scholarly journals PRER: A Patient Representation with Pairwise Relative Expression of Proteins on Biological Networks

2020 ◽  
Author(s):  
Halil İbrahim Kuru ◽  
Mustafa Buyukozkan ◽  
Oznur Tastan
Keyword(s):  
Protein Expression ◽  
Biological Networks ◽  
Expression Profiles ◽  
Predictive Performance ◽  
Survival Models ◽  
Survival Prediction ◽  
Expression Levels ◽  
Biased Random Walk ◽  
Aggregate Information ◽  
Ppi Networks

AbstractChanges in protein and gene expression levels are often used as features to predictive models such as survival prediction. A common strategy to aggregate information on individual proteins is to integrate the expression information with biological networks. We propose a novel patient representation in this work where we integrate proteins’ expression levels with the protein-protein interaction (PPI) networks. Patient representation with PRER (Pairwise Relative Expressions with Random walks) uses the neighborhood of a protein to capture the dysregulation patterns in protein abundance. Specifically, PRER computes a feature vector for a patient by comparing the source protein’s protein expression level with other proteins’ levels in its neighborhood. This neighborhood of the source protein is derived using a biased random-walk strategy on the network. We test PRER’s performance through a survival prediction task in 10 different cancers using random forest survival models. PRER representation yields a statistically significant predictive performance in 9 out of 10 cancer types when compared to a representation based on individual protein expression. We also identify important proteins that are not important in the models trained with the expression values but emerge as predictive in models trained with PRER features. The set of identified relations provides a valuable collection of biomarkers with high prognostic value. PRER representation can be used for other complex diseases and prediction tasks that use molecular expression profiles as input. PRER is freely available at: https://github.com/hikuru/PRER

scholarly journals PRER: A patient representation with pairwise relative expression of proteins on biological networks

2021 ◽  
Vol 17 (5) ◽  
pp. e1008998
Author(s):  
Halil İbrahim Kuru ◽  
Mustafa Buyukozkan ◽  
Oznur Tastan
Keyword(s):  
Biological Networks ◽  
Expression Profiles ◽  
Predictive Performance ◽  
Survival Models ◽  
Survival Prediction ◽  
Protein Biomarkers ◽  
Expression Levels ◽  
Biased Random Walk ◽  
Aggregate Information ◽  
Ppi Networks

Changes in protein and gene expression levels are often used as features in predictive modeling such as survival prediction. A common strategy to aggregate information contained in individual proteins is to integrate the expression levels with the biological networks. In this work, we propose a novel patient representation where we integrate proteins’ expression levels with the protein-protein interaction (PPI) networks: Patient representation with PRER (Pairwise Relative Expressions with Random walks) (PRER). PRER captures the dysregulation patterns of proteins based on the neighborhood of a protein in the PPI network. Specifically, PRER computes a feature vector for a patient by comparing the source protein’s expression level with other proteins’ levels that are within its neighborhood. The neighborhood of the source protein is derived by biased random-walk strategy on the network. We test PRER’s performance in survival prediction task in 10 different cancers using random forest survival models. PRER yields a statistically significant predictive performance in 9 out of 10 cancers when compared to the same model trained with features based on individual protein expressions. Furthermore, we identified the pairs of proteins that their interactions are predictive of patient survival but their individual expression levels are not. The set of identified relations provides a valuable collection of protein biomarkers with high prognostic value. PRER can be used for other complex diseases and prediction tasks that use molecular expression profiles as input. PRER is freely available at: https://github.com/hikuru/PRER.

scholarly journals EPEN-10. UNRAVELLING THE TUMOR IMMUNE MICROENVIRONMENT OF POSTERIOR FOSSA A EPENDYMOMAS ON RNA AND PROTEIN EXPRESSION LEVELS

2021 ◽  
Vol 23 (Supplement_1) ◽  
pp. i15-i15
Author(s):  
Fenna F. Feenstra ◽  
Friso Calkoen ◽  
Johan M Kros ◽  
Lennart Kester ◽  
Mariëtte Kranendonk ◽  
...  
Keyword(s):  
Gene Expression ◽  
Protein Expression ◽  
Posterior Fossa ◽  
Antigen Processing ◽  
Sequence Data ◽  
Expression Profiles ◽  
Immune Microenvironment ◽  
Expression Levels ◽  
Tumor Immune Microenvironment ◽  
Immune Related Genes

