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Conserved minimal CD8 cytotoxic immune-state unit in an IL18R1-associated immune program in hepatocellular carcinoma.
BACKGROUND AND AIMS: The conserved human correlate of interleukin-18 receptor 1 (IL-18R1)-associated immune states in hepatocellular carcinoma (HCC) remains undefined. We aimed to identify a minimal, reproducible, immune-state component associated with IL18R1 across independent human HCC cohorts and evaluate its clinical relevance. METHODS: Publicly available single-cell RNA sequencing datasets from five independent HCC cohorts were analyzed using a unified framework. Paired tumor-adjacent cohorts were prioritized for primary analyses, whereas tumor-only cohorts served as supportive datasets. CD8 T cells were stratified by IL18R1 expression, and tumor-specific enrichment was quantified using difference-in-differences. We validated the spatial transcriptomics using a murine liver tumor Xenium dataset. Clinical relevance was evaluated using The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) bulk RNA-seq data. RESULTS: IL18R1-positive CD8 T cells exhibited a tumor-specific, C-C motif chemokine ligand 5 (CCL5)-centered cytotoxic transcriptional program, consistently enriched across datasets. Alternative IL18R1 stratification confirmed the identified immune program's robustness. Spatial transcriptomics localized this cytotoxic state in tumor-associated CD8 T cells and highlighted increased expression of Ccl5 and Cxcr3. Tumor-only cohorts demonstrated substantial heterogeneity, highlighting the importance of paired cohort design. In TCGA-LIHC, a composite CCL5-centered cytotoxic core score, not CCL5 alone, was associated with improved overall survival, although this attenuated after adjusting for clinical covariates. CONCLUSIONS: The dominant human correlate of an IL18R1-associated immune state in HCC is a compact, CCL5-centered CD8 cytotoxic module, not a broad inflammatory response. This spatially validated unit provides a reproducible framework for HCC immune-state characterization and stratification.
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Integrated analysis and validation reveal ACAP1 as a novel prognostic biomarker associated with tumor immunity in lung adenocarcinoma.
ADP-ribosylation factor (Arf)-GTPase-activating protein (GAP) with coiled-coil, ankyrin repeat and PH domains 1 (ACAP1) has been reported to serve as an adaptor for clathrin coat complex playing a role in endocytic recycling and cellular migration. The potential role of ACAP1 in lung adenocarcinoma (LUAD) has not been yet completely defined. We performed the comprehensive analyses, including gene expression, survival analysis, genetic alteration, function enrichment, and immune characteristics. ACAP1 was remarkably downregulated in tumor tissues, and linked with the clinicopathologic features in LUAD patients. Prognostic analysis demonstrated that low ACAP1 expression was correlated with unsatisfactory overall survival (OS) and disease specific survival (DSS) in LUAD patients. Moreover, ACAP1 could be determined as a prognostic biomarker according to Cox proportional hazard model and nomogram model. We also confirmed that ACAP1 was downregulated in two LUAD cell lines, comparing to normal lung cell. Overexpression of ACAP1 caused a profound attenuation in cell proliferation, migration, invasion, and promoted cell apoptosis. Additionally, functional enrichment analyses confirmed that ACAP1 was highly correlated with T cell activation and immune response. Then, we further conducted immune landscape analyses, including single cell RNA sequencing, immune cells infiltration, and immune checkpoints. ACAP1 expression was positively associated with the infiltrating level of immune cells in TME and the expression of immune checkpoint molecules. This study first comprehensively analyzed molecular expression, clinical implication, and immune landscape features of ACAP1 in LUAD, suggesting that ACAP1 was predictive of prognosis and could serve as a potential biomarker predicting immunotherapy response for LUAD patients.
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Identification of Immuno-Diagnostic and Therapeutic Markers in Tumors and Inflammatory Diseases Based on Single-Cell and Machine Learning Methods.
