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A multi-omic machine learning approach deconstructs the role of amino acid metabolism in the immune microenvironment and prognosis of colon adenocarcinoma.
BACKGROUND: The clinical heterogeneity of colon adenocarcinoma (COAD) complicates patient prognosis and treatment. While metabolic reprogramming is a key driver of tumor progression, the role of histidine metabolism in the COAD immune microenvironment remains unclear. METHODS: Through an integrated analysis of public single-cell and bulk transcriptomic data, we mapped the COAD cellular atlas and assessed histidine metabolism activity. A prognostic Histidine Metabolism-related Model (HRM) was constructed using an ensemble of 101 machine learning algorithms, and its biological underpinnings were explored. The function of the key gene, TRIP6, was validated via in vitro experiments. RESULTS: Single-cell analysis identified epithelial cells as the hub of histidine metabolism, which remodels TME intercellular communication. The machine learning-derived HRM robustly stratified patient prognosis across a primary and two validation cohorts. High HRM scores correlated with an "infiltrated-exhausted" immune phenotype, characterized by high immune infiltration alongside elevated checkpoint expression. The key signature gene TRIP6 was identified as a driver of poor prognosis and an immunosuppressive state, and its silencing suppressed malignant phenotypes in vitro. CONCLUSION: Histidine metabolism is a critical regulator of the COAD immune microenvironment. Our prognostic model, the HRM, provides a clinically relevant tool for risk stratification, while its key mediator, TRIP6, represents a novel therapeutic target linking tumor metabolism to immune evasion.
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Transcriptome profiling reveals liver metastasis-associated genes in pancreatic ductal adenocarcinoma.
Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal malignancies worldwide. However, the mechanisms underlying the acquisition of the metastatic potential in PDAC has not been well understood. In this study, we identified a total of 154 genes upregulated in primary tissues of PDAC with liver metastasis using the Genome Cancer Atlas (TCGA) and GSE151580 cohorts. The epithelial-mesenchymal transition and glycolysis were enriched by the liver metastasis-related genes, indicating that the liver metastasis-related genes might be functionally relevant to liver metastasis in PDAC. Moreover, we also found that the liver metastasis-related genes were primarily regulated at epigenetic level. Particularly, SFN, a cell cycle checkpoint protein, and KRT19, a marker gene for ductal cells, were predicted to be regulated by multiple methylation sites at the promoter. Clinically, we for the first time defined a liver metastasis score (LMS), which was derived from liver metastasis-related genes, and closely associated with clinical characteristics such as disease type and tumor grade, in PDAC. Furthermore, we also divided the samples into high and low LMS groups using three cohorts with long-term follow-up (TCGA, GSE71729, and E-MTAB-6134), which exhibited significantly different prognostic outcomes across three PDAC cohorts, suggesting that the LMS might be a good indicator for risk stratification in PDAC. Furthermore, we also found that the liver metastasis-related genes were primarily expressed in malignant ductal cells by integrative analysis of the bulk and single-cell gene expression data. Moreover, the malignant ductal cells and M0 macrophages were highly correlated with LMS, indicating that the two cell types might function as tumor-promoting cells in PDAC. In summary, the systematic analysis for the first time defined an LMS score to evaluate the risk of liver metastasis in PDAC and revealed that malignant ductal cells might promote PDAC liver metastasis, which greatly improves our understanding of the liver metastasis-related genes, their underlying mechanisms, and association with prognosis in PDAC.
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CIAO1 as a crucial signature gene of cuproptosis in gastric cancer.
