Network Biomarkers Constructed from Gene Expression and Protein-Protein Interaction Data for Accurate Prediction of Leukemia

Leukemia is a leading cause of cancer deaths in the developed countries. Great efforts have been undertaken in search of diagnostic biomarkers of leukemia. However, leukemia is highly complex and heterogeneous, involving interaction among multiple molecular components. Individual molecules are not necessarily sensitive diagnostic indicators. Network biomarkers are considered to outperform individual molecules in disease characterization. We applied an integrative approach that identifies active network modules as putative biomarkers for leukemia diagnosis. We first reconstructed the leukemia-specific PPI network using protein-protein interactions from the Protein Interaction Network Analysis (PINA) and protein annotations from GeneGo. The network was further integrated with gene expression profiles to identify active modules with leukemia relevance. Finally, the candidate network-based biomarker was evaluated for the diagnosing performance. A network of 97 genes and 400 interactions was identified for accurate diagnosis of leukemia. Functional enrichment analysis revealed that the network biomarkers were enriched in pathways in cancer. The network biomarkers could discriminate leukemia samples from the normal controls more effectively than the known biomarkers. The network biomarkers provide a useful tool to diagnose leukemia and also aids in further understanding the molecular basis of leukemia.

[1]  T. Haferlach,et al.  BAALC-associated gene expression profiles define IGFBP7 as a novel molecular marker in acute leukemia , 2010, Leukemia.

[2]  Rafael C. Jimenez,et al.  The IntAct molecular interaction database in 2012 , 2011, Nucleic Acids Res..

[3]  Jennifer L. McNeer,et al.  Pediatric Acute Lymphoblastic Leukemia: From Diagnosis to Prognosis. , 2015, Pediatric annals.

[4]  Rod K. Nibbe,et al.  Discovery and Scoring of Protein Interaction Subnetworks Discriminative of Late Stage Human Colon Cancer*S , 2009, Molecular & Cellular Proteomics.

[5]  Ying Wang,et al.  Molecular Signature of Cancer at Gene Level or Pathway Level? Case Studies of Colorectal Cancer and Prostate Cancer Microarray Data , 2013, Comput. Math. Methods Medicine.

[6]  Gordon K. Smyth,et al.  Use of within-array replicate spots for assessing differential expression in microarray experiments , 2005, Bioinform..

[7]  Bairong Shen,et al.  Integrative analysis reveals disease-associated genes and biomarkers for prostate cancer progression , 2014, BMC Medical Genomics.

[8]  P. Broderick,et al.  Lack of a relationship between the common 8q24 variant rs6983267 and risk of chronic lymphocytic leukemia , 2008, Leukemia.

[9]  P. Shannon,et al.  Cytoscape: a software environment for integrated models of biomolecular interaction networks. , 2003, Genome research.

[10]  Albert Gutierrez,et al.  LEF-1 is a prosurvival factor in chronic lymphocytic leukemia and is expressed in the preleukemic state of monoclonal B-cell lymphocytosis. , 2010, Blood.

[11]  Dmitrij Frishman,et al.  MIPS: a database for genomes and protein sequences , 1999, Nucleic Acids Res..

[12]  Christie S. Chang,et al.  The BioGRID interaction database: 2013 update , 2012, Nucleic Acids Res..

[13]  Ioannis Xenarios,et al.  DIP: The Database of Interacting Proteins: 2001 update , 2001, Nucleic Acids Res..

[14]  Sandhya Rani,et al.  Human Protein Reference Database—2009 update , 2008, Nucleic Acids Res..

[15]  A. Scorilas,et al.  The novel member of the BCL2 gene family, BCL2L12, is substantially elevated in chronic lymphocytic leukemia patients, supporting its value as a significant biomarker. , 2011, The oncologist.

[16]  Pearlly Yan,et al.  Epigenetics meets genetics in acute myeloid leukemia: clinical impact of a novel seven-gene score. , 2014, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[17]  S. Deaglio,et al.  In-tandem insight from basic science combined with clinical research: CD38 as both marker and key component of the pathogenetic network underlying chronic lymphocytic leukemia. , 2006, Blood.

[18]  Ying Wang,et al.  Identifying novel prostate cancer associated pathways based on integrative microarray data analysis , 2011, Comput. Biol. Chem..

[19]  Jiri Zavadil,et al.  MYB transcriptionally regulates the miR-155 host gene in chronic lymphocytic leukemia. , 2011, Blood.

