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Upregulation of ADAR Promotes Breast Cancer Progression and Serves as a Potential Therapeutic Target.
BACKGROUND: Breast cancer (BC) is the most common cause of cancer death worldwide, and its incidence is increasing every year. This study aims to investigate the expression characteristics of ADAR gene in breast cancer and to explore its role in the occurrence and development of BC and its possible mechanism. METHODS: TCGA portal was used to detect the expression of ADAR in cancer including BC, and its correlation with clinicopathological data as well as other genes was analyzed via UALCAN database. The TISCH database evaluated the expression of ADAR in different types of cell populations in BC at the single-cell level. The Kaplan-Meier plotter database was used to predict the correlation between ADAR expression and BC patient prognosis. The Human Protein Atlas was used to detect the expression of ADAR in tissues and location of ADAR mRNA in cells. Moreover, the relationships between immune response and ADAR expression in BC were assessed with the use of the TISIDB. Metascape and STRING were applied to predict ADAR with other protein interactions. Finally, the effect generated by ADAR expression on cell proliferating, invading, and migrating processes was assessed in vitro with knockdown and overexpression strategies. RESULTS: ADAR was significantly upregulated in BC tissues compared to paracancerous tissues. Single-cell RNA analysis showed that ADAR was specifically upregulated in cancer cell clusters and was also expressed in stromal and immune cell clusters. The upregulation of ADAR was positively correlated with clinicopathological stage and negatively correlated with BC prognosis. Experimental processes in vitro revealed ADAR knockdown hindered, proliferated, invaded, and migrated levels of BC cells, whereas over expression of ADAR played the opposite effect. ADAR protein, which may interact with OASL, STAT2, and IFIT3, was mainly located in the nucleoli in cells and primarily involved DNA modification and apoptotic signaling pathway. Immune factors may interact with ADAR in BC, and ADAR was found noticeably linked with immunosuppressor such as IL10, CD274, and IDO1. CONCLUSION: ADAR is significantly upregulated in breast cancer tissues, which may promote the progression of BC through the interaction of cancer cells, stromal cells, and immune cells. Targeting ADAR may offer new hope in treating breast cancer.
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Integrated multi-omics analysis reveals NOL11 as a novel prognostic biomarker for hepatocellular carcinoma.
BACKGROUND: Hepatocellular carcinoma (HCC) is one of the most common aggressive malignant tumors worldwide with poor clinical outcomes and high mortality rates, highlighting the pressing need to identify reliable biomarkers. Nucleolar protein 11 (NOL11), as an essential element for ribosomal biosynthesis, has been implicated in the development and progression of various diseases. However, the potential role of NOL11 in HCC remains elusive. METHODS: The expression level of NOL11 was investigated by The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, and the correlation between NOL11 and the clinical characteristics was analyzed. Furthermore, spatial-temporal expression patterns of NOL11 were delineated based on single-cell and spatial transcriptomics. Cox regression and ROC analyses were performed to evaluate the prognostic and diagnostic value of NOL11. Additionally, Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Ontology (GO), and gene set enrichment analysis (GSEA) were conducted to elucidate the functional pathways and potential mechanisms associated with NOL11. The association of NOL11 with immune infiltration was also explored using single-sample Gene Set Enrichment Analysis (ssGSEA). The integrated bioinformatics analysis was utilized to identify the drug sensitivity, and further drug validation was employed via molecular docking. Finally, the biological function of NOL11 was validated through in vitro experiments. RESULTS: The expression of NOL11 was significantly upregulated in HCC, especially in tumor cells. Elevated NOL11 levels closely correlated with worse clinicopathological features, poor prognosis, and immune infiltration. Functional enrichment analysis revealed NOL11's potential involvement in multiple cancer-associated signaling pathways, including the cell cycle, DNA replication, and cell metabolism. Furthermore, we found that common chemotherapy drugs, such as gemcitabine, trametinib, and paclitaxel were sensitive to the expression of NOL11 and exhibited a strong molecular combination with NOL11. Lastly, NOL11 knockdown suppressed the proliferation, migration, and invasion of HCC cells. CONCLUSION: Our findings suggest that NOL11 is a promising diagnostic and prognostic biomarker for HCC, providing new insights into the treatment decisions for HCC patients.