Gastric cancer is a global health challenge, necessitating the identification of novel biomarkers and therapeutic targets. The present study aimed to assess the role of cuproptosisrelated genes (CRGs) in gastric cancer, with the goal of establishing a predictive model consisting of key regulators with prognostic significance, thereby enabling the identification of key genes. Data from The Cancer Genome Atlas and Gene Expression Omnibus databases were used to analyze CRGs in stomach adenocarcinoma (STAD). Least absolute shrinkage and selection operator regression analysis was applied to create a risk model, and its predictive accuracy was confirmed for several clinical subgroups. Moreover, the prognostic value of essential regulators was evaluated through multiple analyses. A risk model with 15 CRGs was used to effectively predict STAD prognosis, demonstrating areas under the receiver operating characteristic curve values of 0.822, 0.811 and 0.922 for 1-, 3- and 5-year overall survival rates, respectively. The risk scores were associated with survival and tumor site. Among the CRGs, the gene for cytosolic iron-sulfur assembly component 1 (CIAO1) was revealed to be critical and associated with histological type, age and treatment outcome. Moreover, single-cell analysis demonstrated that CIAO1 is highly expressed in numerous types of cancer cells, and a high expression of CIAO1 was associated with upregulated transcription levels of immune checkpoints, increased tumor mutation load and decreased immune scores, highlighting its complex role in the tumor microenvironment. CIAO1 knockdown experiments were performed, and eliminating CIAO1 was associated with a reduction in the levels of iron-sulfur proteins and an increase in heat shock protein 70 expression, thereby inducing copper-dependent cell death. Furthermore, treatment with the drugs dasatinib and AT-9283 were associated with an inhibition of CIAO1 expression in gastric cancer cells, and decreased rates of tumor spread and invasion. Taken together, the findings of the present study suggest that CIAO1 is a promising biomarker both for assessing prognosis and evaluating the tumor immune microenvironment of gastric cancer.
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Comprehensive analysis of diverse cytokine patterns in the prognosis and tumor microenvironment of lung adenocarcinoma.
BACKGROUND: The aim of this study was to elucidate the comprehensive roles of cytokines in lung adenocarcinoma (LUAD). METHODS: Cytokine-related genes were analyzed in LUAD using sequencing datasets from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO). Gene Set Variation Analysis (GSVA) was employed to quantify enrichment of four cytokine families—interferons (IFNs), tumor necrosis factors (TNFs), interleukins (ILs), and chemokines (CKs)—across samples. Differential expression and survival analyses assessed the impact of each family on LUAD prognosis. A prognostic model based on TNF family genes was constructed using the least absolute shrinkage and selection operator (LASSO) regression. Functional enrichment of the TNF Score (TNFS) was performed with ClusterProfiler. Finally, the role of the key gene TNFRSF11A was validated by small interfering RNA (siRNA) knockdown, followed by Transwell migration, colony formation, and wound-healing assays. RESULTS: Integrated expression and survival analyses identified the TNF family as a critical regulator in LUAD. TNF scores were significantly elevated in tumor versus normal tissues, whereas CK enrichment predicted improved survival and high TNF enrichment predicted poorer survival. Differential profiling in TCGA and GEO cohorts revealed five overexpressed TNF genes—TNFRSF11A, TNFRSF13B, TNFRSF17, TNFSF13, and TNFSF11—correlated with adverse outcomes. A five-gene LASSO-derived risk model demonstrated strong prognostic performance for overall, disease-free, disease-specific, and progression-free survival. High TNFS was associated with dysregulation of cell-cycle, adhesion, and cytokine signaling pathways, as well as altered infiltration of T cells, B cells, and macrophages. Single-cell analysis further revealed that malignant cells with high TNFRSF11A expression exhibited significantly enhanced intercellular communication with fibroblasts and CD8 + T cells, characterized by the activation of specific signaling pathways. TNFRSF11A emerged as the top contributor, with expression positively correlated with smoking history, advanced TNM stage, and recurrence. Functional assays confirmed that TNFRSF11A knockdown significantly impaired LUAD cell proliferation, migration, and invasion. CONCLUSION: The TNF cytokine family plays a pivotal role in LUAD. TNFRSF11A, previously unstudied in this context, represents a novel key driver of LUAD progression.
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A rare PRIMER cell state in plant immunity.
