RNA-based ovarian cancer research from ‘a gene to systems biomedicine’ perspective

Figures & data

Table 1. The gene expression studies that identify biomarkers of ovarian cancer disease mechanisms.

Sample size and clinical type	Methods	Findings	Proposed mechanism	Reference
172 high-grade serous ovarian carcinoma and 12 normal ovarian tissues	RT-PCR	Compared with normal ovarian tissue, the level of ELOVL6 mRNA was significantly lower in high-grade serous ovarian carcinoma.	Low expression of ELOVL6 may correlate with the poor differentiation and drug resistance in high-grade serous ovarian carcinoma.	Li et al. 2016b
66 high-grade serous carcinoma patients effusions	RT-PCR	HuR is widely expressed in metastatic high-grade ovarian serous carcinoma at the mRNA level.	Higher HUR mRNA levels were significantly related to poor overall survival.	Davidson et al. 2016
44 ovarian tumor tissues and 7 fallopian tube tissues as a control	RT-PCR	Fibulin-5 was down-regulated in ovarian carcinomas compared with control tissues.	It was suggested that fibulin-5 act as a tumor suppressor by inhibiting by migration of tumor cells.	Heo et al. 2015
40 human primary epithelial ovarian carcinoma tissues	RT-PCR	CCNI and KDR mRNA expression was detected in all EOC tissues.	A significant positive correlation between the gene expression of KDR and CCNI.	Cybulski et al. 2015
52 benign ovarian tumors, 106 EOC samples and 30 specimens of normal ovaries	RT-PCR	PRSS3 mRNA levels were significantly higher in both epithelial ovarian cancers and benign tumor tissues relative to normal tissues.	PRSS3 overexpression can be used as a predictor of clinical outcome in patients with ovarian cancer and may therefore represent a new prognostic marker.	Ma et al. 2015
140 epithelial ovarian cancer and 20 normal ovarian tissue samples	RT-PCR	SLP-2 mRNA was expressed at higher levels in all epithelial ovarian cancer tissues compared to adjacent noncancerous tissues.	SLP-2 overexpression was associated with disease progression and poor survival outcomes for patients with epithelial ovarian cancer therefore SLP-2 may be regarded as a potential prognostic factor.	Sun et al. 2015
72 tumor, 15 para-carcinoma and 10 normal ovarian tissues	RT-PCR	IGF2 is frequently increased in human ovarian tissues.	High expression of IGF2 may be a significant prognostic factor for poor survival in ovarian cancer.	Dong et al. 2015
24 tumor and 9 healthy tissues	Microarray	CASP8 and BAD genes were under expressed in tumors biopsies vs. normal samples.	Down regulation of apoptosis signaling pathways in ovarian cancer.	Borhani et al. 2014
18 normal ovaries, 28 benign tumors, 55 serous borderline ovarian tumors, and 135 EOC	RT-PCR	GOLPH3 expression is up-regulated in cancerous tissues vs. control.	GOLPH3 will involve in ovarian cancer formation.	Ma et al. 2014
30 ovarian (15 high and 15 low stage) cancer samples and 9 healthy tissues	RT-PCR	ROR1 expression is relatively higher in cancerous tissues when compared to control.	ROR1 expression is associated with worse prognosis and have potential to be a prognostic marker in ovarian cancer.	Zhang et al. 2014a
53 ovary tumors and 30 healthy tissues	RT-PCR	Mammaglobin A, lipophilin A, lipophilin B and RYD5 gene expressions are considerably high in ovarian cancers relative to healthy tissues.	Lipophilin B over-expression has a potential to be a prognostic biomarker in ovarian cancer.	Bignotti et al. 2013
38 ovarian carcinoma and 21 non-diseased samples	RT-PCR	AKAP4 was generally expressed (89%) in diseased samples but not by controls.	AKAP4 gene as a candidate therapeutic target in ovarian cancer.	Agarwal et al. 2013
90 ovarian carcinoma tumors with 10 healthy samples	RT-PCR	KIF14 expression was raised in tumor samples.	KIF14 is a potential therapeutic target and prognostic marker for ovarian cancer.	Thériault et al. 2012
4 mm-thick paraffin sections of ovarian tumor biopsies contained 146 samples	RT-PCR	EPCR gene expression was detected the presence of in ascitic cell clusters of ovarian cancers.	EPCR expressed by ovarian cancer cells could be a possible biomarker of cancer.	Ducros et al.2012
32 ovarian serous papillary adenocarcinoma and 10 healthy ovarian tissues	RT-PCR	DKK1 was up-regulated in ovarian cancer tissues relative to healthy tissues.	DKK1 attended in tumor invasion and have a potential to be a prognostic biomarker in ovarian serous papillary adenocarcinomas.	Wang and Zhang 2011
78 ovarian tumor and 12 normal ovarian surface tissues	RT-PCR	CHD5 expression was down-regulated by at least twofold in 32 of 72 (41%) invasive EOC.	CHD5 is down-regulated and appears to be an adverse predictor candidate of ovarian cancer disease.	Wong et al. 2011
28 invasive and 7 low malignant potential serous ovarian tumors with the 7 serous benign ovarian tumors and 4 healthy tissues	Microarray	C6orf31, PDGFRA were down while SLPI and WNT7A were up regulated in LMP and invasive group when compared with other two control groups.	C6orf31, PDGFRA, SLPI and WNT7A genes may play a significant role in ovarian cancer.	Merritt et al. 2009
177 tumor samples	Microarray and RT-PCR	Microarray analysis identified a profile of 34 differentially expressed genes. High BTF4 and GCS expression is related to better prognosis.	BTF4 and GCS could be a potential prognostic marker in EOC.	Le Page et al. 2008
20 patients EOC tissues	RT-PCR	SPAG9 mRNA expression was detected in 90% of EOC tissues.	SPAG9 could be a potential target for the development of diagnostic and therapeutic methods in EOC.	Garg et al. 2007
76 epithelial ovarian cancer surgical specimens	RT-PCR	Different expressions profile of metastin and AXOR12 were detected in ovarian cancer samples.	Metastin/AXOR12 signaling might suppress the invasive ovarian cancer phenotype.	Hata et al. 2007
19 ovarian serous papillary carcinomas (OSPC) and 15 controls	Microarray	Kallikreins 6/7/8/10/11, claudin 3/4 and B7-H4 genes were significantly up-regulated in tumor samples.	Kallikreins 6/7/8/10/11, claudin 3/4 and B7-H4 genes were proposed candidate diagnostic and therapeutic biomarkers.	Bignotti et al. 2006

