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Potential colon cancer biomarker search using more than two performance measures in a multiple criteria optimization approach.

Cancer is the second most relevant cause of death worldwide (1). Microarray experiments aim to measure the change in the genetic expression of tens of thousands of genes simultaneously and have been used to generate many of the genetic pipelines in cancer research (2). When considering normal and cancer tissues, genes with the highest differential expression between these states are potential cancer biomarkers. Many and varied methodologies have been proposed for the identification of these genes (3). Our research group has proposed that the identification of potential cancer biomarkers using microarray data be carried out through Multiple Criteria Optimization (MCO) techniques (4).

An MCO problem aims to find the best compromises between two or more conflicting criteria considered. The best compromises are located in the so-called "efficient frontier" of the MCO problem. Results of two or more analyses for a set of genes can be used as conflicting criteria that can be accommodated in an MCO problem. Our hypothesis is that the genes located in the efficient frontier of the related MCO problem are potential cancer biomarkers.

Data Envelopment Analysis (DEA) has been identified as being particularly well suited to the task of identifying the efficient frontiers of MCO problems (5). Here, the ability of the proposed method is explored using more than two performance measures and solved through DEA. Results show that the method identifies a consistent set of genes when increasing the number of performance measures in the MCO problem. It is also observed that convergence of a larger number of potential biomarkers is faster with additional criteria, i.e., more p-values.

Methods

Microarray Data

A colon cancer microarray database was selected for the described exploration. This database was first reported in Alon et al. (6) and is available at www.molbio.princeton.edu/ colondata. It contains the measured expression of 6,500 genes in 22 normal tissues and 40 cancer tissues, all of which were characterized using Affymetrix Hum6000 arrays.

Statistical Analysis

Statistical comparisons between the normal and cancer replicates were performed using the Mann-Whitney nonparametric test. The procedure is illustrated in Figure 1. P-values from different statistical analyses were obtained using partial permutations leaving one, two, or three tissues out of each state; the excluded tissues were selected randomly. From these comparisons, a total of ten different p-values were obtained for each gene. A p-value in the Mann-Whitney test is understood as representing the probability of finding a particular difference of medians between the two states by pure chance. Thus, to favor finding truly significant differences, low p-values are sought.

[FIGURE 1 OMITTED]

Multiple Criteria Optimization and Data Envelopment Analysis

Considering that the aim is to find those genes that change their expressions to the greatest degree between the different states, a p-value can be seen as a criterion to be minimized: smaller p-values show stronger evidence for the rejection of the stated null hypothesis, which relates to not having a significant difference between the two states. Thus, one can build an MCO problem considering the different p-values available for each gene as criteria intended to be minimized simultaneously.

The case of an MCO problem using two different p-values is presented in Figure 2(a). Given the minimization objective for both p-values, the efficient frontier is located in the southwest corner. In order to use DEA to find the efficient frontier, it is necessary to maximize at least one of the conflicting criteria, so a transformation (shown in Figure 2b) should be performed on at least one of the considered p-values. For the instances presented here, half of the p-values were transformed.

Graphical representation becomes complicated when using more than two p-values; however, the use of DEA to find the efficient frontier can be extended to the desired number of dimensions easily without a loss of generality. Banker-Charnes-Cooper (BCC) input- and output-oriented DEA models were used for the frontier search. For this search, genes identified in the previous frontier were removed from the original list and the search process repeated until the tenth frontier was reached. The instances presented here correspond to the use of 2, 4 and 8 p-values for the MCO problem. Results obtained from the different combinations were compared.

In order to express the idea of the method in a simple manner, one can think of a small p-value for a particular gene as being an indicator of its importance. Taking tissues out of the dataset creates a series of somewhat different datasets that allow the computation of multiple p-values for all genes. If all of the p-values for a particular gene are small, then it is likely that that gene will be significantly differentially expressed. Genes with these characteristics tend to cluster along the particular edges of the set of genes under analysis. MCO's objective is to find this specific edge (efficient frontier) and the genes lying on it.

MCO Gene Selection Validation

Since the purpose of this study was to see how useful it would be to model the microarray data analysis for potential biomarker identification as an MCO problem, the results needed to be validated. The validation was performed by undertaking a literature search for the genes identified by this method that changed their expression to the greatest degree between normal and cancer tissues.

[FIGURE 2 OMITTED]

Results

One of the most notable results was that the number of genes identified in the efficient frontiers increased as the number of p-values that were considered in the model also increased. Table 1 shows the genes found in the different combinations of p-values; the information about each identified gene is followed by the frontier where each gene was localized in the corresponding run.

All of the genes selected by the analysis but one, GTF3A, have been previously reported to change their expressions in colorectal cancer and/or other cancer types (Table 2). These reports are based on in vitro and/or in vivo experiments. Even though the role of GTF3A in cancer is still not confirmed, it is quite possible that changes in its expression could be related to cancer development. The GTF3A gene product is a transcription factor that regulates expression of 5 S ribosomal RNA.

Discussion

The MCO method of searching for biomarkers using available microarray data was demonstrated to be robust through the use of a different number of statistical p-values identifying a consistent set of genes. This approach may contribute to the rapid identification of genes by their biological validation as contributors to cancer. The method can also be explored using different DEA models and different types of available data, thus opening several opportunities for the meta-analysis of microarray experiments.

