A Diagnostic Gene Expression Signature for Bladder Cancer Can Stratify Cases into Prescribed Molecular Subtypes and Predict Outcome.
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ABSTRACT:
SUBMITTER: Chen R
PROVIDER: S-EPMC9332739 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Current clinical judgment in bladder cancer (BC) relies primarily on pathological stage and grade. We investigated whether a molecular classification of tumor cell differentiation, based on a developmental biology approach, can provide additional prognostic information. Exploiting large preexisting gene-expression databases, we developed a biologically supervised computational model to predict markers that correspond with BC differentiation. To provide mechanistic insight, we assessed relative tumorigenicity and differentiation potential via xenotransplantation. We then correlated the prognostic utility of the identified markers to outcomes within gene expression and formalin-fixed paraffin-embedded (FFPE) tissue datasets. Our data indicate that BC can be subclassified into three subtypes, on the basis of their differentiation states: basal, intermediate, and differentiated, where only the most primitive tumor cell subpopulation within each subtype is capable of generating xenograft tumors and recapitulating downstream populations. We found that keratin 14 (KRT14) marks the most primitive differentiation state that precedes KRT5 and KRT20 expression. Furthermore, KRT14 expression is consistently associated with worse prognosis in both univariate and multivariate analyses. We identify here three distinct BC subtypes on the basis of their differentiation states, each harboring a unique tumor-initiating population.
| S-EPMC3277552 | biostudies-other
Project description:Bladder cancer (BC) is the most frequent malignancy of the urinary tract with a high incidence in men and smokers. Currently, there are no non-invasive markers useful for BC diagnosis and subtypes classification that could overcome invasive procedures such as cystoscopy. Dysregulated miRNA profiles have been associated with numerous cancers, including BC. Cell-free miRNAs are abundantly present in a variety of biofluids including urine and make them promising candidates in cancer biomarker discovery. In the present study, the identification of miRNA fingerprints associated with different BC status was performed by next-generation sequencing on urine samples from 66 BC and 48 controls. Three signatures based on dysregulated miRNAs have been identified by regression models, assessing the power to discriminate different BC subtypes. Altered miRNAs according to invasiveness and grade were validated by qPCR on 112 cases and 65 controls (among which 46 cases and 16 controls were an independent group of subjects while the rest were replica samples). The area under the curve (AUC) computed including three miRNAs (miR-30a-5p, let-7c-5p and miR-486-5p) altered in all BC subtypes showed a significantly increased accuracy in the discrimination of cases and controls (AUC model = 0.70; p-value = 0.01). In conclusions, the non-invasive detection in urine of a selected number of miRNAs altered in different BC subtypes could lead to an accurate early diagnosis of cancer and stratification of patients.
| S-EPMC5945522 | biostudies-literature
Project description:BackgroundAs Systemic Sclerosis (SSc) is a connective tissue ailment that impacts various bodily systems. The study aims to clarify the molecular subtypes of SSc, with the ultimate objective of establishing a diagnostic model that can inform clinical treatment decisions.MethodsFive microarray datasets of SSc were retrieved from the GEO database. To eliminate batch effects, the combat algorithm was applied. Immune cell infiltration was evaluated using the xCell algorithm. The ConsensusClusterPlus algorithm was utilized to identify SSc subtypes. Limma was used to determine differential expression genes (DEGs). GSEA was used to determine pathway enrichment. A support vector machine (SVM), Random Forest(RF), Boruta and LASSO algorithm have been used to select the feature gene. Diagnostic models were developed using SVM, RF, and Logistic Regression (LR). A ROC curve was used to evaluate the performance of the model. The compound-gene relationship was obtained from the Comparative Toxicogenomics Database (CTD).ResultsThe identification of three immune subtypes in SSc samples was based on the expression profiles of immune cells. The utilization of 19 key intersectional DEGs among subtypes facilitated the classification of SSc patients into three robust subtypes (gene_ClusterA-C). Gene_ClusterA exhibited significant enrichment of B cells, while gene_ClusterC showed significant enrichment of monocytes. Moderate activation of various immune cells was observed in gene_ClusterB. We identified 8 feature genes. The SVM model demonstrating superior diagnostic performance. Furthermore, correlation analysis revealed a robust association between the feature genes and immune cells. Eight pertinent compounds, namely methotrexate, resveratrol, paclitaxel, trichloroethylene, formaldehyde, silicon dioxide, benzene, and tetrachloroethylene, were identified from the CTD.ConclusionThe present study has effectively devised an innovative molecular subtyping methodology for patients with SSc and a diagnostic model based on machine learning to aid in clinical treatment. The study has identified potential molecular targets for therapy, thereby offering novel perspectives for the treatment and investigation of SSc.
