Identification of Genes Associated with Breast Cancer Metastasis to Bone on a Protein–Protein Interaction Network with a Shortest Path Algorithm

Identifying allergic-rhinitis-associated genes with random-walk-based method in PPI network

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.

Graph Neural Network Model for Prediction of Non-Small Cell Lung Cancer Lymph Node Metastasis Using Protein-Protein Interaction Network and 18F-FDG PET/CT Radiomics

The image texture features obtained from 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) images of non-small cell lung cancer (NSCLC) have revealed tumor heterogeneity. A combination of genomic data and radiomics may improve the prediction of tumor prognosis. This study aimed to predict NSCLC metastasis using a graph neural network (GNN) obtained by combining a protein-protein interaction (PPI) network based on gene expression data and image texture features. 18F-FDG PET/CT images and RNA sequencing data of 93 patients with NSCLC were acquired from The Cancer Imaging Archive. Image texture features were extracted from 18F-FDG PET/CT images and area under the curve receiver operating characteristic curve (AUC) of each image feature was calculated. Weighted gene co-expression network analysis (WGCNA) was used to construct gene modules, followed by functional enrichment analysis and identification of differentially expressed genes. The PPI of each gene module and genes belonging to metastasis-related processes were converted via a graph attention network. Images and genomic features were concatenated. The GNN model using PPI modules from WGCNA and metastasis-related functions combined with image texture features was evaluated quantitatively. Fifty-five image texture features were extracted from 18F-FDG PET/CT, and radiomic features were selected based on AUC (n = 10). Eighty-six gene modules were clustered by WGCNA. Genes (n = 19) enriched in the metastasis-related pathways were filtered using DEG analysis. The accuracy of the PPI network, derived from WGCNA modules and metastasis-related genes, improved from 0.4795 to 0.5830 (p < 2.75 × 10-12). Integrating PPI of four metastasis-related genes with 18F-FDG PET/CT image features in a GNN model elevated its accuracy over a without image feature model to 0.8545 (95% CI = 0.8401-0.8689, p-value < 0.02). This model demonstrated significant enhancement compared to the model using PPI and 18F-FDG PET/CT derived from WGCNA (p-value < 0.02), underscoring the critical role of metastasis-related genes in prediction model. The enhanced predictive capability of the lymph node metastasis prediction GNN model for NSCLC, achieved through the integration of comprehensive image features with genomic data, demonstrates promise for clinical implementation.

LncRNA ENST869 Targeting Nestin Transcriptional Region to Affect the Pharmacological Effects of Chidamide in Breast Cancer Cells

Breast cancer is one of the leading threats to the health of women. It has the highest incidence and mortality in women worldwide. Although progress has been made in the development and application of anti-breast cancer drugs such as Chidamide and others, the occurrence of drug resistance limits the effective application of chemotherapies. The purpose of this study is to explore the role of LncRNA in the pharmacological effect of Chidamide in breast cancer therapy. The human breast cancer MCF-7 or MDA-MB-231 cells were used as the research cell models. The RNA library screening and high-throughput sequencing comparative analysis was conducted. The binding of LncRNA and its downstream target genes in RNA and protein levels was tested. The results showed that the expression of LncRNA ENST869 in cells treated with Chidamide increased significantly, as demonstrated by real-time PCR and cell viability assay. RNAplex analysis showed that LncRNA ENST869 and Nestin mRNA may interact. RNA interference and Western blot analysis indicated that LncRNA ENST869 could target and regulate the expression of Nestin. Luciferase assay and RNA-protein pulldown showed that LncRNA ENST869 affected Nestin transcription. There might be a highly active binding region of LncRNA ENST869 in regulating Nestin transcriptional activity within the site of 250 bp upstream of the transcription starting point of Nestin. In addition, LncRNA ENST869 did not directly interact with Nestin protein to affect its activity. In conclusion, our results demonstrated that LncRNA ENST869 could affect the function of Nestin in breast cancer cells treated with Chidamide. Nestin is a key player in influencing the pharmacological activity of Chidamide and an essential factor in drug resistance of breast cancer cells.

