Abstract
Background
Ovarian cancer (OCA), the fifth leading deaths cancer to women, is
famous for its low survival rate in epithelial ovarian cancer cases,
which is very complicated and hard to be diagnosed from asymptomatic
nature in the early stage. Thus, it is urgent to develop an effective
genetic prognostic strategy.
Methods
Current study using the Database for Annotation, Visualization and
Integrated Discovery tool for the generation and analysis of
quantitative gene expression profiles; all the annotated gene and
biochemical pathway membership realized according to shared categorical
data from Pathway and Kyoto Encyclopedia of Genes and Genomes;
correlation networks based on current gene screening actualize by
Weighted correlation network analysis to identify therapeutic targets
gene and candidate bio-markers.
Results
3095 differentially expressed genes were collected from genome
expression profiles of OCA patients (n = 53, 35 advanced, 8 early and
10 normal). By pathway enrichment, most genes showed contribution to
cell cycle and chromosome maintenance.1073 differentially expression
genes involved in the 4 dominant network modules are further generated
for prognostic pattern establish, we divided a dataset with random OCA
cases (n = 80) into 3 groups efficiently (p = 0.0323, 95 % CIs in
Kaplan-Meier). Finally, 6 prognosis related genes were selected out by
COX regression analysis, TFCP2L1 related to cancer-stem cell, probably
contributes to chemotherapy efficiency.
Conclusions
Our study presents an integrated original model of the differentially
expression genes related to ovarian cancer progressing, providing the
identification of genes relevant for its pathological physiology which
can potentially be new clinical markers.
Electronic supplementary material
The online version of this article (doi:10.1186/s13048-015-0176-9)
contains supplementary material, which is available to authorized
users.
Keywords: Ovarian cancer, Gene expression profile, Genetic prognostic
pattern, Candidate biomarkers
Background
As the fifth leading cause of cancer related to deaths, ovarian cancer
just have only 30–40 % with a five year survival rate in women [[35]1].
In American, there were 219,800 new cases and around 142,700 women
succumb to this fatal disease in 2014 [[36]2], and the lifetime risk of
epithelial ovarian cancer is one of 72 women [[37]3], what is much more
worse in developing countries [[38]4, [39]5]. Ovarian tumors can be
classified into epithelial (60 %), germ cell (30 %) and sex-cord
stromal tumors (8 %) [[40]6], among which the vast majority of
malignant ovarian cancers (80 %-85 %) [[41]7] began in the ovarian
epithelium. Unfortunately, ovarian cancer is highly asymptomatic at
early stages, the epithelial ovarian cancer (EOC) is hard to be
diagnosed due to the multitude of clinical and histopathological
aspects [[42]8], lack of precursor lesions [[43]9] and their evolution
[[44]10], thus most patients with EOC are diagnosed at advanced stage
and have a poor prognosis. It is reported that only 30 % in stage I or
II could be cured by surgery with five-year survival rate of 90 %, in
contrast, EOC in stages III or IV could spread throughout widely with
5-year survival of less than 30 % [[45]11, [46]12]. Besides, malignant
ovarian germ cell tumor is hard to be distinguished by medical image
and always happen to young women below the age of 20.
Recently, more and more reports declared that ovarian cancer has home
history, about 10 % of EOC cases are related to inherited genes like
BCRA1and BCRA2 [[47]13, [48]14], and ovarian germ cell tumors can be
cancerous or non-cancerous tumors depend on genome difference
[[49]15–[50]17]. Actually, cancer is a disease single of genomes or
networks of molecular interaction and control, advanced ovarian cancer
with a high relapse rate related to the acquirement of chemo
resistance, due to it’s ability to converting the tumor cells back into
a stem cell-like state. Luckily, several existing drugs [[51]18,
[52]19, [53]14] can attack the pathway and reverse the cellular
transformation, thus ‘re-sensitizing’ the tumor to treatment. For these
reasons, it is urgently to develop effective strategies to stratify
early and advance stage patients.
Correlation networks are increasingly being used in bioinformatics
applications like generating modules (clusters) of highly correlated
genes, summarizing such modules using an intra-modular hub gene or the
eigengene, and analysis of modules’ networks or calculating module
membership measures, which can be used to identify candidate biomarkers
or therapeutic targets. Currently, we use weighted correlation network
analysis (WGCNA) to correlate networks facilitate network based
standardized and screened gene, aim at establish an a feasible genetic
method to prognostic of outcome of individual’s ovarian carcinoma,
especially the bottleneck problem of epithelial ovarian cancer and
malignant ovarian germ cell cancer, therefore, making an advantage to
choose the most suitable chemotherapy for a certain patient.
