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Hypoxia Regulated Gene Network in Glioblastoma Has Special Algebraic Topology Structures and Revealed Communications Involving Warburg Effect and Immune Regulation

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Abstract

Hypoxia regulated genes (HRGs) formed a complex molecular interaction network (MINW), contributing to many aspects of glioblastoma (GBM) tumor biology. However, little is known about the intrinsic structures of the HRGs–MINW, mainly due to a lack of analysis tools to decipher MINWs. By introducing general hyper-geometric distribution, we obtained a statistically reliable gene set of HRGs (SR-HRGs) from several datasets. Next, MINWs were reconstructed from several independent GBM expression datasets. Algebraic topological analysis was performed to quantitatively analyze the amount of equivalence classes of cycles in various dimensions by calculating the Betti numbers. Persistent homology analysis of a filtration of growing networks was further performed to examine robust topological structures in the network by investigating the Betti curves, life length of the cycles. Random networks with the same number of node and edge and degree distribution were produced as controls. As a result, GBM–HRGs–MINWs reconstructed from different datasets exhibited great consistent Betti curves to each other, which were significantly different from that of random networks. Furthermore, HRGs–MINWs reconstructed from normal brain expression datasets exhibited topological structures significantly different from that of GBM–HRGs–MINWs. Analysis of cycles in GBM–HRGs–MINWs revealed genes that had clinical implications, and key parts of the cycles were also identified in reconstructed protein–protein interaction networks. In addition, the cycles are composed by genes involved in the Warburg effect, immune regulation, and angiogenesis. In summary, GBM–HRGs–MINWs contained abundant molecular interacting cycles in different dimensions, which are composed by genes involved in multiple programs essential for the tumorigenesis of GBM, revealing novel interaction diagrams in GBM and providing novel potential therapeutic targets.

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Abbreviations

ARACNe:

Algorithm for the reconstruction of accurate cellular networks

CI:

Confidential interval

CSC:

Cancer stem cell

DD:

Degree distribution

DPI:

Data processing inequality

D gene :

Connectivity degree of the gene

GBM:

Glioblastoma

GBM–HRGs–MINWs:

Molecular interaction network of hypoxia regulated gene reconstructed from GBM expression datasets

GSCs:

Glioblastoma stem like cells

HRGs:

Hypoxia regulated genes

MI:

Mutual information

MINW:

Molecular interaction network

N edge :

Number of edges

N node :

Number of nodes

NCgene :

Number of cycles involved for a gene

NCq-clique :

Number of cycles involved for a q dimensional clique (or simplex)

Normal-HRGs–MINWs:

Molecular interaction network of hypoxia regulated gene reconstructed from normal brain expression datasets

NSC:

Neural stem cells

PH:

Persistent homology

PPI:

Protein–protein interaction

PPI–MINWs:

Protein–protein interaction MINWs

PPI–HRGs–MINWs:

PPI–MINWs reconstructed with the HRGs

q-simplex:

q dimensional simplex (fully connected (q + 1) nodes)

q-cycle:

q dimensional cycle

q-sig-cycle:

q dimensional cycle whose life is statistically long (longer than the 95% bound of q-cycle life in random-MINWs)

q-end-cycles:

q dimensional cycle in the end point of filtration

RandomExp:

Random gene expression datasets

RandomSameDD-MINWs:

Random-MINWs with the same degree distribution (DD) to the corresponding MINWs

SR-HRGs:

Statistically reliable gene set of HRGs

SRExp:

Rank values ordered by expression levels for each gene

sub-cycles-3genes:

Sub-cycles containing three genes that are presented in both GBM–HRG–MINWs and PPI–HRGs–MINW

TF:

Transcription factor

Up-HRGs:

Up-regulated HRGs

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Funding

This study was funded by National Natural Science Foundation of China (Grant Numbers: 81502143, 51675411, 81671302) and foundation of fourth military medical university (2018JSTS05).

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  1. Xing-gang Mao and Xiao-yan Xue contributed equally to the study.

Authors and Affiliations

  1. Department of Neurosurgery, Xijing Hospital, Fourth Military Medical University, Xi’an, Shaanxi, People’s Republic of China

    Xing-gang Mao, Liang Li & Xiang Zhang

  2. Department of Pharmacology, School of Pharmacy, Fourth Military Medical University, Xi’an, Shaanxi, People’s Republic of China

    Xiao-yan Xue

  3. State Key Laboratory for Manufacturing System Engineering, School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, 710054, People’s Republic of China

    Ling Wang

  4. Department of Neurosurgery, Tangdu Hospital, Fourth Military Medical University, Xi’an, Shaanxi, People’s Republic of China

    Liang Wang

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  1. Xing-gang Mao
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Mao, Xg., Xue, Xy., Wang, L. et al. Hypoxia Regulated Gene Network in Glioblastoma Has Special Algebraic Topology Structures and Revealed Communications Involving Warburg Effect and Immune Regulation. Cell Mol Neurobiol 39, 1093–1114 (2019). https://doi.org/10.1007/s10571-019-00704-5

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