The construction and analysis of tumor-infiltrating immune cell and ceRNA networks in recurrent soft tissue sarcoma

Huang, Runzhi; Meng, Tong; Chen, Rui; Yan, Penghui; Zhang, Jie; Hu, Peng; Zhu, Xiaolong; Yin, Huabin; Song, Dianwen; Huang, Zongqiang

doi:10.18632/aging.102424

Cited by 42 publications

(52 citation statements)

References 55 publications

(65 reference statements)

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“…STSs is not only a heterogeneous tumor, but also has been found presenting in almost every part of the human body, and patients with different histological types are significantly distinct in clinical outcomes. Thus, analysis combined with the comprehensive clinical characteristics, such as age, gender, tissue or organ of origin, and histological, might effectively improve the accuracy of prognosis in STSs patients [38]. In this study, the distribution of the disease-specific OS in these 4 distinct prognostic subtypes of STSs was seemed to be predicted, as well as original sites, histological classification, age, and gender distribution.…”

Section: Discussionmentioning

confidence: 81%

DNA methylation patterns–based subtype distinction and identification of soft tissue sarcoma prognosis

Fan

et al. 2020

Preprint

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Background: Soft tissue sarcomas (STSs) are heterogeneous at the clinical with a variable tendency of aggressive behavior. Methods: In this study, we constructed a specific DNA methylation-based classification to identify the distinct prognosis-subtypes of STSs based on the DNA methylation spectrum from the TCGA database.Results: Eventually, samples were clustered into four subgroups, and their survival curves were distinct from each other. Meanwhile, the samples in each subgroup reflected differentially in several clinical features. Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis was also conducted on the genes of the corresponding promoter regions of the above‐described specific methylation sites, revealing that these genes were mainly concentrated in certain cancer‑associated biological functions and pathways. In addition, we calculated the differences among clustered methylation sites and performed the specific methylation sites with LASSO algorithm. The selection operator algorithm was employed to derive a risk signature model, and a prognostic signature based on these methylation sites performed well for risk stratification in STSs patients. At last, a nomogram consisted of clinical features and risk score was developed for the survival prediction. Conclusion: In conclusion, this study declares that DNA methylation-based STSs subtype classification is highly relevant for future development of personalized therapy as it identifies the prediction value of patient prognosis.

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Section: Discussionmentioning

confidence: 81%

DNA methylation patterns–based subtype distinction and identification of soft tissue sarcoma prognosis

Fan

et al. 2020

Preprint

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“…Analyzing the tumor microenvironment by CIBERSORT algorithm to calculate the immune scores and stromal scores, there have been some literatures reported that there were a signi cant relationship between tumor microenvironment and tumor prognosis [25,26]. Though, Runzhi Huang el at [11]. have analyzed the relationship between sarcoma recurrence and tumor immune microenvironment with TCGA data, this is the rst report to predict the prognosis of sarcoma patients by using TCGA data to calculate the immune score to construct a prognostic model.…”

Section: Discussionmentioning

confidence: 99%

“…The tumor microenvironment (TME) is the cellular milieu consisting mesenchymal cells, endothelial cells, tumor in ltrating immune cells (TIIC), extracellular matrix(ECM) molecules and in ammatory mediators [7], which can provide the metabolites and controls factors of proliferation and diffusion for many tumors. Tumor microenvironment has also been reported to seriously affect gene expression in tumor tissues [8][9][10][11]. Immune cells and stromal cells are the two main types of non-tumor components in the tumor microenvironment, and are considered to have important signi cance for the diagnosis and prognostic evaluation of tumors [12].…”

Section: Introductionmentioning

confidence: 99%

The prediction of survival and analysis of microenvironment in patients with sarcoma

