A breast cancer prognostic signature predicts clinical outcomes in multiple tumor types.
ABSTRACT Epidemiological studies indicate an increased risk of subsequent primary ovarian cancer from women with breast cancer. We have recently identified a 28-gene expression signature that predicts, with high accuracy, the clinical course in a large population of breast cancer patients. This prognostic gene signature also accurately predicts response to chemotherapy commonly used for treating breast cancer, including CMF, Tamoxifen, Paclitaxel, Docetaxel and Doxorubicin (Adriamycin), in a panel of 60 cancer cell lines of nine different tissue origins. This prompted us to investigate whether this prognostic gene signature could also predict clinical outcome in other cancer types of epithelial origins, including ovarian cancer (n=124), colon tumors (n=74) and lung adenocarcinomas (n=442). The results show that the gene expression signature contributes significantly more accurate (P<0.05; compared with random prediction) prognostic information in multiple cancer types independent of established clinical parameters. Furthermore, the functional pathway analysis with curated database delineated a biological network with tight connections between the signature genes and numerous well established cancer hallmarks, indicating important roles of this prognostic gene signature in tumor genesis and progression.
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ABSTRACT: Why does a constant barrage of DNA damage lead to disease in some individuals, while others remain healthy? This article surveys current work addressing the implications of inter-individual variation in DNA repair capacity for human health, and discusses the status of DNA repair assays as potential clinical tools for personalized prevention or treatment of disease. In particular, we highlight research showing that there are significant inter-individual variations in DNA repair capacity (DRC), and that measuring these differences provides important biological insight regarding disease susceptibility and cancer treatment efficacy. We emphasize work showing that it is important to measure repair capacity in multiple pathways, and that functional assays are required to fill a gap left by genome wide association studies, global gene expression and proteomics. Finally, we discuss research that will be needed to overcome barriers that currently limit the use of DNA repair assays in the clinic.DNA repair 04/2014; · 3.36 Impact Factor
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ABSTRACT: INTRODUCTION: The traditional staging system is inadequate to identify those patients with stage II colorectal cancer (CRC) at high risk of recurrence or with stage III CRC at low risk. A number of gene expression signatures to predict CRC prognosis have been proposed, but none is routinely used in the clinic. The aim of this work was to assess the prediction ability and potential clinical usefulness of these signatures in a series of independent datasets. METHODS: A literature review identified 31 gene expression signatures that used gene expression data to predict prognosis in CRC tissue. The search was based on the PubMed database and was restricted to papers published from January 2004 to December 2011. Eleven CRC gene expression datasets with outcome information were identified and downloaded from public repositories. Random Forest classifier was used to build predictors from the gene lists. Matthews correlation coefficient was chosen as a measure of classification accuracy and its associated p-value was used to assess association with prognosis. For clinical usefulness evaluation, positive and negative post-tests probabilities were computed in stage II and III samples. RESULTS: Five gene signatures showed significant association with prognosis and provided reasonable prediction accuracy in their own training datasets. Nevertheless, all signatures showed low reproducibility in independent data. Stratified analyses by stage or microsatellite instability status showed significant association but limited discrimination ability, especially in stage II tumors. From a clinical perspective, the most predictive signatures showed a minor but significant improvement over the classical staging system. CONCLUSIONS: The published signatures show low prediction accuracy but moderate clinical usefulness. Although gene expression data may inform prognosis, better strategies for signature validation are needed to encourage their widespread use in the clinic.PLoS ONE 11/2012; 7(11):e48877. · 3.53 Impact Factor
