zbMATH — the first resource for mathematics

Prediction of metastasis in advanced colorectal carcinomas using CGH data. (English) Zbl 1382.92167
Summary: Logistic regression model (LRM) and artificial neural networks (ANNs) as two nonlinear models have been used to establish a novel two-stage hybrid modeling procedure for prediction of metastasis in advanced colorectal carcinomas. Two different datasets were used in training and testing procedures. For the first stage of hybrid modeling procedure, LRM was used to evaluate the contribution of DNA sequence copy number aberrations detected by Comparative Genomic Hybridization in advanced colorectal carcinoma and its metastasis. Then, the most effective parameters were selected by the LRM. Selected effective parameters among 565 detected chromosomal gains and losses were as follows: gain of 20q11.2, loss of 1q42, loss of 13q34, gain of 5q12, gain of 17p13, loss of 2q22, loss of 11q24 and gain of 2p11.2. Consequently, neural network models were constructed and fed by the parameters selected by LRM to build hybrid predictors on the two databases during self-consistency and jackknife tests, and performance of the hybrid model was verified. The results showed that our two-stage hybrid model approach is very promising for prediction of metastasis in advanced colorectal carcinomas.
92C50 Medical applications (general)
92B20 Neural networks for/in biological studies, artificial life and related topics
62P10 Applications of statistics to biology and medical sciences; meta analysis
Full Text: DOI
[1] Al-Mulla, F.; Keith, W. N.; Pickford, I. R.; Going, J. J.; Birnie, G. D., Comparative genomic hybridization analysis of primary colorectal carcinomas and their synchronous metastases, Genes Chromosome Cancer, 24, 306-314, (1999)
[2] Arribas, R.; Ribas, M.; Risques, R. A.; Masramon, L.; Tortola, S.; Marcuello, E.; Aiza, G.; Miro, R.; Capella, G.; Peinado, M. A., Prospective assessment of allelic losses at 4p 14-16 in colorectal cancer: two mutational patterns and a locus associated with poorer survival, Clin. Cancer Res., 5, 3454-3459, (1999)
[3] Aust, D. E.; Willenbucher, R. F.; Terdiaman, J. P.; Ferrell, L. D.; Chang, C. G.; Moore, D. H.; Molinaro-Clark, A.; Baretton, G. B.; Loehrs, U.; Waldman, F. M., Chromosomal alterations in ulcerative colitis-related and sporadic colorectal cancer by comparative genomic hybridization, Hum. Pathol., 31, 109-114, (2000)
[4] Bartosch-Härlid, A.; Andersson, B.; Aho, U.; Nilsson, J.; Andersson, R., Artificial neural networks in pancreatic disease, Br. J. Surg., 95, 7, 817-826, (2007)
[5] Bourdès, V. S.; Bonnevay, S.; Lisboa, P. J.; Aung, M. S.; Chabaud, S.; Bachelot, T.; Perol, D.; Negrier, S., Breast cancer predictions by neural networks analysis: a comparison with logistic regression, (Conf. Proc. IEEE Eng. Med. Biol. Soc., 2007, (2007)), 5424-5427
[6] Cai, Y. D., Predicting protein-protein interactions from sequences in a hybridization space, J. Proteome Res., 5, 316-322, (2006)
[7] Cai, Y. D.; Zhou, G. P., Predicting enzyme family classes by hybridizing gene product composition and pseudo amino acid composition, J. Theor. Biol., 234, 145-149, (2005)
[8] Chen, W.; Feng, P.; Yang, H.; Ding, H., Irna-AI: identifying the adenosine to inosine editing sites in RNA sequences, Oncotarget, 8, 3, 4208-4217, (2017)
[9] Chen, W.; Feng, P. M.; Deng, E. Z., Itis-psetnc: a sequence-based predictor for identifying translation initiation site in human genes using pseudo trinucleotide composition, Anal. Biochem., 462, 76-83, (2014)
