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.

LncRNA NDRG1 aggravates osteosarcoma progression and regulates the PI3K/AKT pathway by sponging miR-96-5p.

BACKGROUND: Osteosarcoma (OS) is the most common primary malignant bone tumors in children and adolescents. Large numbers of studies have focused on the long non-coding RNA (lncRNA) that plays essential roles in the progression of osteosarcoma. Nevertheless, the functions and underlying mechanisms of LncRNA NDRG1 in osteosarcoma remain unknown. METHODS: Differentially expressed lncRNAs between osteosarcoma and adjacent normal tissues were identified through RNA sequencing. The role of LncRNA NDRG1 in osteosarcoma proliferation and metastasis were investigated through in vitro and in vivo functional experiments. The interaction between LncRNA NDRG1 and miR-96-5p was verified through bioinformatic analysis and luciferase reporter assay. Regulation relationship between LncRNA NDRG1 and miR-96-5p was further evaluated by the rescue experiments. Additionally, the changes in the expression of epithelial-mesenchymal transition (EMT) and the PI3K/AKT pathway were verified by Western blot. RESULTS: LncRNA NDRG1 was up-regulated in osteosarcoma cell lines and tissues and the expression of LncRNA NDRG1 was correlated with the overall survival of osteosarcoma patients. Functional experiments exhibited that LncRNA NDRG1 aggravated osteosarcoma proliferation and migration in vitro; meanwhile, animals experiments showed that LncRNA NDRG1 promoted osteosarcoma growth and metastasis in vivo. Mechanistically, LncRNA NDRG1 was found to aggravate osteosarcoma progression and regulate the PI3K/AKT pathway by sponging miR-96-5p. CONCLUSIONS: LncRNA NDRG1 aggravates osteosarcoma progression and regulates the PI3K/AKT pathway by sponging miR-96-5p. Therefore, LncRNA NDRG1 could act as a prognostic marker and a therapeutic target for osteosarcoma in the future.

Deubiquitinase PSMD14 promotes tumorigenicity of glioblastoma by deubiquitinating and stabilizing ß-catenin.

The deubiquitinating enzyme 26S proteasome non-ATPase regulatory subunit 14 (PSMD14), a member of the JAB1/MPN/Mov34 metalloenzyme (JAMM) family, has been shown to function as an oncogene in various human cancers. However, the function of PSMD14 in glioma and the underlying mechanism remain unclear. In this study, our findings reveal a dramatic upregulation of PSMD14 in GBMs, which is associated with poor survival outcomes. Knocking down PSMD14 is associated with decreased proliferation and invasion of GBM cells in vitro and inhibited tumor growth in a xenograft mouse model. Mechanistically, PSMD14 directly interacts with ß-catenin, leading to a decrease in the K48-linked ubiquitination of ß-catenin and subsequent ß-catenin stabilization. Increased ß-catenin expression significantly reverses the inhibitory effects of PSMD14 knockdown on the migration, invasion, and tumor growth of GBM cells. Moreover, we observed a significant correlation between PSMD14 and ß-catenin expression in human GBM samples. In summary, our results reveal that PSMD14 is a crucial deubiquitinase that is responsible for stabilizing the ß-catenin protein, highlighting its potential for use as a therapeutic target for GBM.

Predicting Intracranial Aneurysm Rupture: A Multifactor Analysis Combining Radscore, Morphology, and PHASES Parameters.

RATIONALE AND OBJECTIVES: We aimed at developing and validating a nomogram and machine learning (ML) models based on radiomics score (Radscore), morphology, and PHASES to predict intracranial aneurysm (IA) rupture. MATERIALS AND METHODS: We collected 440 patients with IAs in our hospital from 2015 to 2023, totaling 475 IAs (214 ruptured and 261 unruptured). A 7:3 random split was utilized to allocate participants into training and testing sets. To optimize the selection of radiomics features extracted from digital subtraction angiography, we employed t-tests and LASSO regression. Subsequently, we built single-factor and multifactor logistic regression (LR) models, alongside a nomogram. Furthermore, we employed four ML algorithms. After a comprehensive evaluation, including area under the curve (AUC), calibration curves, decision curve analysis (DCA), and other metrics, the best model was determined. RESULTS: The AUCs for LR models P (PHASES), M (Morphology), and R (Radscore) in the testing set were 0.859, 0.755, and 0.803, respectively, while those for multifactor models R+M (Radscore and Morphology), R+P (Radscore and PHASES), and R+M+P (Radscore, Morphology, and PHASES) were 0.818, 0.899, and 0.887, respectively. The AUCs of random forest, extreme gradient boosting, gradient boosting machine, and light gradient boosting machine were 0.880, 0.888, 0.891, and 0.892 in testing set, respectively. In the training set, the LR model showed significant differences in AUCs compared with the four ML models (all p < 0.05). However, in the testing set, no statistically significant differences were found between them (all p > 0.05). Both ML models and the nomogram exhibit excellent performance in DCA and calibration curves. CONCLUSION: Nomogram and ML models based on Radscore, morphology, and PHASES show high precision in predicting aneurysm rupture.