BACKGROUND: Chemotherapy is the mainstay of systemic treatment for colorectal cancer (CRC), but drug resistance and tumor heterogeneity markedly limit survival. Mitochondrial metabolism is increasingly recognized as a regulator of tumor progression, immune contexture, and therapeutic response. However, the prognostic relevance of mitochondrial metabolism-related transcriptional patterns and their relationships with chemotherapy-response signatures and immune features in CRC remain incompletely understood. METHODS: Clinical and transcriptomic data from The Cancer Genome Atlas colorectal cancer cohort (TCGA-CRC) and Gene Expression Omnibus cohorts GSE17537 and GSE104645 were analyzed. A curated set of mitochondrial metabolism-related genes (MMRGs) were used to calculate an MMRG score, whereas genes upregulated in chemotherapy-sensitive cases in GSE104645 were used to derive a drug-sensitivity gene-set score by single-sample gene set enrichment analysis (ssGSEA). Weighted gene coexpression network analysis (WGCNA), differential expression analysis, univariate Cox regression, least absolute shrinkage and selection operator regression, and multivariate Cox regression were used to construct a five-gene RiskScore. Prognostic associations were assessed using Kaplan-Meier curves, time-dependent receiver operating characteristic curves, external validation, and subgroup analyses. Immune-cell enrichment and pRRophetic-predicted half-maximal inhibitory concentration values were evaluated, and CD36 was examined using in vitro phenotypic assays. RESULTS: Tumors showed lower MMRG scores than normal tissues, and higher MMRG scores were associated with better overall survival (OS). The drug-sensitivity gene-set score was higher in tumors but was not significantly associated with OS. A five-gene signature (CD36, CPA3, FCRL2, POU2F2, and S100B) generated a RiskScore that separated OS outcomes in the TCGA training, testing, and overall cohorts, as well as GSE17537, although its time-dependent discrimination varied across cohorts and follow-up periods. Similar survival associations were observed within subgroups defined by stage, age, and sex; however, independent or incremental prognostic value was not evaluated. High-risk tumors exhibited altered immune-cell enrichment, and the RiskScore was correlated with pRRophetic-predicted drug IC50 values after Benjamini-Hochberg correction. CD36 expression was higher in SW1116 than in NCM460 cells, and its knockdown inhibited proliferation, migration, and invasion. Mitochondrial function was not directly measured in these experiments. CONCLUSION: The five-gene RiskScore was associated with OS, immune-cell enrichment, and in silico drug-sensitivity estimates in CRC. It may serve as a candidate tool for prognostic stratification, but its incremental clinical value and treatment-predictive utility require prospective validation. The CD36 knockdown experiments support a tumor-promoting phenotype in SW1116 cells but do not establish dependence on mitochondrial metabolic reprogramming.
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Machine Learning Developed a MYC Expression Feature-Based Signature for Predicting Prognosis and Chemoresistance in Pancreatic Adenocarcinoma.
MYC has been identified to profoundly influence a wide range of pathologic processes in cancers. However, the prognostic value of MYC-related genes in pancreatic adenocarcinoma (PAAD) remains unclarified. Gene expression data and clinical information of PAAD patients were obtained from The Cancer Genome Atlas (TCGA) database (training set). Validation sets included GSE57495, GSE62452, and ICGC-PACA databases. LASSO regression analysis was used to develop a risk signature for survival prediction. Single-cell sequencing data from GSE154778 and CRA001160 datasets were analyzed. Functional studies were conducted using siRNA targeting RHOF and ITGB6 in PANC-1 cells. High MYC expression was found to be significantly associated with a poor prognosis in patients with PAAD. Additionally, we identified seven genes (ADGRG6, LINC00941, RHOF, SERPINB5, INSYN2B, ITGB6, and DEPDC1) that exhibited a strong correlation with both MYC expression and patient survival. They were then utilized to establish a risk model (MYCsig), which showed robust predictive ability. Furthermore, MYCsig demonstrated a positive correlation with the expression of HLA genes and immune checkpoints, as well as the chemotherapy response of PAAD. RHOF and ITGB6, expressed mainly in malignant cells, were identified as key oncogenes regulating chemosensitivity through EMT. Downregulation of RHOF and ITGB6 reduced cell proliferation and invasion in PANC-1 cells. The developed MYCsig demonstrates its potential in enhancing the management of patients with PAAD by facilitating risk assessment and predicting response to adjuvant chemotherapy. Additionally, our study identifies RHOF and ITGB6 as novel oncogenes linked to EMT and chemoresistance in PAAD.
