Open Access

Survival associated pathway identification with group L p penalized global AUC maximization

Algorithms for Molecular Biology20105:30

DOI: 10.1186/1748-7188-5-30

Received: 7 January 2010

Accepted: 16 August 2010

Published: 16 August 2010

Abstract

It has been demonstrated that genes in a cell do not act independently. They interact with one another to complete certain biological processes or to implement certain molecular functions. How to incorporate biological pathways or functional groups into the model and identify survival associated gene pathways is still a challenging problem. In this paper, we propose a novel iterative gradient based method for survival analysis with group L p penalized global AUC summary maximization. Unlike LASSO, L p (p < 1) (with its special implementation entitled adaptive LASSO) is asymptotic unbiased and has oracle properties [1]. We first extend L p for individual gene identification to group L p penalty for pathway selection, and then develop a novel iterative gradient algorithm for penalized global AUC summary maximization (IGGAUCS). This method incorporates the genetic pathways into global AUC summary maximization and identifies survival associated pathways instead of individual genes. The tuning parameters are determined using 10-fold cross validation with training data only. The prediction performance is evaluated using test data. We apply the proposed method to survival outcome analysis with gene expression profile and identify multiple pathways simultaneously. Experimental results with simulation and gene expression data demonstrate that the proposed procedures can be used for identifying important biological pathways that are related to survival phenotype and for building a parsimonious model for predicting the survival times.

Background

Biologically complex diseases such as cancer are caused by mutations in biological pathways or functional groups instead of individual genes. Statistically, genes sharing the same pathway have high correlations and form functional groups or biological pathways. Many databases about biological knowledge or pathway information are available in the public domain after many years of intensive biomedical research. Such databases are often named metadata, which means data about data. Examples of such databases include the gene ontology (GO) databases (Gene Ontology Consortium, 2001), the Kyoto Encyclopedia of Genes and Genomes (KEGG) database [2], and several other pathways on the internet (e.g., http://www.superarray.com; http://www.biocarta.com). Most current methods, however, are developed purely from computational points without utilizing any prior biological knowledge or information. Gene selections with survival outcome data in the statistical literature are mainly within the penalized Cox or additive risk regression framework [38]. The L1 and L p (p < 1) penalized Cox regressions can work for simultaneous individual gene selection and survival prediction and have been extensively studied in statistics and bioinformatics literature [811]. The performance of the survival model is evaluated by the global area under the ROC curve summary (GAUCS) [12]. Unfortunately, those methods are mainly for individual gene selections and cannot be used to identify pathways directly. In microarray analysis, several popular tools for pathway analysis, including GENMAP, CHIPINFO, and GOMINER, are used to identify pathways that are over-expressed by differentially expressed genes. These gene set enrichment analysis (GSEA)methods are very informative and are potentially useful for identifying pathways that related to disease status [13]. One drawback with GSEA is that it considers each pathway separately and the pathway information is not utilized in the modeling stage. Wei and Li [14] proposed a boosting algorithm incorporating related pathway information for classification and regression. However, their method can only be applied for binary phenotypes. Since most complex diseases such as cancer are believed to be associated with the activities of multiple pathways, new statistical methods are required to select multiple pathways simultaneously with the time-to-event phenotypes.

A ROC curve provides complete information on the set of all possible combinations of true-positive and false-positive rates, but is also more generally useful as a graphic characterization of the magnitude of separation between the case and control distributions. AUC is known to measure the probability that the marker value (score) for a randomly selected case exceeds the marker value for a randomly selected control and is directly related to the Mann-Whitney U statistic [15, 16]. In survival analysis, a survival time can be viewed as a time-varying binary outcome. Given a fixed time t, the instances for which t i = t are regarded as cases and samples with t i > t are controls. The global AUC summary (GAUCS) is then defined as GAUCS = P (M j > M k |t j < t k ), which indicates that subject who died at an earlier time has a larger score value, where M is a score function. Heagerty and Zheng [12] have shown that GAUCS is a weighted average of the area under time-specific ROC curves. Liu et al. [17] proposed a L1 penalized quadratic support vector machine (SVM) method for GAUCS maximization and individual gene selection with survival outcomes and showed the method outperformed the Cox regression. However, that method is only for gene selections and can not be directly used for identifying pathways without additional criteria. Group LASSO (L1) related penalized methods have been extensively studied recently in logistic regression [18], multiple kernel learning [19], and microarray analysis [20] with binary phenotypes. The methods are designed for selecting groups of variables and identifying important covariate groups (pathways). However, LASSO is biased. L p (p ≤ 1) (with one specific implementation entitled adaptive LASSO [1, 21]) is asymptotic unbiased and has oracle properties. Therefore, it is reasonable to extend the L p to group L p for pathway identifications. In this paper, we therefore extend L p to group L p penalty and develop a novel iterative gradient based algorithm for GAUCS maximization (IGGAUCS), which can effectively integrate genomic data and biological pathway information and identify disease associated pathways with right censored survival data. In Section 2, we formulate the GAUCS maximization and group L p penalty model and propose an efficient EM algorithm for survival prediction. Its performance is compared with its group lasso penalized Cox regression (implemented by ourselves). We also propose an integrated algorithm with GAUCS for microarray data analysis. The proposed approach is demonstrated with simulation and gene expression examples in Section 3. Concluding remarks are discussed in Section 4.