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.

Significance of liquid-liquid phase separation (LLPS)-related genes in breast cancer: a multi-omics analysis.

Currently, the role of liquid-liquid phase separation (LLPS) in cancer has been preliminarily explained. However, the significance of LLPS in breast cancer is unclear. In this study, single cell sequencing datasets GSE188600 and GSE198745 for breast cancer were downloaded from the GEO database. Transcriptome sequencing data for breast cancer were downloaded from UCSC database. We divided breast cancer cells into high-LLPS group and low-LLPS group by down dimension clustering analysis of single-cell sequencing data set, and obtained differentially expressed genes between the two groups. Subsequently, weighted co-expression network analysis (WGCNA) was performed on transcriptome sequencing data, and the module genes most associated with LLPS were obtained. COX regression and Lasso regression were performed and the prognostic model was constructed. Subsequently, survival analysis, principal component analysis, clinical correlation analysis, and nomogram construction were used to evaluate the significance of the prognostic model. Finally, cell experiments were used to verify the function of the model's key gene, PGAM1. We constructed a LLPS-related prognosis model consisting of nine genes: POLR3GL, PLAT, NDRG1, HMGB3, HSPH1, PSMD7, PDCD2, NONO and PGAM1. By calculating LLPS-related risk scores, breast cancer patients could be divided into high-risk and low-risk groups, with the high-risk group having a significantly worse prognosis. Cell experiments showed that the activity, proliferation, invasion and healing ability of breast cancer cell lines were significantly decreased after knockdown of the key gene PGAM1 in the model. Our study provides a new idea for prognostic stratification of breast cancer and provides a novel marker: PGAM1.

Transcriptome analysis reveals tumor antigen and immune subtypes of melanoma.

Purpose: To screen potential tumor antigens for melanoma vaccine development and identify different immune subtypes. Methods: Transcriptional data (HTSEQ-FPKM) and clinical information of a 472 Melanoma cohort GDC TCGA Melanoma (SKCM) were downloaded from the UCSC XENA website (http://xena.ucsc.edu/). Subsequently, transcriptome data and clinical information of 210 melanoma cohort GSE65904 were downloaded from Gene Expression Omnibus (GEO), a large global public database. All the transcriptome expression data matrices were log2 transformed for subsequent analysis. GEPIA, TIMER, and IMMPORT databases are also used for analysis. Cell function experiments were performed to validate the role of the IDO1 gene in melanoma cell line A375. Results: Our study provides potential tumor antigens for vaccine development in melanoma patients: GZMB, GBP4, CD79A, APOBEC3F, IDO1, JCHAIN, LAG3, PLA2G2D, XCL2. In addition, we divide melanoma patients into two immune subtypes that have significant differences in tumor immunity and may have different responses to vaccination. In view of the unclear role of IDO1 in melanoma, we selected IDO1 for cell assay validation. Cell function assay showed that IDO1 was significantly overexpressed in the melanoma A375 cell line. After IDO1 knockdown, the activity, invasion, migration and healing ability of A375 cell lines were significantly decreased. Conclusion: Our study could provide a reference for the development of vaccines for melanoma patients.

USP21 promotes self-renewal and tumorigenicity of mesenchymal glioblastoma stem cells by deubiquitinating and stabilizing FOXD1.

Recent studies suggest that Forkhead box D1 (FOXD1) plays an indispensable role in maintaining the mesenchymal (MES) properties of glioblastoma (GBM) stem cells (GSCs). Thus, understanding the mechanisms that control FOXD1 protein expression is critical for guiding GBM treatment, particularly in patients with therapy-resistant MES subtypes. In this study, we identify the ubiquitin-specific peptidase 21 (USP21) as a critical FOXD1 deubiquitinase in MES GSCs. We find that USP21 directly interacts with and stabilizes FOXD1 by reverting its proteolytic ubiquitination. Silencing of USP21 enhances polyubiquitination of FOXD1, promotes its proteasomal degradation, and ultimately attenuates MES identity in GSCs, while these effects could be largely restored by reintroduction of FOXD1. Remarkably, we show that disulfiram, a repurposed drug that could block the enzymatic activities of USP21, suppresses GSC tumorigenicity in MES GSC-derived GBM xenograft model. Additionally, we demonstrate that USP21 is overexpressed and positively correlated with FOXD1 protein levels in GBM tissues, and its expression is inversely correlated with patient survival. Collectively, our work reveals that USP21 maintains MES identity by antagonizing FOXD1 ubiquitination and degradation, suggesting that USP21 is a potential therapeutic target for the MES subtype of GBM.

