A Voxel-Based Radiographic Analysis Reveals the Biological Character of Proneural-Mesenchymal Transition in Glioblastoma

Introduction: Proneural and mesenchymal subtypes are the most distinct demarcated categories in classification scheme, and there is often a shift from proneural type to mesenchymal subtype in the progression of glioblastoma (GBM). The molecular characters are determined by specific genomic methods, however, the application of radiography in clinical practice remains to be further studied. Here, we studied the topography features of GBM in proneural subtype, and further demonstrated the survival characteristics and proneural-mesenchymal transition (PMT) progression of samples by combining with the imaging variables.Methods: Data were acquired from The Cancer Imaging Archive (TCIA, http://cancerimagingarchive.net). The radiography image, clinical variables and transcriptome subtype from 223 samples were used in this study. Proneural and mesenchymal subtype on GBM topography based on overlay and Voxel-based lesion-symptom mapping (VLSM) analysis were revealed. Besides, we carried out the comparison of survival analysis and PMT progression in and outside the VLSM-determined area.Results: The overlay of total GBM and separated image of proneural and mesenchymal subtype revealed a correlation of the two subtypes. By VLSM analysis, proneural subtype was confirmed to be related to left inferior temporal medulla, and no significant voxel was found for mesenchymal subtype. The subsequent comparison between samples in and outside the VLSM-determined area showed difference in overall survival (OS) time, tumor purity, epithelial-mesenchymal transition (EMT) score and clinical variables.Conclusions: PMT progression was determined by radiography approach. GBM samples in the VLSM-determined area tended to harbor the signature of proneural subtype. This study provides a valuable VLSM-determined area related to the predilection site, prognosis and PMT progression by the association between GBM topography and molecular characters.

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EMT-related gene expression is positively correlated with immunity and may be derived from stromal cells in osteosarcoma

PeerJ ◽

10.7717/peerj.8489 ◽

2020 ◽

Vol 8 ◽

pp. e8489

Author(s):

Yin-xiao Peng ◽

Bin Yu ◽

Hui Qin ◽

Li Xue ◽

Yi-jian Liang ◽

...

Keyword(s):

Gene Expression ◽

Stromal Cells ◽

Epithelial Mesenchymal Transition ◽

Related Gene ◽

Prognostic Information ◽

Poor Overall Survival ◽

Mesenchymal Transition ◽

Immune Activity ◽

Tumor Purity ◽

Microarray Datasets

Background Osteosarcoma is the most common type of bone cancer in children and young adults. Recent studies have shown a correlation between epithelial–mesenchymal transition (EMT)-related gene expression and immunity in human cancers. Here, we investigated the relationship among EMT, immune activity, stromal activity and tumor purity in osteosarcoma. Methods We defined EMT gene signatures and evaluated immune activity and stromal activity based on the gene expression and clinical data from three independent microarray datasets. These factors were evaluated by single sample Gene Set Enrichment Analyses and the ESTIMATE tool. Finally, we analyzed the key source of EMT gene expression in osteosarcoma using microarray datasets from the Gene Expression Omnibus and human samples that we collected. Results EMT-related gene expression was positively correlated with immune and stromal activity in osteosarcoma. Tumor purity was negatively correlated with EMT, immune activity and stromal cells. We further demonstrated that high EMT gene expression could significantly predict poor overall survival (OS) and recurrence-free survival (RFS) in osteosarcoma patients, while high immune activity cannot. However, combining these factors could have further prognostic value for osteosarcoma patients in terms of OS and RFS. Finally, we found that stromal cells may serve as a key source of EMT gene expression in osteosarcoma. Conclusion The results of this study reveal that the expression of EMT genes and immunity are positively correlated, but these signatures convey disparate prognostic information. Furthermore, the results indicate that EMT-related gene expression may be derived from stromal rather than epithelial cancer cells.

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