Group L p Penalized GAUCS Maximization

Consider we have a set of n independent observations { t i , δ i , x i } i = 1 n , where δ i is the censoring indicator and t i is the survival time (event time) if δ i = 1 or censoring time if δ i = 0, and x i is the m-dimensional input vector of the i th sample. We denote N i * ( t ) = 1 ( t i t ) and the corresponding increment d N i * ( t ) = N i * ( t ) N i * ( t ) . The time-dependent sensitivity and specificity are defined by sensitivity (c, t): Pr ( M i > c | t i = t ) = Pr ( M i > c | d N i * ( t ) = 1 ) and specificity (c, t): ( Pr ( M i c | t i > t ) = Pr ( M i c | N i * ( t ) = 0 ) . Here sensitivity measures the expected fraction of subjects with a marker greater than c among the subpopulation of individuals who die (cases) at time t, while specificity measures the fraction of subjects with a marker less than or equal to c among those who survive (controls) beyond time t. With this definition, a subject can play the role of a control for an early time, t < t i , but then play the role of case when t = t i . Then, ROC curves are defined as ROC t (q) = TP t {[FP t ]-1(q)} for q ϵ [0, 1], and the area under the ROC curve for time t is A U C ( t ) = 0 1 R O C t ( q ) d q , where TP t and FP t are the true and false positive rate at time t respectively, and [FP t ]-1(q) = inf c {c : FP t (c) ≤ q}. ROC methods can be used to characterize the ability of a marker to distinguish cases at time t from controls at time t. However, in many applications there is no prior time t identified and thus a global accuracy summary is defined by averaging over t:
G A U C S = 2 A U C ( t ) g ( t ) S ( t ) d t = Pr ( M j > M k | t j < t k ) ,
(1)

which indicates the probability that the subject who died (cases) at the early time has a larger value of the marker, where S(t) and g(t) are the survival and corresponding density functions, respectively.

Assuming there are r clusters in the input covariates, our primary aim is to identify a small number of clusters associated with survival time t i . Mathematically, for each input x i ϵ m , we are given a decomposition of m as a product of r clusters:

m = m 1 × ... × m r , so that each data point x i can be decomposed into r cluster components, i.e. x i = (xi 1,...,x ir ), where each x il is in general a vector. We define M(x) = w T x to be the risk score function, where w = ( w 1 , w 2 , ... , w r ) T m 1 + , ... , + m r is the vector of coefficients that has the same cluster decomposition as x i . We denote M i = M(x i ) for simplicity. Our goal is to encourage the sparsity of vector w at the level of clusters; in particular, we want most of its multivariate components w l to be zero. The natural way to achieve this is to explore the combination of L p (0 ≤ p ≤ 1) norm and L2 norm. Since w is defined by clusters, we define a weighted group L p norm
L p = l = 1 r d l | w l | p ,
where within every group, an L2 norm is used ( | w l | = ( w l T w l ) 1 / 2 ) and d l can be set to be 1 if all clusters are equally important. Note that group L p = L2 if r = 1 and d l = 1, and group L p = L p when r = m and d l = 1. We can define the optimization problem
max G A U C S = max P r ( M j > M k | t j < t k ) s . t . L p < β ,
(2)

where M j = w T x j . The ideal situation is that M(x j ) > M(x k ) or w T (x j - x k ) > 0, couple (x j , x k ) with corresponding times t j < t k (or j < k) and δ j = 1.

Pr(M j > M k |t j < t k ) can be estimated as
G A U C S = P r ( M j > M k | t j < t k ) = j < k δ j = 1 k = 2 n 1 M j > M k j < k δ j = 1 k = 2 n 1 ,
(3)

where 1a>b = 1 if a > b, and 0 otherwise. Obviously, GAUCS is a measure to rank the patients' survival time. The perfect GAUCS = 1 indicates that the order of all patients' survival time are predicted correctly and GAUCS = 0.5 indicates for a completely random choice.

One way to approximate step function 1 M j > M k is to use a sigmoid function σ ( z ) = 1 1 + e z and let N = j < k δ j = 1 k = 2 n 1 , then
G A U C S = j < k δ j = 1 k = 2 n σ ( w T ( x j x k ) ) N .
(4)
Equation (4) is nonconvex function and can only be solved with the conjugate gradient method to find a local minimum. Based on the property that the arithmetic average is greater than the geometric average, we have
j < k δ j = 1 k = 2 n σ ( w T ( x j x k ) ) N 1 N j < k δ j = 1 k = 2 n σ ( w T ( x j x k ) ) .
We can, therefore, maximize the following log likelihood lower bound of equation (4).
E p = 1 N j < k δ j = 1 k = 2 n log σ ( w T ( x j x k ) ) λ L p = 1 N j < k δ j = 1 k = 2 n log σ ( w T ( x j x k ) ) λ l = 1 r d l | w l | p ,
(5)

where λ is a penalized parameter controlling model complexity. Equation (5) is the maximum a posterior (MAP) estimator of w with Laplace prior provided we treat the sigmoid function as the pair-wise probability, i.e. Pr(M j > M k ) = σ(w T (x j - x k )). When p = 1, E p is a convex function. A global optimal solution is guaranteed.

