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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.
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An externally validated lactylation-associated prognostic signature for overall survival prediction in lung adenocarcinoma identifies TUBA1C as a candidate gene.
BACKGROUND: Lung adenocarcinoma (LUAD) is characterized by marked prognostic heterogeneity. Although lactylation has been implicated in tumor progression and immune regulation, the clinical relevance of lactylation-related transcriptional programs in LUAD remains insufficiently defined. This study aimed to develop and externally validate a lactylation-related gene signature for overall survival prediction in LUAD and to prioritize candidate genes for further biological investigation. METHODS: We conducted a retrospective prediction model development and external validation study by integrating single-cell and bulk transcriptomic data. The Cancer Genome Atlas (TCGA)-LUAD was used as the model development cohort, whereas GSE31210 and GSE72094 were used as independent external validation cohorts, including 503, 226, and 398 patients, respectively. Overall survival was defined as the primary outcome. An optimized lactylation-related gene signature (LRGS) was constructed using machine learning strategies, and its predictive performance was evaluated using the concordance index, Kaplan-Meier survival analysis, and time-dependent receiver operating characteristic (ROC) analysis. In addition, pathway enrichment, tumor microenvironment, genomic alteration, and intercellular communication analyses were performed. Immunohistochemistry and reverse transcription quantitative polymerase chain reaction (RT-qPCR) were further used to validate TUBA1C expression in LUAD tissues and cell lines. RESULTS: The optimal model [StepCox (forward) + random survival forest (RSF)] achieved C-index values of 0.935, 0.668, and 0.637 in the TCGA-LUAD, GSE31210, and GSE72094 cohorts, respectively. The final LRGS consisted of 15 genes and stratified patients into high- and low-risk groups with significantly different overall survival across all cohorts (all P<0.001). Time-dependent ROC analysis demonstrated favorable predictive performance, with areas under the curve (AUCs) of 0.96, 0.98, and 0.99 at 1, 2, and 3 years in TCGA-LUAD. Multivariate Cox analysis confirmed that LRGS was an independent prognostic factor. High-risk tumors were associated with enhanced glycolysis, hypoxia, and PI3K-AKT-mTOR signaling, as well as a less immune-active tumor microenvironment. Single-cell ligand-receptor analysis further inferred relatively increased transforming growth factor-beta (TGF-β), vascular endothelial growth factor (VEGF), and C-X-C motif chemokine ligand (CXCL) communication patterns in the high-risk group. TUBA1C was prioritized as a candidate gene, and showed higher expression in LUAD tissues and NSCLC cell lines in preliminary validation assays. CONCLUSIONS: LRGS may support prognostic stratification of LUAD based on lactylation-related transcriptional features. In addition, TUBA1C may be a potential biomarker worthy of further functional investigation.
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Stromal NNMT overexpression as an independent prognostic biomarker in lung adenocarcinoma and breast carcinoma.
This study investigated the expression profile and prognostic significance of nicotinamide N-methyltransferase (NNMT) in pan-cancer stroma, with focused validation in lung adenocarcinoma (LUAD) and breast carcinoma (BC). Integrated analysis of Genotype-Tissue Expression and The Cancer Genome Atlas databases revealed tumor-specific NNMT mRNA upregulation in these malignancies. Subsequently, the relationship between NNMT mRNA expression levels and clinical parameters across multiple cancer types was examined, with particular emphasis on LUAD, BC, and CRC. Single-cell RNA sequencing data further revealed elevated NNMT expression specifically within stromal compartments, with fibroblasts exhibiting the highest NNMT expression levels among stromal cell populations. Protein-level validation using the Human Protein Atlas database, the National Cancer Institute's Proteomics Data Commons, and the quantitative immunohistochemistry of our independent clinical cohorts demonstrated elevated stromal NNMT protein expression, particularly in cancer-associated fibroblasts. Cox regression analysis and survival curve analysis established stromal NNMT expression as an independent prognostic factor for overall survival in LUAD and BC patients (P < .05). In conclusion, high stromal NNMT levels correlated with poor prognosis, establishing it as a promising biomarker for risk stratification in LUAD and BC.
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The spatiotemporal transcriptional profiling of murine brain during cerebral malaria progression and after artemisinin treatment.