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ABSTRACT: The malignancy-risk gene signature is composed of numerous proliferative genes and has been applied to predict breast cancer risk. We hypothesized that the malignancy-risk gene signature has prognostic and predictive value for early-stage non-small cell lung cancer (NSCLC) patients. The ability of the malignancy-risk gene signature to predict overall survival (OS) of early-stage NSCLC patients was tested using a large NSCLC microarray dataset from the Director's Challenge Consortium (n = 442) and two independent NSCLC microarray datasets (n = 117 and 133, for the GSE13213 and GSE14814 datasets, respectively). An overall malignancy-risk score was generated by principal component analysis to determine the prognostic and predictive value of the signature. An interaction model was used to investigate a statistically significant interaction between adjuvant chemotherapy (ACT) and the gene signature. All statistical tests were two-sided. The malignancy-risk gene signature was statistically significantly associated with OS (P < .001) of NSCLC patients. Validation with the two independent datasets demonstrated that the malignancy-risk score had prognostic and predictive values: Of patients who did not receive ACT, those with a low malignancy-risk score had increased OS compared with a high malignancy-risk score (P = .007 and .01 for the GSE13212 and GSE14814 datasets, respectively), indicating a prognostic value; and in the GSE14814 dataset, patients receiving ACT survived longer in the high malignancy-risk score group (P = .03), and a statistically significant interaction between ACT and the signature was observed (P = .02). The malignancy-risk gene signature was associated with OS and was a prognostic and predictive indicator. The malignancy-risk gene signature could be useful to improve prediction of OS and to identify those NSCLC patients who will benefit from ACT.CancerSpectrum Knowledge Environment 12/2011; 103(24):1859-70. · 14.07 Impact Factor
Abstract. Epidemiological studies indicate an increased risk
of subsequent primary ovarian cancer from women with
breast cancer. We have recently identified a 28-gene expression
signature that predicts, with high accuracy, the clinical course
in a large population of breast cancer patients. This prognostic
gene signature also accurately predicts response to chemo-
therapy commonly used for treating breast cancer, including
CMF, Tamoxifen, Paclitaxel, Docetaxel and Doxorubicin
(Adriamycin), in a panel of 60 cancer cell lines of nine dif-
ferent tissue origins. This prompted us to investigate whether
this prognostic gene signature could also predict clinical
outcome in other cancer types of epithelial origins, including
ovarian cancer (n=124), colon tumors (n=74) and lung adeno-
carcinomas (n=442). The results show that the gene expression
signature contributes significantly more accurate (P<0.05;
compared with random prediction) prognostic information
in multiple cancer types independent of established clinical
parameters. Furthermore, the functional pathway analysis
with curated database delineated a biological network with
tight connections between the signature genes and numerous
well established cancer hallmarks, indicating important
roles of this prognostic gene signature in tumor genesis and
For women with breast cancer, an increased risk of primary
ovarian cancer has been observed from epidemiological
studies (1). This risk is highest for women with early-onset
breast cancer (younger than age 50 years at diagnosis). To
date, two major genes, BRCA1 and BRCA2, have been identi-
fied to be associated with susceptibility to breast and ovarian
cancer. However, mutations in these two genes only account
for 2-3% of all breast cancers (2). It has been proposed that
additional genes that are associated with susceptibility to
breast and ovarian cancer exist (2). Identification of other
susceptibility genes could provide crucial information to guide
clinicians to assess the risk of subsequent ovarian cancer in
breast cancer patients.
Previously, we identified a 28-gene breast cancer prog-
nostic signature in a population-based study (3). A unified
classification scheme was later developed for patient stratifi-
cation based on the expression patterns of the 28-gene signa-
ture. The prognostic categorization system was validated
with more than 2000 breast cancer patient samples quantified
with heterogeneous DNA microarray platforms (4). This
prognostic gene signature was also found to predict response
to chemotherapy commonly used for treating breast cancer,
including CMF, Tamoxifen, Paclitaxel, Docetaxel and
Doxorubicin (Adriamycin), in a panel of 60 cancer cell lines
(NCI-60) of nine different tissue origins (4). Based on these
results, we hypothesize that the 28-gene prognostic signature
reveals molecular characteristics important to tumor genesis
and progression. To test this hypothesis, we first sought to
investigate whether the 28-gene signature reveals common
biological processes involved in recurrence and metastases
of breast and ovarian cancer. Next, we sought to explore
whether the 28-gene prognostic signature could also predict
clinical outcome in other cancer types with epithelial origin,
including colon cancer and non-small cell lung cancer.
Materials and methods
Patients and samples
Ovarian cancer. The ovarian cancer cohort (n=124) was
retrieved from Bild et al (5). Of these ovarian cancer patients,
94.4% (117/124) had advanced stages (III and IV).
Colon cancer. The first cohort contained 50 patients with
stage II colon adenocarcinoma (6). None of the patients had
emergency surgery or received any adjuvant chemotherapy.