The IGGAUCS Algorithm

In order to find the w that maximizes E p , we need to find the first order derivative. Since group L p with p ≤ 1 is not differentiable at |w l | = 0, differentiable approximations of group L p is required. We propose a local quadratic approximation for group |w l | p based on convex duality and local variational methods [23, 24]. Fan and Li [25] proposed a similar approximation for single variable. The adaptive LASSO approach proposed by Zou and Li [21] is a LASSO (linear bound) approximation for L p penalty. The drawback with that approach is that LASSO itself is not differentiable at |w l | = 0. Since |w l | p is concave when p < 1, we can have
f ( w l ) = | w l | p = min η l { η l | w l | 2 g ( η l ) } g ( η l ) = min | θ l | { η l | θ l | 2 f ( θ l ) } ,
(6)
where the function g(.) is the dual function of f(.) in variational analysis. Geometrically, g(η l ) represents the amounts of vertical shift applied to η l |w l |2 to obtain a quadratic upper bound with precision parameter η l , that touches f(w l ). Taking the first order derivative for η l θ l 2 f ( θ l ) , the minimum occurs at a solution of stationary equation when θ l ≠ 0,
2 η l | θ l | f ( θ l ) = 0 η l = f ( θ l ) 2 | θ l | ,
and f'(θ l ) = p|θ l |p-1. Substituting into f(w l ), we have the variational bound:
| w l | p f ( θ l ) 2 θ l ( | w l | 2 | θ l | 2 ) + f ( θ l ) = 1 2 { p | θ l | p 2 | w l | 2 + ( 2 p ) | θ l | p } ,
(7)
where θ l denote variational parameters. With the local quadratic bound, we have the following smooth lower bound.
E p = 1 N j < k δ j = 1 k = 2 n log σ ( w T ( x j x k ) ) λ l = 1 r d l | w l | p 1 N j < k δ j = 1 k = 2 n log σ ( w T ( x j x k ) ) λ l = 1 r d l 2 { p | θ l | p 2 | w l | 2 + ( 2 p ) | θ l | p } = E ( w , θ ) .
(8)

In equation (8), the lower bound E(w, θ) is differentiable w.r.t both w and θ. We therefore propose a EM algorithm to maximize E(w, θ) w.r.t w while keeping θ fixed and maximize E(w, θ) w.r.t the variational parameter θ to tighten the variational bound while keeping w fixed. Convergence to the local optimum is guaranteed. Since maximization w.r.t the variational parameters 0 = (|θ1|,|θ2|,..., |θ r |), with w being fixed, can be solved with the stationary equation E ( w , θ ) | θ l | = 0 we have θ l = w l , for l = 1, 2,..., r.

Given r candidate pathways potentially associated with the survival time, m l survival associated genes with the expression of x l on each pathway l (l = 1, 2,..., r), and letting w = (w1, w2,..., w r ) T be a vector of the corresponding coefficients and g ( w ) = w E ( w , θ ) w , we have the following iterative gradient algorithm for E(w, θ) maximization:

The IGGAUCS Algorithm

Given p, λ, and ϵ = 10-6, initializing w 1 = ( w 1 1 , w 2 1 , ... , w r 1 ) T randomly with nonzero w l 1 , l = 1 , ... , r , and set θ1 = w1.

Update w with θ fixed:

wt+1= w t + α t d t , where t: the number of iterations and α t : the step size, d t is updated with the conjugate gradient method:

d t = g(w t ) + u t d t and u t = [ g ( w t ) g ( w t 1 ) ] T g ( w t ) g ( w t 1 ) T g ( w t 1 ) .

Update θ with w fixed:

θt+1= wt+1

Stop when |wt+1- w t | < ϵ or maximal number of iterations exceeded.

Choice of Parameters

There are two parameters p and λ in this method, which can be determined through 10-fold cross validation. One efficient way is to set p = 0.1, 0.2,..., and 1 respectively, and search for an optimal λ for each p using cross validation. The best (p, λ) pair will be found with the maximal test GAUCS value. Theoretically when p = 1, E(w, θ) is convex and we can find the global maximum easily, but the solution is biased and small values of p would lead to better asymptotic unbiased solutions. Our results with limited experiments show that optimal p usually happens at a small p such as p = 0.1. For comparison purposes, we implement the popular Cox regression with group LASSO (GL1Cox), since there is no software available in the literature. Our implementation is based on group LASSO penalized partial log-likelihood maximization. The best λ is searched from λ ϵ [0.1, 25] for IGGAUCS and from λ ϵ [0.1, 40] for GL1Cox method with the step size of 0.1, as the L p penalty goes to zero much quicker than L1. We suggest that the larger step size such as 0.5 can be used for most applications, since the test GAUCS does not change dramatically with a small change of λ.

Computational Results

Simulation Data

We first perform simulation studies to evaluate how well the IGGAUCS procedure performs when input data has a block structure. We focus on whether the important variable groups that are associated with survival outcomes can be selected using the IGGAUCS procedure and how well the model can be used for predicting the survival time for future patients. In our simulation studies, we simulate a data set with a sample size of 100 and 300 input variables with 100 groups (clusters). The triple variables x1 - x3, x4 - x6, x7 - x9,..., x298 - x300 within each group are highly correlated with a common correlation γ and there are no correlations between groups. We set γ = 0.1 for weak correlation, γ = 0.5 for moderate, and γ = 0.9 for strong correlation in each triple group and generate training and test data sets of sample size 100 with each γ respectively from a normal distribution with the band correlation structure. We assume that the first three groups(9 covariates) (x1 - x3, x4 - x6, x7 - x9) are associated with survival and the 9 covariates are set to be w = [-2.9 2.1 2.4 1.6 -1.8 1.4 0.4 0.8 -0.5] t . With this setting, 3 covariates in the first group have the strongest association (largest covariate values) with survival time and 3 covariates in group 3 have less association with survival time. The survival time is generated with H = 100 exp(-w T x + ε) and the Weibull distribution, and the census time is generated from 0.8*median(time) plus a random noise. Based on this setting, we would expect about 25% - 35% censoring. To compare the performance of IGGAUCS and GL1Cox, we build the model based on training data set and evaluate the model with the test data set. We repeat this procedure 100 times and use the time-independent GAUCS to assess the predictive performance.