Abstract Background Ependymomas account for 8–10% of pediatric brain tumors, and the standard therapy of surgery and radiation has not changed for the past two decades. Characterization of the tumor immune microenvironment (TIME) is of great importance in order to find better therapies. However, the TIME of ependymomas is still not defined. In this retrospective observational study we aimed to unravel the TIME of ependymomas at mRNA and protein expression levels. Methods Ependymoma samples from two locations were selected: Posterior Fossa (PF-A, n=8), and supratentorial (ST, n=5). Targeted gene expression profile using the PanCancer immune profile panel of NanoString technology was performed. Data were analyzed using the nSolver software. In addition, 8 samples were subjected to RNA bulk sequencing, and the sequenced data were connected to the expression data of the same samples. To validate some of the findings, immunohistochemistry was performed. Results Unsupervised hierarchical clustering showed that PF-A ependymomas can be divided into two groups based on the expression of their immune-related genes. PF-A that showed high immune-expression clustered closely to the ST ependymomas. Significant expressions of genes related to “antigen-processing” and “adhesion” pathways were found in the immune-active groups. On the contrary, the PF-A that had low expressions of immune-related genes showed a high expression of BMI1 that has a prognostic and therapeutic value. Connecting gene expression to bulk sequence data validated the findings. In addition, immunohistochemical analysis confirmed that protein expression for some of the findings. Conclusion The TIME varies in ependymomas based on the location of the tumor. Moreover, the immune-related expression profiles indicated that PF-A ependymomas can be divided into two groups: immune-active and immune-not active PF-A. The prognostic and therapeutic values of the immune activity of PF-A should be further studied.

scholarly journals Role of miR-10b-5p in the prognosis of breast cancer

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7728 ◽  
Author(s):  
Junmin Wang ◽  
Yanyun Yan ◽  
Zhiqi Zhang ◽  
Yali Li
Keyword(s):  
Breast Cancer ◽  
Target Genes ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Cancer Development ◽  
Clinicopathological Parameters ◽  
Aberrant Expression ◽  
Expression Levels ◽  
Ppi Networks

Breast cancer is the leading cause of cancer-related death in women worldwide. Aberrant expression levels of miR-10b-5p in breast cancer has been reported while the molecular mechanism of miR-10b-5p in tumorigenesis remains elusive. Therefore, this study was aimed to investigate the role of miR-10b-5p in breast cancer and the network of its target genes using bioinformatics analysis. In this study, the expression profiles and prognostic value of miR-10b-5p in breast cancer were analyzed from public databases. Association between miR-10b-5p and clinicopathological parameters were analyzed by non-parametric test. Moreover, the optimal target genes of miR-10b-5p were obtained and their expression patterns were examined using starBase and HPA database. Additionally, the role of these target genes in cancer development were explored via Cancer Hallmarks Analytics Tool (CHAT). The protein–protein interaction (PPI) networks were constructed to further investigate the interactive relationships among these genes. Furthermore, GO, KEGG pathway and Reactome pathway analyses were carried out to decipher functions of these target genes. Results demonstrated that miR-10b-5p was down-regulated in breast cancer and low expression of miR-10b-5p was significantly correlated to worse outcome. Five genes, BIRC5, E2F2, KIF2C, FOXM1, and MCM5, were considered as potential key target genes of miR-10b-5p. As expected, higher expression levels of these genes were observed in breast cancer tissues than in normal tissues. Moreover, analysis from CHAT revealed that these genes were mainly involved in sustaining proliferative signaling in cancer development. In addition, PPI networks analysis revealed strong interactions between target genes. GO, KEGG, and Reactome pathway analysis suggested that these target genes of miR-10b-5p in breast cancer were significantly involved in cell cycle. Predicted target genes were further validated by qRT-PCR analysis in human breast cancer cell line MDA-MB-231 transfected with miR-10b mimic or antisense inhibitors. Taken together, our data suggest that miR-10b-5p functions to impede breast carcinoma progression via regulation of its key target genes and hopefully serves as a potential diagnostic and prognostic marker for breast cancer.

scholarly journals A multi-method approach for proteomic network inference in 11 human cancers

2015 ◽  
Author(s):  
Yasin Şenbabaoğlu ◽  
Selçuk Onur Sümer ◽  
Giovanni Ciriello ◽  
Nikolaus Schultz ◽  
Chris Sander
Keyword(s):  
Protein Expression ◽  
Tyrosine Kinases ◽  
Network Inference ◽  
Mapk Pathway ◽  
Expression Profiles ◽  
The Cancer Genome Atlas ◽  
Research Network ◽  
Post Translational Modification ◽  
Ppi Networks ◽  
Tumor Types

