Introduction
Esophageal cancer (EC) is the tenth most common
cause of cancer-associated mortality in United States in 2018
(1), and consists of two
histological types; esophageal squamous cell carcinoma (ESCC) and
esophageal adenocarcinoma (2). ESCC
has been the more common histological type of EC for centuries,
particularly in Eastern Asia, and accounted for ~90% of all cases
of EC worldwide, in 2012 (3). In
addition, ESCC was the fourth most fatal type of cancer in China,
in 2015 (4). A common treatment
method for ESCC is esophagectomy, followed by radiation or
neoadjuvant chemotherapy for the residual tumor tissues (5–7).
However, these treatment regimens have failed to substantially
improve the survival for patients with ESCC over the last few
decades (8). Despite the rapid and
successful identification of prognostic biomarkers by genome-wide
gene expression profiling in numerous types of cancer, including
breast cancer (9–11), lung adenocarcinoma (12), non-small cell lung cancer (13) and rectal adenocarcinoma (14), only a small number of biomarkers for
ESCC have been identified, including cortactin (15) and ribonuclease 3 (RNASEN; also known
as drosha ribonuclease III) (16).
The intra- and inter-patient molecular heterogeneity
of cancer has been repeatedly reported (17–25), and
makes it difficult to identify and establish universal prognostic
biomarkers. To assist in the development of novel prognostic
biomarkers, a number of prognostic databases have been established
for breast, ovarian, lung and gastric cancer, and numerous new
prognostic molecular markers have been identified (26–29).
However, to the best of our knowledge, there is no relevant
prognostic database for ESCC.
To facilitate the validation of prognostic
biomarkers of ESCC and the evaluation of their sensitivity in
independent studies, and to help clinicians and scientists improve
their understanding of the clinically relevant gene expression
data, the present study developed an online consensus survival tool
for ESCC, termed OSecc, a web-based interactive survival analysis
tool for ESCC. Using OSescc enables individuals to assess overall
survival (OS) and relapse free survival (RFS) based on the
expression of given genes or probes. OSescc is accessible at
bioinfo.henu.edu.cn/DBList.jsp.
Materials and methods
Data acquisition
Datasets were obtained from Gene Expression Omnibus
(GEO; ncbi.nlm.nih.gov/geo/) and The Cancer
Genome Atlas (TCGA; cancergenome.nih.gov) by searching with the keyword
‘esophageal carcinoma’, followed by manually checking for ‘more
than 20 ESCC cases with clinical survival data and gene expression
profiling data’.
Development of OSescc
The OSescc server is hosted in a Windows Tomcat
server. Server-side scripts are developed in Java v8 (https://www.oracle.com/technetwork/java/javaee/overview/index.html),
which control the request of analysis and the return of the
results. The database system is managed by a Microsoft Standard
Query Language (SQL) server 2008 (https://www.microsoft.com/de-de/download/details.aspx?id=22985),
which stores the gene expression and clinical data. The RODBC
package of R serves as a connecting layer between the R v3.5.2
(https://www.r-project.org/) and the SQL
server database. The Java Database Connectivity driver serves as a
connecting layer between the Java and the SQL Server database. The
R package ‘survival’ was used to generate Kaplan-Meier (KM)
survival curves and calculate the hazard ratio (HR) and log-rank
P-value. The central server for OSescc can be accessed at
bioinfo.henu.edu.cn/DBList.jsp. A
system architecture diagram is presented in Fig. 1. Screenshots of the database
interface and the result retrieval are presented in Fig. 2.
Results
Two datasets that met the criteria were obtained
from GEO (GSE53625) (30) and TCGA
(31) database (Table I). These datasets were composed of a
total of 264 unique ESCC cases with comprehensive clinical
information, including smoking history, alcohol history, TNM stage
(32), lymph node status, country
and ethnicity.
 | Table I.Clinical characteristics of the
patients with esophageal squamous cell carcinoma collected in
OSescc. |
Table I.
Clinical characteristics of the
patients with esophageal squamous cell carcinoma collected in
OSescc.