[20]  M. Korenberg,et al.  Microarray Data Analysis , 2007, Methods in Molecular Biology.

[21]  Bairong Shen,et al.  Identification of candidate miRNA biomarkers from miRNA regulatory network with application to prostate cancer , 2014, Journal of Translational Medicine.

[22]  T. Ideker,et al.  Network-based classification of breast cancer metastasis , 2007, Molecular systems biology.

[23]  Mingming Jia,et al.  COSMIC: mining complete cancer genomes in the Catalogue of Somatic Mutations in Cancer , 2010, Nucleic Acids Res..

[24]  Y. Benjamini,et al.  Controlling the false discovery rate in behavior genetics research , 2001, Behavioural Brain Research.

[25]  Brad T. Sherman,et al.  Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources , 2008, Nature Protocols.

[26]  H. Döhner,et al.  Experimental Therapeutics , Molecular Targets , and Chemical Biology A Novel Paradigm to Trigger Apoptosis in Chronic Lymphocytic Leukemia , 2009 .

[27]  G. Mills,et al.  Small Molecule ErbB Inhibitors Decrease Proliferative Signaling and Promote Apoptosis in Philadelphia Chromosome–Positive Acute Lymphoblastic Leukemia , 2013, PloS one.

[28]  J. Miguel,et al.  Gene expression profiling of B lymphocytes and plasma cells from Waldenström's macroglobulinemia: comparison with expression patterns of the same cell counterparts from chronic lymphocytic leukemia, multiple myeloma and normal individuals , 2007, Leukemia.

[29]  Jiajia Chen,et al.  Discovery and characterization of long intergenic non-coding RNAs (lincRNA) module biomarkers in prostate cancer: an integrative analysis of RNA-Seq data , 2015, BMC Genomics.

[30]  Xiaoli Xie,et al.  KEGG-PATH: Kyoto encyclopedia of genes and genomes-based pathway analysis using a path analysis model. , 2014, Molecular bioSystems.

[31]  Livia Perfetto,et al.  MINT, the molecular interaction database: 2012 update , 2011, Nucleic Acids Res..

[32]  C. Pui,et al.  Biology, risk stratification, and therapy of pediatric acute leukemias: an update. , 2011, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[33]  J. Timmer,et al.  untreated indolent B-cell lymphoma and very early CLL Definition and characterization of the systemic T-cell dysregulation in , 2011 .

[34]  Yu-Chao Wang,et al.  A network-based biomarker approach for molecular investigation and diagnosis of lung cancer , 2011, BMC Medical Genomics.

[35]  Guido Marcucci,et al.  The prognostic and functional role of microRNAs in acute myeloid leukemia. , 2011, Blood.

[36]  Jianmin Wu,et al.  PINA v2.0: mining interactome modules , 2011, Nucleic Acids Res..

[37]  Xiangdong Wang,et al.  Role of clinical bioinformatics in the development of network-based Biomarkers , 2011, Journal of Clinical Bioinformatics.

[38]  Jiajia Chen,et al.  MicroRNA biomarker identification for pediatric acute myeloid leukemia based on a novel bioinformatics model , 2015, Oncotarget.

[39]  Corey A. Kemper,et al.  Reverse-engineering the genetic circuitry of a cancer cell with predicted intervention in chronic lymphocytic leukemia , 2012, Proceedings of the National Academy of Sciences.

[40]  J. Gribben,et al.  Chronic lymphocytic leukemia cells induce changes in gene expression of CD4 and CD8 T cells. , 2005, The Journal of clinical investigation.

[41]  G. Marcucci,et al.  Molecular prognostic factors in cytogenetically normal acute myeloid leukemia , 2012, Expert review of hematology.

[42]  T. G. Marr,et al.  Gene Expression Differences between Enriched Normal and Chronic Myelogenous Leukemia Quiescent Stem/Progenitor Cells and Correlations with Biological Abnormalities , 2011, Journal of oncology.

[43]  C. Bloomfield,et al.  The 2008 revision of the World Health Organization (WHO) classification of myeloid neoplasms and acute leukemia: rationale and important changes. , 2009, Blood.

[44]  T. Haferlach,et al.  Leukemia diagnosis: today and tomorrow , 2015, European journal of haematology.

[45]  Soheil Meshinchi,et al.  Identification of genes with abnormal expression changes in acute myeloid leukemia , 2008, Genes, chromosomes & cancer.

[46]  Jiajia Chen,et al.  Deciphering oncogenic drivers: from single genes to integrated pathways , 2015, Briefings Bioinform..