We have studied the changes in protein-protein interaction network of 38 different tissues of the human body.123 gene expression samples from these tissues were used to construct human protein-protein interaction network. This network is then pruned using the gene expression samples of each tissue to construct different protein-protein interaction networks corresponding to different studied tissues of the body. This study is helpful for understanding how human protein interactions change in different tissues. In this way, similar tissues of the body and special functions of each tissue, corresponding to their individualized subnetworks, can be identified. We have calculated graph parameters for studying these protein-protein interaction networks and hubs and non-hubs of the studied protein-protein interaction networks are identified. We found a common subnetwork among protein-protein interaction networks of the studied tissues and a tree of tissue similarities has been constructed. We have also found that average correlation coefficient of hubs in human protein-protein interaction networks obeys a normal-like distribution though it is not possible to separate party and date hubs.

Aim: In the current study, we analysised only the articles that investigate serum proteome profile of cirrhosis patients or HCC patients versus healthy controls. Background: Increased understanding of cancer biology has enabled identification of molecular events that lead to the discovery of numerous potential biomarkers in diseases. Protein-protein interaction networks is one of aspect that could elevate the understanding level of molecular events and protein connections that lead to the identification of genes and proteins associated with diseases. Methods: Gene expression data, including 63 gene or protein names for hepatocellular carcinoma and 29 gene or protein names for cirrhosis, were extracted from a number of previous investigations. The networks of related differentially expressed genes were explored using Cytoscape and the PPI analysis methods such as MCODE and ClueGO. Centrality and cluster screening identified hub genes, including APOE, TTR, CLU, and APOA1 in cirrhosis. Results: CLU and APOE belong to the regulation of positive regulation of neurofibrillary tangle assembly. HP and APOE involved in cellular oxidant detoxification. C4B and C4BP belong to the complement activation, classical pathway and acute inflammation response pathway. Also, it was reported TTR, TFRC, VWF, CLU, A2M, APOA1, CKAP5, ZNF648, CASP8, and HSP27 as hubs in HCC. In HCC, these include A2M that are corresponding to platelet degranulation, humoral immune response, and negative regulation of immune effector process. CLU belong to the reverse cholesterol transport, platelet degranulation and human immune response. APOA1 corresponds to the reverse cholesterol transport, platelet degranulation and humoral immune response, as well as negative regulation of immune effector process pathway. Conclusion: In conclusion, this study suggests that there is a common molecular relationship between cirrhosis and hepatocellular cancer that may help with identification of target molecules for early treatment that is essential in cancer therapy.

Background: In cancer-related diseases, early detection and control of disease progression are very important for successful treatment. Breast cancer is a significant problem due to its high mortality rate in the female population worldwide. By the early diagnosis of breast cancer, the 5-year survival rate reaches 93 to 98%. In this study, to identify breast cancer biomarkers, we construct new protein-protein interaction (PPI) and miRNAs-mRNAs networks by analyzing upregulated and downregulated genes in breast cancer patients.Method: In this in silico study, two gene expression profile datasets, with the accession numbers GSE42568 and GSE154255, were downloaded from the GEO database. GEO2R was used to obtain differentially expressed mRNA (DEMs) and miRNAs (DEMIs) based on |logFC|>2 and adjusted P-value <0.05. Gene Ontology and KEGG Pathway Enrichment Analysis were performed by EnrichR. STRING v9. 1 and cytoHubba plugin in Cytoscape (v3.9.1) were used to investigate PPI network construction and identification of hub genes. Finally, key microRNAs (miRNAs) were predicted.Results: After protein-protein interaction analysis, a total of 10 upregulated DEMs (DLGAP5, CCNB1, TTK, NUSAP1, RRM2, BUB1B, CDK1, CENPF, TOP2A, and ASPM) and 10 downregulated DEMs (PPARG, LIPE, CD36, FABP4, SCD, LPL, DGAT2, PNPLA2, ACSL1, and LEP) were screened as hub genes. Based on miRNAs-mRNAs networks, 4 key miRNAs including hsa-miR-182-5p, hsa-miR-96-5p, hsa-miR-335-3p, and hsa-miR-32-5p play a critical role in network regulation.Conclusion: Our study presents PPI and miRNAs-mRNAs networks for identifying molecular biomarkers in breast cancer. The introduced biomarkers open a new approach to diagnostic and therapeutic indicators for clinical applications.

Predicting missing links in noisy protein-protein interaction networks is an essential~computational method. Recently, attributed network embedding methods have been shown to be significantly effective in generating low-dimensional representations of nodes to predict links; in these representations, both the nodes'features and the network's topological information are preserved. Recent research suggests that models based on paths of length 3 between two nodes are more accurate than models based on paths of length 2 for predicting missing links in a protein-protein interaction network. In the present study, an attributed network embedding method termed ANE-SITI is recommended to combine protein sequence information and network topological information. In addition, to improve accuracy, network topological information also considers paths of length 3 between two proteins. The results of this experiment demonstrate that ANE-SITI outperforms the compared methods on various~protein-protein interaction (PPI) networks.

Type 1 diabetes (T1D) occurs as a consequence of an autoimmune attack against pancreatic β-cells. Due to a lack of a clear understanding of the T1D pathogenesis, the identification of effective therapies for T1D is the active area in the research. The study purpose was to prioritize potential drugs and targets in T1D via systems biology approach. Gene expression data of peripheral blood mononuclear cells (PBMCs) and pancreatic β-cells in T1D were analyzed and differential expressed genes were integrated with protein-protein interactions (PPI) data. Multiple topological centrality parameters of extracted query-query PPI (QQPPI) networks were calculated and the interaction of more central proteins with drugs was investigated. Molecular docking was performed to further predict the interactions between drugs and the binding sites of targets. Central proteins were identified by the analysis of PBMC (MYC, ERBB2, PSMA1, ABL1 and HSP90AA1) and pancreatic β-cells (HSP90AB1, ESR1, RELA, RAC1, NFKB1, NFKB2, IKBKE, ARRB2 and SRC) QQPPI networks. Thirteen drugs which targeted eight central proteins were identified by further analysis of drug-target interactions. Some drugs which investigated for diabetes treatment in the experimental models of T1D were prioritized by literature verification, including melatonin, resveratrol, lapatinib, geldanamycin, eugenol and fostaminib. Finally, according on molecular docking analysis, lapatinib-ERBB2 and eugenolESR1 exhibited highest and lowest binding energy, respectively. This study presented promising results for the prioritization of potential drug-targets which might facilitate T1D targeted therapy and its drug discovery process more effectively.