Molecular classification of bladder cancer is becoming increasingly important for its clinical management. And, the current classifications are primarily based on gene expression profiles. We identified four immunotypes of bladder cancer (referred to as C1-C4) based on gene expression profiles performed by immune-related gene sets in three independent data sets, and proved that this classification is effective and reproducible. We found that C2 is an immune-infiltrating type and C4 is an immune "desert" type. These types are characterized by the up-and downregulation of genes encoding numerous immune checkpoint proteins and HLA and regulating human immune cell subgroups. The survival rate was better for the C2 subtype than for other subtypes. We believe that this can be explained by the antitumor effects of CD4 memory T cells and CD8 T cells as well as their ability to circumvent M0 macrophage antitumor immunity. In addition, C2 was most sensitive to not only anti-PD-1 immunosuppressive therapy, but also conventional chemotherapeutics such as gemcitabine and bleomycin. The C4 subtype was most sensitive to the chemotherapy drugs cisplatin and doxorubicin. This theoretical framework may guide the personalized treatment of bladder cancer in the future. It is worth noting that the C2 immune infiltration type positively correlates with a variety of stromal components, such as enrichment of endothelial cells and fibroblasts, epithelial-mesenchymal transition, and angiogenesis, together with enrichment of seven kinds of stem cells. We further identified tumor-related JAK-STAT and other signaling pathways in the C2 subtype, along with important mutations in the proteins involved in these pathways, revealing the complex mechanism underlying tumor immune escape. Our results, and particularly the identification of hub genes specific to the C2 and C4 subtypes, provide a reference for the development of immunotherapeutic agents against bladder cancer.

Background Currently, no molecular classification is established for bladder cancer based on metabolic characteristics. Therefore, we conducted a comprehensive analysis of bladder cancer metabolism-related genes using multiple publicly available datasets and aimed to identify subtypes according to distinctive metabolic characteristics. Methods RNA-sequencing data of The Cancer Genome Atlas were subjected to non-negative matrix fractionation to classify bladder cancer according to metabolism-related gene expression; Gene Expression Omnibus and ArrayExpress datasets were used as validation cohorts. The sensitivity of metabolic types to predicted immunotherapy and chemotherapy was assessed. Kaplan–Meier curves were plotted to assess patient survival. Differentially expressed genes between subtypes were identified using edgeR. The differences among identified subtypes were compared using the Kruskal–Wallis non-parametric test. To better clarify the subtypes of bladder cancer, their relationship with clinical characteristics was examined using the Fisher’s test. We also constructed a risk prediction model using the random survival forest method to analyze right-censored survival data based on key metabolic genes. To identify genes of prognostic significance, univariate Cox regression, lasso analysis, and multivariate regression were performed sequentially. Results Three bladder cancer subtypes were identified according to the expression of metabolism-related genes. The M1 subtype was characterized by high metabolic activity, low immunogenicity, and better prognosis. M2 exhibited moderate metabolic activity, high immunogenicity, and the worst prognosis. M3 was associated with low metabolic activity, low immunogenicity, and poor prognosis. M1 showed the best predicted response to immunotherapy, whereas patients with M1 were predicted to be the least sensitive to cisplatin. By contrast, M2 showed the worst predicted response to immunotherapy but was predicted to be more sensitive to cisplatin, doxorubicin, and other first-line anticancer drugs. M3 was the most sensitive to gemcitabine. The risk model based on metabolic genes effectively predicted the prognosis of bladder cancer patients. Conclusions Metabolic classification of bladder cancer has potential clinical value and therapeutic feasibility by inhibiting the associated pathways. This classification can provide valuable insights for developing precise bladder cancer treatment.

