scholarly journals Prediction of a potential drug target based on protein druggability for thyroid cancer

2020 ◽  
Author(s):  
YiFei Yang ◽  
Bin Yu ◽  
Xiu-Xia Zhang ◽  
Yun-Hua Zhu
Keyword(s):  
Thyroid Cancer ◽  
Targeted Therapy ◽  
Drug Target ◽  
Drug Repositioning ◽  
The Cancer Genome Atlas ◽  
Targeted Drugs ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
Cancer Genome Atlas ◽  
Targeted Drug

Abstract Background: Thyroid cancer is a common endocrine malignancy; however, its treatment is still surgical. With the development and application of targeted therapy in cancer treatment, there are great development prospects in researching targeted drugs for thyroid cancer. Methods: Differentially expressed mRNAs between thyroid cancerous tissue and normal thyroid tissues were screened from The Cancer Genome Atlas (TCGA) database. Using weighted gene coexpression network analysis (WGCNA) to build co-expression modules and combined with differentially methylated gene (DMG) analysis. The druggability was analyzed by PockDrug-Server. Due to drug repositioning to seek targeted drugs to treat thyroid cancer we constructed a protein-protein interaction (PPI) network, and screened out a drug target of thyroid cancer. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) were used to analysis the protein enrichment of PPI network. Results: In the present study, the red module was significantly correlated with thyroid cancer. With DMG analysis, we screened out three genes: HEY2 , TNIK and LRP4 . These three genes were hypomethylation in tumors. The druggability based on PockDrug-Server predicted that only TNIK had protein pocket druggability. With PPI model for TNIK, there were ten genes interacted with TNIK. These genes were enriched in the MAPK and Wnt pathways, which are correlated with tumor proliferation, differentiation, and development. Upon searching for drugs against these 10 genes in Drugbank, it was determined that the targeted drug Binimetinib which is MEK1/2 inhibition. Therefore, we hypothesized that Binimetinib can be used as a targeted drug and TNIK can be regard as drug target for thyroid cancer therapy. Conclusion: Our research provides a bioinformatics method for screening drugs target and provides a theoretical basis for targeted therapy for thyroid cancer.

scholarly journals Prognostic Biomarker SYK and its Correlation with Immune Infiltrates in Glioma

2021 ◽  
Author(s):  
Pei Liu ◽  
Jiamin Guo ◽  
Xiaoxiao Xu ◽  
Haixin Sun ◽  
Zheng Gong
Keyword(s):  
Development Process ◽  
Prognostic Biomarker ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Ppi Network ◽  
Glioma Patient ◽  
Gene Set Enrichment ◽  
Protein Protein Interaction ◽  
Cancer Genome Atlas

Abstract Background: Tumor microenvironment (TME) has great effects on the development process of glioma, and we sought to identify effective prognostic factors by analyzing data from patients with glioma. In this paper, CIBERSORT and ESTIMATE calculations were employed to figure up the ratio of tumor-infiltrating immune cells (TICs) and the quantity of immune and stromal components in 698 glioma dates from The Cancer Genome Atlas (TCGA) database. In addition, differentially expressed genes (DEGs) were studied by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, and single genes associated with prognosis were identified by PPI network and COX combined analysis. Results: Immune and stromal scores of TME were significantly correlated with glioma patient survival. Through protein–protein interaction (PPI) network and regression analysis of COX, we finally determined that SYK was the best prognostic factor for patients with glioma. Gene Set Enrichment Analysis (GSEA) and CIBERSORT analysis were also employed, with the former showed that high-expression SYK group’s genes are principally enriched immune-related activities and the latter revealed that SYK expression was positively associated with T cells CD4 memory resting and Monocytes. All the above experimental analyses provided the theoretical basis for the biological prediction of SYK.Conclusions: SYK contributes to immune predictors in glioma patients by facilitating the shift of TME from immune dominance to metabolic activity, which provides promising insights into the treatment of glioma.

