scholarly journals Investigation of Immune‐related Makers Associated with Tumor-infiltrating T Cells in Ovarian Cancer

2020 ◽  
Author(s):  
Haihong Liao ◽  
Shuwen Han ◽  
Yuefen Pan ◽  
Jiamin Xu ◽  
Quan Qi ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
T Cells ◽  
Network Analysis ◽  
The Cancer Genome Atlas ◽  
Cancer Prognosis ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Target Network ◽  
Patient Prognosis

Abstract Background: Tumor-infiltrating T cells in the tumor microenvironment are the biological basis of immunotherapy and promising predictors of cancer prognosis. Aim: The aim of this study was to investigate immune‐related RNAs associated with tumor-infiltrating T cells in ovarian cancer (OV).Methods: The gene expression data of patients with OV were downloaded from The Cancer Genome Atlas (TCGA) database. The immune and stromal scores were calculated and the differentially expressed mRNAs (DEGs) were screened. The abundance of six types of infiltrating immune cells was investigated, and the immune-related DEGs associated with tumor-infiltrating CD4+ and CD8+ T cells were explored by correlation analyses. Subsequently, multiple analyses, i.e., protein-protein interaction (PPI) network analysis, competing endogenous RNA (ceRNA; lncRNA-miRNA-target) network analysis, and small-molecule target network analysis, were performed. Results: In total, 37 and 49 immune-related DEGs of CD4+ and CD8+ T cells were screened, respectively. PPI network results showed that granzyme B (GZMB) was a hub node in the two PPI networks constructed by immune-related DEGs of CD4+ and CD8+ T cells. Moreover, the ceRNA chr22-38_28785274-29006793.1/has-miR-1249-5p/CD3E was obtained from the two constructed ceRNA networks related to CD4+ and CD8+ T cells. Survival analysis revealed that key immune-related DEGs of CD4+ and CD8+ T cells, such as GZMB and CD3E, were positively correlated with patient prognosis. Conclusion: GZMB and ceRNAs, such as chr22-38_28785274-29006793.1/has-miR-1249-5p/CD3E, may mediate the role of tumor-infiltrating T cells in OV. GZMB and CD3E may be used as promising T cell-related biomarkers with prognostic value in OV.

scholarly journals Exploration of the regulatory mechanism for hepatocellular carcinoma based on ceRNA networks analysis and immune infiltration

2021 ◽  
Author(s):  
Ning Huang ◽  
Qiang Chen ◽  
Xiaoyi Wang
Keyword(s):  
Hepatocellular Carcinoma ◽  
T Cells ◽  
Network Analysis ◽  
Correlation Analysis ◽  
Immune Cells ◽  
Regulatory Mechanism ◽  
Immune Cell ◽  
Risk Model ◽  
The Cancer Genome Atlas ◽  
Ppi Network

Abstract Background Hepatocellular carcinoma (HCC) as malignant cancer has been deeply investigated for its widespread distribution and extremely high mortality rate worldwide. Despite efforts to understand the regulatory mechanism in HCC, it remains largely unknown. Methods The RNA (mRNAs, lncRNAs, and miRNAs) profiles were downloaded from The Cancer Genome Atlas (TCGA) database. Based on the Weighted Gene Co-expression Network Analysis (WGCNA), the hub differentially expressed RNAs (DERNAs) were screened out. The competing endogenous RNA (ceRNA) and Protein and Protein Interaction (PPI) network were constructed based on the hub DERNAs. The Cox and LASSO regression analysis were used to find the independent prognostic ceRNAs. We performed the “CIBERSORT” algorithm estimate the abundance of immune cells. The correlation analysis was applied to determine the relationship between HCC-related immune cells and prognostic ceRNAs. GEPIA and TIMER database were used to explore the association of critical genes with survival and immune cell infiltration, respectively. Results A total of 524 hub RNAs (507 DEmRNAs, 13 DElncRNAs and 4 DEmiRNAs) were identified in the turquoise module (cor = 0.78, P = 4.7e − 198) using WGCNA algorithm. PPI network analysis showed that NDC80, BUB1B and CCNB2 as the critical genes in HCC. Subsequently, survival analysis revealed that the low expression of NDC80 and BUB1B resulted in a longer overall survival (OS) time for HCC patients in GEPIA database. These critical genes and several immune cells were all significantly positive correlated in TIMER database. The ceRNA network were establish, and were incorporated to risk model. Subsequently, ROC curve showed that the area under the curve (AUC) of the 1-, 3-, and 5-year were 0.762, 0.705, and 0.688, respectively. Out of the 22 cell types, T cells CD4 memory resting were identified as the HCC-related immune cells by systematic analysis. The correlation analysis shown that T cells CD4 memory resting is negatively associated with both AL021453.1 (R = − 0.44, P = 0.00049) and CCDC137 (R = − 0.47, P = 2e-04). Conclusion The current study provide potential prognostic signatures and therapeutic targets for HCC.

