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Single-Cell Sequencing Reveals the Heterogeneity of Glioma and Identifies IGFBP2 as A Potential Therapeutic Target.
Background: Glioma is among the most malignant brain tumors, and its heterogeneity contributes significantly to treatment failure. Comprehensive profiling of cellular and molecular heterogeneity across different glioma stages and recurrence states is crucial for understanding therapeutic resistance and identifying novel targets. Accordingly, this study sought to systematically characterize the cellular and molecular heterogeneity of glioma across different stages and recurrence states using single-cell RNA sequencing, and to identify prognostic subtypes and potential therapeutic targets. Methods: We integrated public single-cell RNA sequencing data from glioma specimens, including lower-grade glioma (LGG), glioblastoma (GBM), and paired primary and recurrent tumors. Using these datasets, we identified distinct cellular subpopulations and their molecular signatures. Based on these glioma cell subpopulations, we reclassified gliomas from The Cancer Genome Atlas (TCGA) database into molecular subtypes and constructed a prognostic model. The functional role of a key candidate gene, insulin-like growth factor binding protein 2 (IGFBP2), was validated using in vitro knockdown experiments in mouse and human tumor cells and in vivo therapeutic studies in murine models, including combination therapy with anti-programmed cell death protein 1 (anti-PD-1) immune checkpoint blockade. Results: This analysis revealed that T cells in GBM and recurrent samples were predominantly exhausted, characterized by upregulation of PD-1 and T-cell immunoglobulin and mucin-domain containing-3 (Tim3), while myeloid cells exhibited an immunosuppressive phenotype with elevated expression of macrophage migration inhibitory factor (MIF) and cluster of differentiation (CD)276. We identified 12 distinct glioma cell subpopulations with varying proliferative and hypoxic signatures. Based on these subpopulations, TCGA gliomas were reclassified into two major subtypes. One subtype, enriched with myeloid-derived suppressor cells (MDSCs) and regulatory T cells (Tregs), was associated with poorer patient prognosis. A prognostic model was successfully established using differentially expressed genes between the subtypes. Furthermore, IGFBP2 was highly expressed in glioma cells, and its expression negatively correlated with T cell infiltration. In vitro knockdown of IGFBP2 downregulated programmed death-ligand 1 (PD-L1) expression on tumor cells. In vivo, IGFBP2 knockdown significantly suppressed tumor growth (p < 0.01) and extended survival in tumor-bearing mice (p < 0.05), and its combination with anti-PD-1 therapy markedly enhanced antitumor efficacy. Conclusions: This study provides deeper insights into the cellular ecosystem and heterogeneity of glioma, linking specific cellular features to patient prognosis. We identify IGFBP2 may play a role in regulating immunosuppressive tumor microenvironment and a potential therapeutic target.
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A clinical prognostic model related to T cells based on machine learning for predicting the prognosis and immune response of ovarian cancer.
BACKGROUND: Ovarian cancer (OV) is regarded as one of the most lethal malignancies affecting the female reproductive system, with individuals diagnosed with OV often facing a dismal prognosis due to resistance to chemotherapy and the presence of an immunosuppressive environment. T cells serve as a crucial mediator for immune surveillance and cancer elimination. This study aims to analyze the mechanism of T cell-associated markers in OV and create a prognostic model for clinical use in enhancing outcomes for OV patients. METHODS: Based on the single-cell dataset GSE184880, this study used single-cell data analysis to identify characteristic T cell subsets. Analysis of high dimensional weighted gene co-expression network analysis (hdWGCNA) is utilized to identify crucial gene modules along with their corresponding hub genes. A grand total of 113 predictive models were formed utilizing ten distinct machine learning algorithms along with the combination of the cancer genome atlas (TCGA)-OV dataset and the GSE140082 dataset. The most dependable clinical prognostic model was created utilizing the leave one out cross validation (LOOCV) framework. The validation process for the models was achieved by conducting survival curve analysis and receiver operating characteristic (ROC) analysis. The relationship between risk scores and immune cells was explored through the utilization of the Cibersort algorithm. Additionally, an analysis of drug sensitivity was carried out to anticipate chemotherapy responses across various risk groups. The genes implicated in the model were authenticated utilizing qRT-PCR, cell viability experiments, and EdU assay. RESULTS: This study developed a clinical prognostic model that includes ten risk genes. The results obtained from the training set of the study indicate that patients classified in the low-risk group experience a significant survival advantage compared to those in the high-risk group. The ROC analysis demonstrates that the model holds significant clinical utility. These results were verified using an independent dataset, strengthening the model's precision and dependability. The risk assessment provided by the model also serves as an independent prognostic factor for OV patients. The study also unveiled a noteworthy relationship between the risk scores calculated by the model and various immune cells, suggesting that the model may potentially serve as a valuable tool in forecasting responses to both immune therapy and chemotherapy in ovarian cancer patients. Notably, experimental evidence suggests that PFN1, one of the genes included in the model, is upregulated in human OV cell lines and has the capacity to promote cancer progression in in vitro models. CONCLUSION: We have created an accurate and dependable clinical prognostic model for OV capable of predicting clinical outcomes and categorizing patients. This model effectively forecasts responses to both immune therapy and chemotherapy. By regulating the immune microenvironment and targeting the key gene PFN1, it may improve the prognosis for high-risk patients.