Materials
As the paper did not involve any human or animal study, there was no
need for any ethical approval.
Literature selecting and building
For analysis of differential genome-wide expression between patients in
different cancer stages, we selected [54]GSE12470 dataset [[55]20],
including gene array data from 35 advanced ovarian cancer patients, 8
early ovarian cancer patients and 10 non-cancer persons.
For prognostic analysis on different types of ovarian cancer,
[56]GSE14764 data set [[57]21] was selected, which includes genome-wide
expression data from 80 ovarian cancer patients. In addition,
[58]GSE63885 dataset [[59]22] and [60]GSE49997 dataset [[61]23] were
tested to verify the established prognostic analysis model, these two
data sets are consisted of genome-wide expression data from 101
candidates with differential ovarian cancer and mRNA expression data
from 204 candidates suffered from ovarian cancer respectively.
Database search
Gene Expression Omnibus [[62]24] functional genomics repository was
searched for the relationships between the probe in the platforms used
in the selected datasets and corresponding genes. One probe set
(contain several probes, N ≥1) matching one target gene, therefore
average value [[63]25] of different corresponding probe IDs is
represent one gene expression level. Skew distribution of gene
expression was transformed to skew normal distribution by log2
transformed and final probe set level data was generated through Robust
Multichip Analysis [[64]26] (a model-based algorithms) with default
parameters [[65]27].
Screening of differentially expressed genes
After expression data for post-processing of standardization, we
directly employed a more mature significance analysis of microarrays
(SAM) algorithm [[66]28]. Differentially expressed genes were screened
by using t-test and analysis of variance, if N is large number of our
genes, it will generate a lot of false positives, then use controlling
the FDR (false discovery rate) values corrected for multiple testing in
the false-positive rate. Calculate the relative difference statistic d:
[MATH: d=X1′−X2
′s−s0<
/mn> :MATH]
d, statistic measures the relative differences in gene expression are
corrected d statistics. X1 ‘represents the average expression level of
a state under genes, X2’ represents the average expression levels of
gene, s represents the variance of the gene.
Construction of co-expression network and module-mining
Construction of co-expression network mining based on the
differentially expression value, weighted correlation network analysis
(WGCNA) [[67]29] was used for finding modules of correlated
differential genes, summarizing such modules, relating modules to one
another, and weighting module’s membership and contributed genes. All
the genes used in WGCNA methods had been screened as previously
described.
Screening of differentially expressed module
Specific gene regulatory network module were screened in two
conditions, and then determine the gene for each module in the two
states within the overall expression differences, using a global
analysis of variance method [[68]30], Global-Ancova method based on
correlation analysis of variance test set and a set of functional gene
phenotype, P value tested with less than 0.05 network modules selected
as differentially expressed module. The method may be R language
Global-Ancova package implementation.
Enrichment analysis of gene function
For a group identified gene sets, we used DAVID [[69]31] tool - a
software is based on the hyper geometric enrichment test methods of
distribution test, to achieve function and the Kyoto Encyclopedia of
Genes and Genomes (KEGG, [70]http://www.genome.jp/kegg/) [[71]32]
pathway enrichment analysis.
Survival analysis
By statistical analysis, we able to achieve some network modules
consisted of differential selected genes with some chemotherapy related
regulation factors. In view of these differential genes, we classified
dataset [72]GSE14764, [73]GSE63885 and [74]GSE49997 into subgroups, all
the candidates are treated by chemotherapy. Prognosis analyses were
conducted by SURVICAL package in R environment, and Kaplan-Meier
estimates of overall survival (OS) respective 95 % confidence intervals
(CIs) were provided for each cluster. In addition, for each dataset,
Cox regression modeling [[75]33] was used to control and assess for
statistically significant prognostic factors, included adjustments for
age, histology, and stage. Then the Pairwise comparisons between
clusters were carried out in Cox model, based on calculated p-values,
genes with p < 0.05 are considered to be relevant to the clinical
characteristics and prognosis of ovarian cancer.
Results
Overall gene-expression profiling standardization
As previously described, we generated original genome expression
profiling and mRNA expression data got from each data set
([76]GSE12470, [77]GSE14764, [78]GSE49997 and [79]GSE63885), after
using GEO database matching the probe ID in the platform to Gene
Symbols, corresponding genes and gene’s IDs were collected from these
data sets respectively. A quantitative genome expression distributions
map are showed in type of box-plots (see Fig. [80]1), values from each
dataset were linearized when provided as logarithms, raw files were
converted into pre-processed data by RMA with default parameters
[[81]27].