Protein expression and post-translational modification levels are tightly regulated in neoplastic cells to maintain cellular processes known as ?cancer hallmarks?. The first Pan-Cancer initiative of The Cancer Genome Atlas (TCGA) Research Network has aggregated protein expression profiles for 3,467 patient samples from 11 tumor types using the antibody based reverse phase protein array (RPPA) technology. The resultant proteomic data can be utilized to computationally infer protein-protein interaction (PPI) networks and to study the commonalities and differences across tumor types. In this study, we compare the performance of 13 established network inference methods in their capacity to retrieve literature-curated pathway interactions from RPPA data. We observe that no single method has the best performance in all tumor types, but a group of six methods, including diverse techniques such as correlation, mutual information, and regression, consistently rank highly among the tested methods. A consensus network from this high-performing group reveals that signal transduction events involving receptor tyrosine kinases (RTKs), the RAS/MAPK pathway, and the PI3K/AKT/mTOR pathway, as well as innate and adaptive immunity signaling, are the most significant PPIs shared across all tumor types. Our results illustrate the utility of the RPPA platform as a tool to study proteomic networks in cancer.

NEAT1 Is a Novel Oncogenic LncRNA and Correlated with miR-143 in Pediatric Oligodendrogliomas

2021 ◽  
Vol 56 (2) ◽  
pp. 133-139
Author(s):  
Secil Ak Aksoy ◽  
Melis Mutlu ◽  
Rabia Nur Balcin ◽  
Mevlut Ozgur Taskapilioglu ◽  
Cagla Tekin ◽  
...  
Keyword(s):  
Noncoding Rna ◽  
Expression Profiles ◽  
Prognostic Significance ◽  
Disease Free Survival ◽  
Pediatric Brain Tumors ◽  
Normal Brain ◽  
Diffuse Glioma ◽  
Expression Levels ◽  
Treatment Models ◽  
New Treatment

Introduction: The noncoding RNAs (ncRNAs) play a role in biological processes of various cancers including gliomas. The majority of these transcripts are uniquely expressed in differentiated tissues or specific glioma types. Pediatric oligodendroglioma (POG) is a rare subtype of diffuse glioma and accounts for <1% of pediatric brain tumors. Because histologically POG resembles adult OG, the same treatment is applied as adults. However, the significance in predicting outcomes in POG patients is unclear. In this study, we aimed to investigate the prognostic significance of expression ­profiles of microRNA (miRNA) and long noncoding RNA ­(LncRNA) in POGs. Methods: We investigated the levels of 13 known miRNAs and 6 LncRNAs in tumor samples from 9 patients with primary POG by using RT-PCR and analyzed their association with outcomes. Results: The expression levels of miR-21, miR-106a, miR-10b, and LncRNA NEAT1 were higher, and the expression level of miR-143 was lower in POG tissues compared with normal brain tissues (p = 0.006, p = 0.032, p = 0.034, p = 0.002, and p = 0.001, respectively). High levels of NEAT1 and low expression of miR-143 were associated with decreased probability of short disease-free survival (p = 0.018 and p = 0.022, respectively). Discussion: NEAT1 and miR-143 levels could serve as reciprocal prognostic predictors of disease progression in patients with POG. New treatment models to regulate the expression levels of NEAT1 and miR-143 will bring a new approach to the therapy of POG.

scholarly journals Pirfenidone attenuates synovial fibrosis and postpones the progression of osteoarthritis by anti-fibrotic and anti-inflammatory properties in vivo and in vitro

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Qilu Wei ◽  
Ning Kong ◽  
Xiaohui Liu ◽  
Run Tian ◽  
Ming Jiao ◽  
...  
Keyword(s):  
Protein Expression ◽  
Cell Counting ◽  
Enzyme Linked Immunosorbent Assay ◽  
Fast Green ◽  
Expression Levels ◽  
Tnf Α ◽  
Anti Inflammatory ◽  
Safranin O ◽  
Tgf Β1