Use of a Network-Based Method to Identify Latent Genes Associated with Hearing Loss in Children

Hearing loss is a total or partial inability to hear. Approximately 5% of people worldwide experience this condition. Hearing capacity is closely related to language, social, and basic emotional development; hearing loss is particularly serious in children. The pathogenesis of childhood hearing loss remains poorly understood. Here, we sought to identify new genes potentially associated with two types of hearing loss in children: congenital deafness and otitis media. We used a network-based method incorporating a random walk with restart algorithm, as well as a protein-protein interaction framework, to identify genes potentially associated with either pathogenesis. A following screening procedure was performed and 18 and 87 genes were identified, which potentially involved in the development of congenital deafness or otitis media, respectively. These findings provide novel biomarkers for clinical screening of childhood deafness; they contribute to a genetic understanding of the pathogenetic mechanisms involved.

A Random Walk-Based Method to Identify Candidate Genes Associated With Lymphoma

Lymphoma is a serious type of cancer, especially for adolescents and elder adults, although this malignancy is quite rare compared with other types of cancer. The cause of this malignancy remains ambiguous. Genetic factor is deemed to be highly associated with the initiation and progression of lymphoma, and several genes have been related to this disease. Determining the pathogeny of lymphoma by identifying the related genes is important. In this study, we presented a random walk-based method to infer the novel lymphoma-associated genes. From the reported 1,458 lymphoma-associated genes and protein–protein interaction network, raw candidate genes were mined by using the random walk with restart algorithm. The determined raw genes were further filtered by using three screening tests (i.e., permutation, linkage, and enrichment tests). These tests could control false-positive genes and screen out essential candidate genes with strong linkages to validate the lymphoma-associated genes. A total of 108 inferred genes were obtained. Analytical results indicated that some inferred genes, such as RAC3, TEC, IRAK2/3/4, PRKCE, SMAD3, BLK, TXK, PRKCQ, were associated with the initiation and progression of lymphoma.

The genomic architecture of metastasis in breast cancer: focus on mechanistic aspects, signalling pathways and therapeutic strategies

Breast cancer is a multifactorial, heterogeneous disease and the second most frequent cancer amongst women worldwide. Metastasis is one of the most leading causes of death in these patients. Early-stage or locally advanced breast cancer is limited to the breast or nearby lymph nodes. When breast cancer spreads to farther tissues/organs from its original site, it is referred to as metastatic or stage IV breast cancer. Normal breast development is regulated by specific genes and signalling pathways controlling cell proliferation, cell death, cell differentiation and cell motility. Dysregulation of genes involved in various signalling pathways not only leads to the formation of primary tumour but also to the metastasis as well. The metastatic cascade is represented by a multi-step process including invasion of the local tumour cell followed by its entry into the vasculature, exit of malignant cells from the circulation and ultimately their colonization at the distant sites. These stages are referred to as formation of primary tumour, angiogenesis, invasion, intravasation and extravasation, respectively. The major sites of metastasis of breast cancer are the lymph nodes, bone, brain and lung. Only about 28% five-year survival rate has been reported for stage IV breast cancer. Metastasis is a serious concern for breast cancer and therefore, various therapeutic strategies such as tyrosine kinase inhibitors have been developed to target specific dysregulated genes and various signalling pathways involved in different steps of metastasis. In addition, other therapies like hyperbaric oxygen therapy, RNA interference and CRISPR/Cas9 are also being explored as novel strategies to cure the stage IV/metastatic breast cancer. Therefore, the current review has been compiled with an aim to evaluate the genetic basis of stage IV breast cancer with a focus on the molecular mechanisms. In addition, the therapeutic strategies targeting these dysregulated genes involved in various signalling pathways have also been discussed. Genome editing technologies that can target specific genes in the affected areas by making knock-in and knock-out alternations and thereby bring significant treatment outcomes in breast cancer have also been summarized.

