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Identification of anoikis-related gene signatures and construction of the prognosis model in prostate cancer.
BACKGROUND: One of the primary reasons for tumor invasion and metastasis is anoikis resistance. Biochemical recurrence (BCR) of prostate cancer (PCa) serves as a harbinger of its distant metastasis. However, the role of anoikis in PCa biochemical recurrence has not been fully elucidated. METHODS: Differential expression analysis was used to identify anoikis-related genes based on the TCGA and GeneCards databases. Prognostic models were constructed utilizing LASSO regression, univariate and multivariate Cox regression analyses. Moreover, Gene Expression Omnibus datasets (GSE70770 and GSE46602) were applied as validation cohorts. Gene Ontology, KEGG and GSVA were utilized to explore biological pathways and molecular mechanisms. Further, immune profiles were assessed using CIBERSORT, ssGSEA, and TIDE, while anti-cancer drugs sensitivity was analyzed by GDSC database. In addition, gene expressions in the model were examined using online databases (Human Protein Atlas and Tumor Immune Single-Cell Hub). RESULTS: 113 differentially expressed anoikis-related genes were found. Four genes (EEF1A2, RET, FOSL1, PCA3) were selected for constructing a prognostic model. Using the findings from the Cox regression analysis, we grouped patients into groups of high and low risk. The high-risk group exhibited a poorer prognosis, with a maximum AUC of 0.897. Moreover, larger percentage of immune infiltration of memory B cells, CD8 Tcells, neutrophils, and M1 macrophages were observed in the high-risk group than those in the low-risk group, whereas the percentage of activated mast cells and dendritic cells in the high-risk group were lower. An increased TIDE score was founded in the high-risk group, suggesting reduced effectiveness of ICI therapy. Additionally, the IC50 results for chemotherapy drugs indicated that the low-risk group was more sensitive to most of the drugs. Finally, the genes EEF1A2, RET, and FOSL1 were expressed in PCa cases based on HPA website. The TISCH database suggested that these four ARGs might contribute to the tumor microenvironment of PCa. CONCLUSION: We created a risk model utilizing four ARGs that effectively predicts the risk of BCR in PCa patients. This study lays the groundwork for risk stratification and predicting survival outcomes in PCa patients with BCR.
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Identification and validation of a fatty acid metabolism gene signature for the promotion of metastasis in liver cancer.
Metastasis is a fatal status for liver cancer, and the identification of an effective prediction model and promising therapeutic target is essential. Given the known relationship between fatty acid (FA) metabolism and the liver, the present study aimed to investigate dysregulation of genes associated with FA metabolism in liver cancer. Bioinformatics analyses were performed on data from patients with hepatocellular carcinoma (HCC) obtained from The Cancer Genome Atlas database using R software packages. Online public tools such as the Human Protein Atlas, Tumor Immune Single-Cell Hub and the University of Alabama at Birmingham Cancer Data Analysis portal were also utilized. Some essential results were further verified using in vitro experiments using HepG2 liver cancer cells. A signature consisting of three genes associated with the progression and prognosis of HCC and FA metabolism was identified. When samples were scored based on the expression of these genes and divided according to the median value, the higher score group showed a worse outcome and repressive immune microenvironment than the lower score group. Downstream pathways such as hypoxia, IL6/JAK/STAT3 and epithelial-mesenchymal transition were found to be significantly activated in the higher score group. As the core factor in the signature, mitochondrial ribosomal protein L35 (MRPL35) was found to be upregulated in HCC and to have certain impacts on the dysregulation of effective immunity. Further investigations and in vitro experiments indicated that MRPL35 facilitates the migration and invasion abilities of liver cancer, and the resistance of HCC to treatment. These findings have important implications regarding the characteristics and mechanisms of metastasis in liver cancer, and provide a promising signature based on FA metabolism-related genes that may be used to predict outcomes and explored as a novel therapeutic target in liver cancer.
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SOX11 and GLIS2: Novel Biomarkers for Understanding the Progression of Oral Leukoplakia.