Cerebral malaria (CM) is a severe encephalopathy caused by Plasmodium parasite infection, resulting in thousands of annual deaths and neuro-cognitive sequelae even after anti-malarial drugs treatment. Despite efforts to dissect the mechanism, the cellular transcriptomic reprogramming within the spatial context remains elusive. Here, we constructed single-cell and spatial transcriptome atlases of experimental CM (ECM) male murine brain tissues with or without artesunate (ART) treatment. We identified activated inflammatory endothelial cells during ECM, characterized by a disrupted blood-brain barrier, increased antigen presentation, and leukocyte adhesion. We also observed that inflammatory microglia enhance antigen presentation pathway such as MHC-I to CD8+ cytotoxic T cells. The latter underwent an inflammatory state transition with up-regulated cytokine expression and cytotoxic activity. Multi-omics analysis revealed that the activated interferon-gamma response of injured neurons during ECM and persisted after ART treatment. Overall, our research provides valuable resources for understanding malaria parasite-host interaction mechanisms and adjuvant therapy development.
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The molecular feature of macrophages in tumor immune microenvironment of glioma patients.
BACKGROUND: Gliomas are one of the most common types of primary tumors in central nervous system. Previous studies have found that macrophages actively participate in tumor growth. METHODS: Weighted gene co-expression network analysis was used to identify meaningful macrophage-related gene genes for clustering. Pamr, SVM, and neural network were applied for validating clustering results. Somatic mutation and methylation were used for defining the features of identified clusters. Differentially expressed genes (DEGs) between the stratified groups after performing elastic regression and principal component analyses were used for the construction of MScores. The expression of macrophage-specific genes were evaluated in tumor microenvironment based on single cell sequencing analysis. A total of 2365 samples from 15 glioma datasets and 5842 pan-cancer samples were used for external validation of MScore. RESULTS: Macrophages were identified to be negatively associated with the survival of glioma patients. Twenty-six macrophage-specific DEGs obtained by elastic regression and PCA were highly expressed in macrophages at single-cell level. The prognostic value of MScores in glioma was validated by the active proinflammatory and metabolic profile of infiltrating microenvironment and response to immunotherapies of samples with this signature. MScores managed to stratify patient survival probabilities in 15 external glioma datasets and pan-cancer datasets, which predicted worse survival outcome. Sequencing data and immunohistochemistry of Xiangya glioma cohort confirmed the prognostic value of MScores. A prognostic model based on MScores demonstrated high accuracy rate. CONCLUSION: Our findings strongly support a modulatory role of macrophages, especially M2 macrophages in glioma progression and warrants further experimental studies.
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The coagulation-related genes for prognosis and tumor microenvironment in pancreatic ductal adenocarcinoma.
BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is a malignancy characterized by challenging early diagnosis and poor prognosis. It is believed that coagulation has an impact on the tumor microenvironment of PDAC. The aim of this study is to further distinguish coagulation-related genes and investigate immune infiltration in PDAC. METHODS: We gathered two subtypes of coagulation-related genes from the KEGG database, and acquired transcriptome sequencing data and clinical information on PDAC from The Cancer Genome Atlas (TCGA) database. Using an unsupervised clustering method, we categorized patients into distinct clusters. We investigated the mutation frequency to explore genomic features and performed enrichment analysis, utilizing Gene Ontology (GO) and Kyoto Encyclopedia of Genes (KEGG) to explore pathways. CIBERSORT was used to analyze the relationship between tumor immune infiltration and the two clusters. A prognostic model was created for risk stratification, and a nomogram was established to assist in determining the risk score. The response to immunotherapy was assessed using the IMvigor210 cohort. Finally, PDAC patients were recruited, and experimental samples were collected to validate the infiltration of neutrophils using immunohistochemistry. In addition, and identify the ITGA2 expression and function were identified by analyzing single cell sequencing data. RESULTS: Two coagulation-related clusters were established based on the coagulation pathways present in PDAC patients. Functional enrichment analysis revealed different pathways in the two clusters. Approximately 49.4% of PDAC patients experienced DNA mutation in coagulation-related genes. Patients in the two clusters displayed significant differences in terms of immune cell infiltration, immune checkpoint, tumor microenvironment and TMB. We developed a 4-gene prognostic stratified model through LASSO analysis. Based on the risk score, the nomogram can accurately predict the prognosis in PDAC patients. We identified ITGA2 as a hub gene, which linked to poor overall survival (OS) and short disease-free survival (DFS). Single-cell sequencing analysis demonstrated that ITGA2 was expressed by ductal cells in PDAC. CONCLUSIONS: Our study demonstrated the correlation between coagulation-related genes and the tumor immune microenvironment. The stratified model can predict the prognosis and calculate the benefits of drug therapy, thus providing the recommendations for clinical personalized treatment.
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Functional enrichment analysis of LYSET and identification of related hub gene signatures as novel biomarkers to predict prognosis and immune infiltration status of clear cell renal cell carcinoma.
