Dissortativity and duplications in oral cancer

Abstract

More than 300,000 new cases worldwide are being diagnosed with oral cancer annually. Complexity of oral cancer renders designing drug targets very difficult. We analyse proteinprotein interaction network for the normal and oral cancer tissue and detect crucial changes in the structural properties of the networks in terms of the interactions of the hub proteins and the degree-degree correlations. Further analysis of the spectra of both the networks, while exhibiting universal statistical behavior, manifest distinction in terms of the zero degeneracy, providing insight to the complexity of the underlying system.Oral cancer refers to a subgroup of head and neck malignancies that develop at the lips, tongue, salivary glands, gingiva, floor of the mouth, oropharynx, buccal surfaces and other intra-oral locations according to the international classification of diseases [1]. More than 300,000 new cases worldwide are being diagnosed with Oral squamous cell carcinoma (OSCC) annually [2]. WHO estimates it particularly to be the eighth most common cancer worldwide [3] and it is of significant public health importance to developing countries such as in Indian subcontinent where it ranks among the top three types of cancer and accounts for over 30% of all cancers [4][5][6]. Despite the growing propensity for oral cancer, majority of the research works have focused only on breast cancer [7], colon cancer [8] and lymphomas [9,10]. Hence, the identification of genetic changes specific to oral cancer is very crucial because it provides an opportunity to use this information for the development of drugs to treat this disease [11]. Availability of huge amount of proteomic data [12] and information on the highly interlinked internal organization of the cell metabolism, signal transduction, transport etc.[13], provides us a scope to identify and understand, the principles that govern the behaviour of cells. We analyse the proteomic data using the network theory framework [14,15] which is holistic approach that enables us to capture the intrinsic properties of the underlying system in a cost-effective and time-efficient manner.Previous attempts to understand various diseases under the network theory reveal that various types of cancers are interlinked through some pathways which are altered in different diseases [16]. This study explores the networks of the normal oral tissue and its diseasome, by characterizing their structural and spectral properties. This approach yields a comprehensive picture of the patterns and principles governing oral cancer which otherwise would not be apparent from the study of individual proteins [17]. We find significant variations between the structural and spectral properties of the Normal and Cancer network. The change in the degree-degree correlation provides quantitative information about the consequences of altered cell behaviour from the normal to cancer state. Further, the node duplication presents a clue to the abrupt transformation of the normal cells to the cancer cells which can be fu...

Network spectra for drug-target identification in complex diseases: new guns against old foes

“…2 ). Similar distribution have been observed for PPI networks of various cancers and their normal counterparts in an another study ( Shinde and et al 2015 ; Rai and et al 2017 ). The spectral density of networks pertaining triangular structure depicts a scale-free network topology and a sparsely connected network structure ( Rai et al 2014 ; Rai and et al 2017 ; Agrawal and et al 2014 ; Sarkar and Jalan 2016 ).…”

“…This observation indicates that, not only a particular degree sequence, but also the nature by which these proteins interact in the network contribute on the occurrence of high degeneracy at the zero eigenvalues in the real networks. Another study related to the PPI networks of normal and cancer oral tissue proteome data reveals that despite similar overall spectral properties (Shinde et al 2015 ), the height of the peak at zero eigenvalue differs considerably in both the networks (Table 3 and Fig. 3 ).…”

“…Apart from that they compared their real-world networks with corresponding random models such as ER and configuration models. It was found that there exists a very high degeneracy in the real-world biological networks as compared to their corresponding model networks (Shinde et al 2015 ; Rai et al 2014 ; Rai et al 2017 ; Rai et al 2015 ). The corresponding configuration models, which generate a network from a given degree sequence, also does not exhibit a high degeneracy at the zero eigenvalue.…”

Understanding cancer complexome using networks, spectral graph theory and multilayer framework

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Longitudinal network theory approaches identify crucial factors affecting sporulation efficiency in yeast

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