| Characteristic | GSE53625 | TCGA |
|---|
| Cohort size, n | 179 | 85 |
| Alcohol drinker, n
(%) |
|
Yes | 106 (59.22) | 61 (71.76) |
| No | 73 (40.78) | 23 (27.06) |
| NA | 0 (0.00) | 1 (1.18) |
| Smoker, n (%) |
|
Yes | 114 (63.69) | 56 (65.88) |
| No | 65 (36.31) | 27 (31.76) |
| NA | 0 (0.00) | 2 (2.36) |
| TNM stage, n
(%) |
| I | 10 (5.59) | 1 (1.18) |
| II | 77 (43.01) | 24 (28.24) |
|
III | 92 (51.40) | 13 (15.29) |
| IV | 0 (0.00) | 4 (4.70) |
| NA | 0 (0.00) | 43 (50.59) |
| Lymph node status,
n (%) |
|
Yes | 0 (0.00) | 20 (23.53) |
| No | 0 (0.00) | 34 (40.00) |
| NA | 179 (100.00) | 31 (36.47) |
| Ethnicity, n
(%) |
|
Caucasian | 0 (0.00) | 40 (47.06) |
|
Asian | 179 (100.00) | 40 (47.06) |
|
African-American | 0 (0.00) | 2 (2.35) |
| NA | 0 (0.00) | 3 (3.53) |
In OSescc, users could input the gene symbol into
the ‘Gene symbol’ dialog window (as presented in Fig. 2) to assess the prognostic
significance of interested genes/probes in ESCC. As a result, the
KM plot would display the association between the inquired gene and
the OS or RFS in which the samples can be stratified by different
expression levels of the selected genes or probes depending on the
user's choice of median, quartile or 30%. The results can be
retrieved in a cohort-specific manner or a cohort-combined manner
to increase the statistic power, and the HR and log-rank test
P-value are generated. In addition, the population can be
co-stratified by alcohol, smoking, TNM stage, lymph node status,
country and ethnicity prior to running the analysis to investigate
the subclass specific prognostic power.
Clinicians may also be interested in the association
between survival and the expression of an individual
prognosis-associated probe or transcript variant. Therefore, the
present study developed a function by which OSescc can generate a
KM plot not only in a gene-specific manner, with the mean value
expression of multiple probes for a given gene, but also in a
probe-specific manner, with the expression of a single probe for a
given gene, on the GSE53625 dataset. This particular function
enables individuals to compare the outcomes from a single probe or
from multiple probes, which in turn improves the evaluation of the
validity and reliability of the potential prognosis biomarkers. By
doing so, the current study was able to further analyze four groups
of genes.
The first group contained 79 genes; each gene in
this group possessed multiple probes and all probes predicted the
prognosis with statistical significance towards the same trend of
either good or bad prognosis. Therefore, genes in this group could
serve as the prognostic biomarkers with high degrees of prediction
power.
The second group contained only two genes. Probes of
either gene contradicted with each other with regard to the
predicted prognosis and distinct HR. For example, the ERCC excision
repair 5 (ERCC5) gene had two probes (probe nos. 55483 and 152357)
in GSE53625. A high expression of probe no. 55483 predicted a
significantly worse overall survival rate (HR, 1.9514, P=0.0013),
whereas a high expression of probe no. 152375 indicated a
significantly improved overall survival rate (HR, 0.5545;
P=0.0178). The contradiction between the two probes for the ERCC5
gene resulted in an insignificant prognostic prediction and
decreased the sensitivity of outcome prediction using the mean
value expression detected by the two probes (Fig. 3).
The third group included 532 genes, each of which
had multiple probes. Notably, for each given gene, although ≥1
probe could offer a statistically significant prognosis prediction,
the prediction based on the mean value expression of all probes was
not significant. An example is the colony stimulating factor 1
gene, which was detected using three probes (probe nos. 19819,
54057 and 116310), with P-values of 0.7295, 0.2978 or 0.0031,
respectively. Whereas probe no. 116310 appeared to be a promising
prognosis predictor with P=0.0031, the P-value of log-rank test for
the mean value expression of the three probes was 0.9823, which
indicates the prognosis prediction was insignificant (Fig. 4).
The last group included 135 genes, all with multiple
probes. Notably, for each gene, although none of the probes
predicted the prognosis with a statistically significant power, the
prognosis prediction based on the mean value of all probes was
significant. In other words, for genes in this group, the prognosis
prediction in a gene-specific manner, but not in a probe-specific
manner, was statistically significant. For example, the
β-1,3-N-acetylglucosaminyltransferase 7 gene has two probes (probe
nos. 65517 and 69543). The prognostic significance of each probe
was 0.64075 and 0.76305, respectively. However, the P-value of
log-rank test for the mean value expression detected by the two
probes was 0.0081 (Fig. 5).
Previous studies have reported several potential
biomarkers for ESCC prognosis, including ABL proto-oncogene 1,
non-receptor tyrosine kinase (ABL1) (33), transcription elongation factor A
protein like 1 (TCEAL1) (34),
tropomyosin 2 (35), cystatin C
(36), REV3 like, DNA directed
polymerase ζ catalytic subunit (37)
and urokinase-type plasminogen activator (38). In OSescc, as expected, ABL1 and
TCEAL1 demonstrated significant prognostic prediction in the GEO
and TCGA datasets (Fig. 6).
