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

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. Electronic supplementary material The online version of this article (10.1186/s12967-019-1908-1) contains supplementary material, which is available to authorized users.

High expression of stromal signatures correlated with macrophage infiltration, angiogenesis and poor prognosis in glioma microenvironment

“…The five stromal signatures including HSPG2, FOXF1, KDR, COL3A1 and SRPX2. Sun et al (2019) constructed the multicellular gene network between gliomas and macrophages, they found macrophage-related gene signature had good prognostic value for predicting resistance to targeted therapeutics and survival of glioma patients. We found the relationship between stromal signatures and macorphages signatures in gliomas.…”

High expression of GPNMB indicates an unfavorable prognosis in glioma: Combination of data from the GEO and CGGA databases and validation in tissue microarray

“…In addition to the effects of GPNMB on glioma angiogenesis, migration and invasion, several recent studies have suggested other potential mechanisms of GPNMB-mediated glioma progression which involve glioma-associated microglia/macrophage (GAM) polarization towards the M2 phenotype ( 55 – 57 ). GAM with the M2 phenotype has protumoral and immunosuppressive properties ( 58 , 59 ).…”

Prognostic Inflammasome‐Related Signature Construction in Kidney Renal Clear Cell Carcinoma Based on a Pan‐Cancer Landscape

“…Given the association between the signature genes and immune states, these genes might be related to immune therapeutic response of KIRC. Scholars have combined preclinical RNA-Seq data with clinical gene expression profile to establish predictive signatures of prognosis and therapeutic response for gliomas [52,53]. Inspired by their work, we would further use single-cell RNA-Seq data to explore the roles of the signature genes in immunotherapy.…”