We first compare the performance of IGGAUCS and GL1Cox methods with the frequency of each of these three groups being selected under two different correlation structures based on 100 replications. The results are in Table 1. Table 1 shows that IGGAUCS with p = 0.1 outperforms the GL1Cox in that IGGAUCS can identify the true group structures more frequently under different inner group correlation structures. Its performance is much better than GL1Cox regression, when the inner correlation in a group is high (γ = 0.9) and the variables within a group have weak association with survival time.
Table 1

Frequency of Three Survival Associated Groups Selected in 100 Replications

 

IGGAUCS/GL1Cox

Parameters

γ = 0.1

γ = 0.5

γ = 0.9

w1 = -2.9

   

w2 = 2.1

100/100

100/100

100/100

w3 = 2.4

   

w4 = 1.6

   

w5 = -1.8

100/78

100/84

100/96

w6 = 1.4

   

w7 = 0.4

   

w8 = 0.8

47/2

53/4

94/24

w9 = -0.5

   
To compare more about the performance of IGGAUCS and GL1Cox in parameter estimation, we show the results for each parameter with different inner correlation structures (0.1, 0.5, 0.9) in Figure 1. For each parameter in Figure 1, the left bar represents the parameter estimated from GL1Cox, the middle bar is the true value of the parameter, and the right bar indicates parameter estimated from IGGAUCS. We observe that both the GL1Cox and IGGAUCS methods estimated the sign of the parameters correctly for the first two groups. However both methods can only estimate the sign of w8 correctly in group 3 with smaller coefficients. Moreover, ŵ estimated from IGGAUCS is much closer to the true w than that from GL1Cox, especially when the covariates are larger. This indicates that the L p (p = 0.1) penalty is less biased than the L1 penalty. The estimators of IGGAUCS are larger than that of GL1Cox with weak, moderate, and strong correlations. Finally, the test global AUC summaries (GAUCSs) of IGGAUCS and GL 1Cox with 100 replications are shown in Table 2. Table 2 shows that IGGAUCS performs better than GL1Cox regression. This is reasonable, since our method, unlike Cox regression which maximizes a partial log likelihood, directly maximizes the penalized GAUCS. One interesting result is that the test GAUCSs become smaller as the inner group correlation coefficient γ increases from 0.1 to 0.9. We also apply the gene harvesting method proposed by Hastie et al. (2001) and discussed by Segal (2006) [7, 26] to the simulation data, but don't show the results in Table 2. The gene harvest method uses the average gene expression in each group (cluster) and ignore the variance among genes within the group. The prediction performances are poor with test GAUCS of 0.57 ± 0.02 and 0.65 ± 0.016 respectively, when the correlations among genes are weak (γ = 0.1) and moderate (γ = 0.5). Its performance is slightly better with the test GAUCS of 0.75 ± 0.024, when γ = 0.9, but this4 performance is still not as good as either IGGAUCS or GL1Cox. One explantation is that the group is more heterogeneous with weaker correlations among variables, and the average does not provide a meaningful summary. Moreover, we cannot identify the survival association of individual variables using gene harvesting.
Table 2

Test GAUCS of Simulated Data w ith Different Correlation Structures

Correlation

IGGAUCS

GL1Cox

γ = 0.1

0.921(±0.023)

0.897(±0.031)

γ = 0.5

0.889(±0.021)

0.871(±0.024)

γ = 0.9

0.866(±0.017)

0.828(±0.025)

https://static-content.springer.com/image/art%3A10.1186%2F1748-7188-5-30/MediaObjects/13015_2010_Article_103_Fig1_HTML.jpg
Figure 1

True and Estimated Parameters. The true and estimated parameters with the simulation data are shown in Figure 2. The left bars represent each parameter estimated from GL1Cox, the middle bars are the true value of the parameter, and the right bars indicate parameters estimated form IGGAUCS.

Follicular Lymphoma (FL) Data

Follicular lymphoma is a common type of Non-Hodgkin Lymphoma (NHL). It is a slow growing lymphoma that arises from B-cells, a type of white blood cell. It is also called an "indolent" or "low-grad" lymphoma for its slow nature, both in terms of its behavior and how it looks under the microscope. A study was conducted to predict the survival probability of patients with gene expression profiles of tumors at diagnosis [27].