Furthermore, OSescc could detect the variations of a prognostic
biomarker among different cohorts. For example, RNASEN, which has
previously been reported as a biomarker of worse prognosis in
patients with ESCC (15), was also
demonstrated to be associated with worse prognosis by OSescc in the
TCGA dataset; however, the prognosis analysis based on the GSE53625
dataset was not statistically significant (Fig. 7).
 | Figure 6.Kaplan-Meier plots for two previously
published esophageal squamous cell carcinoma biomarkers
cross-validated between TCGA and GSE53625 datasets. (A) Prognostic
significance of gene ABL1 in GSE53625. (B) Prognostic significance
of gene ABL1 in TCGA. (C) Prognostic significance of gene TCEAL1 in
GSE53625. (D) Prognostic significance of gene TCEAL1 in TCGA. ESCC
cases in TCGA, n=85 and in GSE53625, n=179. TCGA, The Cancer Genome
Atlas; ABL1, ABL proto-oncogene 1, non-receptor tyrosine kinase;
TCEAL1, transcription elongation factor A protein like 1; HR,
hazard ratio; CI, confidence interval; OSecc, online consensus
survival tool for esophageal squamous cell carcinoma; ESCC,
esophageal squamous cell carcinoma. |
Discussion
Discovering prognostic biomarkers is important for
translational cancer research. The present study analyzed two large
gene expression profiling datasets of patients with ESCC with
comprehensive clinical follow-up information from GEO and TCGA, and
developed OSescc, an online free tool, to assess the prognostic
power of potential prognostic biomarkers.
The advantage of OSescc is its convenience to assess
and validate potential prognostic biomarkers for ESCC. It can
assist clinicians and biologists to determine the prognostic power
of given genes in an easy and interactive way. The disadvantage of
current study is the sample size; only two available cohorts were
implemented in OSescc. This is one reason why the OSescc database
was generated to collect all available ESCC clinical follow-up
information and gene expression profiling data to facilitate the
prognosis analysis for other researchers. When novel ESCC clinical
and profiling data becomes available, they will be add to the
OSescc database to improve its power and reliability. OSescc was
developed to assist researchers and clinicians to investigate the
prognostic value of interested genes specific to patients with
ESCC. So far, the clinical and gene expression data collected from
GEO and TCGA only contained patients with ESCC. In addition to
OSescc, we are working on developing online survival tools for
other types of cancer and these prognostic tools for other types of
cancer should be publically accessible in the future.
Furthermore, the prognosis of patients with ESCC had
been demonstrated to be highly dependent on biological factors of
the patient, including immune function, nutrition, alcohol drinking
and smoking status (39,40); however, the aim of current study was
to evaluate the association between gene and prognosis.
Nevertheless, certain factors, including TNM stage, smoking,
alcohol, lymph node status, country and ethnicity, were also
implemented in OSescc to facilitate users to limit their prognostic
analysis in particular groups of ESCC. In summary, OSescc may
become a guidance tool for selecting suitable prognostic markers
for ESCC.
Acknowledgements
Not applicable.
Funding
This work was supported by the National Natural
Science Foundation of China (grant no. 81602362), the Program for
Science and Technology Development in Henan Province (grant no.
162102310391), the Program for Young Key Teacher of Henan Province
(grant no. 2016GGJS-214), Supporting Grants of Henan University
(grant nos. 2015YBZR048 and B2015151), the Program for Innovative
Talents of Science and Technology in Henan Province (grant no.
18HASTIT048) and the Yellow River Scholar Program (grant no.
H2016012).
Availability of data and materials
The datasets used and analyzed during the present
study are available from the corresponding author on reasonable
request.
Authors' contributions
QW, FW, WZ, YT, XL and XG conceived and designed the
study, designed and implemented the software and drafted the
manuscript. QW, FW, LX, WZ, YT, JL, JX, YL, XZ, YW, XL and XG
acquired and analyzed the data, and designed and implemented the
database. QW, FW, LX, WZ, YT, YW, XL and XZ critically revised the
manuscript for intellectual content.
Ethics approval and consent to
participate
Not applicable.
Patient consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing
interests.
Glossary
Abbreviations
Abbreviations:
|
OSescc
|
online consensus survival tool for
esophageal squamous cell carcinoma
|
|
TCGA
|
The Cancer Genome Atlas
|
|
GEO
|
Gene Expression Omnibus
|
|
OS
|
overall survival
|
|
RFS
|
relapse free survival
|
|
ESCC
|
esophageal squamous cell carcinoma
|
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