[10] Chen, W.; Feng, P. M.; Lin, H., Irspot-psednc: identify recombination spots with pseudo dinucleotide composition, Nucleic Acids Res., 41, 6, e68, (2013)
[11] Chen, W.; Tang, H.; Ye, J.; Lin, H., Irna-pseu: identifying RNA pseudouridine sites, Mol. Ther. Nucleic Acids, 5, e332, (2016)
[12] Cheng, X.; Zhao, S. G., Iatc-mhyb: a hybrid multi-label classifier for predicting the classification of anatomical therapeutic chemicals, Oncotarget, (2017)
[13] Chou, K. C., Some remarks on protein attribute prediction and pseudo amino acid composition (50th anniversary year review), J. Theor. Biol., 273, 1, 236-247, (2015)
[14] Chou, K. C., Impacts of bioinformatics to medicinal chemistry, Med. Chem., 11, 3, 218-234, (2015)
[15] De Angelis, P. M.; Clausen, O. P.; Schjolberg, A.; Stokke, T., Chromosomal gains and losses in primary colorectal carcinomas detected by CGH and their associations with tumor DNA poidy genotypes and phenotypes, Br. J. Cancer, 80, 526-535, (1999)
[16] Fontaine, J. F.; Mirebeau-Prunier, D.; Franc, B.; Triau, S.; Rodien, P.; Houlgatte, R.; Malthièry, Y.; Savagner, F., Microarray analysis refines classification of non-medullary thyroid tumours of uncertain malignancy, Oncogene, 27, 15, 2228-2236, (2008)
[17] Guo, S. H.; Deng, E. Z.; Xu, L. Q., Inuc-pseknc: a sequence-based predictor for predicting nucleosome positioning in genomes with pseudo k-tuple nucleotide composition, Bioinformatics, 30, 1522-1529, (2014)
[18] Helen, E. A.; Timothy, J. S.; Janice, A. R.; David, W. H., Analysis of colorectal tumor progression by microdissection and comparative genomic hybridization, Genes Chromosome Cancer, 37, 369-380, (2003)
[19] Hidaka, S.; Yasutake, T.; Takeshita, H.; Kondo, M.; Tsuji, T.; Nanashima, A.; Sawai, T.; Yamaguchi, H.; Nakagoe, T.; Ayabe, H.; Tagawa, Y., Differences in 20q13.2 copy number between colorectal cancer with and without liver metastases, Clin. Cancer Res., 2712, 2712-2717, (2000)
[20] Hosmer, D. W.; Lemeshow, S., Applied logistic regression, (1989), Wiley New York · Zbl 0715.62125
[21] Houlston, R. S.; Tomlinson, I. P.M., Genetic prognostic markers in colorectal cancer, Mol. Pathol., 50, 281-288, (1997)
[22] Jahandideh, S.; Abdolmaleki, P.; Jahandideh, M.; Sadat Hayatshahi, S. H., Novel hybrid method for the evaluation of parameters contributing in determination of protein structural classes, J. Theo. Biol., 244, 275-281, (2007)
[23] Jia, J.; Liu, Z.; Xiao, X., Ippi-esml: an ensemble classifier for identifying the interactions of proteins by incorporating their physicochemical properties and wavelet transforms into pseaac, J. Theor. Biol., 377, 47-56, (2015)
[24] Jia, J.; Liu, Z.; Xiao, X., Psuc-lys: predict lysine succinylation sites in proteins with pseaac and ensemble random forest approach, J. Theor. Biol., 394, 223-230, (2016) · Zbl 1343.92153
[25] Jia, J.; Zhang, L.; Liu, Z., Psumo-CD: predicting sumoylation sites in proteins with covariance discriminant algorithm by incorporating sequence-coupled effects into general pseaac, Bioinformatics, 32, 20, 3133-3141, (2016)
[26] Jiang, Y.; Huang, T.; Chen, L.; Gao, Y. F., Signal propagation in protein interaction network during colorectal cancer progression, Biomed Res Int (BMRI), (2013)
[27] Juan, H. F.; Huang, H. C., Bioinformatics: microarray data clustering and functional classification, Methods Mol. Biol., 382, 405-416, (2007)