scholarly journals Gene Co-Expression Network Analysis Confirms Four Independent Predictors of Survival In Ovarian Cancer Patients Under Optimal Debulking Surgery

2021 ◽  
Author(s):  
Cong Zhang ◽  
Tao Zhu ◽  
Ting Hu ◽  
Qian Sun
Keyword(s):  
Ovarian Cancer ◽  
Network Analysis ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Gene Clusters ◽  
Debulking Surgery ◽  
Screening Criteria ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Optimal Debulking

Abstract Background: Serious ovarian cancer (OvCa) is the most common histological type of epithelial OvCa with poor prognosis. Despite received optimal cytoreduction and standard chemotherapy, a large proportion of patients are forced to recurrence or death within three years. To identify exact prognostic biomarkers associated with overall survival (OS) is urgent requirements of exploring rapid tumor progression mechanisms and developing novel strategies for immunotherapy.Methods: The gene expression profiles of GSE49997, GSE9891 and TCGA were screened through rigorous criteria using R software and Bioconductor package. Weighted gene co-expression network analysis (WGCNA) was constructed to figure out gene clusters associated with OS. Protein-protein interaction (PPI) networks were built through STRING website. Prognostic values of potential biomarkers were validated using forest map and Kaplan-Meier analysis.Results: According to screening criteria, 788 samples and 10402 genes were reserved as the modeling dataset. We detected five modules related to OS and intersected 108 genes through WGCNA after random sampling. PPI network analysis, Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed potential mechanisms of above biomarkers. Conclusions: Four exact biomarkers (CANT1, P4HB, DUS1L and SIRT7) were confirmed as independent predictors of survival in OvCa patients with success of debulking surgery, which might provide promising biomarkers for prognostic judgement in ovarian cancer.

scholarly journals Differential Expression of mRNAs in Peripheral Blood Related to Prodrome and Progression of Alzheimer’s Disease

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Weishuang Xue ◽  
Jinwei Li ◽  
Kailei Fu ◽  
Weiyu Teng
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Peripheral Blood ◽  
Bioinformatics Analysis ◽  
Gene Expression Omnibus ◽  
Venn Diagram ◽  
Ppi Network ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Ppi Networks

Alzheimer’s disease (AD) is a chronic progressive neurodegenerative disease that affects the quality of life of elderly individuals, while the pathogenesis of AD is still unclear. Based on the bioinformatics analysis of differentially expressed genes (DEGs) in peripheral blood samples, we investigated genes related to mild cognitive impairment (MCI), AD, and late-stage AD that might be used for predicting the conversions. Methods. We obtained the DEGs in MCI, AD, and advanced AD patients from the Gene Expression Omnibus (GEO) database. A Venn diagram was used to identify the intersecting genes. Gene Ontology (GO) and Kyoto Gene and Genomic Encyclopedia (KEGG) were used to analyze the functions and pathways of the intersecting genes. Protein-protein interaction (PPI) networks were constructed to visualize the network of the proteins coded by the related genes. Hub genes were selected based on the PPI network. Results. Bioinformatics analysis indicated that there were 61 DEGs in both the MCI and AD groups and 27 the same DEGs among the three groups. Using GO and KEGG analyses, we found that these genes were related to the function of mitochondria and ribosome. Hub genes were determined by bioinformatics software based on the PPI network. Conclusions. Mitochondrial and ribosomal dysfunction in peripheral blood may be early signs in AD patients and related to the disease progression. The identified hub genes may provide the possibility for predicting AD progression or be the possible targets for treatments.