| S-EPMC10577296 | biostudies-literature
Project description:In 2014, there was a burst of studies on the molecular subtypes of bladder cancer in the published literature that was made possible by the advances in high-throughput technologies. Based on gene expression profiling, the major molecular classification subdivisions were basal and luminal subtypes, which resembled to those observed in breast cancers. These basal and luminal subtypes were further subdivided by TCGA into squamous, infiltrated, luminal-papillary, luminal/genomically unstable (GU), and neuronal/small cell carcinoma (SCC) subtypes. Recently, an international subtypes consensus project further expanded on the TCGA subtypes by defining a consensus molecular classification (CMC). A multidisciplinary team of experts generated CMC to overcome the difficulties of clinical applications due to several published bladder cancer molecular classifications with various nomenclatures and molecular features. It included six molecular subtypes with the addition of one more luminal subtype (luminal nonspecified) compared to the TCGA subtype classification. The initial research efforts have focused on the characterization of each subtype at the molecular and histopathologic levels, but more recent studies have examined their significance in terms of clinical utility, i.e., biomarkers that inform prognostication and/or to predict therapeutic responses to be tested in future clinical trials. This review provides an overview of recent investigations into the relationship between molecular subtypes and the clinical management of patients with bladder cancer.
| S-EPMC7807369 | biostudies-literature
Project description:Background: Considering the heterogeneity and complexity of epigenetic regulation in bladder cancer, the underlying mechanisms of global DNA methylation modification in the immune microenvironment must be investigated to predict the prognosis outcomes and clinical response to immunotherapy. Methods: We systematically assessed the DNA methylation modes of 985 integrated bladder cancer samples with the unsupervised clustering algorithm. Subsequently, these DNA methylation modes were analyzed for their correlations with features of the immune microenvironment. The principal analysis algorithm was performed to calculate the DMRscores of each samples for qualification analysis. Findings: Three DNA methylation modes were revealed among 985 bladder cancer samples, and these modes are related to diverse clinical outcomes and several immune microenvironment phenotypes, e.g., immune-desert, immune-inflamed, and immune-excluded ones. Then patients were classified into high- and low-DMRscore subgroups according to the DMRscore, which was calculated based on the expression of DNA methylation related genes (DMRGs). Patients with the low-DMRscore subgroup presented a prominent survival advantage that was significantly correlated to the immune-inflamed phenotype. Further analysis revealed that patients with low DMRscores exhibited less TP53 wild mutation, lower cancer stage and molecular subtypes were mainly papillary subtypes. In addition, an independent immunotherapy cohort confirmed that DMRscore could serve as a signature to predict prognosis outcomes and immune responses. Conclusion: Global DNA methylation modes can be used to predict the immunophenotypes, aggressiveness, and immune responses of bladder cancer. DNA methylation status assessments will strengthen our insights into the features of the immune microenvironment and promote the development of more effective treatment strategies.
