Up-regulation of GINS1 highlighted a good diagnostic and prognostic potential of survival in three different subtypes of human cancer

Cancer is a fatal malignancy and its increasing worldwide prevalence demands the discovery of more sensitive and reliable molecular biomarkers. To investigate the GINS1 expression level and its prognostic value in distinct human cancers using a series of multi-layered in silico approach may help to establish it as a potential shared diagnostic and prognostic biomarker of different cancer subtypes. The GINS1 mRNA, protein expression, and promoter methylation were analyzed using UALCAN and Human Protein Atlas (HPA), while mRNA expression was further validated via GENT2. The potential prognostic values of GINS1 were evaluated through KM plotter. Then, cBioPortal was utilized to examine the GINS1-related genetic mutations and copy number variations (CNVs), while pathway enrichment analysis was performed using DAVID. Moreover, a correlational analysis between GINS1 expression and CD8+ T immune cells and a the construction of gene-drug interaction network was performed using TIMER, CDT, and Cytoscape. The GINS1 was found down-regulated in a single subtypes of human cancer while commonly up-regulated in 23 different other subtypes. The up-regulation of GINS1 was significantly correlated with the poor overall survival (OS) of Liver Hepatocellular Carcinoma (LIHC), Lung Adenocarcinoma (LUAD), and Kidney renal clear cell carcinoma (KIRC). The GINS1 was also found up-regulated in LIHC, LUAD, and KIRC patients of different clinicopathological features. Pathways enrichment analysis revealed the involvement of GINS1 in two diverse pathways, while few interesting correlations were also documented between GINS1 expression and its promoter methylation level, CD8+ T immune cells level, and CNVs. Moreover, we also predicted few drugs that could be used in the treatment of LIHC, LUAD, and KIRC by regulating the GINS1 expression. The expression profiling of GINS1 in the current study has suggested it a novel shared diagnostic and prognostic biomarker of LIHC, LUAD, and KIRC.

Identification of Key Genes and Pathways in Osteosarcoma by Bioinformatics Analysis

Purpose. Osteosarcoma (OS) is the most primary bone malignant tumor in adolescents. Although the treatment of OS has made great progress, patients’ prognosis remains poor due to tumor invasion and metastasis. Materials and Methods. We downloaded the expression profile GSE12865 from the Gene Expression Omnibus database. We screened differential expressed genes (DEGs) by making use of the R limma software package. Based on Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and Gene Set Enrichment Analysis, we performed the function and pathway enrichment analyses. Then, we constructed a Protein-Protein Interaction network and screened hub genes through the Search Tool for the Retrieval of Interacting Genes. Result. By analyzing the gene expression profile GSE12865, we obtained 703 OS-related DEGs, which contained 166 genes upregulated and 537 genes downregulated. The DEGs were primarily abundant in ribosome, cell adhesion molecules, ubiquitin-ubiquitin ligase activity, and p53 signaling pathway. The hub genes of OS were KDR, CDH5, CD34, CDC42, RBX1, POLR2C, PPP2CA, and RPS2 through PPI network analysis. Finally, GSEA analysis showed that cell adhesion molecules, chemokine signal pathway, transendothelial migration, and focal adhesion were associated with OS. Conclusion. In this study, through analyzing microarray technology and bioinformatics analysis, the hub genes and pathways about OS are identified, and the new molecular mechanism of OS is clarified.

Large-Scale Gastric Cancer Susceptibility Gene Identification Based on Gradient Boosting Decision Tree

The early clinical symptoms of gastric cancer are not obvious, and metastasis may have occurred at the time of treatment. Poor prognosis is one of the important reasons for the high mortality of gastric cancer. Therefore, the identification of gastric cancer-related genes can be used as relevant markers for diagnosis and treatment to improve diagnosis precision and guide personalized treatment. In order to further reveal the pathogenesis of gastric cancer at the gene level, we proposed a method based on Gradient Boosting Decision Tree (GBDT) to identify the susceptible genes of gastric cancer through gene interaction network. Based on the known genes related to gastric cancer, we collected more genes which can interact with them and constructed a gene interaction network. Random Walk was used to extract network association of each gene and we used GBDT to identify the gastric cancer-related genes. To verify the AUC and AUPR of our algorithm, we implemented 10-fold cross-validation. GBDT achieved AUC as 0.89 and AUPR as 0.81. We selected four other methods to compare with GBDT and found GBDT performed best.