Network-Based Target Prioritization and Drug Candidate Identification for Multiple Sclerosis: From Analyzing "Omics Data" to Druggability Simulations

Multiple sclerosis (MS) is the most common chronic inflammatory demyelinating disease of the central nervous system. While the drugs currently available for MS provide symptomatic benefit, there is no curative treatment. The emergence of large-scale multiomics data and network theory provide new opportunities for drug discovery in MS, as these are promising strategies for developing novel drugs. In this study, we proposed a computational framework that combined biomolecular network modeling and structural dynamics analysis to facilitate the discovery of new drugs with potential activity in MS. First, we developed a new shortest path-based algorithm that prioritized differentially expressed genes using a newly topological and functional exploration of protein-protein interaction network. Then, pathway enrichment analysis and an assessment of target druggability suggested that TNF-α-induced protein 3 (TNFAIP3), which is involved in NF-κ B signaling, could be a potential therapeutic target for MS. Finally, druggability simulations and mutation enrichment analysis of the TNFAIP3 dimer presented two druggable sites. Follow-up pharmacophore model-based virtual screening of the two sites yielded 30 hit compounds with low energy scores. In summary, this novel method based on analyzing "omics data" and performing druggability simulations, is a systematic approach that unravels disease mechanisms and links them to the chemical space to develop treatments and can be applied to other complex diseases.

RWR-algorithm-based dissection of microRNA-506-3p and microRNA-140-5p as radiosensitive biomarkers in colorectal cancer

Radiotherapy resistance is one of the main causes for treatment failure in colorectal cancer (CRC), and it is associated with the deregulation of certain microRNAs. In this study, we constructed the microRNA-mRNA network consisting of 2275 microRNAs and 7045 target genes, collected the known microRNAs related to CRC-radiosensitivity (CRCR) (n=18) as the seed nodes, and applied the algorithm of random walk with restart (RWR) to the network to identify novel CRCR-related microRNAs (n=263). In functional analysis, 263 novel microRNAs shared a high proportion of the same biological processes and pathways with the known microRNAs. In topological analysis of the sub-network of the 263 microRNAs and their targets, hsa-mir-506-3p and hsa-mir-140-5p were identified as network hub nodes. In plasma, radiosensitive patients had a higher expression level of hsa-mir-506-3p and hsa-mir-140-5p than radioresistant patients. In experimental validation, both hsa-mir-506-3p and hsa-mir-140-5p over-expression could obviously decrease the cell proliferation, survival rate and colonality in CRC cells after radiation. In conclusion, this study combined the novel network-based method with experimental validation, and identified two novel radiosensitive biomarkers of hsa-mir-506-3p and hsa-mir-140-5p in CRC.

Innovative Options for Bone Metastasis Treatment: An Extensive Analysis on Biomaterials-Based Strategies for Orthopedic Surgeons

Bone is the third most frequent site of metastasis, with a particular incidence in breast and prostate cancer patients. For example, almost 70% of breast cancer patients develop several bone metastases in the late stage of the disease. Bone metastases are a challenge for clinicians and a burden for patients because they frequently cause pain and can lead to fractures. Unfortunately, current therapeutic options are in most cases only palliative and, although not curative, surgery remains the gold standard for bone metastasis treatment. Surgical intervention mostly provides the replacement of the affected bone with a bioimplant, which can be made by materials of different origins and designed through several techniques that have evolved throughout the years simultaneously with clinical needs. Several scientists and clinicians have worked to develop biomaterials with potentially successful biological and mechanical features, however, only a few of them have actually reached the scope. In this review, we extensively analyze currently available biomaterials-based strategies focusing on the newest and most innovative ideas while aiming to highlight what should be considered both a reliable choice for orthopedic surgeons and a future definitive and curative option for bone metastasis and cancer patients.

Identifying Methylation Pattern and Genes Associated with Breast Cancer Subtypes

Breast cancer is regarded worldwide as a severe human disease. Various genetic variations, including hereditary and somatic mutations, contribute to the initiation and progression of this disease. The diagnostic parameters of breast cancer are not limited to the conventional protein content and can include newly discovered genetic variants and even genetic modification patterns such as methylation and microRNA. In addition, breast cancer detection extends to detailed breast cancer stratifications to provide subtype-specific indications for further personalized treatment. One genome-wide expression–methylation quantitative trait loci analysis confirmed that different breast cancer subtypes have various methylation patterns. However, recognizing clinically applied (methylation) biomarkers is difficult due to the large number of differentially methylated genes. In this study, we attempted to re-screen a small group of functional biomarkers for the identification and distinction of different breast cancer subtypes with advanced machine learning methods. The findings may contribute to biomarker identification for different breast cancer subtypes and provide a new perspective for differential pathogenesis in breast cancer subtypes.

