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Molecular logic of cellular diversification in the mouse cerebral cortex.
The mammalian cerebral cortex has an unparalleled diversity of cell types, which are generated during development through a series of temporally orchestrated events that are under tight evolutionary constraint and are critical for proper cortical assembly and function1,2. However, the molecular logic that governs the establishment and organization of cortical cell types remains unknown, largely due to the large number of cell classes that undergo dynamic cell-state transitions over extended developmental timelines. Here we generate a comprehensive atlas of the developing mouse neocortex, using single-cell RNA sequencing and single-cell assay for transposase-accessible chromatin using sequencing. We sampled the neocortex every day throughout embryonic corticogenesis and at early postnatal ages, and complemented the sequencing data with a spatial transcriptomics time course. We computationally reconstruct developmental trajectories across the diversity of cortical cell classes, and infer their spatial organization and the gene regulatory programs that accompany their lineage bifurcation decisions and differentiation trajectories. Finally, we demonstrate how this developmental map pinpoints the origin of lineage-specific developmental abnormalities that are linked to aberrant corticogenesis in mutant mice. The data provide a global picture of the regulatory mechanisms that govern cellular diversification in the neocortex.
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Unveiling the comorbidity hub: WT1 drives renal cancer progression in chronic kidney disease and confers sirolimus vulnerability.
Chronic kidney disease (CKD) is epidemiologically linked to renal cell carcinoma (RCC); however, the biological mechanisms underlying this association remain unclear. In the present study, a meta-analysis confirmed CKD as a significant risk factor for incident RCC (pooled odds ratio, 2.55; 95% confidence interval, 1.80-3.59; P<0.00001), with a marked heterogeneity (I²=97%) that was largely explained by geography as North American studies showed stronger effects than East Asian studies (P=0.01). Integrating The Cancer Genome Atlas, Gene Expression Omnibus and immunogenic cell death-related genes, 17 CKD-associated genes (CKDGs) were found to be dysregulated in both RCC and CKD. Functional analyses implicated these genes in kinase signaling (MAPK/PI3K-Akt) and epithelial proliferation. Re-analysis of publicly available single-cell RNA-sequencing data revealed that CKDGs were enriched in tumor-infiltrating T cells, where high CKDG activity was correlated with enhanced cytotoxicity, reduced exhaustion and active ligand-receptor crosstalk. Unsupervised clustering of bulk tumor data defined two CKDG-based subtypes; the immune-rich Cluster 2 showed elevated checkpoint expression (programmed cell death protein 1, cytotoxic T-lymphocyte-associated protein 4 and lymphocyte-activation gene 3) and higher tumor immune dysfunction and exclusion scores, suggesting potential responsiveness to immunotherapy despite a poorer survival. From the CKDGs, an 8-gene prognostic signature that stratified patients by survival was built (P<0.0001). Phenome-wide association study singled out Wilms tumor 1 (WT1) as the only CKDG significantly associated with kidney cancer (P=0.039). Computational screening prioritized sirolimus as a high-affinity WT1 binder (ΔG= -122.89 kJ/mol). In vitro, sirolimus (IC50=15.09 µg/ml) dose-dependently suppressed UOK276 cell proliferation and invasion as well as induced apoptosis. Collectively, the present study identified WT1 as a molecular nexus in RCC-CKD comorbidity and provided mechanistic rationale for repurposing sirolimus in this context.
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SGSM2 in Uveal Melanoma: Implications for Survival, Immune Infiltration, and Drug Sensitivity.
BACKGROUND: The abnormal expression of small G protein signaling modulator 2 (SGSM2) is related to the occurrence of thyroid cancer and breast cancer. However, the role of SGSM2 in uveal melanoma (UVM) is unclear. OBJECTS: To elucidate this ambiguity, our study utilized bioinformatics analysis and experimental validation. METHODS: The expression of SGSM2 was detected in UVM cell lines through quantitative real-- time PCR (qRT-PCR). We utilized the Cancer Genome Atlas (TCGA) database to assess the relationship between SGSM2 expression and clinical characteristics, as well as its prognostic significance in UVM. Furthermore, the study examined potential regulatory networks involving SGSM2 in relation to immune infiltration, immune checkpoint genes, microsatellite instability (MSI), and drug sensitivity in UVM. The study also examined SGSM2 expression in UVM single-cell sequencing data. RESULTS: SGSM2 was highly expressed in UVM cell lines. Moreover, elevated levels of SGSM2 in UVM patients were significantly linked to poorer overall survival (OS) (p < 0.001), progress- free survival (PFS) (p < 0.001), and disease-specific survival (DSS) (p < 0.001). Additionally, SGSM2 expression was identified as an independent prognostic factor in UVM patients (p < 0.001). SGSM2 was associated with several pathways, including the calcium signaling pathway, natural killer cell-mediated cytotoxicity, cell adhesion molecules (CAMs), and others. The study revealed that SGSM2 expression in UVM is linked to immune infiltration, immune checkpoint genes, and MSI. Additionally, a significant inverse correlation was observed between SGSM2 expression and the compounds GSK690693, TL-2-105, PHA-793887, Tubastatin A, and SB52334 in UVM patients. CONCLUSION: SGSM2 may not only serve as an important indicator for prognostic assessment. Still, it may also be a key target for the development of new therapeutic approaches, providing new perspectives on the treatment of UVM patients.
