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Identifying genes associated with Sorafenib resistance in hepatocellular carcinoma to develop risk model.
BACKGROUND: Hepatocellular carcinoma (HCC) poses a considerable global health challenge, notably due to the resistance to sorafenib therapy, which significantly impedes effective treatment strategies. This study aimed to identify potential resistance-associated genes and develop a robust prognostic model to predict patient outcomes. METHODS: We utilized transcriptomic data from the gene expression omnibus (GEO) database, focusing on sorafenib-resistant Huh7 and MHCC97H cell lines (GSE94550 and GSE176151), and integrated expression, mutation, and clinical data from the cancer genome atlas (TCGA) and international cancer genome consortium (ICGC) databases. Single-cell RNA sequencing data (GSE149614) were processed with the Seurat and Harmony R packages for quality control and integration. Differential gene expression analysis, consensus clustering, and principal component analysis were performed to identify significant genes and stratify patients based on prognostic outcomes. RESULTS: The analysis revealed 305 potential resistance-associated genes, with a seven-gene (ANAPC13, NCAPD2, KIF2C, CDK5RAP2, MANBA, PPAT, and LPCAT1) risk model demonstrating significant prognostic capability, indicated by area under curve values of 0.824, 0.746, and 0.717 for 1, 3, and 5-year survival predictions, respectively. Notably, immune cell infiltration analyses highlighted significant correlations between risk scores and specific immune cell types, suggesting potential therapeutic targets. Drug sensitivity analysis further identified various compounds with lower IC50 values in high-risk groups. To facilitate clinical application, a nomogram plot was designed. CONCLUSION: This study provides a comprehensive framework for understanding sorafenib resistance in HCC, alongside a validated prognostic model that holds potential for clinical application.
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Single-cell transcriptomic atlas of enteroendocrine cells along the murine gastrointestinal tract.
BACKGROUND: Enteroendocrine cells (EECs) produce over 20 gut hormones which contribute to intestinal physiology, nutrient metabolism and the regulation of food intake. The objective of this study was to generate a comprehensive transcriptomic map of mouse EECs from the stomach to the rectum. METHODS: EECs were purified by flow-cytometry from the stomach, upper small intestine, lower small intestine, caecum and large intestine of NeuroD1-Cre mice, and analysed by single cell RNA sequencing. Regional datasets were analysed bioinformatically and combined into a large cluster map. Findings were validated by L-cell calcium imaging and measurements of CCK secretion in vitro. RESULTS: 20,006 EECs across the full gastrointestinal tract could be subdivided based on their full transcriptome into 10 major clusters, each exhibiting a different pattern of gut hormone expression. EECs from the stomach were largely distinct from those found more distally, even when expressing the same hormone. Cell clustering was also observed when performed only using genes related to GPCR cell signalling, revealing GPCRs predominating in different EEC populations. Mc4r was expressed in 55% of Cck-expressing cells in the upper small intestine, where MC4R agonism was found to stimulate CCK release in primary cultures. Many individual EECs expressed more than one hormone as well as machinery for activation by multiple nutrients, which was supported by the finding that the majority of L-cells exhibited calcium responses to multiple stimuli. CONCLUSIONS: This comprehensive transcriptomic map of mouse EECs reveals patterns of GPCR and hormone co-expression that should be helpful in predicting the effects of nutritional and pharmacological stimuli on EECs from different regions of the gut. The finding that MC4R agonism stimulates CCK secretion adds to our understanding of the melanocortin system.
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Identification of Immune Cell Infiltration and Immune-Related Genes in the Tumor Microenvironment of Glioblastomas.
Glioblastoma (GBM) is one of the most prevalent malignant brain tumors with poor prognosis. Increasing evidence has revealed that infiltrating immune cells and other stromal components in the tumor microenvironment (TME) are associated with prognosis of GBM. The aim of the present study was to identify immune cells and immune-related genes extracted from TME in GBM. RNA-sequencing and clinical data of GBM were downloaded from The Cancer Genome Atlas (TCGA). Four survival-related immune cells were identified via Kaplan-Meier survival analysis and immune-related differentially expressed genes (DEGs) screened. Functional enrichment and protein-protein interaction (PPI) networks for the genes were constructed. In addition, we identified 24 hub genes and the expressions of 6 of the genes were significantly associated with prognosis of GBM. Finally, the genes were validated in single-cell sequencing studies of GBM, and the immune cells validated in an independent GBM cohort from the Chinese Glioma Genome Atlas (CGGA). Overall, 24 immune-related genes infiltrating the tumor microenvironment were identified in the present study, which could serve as novel biomarkers and immune therapeutic targets.