Transcriptome Analysis of Solute Carrier-Associated Genes in Hepatocellular Carcinoma: Friend or Foe?

Hepatocellular carcinoma (HCC) is one of the most common types of cancer, and its treatment remains difficult. Since the early symptoms of HCC are not obvious, many HCC patients are already at an advanced stage of the disease at the time of diagnosis. Although current targeted therapy and immunotherapy have been initially effective in HCC patients, several patients have shown low response rates or developed drug resistance, which leads to tumor progression and even death. Hence, there is an urgent need for new biomarkers to guide the prognosis and treatment of HCC. In our study, a prognostic signature consisting of nine SLC genes was constructed in HCC by comprehensive analysis. By calculating risk scores, HCC patients could be divided into high-risk and low-risk groups, with the high-risk group having a significantly poorer prognosis. In addition, we found a hub gene, SLC7A11, which is a robust prognostic marker of HCC. In conclusion, our study can serve as a reference for the prognostic evaluation and treatment of HCC.

Integrative analysis of TRPV family to prognosis and immune infiltration in renal clear cell carcinoma.

The transient receptor potential vanilloid (TRPV) family has been preliminarily discovered to play an important role in various cancers, including clear cell renal cell carcinoma (ccRCC), which is closely associated with immune infiltration. However, the expression and prognosis of TRPV family and tumor-infiltrating immune cells in ccRCC are obscure. This study aimed to explore the prognostic and therapeutic value of the TRPV family expression in ccRCC from the perspective of bioinformatics. We analyzed the transcriptome and clinical data of kidney renal clear cell carcinoma (KIRC) from The Cancer Genome Atlas (TCGA) database. A clustering analysis and immune infiltration analysis were conducted to investigate the influence of the TRPV family genes on ccRCC. Our study found that the TRPV family is an excellent prognostic stratification for ccRCC. Among them, TRPV3 is the most significant prognostic marker of ccRCC. In addition, we performed a drug sensitivity analysis to identify the drugs with the strongest association with TRPV3. As a result, the TRPV family, particularly TRPV3, can act as a prognostic biomarker in ccRCC to determine prognosis and levels of immune infiltration.

Pan-cancer analysis revealed the significance of the GTPBP family in cancer.

BACKGROUND: At present, cancer is still one of the principal diseases to represent a serious danger to human health. Although research on the pathogenesis and treatment of cancer is progressing rapidly, the current knowledge on this topic is far from sufficient. Some tumors with poor prognoses lack effective prognostic biomarkers. METHODS: Firstly, the Wilcoxon test was used to analyse the expression of GTPBP1-GTPBP10 in cancerous and normal tissues. Subsequently, we explored the expression of GTPBP1-10 in cancer by way of a paired t-test and plotted the survival curve using KM and univariate Cox regression analysis to explore the relationship between GTPBP1-10 and the prognosis of cancer. We then explored the significance of the GTPBP family in the tumor microenvironment. RESULTS: The results showed that many members of the GTPBP family are differentially expressed in a variety of cancers and alter the prognosis of a number of cancers. Members of the GTPBP family may serve as novel prognostic markers for these tumors. Moreover, members of the GTPBP family are correlated with the immune microenvironment of tumors, which is valuable in terms of adding to our understanding of the mechanisms of tumor genesis. Finally, we identified drugs showing a high correlation with GTPBP family members, which are therefore conducive to the development of GTPBP family member-based treatment regimens. CONCLUSIONS: The 10 members of the GTPBP family have prognostic value in multiple tumor types and are associated with the immune microenvironment. Our study may provide a reference for the diagnosis and treatment of tumors.

Transcriptome Analysis and Single-Cell Sequencing Analysis Constructed the Ubiquitination-Related Signature in Glioma and Identified USP4 as a Novel Biomarker.

