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Novel prognostic matrisome-related gene signature of head and neck squamous cell carcinoma.
Background: Head and neck squamous cell carcinoma (HNSCC) is a common malignancy of the mucosal epithelium of the oral cavity, pharynx, and larynx. Laryngeal squamous cell carcinoma (LSCC) and oral squamous cell carcinoma are common HNSCC subtypes. Patients with metastatic HNSCC have a poor prognosis. Therefore, identifying molecular markers for the development and progression of HNSCC is essential for improving early diagnosis and predicting patient outcomes. Methods: Gene expression RNA-Seq data and patient clinical traits were obtained from The Cancer Genome Atlas-Head and Neck Squamous Cell Carcinoma (TCGA-HNSC) and Gene Expression Omnibus databases. Differentially expressed gene (DEG) screening was performed using the TCGA-HNSC dataset. Intersection analysis between the DEGs and a list of core matrisome genes obtained from the Matrisome Project was used to identify differentially expressed matrisome genes. A prognostic model was established using univariate and multivariate Cox regression analyses, least absolute shrinkage, and selection operator (LASSO) regression analysis. Immune landscape analysis was performed based on the single-sample gene set enrichment analysis algorithm, Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, prognostic value, receiver operating characteristic curve analysis, and gene mutation analyses. Immunohistochemical results regarding prognostic protein levels were obtained from the Human Protein Atlas. Single-gene RNA-sequencing data were obtained from GSE150321 and GSE172577 datasets. CCK-8 and Transwell assays were used to confirm cell proliferation and migration. Results: A total of 1,779 DEGs, including 939 upregulated and 840 downregulated genes, between tumor and normal samples were identified using the TCGA-HNSC microarray data. Intersection analysis revealed 52 differentially expressed matrisome-related genes. After performing univariate and multivariate Cox regression and LASSO analyses, a novel prognostic model based on six matrisome genes (FN1, LAMB4, LAMB3, DMP1, CHAD, and MMRN1) for HNSCC was established. This risk model can successfully predict HNSCC survival. The high-risk group had worse prognoses and higher enrichment of pathways related to cancer development than the low-risk group. Silencing LAMB4 in HNSCC cell lines promoted cell proliferation and migration. Conclusion: This study provides a novel prognostic model for HNSCC. Thus, FN1, LAMB4, LAMB3, DMP1, CHAD, and MMRN1 may be the promising biomarkers for clinical practice.
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Identification of Malignant Cell Populations Associated with Poor Prognosis in High-Grade Serous Ovarian Cancer Using Single-Cell RNA Sequencing.
To reveal tumor heterogeneity in ovarian cancer, we performed single-cell RNA sequencing (RNA-seq). We obtained The Cancer Genome Atlas (TCGA) survival data and TCGA gene expression data for a Kaplan-Meier plot showing the association of each tumor population with poor prognosis. As a result, we found two malignant tumor cell subtypes associated with poor prognosis. Next, we performed trajectory analysis using scVelo and Monocle3 and cell-cell interaction analysis using CellphoneDB. We found that one malignant population included the earliest cancer cells and cancer stem-like cells. Furthermore, we identified SLC3A1 and PEG10 as the marker genes of cancer-initiating cells. The other malignant population expressing CA125 (MUC16) is associated with a decrease in the number of tumor-infiltrating cytotoxic T lymphocytes (CTLs). Moreover, cell-cell interaction analysis implied that interactions mediated by LGALS9 and GAS6, expressed by this malignant population, caused the CTL suppression. The results of this study suggest that two tumor cell populations, including a cancer-initiating cell population and a population expressing CA125, survive the initial treatment and suppress antitumor immunity, respectively, and are associated with poor prognosis. Our findings offer a new understanding of ovarian cancer heterogeneity and will aid in the development of diagnostic tools and treatments.
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Single-cell analyses EMP1 as a marker of the ratio of M1/M2 macrophages is associated with EMT, immune infiltration, and prognosis in bladder cancer.
BACKGROUND: Bladder cancer is among the most lethal urinary system cancers across the globe. Macrophage 1 and Macrophage 2 play an essential role in the pathogenesis of tumors. Nevertheless, prior studies failed to investigate the implication of the two cells, working in combination, in the development, growth, progression and metastasis of bladder cancer. METHODS: We computed the M1/M2 ratio of the samples retrieved from The Cancer Genome Atlas (TCGA) by using the Cibersortx algorithm and calculated the ratio in 32 patients in our series by employing flow cytometry. SurvivalRandomForest was utilized to reduce the dimension of the list of the M1/M2-related genes, with an aim to obtain the most survival-predictive gene (EMP1) encoding epithelial membrane protein 1 (EMP1). The EMP1 was biologically characterized by using Gene Set Enrichment Analysis (GSEA), Gene Set Variation Analysis (GSVA), and Gene Ontology (GO). The single-cell transcriptome (sc-RNA) analysis was then applied to further look into the function of EMP1. Finally, Cellchat was employed to examine the interaction between macrophages and epithelium cells. RESULTS: The results showed that higher M1/M2 ratio was found to be associated with a more favorable prognosis of bladder cancer. EMP1 was identified to be the key gene indicative of M1/M2 ratio and higher EMP1 expression was associated with poor prognosis. Further analyses showed that EMP1 might promote tumor invasion and metastasis via epithelial-mesenchymal transition (EMT) and focal adhesion (FA). Moreover, the expression level of EMP1 could serve as an indicator of immunotherapy efficacy. The scRNA-seq data indicated that EMP1 in cancer cells was strongly associated with tumor proliferation. Finally, the Cellchat results exhibited that EMP1 might promote the interaction between macrophages and cancer cells through the fibronectin 1-syndecan 1 (FN1-SDC1) pathway. CONCLUSION: Our study identified EMP1, an M1/M2-related gene, the expression of which may act as a prognostic indicator for the proliferation, metastasis, and response to immunotherapy. EMP1 might be involved in the regulation on M1/M2 ratio.