scholarly journals Key Markers Involved in the Anticolon Cancer Response of CD8+ T Cells through the Regulation of Cholesterol Metabolism

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Liang Dong ◽  
Xi Yang ◽  
Yangyanqiu Wang ◽  
Yin Jin ◽  
Qing Zhou ◽  
...  
Keyword(s):  
T Cells ◽  
T Cell ◽  
Network Analysis ◽  
Gene Family ◽  
Cd8 T Cells ◽  
Cholesterol Metabolism ◽  
Differential Expression Analysis ◽  
High Expression ◽  
Ppi Network ◽  
Low Expression

Background. T cell-mediated antitumor immune response is the basis of colorectal cancer (CRC) immunotherapy. Cholesterol plays an important role in T cell signal transduction and function. Apolipoprotein E (APOE) plays a major role in cholesterol metabolism. Objective. To screen and analyze key markers involved in the anticolon cancer response of CD8+ T cells through the regulation of cholesterol metabolism. Methods. Based on the median cutoff of the expression value of APOE according to the data downloaded from The Cancer Genome Atlas and Gene Expression Omnibus database, patients were grouped into low and high expression groups. Differences in clinical factors were assessed, and survival analysis was performed. Differentially expressed genes (DEGs) in the high and low expression groups were screened, followed by the analysis of differences in tumor-infiltrating immune cells and weighted gene coexpression network analysis results. The closely related genes to APOE were identified, followed by enrichment analysis, protein–protein interaction (PPI) network analysis, and differential expression analysis. Immunohistochemical staining (IHC) was used to detect the expression of CD8 in CRC tissues. Results. There were significant differences in prognosis and pathologic_N between the APOE low and high expression groups. A total of 2,349 DEGs between the high and low expression groups were selected. A total of 967 genes were obtained from the blue and brown modules. The probability of distribution of CD8+ T cells differed significantly between the two groups, and 320 closely related DEGs of APOE were screened. Genes including the HLA gene family, B2M, IRF4, and STAT5A had a higher degree in the PPI network. GEO datasets verified the prognosis and the related DEGs of APOE. IHC staining verified the relationship between the distribution of CD8+ T cells and APOE expression. Conclusion. Genes including the HLA gene family, B2M, IRF4, and STAT5A might be the key genes involved in the anticolon cancer response of CD8+ T cells through the regulation of cholesterol metabolism.

scholarly journals A Non-negative Matrix Factorization Based Method for Identifying Essential Proteins

2021 ◽  
Author(s):  
Zhihong Zhang ◽  
Sai Hu ◽  
Wei Yan ◽  
Bihai Zhao ◽  
Lei Wang
Keyword(s):  
Protein Interaction ◽  
Matrix Factorization ◽  
Biological Data ◽  
Protein Domain ◽  
Biological Information ◽  
Ppi Network ◽  
Essential Proteins ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Non Negative Matrix Factorization

Abstract BackgroundIdentification of essential proteins is very important for understanding the basic requirements to sustain a living organism. In recent years, various different computational methods have been proposed to identify essential proteins based on protein-protein interaction (PPI) networks. However, there has been reliable evidence that a huge amount of false negatives and false positives exist in PPI data. Therefore, it is necessary to reduce the influence of false data on accuracy of essential proteins prediction by integrating multi-source biological information with PPI networks.ResultsIn this paper, we proposed a non-negative matrix factorization and multiple biological information based model (NDM) for identifying essential proteins. The first stage in this progress was to construct a weighted PPI network by combing the information of protein domain, protein complex and the topology characteristic of the original PPI network. Then, the non-negative matrix factorization technique was used to reconstruct an optimized PPI network with whole enough weight of edges. In the final stage, the ranking score of each protein was computed by the PageRank algorithm in which the initial scores were calculated with homologous and subcellular localization information. In order to verify the effectiveness of the NDM method, we compared the NDM with other state-of-the-art essential proteins prediction methods. The comparison of the results obtained from different methods indicated that our NDM model has better performance in predicting essential proteins.ConclusionEmploying the non-negative matrix factorization and integrating multi-source biological data can effectively improve quality of the PPI network, which resulted in the led to optimization of the performance essential proteins identification. This will also provide a new perspective for other prediction based on protein-protein interaction networks.

