Multicellular gene network analysis identifies a macrophage-related gene signature predictive of therapeutic response and prognosis of gliomas

Xiaoqiang Sun, Xiaoping Liu, Mengxue Xia, Yongzhao Shao, Xiaohua Douglas Zhang

Research output: Contribution to journalArticlepeer-review

26 Scopus citations

Abstract

Background: The tumor-associated microenvironment plays important roles in tumor progression and drug resistance. However, systematic investigations of macrophage-tumor cell interactions to identify novel macrophage-related gene signatures in gliomas for predicting patient prognoses and responses to targeted therapies are lacking. Methods: We developed a multicellular gene network approach to investigating the prognostic role of macrophage-tumor cell interactions in tumor progression and drug resistance in gliomas. Multicellular gene networks connecting macrophages and tumor cells were constructed from re-grouped drug-sensitive and drug-resistant samples of RNA-seq data in mice gliomas treated with BLZ945 (a CSF1R inhibitor). Subsequently, a differential network-based COX regression model was built to identify the risk signature using a cohort of 310 glioma samples from the Chinese Glioma Genome Atlas database. A large independent validation set of 690 glioma samples from The Cancer Genome Atlas database was used to test the prognostic significance and accuracy of the gene signature in predicting prognosis and targeted therapeutic response of glioma patients. Results: A macrophage-related gene signature was developed consisting of twelve genes (ANPEP, DPP4, PRRG1, GPNMB, TMEM26, PXDN, CDH6, SCN3A, SEMA6B, CCDC37, FANCA, NETO2), which was tested in the independent validation set to examine its prognostic significance and accuracy. The generation of 1000 random gene signatures by a bootstrapping scheme justified the non-random nature of the macrophage-related gene signature. Moreover, the discovered gene signature was verified to be predictive of the sensitivity or resistance of glioma patients to molecularly targeted therapeutics and outperformed other existing gene signatures. Additionally, the macrophage-related gene signature was an independent and the strongest prognostic factor when adjusted for clinicopathologic risk factors and other existing gene signatures. Conclusion: The multicellular gene network approach developed herein indicates profound roles of the macrophage-mediated tumor microenvironment in the progression and drug resistance of gliomas. The identified macrophage-related gene signature has good prognostic value for predicting resistance to targeted therapeutics and survival of glioma patients, implying that combining current targeted therapies with new macrophage-targeted therapy may be beneficial for the long-term treatment outcomes of glioma patients.

Original languageEnglish
Article number159
JournalJournal of Translational Medicine
Volume17
Issue number1
DOIs
StatePublished - May 16 2019

Bibliographical note

Funding Information:
X.S. was funded by the National Natural Science Foundation of China (11871070, 61503419), the Guangdong Nature Science Foundation (2016A030313234, 2014A030310355) and the Opening Project of Guangdong Province Key Laboratory of Computational Science at the Sun Yat-Sen University (2018003). Y.S. was funded by the NIH/NCI Grant 5P30CA016087. X.D.Z was supported by research Grants SRG2016-00083-FHS, FHS-CRDA-029-002-2017 and MYRG2018-00071-FHS at the University of Macau. The funding body played no role in the design of the study; collection, analysis, and interpretation of data; or writing of the manuscript.

Publisher Copyright:
© 2019 The Author(s).

Keywords

  • Biomarker
  • Drug resistance
  • Glioma
  • Macrophages
  • Multicellular gene network
  • Prognostic signature

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology (all)

Fingerprint

Dive into the research topics of 'Multicellular gene network analysis identifies a macrophage-related gene signature predictive of therapeutic response and prognosis of gliomas'. Together they form a unique fingerprint.

Cite this