Articles Online (Volume 15, Issue 6)

Original Research

Proteomic Analysis Shows Constitutive Secretion of MIF and p53-associated Activity of COX-2−/− Lung Fibroblasts

Mandar Dave, Abul B.M.M.K. Islam, Roderick V. Jensen, Agueda Rostagno, Jorge Ghiso, Ashok R. Amin

The differential expression of two closelyassociated cyclooxygenase isozymes, COX-1 and COX-2, exhibited functions beyond eicosanoid metabolism. We hypothesized that COX-1 or COX-2 knockout lung fibroblasts may display altered protein profiles which may allow us to further differentiate the functional roles of these isozymes at the molecular level. Proteomic analysis shows constitutive production of macrophage migration inhibitory factor (MIF) in lung fibroblasts derived from COX-2−/− but not wild-type (WT) or COX-1−/− mice. MIF was spontaneously released in high levels into the extracellular milieu of COX2−/− fibroblasts seemingly from the preformed intracellular stores, with no change in the basal gene expression of MIF. The secretion and regulation of MIF in COX-2−/− was “prostaglandin-independent.” GO analysis showed that concurrent with upregulation of MIF, there is a significant surge in expression of genes related to fibroblast growth, FK506 binding proteins, and isomerase activity in COX-2−/− cells. Furthermore, COX-2−/− fibroblasts also exhibit a significant increase in transcriptional activity of various regulators, antagonists, and co-modulators of p53, as well as in the expression of oncogenes and related transcripts. Integrative Oncogenomics Cancer Browser (IntroGen) analysis shows downregulation of COX-2 and amplification of MIF and/or p53 activity during development of glioblastomas, ependymoma, and colon adenomas. These data indicate the functional role of the MIF-COX-p53 axis in inflammation and cancer at the genomic and proteomic levels in COX-2-ablated cells. This systematic analysis not only shows the proinflammatory state but also unveils a molecular signature of a pro-oncogenic state of COX-1 in COX-2 ablated cells.

Page 339–351

Original Research

Interactome Analysis of Microtubule-targeting Agents Reveals Cytotoxicity Bases in Normal Cells

Andrés Julián Gutiérrez-Escobar, Gina Méndez-Callejas

Cancer causes millions of deaths annually and microtubule-targeting agents (MTAs) are the most commonly-used anti-cancer drugs. However, the high toxicity of MTAs on normal cells raises great concern. Due to the non-selectivity of MTA targets, we analyzed the interaction network in a non-cancerous human cell. Subnetworks of fourteen MTAs were reconstructed and the merged network was compared against a randomized network to evaluate the functional richness. We found that 71.4% of the MTA interactome nodes are shared, which affects cellular processes such as apoptosis, cell differentiation, cell cycle control, stress response, and regulation of energy metabolism. Additionally, possible secondary targets were identified as client proteins of interphase microtubules. MTAs affect apoptosis signaling pathways by interacting with client proteins of interphase microtubules, suggesting that their primary targets are non-tumor cells. The paclitaxel and doxorubicin networks share essential topological axes, suggesting synergistic effects. This may explain the exacerbated toxicity observed when paclitaxel and doxorubicin are used in combination for cancer treatment.

Page 352–360


OrthoGNC: A Software for Accurate Identification of Orthologs Based on Gene Neighborhood Conservation

