The gene expression profiling of hepatocellular carcinoma by a network analysis approach shows a dominance of intrinsically disordered proteins (IDPs) between hub nodes.
We have analyzed the transcriptomic data from patients with hepatocellular carcinoma (HCC) after viral HCV infection at the various stages of the disease by means of a networking analysis using the publicly available E-MTAB-950 dataset. The data was compared with those obtained in our group from HepG2 cells, a cancer cell line that lacks the viral infection. By sequential pruning of data, and also taking into account the data from cells of healthy patients as blanks, we were able to obtain a distribution of hub genes for the various stages that characterize the disease and finally, we isolated a metabolic sub-net specific to HCC alone. The general picture is that the basic organization to energetically and metabolically sustain the cells in both the normal and diseased conditions is the same, but a complex cluster of sub-networks controlled by hub genes drives the HCC progression with high metabolic flexibility and plasticity. In particular, we have extracted a sub-net of genes strictly correlated to other hub genes of the network from HepG2 cells, but specific for the HCC and mainly devoted to: (i) control at chromatin levels of cell division; (ii) control of ergastoplasmatic stress through protein degradation and misfolding; (iii) control of the immune response also through an increase of mature T-cells in the thymus. This sub-net is characterized by 26 hub genes coding for intrinsically disordered proteins with a high ability to interact with numerous molecular partners. Moreover, we have also noted that periphery molecules, that is, with one or very few interactions (e.g., cytokines or post-translational enzymes), which do not have a central role in the clusters that make up the global metabolic network, essentially have roles as information transporters. The results evidence a strong presence of intrinsically disordered proteins with key roles as hubs in the sub-networks that characterize the various stages of the disease, conferring a structural plasticity to the net nodes but an inherent functional versatility to the whole metabolic net. Thus, our present article provides a novel way of targeting the intrinsic disorder in HCC networks to dampen the cancer effects and provides new insight into the potential mechanisms of HCC. Taken together, the present findings suggest novel targets to design strategies for drug design and may support a rational intervention in the pharmacotherapy of HCC and other associated diseases.
Dottorato in Biologia Computazionale, Dipartimento di Biochimica, Biofisica e Patologia generale, Seconda Università degli Studi di Napoli, Napoli, Italy.
Sakshi
S
Singh
Giovanni
G
Colonna
Giovanni
G
Di Bernardo
Francesca
F
Bergantino
Marcella
M
Cammarota
Giuseppe
G
Castello
Susan
S
Costantini
D006528:Carcinoma, Hepatocellular / Q000235:genetics*; D030541:Databases, Genetic; D020869:Gene Expression Profiling; D053263:Gene Regulatory Networks; D056945:Hep G2 Cells; D016174:Hepacivirus / Q000502:physiology; D006526:Hepatitis C / Q000235:genetics / Q000821:virology; D006801:Humans; D064267:Intrinsically Disordered Proteins / Q000235:genetics*; D008103:Liver Cirrhosis / Q000235:genetics; D008113:Liver Neoplasms / Q000235:genetics*
D064267:Intrinsically Disordered Proteins