features, spatial distribution, and the developmental trajectories of five decidual NK (dNK) subsets have been elaborately illustrated. In RM patients, a dNK subset that supports embryonic growth is diminished in proportion, while the ratio of another dNK subset with cytotoxic and immune-active signature is significantly increased. Notably, a unique pro-inflammatory CD56+CD16+ dNK subset substantially accumulates in RM decidua. These findings reveal a comprehensive cellular and molecular atlas of decidual and peripheral leukocytes in human early pregnancy and provide an in-depth insight into the immune pathogenesis for early pregnancy loss.
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Identification and validation of the clinical prediction model and biomarkers based on chromatin regulators in colon cancer by integrated analysis of bulk- and single-cell RNA sequencing data.
BACKGROUND: Chromatin regulators (CRs) are implicated in the development of cancer, but a comprehensive investigation of their role in colon adenocarcinoma (COAD) is inadequate. The purpose of this study is to find CRs that can provide recommendations for clinical diagnosis and treatment, and to explore the reasons why they serve as critical CRs. METHODS: We obtained data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Weighted Gene Co-Expression Network Analysis (WGCNA) screened tumor-associated CRs. LASSO-Cox regression was used to construct the model and to screen key CRs together with support vector machine (SVM), the univariate Cox regression. We used single-cell data to explore the expression of CRs in cells and their communication. Immune infiltration, immune checkpoints, mutation, methylation, and drug sensitivity analyses were performed. Gene expression was verified by quantitative real-time reverse transcription-polymerase chain reaction (qRT-PCR). Pan-cancer analysis was used to explore the importance of hub CRs. RESULTS: We finally obtained 32 tumor-associated CRs. The prognostic model was constructed based on RCOR2, PPARGC1A, PKM, RAC3, PHF19, MYBBP1A, ORC1, and EYA2 by the LASSO-Cox regression. Single-cell data revealed that the model was immune-related. Combined with immune infiltration analysis, immune checkpoint analysis, and tumor immune dysfunction and exclusion (TIDE) analysis, the low-score risk group had more immune cell infiltration and better immune response. Mutation and methylation analysis showed that multiple CRs may be mutated and methylated in colon cancer. Drug sensitivity analysis revealed that the low-risk group may be more sensitive to several drugs and PKM was associated with multiple drugs. Combined with machine learning, PKM is perhaps the most critical gene in CRs. Pan-cancer analysis showed that PKM plays a role in the prognosis of cancers. CONCLUSIONS: We developed a prognostic model for COAD based on CRs. Increased expression of the core gene PKM is linked with a poor prognosis in several malignancies.