BACKGROUND: Oral leukoplakia (LP) is a common precancerous lesion that can progress to oral squamous cell carcinoma (OSCC). Although various risk factors have been identified, the molecular mechanisms and biomarkers of LP remain insufficiently understood. METHODS: This study integrated single-cell RNA sequencing with bulk RNA sequencing data from leukoplakia tissues to investigate the transcriptional regulatory mechanisms within LPtissues. Our aim is to identify key molecular markers and their associated pathways. Furthermore, by analyzing survival data from OSCC in the The Cancer Genome Atlas(TCGA) database, we assessed the prognostic significance of these molecular markers in tumors. Finally, we validated the potential roles of relevant molecular markers in the progression of leukoplakia using patient LP tissue samples. RESULTS: The analysis revealed that single-cell RNA sequencing data indicated that fibroblasts in LP tissues possess a unique gene regulatory network, exhibiting significantly different transcriptional characteristics compared to normal tissues. We identified SOX11 and GLIS2 as LP-specific molecular markers, with their expression significantly elevated in LP tissues compared to normal tissues. Transcriptomic profiling of LP reveals that SOX11 and GLIS2 positively correlate with the activation of epithelial-mesenchymal transition (EMT) and angiogenesis. Additionally, analysis of OSCC samples demonstrated that high expression levels of SOX11 and GLIS2 were associated with poor prognosis. Immunohistochemical staining further showed a gradual increase in the expression of SOX11 and GLIS2 during the progression of leukoplakia, supporting their potential application as biomarkers. CONCLUSION: As key transcription factors in LP, SOX11 and GLIS2 may influence the occurrence and development of lesions by regulating EMT and angiogenesis biological processes. They have the potential to serve as biomarkers and therapeutic targets for LP and OSCC, providing new insights into understanding the mechanisms of LP and developing treatment strategies.
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The landscape in telomere related gene prognostic signature for survival and medication treatment effectiveness prediction in hepatocellular carcinoma.
OBJECTIVE: Telomeres, made of repetitive DNA sequences and shelterin complexes, which were found at the ends of chromosomes and had been extensively studied in cancer research. However, in hepatocellular carcinoma (HCC) was still relatively scarce. In this study, we investigated the correlation between telomerase-related genes (TRGs) and the prognosis and immunotherapy of HCC patients to enhance clinical outcomes. METHODS: In this work, TRGs were gathered using TelNet, while clinical information and gene expression data for HCC patients were retrieved from the Cancer Genome Atlas (TCGA) database. A risk prediction model based on TRGs was created using COX and Lasso regression analyses, with ROC curves used to assess prognostic efficacy. Univariate and multifactorial COX regression analyses were used to determine if the risk model had an independent impact on prognosis. Nomograms were created to enhance clinical usability, and calibration curves were used to assess predictive ability at various time points. The Tumor Immune Dysfunction and Exclusion (TIDE) score was used to analyze differences in immune infiltrating cells between risk groups. The study analyzed the relationship between risk ratings and drug treatment effectiveness using data from the CellMiner database. The hub gene was identified and its relationship to prognostic markers of HCC patients was examined. The expression of hub genes in immune cell subpopulations was also investigated by single-cell data. RESULTS: 2093 TRGs were identified, with 949 showing significant differences in expression between HCC and paracancerous tissues. Seven risk genes were overexpressed in tumor tissues, leading to lower survival rates in high-risk patients. Risk model could independently predict the prognosis of HCC patients. Analysis of tumor immune infiltrating cells revealed significant differences in cell abundance between risk groups, with notable variations in immune subset enrichment between subgroups. Higher risk scores correlated with increased sensitivity to sorafenib, mitoxantrone, oxaliplatin, gemcitabine, and entinostat, while sensitivity decreased for vincristine, etc. CDCA8 was identified as a key gene in the Protein Interaction Network, while high expression associated with poorer overall survival, tumor proliferation and metastasis. The results of single-cell data analysis suggest that CDCA8 may promote the development of HCC by affecting T lymphocytes. CONCLUSION: The TRG-based risk model could predict HCC patient prognosis and closely linked to tumor immune environment, which could offer new possibilities for clinical treatment.
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Multi-omics analyses related to unfolded protein response in prostate cancer implicate pro-tumor role of IFRD1.
INTRODUCTION: The unfolded protein response (UPR) promotes prostate cancer (PCa) progression, yet its multi-omics landscape and clinical utility remain undefined. METHODS: We integrated single-cell and bulk transcriptomic datasets, and identified UPR-related genes (UPRRGs) through a combination of differential expression analysis and weighted gene co-expression network analysis (WGCNA), based on which we further developed a consensus UPR-related signature (UPRRS) using a machine learning framework. The UPRRGs were further characterized by functional enrichment, cell-cell communication, and survival analyses. A clinically applicable nomogram integrating UPR-related prognostic genes was constructed for prognostic prediction. Through in silico and in vitro analyses, we validated the clinical relevance between the hub UPRRGs and PCa progression. RESULTS: Single-cell analyses revealed elevated UPR activity in prostate epithelial cells, most prominently within the LE-KLK3 subpopulation. These cells exhibited enhanced ligand-receptor interactions in TNF, VEGF and NOTCH signaling axes. A seven-UPRRG signature (including IFRD1, DDIT3, HSPA5) demonstrated robust prognostic performance in the TCGA training set and three external validation cohorts (C-index > 0.82; AUC > 0.80). Multivariate Cox analysis confirmed UPRRS as an independent prognostic factor beyond clinical stage and Gleason score. Mechanistically, the UPRRS-high subgroup displayed an immunosuppressive microenvironment and reduced sensitivity to multiple chemotherapeutics. In vitro knock-down of IFRD1 markedly attenuated PCa cell proliferation and migration. CONCLUSION: We provide the first systematic single-cell atlas of UPR heterogeneity in PCa and develop a clinically translatable UPRRS prognostic model. IFRD1, a key driver, emerges as a dual diagnostic and therapeutic target, offering both theoretical and experimental foundations for precision stratification and individualized management of PCa.
