Objectives: To analyze the potential causal-effect of gut microbiota (GM) on neuroblastoma (NB) risk using a Mendelian randomization (MR) study. Methods: A two-sample MR study was conducted using summary statistics of the GM from the largest available meta-analysis of genome-wide association studies conducted by the MiBioGen consortium. Pooled statistics for childhood NB were obtained from the IEU Consortium release data (1627 cases and 3254 controls). Inverse variance-weighted, weighted median, MR-Egger, and weighted mod were used to examine the causal relationship between GM and childhood NB. Single-nucleotide polymorphism (SNP) genes of positive GM were extracted using the PLINK program, and correlations between key SNP genes and tumor-regulated genes were analyzed. Functional enrichment analysis and transcription factor prediction were performed. Results: Inverse variance weighted (IVW) results indicated that Erysipelotrichia exerted a protective effect against childhood NB (odds ratio = 0.371, 95% Confidence interval: 0.173 - 0.795, P = 0.011) and that Oscillospira exerted a risk effect against childhood NB (odds ratio = 2.378, 95% Confidence interval: 1.121 - 5.043, P = 0.024), indicating the association of GM with childhood NB. Further screening analysis using the IVW test revealed a reliable causal relationship between Erysipelotrichia and NB. Two SNP genes (MUC4 and PELI2) of Erysipelotrichia were extracted and analyzed. Both key genes were significantly associated with tumor-regulated genes, enriched in several pathways associated with tumor progression, and correlated with several upstream transcription factors. Conclusions: It was observed that Erysipelotrichia is causally associated with NB using a two-sample MR study. Furthermore, the discovery of two SNP genes, MUC4 and PELI2, provides potential targets for the diagnosis and treatment of NB.

Background: Neuroblastoma (NBL) is a rare malignant tumor of the peripheral sympathetic nervous system in children with a low overall survival rate. Recent studies have revealed the important role of necroptosis in the occurrence and development of many kinds of tumors. In this study, a prognostic model based on necroptosis-related genes was constructed for NBL.Methods: Expression profiles and clinical information for patients with NBL were downloaded from TARGET. Data for necroptosis-related genes were extracted for Cox regression and lasso regression analyses to evaluate factors associated with prognosis and to construct a prognostic model. Data from the GEO datasets GSE62564 and GSE85047 were used for external verification. Associations between risk scores were calculated, and immune infiltration, drug sensitivity, and mutation analyses were conducted. Functional enrichment analyses of genes in the prognostic model were performed.Results: Six necroptosis-related genes (i.e., CYLD, JAK1, APC, ERH, CNBP, and BAX) were selected to construct a prognostic risk model. The risk score was highly correlated with levels of infiltration of multiple immune cells and sensitivity to common antineoplastic drugs. In addition, the risk score was identified as an independent prognostic factor for patients with NBL.Conclusion: We constructed and validated a prognostic model based on necroptosis-related genes, providing insights into the development and progression of NBL and a basis for improved management. In addition to providing a tool for clinical decision-making, these findings support the importance of necroptosis in NBL and may guide the development of therapeutic strategies targeting this process.

Background: Neuroblastoma (NB) is the most common extracranial solid tumor in children, and the AURKA gene encodes a protein kinase involved in cell cycle regulation that plays an oncogenic role in a variety of human cancers. The aim of this study was to validate the biological function and prognostic significance of AURKA in NB using basic experiments and bioinformatics. Methods: Data obtained from Target and GEO databases were analyzed using various bioinformatic techniques. The expression of AURKA in 77 NB samples was detected by immunohistochemistry (IHC) method. The lentiviral RNA interference technique was employed to downregulate AURKA gene expression in NB cell lines. Additionally, cell counting kit-8 and flow cytometry analysis were conducted to investigate the impact of AURKA expression on cell proliferation, cell cycle progression, and apoptosis. Results: A bioinformatic analysis showed that patients with NB in the AURKA-high-expression group had shorter OS (Overall Survival). Immune cell infiltration analysis showed that only activated CD4 T cell and type 2 T helper cell infiltration levels were higher in the AURKA-high-expression group than in the AURKA-low-expression group, with the infiltration levels of most other immune cells and cytokines lower in the high-expression group. Furthermore, the enhanced infiltration of activated CD4 T cells was associated with worse OS in patients with NB. IHC results showed that the AURKA expression was correlated with MYCN status and INSS stage. Log-rank test showed that pathological type, MYCN status, INSS stage, COG risk group, and AURKA expression was related to PFS (Progression-free survival) of NB patients, but COX regression analysis showed that none of the above factors were independently prognostic for PFS. In vitro, shRNA delivered via an AURKA-specific lentivirus significantly and consistently silenced endogenous AURKA expression in the human NB cell line SK-N-AS. This inhibited tumor cell proliferation, induced apoptosis, and caused G2/M-phase cell cycle arrest. Moreover, western blot assay showed significant reductions in the levels of mTOR, p70S6K, and 4E-BP1 phosphorylation in the AURKA-knockdown group. I found in subsequent experiments that NFYB can bind to the AURKA promoter and thus promote AURKA expression. Conclusions: High-level AURKA expression in NB is associated with poor patient prognosis. Silencing AURKA inhibited tumor cell proliferation, induced tumor cell apoptosis, and led to cell cycle arrest in the G2/M phase. Mechanistically, AURKA knockdown inhibited the phosphorylation and the activation of the mTOR1/p70S6K/4E-BP1 signaling pathway. In addition, AURKA was observed to regulate the infiltration levels of various immune cells in the NB tumor microenvironment, resulting in remodeling of the immunosuppressive tumor microenvironment.

