Identify potential driver genes for PAX-FOXO1 fusion-negative rhabdomyosarcoma through frequent gene co-expression network mining

Xiaohui Zhan; Yusong Liu; Asha Jacob Jannu; Shaoyang Huang; Bo Ye; Wei Wei; Pankita H Pandya; Xiufen Ye; Karen E Pollok; Jamie L Renbarger; Kun Huang; Jie Zhang

doi:10.3389/fonc.2023.1080989

Identify potential driver genes for PAX-FOXO1 fusion-negative rhabdomyosarcoma through frequent gene co-expression network mining

Front Oncol. 2023 Jan 30:13:1080989. doi: 10.3389/fonc.2023.1080989. eCollection 2023.

Authors

Xiaohui Zhan¹, Yusong Liu², Asha Jacob Jannu³, Shaoyang Huang⁴, Bo Ye¹, Wei Wei¹, Pankita H Pandya⁵, Xiufen Ye², Karen E Pollok⁵, Jamie L Renbarger⁵, Kun Huang³, Jie Zhang⁶

Affiliations

¹ Department of Bioinformatics, School of Basic Medicine, Chongqing Medical University, Chongqing, China.
² College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, China.
³ Department of Biostatistics and Health Data Science, Indiana University, School of Medicine, Indianapolis, IN, United States.
⁴ Carmel High School, Indianapolis, IN, United States.
⁵ Department of Pediatrics, Indiana University, School of Medicine, Indianapolis, IN, United States.
⁶ Department of Medical and Molecular Genetics, Indiana University, School of Medicine, Indianapolis, IN, United States.

Abstract

Background: Rhabdomyosarcoma (RMS) is a soft tissue sarcoma usually originated from skeletal muscle. Currently, RMS classification based on PAX-FOXO1 fusion is widely adopted. However, compared to relatively clear understanding of the tumorigenesis in the fusion-positive RMS, little is known for that in fusion-negative RMS (FN-RMS).

Methods: We explored the molecular mechanisms and the driver genes of FN-RMS through frequent gene co-expression network mining (fGCN), differential copy number (CN) and differential expression analyses on multiple RMS transcriptomic datasets.

Results: We obtained 50 fGCN modules, among which five are differentially expressed between different fusion status. A closer look showed 23% of Module 2 genes are concentrated on several cytobands of chromosome 8. Upstream regulators such as MYC, YAP1, TWIST1 were identified for the fGCN modules. Using in a separate dataset we confirmed that, comparing to FP-RMS, 59 Module 2 genes show consistent CN amplification and mRNA overexpression, among which 28 are on the identified chr8 cytobands. Such CN amplification and nearby MYC (also resides on one of the above cytobands) and other upstream regulators (YAP1, TWIST1) may work together to drive FN-RMS tumorigenesis and progression. Up to 43.1% downstream targets of Yap1 and 45.8% of the targets of Myc are differentially expressed in FN-RMS vs. normal comparisons, which also confirmed the driving force of these regulators.

Discussion: We discovered that copy number amplification of specific cytobands on chr8 and the upstream regulators MYC, YAP1 and TWIST1 work together to affect the downstream gene co-expression and promote FN-RMS tumorigenesis and progression. Our findings provide new insights for FN-RMS tumorigenesis and offer promising targets for precision therapy. Experimental investigation about the functions of identified potential drivers in FN-RMS are in progress.

Keywords: copy number alteration; frequent co-expression network (fGCN); fusion-negative RMS (FN-RMS); fusion-positive RMS (FP-RMS); upstream regulator.

Grants and funding

This work was funded by Indiana University Precision Health Initiative to JZ and KH; The National Natural Science Foundation of China (grant number No. 32100530) to XZ, 2021 Chongqing Doctoral “Through Train” Scientific Research Project (grant number No. CSTB2022BSXM-JCX0065) to XZ and Youth project of science and technology research program of Chongqing Education Commission of China (grant No. KJQN202200455) to XZ.