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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.
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Mitotic catastrophe heterogeneity: implications for prognosis and immunotherapy in hepatocellular carcinoma.
BACKGROUND AND AIMS: The mitotic catastrophe (MC) pathway plays an important role in hepatocellular carcinoma (HCC) progression and tumor microenvironment (TME) regulation. However, the mechanisms linking MC heterogeneity to immune evasion and treatment response remain unclear. METHODS: Based on 94 previously published highly correlated genes for MC, HCC patients' data from the Cancer Genome Atlas (TCGA) and changes in immune signatures and prognostic stratification were studied. Time and spatial-specific differences for MCGs were assessed by single-cell RNA sequencing and spatial transcriptome (ST) analysis. Multiple external databases (GEO, ICGC) were employed to construct an MC-related riskscore model. RESULTS: Identification of two MC-related subtypes in HCC patients from TCGA, with clear differences in immune signatures and prognostic risk stratification. Spatial mapping further associates low MC tumor regions with significant immune escape-related signaling. Nomogram combining MC riskscore and traditional indicators was validated great effect for early prediction of HCC patient outcomes. CONCLUSION: MC heterogeneity enables immune escape and therapy resistance in HCC. The MC gene signature serves as a reliable prognostic indicator for liver cancer. By revealing clear immune and spatial heterogeneity of HCC, our integrated approach provides contextual therapeutic strategies for optimal clinical decision-making.
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Revealing platelet-related subtypes and prognostic signature in pancreatic adenocarcinoma.
BACKGROUND: Pancreatic adenocarcinoma (PDAC) is a malignant tumor with high heterogeneity and poor prognosis. In this study, we sought to identify the value of platelet-related genes in prognosis and heterogeneity of PDAC through multiple transcriptomic methods. METHODS: Based on datasets from Gene Expression Omnibus and The Cancer Genome Atlas (TCGA), platelet-related genes were screened out, and the TCGA cohort (n = 171) was identified into two subtypes by unsupervised clustering. The platelet-related risk score model (PLRScore) was constructed by univariate Cox and LASSO regression, and the predictive ability was evaluated by Kaplan-Meier test and time-dependent receiver operating characteristic (ROC) curves. The results were validated in two other external validation sets, ICGC-CA (n = 140) and GSE62452 (n = 66). Furthermore, predictive nomogram containing clinical characteristics and PLRScore was established. In addition, we determined the possible correlation between PLRScore and immune infiltration and response of immunotherapy. Finally, we analyzed the heterogeneity of our signature in various types of cells using single-cell analysis. RESULTS: Platelet-related subtypes that have significant difference of overall survival and immune states (p < 0.05) were identified. PLRScore model based on four-gene signature (CEP55, LAMA3, CA12, SCN8A) was constructed to predict patient prognosis. The AUCs of training cohort were 0.697, 0.687 and 0.675 for 1-, 3-and 5-year, respectively. Further evaluation of the validation cohorts yielded similar results. In addition, PLRScore was associated with immune cell infiltration and immune checkpoint expression, and had promising ability to predict response to immunotherapy of PDAC. CONCLUSIONS: In this study, the platelet-related subtypes were identified and the four-gene signature was constructed and validated. It may provide new insights into the therapeutic decision-making and molecular targets of PDAC.
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RANBP1 promotes oral cancer progression through activating YAP1 and NDUFB3 derived from the database of single-cell sequencing.
Oral cavity squamous cell carcinoma (OSCC) is the most common head and neck cancer, thriving in microenvironments composed of cancer cells, stromal tissue, and extracellular matrix. Extensive local invasion and cervical nodal metastasis can lead to poor treatment outcomes for OSCC. We created a single-cell transcriptomic database of OSCC using biopsies from 10 individuals to identify candidate genes. Ran-specific binding protein 1 (RANBP1) was identified as a gene with differential expression between malignant and normal epithelial cells. Analysis of the Cancer Genome Atlas Program (TCGA) OSCC and Taiwanese cohorts confirmed that higher RANBP1 levels are significantly associated with worse prognosis. Functional tests showed that knocking down RANBP1 reduced cell proliferation and invasion by decreasing proteins involved in focal adhesion, invadopodia formation, and epithelial-mesenchymal transition. Inhibiting RANBP1 also decreased vascular spread in zebrafish tumor xenografts, while overexpressing RANBP1 had the opposite effect. The overexpression of RANBP1 significantly increased tumor growth in NOD/SCID xenografts. RANBP1 was positively correlated with the oxidative phosphorylation pathway. Cells with reduced RANBP1 showed lower levels of NADH ubiquinone oxidoreductase subunit B3 (NDUFB3) and had impaired mitochondrial function. Additionally, RANBP1 depletion decreased activation of Yes1 associated transcriptional regulator (YAP1). Increased NDUFB3 expression in RANBP1-overexpressing cells was reversed by YAP1 inhibitor verteporfin treatment or knocking down YAP1. RANBP1 enhances a more invasive microenvironment of OSCC.