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Metabolic reprogramming and prognostic insights in molecular landscapes driven by glycolysis in ovarian cancer.
Ovarian cancer (OC) is a highly fatal gynecological malignancy primarily attributable to late-stage detection and restricted treatment options. Aberrant glycolysis, exemplified by the Warburg effect, facilitates tumor development, immunological evasion, and alteration of the microenvironment. Identifying glycolysis-related biomarkers could provide novel insights into prognosis and potential therapeutic targets for OC.The transcriptomic and clinical information of OC patients were obtained from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and Gene Expression Omnibus (GEO) databases. Differentially expressed glycolysis-related genes (GRGs) were identified and analyzed for their prognostic significance. Consensus clustering was employed to identify glycolysis subtypes, followed by pathway enrichment and immune infiltration analyses. A ten-gene GRG signature was developed with LASSO-Cox regression and verified in various cohorts. Single-cell RNA sequence and drug susceptibility analysis were performed to explore tumor microenvironment heterogeneity and potential therapeutic agents.A total of 457 differentially expressed GRGs were discovered, of which 30 were substantially linked with OC prognosis. Three molecular subtypes were characterized, with cluster C exhibiting the worst prognosis and activation of tumor-associated pathways. A prognostic model comprising ten genes (LMCD1, L1CAM, MYCN, GALT, IDO1, RPL18, XBP1, LPAR3, RUNX3, PLCG1) was developed and validated, demonstrating robust predictive efficacy across various cohorts. Immune analysis revealed substantial disparities in immune infiltration among risk groups, whereas single-cell analysis identified several critical genes essential for metabolism, proliferation, and interactions within the tumor microenvironment.This work highlights the prognostic and therapeutic significance of GRGs in OC. The ten-gene GRG signature serves as a reliable framework for risk assessment and the formulation of individualized treatment regimens. Nonetheless, further experimental validation and extensive clinical research are necessary to enable the application of these findings in clinical practice. These results highlight the potential of targeting glycolytic pathways as a promising approach to improve the management and treatment outcomes of OC.
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Single-cell transcriptional profiling reveals cell type-specific responses to duck reovirus infection in the Bursa of Fabricius of Cairna moschata.
Duck reovirus (DRV) is a universal waterfowl virus that causes significant economic losses in the duck industry. However, the role of the host innate immune response of the Bursa of Fabricius to DRV infection is largely unknown. In the present study, we constructed a single-cell resolution transcriptomic atlas of the Bursa of Fabricius of Cairna moschata after infection with HN10 (a novel DRV). Ten cell-type marker genes were used to annotate the cell type, indicating a high degree of cell heterogeneity in the Bursa of Fabricius. Most of the innate and adaptive immune system-related genes were highly expressed in T cells, B cells, neutrophils, macrophages, and DCs. In the Bursa of Fabricius, the proportions of DCs and macrophages were largely increased by HN10 infection at 14 d, suggesting that DCs and macrophages play important roles in the long-term viral response. Notably, a number of innate and adaptive immune system-related genes were highly expressed at 24 h after HN10 infection, indicating that the Bursa of Fabricius has a very strong immune function even in the early developmental stage. In the immune system, the NOD-like receptor signaling pathway and RIG-I-like receptor signaling pathway were significantly activated at the early stage of HN10 infection, while the Toll-like receptor signaling pathway was significantly activated at the late stage. Enrichment analysis suggested that different immune signaling pathways play roles in specific developmental stages. Our data provide an opportunity to reveal the immune response to DRV infection at the single-cell level.
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Comprehensive analysis of CXCL14 uncovers its role during liver metastasis in colon cancer.
BACKGROUND: The most common cause of death for colon cancer patients is liver metastasis. METHODS: All the data enrolled in this study were downloaded from two public databases, The Cancer Genome Atlas Program, the TCGA-COAD project and Gene Expression Omnibus, GSE41258 project. All the analysis was performed in R software. RESULTS: In our study, we systematically explored the molecules involved in the liver metastasis process of colon cancer. The biological role of these molecules was identified through the GO and KEGG analysis. Moreover, we identified that the molecules SERPINA3, SERPINA1, MMP3, ALDH1A3, PBK and CXCL14 were the independent factors for patients survival. The CXCL14 was selected for further analysis for its most significant P value. Single-cell analysis showed that the CXCL14 was mainly expressed in the fibroblasts. Meanwhile, the biological role of fibroblasts in the colon cancer microenvironment was investigated. Further, the clinical role of CXCL14 in colon cancer was also explored. The result showed that the CXCL14 is a protective factor against colon cancer independent of other clinical parameters like age, gender, clinical stage, and TNM classifications. Then, biological enrichment analysis indicated that the CXCL14 is predominantly involved in the activating of the WNT/β/catenin pathway, pancreas beta cells, peroxisome and bile acid metabolism. Immune infiltration analysis showed that for the patients with high CXCL14 levels, the plasma B cells, CD8 + T cells, neutrophil and NK cells might infiltrate more, in contrast to B cells, monocyte and macrophages. Furthermore, we found that the patients with low CXCL14 expression might be more sensitive to etoposide, rapamycin and sunitinib. CONCLUSIONS: Our result could improve the understanding of the liver metastasis process in colon cancer. Also, CXCL14 was identified as an underlying therapeutic target for colon cancer.
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Machine learning- and WGCNA-mediated double analysis based on genes associated with disulfidptosis, cuproptosis and ferroptosis for the construction and validation of the prognostic model for breast cancer.