[28] Kallioniemi, O. P.; Kallioniemi, A.; Sudar, D.; Rutovitz, D.; Gray, J. W.; Waldman, F.; Pinkel, D., Comparative genomic hybridization: a rapid new method for detecting and mapping DNA amplification in tumors, (Semin. Cancer Biol., 4, (1993)), 41-46
[29] Knösel, T.; Petersen, S.; Schwabe, H.; Schlüns, K.; Stein, U.; Manfred Diete, P. M.S.; Petersen, I., Incidence of chromosomal imbalances in advanced colorectal carcinoma and their metastases, Virchows Arch., 440, 187-194, (2002)
[30] Knösel, T.; Schlüns, K.; Stein, U.; Schwabe, H.; Schlag, P. M.; Dietel, M.; Petersen, I., Chromosomal alterations during lymphatic and liver metastasis formation of colorectal cancer, Neoplasia, 6, 1, 23-28, (2004)
[31] Korn, W. M.; Yasutake, T.; Kuo, W.; Warren, R. S.; Collins, C.; Tomita, M.; Gray, J.; Waldman, F. M., Chromosomal arm 20q gains and other genomic alterations in colorectal cancer metastatic to liver, as analyzed by comparative genomic hybridization and fluorescence in situ hybridization, Genes Chromosome Cancer, 25, 82-90, (1999)
[32] Li, B. Q.; Huang, T.; Liu, L., Identification of colorectal cancer related genes with mrmr and shortest path in protein-protein interaction network, PLoS ONE, 7, 4, e33393, (2012)
[33] Li, L. S.; Kim, N. G.; Kim, S. H.; Park, C.; Kim, H.; Kang, H. J.; Koh, K. H.; Kim, S. N.; Kim, W. H.; Kim, N. K.; Kim, H., Chromosomal imbalances in the colorectal carcinomas with microsatellite instability, Am. J. Pathol., 163, 1429-1436, (2003)
[34] Lin, H.; Deng, E. Z.; Ding, H., Ipro54-pseknc: a sequence-based predictor for identifying sigma-54 promoters in prokaryote with pseudo k-tuple nucleotide composition, Nucleic Acids Res., 42, 12961-12972, (2014)
[35] Liu, B.; Fang, L.; Long, R.; Lan, X., Ienhancer-2L: a two-layer predictor for identifying enhancers and their strength by pseudo k-tuple nucleotide composition, Bioinformatics, 32, 3, 362-369, (2016)
[36] Liu, B.; Long, R.; Chou, K. C., Idhs-EL: identifying dnase I hypersensi-tivesites by fusing three different modes of pseudo nucleotide composition into an en-semble learning framework, Bioinformatics, 32, 2411-2418, (2016)
[37] Liu, B.; Wu, H.; Zhang, D.; Wang, X., Pse-analysis: a python package for DNA/RNA and protein/peptide sequence analysis based on pseudo components and kernel methods, Oncotarget, 8, 3, 4208-4217, (2017)
[38] Liu, B.; Zhang, D.; Xu, R., Combining evolutionary information extracted from frequency profiles with sequence-based kernels for protein remote homology detection, Bioinformatics, 30, 472-479, (2014)
[39] Liu, Z.; Xiao, X.; Yu, D. J.; Jia, J., Prnam-PC: predicting N-methyladenosine sites in RNA sequences via physical-chemical properties, Anal. Biochem., 497, 60-67, (2016)
[40] Mathew, B. W., Comparison of the predicted and observed secondary structure of T4 phage lysozyme, Biochim. Biophys. Acta, 405, 442-451, (1975)
[41] Matsuo, T.; Yamaguchi, S.; Mitsui, S.; Emi, A.; Shimoda, F.; Okamura, H., Control mechanism of the Circadian clock for timing of cell division in vivo, Science, 302, 255-259, (2003)
[42] Mattfeldt, T.; Gottfried, H. W.; Wolter, H.; Schmidt, V.; Kestler, H. A.; Mayer, J., Classification of prostatic carcinoma with artificial neural networks using comparative genomic hybridization and quantitative stereological data, Pathol. Res. Pract., 199, 12, 773-784, (2003)
[43] Meher, P. K.; Sahu, T. K.; Saini, V.; Rao, A. R., Predicting antimicrobial peptides with improved accuracy by incorporating the compositional, physico-chemical and structural features into Chou’s general pseaac, Sci. Rep., 42362, (2017)
