Traditional drug screening methods typically focus on a single protein target and exhibit limited efficiency due to the multifactorial nature of most diseases, which result from disturbances within complex networks of protein-protein interactions rather than single gene abnormalities. Addressing this limitation requires a comprehensive drug screening strategy. Network medicine is rooted in systems biology and provides a comprehensive framework for understanding disease mechanisms, prevention, and therapeutic innovations. This approach not only explores the associations between various diseases but also quantifies the relationships between disease genes and drug targets within interactome networks, thus facilitating the prediction of drug-disease relationships and enabling the screening of therapeutic drugs for specific complex diseases. An increasing body of research supports the efficiency and utility of network-based strategies in drug screening. This review highlights the transformative potential of network medicine in virtual therapeutic screening for complex diseases, offering novel insights and a robust foundation for future drug discovery endeavors.

Allergic rhinitis is a common allergic disease with a complex pathogenesis and many unresolved issues. Studies have shown that the incidence of allergic rhinitis is closely related to genetic factors, and research on the related genes could help further understand its pathogenesis and develop new treatment methods. In this study, 446 allergic rhinitis-related genes were obtained on the basis of the DisGeNET database. The protein-protein interaction network was searched using the random-walk-with-restart algorithm with these 446 genes as seed nodes to assess the linkages between other genes and allergic rhinitis. Then, this result was further examined by three screening tests, including permutation, interaction, and enrichment tests, which aimed to pick up genes that have strong and special associations with allergic rhinitis. 52 novel genes were finally obtained. The functional enrichment test confirmed their relationships to the biological processes and pathways related to allergic rhinitis. Furthermore, some genes were extensively analyzed to uncover their special or latent associations to allergic rhinitis, including IRAK2 and MAPK, which are involved in the pathogenesis of allergic rhinitis and the inhibition of allergic inflammation via the p38-MAPK pathway, respectively. The new found genes may help the following investigations for understanding the underlying molecular mechanisms of allergic rhinitis and developing effective treatments.

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Diabetic retinopathy is a common complication of diabetes mellitus that causes pathogenic damage to the retina. Particularly, the proliferative diabetic retinopathy (PDR) state can cause abnormal angiogenesis in the retina tissues and trigger the retina destruction in advanced stage. In the clinic, the symptoms during the initiation and progression of PDR are relatively unrecognizable. Therefore, various studies have focused on the pathogenesis of PDR. According to published literature, genetic contributions play an irreplaceable role in the initiation and progression of PDR. Although many computational methods, such as shortest path- and random walk with restart-based methods, have been applied in screening the potential pathogenic factors of PDR, advanced computational methods, which may provide essential supplements for previous ones, are still widely needed. In this study, a novel computational method was presented to infer novel PDR-associated genes. Different from previous methods, the method used in this work employed a different network algorithm, that is, the Laplacian heat diffusion algorithm. This algorithm was applied on the protein-protein interaction network reported in the STRING database. Three screening tests were performed to filter the most likely inferred genes. A total of 26 genes were accessed using the proposed method. Compared with the two previous predictions, most of the identified genes were novel, and only one gene was shared. Several inferred genes, such as CSF3, COL18A1, CXCR2, CCR1, FGF23, CXCL11, and IL13, were related to the pathogenesis of PDR.

Accumulating experimental studies have indicated that disease comorbidity causes additional pain to patients and leads to the failure of standard treatments compared to patients who have a single disease. Therefore, accurate prediction of potential comorbidity is essential to design more efficient treatment strategies. However, only a few disease comorbidities have been discovered in the clinic.
In this work, we propose PCHS, an effective computational method for predicting disease comorbidity.
We utilized the HeteSim measure to calculate the relatedness score for different disease pairs in the global heterogeneous network, which integrates six networks based on biological information, including disease-disease associations, drug-drug interactions, protein-protein interactions and associations among them. We built the prediction model using the Support Vector Machine (SVM) based on the HeteSim scores.
The results showed that PCHS performed significantly better than previous state-of-the-art approaches and achieved an AUC score of 0.90 in 10-fold cross-validation. Furthermore, some of our predictions have been verified in literatures, indicating the effectiveness of our method.

Network biology and medicine provide unprecedented opportunities and challenges for deciphering disease mechanisms from integrative viewpoints. The disease genes and their products perform their dysfunctions via physical and biochemical interactions in the form of a molecular network. The topological parameters of these disease genes in the interactome are of prominent interest to the understanding of their functionality from a systematic perspective. In this work, we provide a systems biology analysis of the topological features of complex disease genes in an integrated biomolecular network. Firstly, we identify the characteristics of four network parameters in the ten most frequently studied disease genes and identify several specific patterns of their topologies. Then, we confirm our findings in the other disease genes of three complex disorders (i.e., Alzheimer\'s disease, diabetes mellitus, and hepatocellular carcinoma). The results reveal that the disease genes tend to have a higher betweenness centrality, a smaller average shortest path length, and a smaller clustering coefficient when compared to normal genes, whereas they have no significant degree prominence. The features highlight the importance of gene location in the integrated functional linkages.

