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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.
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NOP16 promotes hepatocellular carcinoma progression and triggers EMT through the Keap1-Nrf2 signaling pathway.
BACKGROUND: Nucleolar protein 16 (NOP16) is present in the protein complex of the nucleolus. The NOP16 promoter contains a c-Myc binding site, and the transcriptional regulation by c-Myc directly regulates NOP16 expression levels. OBJECTIVE: Dysregulation of NOP 16 is currently reported in only a small number of cancers. In this study, the expression profile of NOP 16 in hepatocellular carcinoma (LIHC) and its clinical significance were analyzed. METHODS: NOP16 expression in hepatocellular carcinoma (LIHC) and its relationship with the clinical characters of LIHC were examined using the Cancer Genome Atlas (TCGA), the Gene Expression comprehensive database (GEO), Kaplan-Meier survival analysis, univariate Cox analysis, multivariate Cox analysis, ROC curve analysis of KEGG enrichment, GSEA enrichment, in vitro experiments (e.g., siRNA interference of NOP16 expression in hepatoma cells, Keap1-Nrf2 pathway, cell cycle, cell apoptosis and Transwell assays), and LIHC single-cell sequencing (scRNA). RESULTS: Pan-cancer analysis revealed that NOP16 was highly expressed in 20 cancer types, including LIHC, and high NOP16 expression was an independent adverse prognostic factor in LIHC patients. The expression levels of NOP16 mRNA and protein were significantly increased in tumour tissues of LIHC patients compared to normal tissues. The functions of co-expressed genes were primarily enriched in the cell cycle and reactive oxygen species metabolism. The experimental results showed that knockdown of NOP16 activated the Keap/Nrf2 signalling pathway and inhibited the invasion, migration, and EMT progression of LIHC cells. LIHC scRNA-seq data showed that NOP16 was primarily expressed in T lymphocytes. CONCLUSIONS: NOP16 promoted cancer development in LIHC and caused an imbalance in Keap/Nrf2 signalling, which subsequently caused the aberrant expression of genes typical for EMT, cell cycle progression and apoptosis. NOP16 is a potential prognostic marker and therapeutic target for hepatocellular carcinoma progression.
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Identification of a novel prognostic signature based on vitamin metabolism clustering-related genes in lung adenocarcinoma.
BACKGROUND: Vitamins, and their metabolic processes play essential regulatory roles in controlling proliferation, differentiation, and growth in carcinogenesis. However, the role of vitamin metabolism in lung adenocarcinoma (LUAD) has rarely been reported. Here, we established a novel prognostic model based on vitamin metabolism-related genes in LUAD. METHODS: In this research, we aimed to identify vitamin metabolism associated with differentially expressed genes (DEGs) in LUAD utilizing The Cancer Genome Atlas (TCGA)-LUAD, GSE68465 and GSE72094 data. Unsupervised clustering classified patients into distinct subgroups. By utilizing least absolute shrinkage and selection operator (LASSO)-Cox regression analysis, vitamin metabolism-related genes could be used to construct prognostic model. Then the vitamin metabolism gene-related risk score (VRS) was calculated based on best cut-off splitting. Kaplan-Meier analysis, time-dependent receiver operating characteristic (ROC) analysis, univariate and multivariate Cox analyses, chemotherapeutic drugs sensitivity analysis, immune infiltration analysis and nomogram were conducted to verify our models' accuracy. Finally, CPS1 was identified as a relevant diagnostic marker using Random Forests algorithms, single-cell RNA sequencing data was used to confirm its expression. RESULTS: We investigated the relationship between vitamin metabolism patterns, overall survival (OS), and immune infiltration levels of patients with LUAD. A prognostic signature consisting of 11 genes was developed, which was able to classify patients into high and low VRS groups. Through gene enrichment analysis, cell cycle was mainly enriched. Compared to the low VRS group, the high VRS group exhibited poorer OS, as demonstrated by the Kaplan-Meier survival analysis. Furthermore, VRS was identified as an independent predictor of poor prognosis and poor OS, as indicated by both univariate and multivariate Cox regression analyses. Additionally, a nomogram was constructed to improve the accuracy of survival predictions in LUAD patients. We also found that the two groups of patients might respond differently to immune targets and anti-tumor drugs. CPS1 was identified as a relevant diagnostic marker and the expression was also as confirmed by single-cell RNA sequencing data. CONCLUSIONS: Overall, our findings suggest that vitamin metabolism can influence the prognosis of LUAD patients, and our prognostic signature represents a potentially helpful resource for predicting patient outcomes and informing clinical decision-making.
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Comprehensive analysis of immune ferroptosis gene in renal clear cell carcinoma: prognosis and influence of tumor microenvironment.
OBJECTIVE: We conducted an in-depth study of the immune system and ferroptosis to identify prognostic biomarkers and therapeutic targets for renal clear cell carcinoma. METHODS: Immune ferroptosis-related differentially expressed genes (IFR-DEGs) were selected from The Cancer Genome Atlas (TCGA). A lasso-Cox risk scoring model was established; its prognostic value was determined using prognostic analysis and single multivariate Cox analysis. Model genes were subjected to subcellular fluorescence localization, mRNA and protein expression analyses, and single-cell RNA sequencing localization analysis. Risk score was analyzed using the immune score, immune infiltrating cell correlation, immune checkpoint, TIDE, and drug sensitivity. RESULTS: A total of 103 IFR-DEGs were identified; a risk model comprising ACADSB, CHAC1, LURAP1L, and PLA2G6 was established. The survival curve, single multivariate Cox regression, and receiver operating characteristic (ROC) curve analysis showed that the model had good predictive ability (p < 0.05). It was also validated using the validation set and total cohort. Subcellular fluorescence localization revealed that ACADSB, CHAC1, and PLA2G6 were distributed in the cytoplasm and LURAP1L in the nucleus. The mRNA and protein expression trends were consistent. Single-cell RNA sequencing mapping revealed that ACADSB was enriched in distal tubule cell clusters. In the Kidney renal clear cell carcinoma (KIRC) mutation correlation analysis, 1.56% of the patients were found to have genetic alterations; The Spearman correlation analysis of model gene mutations showed that ACADSB was positively correlated with LURAP1L, which may have a synergistic effect; it was negatively correlated with CHAC1 and PLA2G6, and CHAC1 was negatively correlated with LURAP1L, which may have an antagonistic effect. Model and immune correlation analyses found that high-risk patients had significantly higher levels of CD8+ T cells, regulatory T cells (Tregs), immune checkpoints, immune scores, and immune escape than those in low-risk patients. High-risk patients had a higher susceptibility to small-molecule drugs. CONCLUSION: A novel prognostic model of immune ferroptosis-related genes (ACADSB, CHAC1, LURAP1L, and PLA2G6), which plays an important role in immune infiltration, microenvironment, and immune escape, was constructed. It effectively predicts the survival of patients with KIRC.