Gene expression differences among different MSI statuses in colorectal cancer

Colorectal cancer is the third most common cancer in males and second in females. This disease can be caused by genetic and acquired/environmental factors. Microsatellite instability (MSI) is one of the major mechanisms in colorectal cancer. This mechanism is a specific condition of genetic hyper mutability that results from incompetent DNA mismatch repair. MSI has been applied to classify different colorectal cancer subtypes. However, the effects of MSI status on gene expression are largely unknown. In our study, we integrated the gene expression profile and MSI status of all CRC samples from the TCGA database, and then categorized the CRC samples into three subgroups, namely, MSI‐stable, MSI‐low, and MSI‐high, according to the MSI status. We applied a novel computational method based on machine learning and screened the genes specifically expressed for the different colorectal cancer subtypes. The results showed the distinct mechanisms of the different colorectal cancer subtypes with MSI status and provided the genes that may be the optimal standards to further classify the various molecular subtypes of colorectal cancer with distinct MSI status.

[1]  B. Innis,et al.  Safety and Immunogenicity of Human Serum Albumin-Free MMR Vaccine in US Children Aged 12–15 Months , 2014, Journal of the Pediatric Infectious Diseases Society.

[2]  J. Manson,et al.  Circulating Vitamin D Levels and Risk of Colorectal Cancer in Women , 2015, Cancer Prevention Research.

[3]  Peter J. Park,et al.  The Landscape of Microsatellite Instability in Colorectal and Endometrial Cancer Genomes , 2013, Cell.

[4]  Kurt Hornik,et al.  The support vector machine under test , 2003, Neurocomputing.

[5]  R. Lothe,et al.  Regulator of Chromosome Condensation 2 Identifies High-Risk Patients within Both Major Phenotypes of Colorectal Cancer , 2015, Clinical Cancer Research.

[6]  Matej Horvat,et al.  Microsatellite instability in colorectal cancer , 2011, Radiology and oncology.

[7]  L. Klampfer,et al.  Cytokines, inflammation and colon cancer. , 2011, Current cancer drug targets.

[8]  C. Cremers,et al.  A novel locus for autosomal recessive nonsyndromic hearing impairment, DFNB63, maps to chromosome 11q13.2–q13.4 , 2007, Journal of Molecular Medicine.

[9]  K. Nakai,et al.  Diversification of transcriptional modulation: large-scale identification and characterization of putative alternative promoters of human genes. , 2005, Genome research.

[10]  Katerina Michalickova,et al.  Mutation profiling of mismatch repair-deficient colorectal cncers using an in silico genome scan to identify coding microsatellites. , 2002, Cancer research.

[11]  M. Rudzińska,et al.  Thyroid peroxidase (TPO) expressed in thyroid and breast tissues shows similar antigenic properties , 2017, PloS one.

[12]  S. Berndt,et al.  Incidence and mortality of colorectal cancer in individuals with a family history of colorectal cancer. , 2015, Gastroenterology.

[13]  Jung Eun Lee,et al.  Sex- and gender-specific disparities in colorectal cancer risk. , 2015, World journal of gastroenterology.

[14]  C. Sander,et al.  Analysis of microRNA-target interactions across diverse cancer types , 2013, Nature Structural &Molecular Biology.

[15]  Z. Lou,et al.  Dedicator of Cytokinesis 8 Interacts with Talin and Wiskott-Aldrich Syndrome Protein To Regulate NK Cell Cytotoxicity , 2013, The Journal of Immunology.

[16]  Boris Pasche,et al.  TGF-β signaling alterations and susceptibility to colorectal cancer , 2007 .

[17]  P. Bucher,et al.  Can Survival Prediction Be Improved By Merging Gene Expression Data Sets? , 2009, PloS one.

[18]  R. Mendel,et al.  Evidence for the physiological role of a rhodanese-like protein for the biosynthesis of the molybdenum cofactor in humans. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[19]  Peter W. Laird,et al.  Molecular Characterization of MSI-H Colorectal Cancer by MLHI Promoter Methylation, Immunohistochemistry, and Mismatch Repair Germline Mutation Screening , 2008, Cancer Epidemiology Biomarkers & Prevention.

[20]  B. Wegiel,et al.  Role and Regulation of Myeloid Zinc Finger Protein 1 in Cancer , 2015, Journal of cellular biochemistry.

[21]  J. Jeon,et al.  Exercise barriers in Korean colorectal cancer patients. , 2014, Asian Pacific journal of cancer prevention : APJCP.

