Dataset Information

Single-Cell Profiling of Tumor Microenvironment Heterogeneity in Osteosarcoma Identifies a Highly Invasive Subcluster for Predicting Prognosis.

ABSTRACT: Osteosarcoma is the most common malignant bone tumor in adolescents, and metastasis is the key reason for treatment failure and poor prognosis. Once metastasis occurs, the 5-year survival rate is only approximately 20%, and assessing and predicting the risk of osteosarcoma metastasis are still difficult tasks. In this study, cellular communication between tumor cells and nontumor cells was identified through comprehensive analysis of osteosarcoma single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq data, illustrating the complex regulatory network in the osteosarcoma microenvironment. In line with the heterogeneity of osteosarcoma, we found subpopulations of osteosarcoma cells that highly expressed COL6A1, COL6A3 and MIF and were closely associated with lung metastasis. Then, BCDEG, a reliable risk regression model that could accurately assess the metastasis risk and prognosis of patients, was established, providing a new strategy for the diagnosis and treatment of osteosarcoma.

SUBMITTER: Guo J

PROVIDER: S-EPMC9020875 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Single-Cell Profiling of Tumor Microenvironment Heterogeneity in Osteosarcoma Identifies a Highly Invasive Subcluster for Predicting Prognosis.

Guo Junfeng J Tang Hong H Huang Pan P Guo Junfeng J Shi Youxing Y Yuan Chengsong C Liang Taotao T Tang Kanglai K

Frontiers in oncology 20220406

Osteosarcoma is the most common malignant bone tumor in adolescents, and metastasis is the key reason for treatment failure and poor prognosis. Once metastasis occurs, the 5-year survival rate is only approximately 20%, and assessing and predicting the risk of osteosarcoma metastasis are still difficult tasks. In this study, cellular communication between tumor cells and nontumor cells was identified through comprehensive analysis of osteosarcoma single-cell RNA sequencing (scRNA-seq) and bulk R ...[more]

PMID: 35463309

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Project description:Endometrial carcinoma (EC) is a common malignancy of female genital system which exhibits a unique immune profile. It is a promising strategy to quantify immune patterns of EC for predicting prognosis and therapeutic efficiency. Here, we attempted to identify the possible immune microenvironment-related prognostic markers of EC. We obtained the RNA sequencing and corresponding clinical data of EC from TCGA database. Then, 3 immune scores based on the Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) algorithm were computed. Correlation between above ESTIMATE scores and other immune-related scores, molecular subtypes, prognosis, and gene mutation status (including BRCA and TP53) were further analyzed. Afterwards, gene modules associated with the ESTIMATE scores were screened out through hierarchical clustering analysis and weighted gene co-expression network analysis (WGCNA). Differentially expressed analysis was performed and genes shared by the most relevant modules were found out. KEGG pathway enrichment analysis was conducted to explore the biological functions of those genes. Survival analysis was carried out to identify prognostic immune-related genes and GSE17025 database was further used to confirm the correlation between immune-related genes and the ImmuneScore. The immune-related scores based on ESTIMATE algorithm was closely related to the immune microenvironment of EC. 3 gene modules that had the closest correlations with 3 ESTIMATE scores were obtained. 109 immune-related genes were preliminarily found out and 29 pathways were significantly enriched, most of which were associated with immune response. Univariate survival analysis revealed that there were 14 genes positively associated with both OS and PFS. Among which, 11 genes showed marked correlations with ImmuneScore values in GSE17025 database. Our current study profiled the immune status and identified 14 novel immune-related prognostic biomarkers for EC. Our findings may help to investigate the complicated tumor microenvironment and develop novel individualized therapeutic targets for EC.

Project description:BackgroundMalignant pleural mesothelioma (MPM) is a rare and intractable disease exhibiting a remarkable intratumoral heterogeneity and dismal prognosis. Although immunotherapy has reshaped the therapeutic strategies for MPM, patients react with discrepant responsiveness.MethodsHerein, we recruited 333 MPM patients from 5 various cohorts and developed an in-silico classification system using unsupervised Non-negative Matrix Factorization and Nearest Template Prediction algorithms. The genomic alterations, immune signatures, and patient outcomes were systemically analyzed across the external TCGA-MESO samples. Machine learning-based integrated methodology was applied to identify a gene classifier for clinical application.ResultsThe gene expression profiling-based classification algorithm identified immune-related subtypes for MPMs. In comparison with the non-immune subtype, we validated the existence of abundant immunocytes in the immune subtype. Immune-suppressed MPMs were enriched with stroma fraction, myeloid components, and immunosuppressive tumor-associated macrophages (TAMs) as well exhibited increased TGF-β signature that informs worse clinical outcomes and reduced efficacy of anti-PD-1 treatment. The immune-activated MPMs harbored the highest lymphocyte infiltration, growing TCR and BCR diversity, and presented the pan-cancer immune phenotype of IFN-γ dominant, which confers these tumors with better drug response when undergoing immune checkpoint inhibitor (ICI) treatment. Genetically, BAP1 mutation was most commonly found in patients of immune-activated MPMs and was associated with a favorable outcome in a subtype-specific pattern. Finally, a robust 12-gene classifier was generated to classify MPMs with high accuracy, holding promise value in predicting patient survival.ConclusionsWe demonstrate that the novel classification system can be exploited to guide the identification of diverse immune subtypes, providing critical biological insights into the mechanisms driving tumor heterogeneity and responsible for cancer-related patient prognoses.

Dataset Information

Single-Cell Profiling of Tumor Microenvironment Heterogeneity in Osteosarcoma Identifies a Highly Invasive Subcluster for Predicting Prognosis.

Publications

Single-Cell Profiling of Tumor Microenvironment Heterogeneity in Osteosarcoma Identifies a Highly Invasive Subcluster for Predicting Prognosis.

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