Author/Authors :
Wang Zhi-Hua نويسنده , SHU JIAN-JUN نويسنده , Xiang Dong نويسنده , Wei Wei نويسنده Department of Biostatistics, The University of Texas M.D. Anderson Cancer Center, United States of America , Dong Jun-Jie نويسنده Department of Orthopedics, First Affiliated Hospital of
Kunming Medical University, Kunming, 650032, Yunnan Province,
China , He Shao-Xuan نويسنده Department of Traumatology, The Second Affiliated Hospital
of Kunming Medical University, Kunming, 650011, Yunnan Province,
China , Guo Li-Min نويسنده Department of Traumatology, The Second Affiliated Hospital
of Kunming Medical University, Kunming, 650011, Yunnan Province,
China , Lv Jia نويسنده Department of Traumatology, The Second Affiliated Hospital
of Kunming Medical University, Kunming, 650011, Yunnan Province,
China , Kou Nan-Nan نويسنده Department of Traumatology, The Second Affiliated Hospital
of Kunming Medical University, Kunming, 650011, Yunnan Province,
China
Abstract :
Background Pathway analysis is the first choice for gaining
insight into the underlying biology of disease, as it reduces complexity
and increases explanatory power. Objectives The purpose of our paper was
to investigate dysregulated pathways between ankylosing spondylitis (AS)
patients as well as normal controls based on the pathway interaction
network (PIN) related analysis. Methods This is a case-control
bioinformatics analysis using already published microarray data of AS.
It was conducted in Kunming, China from October 2015 to June 2016. We
recruited the gene expression profile of AS from the ArrayExpress
database (http://www.ebi.ac.uk/arrayexpress/) with the accessing number
of E-GEOD-25101. E-GEOD-25101 existed on A-MEXP-1171 - Illumina
HumanHT-12 v3.0 Expression BeadChip Platform and was comprised of 32
samples (16 AS samples and 16 normal samples). Meanwhile, the
protein-protein interaction (PPI) data and pathway data were retrieved
from Search Tool for the retrieval of interacting genes/proteins
(STRING, http://string-db.org/) as well as Reactome databases,
respectively. Furthermore, according to the principal component analysis
(PCA) method, the seed pathway was selected by computing the activity
score for each pathway. A PIN was constructed dependent on the data and
Pearson correlation coefficient (PCC). Dysregulated pathways were
captured from the PIN by utilizing the seed pathway and the area under
the receiver operating characteristics curve (AUROC) index. Results The
PIN consisted of 1022 pathways and 7314 interactions, of which,
3’-UTR-mediated translational regulation was the seed pathway (absolute
change of activity score = 10.962). Starting from the seed pathway, a
minimum set of pathways with AUROC = 0.902 was extracted from the PIN.
Consequently, a total of 11 dysregulated pathways were identified for AS
compared with normal controls, such as L13a-mediated translational
silencing of Ceruloplasmin expression, GTP hydrolysis, as well as
joining of the 60S ribosomal subunit. Conclusions These results might be
available to provide potential biomarkers to diagnose AS as well as give
a hand to reveal pathological mechanism of this disease.