Differences in gene expression between high- and low-grade serous ovarian cancers: implications for diagnosis and prognosis

Background: The clinical presentations of high-grade serous ovarian cancer (HGSOC) and low-grade serous ovarian cancer (LGSOC) differ. In this study, we aimed to identify the essential molecules for the diagnosis and prognosis of these OC subtypes. Methods: Differentially expressed genes between HGSOC and LGSOC were identified using three GEO series. The functional enrichment analysis was performed to investigate different biological processes and pathways. The protein-protein interaction network was constructed, and the discovered hub genes were frequently validated using prognostic correlation and immunohistochemistry (IHC) in GEPIA and HPA databases. Finally, we screened out BIRC5 and used IHC to detect its expression in 20 cases of borderline serous tumors, 20 cases of LGSOC, and 38 cases of HGSOC, and further analyzed its correlation with clinical characteristics.Results: In comparison with LGSOC, 79 upregulated and 85 downregulated genes were identified in HGSOC. The biological roles of these genes were mainly centered on the cell cycle process and chromosomal segregation. Among the 10 hub genes chosen, BIRC5 is positively related to the overall survival of patients with OC (p = 0.014) and can distinguish OC from normal ovarian tissue. In addition to database analysis, we verify BIRC5 through the specimen resources in our case database. According to the IHC results of our specimens, we found that BIRC5 can not only distinguish HGSOC and LGSOC but also positively correlate with the age, preoperative CA125 level, FIGO stage，and TP53/Ki-67 expression in tumor specimens.Conclusions: BIRC5 is a reliable marker that can distinguish HGSOC from LGSOC, guide prognosis, and be utilized in clinical IHC.

ProtFinder: finding subcellular locations of proteins using protein interaction networks

Protein subcellular localization prediction plays a crucial role in improving our understandings of different diseases and consequently assists in building drug targeting and drug development pipelines. Proteins are known to co-exist at multiple subcellular locations which make the task of prediction extremely challenging. A protein interaction network is a graph that captures interactions between different proteins. It is safe to assume that if two proteins are interacting, they must share some subcellular locations. With this regard, we propose ProtFinder - the first deep learning-based model that exclusively relies on protein interaction networks to predict the multiple subcellular locations of proteins. We also integrate biological priors like the cellular component of Gene Ontology to make ProtFinder a more biology-aware intelligent system. ProtFinder is trained and tested using the STRING and BioPlex databases whereas the annotations of proteins are obtained from the Human Protein Atlas. Our model gives an AUC-ROC score of 90.00% and an MCC score of 83.42% on a held-out set of proteins. We also apply ProtFinder to annotate proteins that currently do not have confident location annotations. We observe that ProtFinder is able to confirm some of these unreliable location annotations, while in some cases complementing the existing databases with novel location annotations.

Supervised machine learning models and protein-protein interaction network analysis of gene expression profiles induced by omega-3 polyunsaturated fatty acids

Background: Omega-3 polyunsaturated fatty acids (PUFAs), such as eicosapentaenoic (EPA) and docosahexaenoic (DHA) acids, have beneficial effects on human health, but their effect on gene expression in elderly individuals (age ≥ 65) is largely unknown. In order to examine this, the gene expression profiles were analyzed in the healthy subjects (n = 96) at baseline and after 26 weeks of supplementation with EPA+DHA to determine up-regulated and down-regulated dif-ferentially expressed genes (DEGs) triggered by PUFAs. The protein-protein interaction (PPI) networks were constructed by mapping these DEGs to a human interactome and linking them to the specific pathways. Objective: This study aimed to implement supervised machine learning models and protein-protein interaction network analysis of gene expression profiles induced by PUFAs. Methods: The transcriptional profile of GSE12375 was obtained from the Gene Expression Om-nibus database, which is based on the Affymetrix NuGO array. The probe cell intensity data were converted into the gene expression values, and the background correction was performed by the multi-array average algorithm. The LIMMA (Linear Models for Microarray Data) algo-rithm was implemented to identify relevant DEGs at baseline and after 26 weeks of supplemen-tation with a p-value < 0.05. The DAVID web server was used to identify and construct the en-riched KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways. Finally, the construction of machine learning (ML) models, including logistic regression, naïve Bayes, and deep neural networks, were implemented for the analyzed DEGs associated with the specific pathways. Results: The results revealed that up-regulated DEGs were associated with neurotrophin/MAPK signaling, whereas the down-regulated DEGs were linked to cancer, acute myeloid leukemia, and long-term depression pathways. Additionally, ML approaches were able to cluster the EPA/DHA-treated and control groups by the logistic regression performing the best. Conclusion: Overall, this study highlights the pivotal changes in DEGs induced by PUFAs and provides the rationale for the implementation of ML algorithms as predictive models for this type of biomedical data.

