Leveraging single-cell sequencing analysis and bulk-RNA sequencing analysis to forecast necroptosis in cutaneous melanoma prognosis.

Cutaneous melanoma, a malignancy of melanocytes, presents a significant challenge due to its aggressive nature and rising global incidence. Despite advancements in treatment, the variability in patient responses underscores the need for further research into novel therapeutic targets, including the role of programmed cell death pathways such as necroptosis. The melanoma datasets used for analysis, GSE215120, GSE19234, GSE22153 and GSE65904, were downloaded from the GEO database. The melanoma data from TCGA were downloaded from the UCSC website. Using single-cell sequencing, we assess the heterogeneity of necroptosis in cutaneous melanoma, identifying distinct cell clusters and necroptosis-related gene expression patterns. A combination of 101 machine learning algorithms was employed to construct a necroptosis-related signature (NRS) based on key genes associated with necroptosis. The prognostic value of NRS was evaluated in four cohorts (one TCGA and three GEO cohorts), and the tumour microenvironment (TME) was analysed to understand the relationship between necroptosis, tumour mutation burden (TMB) and immune infiltration. Finally, we focused on the role of key target TSPAN10 in the prognosis, pathogenesis, immunotherapy relevance and drug sensitivity of cutaneous melanoma. Our study revealed significant heterogeneity in necroptosis among melanoma cells, with a higher prevalence in epithelial cells, myeloid cells and fibroblasts. The NRS, developed through rigorous machine learning techniques, demonstrated robust prognostic capabilities, distinguishing high-risk patients with poorer outcomes in all cohorts. Analysis of the TME showed that high NRS scores correlated with lower TMB and reduced immune cell infiltration, indicating a potential mechanism through which necroptosis influences melanoma progression. Finally, TSPAN10 has been identified as a key target for cutaneous melanoma and is highly associated with poor prognosis. The findings highlight the complex role of necroptosis in cutaneous melanoma and introduce the NRS as a novel prognostic tool with potential to guide therapeutic decisions.

Unravelling the metabolic landscape of cutaneous melanoma: Insights from single-cell sequencing analysis and machine learning for prognostic assessment of lactate metabolism.

This manuscript presents a comprehensive investigation into the role of lactate metabolism-related genes as potential prognostic markers in skin cutaneous melanoma (SKCM). Bulk-transcriptome data from The Cancer Genome Atlas (TCGA) and GSE19234, GSE22153, and GSE65904 cohorts from GEO database were processed and harmonized to mitigate batch effects. Lactate metabolism scores were assigned to individual cells using the 'AUCell' package. Weighted Co-expression Network Analysis (WGCNA) was employed to identify gene modules correlated with lactate metabolism. Machine learning algorithms were applied to construct a prognostic model, and its performance was evaluated in multiple cohorts. Immune correlation, mutation analysis, and enrichment analysis were conducted to further characterize the prognostic model's biological implications. Finally, the function of key gene NDUFS7 was verified by cell experiments. Machine learning resulted in an optimal prognostic model, demonstrating significant prognostic value across various cohorts. In the different cohorts, the high-risk group showed a poor prognosis. Immune analysis indicated differences in immune cell infiltration and checkpoint gene expression between risk groups. Mutation analysis identified genes with high mutation loads in SKCM. Enrichment analysis unveiled enriched pathways and biological processes in high-risk SKCM patients. NDUFS7 was found to be a hub gene in the protein-protein interaction network. After the expression of NDUFS7 was reduced by siRNA knockdown, CCK-8, colony formation, transwell and wound healing tests showed that the activity, proliferation and migration of A375 and WM115 cell lines were significantly decreased. This study offers insights into the prognostic significance of lactate metabolism-related genes in SKCM.

Liquid-liquid phase separation-related gene in gliomas: FABP5 is a potential prognostic marker.

BACKGROUND: The glioma is the most malignant human brain tumor. Early glioma detection and treatment are still difficult. New biomarkers are desperately required to aid in the evaluation of diagnosis and prognosis. METHODS: The single cell sequencing dataset scRNA-6148 for glioblastoma was obtained from the Chinese Glioma Genome Atlas database. Data were gathered for the transcriptome sequencing project. Genes involved in liquid-liquid phase separation (LLPS) were taken out of the DrLLPS database. To find the modules connected to LLPS, the weighted co-expression network was analyzed. Differential expression analysis was used to identify the differentially expressed genes (DEGs) in gliomas. Pseudo-time series analysis, gene set enrichment analysis (GSEA) and immune cell infiltration analysis were used to investigate the role of important genes in the immunological microenvironment. We examined the function of key glioma genes using polymerase chain reaction (PCR) testing, CCK-8 assays, clone generation assays, transwell assays and wound healing assays. RESULTS: FABP5 was identified as a key gene in glioblastoma by multiomics research. Pseudo-time series analysis showed that FABP5 was highly linked with the differentiation of many different types of cells. GSEA revealed that FABP5 was strongly linked to several hallmark pathways in glioblastoma. We looked at immune cell infiltration and discovered a significant link between FABP5, macrophages and T cell follicular helpers. The PCR experiment results demonstrated that FABP5 expression was elevated in glioma samples. Cell experiments showed that FABP5 knockdown dramatically decreased the viability, proliferation, invasion and migration of the LN229 and U87 glioma cell lines. CONCLUSIONS: Our study provides a new biomarker, FABP5, for glioma diagnosis and treatment.