Background: Glioma, the most frequent malignant tumor of the neurological system, has a poor prognosis and treatment problems. Glioma's tumor microenvironment is also little known. Methods: We downloaded glioma data from the TCGA database. The patients in the TCGA database were split into two groups, one for training and the other for validation. The ubiquitination genes were then evaluated in glioma using COX and Lasso regression to create a ubiquitination-related signature. We assessed the signature's predictive usefulness and role in the immune microenvironment after it was generated. Finally, in vitro experiment were utilized to check the expression and function of the signature's key gene, USP4. Results: This signature can be used to categorize glioma patients. Glioma patients can be separated into high-risk and low-risk groups in both the training and validation cohorts, with the high-risk group having a significantly worse prognosis (P<0.05). Following further investigation of the immune microenvironment, it was discovered that this risk grouping could serve as a guide for glioma immunotherapy. The activity, invasion and migration capacity, and colony formation ability of U87-MG and LN229 cell lines were drastically reduced after the important gene USP4 in signature was knocked down in cell tests. Overexpression of USP4 in the A172 cell line, on the other hand, greatly improved clonogenesis, activity, invasion and migration. Conclusions: Our research established a foundation for understanding the role of ubiquitination genes in gliomas and identified USP4 as a possible glioma biomarker.

Integrated Analysis of Genomic and Transcriptomic Profiles Identified the Role of GTP Binding Protein-4 (GTPBP4) in Breast Cancer.

Purpose: To explore the significance of GTP-binding protein 4 (GTPBP4) in breast cancer. Methods: Firstly, GTPBP4 expression analysis was performed in TIMER and UALCAN databases. Subsequently, the TCGA cohort and multiple Gene Expression Omnibus Cohorts were used as validation for GTPBP4 expression. Besides, we also evaluated the diagnostic value of GTPBP4 in TCGA Cohort and multiple GEO Cohorts. The predictive effect of GTPBP4 in breast cancer was then assessed using survival analysis. Then we look at the role of GTPBP4 in the immune milieu and create a Nomogram to help patients with breast cancer understand their prognosis. Finally, in vitro tests were carried out to look at GTPBP4 expression and function in breast cancer cell lines. Results: GTPBP4 is an independent breast cancer prognostic factor that is upregulated in the disease (p < 0.05). Enrichment analysis showed that GTPBP4 was associated with multiple functions and pathways. In addition, GTPBP4 is associated with a variety of immune cell types (p < 0.05). PCR assay showed that GTPBP4 expression was up-regulated in breast cancer cell lines. The activity, migration, and proliferation of breast cancer cells were considerably reduced after GTPBP4 knockdown in the CCK-8, Transwell, and Scratch assays. Conclusions: Our research discovered a new breast cancer biomarker that can be used as a guide for breast cancer diagnosis and treatment.

Single-Cell Sequencing Analysis Based on Public Databases for Constructing a Metastasis-Related Prognostic Model for Gastric Cancer.

Background: Although incidences of gastric cancer have decreased in recent years, the disease remains a significant danger to human health. Lack of early symptoms often leads to delayed diagnosis of gastric cancer, so that many patients miss the opportunity for surgery. Treatment for advanced gastric cancer is often limited. Immunotherapy, targeted therapy, and the mRNA vaccine have all emerged as potentially viable treatments for advanced gastric cancer. However, our understanding of the immune microenvironment of gastric cancer is far from sufficient; now is the time to explore this microenvironment. Methods: In our study, using TCGA dataset and the GEO dataset GSE62254, we performed in-depth transcriptome and single-cell sequencing analyses based on public databases. We analyzed differential gene expressions of immune cells in metastatic and nonmetastatic gastric cancer and constructed a prognostic model of gastric cancer patients based on these differential gene expressions. We also screened candidate vaccine genes for gastric cancer. Results: This prognostic model can accurately predict the prognosis of gastric cancer patients by dividing them into high-risk and low-risk groups. In addition to this, we identified a candidate vaccine gene for gastric cancer: PTPN6. Conclusions: Our study could provide new ideas for the treatment of gastric cancer.

A Necroptosis-Related Prognostic Model of Uveal Melanoma Was Constructed by Single-Cell Sequencing Analysis and Weighted Co-Expression Network Analysis Based on Public Databases.