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Telomemore enables single-cell analysis of cell cycle and chromatin condensation.
Single-cell RNA-seq methods can be used to delineate cell types and states at unprecedented resolution but do little to explain why certain genes are expressed. Single-cell ATAC-seq and multiome (ATAC + RNA) have emerged to give a complementary view of the cell state. It is however unclear what additional information can be extracted from ATAC-seq data besides transcription factor binding sites. Here, we show that ATAC-seq telomere-like reads counter-inituively cannot be used to infer telomere length, as they mostly originate from the subtelomere, but can be used as a biomarker for chromatin condensation. Using long-read sequencing, we further show that modern hyperactive Tn5 does not duplicate 9 bp of its target sequence, contrary to common belief. We provide a new tool, Telomemore, which can quantify nonaligning subtelomeric reads. By analyzing several public datasets and generating new multiome fibroblast and B-cell atlases, we show how this new readout can aid single-cell data interpretation. We show how drivers of condensation processes can be inferred, and how it complements common RNA-seq-based cell cycle inference, which fails for monocytes. Telomemore-based analysis of the condensation state is thus a valuable complement to the single-cell analysis toolbox.
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Multicellular factor analysis of single-cell data for a tissue-centric understanding of disease.
Biomedical single-cell atlases describe disease at the cellular level. However, analysis of this data commonly focuses on cell-type-centric pairwise cross-condition comparisons, disregarding the multicellular nature of disease processes. Here, we propose multicellular factor analysis for the unsupervised analysis of samples from cross-condition single-cell atlases and the identification of multicellular programs associated with disease. Our strategy, which repurposes group factor analysis as implemented in multi-omics factor analysis, incorporates the variation of patient samples across cell-types or other tissue-centric features, such as cell compositions or spatial relationships, and enables the joint analysis of multiple patient cohorts, facilitating the integration of atlases. We applied our framework to a collection of acute and chronic human heart failure atlases and described multicellular processes of cardiac remodeling, independent to cellular compositions and their local organization, that were conserved in independent spatial and bulk transcriptomics datasets. In sum, our framework serves as an exploratory tool for unsupervised analysis of cross-condition single-cell atlases and allows for the integration of the measurements of patient cohorts across distinct data modalities.
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Lumican is a potential predictor on the efficacy of concurrent chemoradiotherapy in cervical squamous cell carcinoma.
PURPOSE: To identify new novel biomarkers for predicting the efficacy of concurrent chemoradiotherapy(CCRT) in cervical squamous cell carcinoma(CESC). METHODS: Gene expression datasets GSE56363, GSE5787, and GSE168009 were analyzed to identify candidate genes to predict the efficacy of CCRT in CESC. Single-cell RNA sequencing (scRNA-seq) data from GSE168652 and CESC patients in The Cancer Genome Atlas(TCGA) were systematically analyzed to explore possible molecular mechanisms. Kaplan-Meier evaluated the correlation between LUM (Lumican) and prognostic significance. The expression of LUM protein in biopsy tissues before CCRT was detected by immunohistochemistry in 15 CESC patients. RESULTS: LUM mRNA levels were significantly upregulated in nonresponders of CESC.patients receiving CCRT and positively correlated with poor therapeutic effect. Furthermore, high expression of LUM influenced the immune microenvironment in CESC patient-derived organoids treated with CCRT. LUM overexpression in CESC cells induced resistance to CCRT, potentially via immune landscape modulation. Gene Set Enrichment Analysis (GSEA) revealed that possible mechanisms underlying resistance to CCRT might involve the PARs and IL1 signaling pathway affecting the immune landscape. CONCLUSIONS: High LUM expression is correlated with poor efficacy in CESC patients receiving CCRT, possibly through the PARs and IL1 signaling pathway affecting the immune landscape.
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Single-cell analysis identifies inflammatory and tissue remodeling tumor-associated macrophages distinct from M1/M2 paradigm.