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Unveiling the Enigmatic Role of SLC35F3 in Lung Adenocarcinoma.
BACKGROUND: The role of solute carrier family 35 member F3 (SLC35F3) in lung adenocarcinoma (LUAD) remains unclear. To address this gap, we conducted a study employing bioinformatics analysis and experimental validation. METHODS: This study aimed to examine the expression patterns of SLC35F3 in various cancer types, particularly focusing on LUAD, by analyzing data from the Cancer Genome Atlas (TCGA) database to evaluate its clinical relevance. The research also explored potential regulatory mechanisms of SLC35F3, including its interactions with immune infiltration, tumor mutational burden (TMB), and drug sensitivity in LUAD. The investigation included analyzing SLC35F3 expression in single-cell sequencing of LUAD cells, examining genetic variations of SLC35F3 in LUAD, and assessing SLC35F3 expression in cell lines using quantitative real-time PCR (qRT-PCR). RESULTS: The aberrant expression of SLC35F3 was observed in both pan-cancer and LUAD. In LUAD patients, a statistically significant increase in SLC35F3 expression was correlated with gender (p < 0.001) and was associated with poorer overall survival (OS) (p = 0.020). The expression of SLC35F3 was identified as an independent prognostic determinant in patients with LUAD (p = 0.032). SLC35F3 exhibited associations with various pathways, including cell cycle and more. SLC35F3 expression demonstrated correlations with immune infiltration, TMB, and some drugs in LUAD. Results indicated significant upregulation of SLC35F3 in both LUAD tissues and cell lines. CONCLUSIONS: SLC35F3 may serve as a prognostic biomarker and immunotherapeutic target for patients with LUAD. CLINICAL TRIAL REGISTRATION: Not applicable.
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Identification of the key gene for hepatocellular carcinoma based on bioinformatics and machine learning and experimental verification.
BACKGROUND: Hepatocellular carcinoma (HCC) is a severe hazard to human health and has a high fatality rate. While deregulated gene expression has been widely linked to hepatocarcinogenesis, many details of how these alterations drive tumor initiation and progression remain to be elucidated. We therefore combined bioinformatics and machine learning strategies to screen for and validate candidate driver genes in HCC. METHODS: Three datasets (GSE78737, GSE98383, and GSE121248) were obtained from the Gene Expression Omnibus (GEO) database. GSE78737 and GSE98383 were combined to form the training set, while GSE121248 was used as the validation set. Initially, differentially expressed genes (DEGs) between HCC and non-HCC (nHCC) in the training set were identified. Enrichment analysis of these DEGs was performed using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA). To identify diagnostic genes, machine learning algorithms including support vector machine-recursive feature elimination (SVM-RFE) and least absolute shrinkage and selection operator (LASSO) were applied. The validation set was employed to confirm the DEGs. Furthermore, immune cell infiltration differences between nHCC and HCC were analyzed using CIBERSORT. GEPIA2.0 was subsequently used to analyze the prognostic significance of the diagnostic genes in HCC, identifying key genes. Finally, the key genes were validated using data from The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC), as well as through immunohistochemistry (IHC) experiments, single-cell, and spatial transcriptomics analysis. RESULTS: A total of 80 DEGs were identified, with 8 upregulated and 72 downregulated. The GO pathways associated with these DEGs were primarily related to responses to alcohol, humoral immune response, vacuolar lumen, chemokine activity, and mannose binding. KEGG pathway analysis revealed that the DEGs were primarily focused on viral protein interactions with cytokines and cytokine receptors. GSEA indicated that the most active processes in HCC included DNA replication, cell cycle, and mismatch repair. Immune cell analysis showed significant overexpression of naive B cells, CD8+ T cells, activated natural killer (NK) cells, M0 macrophages, and dendritic cells in HCC. In contrast, naive CD4+ T cells, gamma delta T cells, and monocytes were significantly lower in HCC compared to nHCC. Machine learning and risk prognosis analysis identified FAM83D as a key gene in HCC, serving as an independent variable affecting HCC prognosis. Increased FAM83D mRNA and protein expression correlated with poor overall survival and prognosis in HCC patients. Additionally, FAM83D expression was significantly related to various immune cells. Further single-cell analysis revealed that FAM83D is predominantly upregulated in malignant cells, and its high expression is strongly associated with poor response to immunotherapy in HCC patients. CONCLUSIONS: FAM83D may be a key gene involved in the development and progression of HCC, contributing to early diagnosis and prognosis assessment. It has the potential to serve as a biomarker for HCC.
