dataOvarian2: Data on time-to-death and 128 gene expressions for 912...
In joint.Cox: Joint Frailty-Copula Models for Tumour Progression and Death in Meta-Analysis
Description
Usage
Format
Details
Source
References
Examples
Description
Meta-analytic data containing 128 gene expressions and time-to-death information for ovarian cancer patients.
The data include time-to-death, residual tumour size (>=1cm> vs. <1cm), and associated 128 gene expressions.
The dataset is a subset of the curated ovarian data of Ganzfried et al (2013).
We prepared the dataset by using "patientselection.config" in "Curated ovarian data" around October 2016.
Usage
1
data("dataOvarian2")
Format
A data frame with 912 observations on the following 132 variables.
t.death: time to death in days
death: death indicator (1=death, 0=alive)
group: study ID; group=4, 9, 12, or 16
debulk: residual tumour size (>=1cm> vs. <1cm)
ANKRD27a numeric vector
AP3S1a numeric vector
APMAPa numeric vector
ARHGAP28a numeric vector
ASAP1a numeric vector
ASAP3a numeric vector
ASB7a numeric vector
B4GALT5a numeric vector
BYSLa numeric vector
C1QTNF3a numeric vector
CASP8a numeric vector
CCL18a numeric vector
CD79Aa numeric vector
CDK19a numeric vector
CLIC4a numeric vector
COL11A1a numeric vector
COL16A1a numeric vector
COL3A1a numeric vector
COL5A1a numeric vector
COL5A2a numeric vector
COMPa numeric vector
COX7A2P2a numeric vector
CPNE1a numeric vector
CRISPLD2a numeric vector
CRYABa numeric vector
CTNNBL1a numeric vector
CXCL12a numeric vector of gene expressions.
The CXCL12 gene expression is a predictive biomarker of survival in ovarian cancer (Popple et al. 2012).
It has been known that CXCL12 promotes tumour growth, participates in tumour metastasis,
and suppresses tumour immunity (Kryczek et al. 2007).
The statistical significance of the CXCL12 expression on survival is first examined by Popple et al. (2012), and
is further confirmed by Ganzfried et al. (2013) based on the meta-analysis of 14 independent studies.
A meta-analysis using a joint model further confirmed that the expression of CXCL12 gene is predictive of
both cancer relapse and death (Emura et al. 2017; 2018)
CXCL9a numeric vector
CYBRD1a numeric vector
CYR61a numeric vector
CYTH3a numeric vector
DDX27a numeric vector
DLGAP4a numeric vector
DNAJC13a numeric vector
DYNLRB1a numeric vector
EFNB2a numeric vector
EIF3Ka numeric vector
ELNa numeric vector
EMP1a numeric vector
ENPP1a numeric vector
FABP4a numeric vector
FAPa numeric vector
FBLa numeric vector
FGF1a numeric vector
FOXN3a numeric vector
FSTL1a numeric vector
GABRG3a numeric vector
GAS1a numeric vector
GFRA1a numeric vector
GJC1a numeric vector
GPATCH1a numeric vector
GZMBa numeric vector
HLA.DOBa numeric vector
HOXA5a numeric vector
HP1BP3a numeric vector
HSD17B6a numeric vector
IL2RGa numeric vector
INHBAa numeric vector
ITGB1a numeric vector
ITPKCa numeric vector
JAM2a numeric vector
JUNa numeric vector
KCNH4a numeric vector
KDELC1a numeric vector
KIAA0355a numeric vector
KINa numeric vector
LEPa numeric vector
LOXa numeric vector
LPLa numeric vector
LSM14Aa numeric vector
LUMa numeric vector
LUZP1a numeric vector
MAPRE1a numeric vector
MCL1a numeric vector
MEOX2a numeric vector
MMP12a numeric vector
N4BP2L2a numeric vector
NCOA3a numeric vector of gene expressions.
The NCOA3 gene encodes a nuclear receptor coactivator, and amplification of the gene
occurs in breast and ovarian cancers (Anzick et al. 1997).
The overexpression of NCOA3 is associated with tumor size (Spears et al. 2012) and
tamoxifen resistance (Osborne et al. 2003), which are involved in the progression.
Yoshida et al. (2005) reported that NCOA3 could contribute to ovarian cancer progression by promoting cell migration.
In Emura et al. (2018), the overexpression of the gene was highly associated with time-to-relapse
(Coefficient=0.194, P-value<0.00001) and time-to-death (Coefficient=0.237, P-value<0.00001).