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Impact of per- and polyfluoroalkyl substances on diabetic kidney disease.
PURPOSE: This study aims to elucidate the mechanistic role of Per- and Polyfluoroalkyl Substances (PFAS) in the pathogenesis and progression of diabetic kidney disease (DKD). METHODS: This study systematically evaluated the toxicity profiles of PFAS compounds utilizing PubChem, ProTox 3.0, and ChEMBL databases. Potential PFAS-related targets were predicted through SwissTargetPrediction and SuperPred platforms. Gene targets associated with DKD were compiled from the GeneCards and OMIM databases. Intersection analysis of PFAS and DKD-related targets was performed to identify candidate genes. A protein-protein interaction network was constructed using STRING to delineate hub targets. Functional enrichment analyses were subsequently conducted via DAVID to elucidate underlying biological processes and pathways. Validation of hub targets encompassed immunohistochemical staining, single-cell expression profiling, subcellular localization assays, and gene expression analyses using external datasets from the Human Protein Atlas (HPA) and Gene Expression Omnibus (GEO). Furthermore, correlations between immune cell infiltration and gene set enrichment analysis (GSEA) were performed to investigate potential mechanistic links. Finally, molecular docking simulations of PFAS compounds with hub proteins were executed using Discovery Studio and CDOCKER to predict binding interactions. RESULTS: A total of 424 PFAS-associated targets were identified, alongside 9,999 potential toxic targets related to DKD. KEGG pathway enrichment analysis revealed that PFAS toxicity in DKD is implicated in critical signaling pathways, including nitrogen metabolism, peroxisome proliferator-activated receptor (PPAR) signaling, endocrine resistance, insulin resistance, and AMP-activated protein kinase (AMPK) signaling. Hub targets identified comprised MMP9, BCL2, CYP3A43, ACE, HNF4A, HSP90AA1, AGTR1, MMP2, AGTR2, and HMGCR. GSEA further indicated that these hub targets may contribute to immune-mediated renal injury. Molecular docking simulations substantiated strong binding affinities between PFAS compounds and the identified hub proteins, supporting their potential mechanistic involvement. CONCLUSION: This study provides a theoretical framework for elucidating the toxic targets and underlying mechanisms through which PFAS contribute to the pathogenesis of DKD.
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An atlas of the aging lung mapped by single cell transcriptomics and deep tissue proteomics.
Aging promotes lung function decline and susceptibility to chronic lung diseases, which are the third leading cause of death worldwide. Here, we use single cell transcriptomics and mass spectrometry-based proteomics to quantify changes in cellular activity states across 30 cell types and chart the lung proteome of young and old mice. We show that aging leads to increased transcriptional noise, indicating deregulated epigenetic control. We observe cell type-specific effects of aging, uncovering increased cholesterol biosynthesis in type-2 pneumocytes and lipofibroblasts and altered relative frequency of airway epithelial cells as hallmarks of lung aging. Proteomic profiling reveals extracellular matrix remodeling in old mice, including increased collagen IV and XVI and decreased Fraser syndrome complex proteins and collagen XIV. Computational integration of the aging proteome with the single cell transcriptomes predicts the cellular source of regulated proteins and creates an unbiased reference map of the aging lung.
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Exploring the role of EBV in multiple sclerosis pathogenesis through EBV interactome.
BACKGROUND: Epstein-Barr virus (EBV) is a known risk factor for multiple sclerosis (MS), even though the underlying molecular mechanisms are unclear and engage multiple immune pathways. Furthermore, the ultimate role of EBV in MS pathogenesis is still elusive. In contrast, Cytomegalovirus (CMV) has been identified as a protective factor for MS. OBJECTIVES: This study aims to identify MS-associated genes that overlap with EBV interactome and to examine their expression in immune and glial cell subtypes. METHODS: We used P-HIPSTer, GWAS, and the Human Protein Atlas (HPA) to derive data on the EBV interactome, MS-associated genes and single-cell gene expression in immune and glial cells. The geneOverlap and dplyr R packages identified overlapping genes. A similar analysis was done for CMV and Adenovirus as negative control. Metascape and GTEx analyzed biological pathways and brain-level gene expression; transcriptomic analysis was performed on glial cells and peripheral blood in MS and controls. All the analyses performed in this study were generated using publicly available data sets. RESULTS: We identified a "core" group of 21 genes shared across EBV interactome, MS genes, and immune and glial cells (p<0.001). Pathway analysis revealed expected associations, such as immune system activation, and unforeseen results, like the prolactin signaling pathway. BCL2 in astrocytes, MINK1 in microglia were significantly upregulated while AHI1 was downregulated in MS compared to controls. CONCLUSIONS: Our findings offer novel insights into EBV and CMV interaction with immune and glial cells in MS, that may shed light on mechanisms involved in disease pathophysiology.