Background: Psoriasis is a chronic immune-mediated skin disorder characterized by excessive keratinocyte proliferation and localized inflammation. A comprehensive understanding of its molecular mechanisms is crucial for improving disease management and developing targeted therapies. Objective: This study aimed to investigate the molecular mechanisms underlying psoriasis by integrating single-cell RNA sequencing with Mendelian randomization (MR) analysis. Methods: Single-cell transcriptomic data from 174 skin samples (92 from psoriasis patients and 82 from healthy controls) were obtained from the GEO database. Data processing was conducted using the Seurat package, including quality control, normalization, dimensionality reduction, and cell-type annotation, ultimately identifying 11 distinct cell populations. MR analysis was then performed using summary statistics from the EBI database (n = 484,598) to assess the putative relationships between candidate genes and psoriasis risk. Results: Seven genetically informed candidate genes were identified as being significantly associated with psoriasis susceptibility. Among them, BIN2 and CAPN12 were linked to an increased risk, while genes such as CXXC5 and KLRD1 were associated with decreased risk. These genes were predominantly expressed in CD4 ⁺ T cells. Functional enrichment analyses, including Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA), revealed their involvement in critical immune-related pathways, such as the IL-17 signaling and NOD-like receptor signaling pathways. Immune infiltration analysis demonstrated an elevated abundance of various immune cell types in psoriasis lesions. Moreover, transcription factor regulatory network analysis suggested that specific transcription factors may regulate the expression of these core genes, thereby contributing to psoriasis pathogenesis. Conclusion: By integrating single-cell RNA sequencing with MR analysis, we identified seven psoriasis-related genes (BIN2, CAPN12, CXXC5, KLRC1, KLRD1, PRF1, and SLFN5) that are highly expressed in CD4 ⁺ T cells. These genes hold promise as potential biomarkers for psoriasis diagnosis and as novel therapeutic targets.

Neuroblastoma (NB) is an embryonic tumour that originates in the sympathetic nervous system and occurs most often in infants and children under 2 years of age. Moreover, it is the most common extracranial solid tumour in children. Increasing studies suggest that intercellular communication within the tumour microenvironment is closely related to tumour development. This study aimed to construct a prognosis-related intercellular communication-associated genes model by single-cell sequencing and transcriptome sequencing to predict the prognosis of patients with NB for precise management. Single-cell data from patients with NB were downloaded from the gene expression omnibus database for comprehensive analysis. Furthermore, prognosis-related genes were screened in the TARGET database based on epithelial cell marker genes through a combination of Cox regression and Lasso regression analyses, using GSE62564 and GSE85047 for external validation. The patients’ risk scores were calculated, followed by immune infiltration analysis, drug sensitivity analysis, and enrichment analysis of risk scores, which were conducted for the prognostic model. I used the Lasso regression feature selection algorithm to screen characteristic genes in NB and developed a 21-gene prognostic model. The risk scores were highly correlated with multiple immune cells and common anti-tumour drugs. Furthermore, the risk score was identified as an independent prognostic factor for NB. In this study, I constructed and validated a prognostic signature based on epithelial marker genes, which may provide useful information on the development and prognosis of NB.

Recent research highlights the pivotal role of N6-methyladenosine (m⁶A) modification and ferroptosis in the evolution of various cancers. This study aimed to establish a prognostic framework centered on genes associated with m⁶A and ferroptosis to enhance the accuracy of prognosis predictions for neuroblastoma (NB) patients, thereby improving targeted therapeutic strategies. Patient data, including expression profiles and clinical information from NB cases, were acquired from The Cancer Genome Atlas. Genes related to m⁶A modification and ferroptosis were identified, and those significant for prognosis were pinpointed using a combination of Cox regression analysis and the least absolute shrinkage and selection operator (LASSO) regression. For further validation, the study utilized external datasets GSE62564 and GSE85047. A prognostic index was computed for each NB patient, followed by analyses of immune cell infiltration and potential drug responsiveness based on the prognostic model. Additionally, enrichment analysis was conducted on the prognostic scores. These scores showed a strong association with the tumor immune environment and the efficacy of prevalent cancer therapies. Moreover, the model’s prognostic score emerged as an independent predictive marker for NB. This research succeeded in creating and confirming a prognostic model rooted in m⁶A and ferroptosis-linked genes, promising to enrich the prognostic understanding and treatment approaches for NB.