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Dendritic cell-related gene signature in pancreatic cancer stratifies patient subtypes and implicates a KCTD14-TNF signaling axis.
BACKGROUND: Pancreatic cancer (PC) is characterized by a profoundly immunosuppressive tumor microenvironment and poor prognosis. Dendritic cells (DCs) are pivotal for antigen presentation and T-cell activation, yet their prognostic and mechanistic roles in PC remain incompletely defined. METHODS: This study performed weighted gene co-expression network analysis (WGCNA) on transcriptomic data from The Cancer Genome Atlas (TCGA) and two Gene Expression Omnibus cohorts (GSE62165, GSE85916) to identify DC-related gene modules. Consensus clustering based on these modules stratified patients into two immune phenotypes. A four-gene DC-related risk score (DCRS) was constructed using LASSO-Cox regression and validated in independent cohorts. Single-cell RNA sequencing data from 25 PC samples (GSE242230) were analyzed through cell clustering analysis, cell-cell communication analysis, and pathway-specific analysis. Functional assays following KCTD14 knockdown in CAPAN-1 and PANC-1 cell lines assessed its impact on proliferation, migration, invasion, and TNF signaling. RESULTS: WGCNA identified 130 overlapping DC-related genes enriched in immune pathways. Two DC-related patient clusters exhibited distinct overall survival (OS) (P < 0.05). The DCRS robustly stratified patients into high- and low-risk groups in both TCGA training and validation sets. DCRS demonstrated good predictive potential for OS and there is a significant difference in OS between the two groups of patients (P < 0.05). Single-cell analysis revealed KCTD14 enrichment in malignant epithelial cells and predicted its interaction with DCs via the TNF-TNFRSF1A axis. In vitro, KCTD14 knockdown significantly reduced PC cell proliferation, colony formation, migration, and invasion, and downregulated TNF-α and TNFRSF1A expression (P < 0.01). CONCLUSION: We identified a novel DC-related gene signature that stratifies PC patients by prognosis and highlights KCTD14 as a novel immunomodulatory oncogene acting through the TNF-TNFR1 axis. Our findings provide a foundation for integrating DCRS into clinical risk assessment and for pursuing KCTD14/TNFR1-targeted therapies to overcome DC-mediated immune suppression in pancreatic cancer.
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SARS-CoV-2 infection induces a long-lived pro-inflammatory transcriptional profile.
BACKGROUND: The immune response to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in COVID-19 patients has been extensively investigated. However, much less is known about the long-term effects of infection in patients and how it could affect the immune system and its capacity to respond to future perturbations. METHODS: Using a targeted single-cell multiomics approach, we have recently identified a prolonged anti-inflammatory gene expression signature in T and NK cells in type 1 diabetes patients treated with low-dose IL-2. Here, we investigated the dynamics of this signature in three independent cohorts of COVID-19 patients: (i) the Oxford COVID-19 Multi-omics Blood Atlas (COMBAT) dataset, a cross-sectional cohort including 77 COVID-19 patients and ten healthy donors; (ii) the INCOV dataset, consisting of 525 samples taken from 209 COVID-19 patients during and after infection; and (iii) a longitudinal dataset consisting of 269 whole-blood samples taken from 139 COVID-19 patients followed for a period of up to 7 months after the onset of symptoms using a bulk transcriptomic approach. RESULTS: We discovered that SARS-CoV-2 infection leads to a prolonged alteration of the gene expression profile of circulating T, B and NK cells and monocytes. Some of the genes affected were the same as those present in the IL-2-induced anti-inflammatory gene expression signature but were regulated in the opposite direction, implying a pro-inflammatory status. The altered transcriptional profile was detected in COVID-19 patients for at least 2 months after the onset of the disease symptoms but was not observed in response to influenza infection or sepsis. Gene network analysis suggested a central role for the transcriptional factor NF-κB in the regulation of the observed transcriptional alterations. CONCLUSIONS: SARS-CoV-2 infection causes a prolonged increase in the pro-inflammatory transcriptional status that could predispose post-acute patients to the development of long-term health consequences, including autoimmune disease, reactivation of other viruses and disruption of the host immune system-microbiome ecosystem.
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Transcriptomic analysis of skeletal muscle regeneration across mouse lifespan identifies altered stem cell states.
In aging, skeletal muscle regeneration declines due to alterations in both myogenic and non-myogenic cells and their interactions. This regenerative dysfunction is not understood comprehensively or with high spatiotemporal resolution. We collected an integrated atlas of 273,923 single-cell transcriptomes and high-resolution spatial transcriptomic maps from muscles of young, old and geriatric mice (~5, 20 and 26 months old) at multiple time points following myotoxin injury. We identified eight immune cell types that displayed accelerated or delayed dynamics by age. We observed muscle stem cell states and trajectories specific to old and geriatric muscles and evaluated their association with senescence by scoring experimentally derived and curated gene signatures in both single-cell and spatial transcriptomic data. This revealed an elevation of senescent-like muscle stem cell subsets within injury zones uniquely in aged muscles. This Resource provides a holistic portrait of the altered cellular states underlying muscle regenerative decline across mouse lifespan.
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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.