scholarly journals PPInfer: a Bioconductor package for inferring functionally related proteins using protein interaction networks

F1000Research ◽  
2017 ◽  
Vol 6 ◽  
pp. 1969
Author(s):  
Dongmin Jung ◽  
Xijin Ge
Keyword(s):  
Protein Interaction ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Biological Processes ◽  
Bioconductor Package ◽  
Biological Functions ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Related Proteins

Interactions between proteins occur in many, if not most, biological processes. This fact has motivated the development of a variety of experimental methods for the identification of protein-protein interaction (PPI) networks. Leveraging PPI data available STRING database, we use network-based statistical learning methods to infer the putative functions of proteins from the known functions of neighboring proteins on a PPI network. This package identifies such proteins often involved in the same or similar biological functions. The package is freely available at the Bioconductor web site (http://bioconductor.org/packages/PPInfer/).

scholarly journals Identification of Key miRNAs in the Treatment of Dabrafenib-Resistant Melanoma

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Guangyu Gao ◽  
Zhen Yao ◽  
Jiaofeng Shen ◽  
Yulong Liu
Keyword(s):  
Drug Resistance ◽  
Molecular Mechanism ◽  
Target Genes ◽  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Pathway Enrichment Analysis ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
Obvious Association

Dabrafenib resistance is a significant problem in melanoma, and its underlying molecular mechanism is still unclear. The purpose of this study is to research the molecular mechanism of drug resistance of dabrafenib and to explore the key genes and pathways that mediate drug resistance in melanoma. GSE117666 was downloaded from the Gene Expression Omnibus (GEO) database and 492 melanoma statistics were also downloaded from The Cancer Genome Atlas (TCGA) database. Besides, differentially expressed miRNAs (DEMs) were identified by taking advantage of the R software and GEO2R. The Database for Annotation, Visualization, and Integrated Discovery (DAVID) and FunRich was used to perform Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis to identify potential pathways and functional annotations linked with melanoma chemoresistance. 9 DEMs and 872 mRNAs were selected after filtering. Then, target genes were uploaded to Metascape to construct protein-protein interaction (PPI) network. Also, 6 hub mRNAs were screened after performing the PPI network. Furthermore, a total of 4 out of 9 miRNAs had an obvious association with the survival rate ( P < 0.05 ) and showed a good power of risk prediction model of over survival. The present research may provide a deeper understanding of regulatory genes of dabrafenib resistance in melanoma.

scholarly journals Repurposing novel therapeutic candidate drugs for coronavirus disease-19 based on protein-protein interaction network analysis

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Masoumeh Adhami ◽  
Balal Sadeghi ◽  
Ali Rezapour ◽  
Ali Akbar Haghdoost ◽  
Habib MotieGhader
Keyword(s):  
Network Analysis ◽  
Protein Interaction ◽  
Target Therapy ◽  
Interaction Network ◽  
Drug Repurposing ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
Candidate Drug ◽  
Starting Point ◽  
Experimental Validations