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Cross-tissue, single-cell stromal atlas identifies shared pathological fibroblast phenotypes in four chronic inflammatory diseases.
BACKGROUND: Pro-inflammatory fibroblasts are critical for pathogenesis in rheumatoid arthritis, inflammatory bowel disease, interstitial lung disease, and Sjögren's syndrome and represent a novel therapeutic target for chronic inflammatory disease. However, the heterogeneity of fibroblast phenotypes, exacerbated by the lack of a common cross-tissue taxonomy, has limited our understanding of which pathways are shared by multiple diseases. METHODS: We profiled fibroblasts derived from inflamed and non-inflamed synovium, intestine, lungs, and salivary glands from affected individuals with single-cell RNA sequencing. We integrated all fibroblasts into a multi-tissue atlas to characterize shared and tissue-specific phenotypes. FINDINGS: Two shared clusters, CXCL10+CCL19+ immune-interacting and SPARC+COL3A1+ vascular-interacting fibroblasts, were expanded in all inflamed tissues and mapped to dermal analogs in a public atopic dermatitis atlas. We confirmed these human pro-inflammatory fibroblasts in animal models of lung, joint, and intestinal inflammation. CONCLUSIONS: This work represents a thorough investigation into fibroblasts across organ systems, individual donors, and disease states that reveals shared pathogenic activation states across four chronic inflammatory diseases. FUNDING: Grant from F. Hoffmann-La Roche (Roche) AG.
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Identifying transposable element expression dynamics and heterogeneity during development at the single-cell level with a processing pipeline scTE.
Transposable elements (TEs) make up a majority of a typical eukaryote's genome, and contribute to cell heterogeneity in unclear ways. Single-cell sequencing technologies are powerful tools to explore cells, however analysis is typically gene-centric and TE expression has not been addressed. Here, we develop a single-cell TE processing pipeline, scTE, and report the expression of TEs in single cells in a range of biological contexts. Specific TE types are expressed in subpopulations of embryonic stem cells and are dynamically regulated during pluripotency reprogramming, differentiation, and embryogenesis. Unexpectedly, TEs are expressed in somatic cells, including human disease-specific TEs that are undetectable in bulk analyses. Finally, we apply scTE to single-cell ATAC-seq data, and demonstrate that scTE can discriminate cell type using chromatin accessibly of TEs alone. Overall, our results classify the dynamic patterns of TEs in single cells and their contributions to cell heterogeneity.
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Identification of TNFRSF21 as an inhibitory factor of osteosarcoma based on a necroptosis-related prognostic gene signature and molecular experiments.
BACKGROUND: Osteosarcoma is one of the most common malignant bone tumors with bad prognosis. Necroptosis is a form of programmed cell death. Recent studies showed that targeting necroptosis was a new promising approach for tumor therapy. This study aimed to establish a necroptosis-related gene signature to evaluated prognosis and explore the relationship between necroptosis and osteosarcoma. METHODS: Data from The Cancer Genome Atlas was used for developing the signature and the derived necroptosis score (NS). Data from Gene Expression Omnibus served as validation. Principal component analysis (PCA), Cox regression, receiver operating characteristic (ROC) curves and Kaplan-Meier survival analysis were used to assess the performance of signature. The association between the NS and osteosarcoma was analyzed via gene set enrichment analysis, gene set variation analysis and Pearson test. Single-cell data was used for further exploration. Among the genes that constituted the signature, the role of TNFRSF21 in osteosarcoma was unclear. Molecular experiments were used to explore TNFRSF21 function. RESULTS: Our data revealed that lower NS indicated more active necroptosis in osteosarcoma. Patients with lower NS had a better prognosis. PCA and ROC curves demonstrated NS was effective to predict prognosis. NS was negatively associated with immune infiltration levels and tumor microenvironment scores and positively associated with tumor purity and stemness index. Single-cell data showed necroptosis heterogeneity in osteosarcoma. The cell communication pattern of malignant cells with high NS was positively correlated with tumor progression. The expression of TNFRSF21 was down-regulated in osteosarcoma cell lines. Overexpression of TNFRSF21 inhibited proliferation and motility of osteosarcoma cells. Mechanically, TNFRSF21 upregulated the phosphorylation levels of RIPK1, RIPK3 and MLKL to promote necroptosis in osteosarcoma. CONCLUSIONS: The necroptosis prognostic signature and NS established in this study could be used as an independent prognostic factor, TNFRSF21 may be a necroptosis target in osteosarcoma therapy.