Twenty-five patients developed a distant metastasis (liver in
ONCOLOGY REPORTS 24: 489-494, 2010
A breast cancer prognostic signature predicts
clinical outcomes in multiple tumor types
YING-WOOI WAN1, YONG QIAN2, SHRUTI RATHNAGIRISWARAN1,
VINCENT CASTRANOVA2and NANCY LAN GUO1
1Mary Babb Randolph Cancer Center/Community Medicine, West Virginia University, Morgantown, WV 26506-9300;
2The Pathology and Physiology Research Branch, Health Effects Laboratory Division, National Institute
for Occupational Safety and Health, Morgantown, WV 26505, USA
Received March 29, 2010; Accepted April 26, 2010
Correspondence to: Dr Nancy L. Guo, 2816 Mary Babb Randolph
Cancer Center/Community Medicine, West Virginia University,
Morgantown, WV 26506-9300, USA
Key words: prognostic gene signature, breast cancer, ovarian cancer,
colon cancer, lung adenocarcinoma
22 patients; lung in 5 patients) within 52 months. The other
25 patients remained disease-free for at least 60 months, with
mean follow-up of 79 months. The second cohort contained
24 patients with stage II colon adenocarcinoma (7). None of
these patients received adjuvant chemotherapy. Ten patients
developed a liver metastasis within 55 months. The other 14
patients remained disease-free for at least 60 months, with
mean follow-up of 72.2 months.
Non-small cell lung cancer. The cohort from Shedden et al
(8) contained 442 lung adenocarcinomas collected from
multiple cancer centers and institutes. Two hundred and
seventy-six patients were in stage I, 94 in stage II and 68 in
stage III and 4 patients with undefined stage.
DNA microarray analysis. The RNA extraction and cDNA
preparation in these studies was described in their original
publications. The ovarian cancer dataset from Bild et al (5)
were assayed with Affymetrix U133A (retrieved with record
GSE3149 from Gene Expression Omnibus). Two colon cancer
datasets were all generated with Affymetrix U133A arrays
(7,6). The lung adenocarcinoma datasets from Shedden et al
(8) were generated with Affymetrix U133A.
Patient stratification in ovarian cancer. The ovarian cancer
cohort (n=124) from Bild et al (5) was used to explore whether
the 28-gene signature reveals molecular portraits common in
breast cancer and ovarian cancer. To avoid over-fitting in
the validation, the data set was randomly partitioned into a
training set (n=82) and a test set (n=42). The 28 gene
predictors were fitted in a Cox hazard proportional model on
the training set, and a survival risk score was generated for
each patient. A high risk score represents a high probability
of post-operative treatment failure, and similarly for a low
risk score. The median of the survival risk scores in the
training set was used as the cut-off point to stratify patients
into different prognostic groups. A patient with a risk score
higher than median risk score was classified into poor-
prognosis group; whereas a patient with a lower risk score
was classified into good-prognosis group. The same cut-off
value and prognostic model were applied to patient strati-
fication in the test set.
Prognostic prediction of recurrence in colon cancer. The
matching genes in the 28-gene signature were identified with
Affymetrix IDs. Twenty-five common genes were found in
each colon cancer cohort. If a gene has multiple probes, the
average expression of multiple probes was used in the classi-
fication. The patient cohort from Barrier et al (6) was used as
training set (n=50), while the cohort from another study by
Barrier et al (7) was used as an independent validation set
(n=24). A training model was built with the 25 signature genes
to classify recurrence in colon cancer patients using a linear
discriminant analysis function in SAS 9.1. A 10-fold cross
validation was used to evaluate the performance of the training
model. This training model was used to predict tumor recur-
rence in each patient in the validation set.
Prognostic categorization of non-small cell lung cancer. The
patient samples collected from the University of Michigan
Cancer Center (UM) and Moffitt Cancer Center (HLM) form
the training set (n=256), whereas the samples obtained from
Memorial Sloan-Kettering Cancer Center (MSK, n=104) and
the Dana-Farber Cancer Institute (DFCI, n=82) constitute an
independent validation set. Gene symbols were used to find
the matching genes in the signature. In the training set (UM
and HLM cohorts), a Cox proportional hazard model was
constructed by using the matching genes as covariates to
predict lung cancer survival after the initial treatment. A risk
score was generated for each patient in this cohort. Based on
the distribution of the risk scores in the training set, a cut-off
point representing the peak value in the histogram was
identified to stratify patients into high- or low-risk groups.