Plants lack specialized and mobile immune cells. Consequently, any cell type that encounters pathogens must mount immune responses and communicate with surrounding cells for successful defence. However, the diversity, spatial organization and function of cellular immune states in pathogen-infected plants are poorly understood1. Here we infect Arabidopsis thaliana leaves with bacterial pathogens that trigger or supress immune responses and integrate time-resolved single-cell transcriptomic, epigenomic and spatial transcriptomic data to identify cell states. We describe cell-state-specific gene-regulatory logic that involves transcription factors, putative cis-regulatory elements and target genes associated with disease and immunity. We show that a rare cell population emerges at the nexus of immune-active hotspots, which we designate as primary immune responder (PRIMER) cells. PRIMER cells have non-canonical immune signatures, exemplified by the expression and genome accessibility of a previously uncharacterized transcription factor, GT-3A, which contributes to plant immunity against bacterial pathogens. PRIMER cells are surrounded by another cell state (bystander) that activates genes for long-distance cell-to-cell immune signalling. Together, our findings suggest that interactions between these cell states propagate immune responses across the leaf. Our molecularly defined single-cell spatiotemporal atlas provides functional and regulatory insights into immune cell states in plants.
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UBE2A as a prognostic indicator across human cancers: Insights from multi‑omics and immune landscape analyses.
Ubiquitin‑conjugating enzyme E2A (UBE2A), a member of the ubiquitin‑conjugating E2 enzyme family, has been implicated in tumor development; however, its role across human cancer types remains incompletely understood. The present study aimed to systematically characterize the expression pattern, prognostic importance and immune relevance of UBE2A in a pan‑cancer context. Public datasets, including The Cancer Genome Atlas, Gene Expression Omnibus and Genotype‑Tissue Expression, were integrated to evaluate UBE2A expression, clinical importance, molecular characteristics, immune associations and biological functions across cancer types. Single‑cell RNA sequencing data were analyzed to investigate the cellular distribution of UBE2A. Functional validation was performed through UBE2A knockdown in breast cancer cell lines. Results indicated that UBE2A was significantly upregulated in a number of cancer types and was associated with unfavorable overall and progression‑free survival. UBE2A expression exhibited significant correlations with immune cell infiltration and immune checkpoint‑associated genes across cancer types. Functional enrichment analyses indicated that UBE2A was primarily involved in cell cycle regulation and proliferative processes. Single‑cell analysis revealed preferential UBE2A expression in proliferative T‑cell populations. Furthermore, in vitro experiments demonstrated that UBE2A knockdown significantly suppressed breast cancer cell proliferation, migration and invasion. Overall, UBE2A was shown to be a promising prognostic biomarker associated with tumor progression, cell cycle activity and immune‑associated characteristics across cancer types. Its preferential expression in proliferative T‑cell populations and oncogenic role in breast cancer suggest that UBE2A may serve as a potential therapeutic target and a candidate biomarker for evaluating tumor prognosis and the tumor immune microenvironment.
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Uncovering the core genetic programs governing plant guard cell biology.
Guard cells (GCs) regulate gas exchange and water loss in plants and have been extensively studied in Arabidopsis thaliana. However, cross-species comparisons at single-cell resolution remain limited. To address this, we aimed to define conserved and divergent transcriptomic signatures of GCs by generating a comparative single-cell atlas encompassing five species: A. thaliana, soybean (Glycine max), tomato (Solanum lycopersicum), rice (Oryza sativa), and sorghum (Sorghum bicolor). We performed single-nucleus RNA sequencing (sNucRNA-seq) on leaf tissues from each species, followed by integrative bioinformatic analyses to identify and annotate GC populations. Orthology-informed transcriptomic comparisons and co-expression analyses were applied to assess shared and species-specific regulatory modules. Our analyses revealed a conserved core transcriptome in GCs across species, enriched for genes involved in stomatal movement, abscisic acid signaling, carbon dioxide signaling, reactive oxygen species metabolism, and ion transport. Conserved expression of transcription factors (e.g. MYB60, FAMA orthologs), transporters (e.g. KAT1, SLAC1), and stress-responsive genes was observed, despite considerable evolutionary divergence. These findings provide a reference framework for cross-species GC biology, reveal conserved regulatory features, and nominate candidate genes for functional validation. This work contributes molecular insights toward improving drought resilience and stomatal efficiency in crop species through targeted gene engineering.