| S-EPMC8678484 | biostudies-literature
Project description:Epithelioid glioblastoma (eGBM) is a newly defined and rare GBM variant in the current WHO 2016 classification. BRAF V600E mutation is overrepresented in these tumors and there is known some morphological overlap with anaplastic epithelioid PXA (ePXA). In order to further elucidate this diagnostic category, we molecularly characterized 64 pediatric and adult examples initially diagnosed as "eGBM." Tumors were analyzed using array based methylation and direct sequencing of the BRAF and TERT genes. Our results demonstrated considerable molecular and clinical heterogeneity among eGBM cohort. Methylation patterns, copy number alterations, and mutational analysis data, in combination with clinical findings disclosed three different, well established tumor subtypes: (i) PXA-like tumors with favorable prognosis, predominantly in children and young adults (38), (ii) IDHwt GBM-like tumors with poor prognosis, mainly occurring in older adults, albeit with more frequent BRAF mutations (17), and (iii) RTK1 pediatric GBM-like neoplasms of intermediate prognosis in children and young adults, associated with chromothripsis and frequent PDGFRA amplifications (9). We conclude that the histopathologically defined eGBM do not represent a single diagnostic entity, but rather at least three molecularly and biologically distinct categories. Therefore, additional molecular testing through genome-wide molecular profiling is recommended to further stratify these rare cases.
| S-EPMC7469088 | biostudies-literature
Project description:Previously, we classified colorectal cancers (CRCs) into five CRCAssigner (CRCA) subtypes with different prognoses and potential treatment responses, later consolidated into four consensus molecular subtypes (CMS). Here we demonstrate the analytical development and validation of a custom NanoString nCounter platform-based biomarker assay (NanoCRCA) to stratify CRCs into subtypes. To reduce costs, we switched from the standard nCounter protocol to a custom modified protocol. The assay included a reduced 38-gene panel that was selected using an in-house machine-learning pipeline. We applied NanoCRCA to 413 samples from 355 CRC patients. From the fresh frozen samples (n = 237), a subset had matched microarray/RNAseq profiles (n = 47) or formalin-fixed paraffin-embedded (FFPE) samples (n = 58). We also analyzed a further 118 FFPE samples. We compared the assay results with the CMS classifier, different platforms (microarrays/RNAseq) and gene-set classifiers (38 and the original 786 genes). The standard and modified protocols showed high correlation (> 0.88) for gene expression. Technical replicates were highly correlated (> 0.96). NanoCRCA classified fresh frozen and FFPE samples into all five CRCA subtypes with consistent classification of selected matched fresh frozen/FFPE samples. We demonstrate high and significant subtype concordance across protocols (100%), gene sets (95%), platforms (87%) and with CMS subtypes (75%) when evaluated across multiple datasets. Overall, our NanoCRCA assay with further validation may facilitate prospective validation of CRC subtypes in clinical trials and beyond.
| S-EPMC6529539 | biostudies-literature
Project description:Mast cells are a major component of the immune microenvironment in tumour tissues and modulate tumour progression by releasing pro-tumorigenic and antitumorigenic molecules. Regarding the impact of mast cells on the outcomes of patients with lung adenocarcinoma (LUAD) patient, several published studies have shown contradictory results. Here, we aimed at elucidating the role of mast cells in early-stage LUAD. We found that high mast cell abundance was correlated with prolonged survival in early-stage LUAD patients. The mast cell-related gene signature and gene mutation data sets were used to stratify early-stage LUAD patients into two molecular subtypes (subtype 1 and subtype 2). The neural network-based framework constructed with the mast cell-related signature showed high accuracy in predicting response to immunotherapy. Importantly, the prognostic mast cell-related signature predicted the survival probability and the potential relationship between TP53 mutation, c-MYC activation and mast cell activities. The meta-analysis confirmed the prognostic value of the mast cell-related gene signature. In summary, this study might improve our understanding of the role of mast cells in early-stage LUAD and aid in the development of immunotherapy and personalized treatments for early-stage LUAD patients.