This result is consistent with the function of these reports.
NCOA6a numeric vector of gene expressions
NOTCH2NLa numeric vector
NR1H3a numeric vector
NUAK1a numeric vector
OATa numeric vector
OMDa numeric vector
PAK4a numeric vector
PCDH9a numeric vector
PDP1a numeric vector
PDPNa numeric vector of gene expressions. The PDPN gene encodes the podoplanin protein.
It is reported that cancer cells with higher PDPN expression have higher malignant potential due to enhanced platelet aggregation,
which promotes alteration of metastasis, cell motility, and epithelial-mesenchymal transition (Shindo et al. 2013).
Zhang et al. (2011) reported that overexpression of PDPN in fibroblasts is significantly associated with a poor prognosis in ovarian carcinoma.
In Emura et al. (2018), the overexpression of the gene was highly associated with time-to-relapse
(Coefficient=0.222, P-value<0.00001) and time-to-death (Coefficient=0.161, P-value<0.0001).
PHF20a numeric vector
PLXNA1a numeric vector
PSMC4a numeric vector
PSMD8a numeric vector
RAB13a numeric vector
RAI14a numeric vector
RARRES1a numeric vector
RBM39a numeric vector
RECQLa numeric vector
RIN2a numeric vector
RND3a numeric vector
RPS16a numeric vector
SACSa numeric vector
SH3PXD2Aa numeric vector
SKIa numeric vector
SLAMF7a numeric vector
SLC37A4a numeric vector
SMG5a numeric vector
SOCS5a numeric vector
SPARCa numeric vector
SSR4a numeric vector
STAU1a numeric vector
SUPT5Ha numeric vector
TBCBa numeric vector
TBCCa numeric vector
TEAD1a numeric vector of gene expressions.
TEAD1 encodes a ubiquitous transcriptional enhancer factor that is a member of the TEA/ATTS domain family.
It is reported that the protein level of TEAD1 was associated with poor prognosis in prostate cancer patients (Knight et al. 2008).
In Emura et al. (2018), the overexpression of the gene was highly associated with
time-to-relapse (Coefficient=0.195, P-value<0.00001) and time-to-death (Coefficient=0.223, P-value<0.00001).
TESK1a numeric vector
TIMP3a numeric vector
TJP1a numeric vector
TP53BP2a numeric vector
TSPAN9a numeric vector
TTI1a numeric vector
TUBB2Aa numeric vector
TUBB6a numeric vector
URI1a numeric vector
USP48a numeric vector
YWHABa numeric vector of gene expressions.
YWHAB encodes a protein belonging to the 14-3-3 family of proteins,
members of which mediate signal transduction by binding to phosphoserine-containing proteins.
It is reported that the protein of YWHAB can regulate cell survival, proliferation, and motility (Tzivion 2006).
Actually, it is reported that overexpression of this gene promotes tumor progression and was associated with
extrahepatic metastasis and worse survival in hepatocellular carcinoma (Liu et al. 2011).
In Emura et al. (2018), the overexpression of the gene was highly associated with
time-to-relapse (Coefficient=0.169, P-value<0.0001) and time-to-death (Coefficient=0.263, P-value<0.00001).
ZFP36a numeric vector
ZFP36L2a numeric vector
ZNF148a numeric vector
Details
4 studies are combined (group=4, 9, 12, and 16). The numbers 4, 9, 12 and 16 corresponds to the IDs
from the original data of Ganzfried et al. (2013).
Source
Ganzfried BF et al. (2013), Curated ovarian data: clinically annotated data for the ovarian cancer transcriptome, Database, Article ID bat013
References
Emura T, Nakatochi M, Murotani K, Rondeau V (2017), A joint frailty-copula model between tumour progression and death for meta-analysis,
Stat Methods Med Res 26(6):2649-66
Emura T, Nakatochi M, Matsui S, Michimae H, Rondeau V (2018), Personalized dynamic prediction of death according to tumour progression and high-dimensional
genetic factors: meta-analysis with a joint model, Stat Methods Med Res 27(9):2842-58
Ganzfried BF, et al. (2013), Curated ovarian data: clinically annotated data for the ovarian cancer transcriptome, Database, Article ID bat013.