Fig. 1.
Fig. 1
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Box-plots of the distribution of gene expression values for analysis of
ovarian cancer gene expression profiling (with a p-value of 0.05 and FC
of 2.0). The abscissa represents each candidate IDs, while the vertical
axis marks the data of genome expression of related patients, all the
datein genome expression profiling are the mean value of many
experiment locations. a [83]GSE12470 dataset, 13356 genes in 53 samples
(10 normal, 35 advanced, 8 early), (b) GSE14764dataset, 80 samples in
different types of ovarian cancer and 13046 genes, C) [84]GSE49997
dataset, 204 samples in various epithelial ovarian cancer and 16150
genes, D) [85]GSE63885 dataset, 101 samples in different ovarian cancer
and mRNA and 20693 genes. a, b, c are consisted of gene-expression
data, while (d) reveals mRNA expression levels. Apparently, most genes’
expression values are approximately in each sample
Ovarian cancer’s genetic screening and pathways analysis
According to the procedure adopted by Dai et al. [[86]26],
pre-processed data of 53 samples (Fig. [87]1a) were analyzed by SAM in
R environment, samples including data from patients in various stage
and non-cancer individuals. Lists of 3095 differentially expressed
genes are collected (Accompanying Table [88]1), showing (i.e., fold
change (FC) equals 2.0) were generated at SAM p-value thresholds of
5 %.
Table 1.
Pathway analyses between normal person and patients in different stages
(top 10)
Source Name p-value q-value Bonferroni
REACTOME Cell Cycle 3.03E-28 8.61E-25
REACTOME Cell Cycle, Mitotic 7.62E-20 2.17E-16
REACTOME Chromosome Maintenance 1.02E-19 2.91E-16
REACTOME Telomere Maintenance 1.09E-19 3.11E-16
REACTOME Deposition of New CENPA-containingNucleosomes at the
Centromere 6.22E-17 1.77E-13
REACTOME Nucleosome assembly 6.22E-17 1.77E-13
REACTOME Meiotic Recombination 2.78E-16 7.90E-13
REACTOME RNA Polymerase I Promoter Opening 3.36E-14 9.56E-11
REACTOME RNA Polymerase I Transcription 8.11E-14 2.31E-10
REACTOME RNA Polymerase I Chain Elongation 9.50E-14 2.70E-10
[89]Open in a new tab
To identify the biological processes associated with these 3095
differential expressed genes, we explore the DAVID;
[90]http://david.abcc.ncifcrf.gov/). Compared with online human genome
database, the top 10 enriched clusters with the 511 genes mainly
distributed at cell cycle including mitosis, deposition of nucleosomes
at the centromere, Chromosome Maintenance including Chromosome,
telomere maintenance and nucleosome assembly, Regulation of RNA
transcription level including RNA polymerase I (Table [91]1,
Accompanying Table [92]2).
Table 2.
Top 10 in weighted gene co-expression network analysis
Genes Degree
RACGAP1 105.7018
UMPS 100.2887
NUSAP1 93.13226
RAD51AP1 92.99357
RAE1 92.71948
CBX3 90.26029
CENPL 89.92775
IARS 89.0546
MRPL3 89.03123
NEK2 87.25454
[93]Open in a new tab
Based on these 511 genes related to top 10 pathways, overall 80
candidates were completely clustered by principal component analysis
(PCA), which indicates a high-performance of differences genetic
screening (Fig. [94]2).
Fig. 2.
Fig. 2
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Clustering map base on 511 screened differential genes. Red spot
indicate healthy individual, spot in blue indicate patient suffering
from ovarian cancer, spots in different colors are effectively
separated from each other
Differences genetic screening and pathways analysis on ovarian cancer in
different stages
By using WGCNA software in R language, gene co-expression networks
(Accompanying Table [96]3) are established from 3095 differential
expression genes (Accompanying Table [97]1). Each gene was weighted and
ranked by calculating the network edges, top 10 are showed in
Table [98]2, Gene RACGAP1 [[99]34], RAD51AP1 [[100]35], RAE1 [[101]36],
NEK2 [[102]37] had been reported as ovarian cancer related genes, while
the others are newly defined related gene. In addition, these 3095
genes were divided into 17 modules (Table [103]3) by the block-wise,
Modules function of WGCNA package. After further screening on
Global-Ancova package using R language and comparing original
gene-expression data set [104]GSE12470, 4 network modules of
differentially expressed cancer genes were identified (Table [105]4,
details showed in Accompanying Table [106]4) as the representative
module to apply function analyses because most of genes in the network
are expressed in the candidate who suffered from cancer.