Inferring novel genes related to oral cancer with a network embedding method and one-class learning algorithms

Oral cancer (OC) is one of the most common cancers threatening human lives. However, OC pathogenesis has yet to be fully uncovered, and thus designing effective treatments remains difficult. Identifying genes related to OC is an important way for achieving this purpose. In this study, we proposed three computational models for inferring novel OC-related genes. In contrast to previously proposed computational methods, which lacked the learning procedures, each proposed model adopted a one-class learning algorithm, which can provide a deep insight into features of validated OC-related genes. A network embedding algorithm (i.e., node2vec) was applied to the protein-protein interaction network to produce the representation of genes. The features of the OC-related genes were used in the training of the one-class algorithm, and the performance of the final inferring model was improved through a feature selection procedure. Then, candidate genes were produced by applying the trained inferring model to other genes. Three tests were performed to screen out the important candidate genes. Accordingly, we obtained three inferred gene sets, any two of which were different. The inferred genes were also different from previous reported genes and some of them have been included in the public Oral Cancer Gene Database. Finally, we analyzed several inferred genes to confirm whether they are novel OC-related genes.

P4HB, a Novel Hypoxia Target Gene Related to Gastric Cancer Invasion and Metastasis

Gastric cancer (GC) is a common tumor-associated lethal disease, and invasiveness and metastasis are primary challenges in its clinical treatment. Hypoxia microenvironment cannot be ignored in the process of metastasis. Hypoxia inducible factor-1α (HIF-1α) is the core component of the hypoxia signaling pathway. The aim of this study was to identify potential hub genes and signaling pathways associated with HIF-1α. We explored the invasiveness- and metastasis-associated phenotype of GC via bioinformatics analysis and molecular studies. Differentially expressed genes (DEGs) were identified in GC cells and HIF-1α-knockdown GC cells. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed, and a protein-protein interaction (PPI) network was constructed. Hub genes were identified via centrality analysis and Molecular Complex Detection (MCODE) module analysis. The findings suggested that prolyl 4-hydroxylase beta polypeptide (P4HB) has strong associations with HIF-1α. Further, we observed that HIF-1α and P4HB were upregulated in SGC-7901 and BGC-823 cells. In addition, inhibition of HIF-1α expression reduced invasion and metastasis in GC cells; this effect was partially reversed by P4HB overexpression. Our results confirm that P4HB plays a significant role in the regulatory network of HIF-1α. Therefore, HIF-1α and P4HB may be considered potential biomarkers of GC.

Inferring novel genes related to colorectal cancer via random walk with restart algorithm

Colorectal cancer (CRC) is the third most common type of cancer. In recent decades, genomic analysis has played an increasingly important role in understanding the molecular mechanisms of CRC. However, its pathogenesis has not been fully uncovered. Identification of genes related to CRC as complete as possible is an important way to investigate its pathogenesis. Therefore, we proposed a new computational method for the identification of novel CRC-associated genes. The proposed method is based on existing proven CRC-associated genes, human protein-protein interaction networks, and random walk with restart algorithm. The utility of the method is indicated by comparing it to the methods based on Guilt-by-association or shortest path algorithm. Using the proposed method, we successfully identified 298 novel CRC-associated genes. Previous studies have validated the involvement of the majority of these 298 novel genes in CRC-associated biological processes, thus suggesting the efficacy and accuracy of our method.

A Binary Classifier for the Prediction of EC Numbers of Enzymes

Background:

Identification of Enzyme Commission (EC) number of enzymes is quite important for understanding the metabolic processes that produce enough energy to sustain life. Previous studies mainly focused on predicting six main functional classes or sub-functional classes, i.e., the first two digits of the EC number.

Objective:

In this study, a binary classifier was proposed to identify the full EC number (four digits) of enzymes.

Methods:

Enzymes and their known EC numbers were paired as positive samples and negative samples were randomly produced that were as many as positive samples. The associations between any two samples were evaluated by integrating the linkages between enzymes and EC numbers. The classic machining learning algorithm, Support Vector Machine (SVM), was adopted as the prediction engine.

Results:

The five-fold cross-validation test on five datasets indicated that the overall accuracy, Matthews correlation coefficient and F1-measure were about 0.786, 0.576 and 0.771, respectively, suggesting the utility of the proposed classifier. In addition, the effectiveness of the classifier was elaborated by comparing it with other classifiers that were based on other classic machine learning algorithms.