Fresh-frozen tumor biopsy specimens and clinical data were obtained from 191 untreated patients who had received a diagnosis of follicular lymphoma between 1974 and 2001. The median age of patients at diagnosis was 51 years (range 23 - 81) and the median follow up time was 6.6 years (range less than 1.0 - 28.2). The median follow up time among patients alive was 8.1 years. Four records with missing survival information were excluded from the analysis. Affymetrix U133A and U133B microarray gene chips were used to measure gene expression levels from RNA samples. A log 2 transformation was applied to the Affymetrix measurement. Detailed experimental protocol can be found in Dave et al. 2004. The data set was normalized for each gene to have mean 0 and variance 1. Because the data is very large and there are many genes with their expressions that either do not change cross samples or change randomly, we filter out the genes by defining a correlation measure with GAUCS for each gene xi R(t, x i ) = |2GAUCS(t, x i ) - 1|, where R = 1 when GAUCS = 0, or 1 and R = 0 when GAUCS = 0.5 (gene x i is not associated with the survival time). We perform the permutation test 1000 times for R to identify 2150 probes associated with survival time. We then identify 49 candidate pathways with 5 and more genes using DAVID. There are total 523 genes on the candidate pathways. Since a gene can be involved in more than one pathway, the number of distinguished genes should be a little less than 500. The 49 biological pathways are given in Table 3. We finally apply IGGAUCS to identify the small number of biological pathways associated with survival phenotypes. We first randomly divide the data into two subsets, one for training with 137 samples, and the other for testing with 50 samples. To avoid overfitting and bias from a particular partition, we randomly partition the data 50 times to estimate the performance of the model with the average of the test GAUCS. The regularization parameter λ is tuned using 10-fold cross-validation with training data only.
Table 3

Candidate Survival Associated Pathways

Pathways

# of Genes

Pathways

# of Genes

Propanoate metabolism

5

Melanoma

10

Type II diabetes mellitus

6

Thyroid cancer

5

Adipocytokine signaling pathway

10

Prostate cancer

13

Melanogenesis

13

Glycolysis/Gluconeogenesis

8

GnRH signaling pathway

11

Butanoate metabolism

6

Insulin signaling pathway

15

Endometrial cancer

11

Sphingolipid metabolism

5

Pancreatic cancer

10

Glycerophospholipid metabolism

9

Colorectal cancer

12

T cell receptor signaling pathway

11

RNA polymerase

6

Hematopoietic cell lineage

10

Huntington's disease

5

Glycerolipid metabolism

6

Focal adhesion

20

Toll-like receptor signaling pathway

13

Apoptosis

12

Antigen processing and presentation

9

Adherens junction

10

Complement and coagulation cascades

8

Tryptophan metabolism

7

ECM-receptor interaction

14

Histidine metabolism

6

Wnt signaling pathway

20

Fatty acid metabolism

10

Ubiquitin mediated proteolysis

15

Acute myeloid leukemia

9

Neuroactive ligand-receptor interaction

28

Bladder cancer

6

gamma-Hexachlorocyclohexane degradation

5

Focal adhesion

20

Calcium signaling pathway

21

ErbB signaling pathway

11

MAPK signaling pathway

31

PPAR signaling pathway

16

Valine, leucine and isoleucine degradation

6

Glioma

7

Pyrimidine metabolism

12

Chronic myeloid leukemia

10

Glycan structures - degradation

5

Non-small cell lung cancer

11

Porphyrin and chlorophyll metabolism

7

  
Since it is possible different pathways may be selected in the cross validation procedure, the relevance count concept [28] was utilized to count how many times a pathway is selected in the cross validation. Clearly, the maximum relevance count for a pathway is 200 with the 10-fold cross validation and 20 repeating. We have selected 8 survival associated pathways with IGGAUCS. The average test GAUCS is 0.892 ± 0.013. Moreover, the parameters (weights) w i and corresponding genes on each pathway indicate the association strength and direction between genes and the survival time. Positive w i s indicate that patients with high expression level die earlier and negative w i s represent that patients live longer with relatively high expression levels. The absolute values of |w i | indicate the strength of association between survival time and the specific gene. Genes on the pathway, estimated parameters, and relevance accounts are given in Table 4.
Table 4

Genes on pathway, relevance accounts, and estimated parameters

w i

GeneID

Gene Name

ECM-receptor interaction (relevance counts: 200)

0.2239

CD36

cd36 antigen (collagen type i receptor, thrombospondin receptor)

0.0409

FNDC1

fibronectin type iii domain containing 1

0.0746

SV2C

synaptic vesicle glycoprotein 2c

0.0804

SDC1

syndecan 1

-0.1255

FN1

fibronectin 1

0.0211

LAMC1

laminin, gamma 1 (formerly lamb2)

-0.0854

GP5

glycoprotein v (platelet)

-0.1130

CD47

cd47 antigen (rh-related antigen, integrin-associated signal transducer)

-0.1296

THBS2

thrombospondin 2

-0.0547

COL1A2

collagen, type i, alpha 2

-0.1024

COL5A2

collagen, type v, alpha 2

0.0861

LAMB4

laminin, beta 4

-0.0315

COL1A1

collagen, type i, alpha 1

0.0395

AGRN

agrin RG

Focal adhesion (relevance counts 145)

0.0054

PAK3

p21 (cdkn1a)-activated kinase 3

0.0446

PIK3R3

phosphoinositide-3-kinase, regulatory subunit 3 (p55, gamma)

0.0044

PDPK1

3-phosphoinositide dependent protein kinase-1

-0.0045

BAD

bcl2-antagonist of cell death

0.0087

PARVA

parvin, alpha

-0.0144

FN1

fibronectin 1

0.0041

LAMC1

laminin, gamma 1 (formerly lamb2)

