scholarly journals Endosialin and Associated Protein Expression in Soft Tissue Sarcomas: A Potential Target for Anti-Endosialin Therapeutic Strategies

Sarcoma ◽  
2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Daniel J. O’Shannessy ◽  
Hongyue Dai ◽  
Melissa Mitchell ◽  
Shane Huntsman ◽  
Stephen Brantley ◽  
...  
Keyword(s):  
Gene Expression ◽  
Soft Tissue ◽  
Protein Expression ◽  
Soft Tissue Sarcomas ◽  
Tissue Expression ◽  
Tissue Type ◽  
Expression Data ◽  
Interacting Protein ◽  
Undifferentiated Sarcoma ◽  
Tumor Types

Endosialin (CD248, TEM-1) is expressed in pericytes, tumor vasculature, tumor fibroblasts, and some tumor cells, including sarcomas, with limited normal tissue expression, and appears to play a key role in tumor-stromal interactions, including angiogenesis. Monoclonal antibodies targeting endosialin have entered clinical trials, including soft tissue sarcomas. We evaluated a cohort of 94 soft tissue sarcoma samples to assess the correlation between gene expression and protein expression by immunohistochemistry for endosialin and PDGFR-β, a reported interacting protein, across available diagnoses. Correlations between the expression of endosialin and 13 other genes of interest were also examined. Within cohorts of soft tissue diagnoses assembled by tissue type (liposarcoma, leiomyosarcoma, undifferentiated sarcoma, and other), endosialin expression was significantly correlated with a better outcome. Endosialin expression was highest in liposarcomas and lowest in leiomyosarcomas. A robust correlation between protein and gene expression data for both endosialin and PDGFR-βwas observed. Endosialin expression positively correlated with PDGFR-βand heparin sulphate proteoglycan 2 and negatively correlated with carbonic anhydrase IX. Endosialin likely interacts with a network of extracellular and hypoxia activated proteins in sarcomas and other tumor types. Since expression does vary across histologic groups, endosialin may represent a selective target in soft tissue sarcomas.

scholarly journals Machine learning analysis of gene expression data reveals novel diagnostic and prognostic biomarkers and identifies therapeutic targets for soft tissue sarcomas

2019 ◽  
Vol 15 (2) ◽  
pp. e1006826 ◽  
Author(s):  
David G. P. van IJzendoorn ◽  
Karoly Szuhai ◽  
Inge H. Briaire-de Bruijn ◽  
Marie Kostine ◽  
Marieke L. Kuijjer ◽  
...  
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Soft Tissue ◽  
Gene Expression Data ◽  
Therapeutic Targets ◽  
Soft Tissue Sarcomas ◽  
Prognostic Biomarkers ◽  
Expression Data ◽  
Learning Analysis

scholarly journals Transcriptome-wide Mendelian randomization study prioritising novel tissue-dependent genes for glioma susceptibility

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jamie W. Robinson ◽  
Richard M. Martin ◽  
Spiridon Tsavachidis ◽  
Amy E. Howell ◽  
Caroline L. Relton ◽  
...  
Keyword(s):  
Gene Expression ◽  
Association Studies ◽  
Tissue Expression ◽  
Tissue Type ◽  
Mendelian Randomisation ◽  
Genome Wide Association Studies ◽  
Causal Pathways ◽  
Genome Wide ◽  
Glioma Risk ◽  
Brain Tissues

AbstractGenome-wide association studies (GWAS) have discovered 27 loci associated with glioma risk. Whether these loci are causally implicated in glioma risk, and how risk differs across tissues, has yet to be systematically explored. We integrated multi-tissue expression quantitative trait loci (eQTLs) and glioma GWAS data using a combined Mendelian randomisation (MR) and colocalisation approach. We investigated how genetically predicted gene expression affects risk across tissue type (brain, estimated effective n = 1194 and whole blood, n = 31,684) and glioma subtype (all glioma (7400 cases, 8257 controls) glioblastoma (GBM, 3112 cases) and non-GBM gliomas (2411 cases)). We also leveraged tissue-specific eQTLs collected from 13 brain tissues (n = 114 to 209). The MR and colocalisation results suggested that genetically predicted increased gene expression of 12 genes were associated with glioma, GBM and/or non-GBM risk, three of which are novel glioma susceptibility genes (RETREG2/FAM134A, FAM178B and MVB12B/FAM125B). The effect of gene expression appears to be relatively consistent across glioma subtype diagnoses. Examining how risk differed across 13 brain tissues highlighted five candidate tissues (cerebellum, cortex, and the putamen, nucleus accumbens and caudate basal ganglia) and four previously implicated genes (JAK1, STMN3, PICK1 and EGFR). These analyses identified robust causal evidence for 12 genes and glioma risk, three of which are novel. The correlation of MR estimates in brain and blood are consistently low which suggested that tissue specificity needs to be carefully considered for glioma. Our results have implicated genes yet to be associated with glioma susceptibility and provided insight into putatively causal pathways for glioma risk.