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Developing a Metabolic-Associated Prognostic Index for Risk Stratification and Therapeutic Guidance in Stage I Lung Adenocarcinoma via Multiomics Analysis.
PURPOSE: Early-stage lung adenocarcinoma (LUAD) exhibits substantial clinical heterogeneity that is not fully explained by TNM staging, highlighting the need for biology-driven prognostic tools. Although metabolic reprogramming is an established cancer hallmark, its systematic prognostic significance in stage I LUAD remains unexplored. MATERIALS AND METHODS: We analyzed stage I LUAD samples from The Cancer Genome Atlas, Gene Expression Omnibus, and European Genome-phenome Archive databases. Weighted gene coexpression network analysis and differential expression analysis were conducted to identify metabolic genes associated with LUAD malignancy and prognosis. A metabolic-associated prognostic index (MAPI) was subsequently developed using machine-learning combinations. The performance of MAPI was evaluated from multiple biologic perspectives and at the single-cell level. Key gene functions were experimentally verified in vitro. RESULTS: MAPI robustly stratified patients into high- and low-risk groups with significantly divergent survival outcomes, outperformed conventional clinicopathologic features and previously published signatures, and emerged as an independent prognostic factor across all validation cohorts. The high-risk group was characterized by enhanced cancer stemness, genetic heterogeneity, metabolic reprogramming, immune exclusion, and reduced responsiveness to immunotherapy. We also pinpointed three potential therapeutic agents (paclitaxel, bortezomib, and vincristine) for high-risk patients. The single-cell RNA sequencing further validated the association between MAPI and malignant progression. Functional analyses demonstrated that knockdown of DEGS1 or PLOD1 significantly suppressed LUAD cell proliferation and migration. CONCLUSION: Our results establish MAPI as a biologically interpretable and clinically applicable tool for risk stratification and precision treatment decision making in patients with stage I LUAD.
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Investigation and verification of GIMAP6 as a robust biomarker for prognosis and tumor immunity in lung adenocarcinoma.
BACKGROUND AND AIM: According to previous reports, GTPase of immunity-associated protein 6 (GIMAP6) is essential for autophagy. However, it is unclear how GIMAP6 affects the development and tumor immunity of lung adenocarcinoma (LUAD). METHODS: In the present study, the role of GIMAP6 in vivo and in vitro was examined using reverse transcription-quantitative PCR, western blotting, and Cell Counting Kit-8, colony formation and Transwell assays. Datasets from The Cancer Genome Atlas and Genotype-Tissue Expression databases were thoroughly analyzed using R software. A nomogram was created using GIMAP6 and prognostic characteristics. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes and Gene Set Enrichment Analysis were applied to explore the potential mechanism of GIMAP6 in lung cancer. The link between GIMAP6 and the immunological landscape was studied using single-cell RNA sequencing datasets from Tumor Immune Estimation Resource (TIMER) 2.0 and Tumor Immune Single-cell Hub. RESULTS: Patients with high GIMAP6 expression had improved overall and disease-specific survival compared with those patients with low GIMAP6 expression. According to the receiver operating characteristic and calibration curve, the nomogram based on T stage, N stage and GIMAP6 had predictive value for prognosis. According to functional enrichment analysis, GIMAP6 was primarily involved in T-cell receptor signaling pathway, chemokine signaling pathway, cytokine and cytokine receptor interaction. GIMAP6 was shown to be favorably linked with the infiltration of immune cells and immune-related molecules, including cytotoxic T-lymphocyte associated protein 4, programmed death-ligand 1, and T cell immunoreceptor with Ig and ITIM domains, by single-cell sequencing and TIMER2.0 analysis. The role of GIMAP6 in lung cancer cell proliferation, invasion, migration and immunity was experimentally verified. CONCLUSION: These findings confirmed that GIMAP6 was an effective prognostic molecule that was involved in the regulation of the immune microenvironment of LUAD, and may become a predictor for the efficacy of immunotherapy.
