Background
Ovarian cancer is the most common lethal gynecologic malignancy in women worldwide, with an estimated 22,280 newly diagnosed cases and approximately 14,240 deaths in 2016 in the United States [
1]. Due to the lack of specific symptoms and effective screening tests, approximately 70 % of ovarian cancer patients have been in advanced-stage (stage III or IV) when they are firstly diagnosed, leading to the 5-year survival rate of less than 30 % [
2]. By contrast, patients who are diagnosed with early-stage (stage I or II) have a 5-year survival rate of up to 70–90 % [
2]. These data indicate the importance to identify the sensitive biomarkers to early distinguish the patients with different prognosis, aiming to determine optimal treatment strategies.
In the past years, remarkable achievements have been obtained in the investigation of prognostic markers for ovarian cancer. For instance, a 10-gene signature (
AEBP1,
COL11A1,
COL5A1,
COL6A2,
LOX,
POSTN,
SNAI2,
THBS2,
TIMP3, and
VCAN) has been validated to be associated with poor overall survival in patients with high-grade serous ovarian cancer [
3]. The presence of a
BRCA1 or
BRCA2 mutation is associated with a better prognosis in patients with invasive ovarian cancer [
4]. A recent study has found that suppression of
ABHD2 in OVCA420 cells increased phosphorylated p38 and ERK, platinum resistance, and side population cells, promoting a malignant phenotype and poor prognosis in serous ovarian cancer [
5]. Furthermore,
CD73 enhances ovarian tumor cell growth and expression of antiapoptotic BCL-2 family members, indicating a role of
CD73 as a prognostic marker of patient survival in high-grade serous ovarian cancer [
6]. Although the aforementioned genes have been shown to be correlated with the prognosis in ovarian cancer, their prognostic accuracy may be limited because the development of disease usually involves several genes and the interaction between them to form a complex pathway. Therefore, it is necessary to identify gene networks and pathways including multiple genes and their interactions, which can be achieved by Reactome functional interaction (FI) network construction as described previously [
7,
8].
In the present study, we aimed to construct the Reactome FIs network to analyze the gene signatures that was significantly related to ovarian cancer patient survival based on gene expression profiling data extracted from The Cancer Genome Atlas (TCGA) database.
Discussion
In this study, a total of 41 modules were obtained from the FI network based on the expression data in the BI dataset. Using MCL network clustering, superpc modeling and Cox PH analysis, two modules, modules 31 and 35, were identified to be significantly associated with prognosis of ovarian cancer patients. Seven genes were included in the two modules (31: DCLRE1A, EXO1, KIAA0101, KIN, PCNA, POLD3, POLD2; 35: DKK3, FABP3, IRF1, AIM2, GBP1, GBP2, IRF2). Furthermore, the genes in module 31 were related to DNA repair or replication, whereas the genes in module 35 were associated with immune and cytokine interferon mediated signaling pathways.
DCLRE1, also known as
SNM1A, belongs to a member of a small gene family that is characterized by a metallo-β-lactamase fold and an appended β-CASP domain that together are proposed to function as a DNA endonuclease to participate in DNA inter-strand cross-link repair [
21]. DNA cross-link repair is beneficial to maintain genomic stability and enables cells to survive DNA damage, contributing to less risk of tumorigenesis [
22]. However, recent studies indicate that the high efficiency of DNA cross-link repair may also promote the excessive proliferation of cells, driving tumor initiation and progression [
23‐
25]. Thus, down-regulation of DNA repair genes may be a promising target for anticancer therapy [
26], which has been demonstrated by the study of Wu et al. [
27]. Wu et al. have found that
DCLRE1A is significantly decreased by bufalin, which promotes lung cancer apoptosis [
27]. In addition, inhibition of DNA cross-link repair was also proved to reverse treatment resistance and improve the therapeutic efficacy [
28].