BACKGROUND: Disulfidptosis, a recently discovered cellular death mechanism, has not been extensively studied in relation to breast cancer (BC). Specifically, no previous research has integrated disulfidptosis-related genes (DRGs), cuproptosis-related genes (CRGs), and ferroptosis-related genes (FRGs) to construct a prognostic signature for BC. METHODS: DRGs, CRGs and FRGs with prognostic potential were identified through Cox regression analysis. A predictive model was constructed by intersecting the core genes obtained from unsupervised cluster analysis and weighted correlation network analysis (WGCNA). Differences in chemotherapy drug sensitivity, immune checkpoint levels were analyzed according to different risk score groups. The expression of the core disulfidptosis gene, SLC7A11, was analyzed using immunofluorescence. RESULTS: Single-cell RNA sequencing analysis revealed differential expression of DRGs in the BC tumor microenvironment. We developed a prognostic model, consisting of six genes, based on machine learning which included unsupervised cluster analysis and Lasso-Cox analysis. An internal training set and a validation set, both derived from the Cancer Genome Atlas-Breast Cancer (TCGA-BRCA) database, GSE20685 and GSE42568 as external validation sets all verified the model's validity. The low-risk group exhibited increased sensitivity to paclitaxel. Additionally, the high-risk group demonstrated significantly higher expression of tumor mutation burden and microsatellite instability compared to the low-risk group. A nomogram confirmed that the risk score can be an independent risk factor for BC. Notably, our findings highlighted the impact of SLC7A11 on the BC tumor microenvironment. Immunofluorescence analysis revealed significantly higher expression of SLC7A11 in BC tissues compared to paracancerous tissues. CONCLUSION: Multiplex analysis based on DRGs, CRGs and FRGs correlated strongly with BC, providing new insights for developing clinical prognostic tools and designing immunotherapy regimens for BC patients.
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Multi-omics pan-cancer study of cuproptosis core gene FDX1 and its role in kidney renal clear cell carcinoma.
BACKGROUND: The mechanism of copper-induced cellular death was newly discovered and termed cuproptosis. Inducing cuproptosis in cancer cells is well anticipated for its curative potential in treating tumor diseases. However, ferredoxin 1 (FDX1), the core regulatory gene in cuproptosis, is rarely studied, and the regulation of FDX1 in tumor biology remains obscure. A comprehensive pan-cancer analysis of FDX1 is needed. METHODS: Thirty-three types of tumors were included with paired normal tissues in The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) datasets. The interaction between transcription, protein, phosphorylation, and promoter methylation levels was analyzed. Survival, immune infiltration, single-cell FDX1 expression, FDX1-related tumor mutational burden (TMB), microsatellite instability (MSI), stemness, tumor immune dysfunction and exclusion (TIDE), and immunotherapy-related analyses were performed. FDX1 protein expression was assessed by kidney renal clear cell carcinoma (KIRC) tissue microarray immunohistochemistry. The function of FDX1 in KIRC was further explored by experiments in 786-O cell lines in vitro. RESULTS: FDX1 is highly expressed in 15 tumor types and lowly expressed in 11 tumor types. The corresponding changes in protein expression, phosphorylation, and promoter methylation level of FDX1 have been described in several tumors. Survival analysis showed that FDX1 was related to favorable or poor overall survival in eight tumors and progression-free survival in nine tumors. Immune infiltration and single-cell analysis indicated the indispensable role of FDX1 expression in macrophages and monocytes. Multiple established immunotherapy cohorts suggested that FDX1 may be a potential predictor of treatment effects for tumor patients. Tissue microarray analysis showed decreased FDX1 expression in KIRC patients' tumor tissues. Knockdown of FDX1 resulted in the downregulation of cuproptosis in kidney renal clear tumor cells. Mechanistically, the FDX1-associated gene expression signature in KIRC is related to the enrichment of genes involved in the tricarboxylic acid (TCA) cycle, NOTCH pathway, etc. Several NOTCH pathway genes were differentially expressed in the high- and low-FDX1 groups in KIRC. CONCLUSION: Our analysis showed that the central regulatory gene of cuproptosis, FDX1, has differential expression and modification levels in various tumors, which is associated with cellular function, immune modulation, and disease prognosis. Thus, FDX1-dependent cuproptosis may serve as a brand-new target in future therapeutic approaches against tumors.
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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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PLEK2 Gene Upregulation Might Independently Predict Shorter Progression-Free Survival in Lung Adenocarcinoma.
OBJECTIVE: This study aimed to explore PLEK2 expression profile, its prognostic value, and the potential genomic alterations associated with its dysregulation in lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC). MATERIALS AND METHODS: Data from The Cancer Genome Atlas (TCGA), The Genotype-Tissue Expression (GTEx), and Kaplan-Meier plotter were used in combination for bioinformatic analysis. RESULTS: PLEK2 mRNA was significantly upregulated in both LUAD and LUSC compared with their respective normal controls. PLEK2 upregulation showed independent prognostic value in progression-free survival (PFS) (HR: 1.169, 95%CI: 1.033 -1.322, p = 0.014). PLEK2 mRNA expression was positively correlated with invasion, cell cycle, DNA damage, and DNA repair of LUAD cells at the single-cell level. Genomic analysis showed that gene-level amplification might not directly lead to increased PLEK2 expression. Methylation profile analysis found 4 CpG sites (cg12199376, cg14437634, cg17641252, and cg06724236) had at least a weakly negative correlation with PLEK2 expression, among which cg12199376, cg14437634 and cg17641252 locate around the first exon of the gene. CONCLUSIONS: Increased PLEK2 expression might be a specific prognostic biomarker of poor PFS in LUAD patients. Its expression had significant positive correlations with invasion, cell cycle, DNA damage, and DNA repair of LUAD cells at the single-cell level. Promoter hypomethylation might be a potential mechanism leading to its upregulation.
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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.