Acknowledgments

M.S.P was supported by a research assistantship from the Industrial Engineering Department at UPRM. Authors acknowledge the support of BioSEI Grant 33 010 3080 301 (awarded to M.C.R.) and that of the PROMEP project (103.5/07/2523, granted to C.I.).

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Erika Watts-Oquendo, BS *; Matilde Sanchez-Pena, MS *; Clara E. Isaza, PhD * [[dagger]]; Mauricio Cabrera-Rios, PhD *

* BioIE Lab, Department of Industrial Engineering, University of Puerto Rico Mayaguez Campus, Mayaguez, Puerto Rico; [[dagger]] Immunology Department, School of Biology, Universidad Autonoma de Nuevo Leon, Mexico

The authors have no conflicts of interest to disclose.

Address correspondence to: Mauricio Cabrera-Rios, PhD, Department of Industrial Engineering, University of Puerto Rico Mayaguez Campus, Call Box 9000, Mayaguez, PR 00681-9000. Email: mauricio.cabrera1@upr.edu
Table 1. List of genes identified using multiple p-values in the
Multiple Criteria Optimization (MCO) problem for the biomarker search.
The number describes the frontier where each gene was found according
to the different number of p-values executions used.

Accession   Gene Symbol   Gene Name
Number

R87126      yq31b10.s1    Soares fetal liver spleen 1NFLS
H08393      WDR77         WD repeat domain 77
R36977      GTF3A         General Transcription factor IIIA
M22382      HSPD1         Heat Shock 60kDa protein 1 (chaperonin)
M26383      IL8           Interleukin 8
X63629      CDH3          Cadherin 3, type 1, P-cadherin (placental)
H40095      yn85b03.s1    Soares adult brain N2b5HB55Y
X12671      HNRNPA1       Heterogeneous nuclear ribonucleoprotein A1
J05032      DARS          Aspartyl-tRNA synthetase
U09564      SRPK1         SRSF protein kinase 1
Z50753      GUCA2B        Guanylate cyclase activator 2B (uroguanylin)
J02854      MYL9          Myosin, light chain 9, regulatory
T47377      S100P         Calcium binding protein P
T86473      NME1          Non-metastatic cells 1, protein (NM23A)
H43887      CFD           Complement factor D (adipsin)
M36634      VIP           Vasoactive intestinal peptide
R08183      HSPE1         Heat Shock 10kDa protein 1 (chaperonin 10)
T71025      MT1G          Metallothionein 1G
U30825      SRSF9         Serine/arginine-rich splicing factor 9
X14958      HMGA1         High mobility group AT-hook 1
M26697      NPM1          Nucleophosmin (nucleolar phosphoprotein B23,
                            numatrin)
R84411      SNRPB         Small nuclear ribonucleoprotein polypeptides
                            B and B1
X12466      SNRPE         Small nuclear ribonucleoprotein
                            polypeptide E
M63391      DES           Desmin

                  p-values used

Accession   2-pv   4-pv   6-pv   8-pv
Number

R87126      1      1      1      1
H08393      2      1      1      1
R36977      3      2      2      1
M22382      4      3      2      1
M26383      5      4      3      2
X63629      5      4      3      2
H40095      5      4      3      2
X12671      5      4      3      2
J05032      6      5      4      2
U09564      6      5      4      2
Z50753      6      4      3      2
J02854      7      6      3      2
T47377      7      5      4      3
T86473      7      6      5      3
H43887      7      5      4      3
M36634      8      7      4      3
R08183      8      6      5      3
T71025      8      7      5      3
U30825      9      7      5      3
X14958      9      7      5      3
M26697      9      7      6      3
R84411      10     8      6      4
X12466      10     8      7      4
M63391      10     8      3      2

Table 2. List of MCO-identified genes with examples of different
types of cancer that have been shown to change their expression

Gene      Cancer type involvement (not a               References
          comprehensive list)

WDRF77    Ovarian, prostate                            7, 8
HSPD1     Colorectal                                   9
IL8       Colorectal                                   10, 11
CDH3      Biliary tract, esophageal                    1, 13
HNRNPA1   Involved in the switch to aerobic
            glycolysis, a process common to cancer     14
            cells.
DARS      Leukemia                                     15
SRPK1     Colorectal                                   16
GUCA2B    Colorectal                                   17
MYL9      Chicken sarcoma model for metastasis,        18, 19
            breast cancer cell motility
S100P     Pancreatic, lung adenocarcinomas, breast,    20, 21, 22, 23
            colon
NME1      Colon                                        24
CFD       Gastric, tongue, colon                       25, 26, 27
VIP       Colorectal                                   28
HSPE1     Proposed to have a role in cancer etiology   29
MTG1      Lung adenocarcinoma, colorectal              30, 31
SRSF9     Regulation of procancerous proteins          32, 33
HMGA1     Prostate, apoptosis inhibition               34, 35
NPM1      Acute myeloid leukemia                       36, 37
SNRPB     Proposed metastasis suppressor gene for      38
            prostate cancer
SNRPE     Hepatocellular carcinoma                     39
DES       Colorectal                                   40

MCO: Multiple Criteria Optimization
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Author:Watts-Oquendo, Erika; Sanchez-Pena, Matilde; Isaza, Clara E.; Cabrera-Rios, Mauricio
Publication:Puerto Rico Health Sciences Journal
Date:Jun 1, 2012
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