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The collagen landscape in cancer: profiling collagens in tumors and in circulation reveals novel markers of cancer-associated fibroblast subtypes.
Cancer-associated fibroblasts (CAFs) deposit and remodel collagens in the tumor stroma, impacting cancer progression and efficacy of interventions. CAFs are the focus of new therapeutics with the aim of normalizing the tumor microenvironment. To do this, a better understanding of CAF heterogeneity and collagen composition in cancer is needed. In this study, we sought to profile the expression of collagens at multiple levels with the goal of identifying cancer biomarkers. We investigated the collagen expression pattern in various cell types and CAF subtypes in a publicly available single-cell RNA sequencing (RNA-seq) dataset of pancreatic ductal adenocarcinoma. Next, we investigated the collagen expression profile in tumor samples across cancer types from The Cancer Genome Atlas (TCGA) database and evaluated if specific patterns of collagen expression were associated with prognosis. Finally, we profiled circulating collagen peptides using a panel of immunoassays to measure collagen fragments in the serum of cancer patients. We found that pancreatic stellate cells and fibroblasts were the primary producers of collagens in the pancreas. COL1A1, COL3A1, COL5A1, COL6A1 were expressed in all CAF subtypes, whereas COL8A1, COL10A1, COL11A1, COL12A1 were specific to myofibroblast CAFs (myCAF) and COL14A1 specific to inflammatory CAFs (iCAF). In TCGA database, myCAF collagens COL10A1 and COL11A1 were elevated across solid tumor types, and multiple associations between high expression and worse survival were found. Finally, circulating collagen biomarkers were elevated in the serum of patients with cancer relative to healthy controls with COL11A1 (myCAF) having the best diagnostic accuracy of the markers measured. In conclusion, CAFs express a noncanonical collagen profile with specific collagen subtypes associated with iCAFs and myCAFs in PDAC. These collagens are deregulated at the cellular, tumor, and systemic levels across different solid tumors and associate with survival. These findings could lead to new discoveries such as novel biomarkers and therapeutic targets. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
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PRKDC-Mediated NHEJ May Play a Crucial Role in Aneuploidy of Chromosome 8-Driven Progression of Ovarian Cancer.
High malignancy is a prominent characteristic of epithelial ovarian cancer (EOC), emphasizing the necessity for further elucidation of the potential mechanisms underlying cancer progression. Aneuploidy and copy number variation (CNV) partially contribute to the heightened malignancy observed in EOC; however, the precise features of aneuploidy and their underlying molecular patterns, as well as the relationship between CNV and aneuploidy in EOC, remain unclear. In this study, we employed single-cell sequencing data along with The Cancer Genome Atlas (TCGA) to investigate aneuploidy and CNV in EOC. The technique of fluorescence in situ hybridization (FISH) was employed using specific probes. The copy number variation within the genomic region of chromosome 8 (42754568-47889815) was assessed and utilized as a representative measure for the ploidy status of individual cells in chromosome 8. Differential expression analysis was performed between different subgroups based on chromosome 8 ploidy. Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), protein-protein interaction (PPI), and hub-gene analyses were subsequently utilized to identify crucial genes involved. By classifying enriched tumor cells into distinct subtypes based on chromosome 8 ploidy combined with TCGA data integration, we identified key genes driving chromosome 8 aneuploidy in EOC, revealing that PRKDC gene involvement through the mediated non-homologous end-joining pathway may play a pivotal role in disease progression. Further validation through analysis of the GEO and TCGA database and survival assessment, considering both mRNA expression levels and CNV status of PRKDC, has confirmed its involvement in the progression of EOC. Further functional analysis revealed an upregulation of PRKDC in both ovarian EOC cells and tissues, with its expression showing a significant correlation with the extent of copy number variation (CNV) on chromosome 8. Taken together, CNV amplification and aneuploidy of chromosome 8 are important characteristics of EOC. PRKDC and the mediated NHEJ pathway may play a crucial role in driving aneuploidy on chromosome 8 during the progression of EOC.