Soheil Jahangiri-Tazehkand, Limsoon Wong, Changiz Eslahchi

Orthology relations can be used to transfer annotations from one gene (or protein) to another. Hence, detecting orthology relations has become an important task in the post-genomic era. Various genomic events, such as duplication and horizontal gene transfer, can cause erroneous assignment of orthology relations. In closely-related species, gene neighborhood information can be used to resolve many ambiguities in orthology inference. Here we present OrthoGNC, a software for accurately predicting pairwise orthology relations based on gene neighborhood conservation. Analyses on simulated and real data reveal the high accuracy of OrthoGNC. In addition to orthology detection, OrthoGNC can be employed to investigate the conservation of genomic context among potential orthologs detected by other methods. OrthoGNC is freely available online at and
同源基因被分类为两大类:直系同源基因(Orthologs)和旁系同源基因。由于Orthologs倾向于有相似功能,所以它可用来从一个基因(或者蛋白)注释转移到另一个基因的注释。因此,检测直系同源关系已成为后基因组时代的一个重要的任务。目前,直系同源关系分析主要有两大类方法:一是基于系统发育学算法,二是基于配对权重序列相似性的分类算法。它们虽然有较高的准确性,但各种各样的基因组事件,如复制和水平基因转移等可能造成错误的直系同源关系分配。在紧密相关的物种间,基因邻接信息可解决直系同源关系推论中的模棱两可的问题。在此,我们提出了一种基于基因邻接保守性来精确预测直系同源关系的软件——OrthoGNC。通过对拟合数据以及真实数据的分析,OrthoGNC表现出超高准确性。此外,OrthoGNC还可用于分析其他方法检测出的潜在orthologs的基因组间的保守性。总体而言,OrthoGNC是一款更加灵活多变、友好且可用于用户自定义参数的软件。它可免费线上使用: 和

Page 361–370


A Novel Nonlinear Parameter Estimation Method of Soft Tissues

Qianqian Tong, Zhiyong Yuan, Mianlun Zheng, Xiangyun Liao, Weixu Zhu, Guian Zhang

The elastic parameters of soft tissues are important for medical diagnosis and virtual surgery simulation. In this study, we propose a novel nonlinear parameter estimation method for soft tissues. Firstly, an in-house data acquisition platform was used to obtain external forces and their corresponding deformation values. To provide highly precise data for estimating nonlinear parameters, the measured forces were corrected using the constructed weighted combination forecasting model based on a support vector machine (WCFM_SVM). Secondly, a tetrahedral finite element parameter estimation model was established to describe the physical characteristics of soft tissues, using the substitution parameters of Young’s modulus and Poisson’s ratio to avoid solving complicated nonlinear problems. To improve the robustness of our model and avoid poor local minima, the initial parameters solved by a linear finite element model were introduced into the parameter estimation model. Finally, a self-adapting Levenberg–Marquardt (LM) algorithm was presented, which is capable of adaptively adjusting iterative parameters to solve the established parameter estimation model. The maximum absolute error of our WCFM_SVM model was less than 0.03 Newton, resulting in more accurate forces in comparison with other correction models tested. The maximum absolute error between the calculated and measured nodal displacements was less than 1.5 mm, demonstrating that our nonlinear parameters are precise.
软组织的弹性参数对于医学诊断和虚拟手术仿真非常重要。本文提出了一种软组织非线性参数估计方法。首先,使用基于支持向量机的加权组合预测模型(weighted combination forecasting model based on a support vector machine WCFM_SVM)校正数据获取平台采集的压力,为非线性参数估计提供高精度的压力数据。其次,建立四面体有限元非线性参数估计模型来描述软组织的物理特性,该模型采用杨氏模量和泊松比的代替参数来避免求解复杂的非线性问题,同时该模型引入了线性有限元模型求解的初始参数,以提高模型的鲁棒性并避免局部极小值问题。最后,采用提出的自适应Levenberg-Marquardt(LM)算法求解上述建立的四面体有限元非线性参数估计模型,本文的自适应Levenberg-Marquardt(LM)算法能够自适应调整迭代参数,以保证非线性参数的准确求解。实验结果表明,与其他压力校正模型相比,本文的WCFM_SVM模型的最大绝对误差小于0.03牛顿,能够得到更精确的压力;此外,采用本文估计的非线性参数计算的节点位移与测量的节点位移之间的最大绝对误差小于1.5毫米,表明了本文非线性参数估计方法的精确性。