Identification of Diagnostic Markers for Breast Cancer Based on Differential Gene Expression and Pathway Network

Background: Breast cancer is the second largest cancer in the world, the incidence of breast cancer continues to rise worldwide, and women’s health is seriously threatened. Therefore, it is very important to explore the characteristic changes of breast cancer from the gene level, including the screening of differentially expressed genes and the identification of diagnostic markers.Methods: The gene expression profiles of breast cancer were obtained from the TCGA database. The edgeR R software package was used to screen the differentially expressed genes between breast cancer patients and normal samples. The function and pathway enrichment analysis of these genes revealed significant enrichment of functions and pathways. Next, download these pathways from KEGG website, extract the gene interaction relations, construct the KEGG pathway gene interaction network. The potential diagnostic markers of breast cancer were obtained by combining the differentially expressed genes with the key genes in the network. Finally, these markers were used to construct the diagnostic prediction model of breast cancer, and the predictive ability of the model and the diagnostic ability of the markers were verified by internal and external data.Results: 1060 differentially expressed genes were identified between breast cancer patients and normal controls. Enrichment analysis revealed 28 significantly enriched pathways (p &lt; 0.05). They were downloaded from KEGG website, and the gene interaction relations were extracted to construct the gene interaction network of KEGG pathway, which contained 1277 nodes and 7345 edges. The key nodes with a degree greater than 30 were extracted from the network, containing 154 genes. These 154 key genes shared 23 genes with differentially expressed genes, which serve as potential diagnostic markers for breast cancer. The 23 genes were used as features to construct the SVM classification model, and the model had good predictive ability in both the training dataset and the validation dataset (AUC = 0.960 and 0.907, respectively).Conclusion: This study showed that the difference of gene expression level is important for the diagnosis of breast cancer, and identified 23 breast cancer diagnostic markers, which provides valuable information for clinical diagnosis and basic treatment experiments.

Unfolding #Anonymous on Twitter

This paper presents a comprehensive empirical investigation of the range of actors, issues and sub-groups related to the hashtag Anonymous on Twitter between 2012 and 2015. Complementing existing studies that have provided in-depth accounts of Anonymous from a specific point of view, this research provides an overview of the network related to the discursive construction of Anonymous on Twitter from a synoptic standpoint. In particular, the analysis covers three dimensions: the structure and dynamics of the #Anonymous interaction network; the range of issues that Anonymous has been associated with; and the relation between Anonymous and its offshoots. This research provides a descriptive characterization of the topological and semantic complexity of Anonymous and invites to reflect on the simplifications that our vocabulary and methods entail vis a vis the complexity of digital entities delimited by and individuated through hashtags.

Integrative In Silico Investigation Reveals the Host-Virus Interactions in Repurposed Drugs Against SARS-CoV-2

The ongoing COVID-19 outbreak have posed a significant threat to public health worldwide. Recently Toll-like receptor (TLR) has been proposed to be the drug target of SARS-CoV-2 treatment, the specificity and efficacy of such treatments remain unknown. In the present study we performed the investigation of repurposed drugs via a framework comprising of Search Tool for Interacting Chemicals (STITCH), Kyoto Encyclopedia of Genes and Genomes (KEGG), molecular docking, and virus-host-drug interactome mapping. Chloroquine (CQ) and hydroxychloroquine (HCQ) were utilized as probes to explore the interaction network that is linked to SARS-CoV-2. 47 drug targets were shown to be overlapped with SARS-CoV-2 network and were enriched in TLR signaling pathway. Molecular docking analysis and molecular dynamics simulation determined the direct binding affinity of TLR9 to CQ and HCQ. Furthermore, we established SARS-CoV-2-human-drug protein interaction map and identified the axis of TLR9-ERC1-Nsp13 and TLR9-RIPK1-Nsp12. Therefore, the elucidation of the interactions of SARS-CoV-2 with TLR9 axis will not only provide pivotal insights into SARS-CoV-2 infection and pathogenesis but also improve the treatment against COVID-19.

Association Between Depression and Breast Cancer: TNF/TNFRSF1β and LEP/LEPR Axis

Depression contributes to enhanced initiation, development and metastasis of breast cancer. Despite epidemiological studies and experimental data suggest that depression and breast cancer may share a common biological mechanism, the results from these studies remain inconsistent. Here, we fully focus on the underlying biological mechanism behind the adverse effects of depression against breast cancer patients, and highlight the practical therapeutic intervention and improving quality of life. Publicly available datasets deposited in the Gene Expression Omnibus (GEO) were downloaded. Gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses of the differentially expressed genes (DEGs), which were extracted by using R tools, were performed. The protein-protein interaction network of the target DEGs was constructed using Cytoscape software and the hub genes were identified. In our study, we found that genes encoding proinflammatory cytokine, such as IL-1β and TNF, had significantly increased expression in depression. Following chronically stimulated by TNFα and IL-1β (usually for 14-18 days), inflammatory cancer-associated fibroblasts (CAFs) had elevated expression of inflammatory genes. Furthermore, the TNF/TNFRSF1β and LEP/LEPR regulatory axes were proven to be hub pathways of the crosstalk between depression and breast cancer. Our findings demonstrate that inflammatory factors are messengers linking depression and breast cancer, and provided further guidance in clinical medication.