Dataset Information

Comprehensive analysis of immune infiltration and gene expression for predicting survival in patients with sarcomas.

ABSTRACT: Tumor microenvironments are strongly related to tumor development, and immune-infiltrating cells and immune-related molecules are potential prognostic markers. However, the shortcomings of traditional measurement methods limit the accurate evaluation of various components in tumor microenvironments. With the rapid advancement of Next-Generation RNA Sequencing technology, dedicated and in-depth analyses of immune filtration within the tumor microenvironment has been achieved. In this study, we combined the bioinformatics analysis methods ESTIMATE, CIBERSORT, and ssGSEA to characterize the immune infiltration of sarcomas and to identify specific immunomodulators of different pathological subtypes. We further extracted a functional enrichment of significant immune-related genes related to improved prognosis, including NR1H3, VAMP5, GIMAP2, GBP2, HLA-E and CRIP1. Overall, the immune microenvironment is an important prognostic determinant of sarcomas and may be a potential resource for developing effective immunotherapy.

SUBMITTER: Chen H

PROVIDER: S-EPMC7880383 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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Comprehensive analysis of immune infiltration and gene expression for predicting survival in patients with sarcomas.

Chen Hongmin H Song Yijiang Y Deng Chuangzhong C Xu Yanyang Y Xu Huaiyuan H Zhu Xiaojun X Song Guohui G Tang Qinglian Q Lu Jinchang J Wang Jin J

Aging 20201209 2

Tumor microenvironments are strongly related to tumor development, and immune-infiltrating cells and immune-related molecules are potential prognostic markers. However, the shortcomings of traditional measurement methods limit the accurate evaluation of various components in tumor microenvironments. With the rapid advancement of Next-Generation RNA Sequencing technology, dedicated and in-depth analyses of immune filtration within the tumor microenvironment has been achieved. In this study, we co ...[more]

PMID: 33316779

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Project description:BackgroundMore than 90% of neuroblastoma patients are cured in the low-risk group while only less than 50% for those with high-risk disease can be cured. Since the high-risk patients still have poor outcomes, we need more accurate stratification to establish an individualized precise treatment plan for the patients to improve the long-term survival rate.ResultsWe focus on extracting features and providing a workflow to improve survival prediction for neuroblastoma patients. With a workflow for gene co-expression network (GCN) mining in microarray and RNA-Seq datasets, we extracted molecular features from each co-expressed module and summarized them into eigengenes. Then we adopted the lasso-regularized Cox proportional hazards model to select the most informative eigengene features regarding association to the risk of metastasis. Nine eigengenes were selected which show strong association with patient survival prognosis. All of the nine corresponding gene modules also have highly enriched biological functions or cytoband locations. Three of them are unique modules to RNA-Seq data, which complement the modules from microarray data in terms of survival prognosis. We then merged all eigengenes from these unique modules and used an integrative method called Similarity Network Fusion to test the prognostic power of these eigengenes for prognosis. The prognostic accuracies are significantly improved as compared to using all eigengenes, and a subgroup of patients with very poor survival rate was identified.ConclusionsWe first compared GCNs mined from microarray and RNA-seq data. We discovered that each data modality yields unique GCNs, which are enriched with clear biological functions. Then we do module unique analysis and use lasso-cox model to select survival-associated eigengenes. Integration of unique and survival-associated eigengenes from both data types provides complementary information that leads to more accurate survival prognosis.ReviewersReviewed by Susmita Datta, Marco Chierici and Dimitar Vassilev.

Project description:BackgroundGastric cancer (GC) is a major public health problem worldwide. In recent decades, the treatment of gastric cancer has improved greatly, but basic research and clinical application of gastric cancer remain challenges due to the high heterogeneity. Here, we provide new insights for identifying prognostic models of GC.MethodsWe obtained the gene expression profiles of GSE62254 containing 300 samples for training. GSE15459 and TCGA-STAD for validation, which contain 200 and 375 samples, respectively. Weighted gene co-expression network analysis (WGCNA) was used to identify gene modules. We performed Lasso regression and Cox regression analyses to identify the most significant five genes to develop a novel prognostic model. And we selected two representative genes within the model for immunohistochemistry staining with 105 GC specimens from our hospital to verify the prediction efficiency. Moreover, we estimated the correlation coefficient between our model and immune infiltration using the CIBERSORT algorithm. The data from GSE15459 and TCGA cohort validated the robustness and predictive accuracy of this prognostic model.ResultsOf the 12 gene modules identified, 1,198 green-yellow module genes were selected for further analysis. Multivariate Cox analysis was performed on genes from univariate Cox regression and Lasso regression analysis using the Cox proportional hazards regression model. Finally, we constructed a five gene prognostic model: Risk Score = [(-0.7547) * Expression (ARHGAP32)] + [(-0.8272) * Expression (KLF5)] + [1.09 * Expression (MAMLD1)] + [0.5174 * Expression (MATN3)] + [1.66 * Expression (NES)]. The prognosis of samples in the high-risk group was significantly poorer than that of samples in the low-risk group (p = 6.503e-11). The risk model was also regarded as an independent predictor of prognosis (HR, 1.678, p < 0.001). The observed correlation with immune cells suggested that this risk model could potentially predict immune infiltration.ConclusionThis study identified a potential risk model for prognosis and immune infiltration prediction in GC using WGCNA and Cox regression analysis.

Dataset Information

Comprehensive analysis of immune infiltration and gene expression for predicting survival in patients with sarcomas.

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Comprehensive analysis of immune infiltration and gene expression for predicting survival in patients with sarcomas.

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