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Expression of PDZ domain-containing proteins is correlated with prognosis and immune infiltration in hepatocellular carcinoma.
BACKGROUND: Hepatocellular carcinoma (HCC)-a predominant type of primary liver cancer-poses a significant global health threat with high incidence and mortality rates. Despite advances in treatment modalities, including surgery, chemotherapy, and immunotherapy, HCC exhibits high relapse rates and low long-term survival, necessitating the identification of novel prognostic markers and treatment targets. This study aims to develop a prognostic model centered on the PDZ domain by identifying key PDZ proteins associated with HCC through bioinformatics analysis of large-scale public datasets, in order to improve prognosis prediction and inform therapeutic strategies. METHODS: Differentially expressed PDZ proteins (DEPs) in HCC were identified through RNA sequencing (RNA-seq) analysis from The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC), and Gene Expression Omnibus (GEO) databases. Cox regression and random survival forest (RSF) modeling were employed to construct a prognostic model and evaluate the prognostic potential of DEPs. Subsequently, the correlation of DEP-related risk scores with immune cell infiltration and genetic variations was analyzed separately. Quantitative polymerase chain reaction (qPCR) was conducted to validate the expression of key DEPs in HCC tissues. RESULTS: A prognostic model for HCC constructed using nine key DEPs demonstrated reliable predictive performance across 1-, 3-, and 5-year survival rates. DEP-related risk scores were significantly associated with immune cell infiltration, with high-risk groups exhibiting an increase in pro-tumor immune cells and a decrease in anti-tumor immune cells. Genetic variations, including single nucleotide polymorphisms (SNPs) and copy number variations (CNVs), also differed between high- and low-risk groups. qPCR validation confirmed that the expression of SNX27, DLG5, PARD3, and RHPN1 was significantly upregulated in HCC tissues. CONCLUSIONS: PDZ proteins may serve as prognostic markers and therapeutic targets in HCC. DEP-related risk scores offer insights into immune infiltration patterns and treatment responsiveness, providing a foundation for future HCC research and development of precision medicine.
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Multi-omics and network toxicology prioritize ADAMTS13 as a candidate gene computationally linked to TDCPP targets in hepatocellular carcinoma.
BACKGROUND: Tris(1,3-dichloro-2-propyl) phosphate (TDCPP), a widely used organophosphate flame retardant, has been increasingly recognized as a potential environmental risk factor for human cancers. However, its potential association with hepatocellular carcinoma (HCC) and the underlying molecular mechanisms remain largely unclear. METHODS: An integrative network toxicology and multi-omics approach was employed to explore the potential computational link between TDCPP‑related targets and HCC transcriptomic changes. TDCPP-associated targets were collected from public databases and intersected with differentially expressed genes from The Cancer Genome Atlas (TCGA). Weighted gene co-expression network analysis (WGCNA) and machine learning algorithms were applied to identify candidate genes. Functional enrichment, immune infiltration, single-cell RNA sequencing, and molecular docking analyses were subsequently performed. RESULTS: A total of six candidate genes were identified, among which ADAMTS13 exhibited strong discriminative performance in the predictive models. Functional analyses suggested that these genes may be involved in pathways related to extracellular matrix organization, immune regulation, and tumor microenvironment remodeling. Immune infiltration and single-cell analyses indicated a potential association between ADAMTS13 expression and tumor microenvironment characteristics. Molecular docking analysis further suggested a potential interaction between TDCPP and ADAMTS13 at the structural level. CONCLUSIONS: These findings suggest a computational association between TDCPP-related targets and HCC transcriptomic changes, with ADAMTS13 emerging as a computationally prioritized candidate gene for further experimental investigation. This study provides a hypothesis-generating framework for understanding possible molecular links between environmental contaminants and hepatocarcinogenesis, but does not establish causality or confirm actual TDCPP exposure in the analyzed patient cohorts.
