A Systems Approach to Gene Ranking from DNA Microarray Data of Cervical Cancer

In this paper we present a method for gene ranking from DNA microarray data. More precisely, we calculate the correlation networks, which are unweighted and undirected graphs, from microarray data of cervical cancer whereas each network represents a tissue of a certain tumor stage and each node in the network represents a gene. From these networks we extract one tree for each gene by a local decomposition of the correlation network. The interpretation of a tree is that it represents the n-nearest neighbor genes on the n-th level of a tree, measured by the Dijkstra distance, and, hence, gives the local embedding of a gene within the correlation network. For the obtained trees we measure the pairwise similarity between trees rooted by the same gene from normal to cancerous tissues. This evaluates the modification of the tree topology due to progression of the tumor. Finally, we rank the obtained similarity values from all tissue comparisons and select the top ranked genes. For these genes the local neighborhood in the correlation networks changes most between normal and cancerous tissues. As a result we find that the top ranked genes are candidates suspected to be involved in tumor growth and, hence, indicates that our method captures essential information from the underlying DNA microarray data of cervical cancer.




References:
[1] R. Bellman, Dynamic Programming. Princeton University Press, 1957
[2] M. Dehmer, Strukturelle Analyse web-basierter Dokumente, Ph.D Thesis,
Department of Computer Science, Technische Universit¨at Darmstadt,
2005
[3] E. W. Dijkstra, A note on two problems in connection with graphs.
Numerische Math., Vol. 1, 1959, 269-271
[4] F. Emmert-Streib., M. Dehmer, J. Kilian: Classification of large Graphs
by a local Tree decomposition, accepted to appear in: Proceedings of
DMIN-05, International Conference on Data Mining, in conjuction with
the 2005 World Congress in Applied Computing, Las Vegas/USA, 2005
[5] T. R. Golub et.al., Molecular Classification of Cancer: Class Discovery
and Class Prediction by Gene Expression Monitoring, Science, Vol. 286,
1999, 531-537
[6] F. Kaden, Graphmetriken und Distanzgraphen. ZKI-Informationen, Akad.
Wiss. DDR, Vol. 2 (82), 1982, 1-63
[7] F. Kaden, Graph metrics and distance-graphs. In: Graphs and other
Combinatorial Topics, ed. M. Fiedler, Teubner Texte zur Math., Leipzig,
Vol. 59, 1983, 145-158
[8] P. J. Kraulis, Molscript: A Program to Produce Both detailed and
schematic plots of protein structures. Journal of Applied Crystallography,
Vol. 24, 1991, 946-950
[9] K.Mori et al., Highly specific marker genes for detecting minimal gastric
cancer cells in cytology negative peritoneal washings, Biochem. Biophys.
Res. Commun. 23;313(4):931-937 (2004).
[10] V. Batagelj and A. Mrvar, Pajek - Program for Large Network Analysis,
Connections 21:47-57 (1998).
[11] R. C. Read and D. G. Corneil, The graph isomorphism disease. Journal
of Graph Theory, Vol. 1, 1977, 339-363
[12] J. Rougemont and P. Hingamp, DNA microarray data and contextual
analysis of correlation graphs. BMC Bioinformatics, Vol. 4, 2003, 4-15
[13] F. Sobik, Graphmetriken und Klassifikation strukturierter Objekte. ZKIInformationen,
Akad. Wiss. DDR, Vol. 2 (82), 1982, 63-122
[14] F. Sobik, Graphmetriken und Charakterisierung von Graphklassen. 27.
Internat. Wiss. Koll., TH-Ilmenau, Vol. 2 (82), 1982, 63-122
[15] J. R. Ullman, An algorithm for subgraph isomorphism. J. ACM, Vol. 23
(1), 1976, 31-42
[16] Y. Wang et al., Gene expression profiles and molecular markers to
predict recurrence of Dukes-B colon cancer, J. Clin. Oncol. 1;22(9):1564-
1571 (2004).
[17] Y. F. Wong et.al. Expression Genomics of Cervical Cancer: Molecular
Classification and Prediction of Radiotherapy Response by DNA Microarray.
Clinical Cancer Research, Vol. 9, 2003, 5486-5492
[18] B. Zelinka, On a certain distance between isomorphism classes of
graphs. ˇ Casopis pro ˇpest. Mathematiky, Vol. 100, 1975, 371-373