Knight JF, et al. (2008), TEAD1 and c-Cbl are novel prostate basal cell markers that correlate with poor clinical outcome in prostate cancer. Br J Cancer 99:1849-58
Kryczek I, et al. (2007), Stroma-derived factor (SDF-1/CXCL12) and human tumor pathogenesis. Am J Physiol 292:987-95
Liu TA, et al. (2011), Increased expression of 14-3-3beta promotes tumor progression and predicts extrahepatic metastasis and worse survival
in hepatocellular carcinoma. Am J Pathol 179:2698-708
Osborne CK, et al. (2003), Role of the estrogen receptor coactivator AIB1 (SRC-3) and HER-2/neu in tamoxifen resistance in breast cancer. J Natl Cancer Inst 95:353-61
Popple A, et al. (2012), The chemokine, CXCL12, is an independent predictor of poor survival in ovarian cancer. Br J Cancer 106:1306-13
Shindo K, et al. (2013), Podoplanin expression in cancer-associated fibroblasts enhances tumor progression of invasive ductal carcinoma of the pancreas. Mol Cancer 12:168
Tzivion G, et al. (2006), 14-3-3 proteins as potential oncogenes. Semin Cancer Biol 16:203-13
Yoshida H, et al. (2005), Steroid receptor coactivator-3, a homolog of Taiman that controls cell migration in the Drosophila ovary,
regulates migration of human ovarian cancer cells. Mol Cell Endocrinol 245:77-85
Zhang Y, et al. (2011), Ovarian cancer-associated fibroblasts contribute to epithelial ovarian carcinoma metastasis by promoting angiogenesis,
lymphangiogenesis and tumor cell invasion. Cancer Lett 303:47-55
Examples
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data(dataOvarian2)
######## univariate Cox ##########
t.death=dataOvarian2$t.death
death=dataOvarian2$death
X.mat=dataOvarian2[,-c(1,2,3,4)] ## gene expression
Symbol=colnames(dataOvarian2)[-c(1,2,3,4)] ## gene symbol
p=ncol(X.mat)
P_value=coef=NULL
for(j in 1:p){
res=summary(coxph(Surv(t.death,death)~X.mat[,j]))$coefficients
P_value=c(P_value,res[5])
coef=c(coef,res[1])
}
data.frame( gene=Symbol[order(P_value)], P=P_value[order(P_value)],
coef=round(coef[order(P_value)],3) )
joint.Cox documentation built on Feb. 4, 2022, 5:08 p.m.
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Description Usage Format Details Source References Examples
Description
Meta-analytic data containing 128 gene expressions and time-to-death information for ovarian cancer patients. The data include time-to-death, residual tumour size (>=1cm> vs. <1cm), and associated 128 gene expressions. The dataset is a subset of the curated ovarian data of Ganzfried et al (2013). We prepared the dataset by using "patientselection.config" in "Curated ovarian data" around October 2016.
Usage
1 | data("dataOvarian2")
|
Format
A data frame with 912 observations on the following 132 variables.
t.death: time to death in days
death: death indicator (1=death, 0=alive)
group: study ID; group=4, 9, 12, or 16
debulk: residual tumour size (>=1cm> vs. <1cm)
ANKRD27a numeric vector
AP3S1a numeric vector
APMAPa numeric vector
ARHGAP28a numeric vector
ASAP1a numeric vector
ASAP3a numeric vector
ASB7a numeric vector
B4GALT5a numeric vector
BYSLa numeric vector
C1QTNF3a numeric vector
CASP8a numeric vector
CCL18a numeric vector
CD79Aa numeric vector
CDK19a numeric vector
CLIC4a numeric vector
COL11A1a numeric vector
COL16A1a numeric vector
COL3A1a numeric vector
COL5A1a numeric vector
COL5A2a numeric vector
COMPa numeric vector
COX7A2P2a numeric vector
CPNE1a numeric vector
CRISPLD2a numeric vector
CRYABa numeric vector
CTNNBL1a numeric vector
CXCL12a numeric vector of gene expressions. The CXCL12 gene expression is a predictive biomarker of survival in ovarian cancer (Popple et al. 2012). It has been known that CXCL12 promotes tumour growth, participates in tumour metastasis, and suppresses tumour immunity (Kryczek et al. 2007). The statistical significance of the CXCL12 expression on survival is first examined by Popple et al. (2012), and is further confirmed by Ganzfried et al. (2013) based on the meta-analysis of 14 independent studies. A meta-analysis using a joint model further confirmed that the expression of CXCL12 gene is predictive of both cancer relapse and death (Emura et al. 2017; 2018)