[44] Meijer, G. A.; Hermsen, M. A.J. A.; Baak, J. P.A.; Diest, P. J.V.; Meuwissen, S. G.M.; Beliën, J. A.M.; Hoovers, J. M.N.; Joenje, H.; Snijders, P. J.F.; Walboomers, J. M.M., Progression from colorectal adenoma to carcinoma is associated with non-random chromosomal gains as detected by comparative genomic hybridization, J. Clin. Pathol., 51, 901-909, (1998)
[45] Nagelkerke, N. J.D., A note on general definition of the coefficient of determination, Biometrika, 78, 691-692, (1991) · Zbl 0741.62069
[46] Nakao, K.; Shibusawa, M.; Tsunoda, A.; Yoshizawa, H.; Murakami, M.; Kusano, M.; Uesugi, N.; Sasaki, K., Genetic changes in primary colorectal cancer by comparative genomic hibridization, Surg. Today, 28, 567-569, (1998)
[47] Ogawa, S.; Goto, W.; Orimo, A.; Hosoi, T.; Ouchi, Y.; Muramats, M.; Inoue, S., Molecular cloning of a novel RING finger-B box-coiled coil (RBCC) protein, terf, expressed in the testis, Biochem. Biophys. Res. Commun., 251, 515-519, (1998)
[48] Paredes-zaglul, A.; Kang, J.; Essig, Y. P.; Mao, W.; Irby, R.; Wloch, M.; Yeatman, T. J., Analysis of colorectal cancer by comparative genomic hybridization: evidence for induction of metastasis phenotype by loss of tumor suppressor genes, Clin. Cancer Res., 4, 879-886, (1998)
[49] Platzer, P.; Madhvi, B. U.; Wilson, K.; Willis, J.; Lutterbaugh, J.; Nosrati, A.; Willson, J. K.V.; Mack, D.; Ried, T.; Markowitz, S., Silence of chromosomal amplification in colon, Cancer Res., 62, 1134-1138, (2000)
[50] Qiu, W. R.; Sun, B. Q.; Xiao, X., Iptm-mlys: identifying multiple lysine PTM sites and their different types, Bioinformatics, 32, 3116-3123, (2016)
[51] Qiu, W. R.; Sun, B. Q.; Xiao, X., Ihyd-psecp: identify hydroxyproline and hydroxylysine in proteins by incorporating sequence-coupled effects into general pseaac, Oncotarget, 7, 28, 44310-44321, (2016)
[52] Rapaport, F.; Barillot, E.; Vert, J. P., Classification of arraycgh data using fused SVM, Bioinformatics, 24, 13, 375-382, (2008)
[53] Rhee, I.; Bachman, K. E.; Park, B. H.; Jair, K. W.; Yen, R. W.C.; Schuebel, K. E.; Cui, H.; Feinberg, A. P.; Lengauer, C.; Kinzler, K. W.; Baylin, S. B.; Vogelstein, B., DNMT1 and DNMT3b cooperate to silence genes in human cancer cells, Nature, 416, 552-556, (2002)
[54] Richard, M.; Pokorny, E. L.; Galandiuk, H. S., What’s new with tumor markers for colorectal cancer?, Dig. Surg., 17, 209-215, (2000)
[55] Ried, T.; Knutsen, R.; Steinbeck, R.; Blegen, H.; Schröck, E.; Heselmeyer, K.; Manuoir, S. D.; Auer, G., Comparative genomic hybridization reveals a specific pattern of chromosomal gains and losses during the genesis of colorectal tumors, Genes Chromosome Cancer, 15, 234-245, (1996)
[56] Robertson, K. D.; Uzvolgyi, E.; Liang, G.; Talmadge, C.; Sumegi, J.; Gonzales, F. A.; Jones, P. A., The human DNA methyltransferases (DNMTs) 1, 3a and 3b: coordinate mrna expression in normal tissues and overexpression in tumors, Nucleic Acids Res., 27, 2291-2298, (1999)
[57] Sartor, H.; Ehlert, F.; Grzeschik, K. H.; Muller, R.; Adolph, S., Assignment of two human cell cycle genes, CDC25C and CCNB1, to 5q31 and 5q12, respectively, Genomics, 13, 911-912, (1992)
[58] Sehhati, M.; Mehridehnavi, A.; Rabbani, H.; Pourhossein, M., Stable gene signature selection for prediction of breast cancer recurrence using joint mutual information, IEEE/ACM Trans. Comput. Biol. Bioinform, 12, 1440-1448, (2015)