-0.0202

PARVG

parvin, gamma

-0.0158

THBS2

thrombospondin 2

0.0134

PPP1R12A

protein phosphatase 1, regulatory (inhibitor) subunit 12a

-0.0044

SOS1

son of sevenless homolog 1 (drosophila)

-0.0084

COL1A2

collagen, type i, alpha 2

-0.0122

COL5A2

collagen, type v, alpha 2

0.0091

LAMB4

laminin, beta 4 RG Homo sapiens

-0.0068

RAF1

v-raf-1 murine leukemia viral oncogene homolog 1

-0.0038

ACTN1

actinin, alpha 1

-0.0034

COL1A1

collagen, type i, alpha 1

-0.0067

GSK3B

glycogen synthase kinase 3 beta

-0.0065

MAPK8

mitogen-activated protein kinase 8

-0.0030

MYL7

myosin, light polypeptide 7, regulatory

Neuroactive ligand-receptor interaction (relevance counts: 200)

0.0894

P2RY6

pyrimidinergic receptor p2y, g-protein coupled, 6

-0.2753

PTAFR

platelet-activating factor receptor

0.1648

GLRA3

glycine receptor, alpha 3

0.0857

FPRL1

formyl peptide receptor-like 1

-0.1783

EDNRA

endothelin receptor type a

0.3233

HRH4

histamine receptor h4

0.2106

GRM2

glutamate receptor, metabotropic 2

-0.1112

GRIN1

glutamate receptor, ionotropic, n-methyl d-aspartate 1

-0.0220

PTHR1

parathyroid hormone receptor 1

0.0971

OPRM1

opioid receptor, mu 1

-0.4303

CTSG

cathepsin g

-0.0404

P2RY8

purinergic receptor p2y, g-protein coupled, 8

-0.0783

BDKRB1

bradykinin receptor b1

0.3247

FSHR

follicle stimulating hormone receptor

-0.1430

ADRA1B

adrenergic, alpha-1b-, receptor

0.1464

C3AR1

complement component 3a receptor 1

0.1120

P2RX2

purinergic receptor p2x, ligand-gated ion channel, 2

0.0311

AVPR1B

arginine vasopressin receptor 1b

0.2646

FPR1

formyl peptide receptor 1

0.2003

GABRA5

gamma-aminobutyric acid (gaba) a receptor, alpha 5

-0.0278

PRLR

prolactin receptor

-0.1070

ADORA1

adenosine a1 receptor

0.2652

HTR7

5-hydroxytryptamine (serotonin) receptor 7 (adenylate cyclase-coupled)

-0.0194

GABRA4

gamma-aminobutyric acid (gaba) a receptor, alpha 4

0.0145

GHRHR

growth hormone releasing hormone receptor

-0.3163

MAS1

mas1 oncogene

-0.0760

PTGER3

prostaglandin e receptor 3 (subtype ep3)

0.2196

PARD3

par-3 partitioning defective 3 homolog (c. elegans)

Ubiquitin mediated proteolysis (relevance counts: 200)

0.0186

UBE2B

ubiquitin-conjugating enzyme e2b (rad6 homolog)

-0.0677

CUL4A

cullin 4a

0.0051

PML

promyelocytic leukemia

-0.1023

UBE3B

ubiquitin protein ligase e3b

-0.1581

UBE3C

ubiquitin protein ligase e3c

0.1390

BTRC

beta-transducin repeat containing

-0.0669

HERC3

hect domain and rld 3

0.00009

RBX1

ring-box 1

0.0011

CUL5

cullin 5

0.0267

ANAPC4

anaphase promoting complex subunit 4

-0.0253

UBE2L3

ubiquitin-conjugating enzyme e2l 3

0.0096

KEAP1

kelch-like ech-associated protein 1

-0.0267

UBE2E1

ubiquitin-conjugating enzyme e2e 1 (ubc4/5 homolog, yeast)

0.0116

CBL

cas-br-m (murine) ecotropic retroviral transforming sequence

0.0328

BIRC6

baculoviral iap repeat-containing 6 (apollon)

Porphyrin and chlorophyll metabolism (relevance counts: 185)

0.0330

BLVRA

biliverdin reductase a

-0.0103

FTH1

ferritin, heavy polypeptide 1

0.0227

ALAD

aminolevulinate, delta-, dehydratase

0.1983

HMOX1

heme oxygenase (decycling) 1

0.0070

UROS

uroporphyrinogen iii synthase (congenital erythropoietic porphyria)

-0.1596

GUSB

glucuronidase, beta

0.0077

UGT2B15

udp glucuronosyltransferase 2 family, polypeptide b15

Calcium signaling pathway (relevance counts: 200)

-0.0025

BST1

bone marrow stromal cell antigen 1

0.0003

BDKRB1

bradykinin receptor b1

-0.0016

PTAFR

platelet-activating factor receptor

-0.0002

ADRA1B

adrenergic, alpha-1b-, receptor

-0.0007

PPP3CC

protein phosphatase 3 (formerly 2b), catalytic subunit, gamma isoform

-0.0001

ADCY7

adenylate cyclase 7

0.0008

GNA11

guanine nucleotide binding protein (g protein), alpha 11 (gq class)