CXCL9a numeric vector
CYBRD1a numeric vector
CYR61a numeric vector
CYTH3a numeric vector
DDX27a numeric vector
DLGAP4a numeric vector
DNAJC13a numeric vector
DYNLRB1a numeric vector
EFNB2a numeric vector
EIF3Ka numeric vector
ELNa numeric vector
EMP1a numeric vector
ENPP1a numeric vector
FABP4a numeric vector
FAPa numeric vector
FBLa numeric vector
FGF1a numeric vector
FOXN3a numeric vector
FSTL1a numeric vector
GABRG3a numeric vector
GAS1a numeric vector
GFRA1a numeric vector
GJC1a numeric vector
GPATCH1a numeric vector
GZMBa numeric vector
HLA.DOBa numeric vector
HOXA5a numeric vector
HP1BP3a numeric vector
HSD17B6a numeric vector
IL2RGa numeric vector
INHBAa numeric vector
ITGB1a numeric vector
ITPKCa numeric vector
JAM2a numeric vector
JUNa numeric vector
KCNH4a numeric vector
KDELC1a numeric vector
KIAA0355a numeric vector
KINa numeric vector
LEPa numeric vector
LOXa numeric vector
LPLa numeric vector
LSM14Aa numeric vector
LUMa numeric vector
LUZP1a numeric vector
MAPRE1a numeric vector
MCL1a numeric vector
MEOX2a numeric vector
MMP12a numeric vector
N4BP2L2a numeric vector
NCOA3a numeric vector of gene expressions. The NCOA3 gene encodes a nuclear receptor coactivator, and amplification of the gene occurs in breast and ovarian cancers (Anzick et al. 1997). The overexpression of NCOA3 is associated with tumor size (Spears et al. 2012) and tamoxifen resistance (Osborne et al. 2003), which are involved in the progression. Yoshida et al. (2005) reported that NCOA3 could contribute to ovarian cancer progression by promoting cell migration. In Emura et al. (2018), the overexpression of the gene was highly associated with time-to-relapse (Coefficient=0.194, P-value<0.00001) and time-to-death (Coefficient=0.237, P-value<0.00001). This result is consistent with the function of these reports.
NCOA6a numeric vector of gene expressions
NOTCH2NLa numeric vector
NR1H3a numeric vector
NUAK1a numeric vector
OATa numeric vector
OMDa numeric vector
PAK4a numeric vector
PCDH9a numeric vector
PDP1a numeric vector
PDPNa numeric vector of gene expressions. The PDPN gene encodes the podoplanin protein. It is reported that cancer cells with higher PDPN expression have higher malignant potential due to enhanced platelet aggregation, which promotes alteration of metastasis, cell motility, and epithelial-mesenchymal transition (Shindo et al. 2013). Zhang et al. (2011) reported that overexpression of PDPN in fibroblasts is significantly associated with a poor prognosis in ovarian carcinoma. In Emura et al. (2018), the overexpression of the gene was highly associated with time-to-relapse (Coefficient=0.222, P-value<0.00001) and time-to-death (Coefficient=0.161, P-value<0.0001).
PHF20a numeric vector
PLXNA1a numeric vector
PSMC4a numeric vector
PSMD8a numeric vector
RAB13a numeric vector
RAI14a numeric vector
RARRES1a numeric vector
RBM39a numeric vector
RECQLa numeric vector
RIN2a numeric vector
RND3a numeric vector
RPS16a numeric vector
SACSa numeric vector
SH3PXD2Aa numeric vector
SKIa numeric vector
SLAMF7a numeric vector
SLC37A4a numeric vector
SMG5a numeric vector
SOCS5a numeric vector
SPARCa numeric vector
SSR4a numeric vector
STAU1a numeric vector
SUPT5Ha numeric vector
TBCBa numeric vector
TBCCa numeric vector
TEAD1a numeric vector of gene expressions. TEAD1 encodes a ubiquitous transcriptional enhancer factor that is a member of the TEA/ATTS domain family. It is reported that the protein level of TEAD1 was associated with poor prognosis in prostate cancer patients (Knight et al. 2008). In Emura et al. (2018), the overexpression of the gene was highly associated with time-to-relapse (Coefficient=0.195, P-value<0.00001) and time-to-death (Coefficient=0.223, P-value<0.00001).