This cut-off risk score and the training model were applied in
prognostic categorization in the validation set (MSK and
Statistical analysis. Patient survival rates were assessed with
Kaplan-Meier analysis using log-rank tests. Cox hazard propor-
tional model was used to generate a risk score for each ovarian
cancer patient based on the 28-gene signature. All statistical
analyses were performed with software package R (9).
Biological pathway analysis. Ingenuity pathway analysis
(IPA) software (Ingenuity Systems, Redwood City, CA) is
a proprietary web-based curated database which provides
contents of gene and protein interactions reported in the
literature. In this study, we used IPA to delineate molecular
networks of genes interacting with the 28-gene signature.
Core analysis identified the most significant biological
functions and processes from the merged network generated
for the 28-gene signature.
Recent studies showed that a prognostic gene signature
identified from breast cancer cells might be able to predict
clinical outcome in multiple tumor types (5,10-12). A set of
28 genes predicted recurrence-free survival (including meta-
stasis and relapse) and overall survival in multiple independent
breast cancer cohorts (3,4). In the present study, we sought to
investigate whether this breast cancer prognostic gene signature
also predicts clinical outcomes in other cancer types of
epithelial origins, including ovarian cancer (n=124), colon
cancer (n=74) and non-small cell lung cancer (n=442).
28-Gene prognostic signature predicts ovarian cancer out-
come. Ovarian cancer is a common malignancy in women,
whose prognosis is bleak due to a usually advanced disease
stage at the time of diagnosis. Common genetic risk factors
of susceptibility to breast and ovarian cancer have recently
been proposed (2). To explore whether the 28-gene signature
reveals common molecular features affecting breast and
ovarian cancer survival, an ovarian cancer cohort from Bild
et al (5) was analyzed. This ovarian cancer cohort (n=124)
was randomly split into a training set (n=82) and a test set
(n=42). A Cox model was built on the training set using the
signature genes as covariates. A survival risk score was gene-
rated for each patient. The median of the risk scores in the
training set was identified as a cut-off point for patient strati-
WAN et al: PROGNOSTIC GENE SIGNATURE PREDICTS OUTCOME IN MULTIPLE CANCERS
fication. Patients with a risk score greater than the cut-off
were stratified into the high-risk group, and otherwise, into
the low-risk group. In the prognostic model evaluation, the
high- and low-risk groups had significantly (log-rank
P<0.0001) different relapse-free survival in the training cohort
in Kaplan-Meier analysis (Fig. 1A). This training model and
stratification scheme were applied to the test set, and gene-
rated significant prognostic stratification (log-rank P=0.0075)
in Kaplan-Meier analysis (Fig. 1B). The details of the prog-
nostic Cox model were provided in http://www.hsc.wvu.edu/
These results indicate that the 28-gene signature reflects
common biological processes involved in breast and ovarian
cancer metastases and relapse. The 28-gene signature could
indentify more aggressive ovarian cancers that were more
likely to develop recurrence after surgical resections and
initial treatment. Therefore, the high risk patients defined
with this gene signature might benefit from second line chemo-
28-Gene prognostic signature is an independent predictor of
colon cancer recurrence. In order to extent the potential
usefulness of the 28-gene prognostic signature, we explored
its value for predicting clinical outcome in patients with
stage II colon cancer. To construct a molecular classifier to
predict colon cancer recurrence, 50 patients with stage II
colon adenocarcinoma (6) were used as training cohort.
Twenty-five genes within the 28-gene signature were identi-
fied from the DNA microarray data. These signature genes
were used to classify recurrence in each patient with the linear
discriminant analysis algorithm. The performance of the clas-
sifier was evaluated in a 10-fold cross validation on the training
set (Table I). The prognostic signature correctly predicted
recurrence in 94% (47/50) of patients, with a sensitivity of
100% (25/25) and a specificity of 88% (22/25). The model
identified in the training cohort was applied to predict recur-
rence in each patient in the validation set (n=24) with a patient
cohort retrieved from Barrier et al (7). In the validation, the
prognostic signature correctly predicted recurrence in 75%
(18/24) patients, with a sensitivity of 80% (8/10) and a speci-
ficity of 71.43% (10/14). Both cohorts contained only stage
II lymph node negative colon adenocarcinomas. These results
indicate that the 28-gene prognostic signature provides
independent prognostic information in addition to tumor
stage. Once validated in larger, independent cohorts this
signature could be potentially used to select lymph node-
negative patients for receiving adjuvant chemotherapy.
