A Weighted Threshold for Detection of Cancerous miRNA Expressions

• Autorzy: Pal J. K. , Ray S. S. , Pal S.K.
• Języki publikacji: EN

Abstrakty

EN

MicroRNAs (miRNA) are one kind of non-coding RNA which play many important roles in eukaryotic cell. Investigations on miRNAs show that miRNAs are involved in cancer development in animal body. In this article, a threshold based method to check the condition (normal or cancer) of miRNAs of a given sample/patient, using weighted average distance between the normal and cancer miRNA expressions, is proposed. For each miRNA, the city block distance between two representatives, corresponding to scaled normal and cancer expressions, is obtained. The average of all such distances for different miRNAs is weighted by a factor, to generate the threshold. The weight factor, which is cancer dependent, is determined through an exhaustive search by maximizing the F score during training. In a part of the investigation, a ranking algorithm for cancer specific miRNAs is also discussed. The performance of the proposed method is evaluated in terms of Matthews Correlation Coefficient (MCC) and by plotting points (1 – Specificity vs: Sensitivity) in Receiver Operating Characteristic (ROC) space, besides the F score. Its efficiency is demonstrated on breast, colorectal, melanoma lung, prostate and renal cancer data sets and it is observed to be superior to some of the existing classifiers in terms of the said indices.

Słowa kluczowe

EN

bioinformatics; cancer detection; miRNA expression analysis; pattern recognition

• Wydawca: IOS Press
• Czasopismo: Fundamenta Informaticae
• Rocznik: 2013
• Tom: Vol. 127, nr 1-4
• Strony: 289--305
• Opis fizyczny: Bibliogr. 17 poz., tab., wykr.

Twórcy

autor

Pal J. K.

• Center for Soft Computing Research, Indian Statistical Institute, 203 B. T. Road, Kolkata- 700108, India

autor

Ray S. S.

• Center for Soft Computing Research, Indian Statistical Institute, 203 B. T. Road, Kolkata- 700108, India

autor

Pal S.K.

• Center for Soft Computing Research, Indian Statistical Institute, 203 B. T. Road, Kolkata- 700108, India

Bibliografia

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• [2] Blenkiron, C., Goldstein, L. D., Thorne, N. P., Spiteri, I., Chin, S., Dunning, M. J., Barbosa-Morais, N. L., Teschendorff, A. E., Green, A. R., Ellis, I. O., Tavar, S., Caldas, C., Miska, E. A.: MicroRNA expression profiling of human breast cancer identifies new markers of tumor subtype, Genome Biology, 8,2007, R214.1- R214.16.
• [3] Calin, G. A., Dumitru, C. D., Shimizu, M., Bichi, R., Zupo, S., Noch, E., Aldler, H., Rattan, S., Keating, M., Rai, K., Rassenti, L., Kipps, T., Negrini, M., Bullrich, F., Croce, C. M.: Frequent deletions and down- regulation of micro-RNA genes miR15 and miR16 at 13q14 in chronic lymphocytic leukemia, Proceedings of the National Academy of Sciences, 99(24), 2002, 15524-15529.
• [4] Einat, P.: Methodologies for High-Throughput Expression Profiling of MicroRNAs, springer, New Jersey, 2006, 139-157.
• [5] Jung, M., Mollenkopf, H.-J., Grimm, C., Wagner, I., Albrecht, M., Waller, T., Pilarsky, C., Johannsen, M., Stephan, C., Lehrach, H., Nietfeld, W., Rudel, T., Jung, K., Kristiansen, G.: MicroRNA profiling of clear cell renal cell cancer identifies a robust signature to define renal malignancy, Journal of Cellular and Molecular Medicine, 13(9b), 2009, 3918-3928.
• [6] Keller, A., Leidinger, P., Borries, A., Wendschlag, A., Wucherpfennig, F., Scheffler, M., Huwer, H., Lenhof, H.-P., Meese, E.: miRNAs in lung cancer - Studying complex fingerprints in patient’s blood cells by microarray experiments, BMC Cancer, 9(353), 2009, 1-10.
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• [8] Lodes, M. J., Caraballo, M., Suciu, D., Munro, S., Kumar, A., Anderson, B.: Detection of Cancer with Serum miRNAs on an Oligonucleotide Microarray, PLoS ONE, 4(7), 2009, e6229.
• [9] Lu, J., Getz, G., Miska, E. A., Alvarez-Saavedra, E., Lamb, J., Peck, D., Sweet-Cordero, A., Ebert, B. L., Mak, R. H., Ferrando, A. A., Downing, J. R., Jacks, T., R.Horvitz, H., Golub, T. R.: MicroRNA expression profiles classify human cancers, Nature, 435(7043), 2005, 834-838.
• [10] Navon, R., Wang, H., Steinfeld, I., Tsalenko, A., Ben-Dor, A., Yakhini, Z.: Novel Rank-Based Statistical Methods Reveal MicroRNAs with Differential Expression in Multiple Cancer Types, PLoS ONE, 4, 2009, e8003.
• [11] Ray, S. S., Halder, S., Kaypee, S., Bhattacharyya, D.: HD-RNAS: an automated hierarchical database of RNA structures, Frontiers in Genetics, 3(59), 2012, 1-10.
• [12] Ray, S. S., Pal, J. K., Pal, S. K.: Computational Approaches for Identifying Cancer miRNA Expressions, Gene Expression, 15(5-6), 243-253.
• [13] Ray, S. S., Pal, S. K.: RNA Secondary Structure Prediction using Soft Computing, IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2012 (accepted).
• [14] Resnick, K. E., Alder, H., Hagan, J. P., Richardson, D. L., Croce, C. M., Cohn, D. E.: The detection of differentially expressed microRNAs from the serum of ovarian cancer patients using a novel real-time PCR platform, Gynecologic Oncology, 112, 2009, 55-59.
• [15] Schaefer, A., Jung, M., Mollenkopf, H.-J., Wagner, I., Stephan, C., Jentzmik, F., Miller, K., Lein, M., Kristiansen, G., Jung, K.: Diagnostic and prognostic implications of microRNA profiling in prostate carcinoma, International Journal of Cancer, 126(5), 2010, 1166-1176.
• [16] Schrauder, M. G., R. Strick, R. D. S.-W., Strissel, P. L., Kahmann, L., Loehberg, C. R., Lux, M. P., Jud, S. M., A. Hartmann, A. H., Bayer, C. M., Bani, M. R., Richter, S., Adamietz, B. R., Wenkel, E., Rauh, C., Beckmann, M. W., Fasching, P. A.: Circulating Micro-RNAs as Potential Blood-Based Markers for Early Stage Breast Cancer Detection, PLoS ONE, 7(1), 2012, e29770.
• [17] Wang, Q., Wang, S., Wang, H., Li, P., Ma, Z.: MicroRNAs: novel biomarkers for lung cancer diagnosis, prediction and treatment, Experimental Biology and Medicine, 237, 2012, 227-235.