Conclusion:

The proposed classifier was quite effective for prediction of EC number of enzymes and was specially designed for dealing with the problem addressed in this study by testing it on five datasets containing randomly produced samples.

Long noncoding RNA HOXA-AS2 regulates the expression of SCN3A by sponging miR-106a in breast cancer

Breast cancer is the most commonly diagnosed cancer that affects women worldwide. This study aimed to investigate the competing endogenous RNAs (ceRNAs) mechanism in breast cancer. Microarray data were downloaded from the University of California Santa Cruz (UCSC) Xena database. The limma package was used to screen the differentially expressed messenger RNAs (DEMs) and differentially expressed long noncoding RNAs (DELs). Subsequently, functional analysis was performed using DAVID tool. After constructing the protein-protein interaction (PPI) network, we identified the major gene modules using the Cytoscape software. Univariate survival analysis in the survival package was performed. Finally, the ceRNA regulatory network was constructed to identify the critical genes. A total of 1380 DEMs and 345 DELs were identified in breast cancer samples compared with normal samples. Functional enrichment analysis showed that DEMs were mainly involved in cell division, and cell cycle. We screened four major gene modules and identified the hub nodes in these functional modules. Several DEMs (including FABP7, C4BPA, and LAMB3) and three long noncoding RNAs (lncRNAs) (LINC00092, SLC26A4.AS1, and COLCA1) exhibited significant correlation with patients' survival outcomes. In the ceRNA network, the lncRNA HOXA-AS2 regulated the expression level of SCN3A by interacting with hsa-miR-106a-5p. Thus, our study investigated the ceRNA mechanism in breast cancer. The results showed that lncRNA HOXA-AS2 might modulate the expression of SCN3A by sponging miR-106a in breast cancer.

Adefovir dipivoxil sensitizes colon cancer cells to vemurafenib by disrupting the KCTD12-CDK1 interaction

Vemurafenib is a B-Raf V600E inhibitor that exerts significant inhibitory effects in melanoma but not in colon cancer, and the mechanism of vemurafenib resistance remains unclear. In this study, bioinformatics analysis of gene profiles in cancer cells treated with vemurafenib or its analog revealed that cell cycle progression is significantly affected by vemurafenib. We found that CDK1 is stably activated in the vemurafenib-resistant (VR) colon cancer sublines that we established, indicating that CDK1 activation is responsible for vemurafenib resistance. As the KCTD12-CDK1 interaction is necessary for CDK1 activation, we screened an FDA-approved drug library consisting of 616 compounds and identified that adefovir dipivoxil (AD), a nucleoside analog for treatment of HBV infections, disrupts the CDK1-KCTD12 interaction and induces G2 phase arrest in the cell cycle. Functional assays demonstrated that AD significantly inhibited colon cancer cell proliferation and tumorigenesis both in vitro and in vivo with no observed side effects. Furthermore, AD sensitized vemurafenib-resistant colon cancer cells and tumor xenografts to vemurafenib. This study reveals that CDK1 activation induces vemurafenib resistance and that AD is a promising therapeutic strategy for colon cancer both as a single agent and in combination with vemurafenib.

Identification of tumor-educated platelet biomarkers of non-small-cell lung cancer

BACKGROUND

Lung cancer is a severe cancer with a high death rate. The 5-year survival rate for stage III lung cancer is much lower than stage I. Early detection and intervention of lung cancer patients can significantly increase their survival time. However, conventional lung cancer-screening methods, such as chest X-rays, sputum cytology, positron-emission tomography (PET), low-dose computed tomography (CT), magnetic resonance imaging, and gene-mutation, -methylation, and -expression biomarkers of lung tissue, are invasive, radiational, or expensive. Liquid biopsy is non-invasive and does little harm to the body. It can reflect early-stage dysfunctions of tumorigenesis and enable early detection and intervention.

METHODS

In this study, we analyzed RNA-sequencing data of tumor-educated platelets (TEPs) in 402 non-small-cell lung cancer (NSCLC) patients and 231 healthy controls. A total of 48 biomarker genes were selected with advanced minimal-redundancy, maximal-relevance, and incremental feature-selection (IFS) methods.