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Integrative single-cell and spatial transcriptomic analyses identify a pathogenic cholangiocyte niche and TNFRSF12A as therapeutic target for biliary atresia.
BACKGROUND AND AIMS: Biliary atresia (BA) is a devastating fibroinflammatory biliary disease that is the leading indication for pediatric liver transplants worldwide. Although cholangiocytes are the primary target cells, the pathogenic mechanisms involving cholangiocytes remain elusive. Here, we aimed to characterize the pathogenic role of cholangiocytes in BA. APPROACH AND RESULTS: Integration of single-cell RNA sequencing of 12 liver tissues (from 9 BA and 3 controls) and the spatial transcriptome of another four liver sections (from 2 BA and 2 controls) provided a comprehensive spatial liver cell atlas of BA. In particular, we identified a cholangiocyte-enriched spatial niche with infiltration of activated HSCs, activated portal fibroblasts, macrovascular endothelial cells, and TREM2 + macrophages that were elevated in the portal triad of BA. This niche was positively correlated with bile duct profiles, liver fibrosis, and poor survival in 2 independent cohorts of patients with BA. Using integrative bioinformatics analyses to mine the cell-cell communication and regulatory network in BA cholangiocytes, we uncovered the fibroinflammatory phenotype of cholangiocytes with TNFSF12-TNFRSF12A as a significant signal. Genetic ablation or blockade of TNFRSF12A suppresses liver injury, inflammation, and bile duct profiles in a mouse model of disease. Using human biliary organoids, we revealed that BA organoids expressed higher levels of CCL2 in response to TNFSF12 stimulation and promoted monocyte chemotaxis via the CCL2-CCR2 axis. CONCLUSIONS: Pathogenic cholangiocytes-enriched niche identifies TNFRSF12A as a potential therapeutic target for BA.
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Primate gastrulation and early organogenesis at single-cell resolution.
Our understanding of human early development is severely hampered by limited access to embryonic tissues. Due to their close evolutionary relationship with humans, nonhuman primates are often used as surrogates to understand human development but currently suffer from a lack of in vivo datasets, especially from gastrulation to early organogenesis during which the major embryonic cell types are dynamically specified. To fill this gap, we collected six Carnegie stage 8-11 cynomolgus monkey (Macaca fascicularis) embryos and performed in-depth transcriptomic analyses of 56,636 single cells. Our analyses show transcriptomic features of major perigastrulation cell types, which help shed light on morphogenetic events including primitive streak development, somitogenesis, gut tube formation, neural tube patterning and neural crest differentiation in primates. In addition, comparative analyses with mouse embryos and human embryoids uncovered conserved and divergent features of perigastrulation development across species-for example, species-specific dependency on Hippo signalling during presomitic mesoderm differentiation-and provide an initial assessment of relevant stem cell models of human early organogenesis. This comprehensive single-cell transcriptome atlas not only fills the knowledge gap in the nonhuman primate research field but also serves as an invaluable resource for understanding human embryogenesis and developmental disorders.
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Systemic characterization of alternative splicing related to prognosis, immune infiltration, and drug sensitivity analysis in ovarian cancer.