scholarly journals Feasibility of using gene expression analysis to study canine soft tissue sarcomas

2010 ◽  
Vol 21 (11-12) ◽  
pp. 577-582 ◽  
Author(s):  
Jennifer A. Mahoney ◽  
Julie C. Fisher ◽  
Stacey A. Snyder ◽  
Marlene L. Hauck
Keyword(s):  
Gene Expression ◽  
Soft Tissue ◽  
Expression Analysis ◽  
Gene Expression Analysis ◽  
Soft Tissue Sarcomas

scholarly journals DNA Methylation Profiling for Diagnosing Undifferentiated Sarcoma with Capicua Transcriptional Receptor (CIC) Alterations

2020 ◽  
Vol 21 (5) ◽  
pp. 1818 ◽  
Author(s):  
Evelina Miele ◽  
Rita De Vito ◽  
Andrea Ciolfi ◽  
Lucia Pedace ◽  
Ida Russo ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Soft Tissue ◽  
Abdominal Wall ◽  
Current Knowledge ◽  
Soft Tissue Sarcomas ◽  
Molecular Techniques ◽  
Round Cell ◽  
Undifferentiated Sarcoma ◽  
Dna Methylation Profiling ◽  
Methylation Profiling

Undifferentiated soft tissue sarcomas are a group of diagnostically challenging tumors in the pediatric population. Molecular techniques are instrumental for the categorization and differential diagnosis of these tumors. A subgroup of recently identified soft tissue sarcomas with undifferentiated round cell morphology was characterized by Capicua transcriptional receptor (CIC) rearrangements. Recently, an array-based DNA methylation analysis of undifferentiated tumors with small blue round cell histology was shown to provide a highly robust and reproducible approach for precisely classifying this diagnostically challenging group of tumors. We describe the case of an undifferentiated sarcoma of the abdominal wall in a 12-year-old girl. The patient presented with a voluminous mass of the abdominal wall, and multiple micro-nodules in the right lung. The tumor was unclassifiable with current immunohistochemical and molecular approaches. However, DNA methylation profiling allowed us to classify this neoplasia as small blue round cell tumor with CIC alterations. The patient was treated with neoadjuvant chemotherapy followed by complete surgical resection and adjuvant chemotherapy. After 22 months, the patient is disease-free and in good clinical condition. To put our experience in context, we conducted a literature review, analyzing current knowledge and state-of-the-art diagnosis, prognosis, and clinical management of CIC rearranged sarcomas. Our findings further support the use of DNA methylation profiling as an important tool to improve diagnosis of non-Ewing small round cell tumors.

scholarly journals Putative biomarkers for predicting tumor sample purity based on gene expression data

BMC Genomics ◽  
2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Yuanyuan Li ◽  
David M. Umbach ◽  
Adrienna Bingham ◽  
Qi-Jing Li ◽  
Yuan Zhuang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Supervised Machine Learning ◽  
Tumor Type ◽  
Expression Data ◽  
Expression Levels ◽  
Gene Set ◽  
Tumor Purity ◽  
Tumor Types ◽  
Cancerous Cells