RESULTS

A support vector-machine (SVM) classifier based on the 48 biomarker genes accurately predicted NSCLC with leave-one-out cross-validation (LOOCV) sensitivity, specificity, accuracy, and Matthews correlation coefficients of 0.925, 0.827, 0.889, and 0.760, respectively. Network analysis of the 48 genes revealed that the WASF1 actin cytoskeleton module, PRKAB2 kinase module, RSRC1 ribosomal protein module, PDHB carbohydrate-metabolism module, and three intermodule hubs (TPM2, MYL9, and PPP1R12C) may play important roles in NSCLC tumorigenesis and progression.

CONCLUSION

The 48-gene TEP liquid-biopsy biomarkers will facilitate early screening of NSCLC and prolong the survival of cancer patients.

Use of Laplacian Heat Diffusion Algorithm to Infer Novel Genes With Functions Related to Uveitis

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.

Deciphering the Relationship between Obesity and Various Diseases from a Network Perspective

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.

Inferring anatomical therapeutic chemical (ATC) class of drugs using shortest path and random walk with restart algorithms

The anatomical therapeutic chemical (ATC) classification system is a widely accepted drug classification scheme. This system comprises five levels and includes several classes in each level. Drugs are classified into classes according to their therapeutic effects and characteristics. The first level includes 14 main classes. In this study, we proposed two network-based models to infer novel potential chemicals deemed to belong in the first level of ATC classification. To build these models, two large chemical networks were constructed using the chemical-chemical interaction information retrieved from the Search Tool for Interactions of Chemicals (STITCH). Two classic network algorithms, shortest path (SP) and random walk with restart (RWR) algorithms, were executed on the corresponding network to mine novel chemicals for each ATC class using the validated drugs in a class as seed nodes. Then, the obtained chemicals yielded by these two algorithms were further evaluated by a permutation test and an association test. The former can exclude chemicals produced by the structure of the network, i.e., false positive discoveries. By contrast, the latter identifies the most important chemicals that have strong associations with the ATC class. Comparisons indicated that the two models can provide quite dissimilar results, suggesting that the results yielded by one model can be essential supplements for those obtained by the other model. In addition, several representative inferred chemicals were analyzed to confirm the reliability of the results generated by the two models. This article is part of a Special Issue entitled: Accelerating Precision Medicine through Genetic and Genomic Big Data Analysis edited by Yudong Cai & Tao Huang.

Identification of genes related to proliferative diabetic retinopathy through RWR algorithm based on protein-protein interaction network

Proliferative diabetic retinopathy (PDR) is one of the most common complications of diabetes and can lead to blindness. Proteomic studies have provided insight into the pathogenesis of PDR and a series of PDR-related genes has been identified but are far from fully characterized because the experimental methods are expensive and time consuming. In our previous study, we successfully identified 35 candidate PDR-related genes through the shortest-path algorithm. In the current study, we developed a computational method using the random walk with restart (RWR) algorithm and the protein-protein interaction (PPI) network to identify potential PDR-related genes. After some possible genes were obtained by the RWR algorithm, a three-stage filtration strategy, which includes the permutation test, interaction test and enrichment test, was applied to exclude potential false positives caused by the structure of PPI network, the poor interaction strength, and the limited similarity on gene ontology (GO) terms and biological pathways. As a result, 36 candidate genes were discovered by the method which was different from the 35 genes reported in our previous study. A literature review showed that 21 of these 36 genes are supported by previous experiments. These findings suggest the robustness and complementary effects of both our efforts using different computational methods, thus providing an alternative method to study PDR pathogenesis.

Prediction of potential drivers connecting different dysfunctional levels in lung adenocarcinoma via a protein-protein interaction network