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Distinct cell states define larval and adult body plans in a hemichordate.
A major gap in our understanding of animal development is how adult body plans arise in animals with indirect development, where adults emerge from the transformation of a distinct larval form during metamorphosis. We address this question by examining cellular changes in the enteropneust hemichordate Schizocardium californicum, a species with a complex lifecycle and dramatic metamorphosis. Employing whole-body single-cell RNA sequencing, we chart the cellular composition and transcriptional dynamics of larval, metamorphic, and adult stages. Our tissue-level atlas reveals that ectodermal and endodermal cell types in larvae and adults occupy distinct transcriptional spaces, showing greater similarity to other cell types within the same life stage than to their counterparts in the opposite stage. In contrast, mesodermal cell types from both larvae and adults cluster closely together, indicating conserved transcriptional profiles. These findings demonstrate that the extensive morphological reorganization during metamorphosis is accompanied by broad shifts in transcriptional identity and reveal life-history stage as a major organizing axis of cellular state during the larva-to-adult transition.
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N-acetyltransferase 10 as a novel prognostic biomarker in papillary renal cell carcinoma: a machine learning and experimental validation study.
BACKGROUND: N-acetyltransferase 10 (NAT10) is a lysine acetyltransferase known for catalyzing the N4-acetylcytidine (ac4C) modifications on RNAs. Recent studies have associated NAT10 with the pathogenesis of various cancers. However, its specific function and prognostic significance in papillary renal cell carcinoma (pRCC) remain poorly understood. This study aimed to explore NAT10's prognostic value and mechanisms in pRCC. METHODS: NAT10 expression and prognostic associations in pancancer were analyzed using The Cancer Genome Atlas (TCGA). Single-cell RNA (scRNA) sequencing data from a previous study were used to characterize NAT10 expression at the single-cell level in pRCC. Pathway enrichment analysis, including gene set variance analysis (GSVA) and overrepresentation analysis, was conducted to investigate the potential mechanisms through which NAT10 exerts its effects. Immune cell infiltration analysis, conducted through the ESTIMATE and CIBERSORT algorithms, was performed to examine the association of NAT10 with the tumor microenvironment (TME). A NAT10-related prognostic model was constructed using least absolute shrinkage and selection operator (LASSO) Cox regression on genes that were both positively correlated with NAT10 and were identified as NAT10-mediated ac4C targets by ac4C RNA immunoprecipitation sequencing The model's performance was validated in the TCGA training set (n=285), with 42 events (deaths) and 243 censored cases, and in the GSE2748 external validation set (n=28), with 13 events (deaths) and 15 censored cases. The association between NAT10-related risk scores and immunotherapy response was assessed via the IMvigor210 cohort. Finally, the aberrant expression of NAT10 was confirmed through immunohistochemistry data from the Human Protein Atlas database and our experimental validations from quantitative real-time polymerase chain reaction (qRT-PCR) and Western blot analyses. RESULTS: NAT10 is upregulated in multiple cancers, including pRCC, and higher NAT10 expression correlates with advanced stages and poorer prognosis. Single-cell RNA-sequencing data confirmed elevated NAT10 expression in malignant pRCC cells. Pathway enrichment and immune cell infiltration analyses indicated that NAT10 is associated with malignancy-related pathways and a disordered TME. A prognostic model was constructed using LASSO Cox regression, with 18 core genes being identified, and demonstrated high predictive accuracy for survival. The model achieved AUC values of 0.97, 0.93, and 0.82 for 1-, 3-, and 5-year survival in the TCGA training set, respectively, while all AUC values in the GSE2748 external validation set were 1. Higher NAT10-related risk scores were linked to poorer immunotherapy response in the IMvigor210 cohort. NAT10's prognostic significance was validated across various cancers, with elevated expression at both the messenger RNA and protein levels confirmed through immunohistochemistry and experimental validation in RCC cell lines. CONCLUSIONS: Our findings suggest that NAT10 is aberrantly expressed in pRCC, is associated with poor prognosis, and contributes to pRCC progression through multiple pathways, offering new insights into the personalized treatment of patients with pRCC.
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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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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.