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The pan-cancer landscape of crosstalk between leukocyte transendothelial migration-related genes and tumor microenvironment relevant to prognosis and immunotherapy response.
BACKGROUND: Leukocyte transendothelial migration-related genes (LTEMGs) play a crucial role in the immune response and have been extensively studied in various pathological conditions, including inflammation, infection, and cancer. In recent years, increasing attention has been given to understanding the biological mechanisms of LTEMGs in the context of tumor progression and metastasis. The potential function of LTEMGs in cancer progression remains unclear. The aim of this study is to systematically delineate the relationship between LTEMGs and tumor prognosis and immune microenvironment at the pan-cancer level, providing new biomarkers for personalized immunotherapy. METHODS: The gene alteration, messenger RNA (mRNA) expression, and prognostic value of LTEMGs in pan-cancer were evaluated using Bulk and single-cell RNA (scRNA) sequence data. The LTEMGs score was calculated by R package "GSVA". The association of LTEMGs score with tumor microenvironment and immunotherapy response were deeply explored. RESULTS: We assessed the mRNA expression of 114 LTEMGs across various cancers, finding significant upregulation in acute myeloid leukemia (LAML) and pancreatic adenocarcinoma (PAAD). Prognostic analysis indicated most LTEMGs were risk factors in low-grade glioma (LGG), PAAD, uveal melanoma (UVM), and LAML. The LTEMGs score, highest in kidney renal clear cell carcinoma (KIRC) and lowest in UVM, was higher in tumor tissues compared to normal tissues in several cancers. The score was a risk factor for overall survival (OS) in LGG, UVM, and others, but protective in KIRC and some others. LTEMGs score correlated positively with Kirsten rat sarcoma viral oncogene homolog (KRAS) signaling, apoptosis, and immune responses. It also correlated with immune and stromal scores, and immune-related pathways. Higher LTEMGs score was linked to greater immune cell infiltration and poorer immunotherapy outcomes. Single-cell analysis revealed higher LTEMGs score in endothelial and monocyte cells, consistent with reduced immunotherapy responsiveness. CONCLUSIONS: Our results reveal that LTEMGs are closely associated with tumor microenvironment. Patients with high LTEMGs score might be resistant to immunotherapy.
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A Single-Cell and Spatial 3D Multi-omic Atlas of Developing Human Basal Ganglia and Inhibitory Neurons.
The human basal ganglia (BG), subcortical nuclei fundamental to motor regulation and cognitive modulation, is constructed from neurons produced during gestation in the adjacent ganglionic eminences (GEs). GEs are transient structures in the ventral prenatal brain that also generate GABAergic inhibitory neurons which migrate to destinations in the BG, cortex and other destinations. This study aims to elucidate the epigenomic and 3D-genomic dynamics involved in the specification and maturation of GEs and GE-derived neurons, using single-nucleus methyl-3C sequencing (snm3C-seq), highly-multiplexed spatial transcriptomics, and chromatin+RNA single-molecule imaging. Our multi-modal data support a heterogeneous temporal progression across GE subregions, with the lateral GE (LGE) showing declining neurogenic activity in mid-gestation and caudal GE (CGE) exhibiting ongoing developmental progression through infancy. We identified regulatory programs that specify subtypes of BG principal cells, medium spiny neurons (MSN), via synchronized maturation of the 3D-epigenome. In infant brains, we found a transient short-range enriched (SE) chromatin conformation during the transition between oligodendrocyte progenitors (OPCs) and oligodendrocytes (ODCs), and a temporary shift toward Long-range Enriched (LE) chromatin conformation in projection neurons, extending previous works showing the differentiation of neurons and glial cells is associated with permanent SE and LE conformation, respectively. Lastly, we found that gene regulatory regions active in MSNs were enriched in loci associated with genetic risk for neuropsychiatric disease. Our study delineates the highly complex, lineage-specific 3D genomic dynamics in ventral progenitors and basal ganglia populations of the perinatal human brain.