[22]  อนิรุธ สืบสิงห์,et al.  Data Mining Practical Machine Learning Tools and Techniques , 2014 .

[23]  G. Freeman,et al.  The microsatellite instable subset of colorectal cancer is a particularly good candidate for checkpoint blockade immunotherapy. , 2015, Cancer discovery.

[24]  R. Roskoski.  The ErbB/HER family of protein-tyrosine kinases and cancer. , 2014, Pharmacological research.

[25]  Boris Pasche,et al.  TGF-beta signaling alterations and susceptibility to colorectal cancer. , 2007, Human molecular genetics.

[26]  Masahiro Inoue,et al.  The consensus molecular subtypes of colorectal cancer as a predictive factor for chemotherapies against metastatic colorectal cancer. , 2018 .

[27]  Jeffrey J Meyer,et al.  Cancer Genome Atlas Network. Comprehensive molecular characterization of human colon and rectal cancer. Nature 2012. (5) , 2013 .

[28]  J. Russo,et al.  Allelic instability as a predictor of survival in Egyptian breast cancer patients. , 1999, International journal of oncology.

[29]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[30]  P. Roach,et al.  GNIP, a Novel Protein That Binds and Activates Glycogenin, the Self-glucosylating Initiator of Glycogen Biosynthesis* , 2002, The Journal of Biological Chemistry.

[31]  E. Lin,et al.  Programmed death 1 blockade, an Achilles heel for MMR-deficient tumors? , 2015, Journal of Hematology & Oncology.

[32]  Jialiang Yang,et al.  Identify Key Sequence Features to Improve CRISPR sgRNA Efficacy , 2017, IEEE Access.

[33]  Fuhui Long,et al.  Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy , 2003, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[34]  J. Langhorst,et al.  A systematic review and meta-analysis of exercise interventions for colorectal cancer patients. , 2014, European journal of cancer care.

[35]  S. Hirohashi,et al.  Promoter hypermethylation contributes to frequent inactivation of a putative conditional tumor suppressor gene connective tissue growth factor in ovarian cancer. , 2007, Cancer research.

[36]  S. Huang,et al.  Adenovirus expressing RIZ1 in tumor suppressor gene therapy of microsatellite-unstable colorectal cancers. , 2001, Cancer research.

[37]  Xiongbin Lu,et al.  Abstract A36: TP53 loss creates therapeutic vulnerability in colorectal cancer , 2017 .

[38]  Céline Delucinge-Vivier,et al.  Identification of CHEK1, SLC26A4, c-KIT, TPO and TG as new biomarkers for human follicular thyroid carcinoma , 2016, Oncotarget.

[39]  L. Aaltonen,et al.  Identification of candidate oncogenes in human colorectal cancers with microsatellite instability. , 2013, Gastroenterology.

[40]  C. Zahnow,et al.  CHD4 Has Oncogenic Functions in Initiating and Maintaining Epigenetic Suppression of Multiple Tumor Suppressor Genes. , 2017, Cancer cell.

[41]  M. Akiyama,et al.  DNA mismatch repair enzymes: genetic defects and autoimmunity. , 2015, Clinica chimica acta; international journal of clinical chemistry.

[42]  J. Platt.  Sequential Minimal Optimization : A Fast Algorithm for Training Support Vector Machines , 1998 .

[43]  Pornpimol Charoentong,et al.  Characterization of the immunophenotypes and antigenomes of colorectal cancers reveals distinct tumor escape mechanisms and novel targets for immunotherapy , 2015, Genome Biology.

[44]  H. Löhmannsröben,et al.  Dual Role of the Molybdenum Cofactor Biosynthesis Protein MOCS3 in tRNA Thiolation and Molybdenum Cofactor Biosynthesis in Humans* , 2012, The Journal of Biological Chemistry.

[45]  James R. Eshleman,et al.  Microsatellite Instability as a Biomarker for PD-1 Blockade , 2016, Clinical Cancer Research.

[46]  Igor Jurisica,et al.  An integrative approach for the identification of prognostic and predictive biomarkers in rectal cancer , 2015, Oncotarget.

[47]  Jeffrey S. Morris,et al.  The Consensus Molecular Subtypes of Colorectal Cancer , 2015, Nature Medicine.

[48]  T. Ørntoft,et al.  Differential expression of DHHC9 in microsatellite stable and instable human colorectal cancer subgroups , 2007, British Journal of Cancer.

[49]  T. M. Rünger,et al.  Assessment of microsatellite instability in a cell line from a patient with xeroderma pigmentosum variant , 1998, Archives of Dermatological Research.

