Prognostic significance of SNCA and its methylation in bladder cancer.

The epidemiological investigation of different cancer types in the global population has reported a decreased risk of bladder cancer (BLCA) in Parkinson's diseases (PD). SNCA a critical gene in PD pathology have been reported involved in tumorigenesis recently. However, the role of SNCA in BLCA remains unclear. This study aimed to explore the potential value of SNCA as a prognostic diagnostic molecular biomarker in BLCA. In this study, we explored the expression pattern, prognostic value and promoter methylation level of SNCA in BLCA by GEPIA2, UALCAN, TCGA, GENT2, GEO and c-BioPortal database. Then, we used LinkedOmics database to obtain the co-expression genes of SNCA for further study by WGCNA. We further investigated the correlations between SNCA expression and six main types of immune cell infiltrations and immune signatures by TIMER. Finally, BLCA cell lines treated with 5-Aza-CdR were used to explore the correlation between increased methylation and downregulated mRNA expression. SNCA was downregulated in tumor tissues in TCGA-BLCA, GENT2 and GEO, which was validated in our cohort by qRT-PCR and immunohistochemistry. SNCA was confirmed as an independent predictor of poor overall survival (OS). LinkedOmics analysis suggested that SNCA regulates cell adhesion molecules, cytokine-cytokine receptor interaction, and complement and coagulation cascades. Twenty-two co-expression gene modules were constructed by WGCNA, and most of them were significantly associated with OS and disease-free survival (DFS). Six key genes (CNTN1, DACT3, MYLK1, PDE2A, RBM24, and ST6GALNAC3) screened also significantly correlated with prognosis. There were significant correlations between SNCA expression and immune infiltrations, especially T cell, suggesting that immune infiltration was one of the reasons for the influence of SNCA on prognosis in BLCA. Analysis by ULACAN and c-BioPortal showed that the promoter methylation of SNCA negatively correlated with its mRNA level. Furthermore, BLCA cell treatment with 5-Aza-CdR revealed that SNCA expression levels were upregulated with decreased methylation. Our research showed that SNCA was downregulated in BLCA and negatively correlation with DNA methylation. High SNCA expression was confirmed as an independent risk for prognosis. SNCA probably plays an important role in the infiltration of immune cells, especially with T cells. Thus, SNCA may be a promising prognostic biomarker in BLCA patients.

Wu Z ，Xia C ，Zhang C ，Yang D ，Ma K ... -  《BMC CANCER》

SNCA correlates with immune infiltration and serves as a prognostic biomarker in lung adenocarcinoma.

The SNCA gene is a critical gene in Parkinson's disease (PD) pathology. Accumulating evidence indicates that SNCA is involved in tumorigenesis; however, the role of SNCA in lung adenocarcinoma (LUAD) remains unclear. This study aimed to explore the potential value of SNCA as a prognostic and diagnostic molecular marker in LUAD. In this study, we explored the expression pattern, prognostic value, and promoter methylation status of SNCA in LUAD based on Oncomine, UALCAN, and Kaplan-Meier Plotter. Then, using TIMER, we investigated the correlation between SNCA expression and immune infiltration. And cBioPortal were used to analysis the correlation between SNCA expression and immune checkpoint. The transcriptome data of A549 cells overexpressing SNCA were used to further study the potential immune role of SNCA in LUAD. The effect of SNCA on proliferation of A549 cells were evaluated by CCK-8, EdU and colony formation. Finally, LUAD cell lines treated with 5-aza-dC were used to explore the correlation between increased promoter methylation and downregulated mRNA expression of SNCA. In general, the expression level of SNCA in LUAD tissue was lower than that in normal tissue, and high expression of SNCA was related to better prognosis. There were significant positive correlations between SNCA expression and immune infiltrations, including CD8+ T cells, macrophages, neutrophils, dendritic cells, B cells, and CD4+ T cells, and immune checkpoints, suggesting that immune infiltration was one of the reasons for the influence of SNCA on prognosis in LUAD. The transcriptome data of A549 cells overexpressing SNCA were further used to screen the relevant immune-related genes regulated by SNCA. Enrichment analysis confirmed that SNCA participates in the PI3K-AKT signaling pathway and other key tumor signaling pathways and regulates the expression of MAPK3, SRC, PLCG1, and SHC1. Cellular proliferation assay showed that SNCA could inhabit the growth of A549 cells via inhibiting activity of PI3K/AKT/ mTOR pathway. Finally, analysis of the methylation level of SNCA promoter showed that the promoter methylation negatively correlated with mRNA level. The expression of SNCA in LUAD cell lines was significantly upregulated by treatment with 5-aza-dC. High methylation of SNCA promoter in LUAD is one of the reasons for the downregulation of SNCA mRNA level. Given that SNCA could inhibit the proliferation of A549 cells and correlates with immune infiltrates, it may serve as a prognostic biomarker in LUAD.

Zhang X ，Wu Z ，Ma K 《BMC CANCER》

Identification of a tumor microenvironment-related seven-gene signature for predicting prognosis in bladder cancer.

Wang Z ，Tu L ，Chen M ，Tong S ... -  《BMC CANCER》

LIG1 is a novel marker for bladder cancer prognosis: evidence based on experimental studies, machine learning and single-cell sequencing.