Uveitis is the inflammation of the uvea and is a serious eye disease that can cause blindness for middle-aged and young people. However, the pathogenesis of this disease has not been fully uncovered and thus renders difficulties in designing effective treatments. Completely identifying the genes related to this disease can help improve and accelerate the comprehension of uveitis. In this study, a new computational method was developed to infer potential related genes based on validated ones. We employed a large protein-protein interaction network reported in STRING, in which Laplacian heat diffusion algorithm was applied using validated genes as seed nodes. Except for the validated ones, all genes in the network were filtered by three tests, namely, permutation, association, and function tests, which evaluated the genes based on their specialties and associations to uveitis. Results indicated that 59 inferred genes were accessed, several of which were confirmed to be highly related to uveitis by literature review. In addition, the inferred genes were compared with those reported in a previous study, indicating that our reported genes are necessary supplements.

The number of obesity cases is rapidly increasing in developed and developing countries, thereby causing significant health problems worldwide. The pathologic factors of obesity at the molecular level are not fully characterized, although the imbalance between energy intake and consumption is widely recognized as the main reason for fat accumulation. Previous studies reported that obesity can be caused by the dysfunction of genes associated with other diseases, such as myocardial infarction, hence providing new insights into dissecting the pathogenesis of obesity by investigating its associations with other diseases. In this study, we investigated the relationship between obesity and diseases from Online Mendelian Inheritance in Man (OMIM) databases on the protein-protein interaction (PPI) network. The obesity genes and genes of one OMIM disease were mapped onto the network, and the interaction scores between the two gene sets were investigated on the basis of the PPI of individual gene pairs, thereby inferring the relationship between obesity and this disease. Results suggested that diseases related to nutrition and endocrine are the top two diseases that are closely associated with obesity. This finding is consistent with our general knowledge and indicates the reliability of our obtained results. Moreover, we inferred that diseases related to psychiatric factors and bone may also be highly related to obesity because the two diseases followed the diseases related to nutrition and endocrine according to our results. Numerous obesity-disease associations were identified in the literature to confirm the relationships between obesity and the aforementioned four diseases. These new results may help understand the underlying molecular mechanisms of obesity-disease co-occurrence and provide useful insights for disease prevention and intervention.

Preeclampsia (PE) is a severe pregnancy complication, which is a leading cause of maternal and fetal mortality. The present study aimed to screen potential biomarkers for the diagnosis and prediction of PE and to investigate the underlying mechanisms of PE development based on the differential expression network (DEN). The microarray datasets E-GEOD-6573 and E-GEOD-48424 were downloaded from the European Bioinformatics Institute database. Differentially expressed genes (DEGs) between the PE and normal groups were screened by Significant Analysis of Microarrays with the cutoff value of a |log2 fold change| of >2, and a false discovery rate of <0.05. The DEN was constructed based on the differential and non-differential interactions observed. In addition, genes with higher connectivity degrees in the DEN were identified on the basis of centrality analysis, while disease genes were also extracted from the DEN. In order to understand the functional roles of genes in DEN, Gene Ontology (GO) and pathway enrichment analyses were performed. The present results indicated that a total of 225 genes were considered as DEGs in the PE group, while 466 nodes and 314 gene interactions were involved in the DEN. Among these 466 nodes, 4 nodes with higher degrees were identified, including ubiquitin C (UBC), small ubiquitin-like modifier 1 (SUMO1), SUMO2 and RAD21 homolog (S. pombe) (RAD21). Notably, UBC was also found to be a disease gene. UBC, RAD21, SUMO2 and SUMO1 were markedly enriched in the regulation of programmed cell death, as well as in the regulation of apoptosis, cell cycle and chromosomal part. In conclusion, based on these results, we suggest that UBC, RAD21, SUMO2 and SUMO1 may be reliable biomarkers for the prediction of the development and progression of PE.

Choroidal neovascularization (CNV) is a serious eye disease that may cause visual loss, especially for older people. Many factors have been proven to induce this disease including age, gender, obesity, and so on. However, until now, we have had limited knowledge on CNV\'s pathogenic mechanism. Discovering the genes that underlie this disease and performing extensive studies on them can help us to understand how CNV occurs and design effective treatments.
In this study, we designed a computational method to identify novel CNV-related genes in a large protein network constructed using the protein-protein interaction information in STRING. The candidate genes were first extracted from the shortest paths connecting any two known CNV-related genes and then filtered by a permutation test and using knowledge of their linkages to known CNV-related genes.
A list of putative CNV-related candidate genes was accessed by our method. These genes are deemed to have strong relationships with CNV.
Extensive analyses of several of the putative genes such as ANK1, ITGA4, CD44 and others indicate that they are related to specific biological processes involved in CNV, implying they may be novel CNV-related genes.
The newfound putative CNV-related genes may provide new insights into CNV and help design more effective treatments. This article is part of a Special Issue entitled \"System Genetics\" Guest Editor: Dr. Yudong Cai and Dr. Tao Huang.