[59] Shen, H. B., Memtype-2L: a web server for predicting membrane proteins and their types by incorporating evolution information through pse-PSSM, Biochem. Biophys. Res. Comm., 360, 339-345, (2007)
[60] Shen, H. B., Review: recent advances in developing web-servers for predicting protein attributes, Nat. Sci., 1, 63-92, (2009)
[61] Shen, H. B., Cell-ploc 2.0: an improved package of web-servers for predicting subcellular localization of proteins in various organisms, Nat. Sci., 2, 1090-1103, (2010)
[62] Statnikov, A.; Aliferis, C., Are random forests better than support vector machines for microarray-based cancer classification?, (AMIA Annu. Sympos Proc., 11, (2007)), 686-690
[63] Statnikov, A.; Wang, L.; Aliferis, C. F., A comprehensive comparison of random forests and support vector machines for microarray-based cancer classification, BMC Bioinform., 22, 9, 319, (2008)
[64] Suehiro, Y.; Umayahara, K.; Ogata, H.; Numa, F.; Yamashita, Y.; Oga, A.; Morioka, H.; Ito, T.; Kato, H.; Sasaki, K., Genetic aberrations detected by comparative genomic hybridization predict outcome in patients with endometrioid carcinoma, Genes Chromosome Cancer, 29, 75-82, (2000)
[65] Tarkkanen, M.; Elomaa, I.; Blomqvist, C.; Kivioja, A. H.; Kellokumpu-Lehtinen, P.; Bohling, T.; Valle, J.; Knuutila, S., DNA sequence copy number increase at 8q: a potential new prognostic marker in high-grade osteosarcoma, Int. J. Cancer, 84, 114-121, (1999)
[66] Weber, R. G.; Sommer, C.; Albert, F. K.; Kiessling, M.; Crèmer, T., Clinically distinct subgroups of glioblastoma multiforme studied by comparative genomic hybridization, Lab. Invest., 74, 108-119, (1996)
[67] Wild, P. J.; Catto, J. W.; Abbod, M. F.; Linkens, D. A.; Herr, A.; Pilarsky, C.; Wissmann, C.; Stoehr, R.; Denzinger, S.; Knuechel, R.; Hamdy, F. C.; Hartmann, A., Artificial intelligence and bladder cancer arrays, Verh. Dtsch. Ges. Pathol., 91, 308-319, (2007)
[68] Willenbucher, R. F.; Aust, D. E.; Chang, C. G.; Zelman, S. J.; Ferrell, L. D.; Moore, D. H.; Waldman, F. M., Genomic instability is an early event during the progression pathway of ulcerativecolitis-related neoplasia, Am. J. Pathol., 154, 1825-1830, (1999)
[69] Willenbucher, R. F.; Zelman, S. J.; Ferrell, L. D.; Moore, D. H.; Waldman, F. M., Chromosomal alterations in ulcerativecolitis-related neoplastic progression, Gastroenterology, 113, 791-801, (1997)
[70] Xiao, X.; Wang, P., Predicting protein quaternary structural attribute by hybridizing functional domain composition and pseudo amino acid composition, J. Appl. Crystallogr., 42, 169-173, (2009)
[71] Xiao, X.; Wang, P., GPCR-2L: predicting G protein-coupled receptors and their 599 types by hybridizing two different modes of pseudo amino acid compositions, 600 Mol. Biosyst., 7, 911-919, (2011)
[72] Xiao, X.; Wang, P.; Lin, W. Z., Iamp-2L: a two-level multi-label classifier for identifying antimicrobial peptides and their functional types, Anal. Biochem., 436, 2, 168-177, (2013)
[73] Xie, S.; Wang, Z.; Okano, M.; Nogami, M.; Li, Y.; He, W. W.; Okumura, K.; Li, E., Cloning, expression and chromosome locations of the human DNMT3 gene family, Gene, 236, 87-95, (1999)
[74] Zhang, C. J.; Tang, H.; Li, W. C.; Lin, H., Iori-human: identify human origin of replication by incorporating dinucleotide physicochemical properties into pseudo nucleotide composition, Oncotarget, 7, 43, 69783-69793, (2016)
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.