Enhancer RNAs (eRNAs) participate in tumor growth and immune regulation through complex signaling pathways. However, the immune-related function of the eRNA-mRNA axis in lung adenocarcinoma (LUAD) is unclear. Data on the expression of eRNAs and mRNAs were downloaded from The Cancer Genome Atlas, GEO, and UCSC Xena, including LUAD, and pan-cancer clinical data and mutational information. Immune gene files were obtained from ImmLnc and ImmPort databases. Survival indices, including relapse-free and overall survival, were analyzed using the Kaplan–Meier and log-rank methods. The level of immune cell infiltration, degree of tumor hypoxia, and tumor cell stemness characteristics were quantified using the single-sample gene set enrichment analysis algorithm. The immune infiltration score and infiltration degree were evaluated using the ESTIMATE and CIBERSORT algorithms. The tumor mutation burden and microsatellite instability were examined using the Spearman test. The LUAD-associated immune-related LINC00987/A2M axis was down-regulated in most cancer types, indicating poor survival and cancer progression. Immune cell infiltration was closely related to abnormal expression of the LINC00987/A2M axis, linking its expression to a possible evaluation of sensitivity to checkpoint inhibitors and response to chemotherapy. Abnormal expression of the LINC00987/A2M axis was characterized by heterogeneity in the degree of tumor hypoxia and stemness characteristics. The abnormal distribution of immune cells in LUAD was also verified through pan-cancer analysis. Comprehensive bioinformatic analysis showed that the LINC00987/A2M axis is a functional and effective tumor suppressor and biomarker for assessing the immune microenvironment and prognostic and therapeutic evaluations of LUAD.

Background Bladder cancer (BLCA) is the fifth most common type of cancer worldwide, with high recurrence and progression rates. Although considerable progress has been made in the treatment of BLCA through accurate typing of molecular characteristics, little is known regarding the various genetic and epigenetic changes that have evolved in stem and progenitor cells. To address this issue, we have developed a novel stem cell typing method. Methods Based on six published genomic datasets, we used 26 stem cell gene sets to classify each dataset. Unsupervised and supervised machine learning methods were used to perform the classification. Results We classified BLCA into three subtypes—high stem cell enrichment (SCE_H), medium stem cell enrichment (SCE_M), and low stem cell enrichment (SCE_L)—based on multiple cross-platform datasets. The stability and reliability of the classification were verified. Compared with the other subtypes, SCE_H had the highest degree of cancer stem cell concentration, highest level of immune cell infiltration, and highest sensitivity not only to predicted anti-PD-1 immunosuppressive therapy but also to conventional chemotherapeutic agents such as cisplatin, sunitinib, and vinblastine; however, this group had the worst prognosis. Comparison of gene set enrichment analysis results for pathway enrichment of various subtypes reveals that the SCE_H subtype activates the important pathways regulating cancer occurrence, development, and even poor prognosis, including epithelial-mesenchymal transition, hypoxia, angiogenesis, KRAS signal upregulation, interleukin 6-mediated JAK-STAT signaling pathway, and inflammatory response. Two identified pairs of transcription factors, GRHL2 and GATA6 and IRF5 and GATA3, possibly have opposite regulatory effects on SCE_H and SCE_L, respectively. Conclusions The identification of BLCA subtypes based on cancer stem cell gene sets revealed the complex mechanism of carcinogenesis of BLCA and provides a new direction for the diagnosis and treatment of BLCA.

Background Thoracic aortic dissection (TAD) is a severe disease with limited understandings in its pathogenesis. Altered DNA methylation has been revealed to be involved in many diseases etiology. Few studies have examined the role of DNA methylation in the development of TAD. This study explored alterations of the DNA methylation landscape in TAD and examined the potential role of cell-free DNA (cfDNA) methylation as a biomarker in TAD diagnosis. Results Ascending aortic tissues from TAD patients (Stanford type A; n = 6) and healthy controls (n = 6) were first examined via whole-genome bisulfite sequencing (WGBS). While no obvious global methylation shift was observed, numerous differentially methylated regions (DMRs) were identified, with associated genes enriched in the areas of vasculature and heart development. We further confirmed the methylation and expression changes in homeobox (Hox) clusters with 10 independent samples using bisulfite pyrosequencing and quantitative real-time PCR (qPCR). Among these, HOXA5, HOXB6 and HOXC6 were significantly down-regulated in TAD samples relative to controls. To evaluate cfDNA methylation pattern as a biomarker in TAD diagnosis, cfDNA from TAD patients (Stanford type A; n = 7) and healthy controls (n = 4) were examined by WGBS. A prediction model was built using DMRs identified previously from aortic tissues on methylation data from cfDNA. Both high sensitivity (86%) and specificity (75%) were achieved in patient classification (AUC = 0.96). Conclusions These findings showed an altered epigenetic regulation in TAD patients. This altered epigenetic regulation and subsequent altered expression of genes associated with vasculature and heart development, such as Hox family genes, may contribute to the loss of aortic integrity and TAD pathogenesis. Additionally, the cfDNA methylation in TAD was highly disease specific, which can be used as a non-invasive biomarker for disease prediction.