BACKGROUND: Alternative splicing (AS) plays an essential role in tumorigenesis and progression. This study intended to construct an innovative prognostic model based on AS events to gain more precise survival prediction and search for potential therapeutic targets in ovarian cancer. METHODS: Seven types of AS events in ovarian serous cystadenocarcinoma (OV) patients with RNA-seq were obtained using The Cancer Genome Atlas (TCGA) SpliceSeq tool and database. Cox and Kaplan-Meier curve analyses were employed to establish the prognostic models. Relying on drug sensitivity data from the CellMiner database, Genomics of Drug Sensitivity (GDS) was adopted to estimate the platinum-sensitive analysis. Furthermore, a prognostic splicing factor (SF)-AS network was constructed using Cytoscape. Finally, in order to explore the influence of the tumor microenvironment on the prognosis of OV patients, we first combined a similar network fusion and consensus clustering (SNF-CC) algorithm to identify three OV subtypes based on survival-related AS events and then utilized single-sample Gene Set Enrichment Analysis (ssGSEA) method to perform immune cell infiltration analysis. RESULTS: A total of 48,049 AS events and 21,841 related genes were selected from 318 OV samples, and 2,206 AS events associated with disease-free survival (DFS) were identified. Multivariate Cox and Kaplan-Meier curve analyses were then employed to establish the prognostic models. Receiver operating characteristic (ROC) analysis from 0.59 to 0.75 showed that these models were highly efficient in distinguishing patient survival. GDS was adopted with the CellMiner database to provide some insights for platinum-sensitive analysis of OV. Furthermore, a prognostic SF-AS network, which discovered a significant connection between SFs and prognostic AS genes, was constructed using Cytoscape. The combined SNF-CC algorithm revealed three distinct OV subtypes based on the prognostic AS events, and the associations between this novel molecular classification and immune cell infiltration were further explored. CONCLUSIONS: We developed a powerful prognostic AS signature for OV and provided a deeper understanding of SF-AS network regulatory mechanisms, as well as platinum-sensitive and cancer immune microenvironments. These results revealed various candidate biomarkers and potential targets for OV treatment strategies.
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TMED2/9/10 Serve as Biomarkers for Poor Prognosis in Head and Neck Squamous Carcinoma.
Background: Head and neck squamous carcinoma (HNSC) is one of the most common malignant tumors with high incidence and poor prognosis. Transmembrane emp24 structural domain (TMED) proteins are involved in protein transport and vesicle budding processes, which have implicated various malignancies' progression. However, the roles of TMEDs in HNSC, especially in terms of development and prognosis, have not been fully elucidated. Methods: We applied TIMER 2.0, UALCAN, GEPIA 2, Kaplan-Meier plotter, GEO, The Human Protein Atlas (HPA), cBioPortal, Linkedomics, Metascape, GRNdb, STRING, and Cytoscape to investigate the roles of TMED family members in HNSC. Results: Compared with normal tissues, the mRNA expression levels of TMED1/2/4/5/7/8/9/10 were significantly increased in the TCGA HNSC dataset. And we combined GEPIA 2 and Kaplan-Meier Plotter to select TMED2/9/10 with prognostic value. Then we detected the levels of mRNA in the GEO HNSC database and the protein expression in HPA. It was found that the mRNA and protein expression levels of TMED2/9/10 were increased in HNSC. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed that TMED2/9/10 and their co-expressed genes promoted the malignant behavior of tumors by participating in biological processes such as intracellular transferase complex, protein transport, focal adhesion, intracellular protein processing. Single-cell analysis and immune infiltration analysis suggested that immune responses of cancer-associated fibroblasts and endothelial cells might be associated with prognosis. Finally, the transcription factors-genes network and protein-protein functional interaction network pointed to genes such as X-box binding protein 1 (XBP1) and TMED7, which might cooperate with TMED2/9/10 to change the progression of HNSC. Conclusions: Our study implied that TMED2/9/10 and related genes mightjointly affect the prognosis of HNSC, providing specific clues for further experimental research, personalized diagnosis strategies, and targeted clinical therapy for HNSC.
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Systematic analysis of UBE2D3 and its association with prognosis, tumor microenvironment, and drug sensitivity in renal clear cell carcinoma.