Lung cancer is a serious disease that threatens an affected individual's life. Its pathogenesis has not yet to be fully described, thereby impeding the development of effective treatments and preventive measures. "Cancer driver" theory considers that tumor initiation can be associated with a number of specific mutations in genes called cancer driver genes. Four omics levels, namely, (1) methylation, (2) microRNA, (3) mutation, and (4) mRNA levels, are utilized to cluster cancer driver genes. In this study, the known dysfunctional genes of these four levels were used to identify novel driver genes of lung adenocarcinoma, a subtype of lung cancer. These genes could contribute to the initiation and progression of lung adenocarcinoma in at least two levels. First, random walk with restart algorithm was performed on a protein-protein interaction (PPI) network constructed with PPI information in STRING by using known dysfunctional genes as seed nodes for each level, thereby yielding four groups of possible genes. Second, these genes were further evaluated in a test strategy to exclude false positives and select the most important ones. Finally, after conducting an intersection operation in any two groups of genes, we obtained several inferred driver genes that contributed to the initiation of lung adenocarcinoma in at least two omics levels. Several genes from these groups could be confirmed according to recently published studies. The inferred genes reported in this study were also different from those described in a previous study, suggesting that they can be used as essential supplementary data for investigations on the initiation of lung adenocarcinoma. This article is part of a Special Issue entitled: Accelerating Precision Medicine through Genetic and Genomic Big Data Analysis edited by Yudong Cai & Tao Huang.

Network-Based Method for Identifying Co- Regeneration Genes in Bone, Dentin, Nerve and Vessel Tissues

Bone and dental diseases are serious public health problems. Most current clinical treatments for these diseases can produce side effects. Regeneration is a promising therapy for bone and dental diseases, yielding natural tissue recovery with few side effects. Because soft tissues inside the bone and dentin are densely populated with nerves and vessels, the study of bone and dentin regeneration should also consider the co-regeneration of nerves and vessels. In this study, a network-based method to identify co-regeneration genes for bone, dentin, nerve and vessel was constructed based on an extensive network of protein–protein interactions. Three procedures were applied in the network-based method. The first procedure, searching, sought the shortest paths connecting regeneration genes of one tissue type with regeneration genes of other tissues, thereby extracting possible co-regeneration genes. The second procedure, testing, employed a permutation test to evaluate whether possible genes were false discoveries; these genes were excluded by the testing procedure. The last procedure, screening, employed two rules, the betweenness ratio rule and interaction score rule, to select the most essential genes. A total of seventeen genes were inferred by the method, which were deemed to contribute to co-regeneration of at least two tissues. All these seventeen genes were extensively discussed to validate the utility of the method.

A network-based method using a random walk with restart algorithm and screening tests to identify novel genes associated with Menière's disease

As a chronic illness derived from hair cells of the inner ear, Menière’s disease (MD) negatively influences the quality of life of individuals and leads to a number of symptoms, such as dizziness, temporary hearing loss, and tinnitus. The complete identification of novel genes related to MD would help elucidate its underlying pathological mechanisms and improve its diagnosis and treatment. In this study, a network-based method was developed to identify novel MD-related genes based on known MD-related genes. A human protein-protein interaction (PPI) network was constructed using the PPI information reported in the STRING database. A classic ranking algorithm, the random walk with restart (RWR) algorithm, was employed to search for novel genes using known genes as seed nodes. To make the identified genes more reliable, a series of screening tests, including a permutation test, an interaction test and an enrichment test, were designed to select essential genes from those obtained by the RWR algorithm. As a result, several inferred genes, such as CD4, NOTCH2 and IL6, were discovered. Finally, a detailed biological analysis was performed on fifteen of the important inferred genes, which indicated their strong associations with MD.

An integrated method for the identification of novel genes related to oral cancer

Cancer is a significant public health problem worldwide. Complete identification of genes related to one type of cancer facilitates earlier diagnosis and effective treatments. In this study, two widely used algorithms, the random walk with restart algorithm and the shortest path algorithm, were adopted to construct two parameterized computational methods, namely, an RWR-based method and an SP-based method; based on these methods, an integrated method was constructed for identifying novel disease genes. To validate the utility of the integrated method, data for oral cancer were used, on which the RWR-based and SP-based methods were trained, thereby building two optimal methods. The integrated method combining these optimal methods was further adopted to identify the novel genes of oral cancer. As a result, 85 novel genes were inferred, among which eleven genes (e.g., MYD88, FGFR2, NF-κBIA) were identified by both the RWR-based and SP-based methods, 70 genes (e.g., BMP4, IFNG, KITLG) were discovered only by the RWR-based method and four genes (L1R1, MCM6, NOG and CXCR3) were predicted only by the SP-based method. Extensive analyses indicate that several novel genes have strong associations with cancers, indicating the effectiveness of the integrated method for identifying disease genes.