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European Journal of Gynaecological Oncology  2020, Vol. 41 Issue (3): 455-461    DOI: 10.31083/j.ejgo.2020.03.5153
Original Research Previous articles | Next articles
Survival-associated transcriptome analysis in ovarian cancer
Xiaofeng Xu1, Xuan Zhou1, Yijin Wang1, 2, Tao Liu1, 3, Jian Fu4, Qian Yang5, Jun Wu1, Huaijun Zhou1()
1Department of Gynecology, Nanjing Drum Tower Hospital, Affiliated to Nanjing University Medical School, Nanjing, 210008, China
2Medical College, Southeast University , Nanjing, 210008, China
3Medical College, Nanjing University, Nanjing, 210008, China
4Department of Gynecology, Suqian People's Hospital of Nanjing Drum Tower Hospital Group, Suqian, 223800, China
5Department of Gynecology and Obstetrics, The pukou Hospital of Nanjing, The Fourth Affiliated Hospital of Nanjing Medical Uni-versity, Nanjing, 210031, China
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Abstract  

Purpose of investigation: Ovarian Cancer (OC) is one of the most lethal gynecologic cancers worldwide. Despite the standard treatment, including radical resection, systemic chemotherapy, and targeted drugs for patients, survival rates remain low. This study provides new ideas for the diagnosis and treatment of Ovarian Cancer. Material and Methods: We performed Kaplan-Meier analysis on the transcriptome of Ovarian Cancer based on RNA-Seq data from The Cancer Genome Atlas (TCGA). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) enrichment were used for pathway and functional enrichment. Protein-protein interaction (PPI) network was constructed and visualized by SRING and Cytoscape. Results: A total of 1693 genes associated with survival were identified. The Kyoto Encyclopedia of Genes and Genomes pathway and Gene Ontology enrichment analysis revealed that these selected genes were differently enriched in numerous functional pathways. The top ten hub genes (RIPK4, HSPA8, FOS, STAT1, CD40LG, FGF2, RAC1, CXCR4, PRPF19, and CXCL10) were identified in our PPI network. Three highly connected cluster modules were differently enriched in several functional pathways. Conclusion: These key biomarkers in Ovarian Cancer may have diagnostic and therapeutic value in the future.

Key words:  Ovarian Neoplasms      Survival Analysis      Transcriptome     
Submitted:  11 January 2019      Accepted:  09 April 2019      Published:  15 June 2020     
Fund: Natural Science Foundation of Jiangsu Province(BK20151096);Key Projects of National Health and Family Plan-ning Commission of Nanjing City(ZKX17015)
*Corresponding Author(s):  Huaijun Zhou     E-mail:  zhouhj2007@126.com

Cite this article: 

Xiaofeng Xu, Xuan Zhou, Yijin Wang, Tao Liu, Jian Fu, Qian Yang, Jun Wu, Huaijun Zhou. Survival-associated transcriptome analysis in ovarian cancer. European Journal of Gynaecological Oncology, 2020, 41(3): 455-461.

URL: 

https://ejgo.imrpress.com/EN/10.31083/j.ejgo.2020.03.5153     OR     https://ejgo.imrpress.com/EN/Y2020/V41/I3/455

Table 1  - KEGG pathway and GO enrichment analysis of genes in module cluster one.
Category Pathway ID Pathway description Count False discovery rate
GOTERM_BP GO.0006875 Cellular metal ion homeostasis 10 2.06E-07
GO.0007187 G-protein coupled receptor signaling pathway, coupled to cyclic nucleotide second messenger 8 2.06E-07
GO.0007204 Positive regulation of cytosolic calcium ion concentration 8 2.06E-07
GOTERM_CC GO.0019005 SCF ubiquitin ligase complex 7 1.71E-11
GO.0005829 Cytosol 14 0.00948
GO.0005829 Plasma membrane part 11 0.0354
GOTERM_MF GO.0048248 CXCR3 chemokine receptor binding 3 0.000117
GO.0004842 Ubiquitin-protein transferase activity 7 0.00017
GO.0001664 G-protein coupled receptor binding 5 0.00503
KEGG_PATHWAY hsa04080 Neuroactive ligand-receptor interaction 7 2.84E-05
hsa04062 Chemokine signaling pathway 6 2.86E-05
hsa04060 Cytokine-cytokine receptor interaction 6 0.000131
Figure 1.  - A total of 1693 genes associated with survival were identified. The survival curves for the first eight genes with the minimum P values were shown. (A) BATF2, (B) CD38, (C) NLRP12, (D) RXFP1, (E) LAMTOR5-AS, (F) ZNF561, (G) ITGAD, (H) SLC22A2.

Figure 2.  - GO and KEGG pathway enrichment analysis of genes associated with survival. (A) The top 5 enriched KEGG pathways of these genes. (B-D) The top 5 enriched GO terms of these genes.

Table 2  - KEGG pathway and GO enrichment analysis of genes in module cluster two.
Category Pathway ID Pathway description Count False discovery rate
GOTERM_BP GO.0000398 mRNA splicing, via spliceosome 10 3.81E-09
GO.0008380 RNA splicing 11 6.07E-09
GO.0006397 mRNA processing 11 3.17E-08
GOTERM_CC GO.0005681 Spliceosomal complex 6 0.000192
GO.0005905 Coated pit 4 0.00183
Ribonucleoprotein complex 8 0.00239
KEGG_PATHWA hsa03040 Spliceosome 8 5.09E-09
hsa04961 Endocrine and other factor-regulated
calcium reabsorption
3 0.00795
hsa04144 Endocytosis 4 0.0238
Figure 3.  - The survival curves for the ten hub genes. (A) RIPK4, (B) HSPA8, (C) FOS, (D) STAT1, (E) CD40LG, (F) FGF2, (G) RAC1, (H) CXCR4, (I) PRPF19, and (J) CXCL10.

Figure 4.  - Three highly connected clusters identified by MCODE algorithm. (A) cluster one, (B) cluster two, (C) cluster three.

Table 3  - KEGG pathway and GO enrichment analysis of genes in module cluster three.
Category Pathway ID Pathway description Count False discovery rate
GOTERM_BP GO.0002696 positive regulation of leukocyte activation 8 1.46E-08
GO.0051249 regulation of lymphocyte activation 8 8.40E-08
GO.0032653 regulation of interleukin-10 production 5 1.29E-07
GOTERM_CC GO.0009897 external side of plasma membrane 7 1.34E-07
GO.0009986 cell surface 8 5.08E-06
GO.0098552 side of membrane 6 9.95E-05
KEGG_PATHWAY hsa04514 Cell adhesion molecules (CAMs) 6 1.06E-07
hsa04060 Cytokine-cytokine receptor interaction 4 0.00298
hsa04640 Hematopoietic cell lineage 3 0.00298
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