Single-cell transcriptome-based multilayer network biomarker for predicting prognosis and therapeutic response of gliomas. (18th April 2019)

• Record Type: Journal Article
• Authors: Zhang, Ji; Guan, Meige; Wang, Qianliang; Zhang, Jiajun; Zhou, Tianshou; Sun, Xiaoqiang
• Is Part Of: Briefings in bioinformatics. Volume 21:Number 3(2020)
• Page Start: 1080
• Page End: 1097
• Publication Date: 2019-04-18
• Subjects: single-cell RNA-seq -- multilayer network biomarker -- tumor microenvironment -- cancer prognosis; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbz040
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: Occurrence and development of cancers are governed by complex networks of interacting intercellular and intracellular signals. The technology of single-cell RNA sequencing (scRNA-seq) provides an unprecedented opportunity for dissecting the interplay between the cancer cells and the associated microenvironment. Here we combined scRNA-seq data with clinical bulk gene expression data to develop a computational pipeline for identifying the prognostic and predictive signature that connects cancer cells and microenvironmental cells. The pipeline was applied to glioma scRNA-seq data and revealed a tumor-associated microglia/macrophage-mediated EGFR/ERBB2 feedback-crosstalk signaling module, which was defined as a multilayer network biomarker (MNB) to predict survival outcome and therapeutic response of glioma patients. We used publicly available clinical data sets from large cohorts of glioma patients to examine the prognostic significance and predictive accuracy of the MNB, which outperformed conventional gene biomarkers and other methods. Additionally, the MNB was found to be predictive of the sensitivity or resistance of glioma patients to molecularly targeted therapeutics. Moreover, the MNB was an independent and the strongest prognostic factor when adjusted for clinicopathologic risk factors and other existing gene signatures. The robustness of the MNB was further tested on additional data sets. Our study presents a promising scRNA-seq transcriptome-based multilayer network approach to elucidate the interactions between tumor cell and tumor-associated microenvironment and to identify prognostic and predictive signatures of cancer patients. The proposed MNB method may facilitate the design of more effective biomarkers for predicting prognosis and therapeutic resistance of cancer patients.