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Vimentin promotes glioma progression and maintains glioma cell resistance to oxidative phosphorylation inhibition.
PURPOSE: Glioma has been demonstrated as one of the most malignant intracranial tumors and currently there is no effective treatment. Based on our previous RNA-sequencing data for oxidative phosphorylation (OXPHOS)-inhibition resistant and OXPHOS-inhibition sensitive cancer cells, we found that vimentin (VIM) is highly expressed in the OXPHOS-inhibition resistant cancer cells, especially in glioma cancer cells. Further study of VIM in the literature indicates that it plays important roles in cancer progression, immunotherapy suppression, cancer stemness and drug resistance. However, its role in glioma remains elusive. This study aims to decipher the role of VIM in glioma, especially its role in OXPHOS-inhibition sensitivity, which may provide a promising therapeutic target for glioma treatment. METHODS: The expression of VIM in glioma and the normal tissue has been obtained from The Cancer Genome Atlas (TCGA) database, and further validated in Human Protein Atlas (HPA) and Chinese Glioma Genome Atlas (CGGA). And the single-cell sequencing data was obtained from TISCH2. The immune infiltration was calculated via Tumor Immune Estimation Resource (TIMER), Estimation of Stromal and Immune Cells in Malignant Tumors using Expression Data (ESTIMATE) and ssGSEA, and the Immunophenoscore (IPS) was calculated via R package. The differentiated expressed genes were analyzed including GO/KEGG and Gene Set Enrichment Analysis (GSEA) between the VIM-high and -low groups. The methylation of VIM was checked at the EWAS and Methsurv. The correlation between VIM expression and cancer stemness was obtained from SangerBox. We also employed DepMap data and verified the role of VIM by knocking down it in VIM-high glioma cell and over-expressing it in VIM-low glioma cells to check the cell viability. RESULTS: Vim is highly expressed in the glioma patients compared to normal samples and its high expression negatively correlates with patients' survival. The DNA methylation in VIM promoters in glioma patients is lower than that in the normal samples. High VIM expression positively correlates with the immune infiltration and tumor progression. Furthermore, Vim is expressed high in the OXPHOS-inhibition glioma cancer cells and low in the OXPHOS-inhibition sensitive ones and its expression maintains the OXPHOS-inhibition resistance. CONCLUSIONS: In conclusion, we comprehensively deciphered the role of VIM in the progression of glioma and its clinical outcomes. Thus provide new insights into targeting VIM in glioma cancer immunotherapy in combination with the current treatment.
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Integrative analysis of the bladder cancer from a cell cycle NCAM1 perspective at both single cell and bulk resolution.