| S-EPMC7191192 | biostudies-literature
Project description:PurposeStudies indicate that molecular subtypes in muscle invasive bladder cancer predict the clinical outcome. We evaluated whether subtyping by a simplified method and established classifications could predict the clinical outcome.Materials and methodsWe subtyped institutional cohort 1 of 52 patients, including 39 with muscle invasive bladder cancer, an Oncomine™ data set of 151 with muscle invasive bladder cancer and TCGA (The Cancer Genome Atlas) data set of 402 with muscle invasive bladder cancer. Subtyping was done using simplified panels (MCG-1 and MCG-Ext) which included only transcripts common in published studies and were analyzed for predicting metastasis, and cancer specific, overall and recurrence-free survival. TCGA data set was further analyzed using the Lund taxonomy, the Bladder Cancer Molecular Taxonomy Group Consensus and TCGA 2017 mRNA subtype classifications.ResultsMuscle invasive bladder cancer specimens from cohort 1 and the Oncomine data set showed intratumor heterogeneity for transcript and protein expression. MCG-1 subtypes did not predict the outcome on univariate or Kaplan-Meier analysis. On multivariate analysis N stage (p ≤0.007), T stage (p ≤0.04), M stage (p=0.007) and/or patient age (p=0.01) predicted metastasis, cancer specific and overall survival, and/or the cisplatin based adjuvant chemotherapy response. In TCGA data set publications showed that subtypes risk stratified patients for overall survival. Consistently the MCG-1 and MCG-Ext subtypes were associated with overall but not recurrence-free survival on univariate and Kaplan-Meier analyses. TCGA data set included 21 low grade specimens of the total of 402 and subtypes associated with tumor grade (p=0.005). However, less than 1% of muscle invasive bladder cancer cases are low grade. In only high grade specimens the MCG-1 and MCG-Ext subtypes could not predict overall survival. On univariate analysis subtypes according to the Bladder Cancer Molecular Taxonomy Group Consensus, TCGA 2017 and the Lund taxonomy were associated with tumor grade (p <0.0001) and overall survival (p=0.01 to <0.0001). Regardless of classification, subtypes had about 50% to 60% sensitivity and specificity to predict overall and recurrence-free survival. On multivariate analyses N stage and lymphovascular invasion consistently predicted recurrence-free and overall survival (p=0.039 and 0.003, respectively).ConclusionsMolecular subtypes reflect bladder tumor heterogeneity and are associated with tumor grade. In multiple cohorts and subtyping classifications the clinical parameters outperformed subtypes for predicting the outcome.
| S-EPMC8327783 | biostudies-literature
Project description:Objective: Advancing age is a major risk factor of atherosclerosis (AS). Nevertheless, the mechanism underlying this phenomenon remains indistinct. Herein, this study conducted a comprehensive analysis of the biological implications of aging-related genes in AS. Methods: Gene expression profiles of AS and non-AS samples were curated from the GEO project. Differential expression analysis was adopted for screening AS-specific aging-related genes. LASSO regression analysis was presented for constructing a diagnostic model, and the discriminatory capacity was evaluated with ROC curves. Through consensus clustering analysis, aging-based molecular subtypes were conducted. Immune levels were estimated based on the expression of HLAs, immune checkpoints, and immune cell infiltrations. Key genes were then identified via WGCNA. The effects of CEBPB knockdown on macrophage polarization were examined with western blotting and ELISA. Furthermore, macrophages were exposed to 100 mg/L ox-LDL for 48 h to induce macrophage foam cells. After silencing CEBPB, markers of cholesterol uptake, esterification and hydrolysis, and efflux were detected with western blotting. Results: This study identified 28 AS-specific aging-related genes. The aging-related gene signature was developed, which could accurately diagnose AS in both the GSE20129 (AUC = 0.898) and GSE43292 (AUC = 0.685) datasets. Based on the expression profiling of AS-specific aging-related genes, two molecular subtypes were clustered, and with diverse immune infiltration features. The molecular subtype-relevant genes were obtained with WGCNA, which were markedly associated with immune activation. Silencing CEBPB triggered anti-inflammatory M2-like polarization and suppressed foam cell formation. Conclusion: Our findings suggest the critical implications of aging-related genes in diagnosing AS and modulating immune infiltrations.
| S-EPMC8792769 | biostudies-literature