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SAP-expressing T peripheral helper cells identify systemic lupus erythematosus patients with lupus nephritis.
INTRODUCTION: T follicular (TFH) and peripheral helper (TPH) cells have been increasingly recognized as a pathogenic subset of CD4 T cells in systemic lupus erythematosus (SLE). The SLAM Associated Protein (SAP) regulates TFH and TPH function by binding to the co-stimulatory signaling lymphocyte activation molecule family (SLAMF) receptors that mediate T cell - B cell interactions. SAP and SLAMF are critical for TPH-dependent B cell maturation into autoantibody-producing plasma cells that characterize SLE pathogenesis. We hypothesized that SAP-expressing TPH cells are involved in the pathogenesis of lupus nephritis (LN). METHODS: Peripheral blood mononuclear cells (PBMC) were isolated using density gradient separation from whole blood. Cells were stained for cell surface markers, followed by permeabilization and staining of intracellular SAP for spectral flow cytometry analysis. We also analyzed SAP expression from renal infiltrating LN T cells using the available single-cell RNA sequencing (scRNA seq) Accelerated Medicines Partnership (AMP) SLE dataset. RESULTS: PBMC from 30 patients with SLE (34 ± 10 years old, 83% female), including 10 patients with LN, were analyzed. We found an increase in total SAP-positive CD4 and CD8 T cells in SLE compared with controls (55.5 ± 2.6 vs. 41.3 ± 3.4, p=0.007, and 52.5 ± 3.0 vs. 39.2 ± 2.8, p=0.007 respectively). In CD4 T cells, the highest SAP expression was in the TPH subset. The frequency of SAP+TPH in circulation correlated with disease activity; SLE patients with renal disease had higher levels of circulating SAP+TPH that remained significant after adjusting for age, sex, race, low complements, and elevated anti-dsDNA (p=0.014). scRNA-seq data of renal infiltrating T cells in LN identified SAP expression to localize to the TFH-like CD4 cluster and GZMK+ CD8 cluster. Increased SAP expression in LN was associated with the differential expression of SLAMF3 and SLAMF7 and granzyme K and EOMES. The existence of two predominant SAP-expressing subsets, the TFH-like CD4 T cells, and GZMK+ effector CD8 T cells, was verified using scRNA-seq data from a human transcriptomic atlas of fifteen major organs. CONCLUSION: The expansion of SAP-expressing T helper cells was associated with LN in our cohort and verified using scRNA-seq data of renal infiltrating T cells. Improved SLAM and SAP signaling understanding can identify new therapeutic targets in LN.
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Identification and validation of SERPINE1 as a prognostic and immunological biomarker in pan-cancer and in ccRCC.