-0.0013

AVPR1B

arginine vasopressin receptor 1b

0.0014

P2RX2

purinergic receptor p2x, ligand-gated ion channel, 2

-0.0013

CACNA1E

calcium channel, voltage-dependent, alpha 1e subunit

0.0004

EDNRA

endothelin receptor type a

0.00009

SLC8A1

solute carrier family 8 (sodium/calcium exchanger), member 1

0.0006

CACNA1B

calcium channel, voltage-dependent, l type, alpha 1b subunit

-0.0013

PLCD1

phospholipase c, delta 1

-0.0029

HTR7

5-hydroxytryptamine (serotonin) receptor 7 (adenylate cyclase-coupled)

0.0014

GRIN1

glutamate receptor, ionotropic, n-methyl d-aspartate 1

0.0028

CAMK2A

calcium/calmodulin-dependent protein kinase (cam kinase) ii alpha

-0.0024

CACNA1I

calcium channel, voltage-dependent, alpha 1i subunit

-0.0002

TNNC1

troponin c type 1 (slow)

0.0009

PTGER3

prostaglandin e receptor 3 (subtype ep3)

0.0012

CACNA1F

calcium channel, voltage-dependent, alpha 1f subunit

Fatty acid metabolism (relevance counts: 192)

0.0043

ACSL3

acyl-coa synthetase long-chain family member 3

0.0675

ALDH2

aldehyde dehydrogenase 2 family (mitochondrial)

-0.0491

ACAT2

acetyl-coenzyme a acetyltransferase 2 (acetoacetyl coenzyme a thiolase)

0.0120

ALDH1B1

aldehyde dehydrogenase 1 family, member b1

0.0597

CYP4A11

cytochrome p450, family 4, subfamily a, polypeptide 11

-0.1447

ACADSB

acyl-coenzyme a dehydrogenase, short/branched chain

0.0736

CPT1A

carnitine palmitoyltransferase 1a (liver)

0.1075

CPT1B

carnitine palmitoyltransferase 1b (muscle)

-0.0366

ACADVL

acyl-coenzyme a dehydrogenase, very long chain

0.2168

ADH4

alcohol dehydrogenase 4 (class ii), pi polypeptide

MAPK signaling pathway (relevance counts: 200)

-0.1570

PLA2G10

phospholipase a2, group x

-0.2415

MAPKAPK5

mitogen-activated protein kinase-activated protein kinase 5

-0.1636

IL1B

interleukin 1, beta

-0.0651

ZAK

sterile alpha motif and leucine zipper containing kinase azk

0.0572

PPP3CC

protein phosphatase 3 (formerly 2b), catalytic subunit, gamma isoform

-0.0674

MAP3K2

mitogen-activated protein kinase kinase kinase 2

-0.1729

JUND

jun d proto-oncogene

-0.1718

SOS1

son of sevenless homolog 1 (drosophila)

0.1082

FGF14

fibroblast growth factor 14

0.3102

PTPN5

protein tyrosine phosphatase, non-receptor type 5

-0.3903

CACNB1

calcium channel, voltage-dependent, beta 1 subunit

0.2678

MAP3K7

mitogen-activated protein kinase kinase kinase 7

0.3176

CACNG8

calcium channel, voltage-dependent, gamma subunit 8

0.0893

FGF19

fibroblast growth factor 19

-0.0853

RRAS2

related ras viral (r-ras) oncogene homolog 2

0.0215

NLK

nemo-like kinase

0.0452

MAP4K4

mitogen-activated protein kinase kinase kinase kinase 4

0.1639

CACNA1E

calcium channel, voltage-dependent, alpha 1e subunit

-0.0290

ARRB1

arrestin, beta 1

-0.1169

STK4

serine/threonine kinase 4

0.1008

CACNA1B

calcium channel, voltage-dependent, l type, alpha 1b subunit

0.0839

MOS

v-mos moloney murine sarcoma viral oncogene homolog

-0.1244

MEF2C

mads box transcription enhancer factor 2, polypeptide c

0.1572

RAF1

v-raf-1 murine leukemia viral oncogene homolog 1

0.1757

MAPK8IP1

mitogen-activated protein kinase 8 interacting protein 1

0.2908

IKBKB

inhibitor of kappa light polypeptide gene enhancer in b-cells, kinase beta

-0.3452

CACNA1I

calcium channel, voltage-dependent, alpha 1i subunit

-0.2473

MAPK8

mitogen-activated protein kinase 8

-0.2339

CACNA1F

calcium channel, voltage-dependent, alpha 1f subunit

0.0979

CD14

cd14 antigen

-0.1047

MRAS

muscle ras oncogene homolog

The eight KEGG pathways identified play an important role in patient survivals and they can be ranked with the average |w j |: j |w j |/L, where L is the number of genes on a pathway as shown in Table 5. The identified 8 KEGG pathways fall into three categories (i) signaling molecules and interaction including the MAPK signaling pathway, the Calcium signaling pathway, Focal adhesion, ECM-receptor interactions and neuroactive ligand-receptor interactions, (ii) metabolic pathways including Fatty acid metabolism and Porphyrin and chlorophyll metabolism, and (iii) nitric oxide and cell stress including Ubiquitin mediated proteolysis. These pathways are involved in different aspects of genetic functions and are vital for cancer patient survivals. We only discuss the MAPK signaling pathway but others can be analyzed in a similar fashion. The top-rank MAPK signaling pathway transduces a large variety of external signals, leading to a wide range of cellular responses, including growth, differentiation, inflammation, and apoptosis invasiveness and ability to induce neovascularization. MAPK signaling pathway has been linked to different cancers including follicular lymphoma [29]. The pathway and genes on pathways are given in Figure 2.
Table 5