Table 2. The expression profiling studies of non-coding RNAs (i.e., miRNAs and lncRNAs) of the ovarian cancer.

Sample size and clinical type	Methods	Findings	Proposed mechanism	Reference
miRNAs
135 epithelial ovarian cancer patients and 54 benign ovarian tumor patients	TaqMan low density miRNA arrays, RT-PCR	expression levels of serum miRNA-20a, miRNA-125b, miRNA-126, miRNA-355, and let-7c were significantly different between malignant and benign ovarian tumor patients	miRNA-125b has the potential to become a novel biomarker for early diagnosis and prognosis prediction of epithelial ovarian cancer	Zhu et al. 2017
Ovarian cancer cell line A2780 and its cisplatin-resistant derivative CP70	Microarrays	miR-21-3p directed increased resistance to cisplatin in a range of ovarian cell lines	miR-21-3p can induce cisplatin resistance in ovarian tumours	Pink et al. 2015
A2780, HEY, SKOV3 cells and primary human ovarian cancer samples	TaqMan RT-PCR, Immunoblotting,	miR-181a was up-regulated and promotes epithelial-to-mesenchymal transition in ovarian cancer	miR-181a promotes TGF-β-mediated epithelial-to-mesenchymal transition via repression of its functional target, Smad7	Parikh et al. 2014
33 ovarian papillary serous cystadeno-carcinoma and 7 normal ovarian tissues, OVCAR3 and A2780 lines	Taqman RT-PCR and RT-PCR	miR-199a and miR-125b were downregulated in ovarian cancer tissues and cell lines	provides a rationale for new therapeutic approach to suppress tumor angiogenesis using miR-199a and miR-125b	He et al. 2013
98 epithelial ovarian cancer tissues and 15 adjacent normal epithelial tissues	TaqMan RT-PCR	miR-100 is downregulated in human epithelial ovarian cancer	low miR-100 expression may be a poor prognostic factor	Peng et al. 2012
4 cell lines 28 serous epithelial ovarian cancer and 28 healthy donors	miRNA expression profiling	miR-200a, miR-200b and miR-200c were highly overexpressed	identified serum microRNAs able to discriminate patients with high grade serous epithelial ovarian cancer	Kan et al. 2012
CD133(+) spheroid-forming subpopulation of the OVCAR3 human ovarian cancer cell line	miRNA microarray and RT-PCR	miR-205, miR-146a, miR-200a, miR-200b were significantly up-regulated while miR-205, miR-146a, miR-200a, miR-200b, and miR-3 were significantly down-regulated	indicate that dysregulation of miRNA may play a role in the stem cell-like properties of ovarian cancer stem cells	Nam et al. 2012
SKOV3 and SKOV3/CIS cells	miRNA microarrays	miR-130a was upregulated in SKOV3/CIS compared to the parental SKOV3 cells	provides important information for the development of targeted gene therapy	Yang et al. 2012
83 human epithelial ovarian cancer samples	RT-PCR	miR-34a expression is decreased in 100%	miR-34 family plays an important role in EOC pathogenesis	Corney et al. 2010