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Genetic association of tertiary lymphoid structure-related gene signatures with HCC based on Mendelian randomization and machine learning and construction of prognosis model.
BACKGROUND: Tertiary lymphoid structures (TLS) are formed in numerous cancer types. However, their value and significance in hepatocellular carcinoma (HCC) is unclear. METHODS: We performed differential genes expression analysis of TLS-related Genes (TLSG) based on The Cancer Genome Atlas (TCGA) database, and performed Mendelian randomization (MR) analysis using expression quantitative trait loci, and then took their intersecting genes. A TLSG prognostic signature (TLSGPS)-based risk score was constructed using Least Absolute Shrinkage and Selection Operator (LASSO), univariate and multivariate COX regression analysis, and survival analysis was then performed. We used the International Cancer Genome Consortium for outside validation. We also performed biological function, tumor mutational burden, immune infiltration, single-cell analysis, CeRNA and drug sensitivity analysis based on TLSGPS. RESULTS: Three TLSGs (HM13, CSTB, CDCA7L) were identified to construct the TLSGPS, which showed good predictive ability and outperformed most prognostic signatures. MR suggested that HM13 (OR = 0.9997, 95 %CI: 0.9994-0.9999, P = 0.014) and CSTB (OR = 0.9997, 95 %CI: 0.9995-0.9999, P = 0.048) were negatively correlated with the risk of HCC onset, while CDCA7L (OR = 1.0004, 1.0001-1.0007, P = 0.0161) was the opposite. The differences in biological functions between the TLSGPS-based high-risk group (HRG) and low-risk group (LRG) involved cell proliferation, differentiation, and drug metabolism. HRG plus high mutations exhibited extremely poor survival. HRG had higher abundance of immune cell-oncogenic phenotypes, higher immune escape ability, and greater sensitivity to Afatinib, Dasatinib, and Gefitinib. CONCLUSION: 3 TLSGs identified by machine learning and MR can predict the onset, prognosis and clinical treatment of HCC patients, and had significant genetic association with HCC.
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Identification of SGMS2 as a molecule involved in natural killer cell recruitment and its in-deep analysis in the liver cancer microenvironment: Evidence from large populations cohort.
BACKGROUND: Liver cancer, a common malignancy within the digestive system, presents with a particularly grim prognosis. Within the immune microenvironment, the role of natural killer (NK) cells in liver cancer remains unclear. METHODS: We sourced data on clinical parameters and gene expressions for liver cancer patients from The Cancer Genome Atlas Program database and carried out all analyses using R software and its relevant codes. RESULTS: In our research, we delved into the genes intertwined with NK cells in hepatocellular carcinoma (HCC). Leveraging the QUANTISEQ and MCPCOUNTER algorithms to quantify NK cells, we spotlighted genes vital to the recruitment of NK cells. Among these genes, GDE1, WDFY3, DNAJB14, PKD2, DGAT2, SGMS2 and MKNK2 showed a strong correlation with patient outcomes. We also mapped out the single-cell expression trajectories of these genes within the HCC milieu. From our findings, SGMS2 emerged as a key gene warranting further scrutiny. Our in-depth analysis of SGMS2 shed light on its influence over specific biological pathways, its contribution to the immune landscape and its role in genomic instability within HCC. Drawing from this, we formulated a predictive model rooted in SGMS2-associated genes. This model showcased remarkable precision across both training and validation cohorts. CONCLUSIONS: Overall, our investigation underscored the profound implications of SGMS2, a gene pivotal to NK cell infiltration, in the landscape of HCC, thereby positioning it as a potential linchpin in oncological strategies.
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Multiomics Dissection of Early-Onset Colorectal Cancer (EOCRC) Reveals an Immunosuppressive Microenvironment and Identifies FAT3 Mutation as a Biomarker of Hot-Like Immune Features.