Background: SERPINE1, a serine protease inhibitor involved in the regulation of the plasminogen activation system, was recently identified as a cancer-related gene. However, its clinical significance and potential mechanisms in pan-cancer remain obscure. Methods: In pan-cancer multi-omics data from public datasets, including The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx), and online web tools were used to analyze the expression of SERPINE1 in different cancers and its correlation with prognosis, genetic alteration, DNA promoter methylation, biological processes, immunoregulator expression levels, immune cell infiltration into tumor, tumor mutation burden (TMB), microsatellite instability (MSI), immunotherapy response and drug sensitivity. Further, two single-cell databases, Tumor Immune Single-cell Hub 2 (TISCH2) and CancerSEA, were used to explore the expression and potential roles of SERPINE1 at a single-cell level. The aberrant expression of SERPINE1 was further verified in clear cell renal cell carcinoma (ccRCC) through qRT-PCR of clinical patient samples, validation in independent cohorts using The Gene Expression Omnibus (GEO) database, and proteomic validation using the Clinical Proteomic Tumor Analysis Consortium (CPTAC) database. Results: The expression of SERPINE1 was dysregulated in cancers and enriched in endothelial cells and fibroblasts. Copy number amplification and low DNA promoter methylation could be partly responsible for high SERPINE1 expression. High SERPINE1 expression was associated with poor prognosis in 21 cancers. The results of gene set enrichment analysis (GSEA) indicated SERPINE1 involvement in the immune response and tumor malignancy. SERPINE1 expression was also associated with the expression of several immunoregulators and immune cell infiltration and could play an immunosuppression role. Besides, SERPINE1 was found to be related with TMB, MSI, immunotherapy response and sensitivity to several drugs in cancers. Finally, the high expression of SERPINE1 in ccRCC was verified using qRT-PCR performed on patient samples, six independent GEO cohorts, and proteomic data from the CPTAC database. Conclusion: The findings of the present study revealed that SERPINE1 exhibits aberrant expression in various types of cancers and is associated with cancer immunity and tumor malignancy, providing novel insights for individualized cancer treatment.
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Integrative analysis of cuproptosis-associated genes for predicting immunotherapy response in single-cell and multi-cohort studies.
BACKGROUND: The role of genes associated with the cuproptosis cell signaling pathway in prognosis and immunotherapy in ovarian cancer (OC) has been extensively investigated. In this study, we aimed to explore these mechanisms and establish a prognostic model for patients with OC using bioinformatics techniques. METHODS: We obtained the single cell sequencing data of ovarian cancer from the Gene Expression Omnibus (GEO) database and preprocessed the data. We analyzed a variety of factors including cuproptosis cell signal score, transcription factors, tumorigenesis and progression signals, gene set variation analysis (GSVA) and intercellular communication. Differential gene analysis was performed between groups with high and low cuproptosis cell signal scores, as well as Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses. Using bulk RNA sequencing data from The Cancer Genome Atlas, we used the least absolute shrinkage and selection operator (LASSO)-Cox algorithm to develop cuproptosis cell signaling pathword-related gene signatures and validated them with GEO ovarian cancer datasets. In addition, we analyzed the inherent rules of the genes involved in building the model using a variety of bioinformatics methods, including immune-related analyses and single nucleotide polymorphisms. Molecular docking is used to screen potential therapeutic drugs. To confirm the analysis results, we performed various wet experiments such as western blot, cell counting kit 8 (CCK8) and clonogenesis tests to verify the role of the Von Willebrand Factor (VWF) gene in two ovarian cancer cell lines. RESULTS: Based on single-cell data analysis, we found that endothelial cells and fibroblasts showed active substance synthesis and signaling pathway activation in OC, which further promoted immune cell suppression, cancer cell proliferation and metastasis. Ovarian cancer has a high tendency to metastasize, and cancer cells cooperate with other cells to promote disease progression. We developed a signature consisting of eight cuproptosis-related genes (CRGs) (MAGEF1, DNPH1, RARRES1, NBL1, IFI27, VWF, OLFML3 and IGFBP4) that predicted overall survival in patients with ovarian cancer. The validity of this model is verified in an external GEO validation set. We observed active infiltrating states of immune cells in both the high- and low-risk groups, although the specific cells, genes and pathways of activation differed. Gene mutation analysis revealed that TP53 is the most frequently mutated gene in ovarian cancer. We also predict small molecule drugs associated with CRGs and identify several potential candidates. VWF was identified as an oncogene in ovarian cancer, and the protein was expressed at significantly higher levels in tumor samples than in normal samples. The high-score model of the cuproptosis cell signaling pathway was associated with the sensitivity of OC patients to immunotherapy. CONCLUSIONS: Our study provides greater insight into the mechanisms of action of genes associated with the cuproptosis cell signaling pathway in ovarian cancer, highlighting potential targets for future therapeutic interventions.