INTRODUCTION: Bladder cancer (BLCA) is a prevalent and deadly form of urinary cancer, and there is a need for effective therapies, particularly for muscle-invasive bladder cancer (MIBC). Cell cycle inhibitors show promise in restoring control of the cell cycle in BLCA cells, but their clinical prognosis evaluation is limited. METHODS: Transcriptome and scRNA-seq data were collected from the Cancer Genome Atlas Program (TCGA)-BLCA and GSE190888 cohort, respectively. R software and the Seurat package were used for data analysis, including cell quality control, dimensionality reduction, and identification of differentially expressed genes. Genes related to the cell cycle were obtained from the genecards website, and a protein-protein interaction network analysis was performed using cytoscape software. Functional enrichment analysis, immune infiltration analysis, drug sensitivity analysis, and molecular docking were conducted using various tools and packages. BLCA cell lines were cultured and transfected for in vitro experimental assays, including RT-qPCR analysis, and CCK-8 cell viability assays. RESULTS: We identified 32 genes as independent risk or protective factors for BLCA prediction. Functional enrichment analysis revealed their involvement in cell cycle regulation, apoptosis, and various signaling pathways. Using these genes, we developed a nomogram for predicting BLCA survival, which displayed high prognosis stratification efficacy in BLCA patients. Four cell cycle associated key genes identified, including NCAM1, HBB, CKD6, and CTLA4. We also identified the main cell types in BLCA patients and investigated the functional differences between epithelial cells based on their expression levels of key genes. Furthermore, we observed a high positive correlative relationship between the infiltration of cancer-associated fibroblasts and the risk score value. Finally, we conducted in vitro experiments to demonstrate the suppressive role of NCAM1 in BLCA cell proliferation. CONCLUSION: These findings suggest that cell cycle associated genes could serve as potential biomarkers for predicting BLCA prognosis and may represent therapeutic targets for the development of more effective therapies. Hopefully, these findings provide valuable insights into the molecular mechanisms and potential therapeutic targets in BLCA from the perspective of cell cycle. Moreover, NCAM1 was a novel cell proliferation suppressor in the BLCA carcinogenesis.
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Childhood-onset lupus nephritis is characterized by complex interactions between kidney stroma and infiltrating immune cells.
Children with systemic lupus erythematosus (SLE) are at increased risk of developing kidney disease, termed childhood-onset lupus nephritis (cLN). Single-cell transcriptomics of dissociated kidney tissue has advanced our understanding of LN pathogenesis, but loss of spatial resolution prevents interrogation of in situ cellular interactions. Using a technical advance in spatial transcriptomics, we generated a spatially resolved, single-cell resolution atlas of kidney tissue from eight patients with cLN and four control individuals. Annotated cells were assigned to 30 reference cell types, including major kidney subsets and infiltrating immune cells. Analysis of spatial distribution demonstrated that individual immune lineages localized to specific regions in cLN kidneys, including myeloid cells that trafficked to inflamed glomeruli and B cells that clustered within tubulointerstitial immune hotspots. Gene expression varied as a function of tissue location, demonstrating how incorporation of spatial data can provide new insights into the immunopathogenesis of SLE. Alterations in immune phenotypes were accompanied by parallel changes in gene expression by resident kidney stromal cells. However, there was little correlation between histologic scoring of cLN disease activity and glomerular cell transcriptional signatures at the level of individual glomeruli. Last, we identified modules of spatially correlated gene expression with predicted roles in induction of inflammation and the development of tubulointerstitial fibrosis. Single-cell spatial transcriptomics allowed insights into the molecular heterogeneity of cLN, paving the way toward more targeted and personalized treatment approaches.
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Integrative mapping of human CD8+ T cells in inflammation and cancer.
CD8+ T cells exhibit remarkable phenotypic diversity in inflammation and cancer. However, a comprehensive understanding of their clonal landscape and dynamics remains elusive. Here we introduce scAtlasVAE, a deep-learning-based model for the integration of large-scale single-cell RNA sequencing data and cross-atlas comparisons. scAtlasVAE has enabled us to construct an extensive human CD8+ T cell atlas, comprising 1,151,678 cells from 961 samples across 68 studies and 42 disease conditions, with paired T cell receptor information. Through incorporating information in T cell receptor clonal expansion and sharing, we have successfully established connections between distinct cell subtypes and shed light on their phenotypic and functional transitions. Notably, our approach characterizes three distinct exhausted T cell subtypes and reveals diverse transcriptome and clonal sharing patterns in autoimmune and immune-related adverse event inflammation. Furthermore, scAtlasVAE facilitates the automatic annotation of CD8+ T cell subtypes in query single-cell RNA sequencing datasets, enabling unbiased and scalable analyses. In conclusion, our work presents a comprehensive single-cell reference and computational framework for CD8+ T cell research.