EXO1 encodes exonuclease and plays important roles in mismatch repair by resecting the damaged strand. Similar to
DCLRE1A,
Exo1 is also shown to be higher expressed in tumor tissues than that in the normal tissues [
29,
30]. A previous study has demonstrated that
FOXM1 facilitates DNA repair through regulating direct transcriptional target
EXO1 to protect ovarian cancer cells from cisplatin-mediated apoptosis, and attenuating
EXO1 expression by small interfering RNA augments the cisplatin sensitivity of ovarian cancer cells [
31].
POLD2 or
POLD3 are both the subunits of DNA polymerase delta that possesses both polymerase and 3′ to 5′ exonuclease activity and plays a critical role in DNA replication and repair [
32].
POLD2 was found to be increased in average 2.5- to almost 20-fold in moderately and poorly differentiated serous carcinomas of epithelial ovarian cancer, eventually leading to poor prognosis [
33].
Furthermore, proliferating cell nuclear antigen (
PCNA) is a ring-shaped homo-triomeric protein that functions as a necessary clamping platform to recruit numerous enzymes involved in DNA replication and repair, such as DNA polymerases, endonuclease, and DNA ligase, ultimately responsible for cell proliferation [
34]. Therefore,
PCNA is widely considered as a biomarker for cancer progression and prognosis. A recent study has found that
PCNA was expressed in 52.2 % of gastric cancer patients, and positive expression of
PCNA was significantly associated with poor 3-year disease-free survival (
p = 0.035) [
35].
KIAA0101 is a 15-kDa protein that has a conserved motif to bind to
PCNA via a yeast two-hybrid system and thus involved in the regulation of DNA repair and cell proliferation [
36]. Similar to
PCNA, overexpression of
KIAA0101 can promote growth and invasion of cancer cells [
37] and predict poor prognosis in cancer patients [
38,
39]. Collectively, these genes in the module 31 may play critical roles in the prognosis of ovarian cancer via regulation of DNA repair and cell proliferation.
In the module 35, 7 genes were included. Interferon regulatory factor 1 (
IRF1) is a member of the interferon regulatory transcription factor (IRF) family, which can cause the inhibition of cell proliferation and stimulation of apoptosis [
40].
IRF2 is a functional antagonist of
IRF1 and may act as an oncogene, promoting the formation and progression of cancer [
41]. A previous study has demonstrated that increased level of
IRF1 is associated with both increased progression-free and overall survival of patients with ovarian carcinoma, and
IRF1 is an independent predictor of platinum resistance and survival in high-grade serous ovarian carcinoma [
42]. Furthermore,
IRF1 directly mediates the interferon-γ (IFN-γ)-induced apoptosis via the activation of caspase-1 gene expression in IFN-γ-sensitive ovarian cancer cells [
43]. However, in a recent study of ovarian cancer, IRF-1 was identified to be up-regulated in ovarian cancer samples compared with healthy ovarian tissue although strong expression of IRF-1 predicted improved disease-free survival and overall survival [
44]. This finding may be attributed to a compensation or adaptation mechanism. Further study indicated the
IRF1 seemed to play a key role in the transcriptional activation of interferon-inducible guanylate binding proteins (
GBP1 and
GBP2) [
45], which subsequently induces T-lymphocyte immune response against the cancer cell spreading and proliferation [
46]. Therefore,
GBP1 and
GBP2 may be also tumor suppressor genes and associated with better prognosis [
47].
AIM2 is another human IFN-inducible protein, which forms the
AIM2 inflammasome with an adaptor protein ASC upon sensing foreign cytoplasmic double-stranded DNA [
48]. The activated
AIM2 inflammasome in macrophages promotes the proteolytic cleavage and secretion of pro-inflammatory cytokines (IL-1β and IL-18) through the activation of caspase-1, leading to cell senescence, apoptosis and preventing cancer progression [
49]. Thereby,
AMI2 may be also correlated with excellent prognosis [
50,
51].