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Ectopic expression of BEX genes in T-cell acute lymphoblastic leukemia.
T-cell acute lymphoblastic leukemia (T-ALL) is a malignant proliferation of T-cell progenitors originating in the thymus. T-ALL is a heterogenous disease involving the dysregulation of various oncogenes/tumor-suppressor (TS) genes. Loss of the TS gene phosphatase and TENsin homolog (PTEN) is a recurrent alteration, which is often associated with a mature T-ALL subgroup expressing a T-cell receptor. Herein, we used a single-cell RNA-sequencing approach to investigate the impact of the absence of PTEN on pathological development of mouse thymocytes. First, our differential gene expression analysis of tumor cells vs physiologic cells uncovers an ectopic expression, in leukemic cells, of the gene encoding Bex1. Then, to determine the relevance of our observation in humans, we queried a public RNA-sequencing database from the TARGET-TCGA (Therapeutically Applicable Research to Generate Effective Treatments-The Cancer Genome Atlas) project. We show that BEX1, BEX2, and BEX5 genes are ectopically expressed in T-ALL samples and we further found that ectopic BEX expression is mainly restricted to the T-ALL subgroup overexpressing TAL1 oncogene. Proximity ligation assays demonstrated the nuclear colocalization of brain-expressed X-linked 1/2 (BEX1/2) proteins with T-cell acute lymphocytic leukemia protein 1 (TAL1) in T-ALL cells. To investigate their functional role, we generated Jurkat cells with a triple knockout of BEX1, BEX2, and BEX5 using CRISPR-CRISPR-associated protein 9. This genetic inactivation led to reduced cell proliferation, a loss of histone H3 lysine 4 monomethylation (H3K4me1) marks notably at genomic regions enriched for E-box motifs, and dysregulation of several TAL1 target genes. Collectively, our findings suggest that BEX1 and BEX2 may contribute to human T-ALL oncogenesis by acting as cofactors within the TAL1 complex.
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Single-cell atlas of tumor cell evolution in response to therapy in hepatocellular carcinoma and intrahepatic cholangiocarcinoma.
BACKGROUND & AIMS: Intratumor molecular heterogeneity is a key feature of tumorigenesis and is linked to treatment failure and patient prognosis. Herein, we aimed to determine what drives tumor cell evolution by performing single-cell transcriptomic analysis. METHODS: We analyzed 46 hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (iCCA) biopsies from 37 patients enrolled in interventional studies at the NIH Clinical Center, with 16 biopsies collected before and after treatment from 7 patients. We developed a novel machine learning-based consensus clustering approach to track cellular states of 57,000 malignant and non-malignant cells including tumor cell transcriptome-based functional clonality analysis. We determined tumor cell relationships using RNA velocity and reverse graph embedding. We also studied longitudinal samples from 4 patients to determine tumor cellular state and its evolution. We validated our findings in bulk transcriptomic data from 488 patients with HCC and 277 patients with iCCA. RESULTS: Using transcriptomic clusters as a surrogate for functional clonality, we observed an increase in tumor cell state heterogeneity which was tightly linked to patient prognosis. Furthermore, increased functional clonality was accompanied by a polarized immune cell landscape which included an increase in pre-exhausted T cells. We found that SPP1 expression was tightly associated with tumor cell evolution and microenvironmental reprogramming. Finally, we developed a user-friendly online interface as a knowledge base for a single-cell atlas of liver cancer. CONCLUSIONS: Our study offers insight into the collective behavior of tumor cell communities in liver cancer as well as potential drivers of tumor evolution in response to therapy. LAY SUMMARY: Intratumor molecular heterogeneity is a key feature of tumorigenesis that is linked to treatment failure and patient prognosis. In this study, we present a single-cell atlas of liver tumors from patients treated with immunotherapy and describe intratumoral cell states and their hierarchical relationship. We suggest osteopontin, encoded by the gene SPP1, as a candidate regulator of tumor evolution in response to treatment.