Early-onset colorectal cancer (EOCRC) exhibits distinct clinical and biological features compared with late-onset CRC (LOCRC), yet its genomic architecture and immune microenvironmental determinants remain incompletely characterized. Here, we integrated multiomics discovery analyses of 69 EOCRC and 461 LOCRC patients from the TCGA cohort, complemented by independent validation, single-cell RNA sequencing, spatial transcriptomics, and systems-level network analysis. We identified 10 genes significantly enriched for mutations in EOCRC, including ANXA7, ZNF267, and POLQ, which displayed distinct domain-specific clustering and were validated in an external cohort. Mutational signature decomposition showed that EOCRC was associated with an SBS87-like signature, whereas LOCRC was dominated by SBS1; because TCGA lacks detailed treatment history, this SBS87-like inference should not be interpreted as direct evidence of prior thiopurine exposure. Immunologically, EOCRC displayed an immunosuppressive microenvironment characterized by elevated regulatory T cells, resting NK cells, and tighter immune-stromal coupling, as further supported by spatial transcriptomic analysis. Single-cell atlas analysis identified M2-polarized, SPP1-expressing macrophage subsets in EOCRC. Weighted gene coexpression network analysis identified a mitochondrial-ribosomal biogenesis module as the transcriptional hallmark of EOCRC. Finally, random forest analysis identified FAT3 mutations as a genomic correlate of a hot-like, immune-activated phenotype in EOCRC. Integrative single-cell analysis localized FAT3 predominantly to cancer-associated fibroblasts, especially myofibroblasts, and an in silico FAT3 perturbation analysis suggested potential downstream programs involving extracellular matrix remodeling, inflammatory signaling, chemokine response, and mitotic spindle regulation. These findings delineate age-stratified genomic and microenvironmental features of EOCRC and generate testable hypotheses for future functional and clinical validation.
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The breast cancer coagulome in the tumor microenvironment and its role in prognosis and treatment response to chemotherapy.
BACKGROUND: The procoagulant phenotype in cancer is linked to thrombosis, cancer progression, and immune response. A novel treatment that reduces the risk of both thrombosis and cancer progression without excess bleeding risk remains to be identified. OBJECTIVES: Here, we aimed to broadly investigate the breast tumor coagulome and its relation to prognosis, treatment response to chemotherapy, and the tumor microenvironment. METHODS: Key coagulation-related genes (n = 35) were studied in a Norwegian cohort with tumor (n = 134) and normal (n = 189) tissue and in the Cancer Genome Atlas (n = 1052) data set. We performed gene set variation analysis in the Norwegian cohort, and in the Cancer Genome Atlas cohort, associations with the tumor microenvironment and prognosis were evaluated. Analyses were performed with cBioPortal, Estimation of Stromal and Immune cells in Malignant Tumors Using Expression Data, Tumor Immune Estimation Resource, the integrated repository portal for tumor-immune system interactions, Tumor Immune Single-cell Hub 2, and the receiver operating characteristic plotter. Six independent breast cancer cohorts were used to study the tumor coagulome and treatment response to chemotherapy. RESULTS: Twenty-two differentially expressed coagulation-related genes were identified in breast tumors. Several coagulome factors were correlated with tumor microenvironment characteristics and were expressed by nonmalignant cells in the tumor microenvironment. PLAT and F8 were independent predictors of better overall survival and progression-free survival, respectively. F12 and PLAU were predictors of worse progression-free survival. The PROCR-THBD-PLAT signature showed a promising predictive value (area under the curve, 0.75; 95% CI, 0.69-0.81; P = 3.6 × 10-17) for combination chemotherapy with fluorouracil, epirubicin, and cyclophosphamide. CONCLUSION: The breast tumor coagulome showed potential in prediction of prognosis and chemotherapy response. Cells within the tumor microenvironment are sources of coagulome factors and may serve as therapeutic targets of coagulation factors.