PURPOSE: The latest research shows that the lysosomal enzyme trafficking factor (LYSET) encoded by TMEM251 is a key regulator of the amino acid metabolism reprogramming (AAMR) and related pathways significantly correlate with the progression of some tumors. The purpose of this study was to explore the potential pathways of the TMEM251 in clear cell renal cell carcinoma (ccRCC) and establish related predictive models based on the hub genes in these pathways for prognosis and tumor immune microenvironment (TIME). METHODS: We obtained mRNA expression data and clinical information of ccRCC samples from The Cancer Genome Atlas (TCGA), E-MATE-1980, and immunotherapy cohorts. Single-cell sequencing data (GSE152938) were downloaded from the Gene Expression Omnibus (GEO) database. We explored biological pathways of the LYSET by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of TMEM251-coexpression genes. The correlation of LYSET-related pathways with the prognosis was conducted by Gene Set Variation Analysis (GSVA) and unsupervised cluster analysis. The least absolute shrinkage and selection operator (LASSO) and Cox regression were used to identify hub prognostic genes and construct the risk score. Immune infiltration analysis was conducted by CIBERSORTx and Tumor Immune Estimation Resource (TIMER) databases. The predictive value of the risk score and hub prognostic genes on immunotherapy responsiveness was analyzed through the tumor mutation burden (TMB) score, immune checkpoint expression, and survival analysis. Immunohistochemistry (IHC) was finally used to verify the expressions of hub prognostic genes. RESULTS: The TMEM251 was found to be significantly correlated with some AAMR pathways. AAGAB, ENTR1, SCYL2, and WDR72 in LYSET-related pathways were finally identified to construct a risk score model. Immune infiltration analysis showed that LYSET-related gene signatures significantly influenced the infiltration of some vital immune cells such as CD4 + cells, NK cells, M2 macrophages, and so on. In addition, the constructed risk score was found to be positively correlated with TMB and some common immune checkpoint expressions. Different predictive values of these signatures for Nivolumab therapy responsiveness were also uncovered in immunotherapy cohorts. Finally, based on single-cell sequencing analysis, the TMEM251 and the hub gene signatures were found to be expressed in tumor cells and some immune cells. Interestingly, IHC verification showed a potential dual role of four hub genes in ccRCC progression. CONCLUSION: The novel predictive biomarkers we built may benefit clinical decision-making for ccRCC. Our study may provide some evidence that LYSET-related gene signatures could be novel potential targets for treating ccRCC and improving immunotherapy efficacy. Our nomogram might be beneficial to clinical choices, but the results need more experimental verifications in the future.
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GNAL-driven calcium signaling reshapes the spatiotemporal immune landscape in ER+ breast cancer: causal insights and prognostic implications.
BACKGROUND: Endocrine-resistant estrogen receptor-positive (ER+) breast cancer often presents with an immune-cold phenotype, yet the upstream regulators driving immune evasion remain unclear. GNAL, a G-protein subunit involved in calcium signaling, has emerged as a potential player in modulating the tumor immune microenvironment, but its role in ER+ breast cancer has not been systematically investigated. This study aims to systematically investigate GNAL's biological functions, molecular mechanisms, and prognostic relevance in endocrine-resistant ER+ breast cancer, as well as its role in regulating the tumor immune microenvironment. METHODS: To elucidate the regulatory role of GNAL, we integrated summary-based Mendelian randomization (SMR), single-cell RNA sequencing, and spatial transcriptomics. Causal inference, cell-type mapping, and intercellular communication networks were analyzed, and a multi-omics prognostic model was constructed and validated across The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) cohorts. RESULTS: SMR analysis identified GNAL as a causal gene for ER+ breast cancer, with loss of expression was associated with increased recurrence risk. GNAL was specifically enriched in stromal stem-like subpopulations and decreased along with the stem cell differentiation trajectory. Spatial analyses revealed that GNAL+ stem-like cells recruited B cells via the MIF-CD74 signaling axis and established long-range communication with endothelial cells. A four-gene risk model (CD24, KDM3B, CEBPD, KRT14) predicted poor prognosis and was independent of Tumor (T), Node (N), and Metastasis (M) staging. High-risk tumors exhibited a 42% reduction in CD8+ T cell infiltration. Molecular docking identified stable hydrogen-bond interactions between GNAL and CEBPD. CONCLUSIONS: GNAL regulates the spatiotemporal immune remodeling of ER+ breast cancer via calcium signaling and stem-like cell differentiation. The multi-omics risk model offers clinical prognostic value and highlights GNAL as a potential target for precision immunotherapy.