Title:Construction and Analysis of mRNA and lncRNA Regulatory Networks Reveal the Key Genes Associated with Prostate Cancer Related Fatigue During Localized Radiation Therapy
Volume: 16
Issue: 2
Author(s): Yechen Wu*, Yaping Gui, Denglong Wu and Qiang Wu*
Affiliation:
- Department of Urology, Baoshan Branch, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, 201900,China
- Department of Urology, Tongji Hospital, Tongji University School of Medicine, Shanghai, 200065,China
Keywords:
Long non-coding RNAs, prostate cancer, fatigue, biomarker, co-expression analysis, genes.
Abstract:
Background: Localized radiation therapy is the first-line option for the treatment of nonmetastatic
prostate cancer (PCa). Previous studies revealed that long non-coding RNAs (lncRNAs) had
crucial roles in disease progression. However, the mechanisms of lncRNAs underlying prostate cancerrelated
fatigue remained largely unclear.
Objective: The present study aimed to uncover the key genes related to PCa related fatigue during localized
radiation therapy by constructing mRNA and lncRNA regulatory networks.
Methods: We analyzed GSE30174, which included 10 control samples and 40 PCa related fatigue
samples, to identify differently expressed lncRNAs and mRNAs in PCa related fatigue. A proteinprotein
interaction network was constructed to reveal the interactions among mRNAs. Co-expression
network analysis was applied to identify the key lncRNAs and reveal the functions of these lncRNAs in
PCa related fatigue.
Results and Discussion: This research found 1271 dysregulated mRNAs and 205 dysregulated
lncRNAs in PCa related fatigue using GSE30174. Bioinformatics analysis showed that PCa related fatigue
with mRNAs and lncRNAs were associated with inflammatory response and immune response
related biological processes. Furthermore, we constructed a PPI network and lncRNA co-expression
network related to fatigue in PCa. Of note, we observed that the dysregulated lncRNAs and mRNAs,
such as SEC61A2, ADCY6, LPAR5, COL7A1, ALB, COL1A1, SNHG1, LINC01215, LINC00926,
GNG4, LMO7, and COL4A6, in PCa related fatigue could predict the outcome of PCa patients.
Conclusions: This research could provide novel mechanisms underlying fatigue and identify new biomarkers
for the prognosis of PCa.