[50]  S. Kliewer,et al.  FGF19 as a Postprandial, Insulin-Independent Activator of Hepatic Protein and Glycogen Synthesis , 2011, Science.

[51]  Wentian Li.  Mutual information functions versus correlation functions , 1990 .

[52]  L. Kasturi,et al.  Biallelic inactivation of hMLH1 by epigenetic gene silencing, a novel mechanism causing human MSI cancers. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[53]  P. Aguiar,et al.  MMR deficiency may lead to a high immunogenicity and then an improvement in anti-PD-1 efficacy for metastatic colorectal cancer. , 2015, Immunotherapy.

[54]  J. Vauthey,et al.  Surgery: KRAS mutations and hepatic recurrence after treatment of colorectal liver metastases , 2017, Nature Reviews Gastroenterology & Hepatology.

[55]  F. Sinicrope.  The role of microsatellite instability testing in management of colorectal cancer. , 2016, Clinical advances in hematology & oncology : H&O.

[56]  D. Laune,et al.  Characterization of an adaptive immune response in microsatellite-instable colorectal cancer , 2014, Oncoimmunology.

[57]  M. Westerfield,et al.  Combinatorial expression of three zebrafish genes related to distal- less: part of a homeobox gene code for the head , 1994, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[58]  A. Valentini,et al.  Detection of selection signatures in Piemontese and Marchigiana cattle, two breeds with similar production aptitudes but different selection histories , 2015, Genetics Selection Evolution.

[59]  J. Herman,et al.  Incidence and functional consequences of hMLH1 promoter hypermethylation in colorectal carcinoma. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[60]  E. Kay,et al.  Diagnostic Potential of Zinc Finger Protein-Specific Autoantibodies and Associated Linear B-Cell Epitopes in Colorectal Cancer , 2015, PloS one.

[61]  Junchi Yan,et al.  Predicting protein-RNA interaction amino acids using random forest based on submodularity subset selection , 2014, Comput. Biol. Chem..

[62]  A. Laties,et al.  Degradation of extracellular ATP by the retinal pigment epithelium. , 2005, American journal of physiology. Cell physiology.

[63]  P. Pauwels,et al.  Immune Cells in Colorectal Cancer: Prognostic Relevance and Role of MSI , 2011, Cancer Microenvironment.

[64]  J. Hedderich,et al.  Expression of DNA mismatch repair proteins MLH1, MSH2, and MSH6 in recurrent glioblastoma , 2015, Neurological research.

[65]  Donald W. Bowden,et al.  Identification of a CA/TG Repeat Polymorphism Proximal to the Human DLX3 Gene , 1998, Human Heredity.

[66]  J. Pollack,et al.  A gene expression signature of genetic instability in colon cancer. , 2005, Cancer research.

[67]  F. Sinicrope,et al.  Microsatellite Instability Testing and Its Role in the Management of Colorectal Cancer , 2015, Current Treatment Options in Oncology.

[68]  Steven J. M. Jones,et al.  Comprehensive molecular characterization of human colon and rectal cancer , 2012, Nature.

[69]  K. Syrigos,et al.  Immune Response Gene Expression in Colorectal Cancer Carries Distinct Prognostic Implications According to Tissue, Stage and Site: A Prospective Retrospective Translational Study in the Context of a Hellenic Cooperative Oncology Group Randomised Trial , 2015, PloS one.

[70]  S. Narod,et al.  BRCA1 and BRCA2 mutations and the risk for colorectal cancer , 2015, Clinical genetics.

[71]  Mohamed F. Ghalwash,et al.  Minimum redundancy maximum relevance feature selection approach for temporal gene expression data , 2017, BMC Bioinformatics.

[72]  V. Brahmachari,et al.  Nucleosomal occupancy and CGG repeat expansion: a comparative analysis of triplet repeat region from mouse and human fragile X mental retardation gene 1 , 2011, Chromosome Research.

[73]  M. Blumenberg,et al.  The homeoprotein DLX3 and tumor suppressor p53 co-regulate cell cycle progression and squamous tumor growth , 2015, Oncogene.

[74]  W. F. Bodmer,et al.  SMAD4 mutations in colorectal cancer probably occur before chromosomal instability, but after divergence of the microsatellite instability pathway , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[75]  Woo Ick Yang,et al.  Genome-wide molecular characterization of mucinous colorectal adenocarcinoma using cDNA microarray analysis. , 2011, Oncology reports.