Project description:Subclassification of tumors based on molecular features may facilitate therapeutic choice and increase the response rate of cancer patients. However, the highly complex cell origin involved in osteosarcoma (OS) limits the utility of traditional bulk RNA sequencing for OS subclassification. Single-cell RNA sequencing (scRNA-seq) holds great promise for identifying cell heterogeneity. However, this technique has rarely been used in the study of tumor subclassification. By analyzing scRNA-seq data for six conventional OS and nine cancellous bone (CB) samples, we identified 29 clusters in OS and CB samples and discovered three differentiation trajectories from the cancer stem cell (CSC)-like subset, which allowed us to classify OS samples into three groups. The classification model was further examined using the TARGET dataset. Each subgroup of OS had different prognoses and possible drug sensitivities, and OS cells in the three differentiation branches showed distinct interactions with other clusters in the OS microenvironment. In addition, we verified the classification model through IHC staining in 138 OS samples, revealing a worse prognosis for Group B patients. Furthermore, we describe the novel transcriptional program of CSCs and highlight the activation of EZH2 in CSCs of OS. These findings provide a novel subclassification method based on scRNA-seq and shed new light on the molecular features of CSCs in OS and may serve as valuable references for precision treatment for and therapeutic development in OS.
Project description:Previous bulk RNA sequencing or whole genome sequencing on clear cell renal cell carcinoma (ccRCC) subtyping mainly focused on ccRCC cell origin or the complex tumor microenvironment (TME). Based on the single-cell RNA sequencing (scRNA-seq) data of 11 primary ccRCC specimens, cancer stem-cell-like subsets could be differentiated into five trajectories, whereby we further classified ccRCC cells into three groups with diverse molecular features. These three ccRCC subgroups showed significantly different outcomes and potential targets to tyrosine kinase inhibitors (TKIs) or immune checkpoint inhibitors (ICIs). Tumor cells in three differentiation directions exhibited distinct interactions with other subsets in the ccRCC niches. The subtyping model was examined through immunohistochemistry staining in our ccRCC cohort and validated the same classification effect as the public patients. All these findings help gain a deeper understanding about the pathogenesis of ccRCC and provide useful clues for optimizing therapeutic schemes based on the molecular subtype analysis.
Project description:Head and neck squamous cell carcinoma (HNSCC) is a highly heterogeneous malignancy with poor prognosis. This article aims to explore the clinical significance of cell differentiation trajectory in HNSCC, identify different molecular subtypes by consensus clustering analysis, and develop a prognostic risk model on the basis of differentiation-related genes (DRGs) for predicting the prognosis of HNSCC patients. Firstly, cell trajectory analysis was performed on single-cell RNA sequencing (scRNA-seq) data, four molecular subtypes were identified from bulk RNA-seq data, and the molecular subtypes were predictive of patient survival, clinical features, immune infiltration status, and expression of immune checkpoint genes (ICGs)s. Secondly, we developed a 10-DRG signature for predicting the prognosis of HNSCC patients by using weighted correlation network analysis (WGCNA), differential expression analysis, univariate Cox regression analysis, and multivariate Cox regression analysis. Then, a nomogram integrating the risk assessment model and clinical features can successfully predict prognosis with favorable predictive performance and superior accuracy. We projected the response to immunotherapy and the sensitivity of commonly used antitumor drugs between the different groups. Finally, we used the quantitative Reverse Transcription-Polymerase Chain Reaction (qRT-PCR) analysis and western blot to verify the signature. In conclusion, we identified distinct molecular subtypes by cell differentiation trajectory and constructed a novel signature based on differentially expressed prognostic DRGs, which could predict the prognosis and response to immunotherapy for patients and may provide valuable clinical applications in the treatment of HNSCC.
Project description:This study aims to investigate the differentiation trajectory of osteosarcoma cells and to construct molecular subtypes with their respective characteristics and generate a multi-gene signature for predicting prognosis. Integrated single-cell RNA-sequencing (scRNA-seq) data, bulk RNA-seq data and microarray data from osteosarcoma samples were used for analysis. Via scRNA-seq data, time-related as well as differentiation-related genes were recognized as osteosarcoma tumor stem cell-related genes (OSCGs). In Gene Expression Omnibus (GEO) cohort, osteosarcoma patients were classified into two subtypes based on prognostic OSCGs and it was found that molecular typing successfully predicted overall survival, tumor microenvironment and immune infiltration status. Further, available drugs for influencing osteosarcoma via prognostic OSCGs were revealed. A 3-OSCG-based prognostic risk score signature was generated and by combining other clinic-pathological independent prognostic factor, stage at diagnosis, a nomogram was established to predict individual survival probability. In external independent TARGET cohort, the molecular types, the 3-gene signature as well as nomogram were validated. In conclusion, osteosarcoma cell differentiation occupies a crucial position in many facets, such as tumor prognosis and microenvironment, suggesting promising therapeutic targets for this disease.