15 normal ovarian tissue sections and 69 malignant tissues	miRNA microarray technologies	most significantly overexpressed miRNAs were miR-200a, miR-141, miR-200c, and miR-200b, whereas miR-199a, miR-140, miR-145, and miR-125b1 were among the most down-modulated miRNAs. miR-21, miR-203, and miR-205, up-modulated in ovarian carcinomas compared with normal tissues	indicate that miRNAs might play a role in the pathogenesis of human epithelial ovarian cancer and identify altered miRNA gene methylation as a possible epigenetic mechanism involved in their aberrant expression	Iorio et al. 2007
A2780, 3AO, OVCAR3, SKOV3, and HO-8910 cell lines and 11 pathologically confirmed ovarian cancer tissues	qRT-PCR, MTT assay, Western blotting, Knockout™ RNAi System, Immunohistochemical staining	HOTAIR overexpression promoted cell cycle progression (and thus cell proliferation) by activating the wnt/β-catenin signaling pathway	HOTAIR might be a potent therapeutic target for ovarian cancer treatment	Li et al. 2016a
109 ovarian cancer and 45 normal ovarian tissue samples and cell lines (SKOV3, IGROV1, A2780, OVCAR3 and HOSE 6.3)	qRT-PCR	CCAT2 in ovarian cancer tissues and cell lines were significantly higher compared with values obtained for adjacent non-tumor tissues and normal ovarian epithelial cells	CCAT2 is a novel factor involved in ovarian cancer progression, and constitutes a potential prognostic biomarker and therapeutic target for patients with ovarian cancer.	Huang et al. 2016
Ovarian cancer tissues and adjacent normal tissues and HO-8910 and OVCAR3 cells	qRT-PCR, western blot assays	AB073614 expression was significantly up-regulated in 85.3% (64/75) cancerous tissues compared with normal counterparts	lncRNA AB073614 acts as a functional oncogene in ovarian cancer development	Cheng et al. 2015
SKOV-3, HO8910 and highly metastatic sublines SKOV-3.ip1, HO8910-PM	microarray analysis and western blotting	ANRIL was highly expressed in both SK-OV-3.ip1 cells and HO8910-PM cells	ANRIL plays an important role in serous ovarian cancer invasion/metastasis and could represent a novel biomarker	Qiu et al. 2015
Epithelial ovarian cancer tissues and SKOV3.ip1, HO8910-PM, and HEY-A8 cell lines	A series of in vitro and in vivo assays	HOTAIR expression was elevated in epithelial ovarian cancer tissues, and HOTAIR levels were highly positively correlated with the FIGO stage	HOTAIR plays a vital role in epithelial ovarian cancer metastasis and could represent a novel prognostic marker and potential therapeutic target	Qiu et al. 2014
SKOV3 and SKOV3.ip1 cell lines	Microarray and RT-qPCR	MALAT1, H19, UCA1, CCAT1, LOC645249, LOC100128881, and LOC100292680 were deregulated	indicates that some lncRNAs might exert a partial or key role in epithelial ovarian cancer metastasis	Liu et al. 2013
Ovarian cancer cell lines (SKOV3 and OVCAR5), tumor tissues and their normal counterpart	Microarray and qPCR	LSINCT5 is overexpressed in ovarian cancer cell lines and tumor tissues, relative to their normal counterpart.	a novel lncRNA LSINCT5 enhances cellular proliferation and may play a significant role in multiple processes in ovarian cancer	Silva et al. 2011

Figure 1. Ovarian cancer associated miRNAs and their target in uncontrolled cell proliferation, abnormal apoptosis, angiogenesis, and inflammation processes.