Page 371–380


A Stack-based Ensemble Framework for Detecting Cancer MicroRNA Biomarkers

Sriparna Saha, Sayantan Mitra, Ravi Kant Yadav

MicroRNA (miRNA) plays vital roles in biological processes like RNA splicing and regulation of gene expression. Studies have revealed that there might be possible links between oncogenesis and expression profiles of some miRNAs, due to their differential expression between normal and tumor tissues. However, the automatic classification of miRNAs into different categories by considering the similarity of their expression values has rarely been addressed. This article proposes a solution framework for solving some real-life classification problems related to cancer, miRNA, and mRNA expression datasets. In the first stage, a multiobjective optimization based framework, non-dominated sorting genetic algorithm II, is proposed to automatically determine the appropriate classifier type, along with its suitable parameter and feature combinations, pertinent for classifying a given dataset. In the second page, a stack-based ensemble technique is employed to get a single combinatorial solution from the set of solutions obtained in the first stage. The performance of the proposed two-stage approach is evaluated on several cancer and RNA expression profile datasets. Compared to several state-of-the-art approaches for classifying different datasets, our method shows supremacy in the accuracy of classification.

Page 381–388


Hybrid Method Based on Information Gain and Support Vector Machine for Gene Selection in Cancer Classification

Lingyun Gao, Mingquan Ye, Xiaojie Lu, Daobin Huang

It remains a great challenge to achieve sufficient cancer classification accuracy with the entire set of genes, due to the high dimensions, small sample size, and big noise of gene expression data. We thus proposed a hybrid gene selection method, Information Gain-Support Vector Machine (IG-SVM) in this study. IG was initially employed to filter irrelevant and redundant genes. Then, further removal of redundant genes was performed using SVM to eliminate the noise in the datasets more effectively. Finally, the informative genes selected by IG-SVM served as the input for the LIBSVM classifier. Compared to other related algorithms, IG-SVM showed the highest classification accuracy and superior performance as evaluated using five cancer gene expression datasets based on a few selected genes. As an example, IG-SVM achieved a classification accuracy of 90.32% for colon cancer, which is difficult to be accurately classified, only based on three genes including CSRP1, MYL9, and GUCA2B.
基因表达数据存在高维、小样本和大量噪声等特点。当采用整个癌症样本的数据进行分类时,很难达到令人满意的分类精度。因此本文提出一种信息增益(information gain, IG)结合支持向量机(support vector machine, SVM)的混合式基因选择算法,称为IG-SVM。首先采用IG过滤不相关和冗余基因,然后利用SVM封装式特征选择方法进一步剔除冗余基因,最后将IG-SVM选择的信息基因子集输入LIBSVM分类器进行癌症分类验证。实验采用五组公开的癌症基因表达数据,在少量信息基因基础上验证了IG-SVM的分类有效性,与已有算法相比具有更优的性能。比如难以实现精确分类的肺癌数据,本文获得的三个信息基因为CSRP1、MYL9和GUCA2B,可以实现90.32%的分类精度。

Page 389–395

Application Note

The Ability of Different Imputation Methods to Preserve the Significant Genes and Pathways in Cancer