Pathway Ranks

Pathway

j |w j |/L

Rank

MAPK signaling pathway

0.1614

1

Neuroactive ligand-receptor interaction

0.1562

2

ECM-receptor interaction

0.0863

3

Fatty acid metabolism

0.0772

4

Porphyrin and chlorophyll metabolism

0.0627

5

Ubiquitin mediated proteolysis

0.0494

6

Focal adhesion

0.0100

7

Calcium signaling pathway

0.0012

8

https://static-content.springer.com/image/art%3A10.1186%2F1748-7188-5-30/MediaObjects/13015_2010_Article_103_Fig2_HTML.jpg
Figure 2

MAPK Signaling Pathway. MAPK signaling pathway and the associated genes. Genes in red are highly expressed in patients who died earlier and genes in yellow are highly expressed in patients who lived longer.

Genes in red color are highly expressed in patients with aggressive FL and genes in yellow are highly expressed in the earlier stage of FL cancers. Many important cancer related genes are identified with our methods. For example, SOS1, one of the RAS genes (e.g., MIM 190020), encodes membrane-bound guanine nucleotide-binding proteins that function in the transduction of signals that control cell growth and differentiation. Binding of GTP activates RAS proteins, and subsequent hydrolysis of the bound GTP to GDP and phosphate inactivates signaling by these proteins. GTP binding can be catalyzed by guanine nucleotide exchange factors for RAS, and GTP hydrolysis can be accelerated by GTPase-activating proteins (GAPs). SOS1 plays a crucial role in the coupling of RTKs and also intracellular tyrosine kinases to RAS activation. The deregulation of receptor tyrosine kinases (RTKs) or intracellular tyrosine kinases coupled to RAS activation has been involved in the development of a number of tumors, such as those in breast cancer, ovarian cancer and leukemia. Another gene, IL1B, is one of a group of related proteins made by leukocytes (white blood cells) and other cells in the body. IL1B, one form of IL1, is made mainly by one type of white blood cell, the macrophage, and helps another type of white blood cell, the lymphocyte, fight infections. It also helps leukocytes pass through blood vessel walls to sites of infection and causes fever by affecting areas of the brain that control body temperature. IL1B made in the laboratory is used as a biological response modifier to boost the immune system in cancer therapy.

As shown in Figure 2, the genes SOS1, IL1B, RAS, CACNB1, MEF2C, JUND, and MAPKAPK5 are highly expressed in patients who were diagnosed earlier and lived longer and the genes FGF14, PTPN5, MOS, RAF1, CD14 are highly expressed in patients who were diagnosed at more aggressive stages and died earlier, which may indicate that oncogenes such such SOS1, JUND, and RAS may initialize FL cancer and genes such as MOS, IKK, and CD14 may cause FL cancer to be more aggressive. There are several causal relations among the identified genes on MAPK. For instance, the down-expressed SOS and RAS cause the up-expressed RAF1 and MOS and the up-stream gene IL1 is coordinately expressed with CASP and the gene MST1/2.

Conclusions

Since a large amount of biological information on various aspects of systems and pathways is available in public databases, we are able to utilize this information in modeling genomic data and identifying pathways and genes and their interactions that might be related to patient survival. In this study, we have developed a novel iterative gradient algorithm for group L p penalized global AUC summary (IGGAUCS) maximization methods for gene and pathway identification, and for survival prediction with right censored survival data and high dimensional gene expression profile. We have demonstrated the applications of the proposed method with both simulation and the FL cancer data set. Empirical studies have shown the proposed approach is able to identify a small number of pathways with nice prediction performance. Unlike traditional statistical models, the proposed method naturally incorporates biological pathways information and it is also different from the commonly used Gene Set Enrichment Analysis (GSEA) in that it simultaneously considers multiple pathways associated with survival phenotypes.

With comprehensive knowledge of pathways and mammalian biology, we can greatly reduce the hypothesis space. By knowing the pathway and the genes that belong to particular pathways, we can limit the number of genes and gene-gene interactions that need to be considered in modeling high dimensional microarray data. The proposed method can efficiently handle thousands of genes and hundreds of pathways as shown in our analysis of the FL cancer data set.

There are several directions for our future investigations. For instance, we may want to further investigate the sensitivity of the proposed methods to the misspecification of the pathway information and misspecification of the model. We may also extend our method to incorporate gene-gene interactions and gene (pathway)- environmental interactions.

Even though we have only applied our methods to gene expression data, it is straightforward to extend our methods to SNP, miRNA CGH, and other genomic data without much modification.

Declarations

Acknowledgements

We thank the Associate Editor and the two anonymous referees for their constructive comments which helped improve the manuscript. ZL was partially supported by grant 1R03CA133899-01A210 from the National Cancer Institute.

Authors’ Affiliations

(1)
Greenebaum Cancer Center, University of Maryland
(2)
Department of Epidemiology and Preventive Medicine, The University of Maryland
(3)
Division of Biostatistics, Department of Pharmacology and Experimental Therapeutics, Thomas Jefferson University
(4)
Department of Oncology and Diagnosis Sciences, The University of Maryland Dental School

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© Liu et al; licensee BioMed Central Ltd. 2010

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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