Table 3.
Differential expressed gene divided into 17 networkmodules
Module_name F.value Gene_num p.approx
Black 0.775321 120 0.32445
Blue 1.993077 546 0.047693
Brown 0.822257 294 0.487956
Cyan 1.898666 24 0.104177
Green 1.501158 200 0.122757
Greenyellow 3.187852 32 0.01002
Grey 1.6997 464 8.81E-06
Lightcyan 2.393375 22 0.066907
Magenta 1.248493 60 0.215386
Midnightblue 0.453455 23 0.710623
Pink 1.184971 75 0.217628
Purple 1.265749 55 0.215887
Red 1.639867 161 0.051571
Salmon 1.295175 31 0.201977
Tan 2.495361 31 0.0358
Turquoise 1.755095 717 0.073338
Yellow 1.025866 240 0.30132
[107]Open in a new tab
Table 4.
Pathway analyses in dominantnetwork modules composed by differential
expression ovarian cancer genes
Module Category Term P-value
Blue KEGG_PATHWAY hsa00150:Androgen and estrogen metabolism 7.72E-07
Blue KEGG_PATHWAY hsa00970:Aminoacyl-tRNA biosynthesis 0.001102
Blue KEGG_PATHWAY hsa00140:Steroid hormone biosynthesis 0.002036
Blue KEGG_PATHWAY hsa00860:Porphyrin and chlorophyll metabolism 0.00247
Blue REACTOME_PATHWAY REACT_1698:Metablism of nucleotides 0.009412
Blue KEGG_PATHWAY hsa00983:Drug metabolism 0.036080937
Greenyellow KEGG_PATHWAY hsa03320:PPAR signaling pathway 0.053198
Greenyellow REACTOME_PATHWAY REACT_602:Metabolism of lipids and
lipoproteins 0.086351
Grey KEGG_PATHWAY hsa04916:Melanogenesis 0.006757
Grey REACTOME_PATHWAY REACT_17044:Muscle contraction 0.015831
Grey REACTOME_PATHWAY REACT_13:Metabolism of amino acids 0.021384
Grey BIOCARTA h_ghrelinPathway:Ghrelin: Regulation of Food Intake and
Energy Homeostasis 0.028498
Grey KEGG_PATHWAY hsa00512:O-Glycan biosynthesis 0.029358
Tan KEGG_PATHWAY hsa00500:Starch and sucrose metabolism 0.001807
Tan REACTOME_PATHWAY REACT_474:Metabolism of carbohydrates 0.002231
[108]Open in a new tab
GO and KEGG analysis on these 4 modules (Table [109]4) shows blue
modules is mainly take part in female metabolism regulation and
controlling: Androgen and estrogen metabolism and Steroid hormone
biosynthesis which straightly related to ovarian functions,
Aminoacyl-tRNA biosynthesis which play a key role in protein synthesis
[[110]38] and has been suggested to be associated with the progression
of various ovarian cancers [[111]39, [112]40], most interested is
porphyrin and chlorophyll metabolism pathways also be involved into
ovarian cancer progression, porphyrin was reported as treatment
elements for ovarian cancer [[113]41], while chlorophyll as important
grapevine iron nutrition for blood [[114]42, [115]43] which most
females are short for it [[116]44], besides, some reporter illustrated
cancer resistance protein can against the porphyrin and chlorophyll
metabolism [[117]45], thus, blue module may potentially denotes the
progress of ovarian cancer and support to our subsequence prognosis
analysis. Besides, gene UMPS ranked second was involved in pathway of
aminoacyl-tRNA biosynthesis, further suggests that UMPS could be
related to a certain ovarian cancer. And gene IARS belongs to drug
metabolism pathway in blue module ranked eighth in Table [118]2,
suggesting that this gene maybe important for applicability of drug
treatment in specific case.
Greenyellow module is mainly related to PPAR signaling pathway, which
is involved in ovarian follicle development [[119]46] and ovarian
cancers progress [[120]47]. Grey module is mainly devoted to
melanogenesis. Presently, no representation shows melanogenesis is
related to cancer progression, but melanogenesis is regarded as a
potential instruction for understanding of complex diseases [[121]48].