Single-cell sequencing analysis of peripheral blood in patients with moyamoya disease.

BACKGROUND: At present, the etiology of moyamoya disease is not clear, and it is necessary to explore the mechanism of its occurrence and development. Although some bulk sequencing data have previously revealed transcriptomic changes in Moyamoya disease, single-cell sequencing data has been lacking. METHODS: Two DSA(Digital Subtraction Angiography)-diagnosed patients with moyamoya disease were recruited between January 2021 and December 2021. Their peripheral blood samples were single-cell sequenced. CellRanger(10 x Genomics, version 3.0.1) was used to process the raw data, demultiplex cellular barcodes, map reads to the transcriptome, and dowm-sample reads(as required to generate normalized aggregate data across samples). There were 4 normal control samples, including two normal samples GSM5160432 and GSM5160434 of GSE168732, and two normal samples of GSE155698, namely GSM4710726 and GSM4710727. Weighted co-expression network analysis was used to explore the gene sets associated with moyamoya disease. GO analysis and KEGG analysis were used to explore gene enrichment pathways. Pseudo-time series analysis and cell interaction analysis were used to explore cell differentiation and cell interaction. RESULTS: For the first time, we present a peripheral blood single cell sequencing landscape of Moyamoya disease, revealing cellular heterogeneity and gene expression heterogeneity. In addition, by combining with WGCNA analysis in public database and taking intersection, the key genes in moyamoya disease were obtained. namely PTP4A1, SPINT2, CSTB, PLA2G16, GPX1, HN1, LGALS3BP, IFI6, NDRG1, GOLGA2, LGALS3. Moreover, pseudo-time series analysis and cell interaction analysis revealed the differentiation of immune cells and the relationship between immune cells in Moyamoya disease. CONCLUSIONS: Our study can provide information for the diagnosis and treatment of moyamoya disease.

Single cell sequencing analysis constructed the N7-methylguanosine (m7G)-related prognostic signature in uveal melanoma.

BACKGROUND: Uveal melanoma is a highly malignant tumor in the eye. Its recurrence and metastasis are common, and the prognosis is poor. METHODS: The transcriptome data of UVM were downloaded from TCGA database, and the single cell sequencing dataset GSE139829 was downloaded from GEO database. Weighted co-expression network analysis was used to explore the modules associated with m7G. Lasso regression was used to construct M7G-related prognostic signature. Immune infiltration analysis was used to explore the significance of the model in the tumor immune microenvironment. Finally, cell assays were used to explore the function of key genes in the MUM-2B and OCM-1 cell lines of UVM. RESULTS: The prognostic signature was constructed by Cox regression and Lasso regression. Patients could be divided into high-risk group and low-risk group by this signature, and the high-risk group had worse prognosis (P<0.05). Cell experiments showed that the proliferation, invasion and migration ability of UVM cell lines were significantly decreased after the knockdown of PAG1, a key gene in signature, which proved that PAG1 might be a potential target of UVM. CONCLUSIONS: Our study explored the significance of m7G in UVM, provided biomarkers for its diagnosis and treatment.

In Vitro Evaluation of Dentin Tubule Occlusion for Novel Calcium Lactate Phosphate (CLP) Paste.

INTRODUCTION: The objective of this in vitro study is to evaluate the effective and long-term occlusion of dentinal tubules using a novel calcium lactate phosphate (CLP) based desensitizing agent. METHODS: Dentin disks (n = 9) were pre-etched using 1 M lactic acid for 30 s and individually treated with Colgate® Pro-Relief™ paste, CLP paste, and double distilled water (ddH2O) by a rubber-cupped handpiece. Dentin disks were analyzed under optical micrographs for pre-treatment, directly after treatment, and 14 days post-treatment. One-way ANOVA and post-hoc Tukey's test were used to determine whether there were any statistically significant differences in dentinal tubule diameter. RESULTS: A significant decrease occurred in the mean tubule diameter for dentin disks treated with CLP paste. A decrease was observed from 3.52 ± 0.83 µm to 2.62 ± 0.42 µm right after treatment, further decreasing to 1.71 ± 0.45 µm after immersion in artificial saliva for 14 days (p < 0.05). CONCLUSIONS: The results suggest that the CLP based desensitizing paste has remineralization properties and provides instant and lasting effectiveness in dentinal tubule occlusion.