Tumor-associated macrophages (TAMs) exert both pro- and antitumoral functions that influence cancer progression and patient prognosis. However, single-cell RNA sequencing (scRNA-seq) studies have revealed that TAM heterogeneity remains incompletely characterized. By performing an unbiased, integrated in silico analyses of publicly available scRNA-seq datasets, comprising samples from blood, tumor, and nontumoral mammary tissue from both patients with breast cancer (BRCA) and healthy individuals, we identified seven transcriptional signatures corresponding to distinct TAM subsets, exhibiting unique functional profiles, including heightened interferon responses, scavenging, and matrix remodeling, the latter two being characteristic of tissue repair. Notably, none of these subsets aligned with the M1/M2 classification of macrophage (Mϕ) polarization. Interferon-associated genes were predominantly enriched in blood monocytes, whereas tissue-repair-associated signatures were more abundant in tissue-resident Mϕ, suggesting that TAMs bearing these signatures resemble monocyte-derived or tissue-resident Mϕs, respectively. Importantly, TAM subsets expressing interferon-associated genes were associated with improved survival compared to tissue-repairing TAMs in a BRCA cohort from The Cancer Genome Atlas. Additionally, one signature was heightened in peripheral monocytes from BRCA patients compared with healthy individuals, which was experimentally validated in a pilot study of Mexican BRCA patients. We concluded that these signatures are a closer description of TAM heterogeneity in BRCA.
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Cellular taxonomy and spatial organization of the murine ventral posterior hypothalamus.
The ventral posterior hypothalamus (VPH) is an anatomically complex brain region implicated in arousal, reproduction, energy balance, and memory processing. However, neuronal cell type diversity within the VPH is poorly understood, an impediment to deconstructing the roles of distinct VPH circuits in physiology and behavior. To address this question, we employed a droplet-based single-cell RNA sequencing (scRNA-seq) approach to systematically classify molecularly distinct cell populations in the mouse VPH. Analysis of >16,000 single cells revealed 20 neuronal and 18 non-neuronal cell populations, defined by suites of discriminatory markers. We validated differentially expressed genes in selected neuronal populations through fluorescence in situ hybridization (FISH). Focusing on the mammillary bodies (MB), we discovered transcriptionally-distinct clusters that exhibit neuroanatomical parcellation within MB subdivisions and topographic projections to the thalamus. This single-cell transcriptomic atlas of VPH cell types provides a resource for interrogating the circuit-level mechanisms underlying the diverse functions of VPH circuits.
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Comprehensive analysis of lower mitochondrial complex I expression is associated with cell metastasis of clear cell renal cell carcinoma.
BACKGROUND: Clear cell renal cell carcinoma (ccRCC) is characterized by high metastasis potential. It is of great importance to explore the mechanisms underlying ccRCC metastasis and to enable development of potent therapeutics. The mitochondrial complex I (CI) had been considered to play an important role in the development of cancers, but less known in ccRCC. METHODS: We utilized available public databases of ccRCC, including single-cell RNA sequencing (scRNA-seq) data GSE73121 and The Cancer Genome Atlas-kidney renal clear cell carcinoma (TCGA-KIRC). Principal component analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (tSNE) analysis were evaluated the heterogeneity of metastatic renal cell carcinoma (mRCC) and primary renal cell carcinoma (pRCC). Protein-protein interaction (PPI) network identified critical gene. Gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) performed to explore the potential biologic pathways. RESULTS: Our study revealed a significant gene expression heterogeneity between pRCC and mRCC. A PPI network based on differentially expressed genes (DEGs) identified electron transport chain (ETC), especially mitochondrial CI, as the key network hub. Further analysis revealed that the role of mitochondrial CI is associated with tumor metastasis and immune responds of ccRCC. Although CI had low frequency mutations in ccRCC, CI expression is associated with the high frequency mutated genes. A prognosis model included 7 CI genes, and these had a significant effect on overall survival (OS). The area under the curve at 1, 3, and 5 years was 0.717, 0.685, and 0.728, respectively. Transcription factor analysis predicted that PPARG possibly is a potential transcription activator of CI genes in ccRCC. CONCLUSIONS: Overall, we found that CI expression is associated with ccRCC progress. CI and PPARG may be potential biomarkers for metastatic ccRCC.
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Construction of a Multitissue Cell Atlas Reveals Cell-Type-Specific Regulation of Molecular and Complex Phenotypes in Pigs.
The systematic characterization of cellular heterogeneity among tissues and cell-type-specific regulation underlying complex phenotypes remains elusive in pigs. Within the Pig Genotype-Tissue Expression (PigGTEx) project, this work presents a single-cell transcriptome atlas of adult pigs encompassing 229 268 high-quality nuclei from 19 tissues, annotated to 67 major cell types. Besides cellular heterogeneity within and across tissues, this work further characterizes prominent tissue-specific features and functions of muscle, epithelial, and immune cells. Through deconvoluting 3921 bulk RNA-seq samples from 17 matching tissues, this work dissects thousands of genetic variants with cell-type interaction effects on gene expression (ieQTL). By colocalizing these ieQTL with variants associated with 268 complex traits, new insights into the cellular mechanisms behind these traits are provided. Moreover, this work highlights that orthologous genes with cell-type-specific regulation in pigs exhibit significant heritability enrichment for some human complex phenotypes. Altogether, this work provides a valuable resource and highlights novel insights in cellular regulation of complex traits for accelerating pig precision breeding and human biomedical research.