Abstract Background Osteoarthritis (OA) is a disease of the entire joint involving synovial fibrosis and inflammation. Pathological changes to the synovium can accelerate the progression of OA. Pirfenidone (PFD) is a potent anti-fibrotic drug with additional anti-inflammatory properties. However, the influence of PFD on OA is unknown. Methods Proliferation of human fibroblast-like synoviocytes (FLSs) after treatment with TGF-β1 or PFD was evaluated using a Cell Counting Kit-8 assay and their migration using a Transwell assay. The expression of fibrosis-related genes (COL1A1, TIMP-1, and ACTA-2) and those related to inflammation (IL-6 and TNF-α) was quantified by real-time quantitative PCR. The protein expression levels of COL1A1, α-SMA (coded by ACTA-2), IL-6 and TNF-α were measured by enzyme-linked immunosorbent assay. A rabbit model of OA was established and then PFD was administered by gavage. The expression of genes related to fibrosis (COL1A1, TIMP-1, and ADAM-12) and inflammation (IL-6 and TNF-α) was measured using RNA extracted from the synovium. Synovial tissue was examined histologically after staining with H&E, Masson’s trichrome, and immunofluorescence. Synovitis scores, the volume fraction of collagen, and mean fluorescence intensity were calculated. Degeneration of articular cartilage was analyzed using a Safranin O-fast green stain and OARSI grading. Results The proliferation of FLSs was greatest when induced with 2.5 ng/ml TGF-β1 although it did not promote their migration. Therefore, 2.5 ng/ml TGF-β1 was used to stimulate the FLSs and evaluate the effects of PFD, which inhibited the migration of FLSs at concentrations as low as 1.0 mg/ml. PFD decreased the expression of COL1A1 while TGF-β1 increased both mRNA and protein expression levels of IL-6 but had no effect on α-SMA or TNF-α expression. PFD decreased mRNA expression levels of COL1A1, IL-6, and TNF-α in vivo. H&E staining and synovitis scores indicated that PFD reduced synovial inflammation, while Masson’s trichrome and immunofluorescence staining suggested that PFD decreased synovial fibrosis. Safranin O-Fast Green staining and the OARSI scores demonstrated that PFD delayed the progression of OA. Conclusions PFD attenuated synovial fibrosis and inflammation, and postponed the progression of osteoarthritis in a modified Hulth model of OA in rabbits, which was related to its anti-fibrotic and anti-inflammatory properties.

scholarly journals Honeysuckle Aqueous Extracts Induced let-7a Suppress EV71 Replication and Pathogenesis In Vitro and In Vivo and Is Predicted to Inhibit SARS-CoV-2

Viruses ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 308
Author(s):  
Ying-Ray Lee ◽  
Chia-Ming Chang ◽  
Yuan-Chieh Yeh ◽  
Chi-Ying F. Huang ◽  
Feng-Mao Lin ◽  
...  
Keyword(s):  
Protein Expression ◽  
Western Blotting ◽  
Expression Profiles ◽  
Plaque Assay ◽  
Virus Titer ◽  
Enterovirus 71 ◽  
Luciferase Reporter ◽  
Pathogen Invasion

Honeysuckle (Lonicera japonica Thunb) is a traditional Chinese medicine (TCM) with an antipathogenic activity. MicroRNAs (miRNAs) are small non-coding RNA molecules that are ubiquitously expressed in cells. Endogenous miRNA may function as an innate response to block pathogen invasion. The miRNA expression profiles of both mice and humans after the ingestion of honeysuckle were obtained. Fifteen overexpressed miRNAs overlapped and were predicted to be capable of targeting three viruses: dengue virus (DENV), enterovirus 71 (EV71) and SARS-CoV-2. Among them, let-7a was examined to be capable of targeting the EV71 RNA genome by reporter assay and Western blotting. Moreover, honeysuckle-induced let-7a suppression of EV71 RNA and protein expression as well as viral replication were investigated both in vitro and in vivo. We demonstrated that let-7a targeted EV71 at the predicted sequences using luciferase reporter plasmids as well as two infectious replicons (pMP4-y-5 and pTOPO-4643). The suppression of EV71 replication and viral load was demonstrated in two cell lines by luciferase activity, RT-PCR, real-time PCR, Western blotting and plaque assay. Furthermore, EV71-infected suckling mice fed honeysuckle extract or inoculated with let-7a showed decreased clinical scores and a prolonged survival time accompanied with decreased viral RNA, protein expression and virus titer. The ingestion of honeysuckle attenuates EV71 replication and related pathogenesis partially through the upregulation of let-7a expression both in vitro and in vivo. Our previous report and the current findings imply that both honeysuckle and upregulated let-7a can execute a suppressive function against the replication of DENV and EV71. Taken together, this evidence indicates that honeysuckle can induce the expression of let-7a and that this miRNA as well as 11 other miRNAs have great potential to prevent and suppress EV71 replication.