scholarly journals High RPS27A Expression Predicts Poor Prognosis in Patients With HPV Type 16 Cervical Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Qiming Wang ◽  
Yan Cai ◽  
Xuewen Fu ◽  
Liang Chen
Keyword(s):  
Cervical Cancer ◽  
Poor Prognosis ◽  
Biological Significance ◽  
Enrichment Analysis ◽  
Hpv Infection ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Protein Protein Interaction ◽  
Cervical Cancer Cells ◽  
Cancer Genome Atlas

In recent years, the incidence and the mortality rate of cervical cancer have been gradually increasing, becoming one of the major causes of cancer-related death in women. In particular, patients with advanced and recurrent cervical cancers present a very poor prognosis. In addition, the vast majority of cervical cancer cases are caused by human papillomavirus (HPV) infection, of which HPV16 infection is the main cause and squamous cell carcinoma is the main presenting type. In this study, we performed screening of differentially expressed genes (DEGs) based on The Cancer Genome Atlas (TCGA) database and GSE6791, constructed a protein–protein interaction (PPI) network to screen 34 hub genes, filtered to the remaining 10 genes using the CytoHubba plug-in, and used survival analysis to determine that RPS27A was most associated with the prognosis of cervical cancer patients and has prognostic and predictive value for cervical cancer. The most significant biological functions and pathways of RPS27A enrichment were subsequently investigated with gene set enrichment analysis (GSEA), and integration of TCGA and GTEx database analyses revealed that RPS27A was significantly expressed in most cancer types. In this study, our analysis revealed that RPS27A can be used as a prognostic biomarker for HPV16 cervical cancer and has biological significance for the growth of cervical cancer cells.

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 Data mining of immune-related prognostic genes in metastatic melanoma microenvironment

2020 ◽  
Vol 40 (11) ◽  
Author(s):  
Wei Han ◽  
Biao Huang ◽  
Xiao-Yu Zhao ◽  
Guo-Liang Shen
Keyword(s):  
Gene Expression ◽  
Tumor Microenvironment ◽  
Metastatic Melanoma ◽  
Interaction Network ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Protein Protein Interaction ◽  
Subtype Identification ◽  
Cancer Genome Atlas ◽  
Immune Related Genes

Abstract Skin cutaneous melanoma (SKCM) is one of the most deadly malignancies. Although immunotherapies showed the potential to improve the prognosis for metastatic melanoma patients, only a small group of patients can benefit from it. Therefore, it is urgent to investigate the tumor microenvironment in melanoma as well as to identify efficient biomarkers in the diagnosis and treatments of SKCM patients. A comprehensive analysis was performed based on metastatic melanoma samples from the Cancer Genome Atlas (TCGA) database and ESTIMATE algorithm, including gene expression, immune and stromal scores, prognostic immune-related genes, infiltrating immune cells analysis and immune subtype identification. Then, the differentially expressed genes (DEGs) were obtained based on the immune and stromal scores, and a list of prognostic immune-related genes was identified. Functional analysis and the protein–protein interaction network revealed that these genes enriched in multiple immune-related biological processes. Furthermore, prognostic genes were verified in the Gene Expression Omnibus (GEO) databases and used to predict immune infiltrating cells component. Our study revealed seven immune subtypes with different risk values and identified T cells as the most abundant cells in the immune microenvironment and closely associated with prognostic outcomes. In conclusion, the present study thoroughly analyzed the tumor microenvironment and identified prognostic immune-related biomarkers for metastatic melanoma.

scholarly journals Artificial intelligence and big data facilitated targeted drug discovery

2019 ◽  
Vol 4 (4) ◽  
pp. 206-213 ◽  
Author(s):  
Benquan Liu ◽  
Huiqin He ◽  
Hongyi Luo ◽  
Tingting Zhang ◽  
Jingwei Jiang
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Drug Discovery ◽  
Three Dimensional ◽  
Data Bank ◽  
The Cancer Genome Atlas ◽  
Biological Databases ◽  
Cancer Genome Atlas ◽  
Targeted Drug ◽  
Targeted Drug Discovery