28-Gene prognostic signature predicts lung cancer survival.
To explore the clinical relevance of the 28-gene prognostic
signature for the prognostication of patients with non-small
cell lung cancer, the lung adenocarcinoma cohorts (UM and
ONCOLOGY REPORTS 24: 489-494, 2010
Figure 1. The 28-gene prognostic signature predicts overall survival in ovarian cancer. Kaplan-Meier analyses of the training cohort (A) and test cohort (B)
from Bild et al (5). The upper curves represent the gene expression-define low risk group, and the lower curve represent the high risk group. The median of
the risk scores with a value of 0.301 generated by fitting the Cox proportional hazard model on the training set was taken as the cut-off in both training and
Table I. Prediction accuracy of colon cancer recurrence using the 28-gene prognostic signature.
Patients within 5-years) (%)
Training set (n=50) (5)100 (25/25)
Specificity (no recurrence
within 5-years) (%)
88 (22/25) 94 (47/50)4.8e-7
Validation set (n=24) (6)
All patients were with tumor stage II at diagnosis. aP<0.05 represents the overall accuracy is significantly higher than that of random
prediction (one-sided Z-tests).
80 (8/10) 71.43 (10/14) 75 (18/24)0.04
WAN et al: PROGNOSTIC GENE SIGNATURE PREDICTS OUTCOME IN MULTIPLE CANCERS
Figure 2. The 28-gene prognostic signature predicts overall survival in lung cancer. (A) Histogram of gene expression-defined risk scores in the training
cohort from Shedden et al (8). The peak value with risk score of -0.75 in the histogram was defined as the cut-off in prognostic categorization. Gene
expression-defined high- (lower curves) and low-risk groups (upper curves) had remarkably different post-operative lung cancer survival in both training (B)
and test cohorts (C).
Figure 3. Functional pathway analysis of the 28-gene prognostic signature using ingenuity pathway analysis. The biological network showed genes interacting
with the signature genes as reported in the literature.
HLM) retrieved from Shedden et al (8) were used as a
training set (n=256). A Cox model of overall survival was
constructed based on the 28-gene signature, with each gene
variable as a covariate. A survival risk score was generated
for every patient, with a higher risk score representing a greater
probability of treatment failure (i.e., death). Based on the
histogram representing distribution of gene expression-defined
risk scores in this cohort (Fig. 2A), a cut-off value of -0.75,
the peak value in the histogram, was used to stratify patients
into high- and low-risk groups. This cut-off value represents
the linear additive expression levels of all the signature genes
in lung cancer patients. This stratification separated patients
into two groups with distinct overall survival (log-rank
P<3.91e-5) in Kaplan-Meier analysis (Fig. 2B). This cut-off
risk score and training model were applied to the validation
set (MSK and DFCI, n=186). The 28-gene signature gene-
rated borderline significant prognostic categorization in the
validation set (log-rank P=0.07; Fig. 2C) in Kaplan-Meier
analysis. In all studied lung adenocarcinoma cohorts, the
low-risk groups had 73.54-82.15% of 2.5-year post-operative
survival rate, representing a significantly better prognosis
compared with the corresponding high-risk groups for which
the 2.5-year survival was ranging from 53.76 to 63.51%.
As the majority of non-small cell lung cancer recurrence
occurs within 2 years after surgery (13), these results indicate
that the 28-gene prognostic signature could be used to
predict post-operative survival in non-small cell lung cancer
Functional pathway analysis. The 28-gene prognostic
signature was able to distinguish more aggressive tumors in
multiple cancer types, indicating that this signature might be
involved in important mechanisms of tumor genesis and
progression. Functional pathway analysis was performed
based on curated database of molecular interactions reported
in the literature using ingenuity pathway analysis. The results
show that the signature genes interact with multiple prominent
cancer signaling pathways, including TP53, TNF and ER, the
BRCA1 breast cancer and ovarian cancer risk gene, the KRT15
stem cell marker, as well as DNA repair proteins RAD51 and
ERCC4 (Fig. 3).