In currently study, we select the modules to evaluate ovarian cancer in
different stages and various types, thus, this module probably take an
important part in subsequence prognosis analysis for patients in
various conditions, beside, the other functions of this modules also
help to analysis cancer proceeding like amino acids metabolism and
energy homeostasis. Tan module is mainly devoted to carbohydrates and
sucrose metabolism, and this is a risk factor for many cancer [[122]49]
and female ovarian health [[123]50], also very important to diagnosis
of advanced ovarian cancer patients [[124]51, [125]52].
All these supported researches and relevance data illustrated that we
had generated network modules from differential expression genes of
various ovarian cancers successfully, and these networks are competent
for predict ovarian cancer’ subgroups, also potentially indicate the
proceeding of ovarian cancer in different patients.
Prognostic analysis of subgroups of ovarian cancers
1073 differential expression genes involved in the 4 dominant network
modules were generated from [126]GSE12470 expression dataset as
previous described. By using SUVIVLE package in R basing on these
differential genes, [127]GSE14764 dataset composed by various ovarian
cancer patients’ gene expression profiles (n = 80) were classified into
3 subgroups (Fig. [128]3a). Pair wise comparisons between clusters
based on p-values were carried out by Kaplan-Meier estimates of OS
respective 95 % confidence intervals (CIs). Kaplan-Meier estimates of
Fig. [129]3a has been showed in Fig. [130]3b with P = 0.0323.
Fig. 3.
Fig. 3
[131]Open in a new tab
Cluster analysis: Heat map profiles of ovarian cancer patients with
1073 extracted differential genes from [132]GSE12470 data set (n = 53).
a Heat map profiles of extracted differentiated genes and various
ovarian cancer patients from [133]GSE14764 dataset (genome expression,
n = 80), the Kaplan-Meier curves are with respect to (b) overall
survival (OS) rite at non-significant P = 0.0323, (c) Heat map profiles
of extracted differentiated genes and various ovarian cancer patients
from [134]GSE49997 dataset (mRNA expression, n = 204), corresponding
Kaplan-Meier curves (d) with a non-significant P = 1.02e - 05, (e) Heat
map profiles of extracted differentiated genes and various subtypes of
epithelial ovarian cancer patients from [135]GSE63885 dataset (genome
expression, n = 101), the Kaplan-Meier curves are with respect to (f)
overall survival (OS) rite at non-significant P =0.0781,A) is for
prognosis trials, (c, e) are used to verify the availability of
selected modules and extracted differential expression genes. All
estimates of OS respective 95 % confidence intervals (Cis)
In order to verify the availability of the prognostic functions of
these 4 modules, we useGSE49997 and [136]GSE63885 datasets to repeat
the same experiment. [137]GSE49997 [[138]23] is composed by mRNA
expression data from epithelial ovarian cancer patients (n = 204) while
[139]GSE63885 datasets are consisted by genome expression data from
various ovarian cancers. According to the original articles, candidates
in [140]GSE49997 [[141]23] dataset are classified into the
clinic-pathologic parameters of the histological serous and non-serous
tumor subtypes, each subtypes can be divided into 2 subclasses derived
from International Federation of Gynecology and Obstetrics
stage-directed supervised classification approach (IFGO). One group’s
(subclass2) conditions deteriorated extremely from a certain time point
and appear much lower livability in both serous and non-serous
histological subtypes than another (subclass1)’s, as revealed by
univariate analysis (hazard ratios [HR] of 3.17 and 17.11,
respectively; P 0.001) and in models corrected for relevant clinic
pathologic parameters (HR 2.87 and 12.42, respectively; P 0.023).
Similarly, candidates in [142]GSE63885 [[143]22] datasets adapt the
same classification approach(IFGO), and they discovered that
histological type could be a confusing factor and gene expression
exploration of ovarian carcinomas should be performed on histologically
homogeneous groups to direct the prognostic analysis on chemotherapy.
In their experiment, clinical endpoints like overall survival,
disease-free survival, tumor response to chemotherapy are not confirmed
by validation either on the same group or on the independent group of
patients, just CLASP1 gene with BRCA1 mutation status related to one
ovarian cancer subclass which tend to deteriorate easily.