Chen

Liang

et al. 2020

Preprint

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Abstract Background: Sarcomas were rare, aggressive, and heterogeneous group of tumors. The degree of tumor microenvironment cells, infiltrating immune cells and stromal cells in the tumor had an important impact on prognosis of sarcoma. The aim of this study was to identify the differentially expressed genes (DEGs) association with immune of sarcoma and potential prognostic immune biomarkers for predicting survival of sarcoma patients. Methods: The gene expression data and clinical data of sarcoma was downloaded from The Cancer Genome Atlas (TCGA) dataset. The immune scores and stromal scores were calculated by ESTIMATE algorithm. The limma package was used to identify the immune DEGs. ClusterProfiler package and STRING were further to analysis the immune DEGs. A prognostic signature was built based the immune gene and clinical data by univariate Cox regression analysis and multivariable Cox analysis. Finally, the prognostic signature was evaluated by functional assessment and the Gene Expression Omnibus (GEO) database. Results: The functional enrichment showed that the up immune DEGs was associated with immune cell activation, proliferation, and adhesion. A single prognostic signature was constructed with seven immune genes, and patient with high risk scores had a worse survival than those with low risk scores. Evaluating showed that the prognostic signature performed well and served as an independent factor in sarcoma. Conclusions: The chemokine receptors or chemokine ligands and immune related genes played a significant role in the mechanism of sarcoma, and the novel immune gene expression had a significant clinical guidance for the prognosis of patient in sarcoma.

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“…Establishment of a ceRNA network could provide novel insights into tumor regulatory networks. The tight relationship has been found between tumor-in ltrating immune cells and ceRNA networks in various tumors like head and neck squamous cell cancer 10 , soft tissue sarcoma 11 and mesothelioma 12 , while this kind of research on BLCA has not been received enough attention. Previous study has successfully constructed a regulatory ceRNA network, which could be a prognostic predictor for BLCA patients and displayed potential biological mechanisms from the angle of ceRNA hypothesis 13 .…”

Section: Introductionmentioning

confidence: 99%

Construction of An Immune-Related CeRNA Network as a Prognostic Signature in Bladder Cancer

Zhou¹,

Tian²,

Yang³

et al. 2020

Preprint

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Background: Immunotherapy has been proved to be effective for bladder cancer (BLCA). However, the molecular network involved in BLCA tumor immune response remains unclear. This study aims to construct an immune-related ceRNA network and to identify the prognostic value. Methods: Based on The Cancer Genome Atlas (TCGA), we used single-sample gene set enrichment analysis (ssGSEA), weighted gene co-expression network analysis (WGCNA) to determine immune-related mRNA, lncRNA and miRNA. Then least absolute shrinkage, and selection operator (LASSO) and Cox regression were performed to identify the mRNAs with high prognostic value, and accordingly, the risk score was calculated. Internal and external validation were performed both in TCGA and GSE13507 with Kaplan-Meier (KM) survival and Receiver Operating Characteristic (ROC) curve analysis. Using the immune-related mRNA, lncRNA and miRNA, a ceRNA network was established via MiRcode, starBase, miRDB, miRTarBase and TargetScan. Besides, we also explore the relationship between the risk score and immune cell infiltration via CIBERSORT algorithm. Results: 5 mRNAs (PCGF3, FASN, DPYSL2, TGFBI and NTF3) were ultimately identified, and KM survival analysis displayed the 5-mRNA risk signature could predict the prognosis of BLCA with high efficacy both in TCGA (p = 1.006e-13) and GSE13507 (p = 7.759e-04). Using miRNA targeting molecular prediction database, an immune-related ceRNA network, including 5 mRNAs, 24 miRNAs and 86 lncRNAs, was constructed. Memory B cells, activated dendritic cells, and regulatory T cells infiltration into tumors were negatively correlated with risk score, while the infiltration levels of macrophages M0, M1 and M2 were positively correlated with risk score. Conclusion: This study helped to better understand the molecular mechanisms of tumor immune response from the view of ceRNA hypothesis, and provided a novel prognostic signature for bladder cancer.

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The construction and analysis of tumor-infiltrating immune cell and ceRNA networks in recurrent soft tissue sarcoma

Cited by 42 publications

References 55 publications

DNA methylation patterns–based subtype distinction and identification of soft tissue sarcoma prognosis

DNA methylation patterns–based subtype distinction and identification of soft tissue sarcoma prognosis

The prediction of survival and analysis of microenvironment in patients with sarcoma

Construction of An Immune-Related CeRNA Network as a Prognostic Signature in Bladder Cancer

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