scholarly journals POS0859 DEEP PHENOTYPING OF DERMATOMYOSITIS BASED ON LIPID FERROPTOSIS-RELATED GENES BY MACHINE LEARNING

2021 ◽  
Vol 80 (Suppl 1) ◽  
pp. 684.1-684
Author(s):  
J. Q. Zhang ◽  
S. X. Zhang ◽  
R. Zhao ◽  
J. Qiao ◽  
M. T. Qiu ◽  
...  
Keyword(s):  
Gene Expression ◽  
T Cells ◽  
Expression Profiles ◽  
Inflammatory Myopathy ◽  
Shanxi Province ◽  
Survival Prediction ◽  
Training Set ◽  
Redox Biology ◽  
Score Model ◽  
Validation Set

Background:Dermatomyositis (DM) is an idiopathic inflammatory myopathy with heterogeneous clinical manifestation that raise challenges regarding diagnosis and therapy1. Ferroptosis is a newly discovered form of regulated cell death that is the nexus between metabolism, redox biology, and rheumatic immune diseases2. However, how ferroptosis maintains the balance of lymphocyte T cells and affect disease activity in DM is unclear.Objectives:To investigate an ferroptosis-related multiple gene expression signature for classification by assessing the global gene expression profile, and calculate the lymphocyte T cells status in the different subsets.Methods:Gene expression profiles of skeletal muscle from DM samples were acquired from GEO database. GSE143323 (30 patients and 20 HCs) was selected as the training set. The GSE3307 contained 21 DM patients and was selected as the validation set. The 60 ferroptosis genes were obtained from previous literature3. The intersection of the global gene and ferroptosis genes was considered the set of significant G-Ferroptosis genes for further analysis. The “NMF” (R-package) was applied as an unsupervised clustering method for sample classification by using G-Ferroptosis genes expression microarray data from the training datasets. An ferroptosis score model was constructed. The performance of the ferroptosis genes-based risk score model constructed by the DM training set was validated in the batch-1 and batch-2 DM sets. Normalized ferroptosis genes training data was used to compare the ssGSEA scores of gene sets between the high risk and low risk group. The statistical software package R (version 4.0.3) was used for all analyses. P value < 0.05 were considered statistically significant.Results:We selected 54 significant G-Ferroptosis genes for further analysis in training set. There were 2 distinct subtypes (high-ferroptosis-score groups and low-ferroptosis-score groups) identified in G-Ferroptosis genes cohort which were also identified in validation datasets (Fig.1A, C, D). Metallothionein 1G (MT1G) was a characteristic gene of low-ferroptosis-score group. The characteristic genes of high-ferroptosis-score group were acyl-CoA synthetase family member 2(ACSF2) and aconitase 1(ACO1) (Fig.1B). Patients in high-ferroptosis-score group had a lower level of Tregs compared with that of low-ferroptosis-score patients in both training and validation set (P <0.05, Fig.1E).Conclusion:The biological process of ferroptosis is associated with the lever of Tregs, suggesting the process of ferroptosis may be involved in the disease progression of DM. Identificating ferroptosis-related features for DM might provide a new idea for clinical treatment.References:[1]DeWane ME, Waldman R, Lu J. Dermatomyositis: Clinical features and pathogenesis. Journal of the American Academy of Dermatology 2020;82(2):267-81. doi: 10.1016/j.jaad.2019.06.1309 [published Online First: 2019/07/08].[2]Liang C, Zhang X, Yang M, et al. Recent Progress in Ferroptosis Inducers for Cancer Therapy. Advanced materials (Deerfield Beach, Fla) 2019;31(51):e1904197. doi: 10.1002/adma.201904197 [published Online First: 2019/10/09].[3]Liang JY, Wang DS, Lin HC, et al. A Novel Ferroptosis-related Gene Signature for Overall Survival Prediction in Patients with Hepatocellular Carcinoma. International journal of biological sciences 2020;16(13):2430-41. doi: 10.7150/ijbs.45050 [published Online First: 2020/08/08].Acknowledgements:This project was supported by National Science Foundation of China (82001740).Open Fund from the Key Laboratory of Cellular Physiology (Shanxi Medical University) (KLCP2019) and Innovation Plan for Postgraduate Education in Shanxi Province (2020BY078).Disclosure of Interests:None declared

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