Comparatively, heat map profiles in current researches (Fig. [144]3c
and Fig. [145]3e showed) showed the samples from [146]GSE49997 and
[147]GSE63885 dataset had been efficiently divided into 2 groups base
on the same differential expressed genes and 4 network modules used in
Fig. [148]3a, which are identical with the original dataset
information. In Kaplan-Meier estimates of OS respective 95 % confidence
intervals (CIs) were provided for these two heat maps with p equals to
1.02e-05 and p equals to 0.0781 respectively. According to these two
verification models and similarities in classification to original data
sources we described above, the selected 1073 different genes in 4
majority network modules is competent to classify ovarian cancer into
subtypes that are prognostic of different chemotherapy outcome,
especially for epithelial ovarian cancer and ovarian germ cell cancer
(especially for stage 4 and stage 5), which are notorious for diagnosis
and distinction at the early stage with analogous morphological
characteristics. In addition, the modules we established may prefer
much more accuracy and practicability, as [149]GSE63885 [[150]22]
datasets with less stringent criteria for gene selection (FDR <10%and
uncorrected p-value <0.001).
For further extraction and prognosis of genes directly related to
ovarian cancer survival, we used univariate COX regression method to
calculate the correlation between genes and survival prognosis within
the module, GSB14764 dataset genes associated with prognosis in a total
of 35 genes; [151]GSE49997 dataset and prognosis related genes, a total
of 47 genes (Additional file [152]1: Table S5); [153]GSE63885 dataset
and prognosis with a total area of Venn diagram with 57 genes
(Additional file [154]1: Table S6). View these three ovarian cancer
prognostic gene intersection situations, find the intersection between
any two relatively small (Fig. [155]4), the intersection of the six
genes LRRC8D, TTC304, TFCP2L1, LIBRINEPOR, PAR52. Outstandingly,
dysregulation of this EPOR may affect the growth of certain tumors
[[156]53, [157]54].
Fig. 4.
Fig. 4
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Three data sets COX univariate regression analysis were screened for
ovarian cancer prognostic gene Venn diagram
Discussion
As previously described, ovarian cancer like epithelial ovarian cancer
and ovarian germ cell cancer has difference subclasses, but it is hard
to distinguish the malignant from carcinoid tumors due to the multitude
of clinical and history pathological aspects [[159]8], lack of
precursor lesions [[160]9] and their evolution [[161]10], which cause
the bad one with a low survival rate and complicated due to frequent
development of resistance to standard therapies and asymptomatic nature
of the early stage. Thus, recently, more and more researches are focus
on genome level analysis aim at recognize collaborative gene and
relatively network modules, which will bring out some newly efficiently
diagnoses, and help to the cancer prevent and treatment to individuals
base on targeted chemotherapy.
Current established genetic ovarian carcinomas prognostic pattern
contains 1073 difference expression genes involved in the 4 dominant
network modules successfully divided a dataset with random OCA cases
(n = 80) into 3 groups (p = 0.0323, 95 % CIs in Kaplan-Meier). Two
other previously reported datasets verified this classification is
available and can be used in both genome (n = 204, p =1.02e-05, 95 %
CIs in Kaplan-Meier) and mRNA (n = 101, p =0.0781, 95 % CIs in
Kaplan-Meier) profiles, also demonstrated that this pattern can be used
to distinguish epithelial ovarian cancer and ovarian germ cell cancer
subclasses that trend todevelopmalignantly.6 prognosis related genes
were selected by COX regression analysis (LRRC8D, TTC30A, TFCP2L1,
LMBR1, EPOR and PARS2), these difference genes regulate modules through
the whole work, rather than a few genes play a prognostic
classification, which can make the outcome much more convincing. Beyond
them, EPOR is famous for its affection to tumor growth [[162]53,
[163]54], support the function to divide the malignant epithelial
ovarian cancer or ovarian germ cell cancer from carcinoid tumors;TTC30A
and LRRC8D are rarely reported before, but recent statistics shows that
these two gene related to immune system, and may have regulation
ability to host protein [[164]55–[165]57], these can be considered in
chemotherapy methods choosing. In addition, corresponding to earlier
pathway analysis (Aminoacyl-tRNA biosynthesis in blue module,
Table [166]4), PARS2 encodes a putative member of the class II family
of aminoacyl-tRNA synthetases, further suggested a highly correlated
gene networks in currently generated modules. What is importantly is
that TFCP2L1 probably contribute to the differentiation of cancer stem
cells, as embryonic stem cell self renewal pathways converge on the
transcription factor Tfcp2l1 [[167]58], and this never been reported
before.
The present study describes a validation analysis of a previously
defined gene signature to establish its relevance as a clinically
useful prognostic factor. While the accuracy of prognostic outcome
restricted by two elements, the routine use of recently published new
prognostic factors in clinical practice has had limited success, and
the updated gene databases.
Acknowledgements