Abstract Background Tumor purity is the percent of cancer cells present in a sample of tumor tissue. The non-cancerous cells (immune cells, fibroblasts, etc.) have an important role in tumor biology. The ability to determine tumor purity is important to understand the roles of cancerous and non-cancerous cells in a tumor. Methods We applied a supervised machine learning method, XGBoost, to data from 33 TCGA tumor types to predict tumor purity using RNA-seq gene expression data. Results Across the 33 tumor types, the median correlation between observed and predicted tumor-purity ranged from 0.75 to 0.87 with small root mean square errors, suggesting that tumor purity can be accurately predicted υσινγ expression data. We further confirmed that expression levels of a ten-gene set (CSF2RB, RHOH, C1S, CCDC69, CCL22, CYTIP, POU2AF1, FGR, CCL21, and IL7R) were predictive of tumor purity regardless of tumor type. We tested whether our set of ten genes could accurately predict tumor purity of a TCGA-independent data set. We showed that expression levels from our set of ten genes were highly correlated (ρ = 0.88) with the actual observed tumor purity. Conclusions Our analyses suggested that the ten-gene set may serve as a biomarker for tumor purity prediction using gene expression data.

scholarly journals Gene expression identifies heterogeneity of metastatic propensity in high-grade soft tissue sarcomas

Cancer ◽  
2012 ◽  
Vol 118 (17) ◽  
pp. 4235-4243 ◽  
Author(s):  
Keith M. Skubitz ◽  
Princy Francis ◽  
Amy P. N. Skubitz ◽  
Xianghua Luo ◽  
Mef Nilbert
Keyword(s):  
Gene Expression ◽  
Soft Tissue ◽  
Soft Tissue Sarcomas ◽  
High Grade

scholarly journals GEDS: A Gene Expression Display Server for mRNAs, miRNAs and Proteins

Cells ◽  
2019 ◽  
Vol 8 (7) ◽  
pp. 675 ◽  
Author(s):  
Xia ◽  
Liu ◽  
Zhang ◽  
Guo
Keyword(s):  
Gene Expression ◽  
Cell Lines ◽  
Gene Expression Data ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Cancer Cell Line ◽  
Tissue Expression ◽  
The Cancer Genome Atlas ◽  
Expression Data ◽  
Protein Levels

High-throughput technologies generate a tremendous amount of expression data on mRNA, miRNA and protein levels. Mining and visualizing the large amount of expression data requires sophisticated computational skills. An easy to use and user-friendly web-server for the visualization of gene expression profiles could greatly facilitate data exploration and hypothesis generation for biologists. Here, we curated and normalized the gene expression data on mRNA, miRNA and protein levels in 23315, 9009 and 9244 samples, respectively, from 40 tissues (The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GETx)) and 1594 cell lines (Cancer Cell Line Encyclopedia (CCLE) and MD Anderson Cell Lines Project (MCLP)). Then, we constructed the Gene Expression Display Server (GEDS), a web-based tool for quantification, comparison and visualization of gene expression data. GEDS integrates multiscale expression data and provides multiple types of figures and tables to satisfy several kinds of user requirements. The comprehensive expression profiles plotted in the one-stop GEDS platform greatly facilitate experimental biologists utilizing big data for better experimental design and analysis. GEDS is freely available on http://bioinfo.life.hust.edu.cn/web/GEDS/.

Gene-expression profiling to classify soft-tissue sarcomas

The Lancet ◽  
2002 ◽  
Vol 359 (9314) ◽  
pp. 1263-1264 ◽  
Author(s):  
Luc Y Dirix ◽  
Allan T van Oosterom
Keyword(s):  
Gene Expression ◽  
Soft Tissue ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Soft Tissue Sarcomas

Aggressive, undifferentiated sarcoma with widespread metastasis in a six-month-old Neopolitan mastiff

1996 ◽  
Vol 32 (2) ◽  
pp. 97-101 ◽  
Author(s):  
NA Sanders ◽  
RL Kerlin ◽  
DM Dambach
Keyword(s):  
Soft Tissue ◽  
Lymph Nodes ◽  
Rapid Growth ◽  
Soft Tissue Sarcomas ◽  
Post Mortem ◽  
Cell Of Origin ◽  
Undifferentiated Sarcoma ◽  
Cervical Mass ◽  
Poor Differentiation

A six-month-old Neopolitan mastiff presented for a rapidly growing cervical mass. Undifferentiated sarcoma was diagnosed at post mortem based on histopathology and immunohistochemistry. Metastases to mediastinum, pleura, lungs, liver, kidneys, omentum, mesentery, and multiple lymph nodes were present. Soft-tissue sarcomas are reported infrequently in children and young dogs. The cell of origin often is difficult to determine due to poor differentiation and rapid growth of these neoplasms.

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