Table 3. Data resources for miRNA and lncRNA annotation and their targets interaction.

Database	Web link	Description	Reference
miRNA
miRBase	http://www.mirbase.org/	Known functional miRNAs	Kozomara and Griffiths-Jones 2013
MiRDB	http://mirdb.org/miRDB/	miRNA-target predictions	Wang 2008
miRecords	http://mirecords.biolead.org/	Experimentally verified miRNA-target interactions	Xiao et al. 2009
miRTarBase	http://mirtarbase.mbc.nctu.edu.tw/	Experimentally verified miRNA-target interactions	Hsu et al. 2014
starBase	http://starbase.sysu.edu.cn/	miRNA-target predictions	Li et al. 2013
miR2Disease	http://www.mir2disease.org/	Experimentally verified miRNA-target interactions and disease association	Jiang et al. 2009
miRCancer	http://mircancer.ecu.edu/	microRNA Cancer Association Database	Xie et al. 2013
TransmiR	http://www.cuilab.cn/transmir	TF-miRNA interactions	Wang et al. 2010
HMDD	http://www.cuilab.cn/hmdd	Experiment-supported evidence for human miRNA and disease associations	Li et al. 2014
microRNA.org	http://www.microrna.org/	miRNA-target interactions	Betel et al. 2008
ChIPBase	http://deepbase.sysu.edu.cn/	TF-miRNA interactions	Yang et al. 2013b
TargetScan	http://www.targetscan.org/	miRNA-target predictions based on sequence complementarity	Agarwal et al. 2015
PicTar	http://pictar.mdc-berlin.de/	miRNA-target predictions based on sequence complementarity	Chen and Rajewsky 2006
RNAhybrid	http://bibiserv.techfak.uni-bielefeld.de/rnahybrid	miRNA-target predictions based on calculations of mRNA secondary structure	Krüger and Rehmsmeier 2006
StarMir	http://sfold.wadsworth.org/	miRNA-target predictions based on calculations of mRNA secondary structure	Rennie et al. 2014
TarBase	http://diana.cslab.ece.ntua.gr/DianaToolsNew/index.php?r=tarbase	Experimentally verified miRNA-target interactions	Vergoulis et al. 2012
miRGator	http://genome.ewha.ac.kr/	miRNA expression	Cho et al. 2013
miRGen	http://www.microrna.gr/mirgen	TF-miRNA interactions	Alexiou et al. 2009
miRNApath	http://lgmb.fmrp.usp.br/mirnapath/	Pathway analyses	Chiromatzo et al. 2007
LNCipedia.org	http://lncipedia.org/	Human annotated lncRNAs	Volders et al. 2015
lncRNAdb	http://www.lncrnadb.org/	Annotated lncRNAs	Quek et al. 2014
NONCODE	http://www.noncode.org/	An integrated knowledge about non-coding RNA	Zhao et al. 2016
lncRNOme	http://genome.igib.res.in/lncRNome/	Resource about lncRNAs in Human.	Bhartiya et al. 2013
NPInter	http://www.bioinfo.org/NPInter/	Interactions between noncoding RNAs and biomolecules	Yuan et al. 2014
ChIPBase	http://deepbase.sysu.edu.cn/	TF-lncRNA/lincRNA regulatory relationships	Yang et al. 2013b
starBase	(http://starbase.sysu.edu.cn/)	lncRNA and target biomolecules interactions	Li et al. 2013
lncBase	http://diana.imis.athena-innovation.gr/	Experimental and predictive miRNA targets on lncRNAs	Paraskevopoulou et al. 2013
lncRNA2Target	http://mlg.hit.edu.cn/lncrna2target/	Resource about differentially expressed genes regulated by lncRNAs	Jiang et al. 2015
lncRNADisease	http://www.cuilab.cn/lncrnadisease	Experimentally supported lncRNA-disease and predicting novel lncRNA-disease association data	Chen et al. 2013
NRED	http://jsm-research.imb.uq.edu.au/nred	ncRNA Expression Database	Dinger et al. 2009
lncRNAtor	http://lncrnator.ewha.ac.kr/	Expression profile, interacting (binding) protein, integrated sequence curation of lncRNAs	Park et al. 2014

Figure 2. RNA based systems biomedicine approach to understand disease pathogenesis and identify biomarkers.

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