Genome-wide association studies utilizing human tissue
samples have enhanced the prognostic capacity of cancer
outcomes. Four breast cancer signatures, including intrinsic
subtypes (14), poor prognosis signature (MammaPrint®) (15),
recurrence score (Oncotype DX®) (16) and wound response
(17), represent largely the same prognostic space (18). Our
identified 28-gene breast cancer prognostic signature predicted
disease-free survival and overall survival in a large population
of more than 2000 breast cancer patient with heterogeneous
disease stage, including both early stage and advanced breast
cancers (3,4). In the evaluation, the 28-gene prognostic signa-
ture is comparable as Oncotype DX and could potentially be
more accurate than the other above mentioned signatures in
terms of predicting disease-free survival and overall survival
in van de Vijver's cohort (15). More importantly, the 28-gene
breast cancer signature showed prognostic ability beyond
early-stage breast cancer. The 28-gene prognostic signature
quantified disease-free survival and overall survival in a
broad patient population including those with advanced stage
(T3/T4), tumor grade III, lymph node metastasis, or negative
estrogen receptor status (ER-) (4). These results indicate that
the 28-gene signature might extend the prognostic space
defined by MammaPrint and Oncotype DX that primarily
target early stage breast cancer. To confirm this conjecture,
this study investigated whether the 28-gene prognostic signa-
ture could predict clinical outcomes in other tumor types of
epithelial origin, including ovarian cancer (n=124), colon
cancer (n=74) and lung adenocarcinoma (n=442).
In each studied cancer type, a patient stratification scheme
was developed based on the expression of the 28-gene
prognostic signature, and was validated on independent
patient cohorts. Based on the clinical outcome provided in
two colon cancer cohorts, a machine learning algorithm linear
discriminant analysis was used in the model construction on
the training set (n=50) with stage II colon carcinoma to predict
the recurrence after surgery. The model accuracy was 94%
on the training cohort in a 10-fold cross validation. This
prognostic model was applied to a test set (n=24) and achieved
an overall accuracy of 75% in the independent validation.
These results are more accurate (P<0.04) compared with
random predictions. In the prognostic validation of lung
adenocarcinoma, a prognostic model was built with Cox
model using the gene expression profiles as covariates. The
cut-off point for prognostic categorization was defined based
on histogram of gene expression defined-risk scores on the
training cohort (n=256). This stratification scheme was applied
to an independent validate set (n=186). The gene signature
separated patients into different prognostic groups with
different (log-rank P=0.07) clinical outcomes in Kaplan-
Meier analysis. Similarly, the Cox model was used in the
prognostic validation on ovarian cancer. In both training and
test cohorts (n=124), the gene expression defined-model
provided significant (log-rank P<0.0075) post-operative
prognostic stratification in Kaplan-Meier analyses.
Epidemiological studies strongly indicate that an associ-
ation exists between breast cancer and the risk of subsequent
ovarian cancer (1). Begfeldt's group found that a primary
breast cancer patient has a 2-fold increased risk of a primary
ovarian cancer. Several genes have been identified to be
associated with susceptibility to breast cancer and ovarian
cancer, including BRCA1, BRCA2, TP53, PTEN and STK11/
LKB1. However, mutations in these genes only account for
very limited portions of breast cancer and ovarian cancer (2).
Identification of other susceptibility genes could provide
essential information to guide clinicians to assess the risk of
subsequent ovarian cancer in breast cancer patients. The 28-
gene signature was shown to be predictive of clinical out-
comes in both breast cancer and ovarian cancer. Furthermore,
the signature genes were shown to interact with TP53 and
BRCA1 in the biological association network (Fig. 3).
Together, this signature might reveal essential genomic
information for estimating the risk of consequent ovarian
cancer in breast cancer patients.
This study confirmed that the identified 28-gene prognostic
signature could predict clinical outcomes in multiple cancer
types with epithelial origins. Thus, this 28-gene signature
ONCOLOGY REPORTS 24: 489-494, 2010
could extend breast cancer prognostic space defined by
MammaPrint and Oncotype DX, among other breast cancer
signatures with potential clinical utility (5,10-12). The
functional pathway analysis with curated IPA database
delineated a biological network with tight connections between
the signature genes and numerous well established cancer
hallmarks, indicating important roles of this prognostic gene
signature in tumor genesis and progression.
We thank Dr Jame Abrahim at West Virginia University for
thoughtful discussions. This research is supported by National
Library of Medicine R01LM009500 (Guo) and NCRR P20
RR16440 Supplement (Guo) from the NIH.
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