Evidence map›Paper›PMID 36793601›Full record

ArticleFrontiers in oncology2023

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 and 2 more

Open access · goldAbstract read
In one paragraph

Article in Frontiers in oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact, top 99% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 0 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors at 4 institutions in 2 countries.

Xiaohui ZhanDepartment of Bioinformatics, School of Basic Medicine, Chongqing Medical University, Chongqing, China.
Yusong LiuCollege of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, China.
Asha Jacob JannuDepartment of Biostatistics and Health Data Science, Indiana University, School of Medicine, Indianapolis, IN, United States.
Shaoyang HuangCarmel High School, Indianapolis, IN, United States.
Bo YeDepartment of Bioinformatics, School of Basic Medicine, Chongqing Medical University, Chongqing, China.
Wei WeiDepartment of Bioinformatics, School of Basic Medicine, Chongqing Medical University, Chongqing, China.
Pankita H PandyaDepartment of Pediatrics, Indiana University, School of Medicine, Indianapolis, IN, United States.
Xiufen YeCollege of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, China.
Karen E PollokDepartment of Pediatrics, Indiana University, School of Medicine, Indianapolis, IN, United States.
Jamie L RenbargerDepartment of Pediatrics, Indiana University, School of Medicine, Indianapolis, IN, United States.
Kun HuangDepartment of Biostatistics and Health Data Science, Indiana University, School of Medicine, Indianapolis, IN, United States.
Jie ZhangDepartment of Medical and Molecular Genetics, Indiana University, School of Medicine, Indianapolis, IN, United States.
Indiana University – Purdue University Indianapolis · USChongqing Medical University · CNHarbin Engineering University · CNIndiana University School of Medicine

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

copy number alterationfrequent co-expression network (fGCN)fusion-negative RMS (FN-RMS)fusion-positive RMS (FP-RMS)upstream regulator

Identifiers

PMID36793601
PMCPMC9924292
OpenAlexW4318483019

What Socratic holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.