BACKGROUND: Dysregulated copper metabolism contributes to kidney renal clear cell carcinoma (KIRC) progression. This study systematically investigated the prognostic and therapeutic implications of copper metabolism-related gene UBE2D3 in KIRC. METHODS: Utilizing The Cancer Genome Atlas (TCGA)-KIRC transcriptomic data and clinical records (n=530), we analyzed differential expression, survival correlation, and clinical significance of copper metabolism-related genes. UBE2D3 was prioritized based on diagnostic/prognostic value. Functional enrichment, immune infiltration [Cell-Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT), Tumor Immune Estimation Resource (TIMER), Tumor Immune Single-cell Hub 2 (TISCH2)], drug sensitivity (oncoPredict), and molecular docking analyses were performed. UBE2D3 protein expression was validated via immunohistochemistry (IHC) in 9 clinical KIRC specimens. RESULTS: UBE2D3 expression was significantly downregulated in KIRC versus normal tissue and inversely correlated with advanced tumor stage/grade (P<0.001). Low UBE2D3 expression predicted poorer overall survival [hazard ratio (HR) =0.67, P=0.002] and served as an independent prognostic factor. UBE2D3 loss associated with altered tumor immune microenvironment, including reduced M1 macrophage infiltration and increased regulatory T cells. Single-cell RNA sequencing confirmed predominant UBE2D3 expression in monocytes/macrophages. Hypermethylation and copy number variation significantly influenced UBE2D3 dysregulation. Drug sensitivity analysis revealed that UBE2D3-low tumors were more responsive to vincristine, bosutinib, and ambazone [median inhibitory concentration difference (IC50 diff.) P<0.01], supported by favorable molecular docking affinities. CONCLUSIONS: UBE2D3 may function as a tumor suppressor in KIRC, as suggested by its downregulation in advanced stages and correlation with improved survival. Correspondingly, its loss appears to correlate with advanced disease, an immunosuppressive tumor microenvironment (TME), and unfavorable prognosis. This potential role in modulating immune cell dynamics and drug sensitivity positions UBE2D3 as a promising biomarker for prognostication and personalized therapy selection in KIRC.
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Lipid metabolism-related gene expression in the immune microenvironment predicts prognostic outcomes in renal cell carcinoma.
BACKGROUND: Rates of renal cell carcinoma (RCC) occurrence and mortality are steadily rising. In an effort to address this issue, the present bioinformatics study was developed with the goal of identifying major lipid metabolism biomarkers and immune infiltration characteristics associated with RCC cases. METHODS: The Cancer Genome Atlas (TCGA) and E-MTAB-1980 were used to obtain matched clinical and RNA expression data from patients diagnosed with RCC. A LASSO algorithm and multivariate Cox regression analyses were employed to design a prognostic risk model for these patients. The tumor immune microenvironment (TIME) in RCC patients was further interrogated through ESTIMATE, TIMER, and single-cell gene set enrichment analysis (ssGSEA) analyses. Gene Ontology (GO), KEGG, and GSEA enrichment approaches were further employed to gauge the mechanistic basis for the observed results. Differences in gene expression and associated functional changes were then validated through appropriate molecular biology assays. RESULTS: Through the approach detailed above, a risk model based on 8 genes associated with RCC patient overall survival and lipid metabolism was ultimately identified that was capable of aiding in the diagnosis of this cancer type. Poorer prognostic outcomes in the analyzed RCC patients were associated with higher immune scores, lower levels of tumor purity, greater immune cell infiltration, and higher relative immune status. In GO and KEGG enrichment analyses, genes that were differentially expressed between risk groups were primarily related to the immune response and substance metabolism. GSEA analyses additionally revealed that the most enriched factors in the high-risk group included the stable internal environment, peroxisomes, and fatty acid metabolism. Subsequent experimental validation in vitro and in vivo revealed that the most significantly differentially expressed gene identified herein, ALOX5, was capable of suppressing RCC tumor cell proliferation, invasivity, and migration. CONCLUSION: In summary, a risk model was successfully established that was significantly related to RCC patient prognosis and TIME composition, offering a robust foundation for the development of novel targeted therapeutic agents and individualized treatment regimens. In both immunoassays and functional analyses, dysregulated lipid metabolism was associated with aberrant immunological activity and the reprogramming of fatty acid metabolic activity, contributing to poorer outcomes.