Bladder cancer, a highly fatal disease, poses a significant threat to patients. Positioned at 19q13.2-13.3, LIG1, one of the four DNA ligases in mammalian cells, is frequently deleted in tumour cells of diverse origins. Despite this, the precise involvement of LIG1 in BLCA remains elusive. This pioneering investigation delves into the uncharted territory of LIG1's impact on BLCA. Our primary objective is to elucidate the intricate interplay between LIG1 and BLCA, alongside exploring its correlation with various clinicopathological factors. We retrieved gene expression data of para-carcinoma tissues and bladder cancer (BLCA) from the GEO repository. Single-cell sequencing data were processed using the "Seurat" package. Differential expression analysis was then performed with the "Limma" package. The construction of scale-free gene co-expression networks was achieved using the "WGCNA" package. Subsequently, a Venn diagram was utilized to extract genes from the positively correlated modules identified by WGCNA and intersect them with differentially expressed genes (DEGs), isolating the overlapping genes. The "STRINGdb" package was employed to establish the protein-protein interaction (PPI) network.Hub genes were identified through the PPI network using the Betweenness Centrality (BC) algorithm. We conducted KEGG and GO enrichment analyses to uncover the regulatory mechanisms and biological functions associated with the hub genes. A machine-learning diagnostic model was established using the R package "mlr3verse." Mutation profiles between the LIG1^high and LIG1^low groups were visualized using the BEST website. Survival analyses within the LIG1^high and LIG1^low groups were performed using the BEST website and the GENT2 website. Finally, a series of functional experiments were executed to validate the functional role of LIG1 in BLCA. Our investigation revealed an upregulation of LIG1 in BLCA specimens, with heightened LIG1 levels correlating with unfavorable overall survival outcomes. Functional enrichment analysis of hub genes, as evidenced by GO and KEGG enrichment analyses, highlighted LIG1's involvement in critical function such as the DNA replication, cellular senescence, cell cycle and the p53 signalling pathway. Notably, the mutational landscape of BLCA varied significantly between LIG1high and LIG1low groups.Immune infiltrating analyses suggested a pivotal role for LIG1 in immune cell recruitment and immune regulation within the BLCA microenvironment, thereby impacting prognosis. Subsequent experimental validations further underscored the significance of LIG1 in BLCA pathogenesis, consolidating its functional relevance in BLCA samples. Our research demonstrates that LIG1 plays a crucial role in promoting bladder cancer malignant progression by heightening proliferation, invasion, EMT, and other key functions, thereby serving as a potential risk biomarker.

Song DM ，Shen T ，Feng K ，He YB ，Chen SL ，Zhang Y ，Luo WF ，Han L ，Tong M ，Jin Y ... -  《Frontiers in Immunology》

Comprehensive analysis of scRNA-Seq and bulk RNA-Seq reveals dynamic changes in the tumor immune microenvironment of bladder cancer and establishes a prognostic model.

The prognostic management of bladder cancer (BLCA) remains a great challenge for clinicians. Recently, bulk RNA-seq sequencing data have been used as a prognostic marker for many cancers but do not accurately detect core cellular and molecular functions in tumor cells. In the current study, bulk RNA-seq and single-cell RNA sequencing (scRNA-seq) data were combined to construct a prognostic model of BLCA. BLCA scRNA-seq data were downloaded from Gene Expression Omnibus (GEO) database. Bulk RNA-seq data were obtained from the UCSC Xena. The R package "Seurat" was used for scRNA-seq data processing, and the uniform manifold approximation and projection (UMAP) were utilized for downscaling and cluster identification. The FindAllMarkers function was used to identify marker genes for each cluster. The limma package was used to obtain differentially expressed genes (DEGs) affecting overall survival (OS) in BLCA patients. Weighted gene correlation network analysis (WGCNA) was used to identify BLCA key modules. The intersection of marker genes of core cells and genes of BLCA key modules and DEGs was used to construct a prognostic model by univariate Cox and Least Absolute Shrinkage and Selection Operator (LASSO) analyses. Differences in clinicopathological characteristics, immune microenvironment, immune checkpoints, and chemotherapeutic drug sensitivity between the high and low-risk groups were also investigated. scRNA-seq data were analyzed to identify 19 cell subpopulations and 7 core cell types. The ssGSEA showed that all 7 core cell types were significantly downregulated in tumor samples of BLCA. We identified 474 marker genes from the scRNA-seq dataset, 1556 DEGs from the Bulk RNA-seq dataset, and 2334 genes associated with a key module identified by WGCNA. After performing intersection, univariate Cox, and LASSO analysis, we obtained a prognostic model based on the expression levels of 3 signature genes, namely MAP1B, PCOLCE2, and ELN. The feasibility of the model was validated by an internal training set and two external validation sets. Moreover, patients with high-risk scores are predisposed to experience poor OS, a larger prevalence of stage III-IV, a greater TMB, a higher infiltration of immune cells, and a lesser likelihood of responding favorably to immunotherapy. By integrating scRNA-seq and bulk RNA-seq data, we constructed a novel prognostic model to predict the survival of BLCA patients. The risk score is a promising independent prognostic factor that is closely correlated with the immune microenvironment and clinicopathological characteristics.

Tan Z ，Chen X ，Zuo J ，Fu S ，Wang H ，Wang J ... -  《Journal of Translational Medicine》