Abstract Background The coronavirus disease-19 (COVID-19) emerged in Wuhan, China and rapidly spread worldwide. Researchers are trying to find a way to treat this disease as soon as possible. The present study aimed to identify the genes involved in COVID-19 and find a new drug target therapy. Currently, there are no effective drugs targeting SARS-CoV-2, and meanwhile, drug discovery approaches are time-consuming and costly. To address this challenge, this study utilized a network-based drug repurposing strategy to rapidly identify potential drugs targeting SARS-CoV-2. To this end, seven potential drugs were proposed for COVID-19 treatment using protein-protein interaction (PPI) network analysis. First, 524 proteins in humans that have interaction with the SARS-CoV-2 virus were collected, and then the PPI network was reconstructed for these collected proteins. Next, the target miRNAs of the mentioned module genes were separately obtained from the miRWalk 2.0 database because of the important role of miRNAs in biological processes and were reported as an important clue for future analysis. Finally, the list of the drugs targeting module genes was obtained from the DGIDb database, and the drug-gene network was separately reconstructed for the obtained protein modules. Results Based on the network analysis of the PPI network, seven clusters of proteins were specified as the complexes of proteins which are more associated with the SARS-CoV-2 virus. Moreover, seven therapeutic candidate drugs were identified to control gene regulation in COVID-19. PACLITAXEL, as the most potent therapeutic candidate drug and previously mentioned as a therapy for COVID-19, had four gene targets in two different modules. The other six candidate drugs, namely, BORTEZOMIB, CARBOPLATIN, CRIZOTINIB, CYTARABINE, DAUNORUBICIN, and VORINOSTAT, some of which were previously discovered to be efficient against COVID-19, had three gene targets in different modules. Eventually, CARBOPLATIN, CRIZOTINIB, and CYTARABINE drugs were found as novel potential drugs to be investigated as a therapy for COVID-19. Conclusions Our computational strategy for predicting repurposable candidate drugs against COVID-19 provides efficacious and rapid results for therapeutic purposes. However, further experimental analysis and testing such as clinical applicability, toxicity, and experimental validations are required to reach a more accurate and improved treatment. Our proposed complexes of proteins and associated miRNAs, along with discovered candidate drugs might be a starting point for further analysis by other researchers in this urgency of the COVID-19 pandemic.

scholarly journals PPInfer: a Bioconductor package for inferring functionally related proteins using protein interaction networks

F1000Research ◽  
2018 ◽  
Vol 6 ◽  
pp. 1969 ◽  
Author(s):  
Dongmin Jung ◽  
Xijin Ge
Keyword(s):  
Protein Interaction ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Biological Processes ◽  
Bioconductor Package ◽  
Biological Functions ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Related Proteins

Interactions between proteins occur in many, if not most, biological processes. This fact has motivated the development of a variety of experimental methods for the identification of protein-protein interaction (PPI) networks. Leveraging PPI data available in the STRING database, we use a network-based statistical learning methods to infer the putative functions of proteins from the known functions of neighboring proteins on a PPI network. This package identifies such proteins often involved in the same or similar biological functions. The package is freely available at the Bioconductor web site (http://bioconductor.org/packages/PPInfer/).

scholarly journals Protein–Protein Interaction Network Analysis Reveals Several Diseases Highly Associated with Polycystic Ovarian Syndrome

2019 ◽  
Vol 20 (12) ◽  
pp. 2959 ◽  
Author(s):  
Balqis Ramly ◽  
Nor Afiqah-Aleng ◽  
Zeti-Azura Mohamed-Hussein
Keyword(s):  
Network Analysis ◽  
Protein Interaction ◽  
Ribosome Biogenesis ◽  
Antigen Processing ◽  
Polycystic Ovarian Syndrome ◽  
Interaction Network ◽  
Pathway Enrichment Analysis ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
Ovarian Syndrome

Based on clinical observations, women with polycystic ovarian syndrome (PCOS) are prone to developing several other diseases, such as metabolic and cardiovascular diseases. However, the molecular association between PCOS and these diseases remains poorly understood. Recent studies showed that the information from protein–protein interaction (PPI) network analysis are useful in understanding the disease association in detail. This study utilized this approach to deepen the knowledge on the association between PCOS and other diseases. A PPI network for PCOS was constructed using PCOS-related proteins (PCOSrp) obtained from PCOSBase. MCODE was used to identify highly connected regions in the PCOS network, known as subnetworks. These subnetworks represent protein families, where their molecular information is used to explain the association between PCOS and other diseases. Fisher’s exact test and comorbidity data were used to identify PCOS–disease subnetworks. Pathway enrichment analysis was performed on the PCOS–disease subnetworks to identify significant pathways that are highly involved in the PCOS–disease associations. Migraine, schizophrenia, depressive disorder, obesity, and hypertension, along with twelve other diseases, were identified to be highly associated with PCOS. The identification of significant pathways, such as ribosome biogenesis, antigen processing and presentation, and mitophagy, suggest their involvement in the association between PCOS and migraine, schizophrenia, and hypertension.

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