Project description:In this study gene expression profiles for 307 cases of advanced bladder cancers were compared to molecular phenotype at the tumor cell level. TUR-B tissue for RNA extraction was macrodissected from the close vicinity of the tissue sampled for immunohistochemistry to ensure high-quality sampling and to minimize the effects of intra-tumor heterogeneity. Despite excellent agreement between gene expression values and IHC-score at the single marker level, broad differences emerge when samples are clustered at the global mRNA versus tumor cell (IHC) levels. Classification at the different levels give different results in a systematic fashion, which implicates that analysis at both levels is required for optimal subtype-classification of bladder cancer.
Project description:ObjectiveHead and neck squamous cell carcinoma (HNSCC) is one of the most common and lethal malignant tumors. We aimed to investigate the HNSCC cell differentiation trajectories and the corresponding clinical relevance.MethodsBased on HNSCC cell differentiation-related genes (HDRGs) identified by single-cell sequencing analysis, the molecular subtypes and corresponding immunity, metabolism, and stemness characteristics of 866 HNSCC cases were comprehensively analyzed. Machine-learning strategies were used to develop a HNSCC cell differentiation score (HCDscore) in order to quantify the unique heterogeneity of individual samples. We also assessed the prognostic value and biological characteristics of HCDscore using the multi-omics data.ResultsHNSCCs were stratified into three distinct molecular subtypes based on HDRGs: active stroma (Cluster-A), active metabolism (Cluster-B), and active immune (Cluster-C) types. The three molecular subtypes had different characteristics in terms of biological phenotype, genome and epigenetics, prognosis, immunotherapy and chemotherapy responses. We then demonstrated the correlations between HCDscore and the immune microenvironment, subtypes, carcinogenic biological processes, genetic variation, and prognosis. The low-HCDscore group was characterized by activation of immunity, enhanced response to anti-PD-1/PD-L1 immunotherapy, and better survival compared to the high-HCDscore group. Finally, by integrating the HCDscore with prognostic clinicopathological characteristics, a nomogram with strong predictive performance and high accuracy was constructed.ConclusionsThis study revealed that the cell differentiation trajectories in HNSCC played a nonnegligible role in patient prognosis, biological characteristics, and immune responses. Evaluating cancer cell differentiation will help to develop more effective immunotherapy, metabolic therapy, and chemotherapy strategies.
Project description:Background: A feature of glioblastoma (GBM) is the cellular and molecular heterogeneity, both within and between tumors. This variability results in a risk for sampling bias and potential tumor escape from future targeted therapy. Heterogeneous gene expression within GBM is well documented, but little is known regarding the epigenetic heterogeneity. We therefore aimed to profile the intra-tumor DNA methylation heterogeneity in GBM. Methods: 3-4 biopsies per tumor from spatially separated regions were collected from 12 GBM patients. We performed genome-wide DNA methylation analysis (~850,000 CpG sites) and compared inter- and intra-tumor variation. Results: All samples were classified as GBM IDH wt or IDH mutated by DNA methylation profiling, but the GBM subtype differed within five tumors. Some GBM samples exhibited higher DNA methylation differences within tumors than between, and many CpG sites (mean: 17,000) had different methylation levels within the tumors. Conclusions: We demonstrated that intra-tumor DNA methylation heterogeneity is a feature of GBM. Although all biopsies were classified as GBM IDH wt/mutated by DNA methylation analysis, the assigned subtype differed in samples from the same patient. The observed DNA methylation heterogeneity within tumors is important to consider for methylation-based biomarkers and future improvements in stratification of GBM patients.
Project description:Most glioblastoma studies incorporate the layer of tumor molecular subtype based on the four-subtype classification system proposed in 2010. Nevertheless, there is no universally recognized and convenient tool for glioblastoma molecular subtyping, and each study applies a different set of markers and/or approaches that cause inconsistencies in data comparability and reproducibility between studies. Thus, this study aimed to create an applicable user-friendly tool for glioblastoma classification, with high accuracy, while using a significantly smaller number of variables. The study incorporated a TCGA microarray, sequencing datasets, and an independent cohort of 56 glioblastomas (LUHS cohort). The models were constructed by applying the Agilent G4502 dataset, and they were tested using the Affymetrix HG-U133a and Illumina Hiseq cohorts, as well as the LUHS cases. Two classification models were constructed by applying a logistic regression classification algorithm, based on the mRNA levels of twenty selected genes. The classifiers were translated to a RT-qPCR assay and validated in an independent cohort of 56 glioblastomas. The classification accuracy of the 20-gene and 5-gene classifiers varied between 90.7-91% and 85.9-87.7%, respectively. With this work, we propose a cost-efficient three-class (classical, mesenchymal, and proneural) tool for glioblastoma molecular classification based on the mRNA analysis of only 5-20 genes, and we provide the basic information for classification performance starting from the wet-lab stage. We hope that the proposed classification tool will enable data comparability between different research groups.
Project description:This SuperSeries is composed of the following subset Series: GSE14818: Gene expression analysis of glioblastoma spheroid cultures I GSE14819: Array CGH analysis of glioblastoma serum grown adherent cultures GSE14820: Array CGH analysis of glioblastoma cell lines GSE14821: Array CGH analysis of single spheroids from glioblastoma spheroid cultures GSE14822: Array CGH analysis of glioblastoma spheroid cultures GSE14823: Array CGH analysis of glioblastomas GSE16666: Gene expression analysis of glioblastoma spheroid cultures II Refer to individual Series