1. Differential connectivity in neoplastic
coexpression networks
TM Creanza1,2, R Anglani1, VC Liuzzi3, A Piepoli4, A Panza4, A Andriulli4, N Ancona1#
1Systems Biology Lab, ISSIA Institute of Intelligent Systems for Automation, CNR, Bari, Italy; 2Center for Complex Systems in Molecular Biology and Medicine, University of
Torino, Italy; 3Dept of Bioscience, Biotechnology and Biopharmaceutical, University of Bari, Italy; 4 Division of Gastroenterology, IRCCS, Casa Sollievo della Sofferenza, Italy
BITS 2014 Eleventh Annual Meeting of the Bioinformatics Italian Society Feb 26-28, 2014 Rome, Italy
Carcinogenesis is a complex process driven by alterations that can occur indifferently in regulatory or coding sites of genes. Indeed, the
coding region alterations and the post-translational modifications can modify the protein activity without affecting the gene expression
level, but altering the interaction pattern with other genes. Hence, these considerations motivate the analysis of changes in gene interaction
networks between normal and cancer conditions. In this framework, we suggest that co-expression network approaches based on the study of
connectivity can reveal those driver genes that play a role in tumor biology due to modifications in gene interactions. As a result, we were
able to show that loss of connectivity in co-expression gene networks is a common trait of cancer tissues and that our connectivity-based
approaches can highlight novel putative cancer genes. Moreover, in the study of gene biosystems, the differential connectivity complements and
extends the informative content provided by differential expression. Finally, we suggest our integrated pathway analysis as a valid hypothesis
generator for the discovery of candidate cancer-related biosystems not still fully investigated.
Loss of connectivity is a common trait of cancer networks
Gene differential connectivity (DC)
Differential connectivity suggests novel
network-based biomarkers
Differential connectivity is complementary to differential expression to reveal cancer related pathways
Differential connectivity highlights known cancer genes
Gene differential connectivity and its interplay with the differential expression
Dataset
(EMTAB829) COLORECTAL CANCER
Affymetrix GeneChip Human Exon 1.0 ST
28 samples (14 cancer and 14 paired normal)
(GSE10072) NSC LUNG CANCER
Affymetrix GeneChip Human Genome U133A
107 samples (49 cancer and 58 normal)
(GSE13911) GASTRIC CANCER
Affymetrix GeneChip Human Genome U133Plus2
69 samples (31 cancer and 38 normal)
(GSE15471) PANCREATIC CANCER
Affymetrix GeneChip Human Exon 1.0 ST
78 samples (39 cancer and 39 paired normal)
(GSE9750) CERVIX CANCER
Affymetrix GeneChip Human Genome U133A
57 samples (33 cancer and 24 normal)
Enrichment analysis in terms of known colon cancer genes PDC PDE
Markowitz et al., New England Journal of Medicine 2009 0.043 0.027
Cancer Gene Census 0.0067 0.763
KEGG Disease H00020 0.06 0.113
Wood et al., Science 2007 0.058 0.703
Given Δi = degree difference of the i-th gene between
normal and cancer condition, a gene is said to be
differentially connected when Δi is statistically significative.
To assess the significance, we randomly assign patients to
one of two groups and we evaluate Δi* for each
permutation. We repeat the shuffle 1000 times to obtain
the random distribution. The differential connection p-value
is evauated comparing the real Δi with the random
distribution. In order to control the expected proportion of
incorrectly rejected null hypotheses, we evaluate Benjamini
Hochberg False Discovery Rate and we put the
significance threshold to 20%.
The ability of detecting organ-specific cancer traits was
tested on curated “core sets” that collect known tumor-
specific hallmark systems from C2-CP collections of
MSigDB. A comparative analysis on these gene sets
shows that considering both changes in gene expression
and alterations in connectivity improves the molecular
characterization of disease mechanisms. For instance, we
performed a meta-analysis of DE and DEC enrichment
combining the p-values associated to the different tissues
(Fisher's combined probability test). The DEC meta-
analysis p-value associated to Reactome Immune
System (P<10-9) turned out to be much smaller than the
corresponding DE value (P=0.02) which is above the
significance level of 0.01. Moreover, in the tissue-specific
enrichment analysis, the DEC enrichment p-values always
result smaller than the corresponding DE values. On the
other hand, the classic pathway analysis is not able to
indicate, for any organ, the Reactome IS as significant at
level of 0.05.
In the case of colon cancer, the second top-ranked gene
for loss of connectivity with P<10-3 is the aryl hydrocarbon
receptor (AhR) that is known to have a crucial role in
suppression of intestinal carcinogenesis by proteasomal
degradation of beta-catenin, which interacts with the
canonical APC-dependent pathway. Moreover, the sixth
top-ranked gene “deleted in polyposis 1” (DIP1) has been
found to have a role of tumor suppressor in colon
carcinogenesis.
DC and DE genes can behave as distinct populations and our bioinformatics analysis supports the idea that
genes involved in cancer that do not change their expression can be highlighted by an analysis of differential
connectivity. Consequently, one can guess that the DC genes are genes harbouring mutations that alter
interactions among gene products without affecting their expression levels.
NORMAL CANCER
Citation: Anglani R, Creanza TM, Liuzzi VC, Piepoli A, Panza A, Andriulli A, Ancona N (2014) “Loss of Connectivity in Cancer Co-Expression Networks”. PLoS ONE 9(1): e87075.
Email: ancona@ba.issia.cnr.it