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PPP1R14B Promotes Lung Adenocarcinoma Tumorigenesis and Paclitaxel Resistance by Increasing ARPC1A Expression.
To investigate the biological functions of PPP1R14B in lung adenocarcinoma and further elucidate its underlying regulatory mechanisms. The Cancer Genome Atlas clinical cohorts, single-cell sequencing, Kaplan-Meier survival analysis, immunohistochemistry, qRT-PCR, and Western blot were performed to assess the expression and prognostic value of PPP1R14B in lung adenocarcinoma. Then, PPP1R14B-silenced A549 cells and PPP1R14B-overexpressing H1650 cells were constructed. Colony formation, CCK-8, EdU assay, flow cytometry, scratch, and Transwell chamber assays were utilized to evaluate cell proliferation, apoptosis, migration, invasion, and paclitaxel (PTX) resistance. To elucidate the molecular mechanism of PPP1R14B, transcriptome sequencing (RNA-seq) and co-immunoprecipitation assays were conducted. Additionally, a xenograft tumor mouse model was established to explore the in vivo role of PPP1R14B. PPP1R14B exhibited high expression in lung adenocarcinoma and correlated with an unfavorable prognosis. Silencing of PPP1R14B markedly suppressed the proliferation, migration, and invasion of A549 cells, while concurrently promoting apoptosis and sensitizing cells to PTX. Nevertheless, overexpression of PPP1R14B in H1650 cells displayed an opposite trend. PPP1R14B directly interacted with ARPC1A and promoted its expression. Overexpression of ARPC1A reversed the inhibitory effects of PPP1R14B silencing on the malignant biological behaviors of A549 cells. In vivo, silencing of PPP1R14B repressed tumor growth, as evidenced by smaller tumor volume, lower tumor weight, fewer Ki67-positive cells, and more TUNEL-positive cells. PPP1R14B directly binds ARPC1A and increases its expression to facilitate lung adenocarcinoma tumorigenesis and PTX resistance.
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Cellular dynamics across aged human brains uncover a multicellular cascade leading to Alzheimer's disease.
Alzheimer's Disease (AD) is a progressive neurodegenerative disease seen with advancing age. Recent studies have revealed diverse AD-associated cell states, yet when and how they impact the causal chain leading to AD remains unknown. To reconstruct the dynamics of the brain's cellular environment along the disease cascade and to distinguish between AD and aging effects, we built a comprehensive cell atlas of the aged prefrontal cortex from 1.64 million single-nucleus RNA-seq profiles. We associated glial, vascular and neuronal subpopulations with AD-related traits for 424 aging individuals, and aligned them along the disease cascade using causal modeling. We identified two distinct lipid-associated microglial subpopulations, one contributed to amyloid-β proteinopathy while the other mediated the effect of amyloid-β in accelerating tau proteinopathy, as well as an astrocyte subpopulation that mediated the effect of tau on cognitive decline. To model the coordinated dynamics of the entire cellular environment we devised the BEYOND methodology which uncovered two distinct trajectories of brain aging that are defined by distinct sequences of changes in cellular communities. Older individuals are engaged in one of two possible trajectories, each associated with progressive changes in specific cellular communities that end with: (1) AD dementia or (2) alternative brain aging. Thus, we provide a cellular foundation for a new perspective of AD pathophysiology that could inform the development of new therapeutic interventions targeting cellular communities, while designing a different clinical management for those individuals on the path to AD or to alternative brain aging.