BACKGROUND: Uveal melanoma(UVM) is the most common intraocular malignancy and has a poor prognosis. The clinical significance of necroptosis(NCPS) in UVM is unclear. METHODS: We first identified necroptosis genes in UVM by single-cell analysis of the GSE139829 dataset from the GEO database and weighted co-expression network analysis of TCGA data. COX regression and Lasso regression were used to construct the prognostic model. Then survival analysis, immune microenvironment analysis, and mutation analysis were carried out. Finally, cell experiments were performed to verify the role of ITPA in UVM. RESULT: By necroptosis-related prognostic model, UVM patients in both TCGA and GEO cohorts could be classified as high-NCPS and low-NCPS groups, with significant differences in survival time between the two groups (P<0.001). Besides, the high-NCPS group had higher levels of immune checkpoint-related gene expression, suggesting that they might be more likely to benefit from immunotherapy. The cell experiments confirmed the role of ITPA, the most significant gene in the model, in UVM. After ITPA was knocked down, the activity, proliferation, and invasion ability of the MuM-2B cell line were significantly reduced. CONCLUSION: Our study can provide a reference for the diagnosis and treatment of patients with UVM.

Combining Single-Cell and Transcriptomic Data Revealed the Prognostic Significance of Glycolysis in Pancreatic Cancer.

Background: Pancreatic cancer (PC), the most common fatal solid malignancy, has a very dismal prognosis. Clinical computerized tomography (CT) and pathological TNM staging are no longer sufficient for determining a patient's prognosis. Although numerous studies have suggested that glycolysis is important in the onset and progression of cancer, there are few publications on its impact on PC. Methods: To begin, the single-sample gene set enrichment analysis (ssGSEA) approach was used to quantify the glycolysis pathway enrichment fraction in PC patients and establish its prognostic significance. The genes most related to the glycolytic pathway were then identified using weighted gene co-expression network analysis (WGCNA). The glycolysis-associated prognostic signature in PC patients was then constructed using univariate Cox regression and lasso regression methods, which were validated in numerous external validation cohorts. Furthermore, we investigated the activation of the glycolysis pathway in PC cell subtypes at the single-cell level, performed a quasi-time series analysis on the activated cell subtypes and then detected gene changes in the signature during cell development. Finally, we constructed a decision tree and a nomogram that could divide the patients into different risk subtypes, according to the signature score and their different clinical characteristics and assessed the prognosis of PC patients. Results: Glycolysis plays a risky role in PC patients. Our glycolysis-related signature could effectively discriminate the high-risk and low-risk patients in both the trained cohort and the independent externally validated cohort. The survival analysis and multivariate Cox analysis indicated this gene signature to be an independent prognostic factor in PC. The prognostic ROC curve analysis suggested a high accuracy of this gene signature in predicting the patient prognosis in PC. The single-cell analysis suggested that the glycolytic pathway may be more activated in epithelial cells and that the genes in the signature were also mainly expressed in epithelial cells. The decision tree analysis could effectively identify patients in different risk subgroups, and the nomograms clearly show the prognostic assessment of PC patients. Conclusion: Our study developed a glycolysis-related signature, which contributes to the risk subtype assessment of patients with PC and to the individualized management of patients in the clinical setting.

RecQ mediated genome instability 2 (RMI2): a potential prognostic and immunological biomarker for pan-cancers.

BACKGROUND: RecQ mediated genome instability 2 (RMI2) is an essential component of the BLM-TopoIIIa-RMI1-RMI2 (BTR) complex. However, the mysterious veil of the potential immunological relationship of RMI2 in tumorigenesis and development has not been revealed. METHODS: We conducted the differential expression (DE) analysis of the RMI2 in pan-cancer using data onto Oncomine, TIMER, and GEPIA databases. Afterward, survival analysis and clinical-stage correlation analysis were performed via the TCGA database. Subsequently, we used R software to further explore the relationship between the expression level of RMI2 and tumor mutation burden (TMB), microsatellite instability (MSI), tumor microenvironment (TME), tumor immune-infiltrated cells (TILs), immune checkpoints (ICP), mismatch repairs (MMRs) -related genes, m6A-related genes, DNA methylation-related genes. Finally, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional networks were also performed for annotation via gene set enrichment analysis (GSEA). RESULTS: The RMI2 expressed remarkably high in most cancer types compared to cancer adjacent normal tissues (P < 0.05). High expression of RMI2 was linked to unfavorable prognosis and advanced stage of disease, especially in LIHC and PAAD. RMI2 expression was related to TMB in 16 cancer types and MSI in 8 cancer types. Furthermore, it is significant positive correlations between RMI2 and stromal and immune cells, ICP-related genes, MMRs-related genes, m6A-related genes, and DNA methylation-related genes. Finally, GSEA analysis revealed that RMI2 was engaged in a variety of signaling pathways in pan-cancers. CONCLUSIONS: RMI2 may serve as a potential biological target and probably assume a crucial part in tumorigenesis and progression.