Rosa Aghdam, Taban Baghfalaki, Pegah Khosravi, Elnaz Saberi Ansari

Deciphering important genes and pathways from incomplete gene expression data could facilitate a better understanding of cancer. Different imputation methods can be applied to estimate the missing values. In our study, we evaluated various imputation methods for their performance in preserving significant genes and pathways. In the first step, 5% genes are considered in random for two types of ignorable and non-ignorable missingness mechanisms with various missing rates. Next, 10 well-known imputation methods were applied to the complete datasets. The significance analysis of microarrays (SAM) method was applied to detect the significant genes in rectal and lung cancers to showcase the utility of imputation approaches in preserving significant genes. To determine the impact of different imputation methods on the identification of important genes, the chi-squared test was used to compare the proportions of overlaps between significant genes detected from original data and those detected from the imputed datasets. Additionally, the significant genes are tested for their enrichment in important pathways, using the ConsensusPathDB. Our results showed that almost all the significant genes and pathways of the original dataset can be detected in all imputed datasets, indicating that there is no significant difference in the performance of various imputation methods tested. The source code and selected datasets are available on
French:Prédire d'important gènes et voies de signalisations à partir de données d'expression incomplètes pourrait faciliter une meilleur compréhension du cancer. Différentes méthodes d'imputation peuvent être appliquées pour estimer les valeurs manquantes. Dans notre étude, nous évaluons différentes méthodes pour leur capacité à conserver les gènes et voies de signalisations importants. remièrement, 5 % des gènes sont considérés aléatoirements comme ayant des données manquantes selon deux types de mecanismes de données manquantes ignorables et non-ignorables.Ensuite, 10 méthodes d'imputation courante ont été appliquées aux jeux de données complets. La méthode SAM (Significance Analysis of Microarrays) a été utilisée pour détecter les gènes significatifs dans des cancers rectaux et pulmonaires afin de monitorer la capacité à conserver les gènes significatifs des différentes approches d'imputation. Afin de déterminer l'impact des différentes méthodes sur l'identification des gènes d'importance, le test du chi-squared a été utilisé pour comparer les proportions de chevauchements entre les gènes significatifs détectés à partir du jeu de données d'origine et ceux détectés à partir des jeux de données tests. De plus, l'enrichissement en voies de signalisation d'intérêt au sein de ces gènes d'intérêt a été testée au moyen de ConsensusPathDB. Nos résultats montrent que presque tout les gènes et pathways significatifs du jeu de donnée d'origine peuvent être détectés dans tout les jeux de données tests, ce qui démontre qu'il n'y a pas de différences significatives dans les performances des différentes méthodes d'imputation testées. Le code source et les jeux de donnée selectionnés sont disponibles sur Farsi: تعیین ژن ها و مسیرهای مهم از روی داده های بیان ژن شامل مقادیر گمشده می تواند باعث درک بهتر برای سرطان شود. برای تخمین مقادیر گمشده می توان از روش های مختلف جانهی استفاده کرد. در این مطالعه، ما عملکرد روش های مختلف جانهی را در حفظ ژن ها و مسیرهای مهم بررسی کردیم. در ابتدا 5٪ ژن ها به طور تصادفی تحت دو نوع مکانیسم گمشدگی قابل چشم پوشی و غیر قابل چشم پوشی با نرخ های گمشده مختلف در نظر گرفته شدند. سپس ده روش شناخته شده برای جانهی داده های گمشده در نظر گرفته شد. بدین منظور از روش SAM برای تشخیص ژن های مهم در سرطان های روده و ریه برای نشان دادن توانایی روش های جانهی در حفظ ژن های مهم استفاده شده است. برای تعیین تاثیر روش های مختلف جانهی در شناسایی ژن های مهم، از آزمون chi-squared برای مقایسه مقادیر همپوشانی بین ژن های مهم شناسایی شده از روی داده های اصلی و داده های جانهی شده استفاده شده است.. علاوه بر این pathway enrichment با استفاده از ConsensusPathDB براساس ژن های مهم انجام گرفت. نتایج ما نشان داد که تقریبا تمام ژن ها و مسیرهای مهم بدست آمده براساس داده اصلی قابل شناسایی از روی داده های جانهی شده بودند، همچنین اختلاف معنی داری در عملکرد روش های مختلف جانهی وجود نداشته است. کدها و مجموعه داده های در در دسترس هستند. Japanese:我々の研究では、重要な遺伝子と重要な経路を保持するために用いられる、様々な帰属の方法に対する調査を行った。例えば、直腸ガンと肺がんにおける重要遺伝子を調査するためにはSAMを用た。また実験にはカイ2乗テスト、パスウェイ総合データベース等を用い相違を比較した。実験の結果、様々な帰属の方法の成果に大きな差異は見受けられなかった。

Page 396–404

Letter to the Editor

Comments on “Vitamin Pharmacogenomics: New Insight into Individual Differences in Diseases and Drug Responses”

Tom Greenfield

Page 405–406

Letter to the Editor

Author’s Reply: Comments on “Vitamin Pharmacogenomics: New Insight into Individual Differences in Diseases and Drug Responses”

Mou-Ze Liu, Hai-Yan He, Wei Zhang

Page 407