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Construction and validation of transcription‑factor‑based prognostic signature for TACE non‑response and characterization of tumor microenvironment infiltration in hepatocellular carcinoma.
Hepatocellular carcinoma (HCC) is one of the most common malignant tumors worldwide. Despite continuous development of treatment methods, overall survival rate of liver cancer is low. Transcatheter arterial chemoembolization (TACE) is a first-choice treatment for advanced liver cancer. Although it is generally effective, a number of patients do not benefit from it. Therefore, the present study was conducted to assess the response of patients following TACE. RNA-sequencing data and corresponding clinical information were extracted from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus databases. Models were constructed using weighted gene co-expression network analysis and least absolute shrinkage and selection operator-Cox regression analysis based on TCGA-LIHC and GSE104580 cohorts. The receiver operating characteristic curve was used for evaluation. Immunoassay, half-maximal inhibitory concentration analysis of risk groups, genomic enrichment analysis and nomogram construction were also performed. The predictive models were validated at the single-cell level using single-cell databases. Finally, the present study examined the expression of TACE refractoriness-related TFs (TRTs) in TACE-resistant and non-resistant cell lines in vitro. A risk categorization approach was created based on screening of four TRTs. The patients were split into high- and low-risk groups. There were significant variations in immune cell infiltration, medication sensitivity and overall survival (OS) between patients in the high-risk and low-risk groups. Multivariate Cox regression analysis showed that the risk score was an independent prognostic factor for OS. In the single-cell gene set, risk score was a good indicator of tumor microenvironment (TME). Reverse transcription-quantitative PCR revealed that three high-risk TRTs were upregulated in TACE-resistant cells. Prognosis and TME status of liver cancer patients following TACE could be assessed using a predictive model based on transcription factor correlation. This predictive model provided a reliable and simplified method to guide the clinical treatment of HCC.
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Single-cell atlas of hepatitis C virus inoculated tree shrew liver reveals immune activation, metabolic reprogramming, and persistent inflammation.
BACKGROUND: Hepatitis C virus (HCV) remains a major global health challenge, largely due to the absence of robust animal models that recapitulate this disease, hindering mechanistic studies and vaccine development. This study employed tree shrews to determine the molecular profiles of distinct liver cell populations following HCV infection. METHODS: Tree shrews received intrahepatic injections of the HCV JFH1 strain and were analysed at two timepoints: 1 week post-inoculation (acute) and 111 weeks post-inoculation (long-term). Viral load was assessed up to 30 weeks. Liver pathology was assessed via haematoxylin and eosin (H&E) staining. Single-cell transcriptomic profiling was performed on liver tissues, while tree shrew primary hepatocytes and ITH6 hepatic cells, as well as human Huh7 cells, were used to characterise cellular changes following HCV infection. FINDINGS: Sixteen major cell types were identified in 157,298 liver cells across HCV-inoculated and control tree shrews. Acute inoculation triggered widespread induction of interferon-stimulated genes (ISGs) across all cell types, including the periportal ISGhigh hepatocyte subcluster, and concurrent reduction in metabolic gene expression in hepatocytes in vivo. Impaired glucose metabolism, as confirmed in HCV-exposed ITH6 cells, likely contributed to this metabolic shift. Erythroblasts were exclusively detected during acute inoculation and exhibited enhanced intercellular communication signatures. Among neutrophils, three distinct subclusters were identified, one of which displayed elevated expression of neutrophil extracellular trap (NET) markers and enhanced NET formation in inoculated livers. INTERPRETATION: This study provides a comprehensive single-cell transcriptomic landscape of HCV-inoculated tree shrew livers. These findings underscore the use of the tree shrew model for advancing mechanistic understanding of HCV pathogenesis, as well as its relevance for therapeutic and vaccine development. FUNDING: This work was supported by grants from National Natural Science Foundation of China (U1902215, U25A20646), National Key Research and Development Plan Program (2022YFF0710900), Key Project of the CAS "Light of West China" Program (xbzg-zdsys-202302), and Yunnan Province (202305AH340006, 202001AS070023).