We have used gene dependency network analysis to understand topological properties under cancer and control condition, the genes with marked topological differences could be regarded as therapeutic target genes. Causal reasoning analysis was used for identification of Deferiprone potential genes, which can explain differential gene expression changes in oral cancer. The development of cancer is a multistep process enabled by occurrence of key hallmark events like ZCL278 sustaining proliferative signaling, evading growth suppressors, resisting apoptotic cell death, enabling replicative immortality, inducing angiogenesis, activating invasion, metastasis and inflammation. Novel literature mining method has been used to associate these cancer hallmarks to genes of our interest. In the present study, the diversity of cancer hallmarks associated with a gene, along with impressive topological profile in dependency and/or causal-network, qualifies a gene to be a potential drug target for oral cancer. Large-scale integration of datasets from oral cancer gene expression studies had been attempted in the past with an objective to mine transcriptional signatures linked with neoplastic transformation or survival. Recently, it has been used to identify frequent somatic drivers for oral carcinogenesis. The task of identifying potential therapeutic targets by integrative analysis, has been attempted for the first time in the current study. With a surge in deaths caused by oral cancer especially in Indian subcontinent region, there is an urgent need to expedite our efforts to find novel therapies for oral cancer. The current study, present a logical framework to find potential therapeutic targets that are associated with multiple cancer hallmarks, and targeting them is thus expected to be a perfect answer to challenges associated with acquired drug-resistance to targeted therapies. The information spread across sibling probes was consolidated with the help of a robust statistic, the Tukey��s biweight. The median related Tukey��s biweight is a robust statistic, which is known to have excellent behaviour in the presence or absence of outliers, because of these attributes, it was implemented in MAS5.0 algorithm used for probe level summarization. Custom scripts were written in perl and R to deal with sibling probes, and the R method ��tbrm �� available with dplR package was used to compute Tukey��s biweight robust mean.