Bladder cancer (BC) is the 9th most common cancer worldwide, and the 6th
most common cancer in men. Its development is linked to chronic inflammation,
genetic susceptibility, smoking, occupational exposures and environmental pollutants.
Aim of this work was to identify a sub-network of genes/proteins modulated by
environmental or arsenic exposure in BC by computational network approaches.
Our studies evidenced the presence of HUB nodes both in “BC and environment”
and “BC and arsenicals” networks. These HUB nodes resulted to be correlated to
circadian genes and targeted by some miRNAs already reported as involved in BC, thus
suggesting how they play an important role in BC development due to environmental
or arsenic exposure. Through data-mining analysis related to putative effect of the
identified HUB nodes on survival we identified genes/proteins and their mutations on
which it will be useful to focus further experimental studies related to the evaluation
of their expression in biological matrices and to their utility as biomarkers of BC developmen