TESK1a numeric vector
TIMP3a numeric vector
TJP1a numeric vector
TP53BP2a numeric vector
TSPAN9a numeric vector
TTI1a numeric vector
TUBB2Aa numeric vector
TUBB6a numeric vector
URI1a numeric vector
USP48a numeric vector
YWHABa numeric vector of gene expressions. YWHAB encodes a protein belonging to the 14-3-3 family of proteins, members of which mediate signal transduction by binding to phosphoserine-containing proteins. It is reported that the protein of YWHAB can regulate cell survival, proliferation, and motility (Tzivion 2006). Actually, it is reported that overexpression of this gene promotes tumor progression and was associated with extrahepatic metastasis and worse survival in hepatocellular carcinoma (Liu et al. 2011). In Emura et al. (2018), the overexpression of the gene was highly associated with time-to-relapse (Coefficient=0.169, P-value<0.0001) and time-to-death (Coefficient=0.263, P-value<0.00001).
ZFP36a numeric vector
ZFP36L2a numeric vector
ZNF148a numeric vector
Details
4 studies are combined (group=4, 9, 12, and 16). The numbers 4, 9, 12 and 16 corresponds to the IDs from the original data of Ganzfried et al. (2013).
Source
Ganzfried BF et al. (2013), Curated ovarian data: clinically annotated data for the ovarian cancer transcriptome, Database, Article ID bat013
References
Emura T, Nakatochi M, Murotani K, Rondeau V (2017), A joint frailty-copula model between tumour progression and death for meta-analysis, Stat Methods Med Res 26(6):2649-66
Emura T, Nakatochi M, Matsui S, Michimae H, Rondeau V (2018), Personalized dynamic prediction of death according to tumour progression and high-dimensional genetic factors: meta-analysis with a joint model, Stat Methods Med Res 27(9):2842-58
Ganzfried BF, et al. (2013), Curated ovarian data: clinically annotated data for the ovarian cancer transcriptome, Database, Article ID bat013.
Knight JF, et al. (2008), TEAD1 and c-Cbl are novel prostate basal cell markers that correlate with poor clinical outcome in prostate cancer. Br J Cancer 99:1849-58
Kryczek I, et al. (2007), Stroma-derived factor (SDF-1/CXCL12) and human tumor pathogenesis. Am J Physiol 292:987-95
Liu TA, et al. (2011), Increased expression of 14-3-3beta promotes tumor progression and predicts extrahepatic metastasis and worse survival in hepatocellular carcinoma. Am J Pathol 179:2698-708
Osborne CK, et al. (2003), Role of the estrogen receptor coactivator AIB1 (SRC-3) and HER-2/neu in tamoxifen resistance in breast cancer. J Natl Cancer Inst 95:353-61
Popple A, et al. (2012), The chemokine, CXCL12, is an independent predictor of poor survival in ovarian cancer. Br J Cancer 106:1306-13
Shindo K, et al. (2013), Podoplanin expression in cancer-associated fibroblasts enhances tumor progression of invasive ductal carcinoma of the pancreas. Mol Cancer 12:168
Tzivion G, et al. (2006), 14-3-3 proteins as potential oncogenes. Semin Cancer Biol 16:203-13
Yoshida H, et al. (2005), Steroid receptor coactivator-3, a homolog of Taiman that controls cell migration in the Drosophila ovary, regulates migration of human ovarian cancer cells. Mol Cell Endocrinol 245:77-85
Zhang Y, et al. (2011), Ovarian cancer-associated fibroblasts contribute to epithelial ovarian carcinoma metastasis by promoting angiogenesis, lymphangiogenesis and tumor cell invasion. Cancer Lett 303:47-55
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | data(dataOvarian2)
######## univariate Cox ##########
t.death=dataOvarian2$t.death
death=dataOvarian2$death
X.mat=dataOvarian2[,-c(1,2,3,4)] ## gene expression
Symbol=colnames(dataOvarian2)[-c(1,2,3,4)] ## gene symbol
p=ncol(X.mat)
P_value=coef=NULL
for(j in 1:p){
res=summary(coxph(Surv(t.death,death)~X.mat[,j]))$coefficients
P_value=c(P_value,res[5])
coef=c(coef,res[1])
}
data.frame( gene=Symbol[order(P_value)], P=P_value[order(P_value)],
coef=round(coef[order(P_value)],3) )
|
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