Different kinds of biological databases publicly available nowadays provide us a goldmine of multidiscipline big data. The Cancer Genome Atlas is a cancer database including detailed information of many patients with cancer. DrugBank is a database including detailed information of approved, investigational and withdrawn drugs, as well as other nutraceutical and metabolite structures. PubChem is a chemical compound database including all commercially available compounds as well as other synthesisable compounds. Protein Data Bank is a crystal structure database including X-ray, cryo-EM and nuclear magnetic resonance protein three-dimensional structures as well as their ligands. On the other hand, artificial intelligence (AI) is playing an important role in the drug discovery progress. The integration of such big data and AI is making a great difference in the discovery of novel targeted drug. In this review, we focus on the currently available advanced methods for the discovery of highly effective lead compounds with great absorption, distribution, metabolism, excretion and toxicity properties.

The impact of BMI on outcomes of patients with metastatic renal cell carcinoma treated with targeted therapy: An external validation data set and analysis of underlying biology from The Cancer Genome Atlas.

2015 ◽  
Vol 33 (7_suppl) ◽  
pp. 405-405 ◽  
Author(s):  
Laurence Albiges ◽  
A. Ari Hakimi ◽  
Xun Lin ◽  
Ronit Simantov ◽  
Emily C. Zabor ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Targeted Therapy ◽  
Prognostic Factor ◽  
Renal Cell ◽  
The Cancer Genome Atlas ◽  
Cancer Genome Atlas ◽  
Tcga Dataset ◽  
The Impact ◽  
Genome Atlas ◽  
High Bmi

405 Background: Obesity is a risk factor for renal cell carcinoma (RCC) and a poor prognostic factor across many tumor types. However, reports have suggested that RCC developing in an obesogenic environment may be more indolent. We recently reported on the favorable impact of body mass index (BMI) on survival in the International mRCC Database Consortium (IMDC). The current work aims to externally validate this finding and characterize the underlying biology. Methods: We conducted an analysis of 4,657 metastatic RCC (mRCC) patients (pts) treated on phase II-III clinical trials sponsored by Pfizer from 2003-2013. We assessed the impact of BMI on overall survival (OS), progression-free survival (PFS) and overall response rate (ORR). Additionally, we analysed metastatic pts from the clear cell RCC (ccRCC) cohort of TCGA dataset to correlate the expression of Fatty Acid Synthase (FASN) with BMI and OS. Results: At targeted therapy (TT) initiation, 1,829 (39%) pts were normal or underweight (BMI <25 kg/m2) and 2,828 (61%) were overweight or obese (BMI ≥25 kg/m2). Overall, the high BMI group had a longer median OS (23.4 months) than the low BMI group (14.5 months) (hazard ratio (HR) = 0.830, p= 0.0008, 95% CI 0.743-0.925) after adjusting for the IMDC prognostic risk group and other risks factors. In addition, pts with high BMI had improved PFS (HR=0.821, 95% CI 0.746-0.903, p<0.0001) and ORR (odds ratio =1.527, 95% CI 1.258-1.855, p<0.001). These results remain valid when stratified by line of therapy. When stratified by histological subtype, the favorable outcome associated with high BMI was only observed in ccRCC. Toxicity patterns did not differ between BMI groups. In the the Cancer Genome Atlas (TCGA) dataset (n=61), there was a trend towards improved OS in the high BMI group (p=0.07). FASN gene expression inversely correlated with both OS (p=0.002) and BMI (p=0.034). Conclusions: In an external cohort,we validate BMI as an independent prognostic factor for improved survival in mRCC. Given that this finding was observed in ccRCC only, we hypothesize that lipid metabolism may be modulated by the fat laden tumors cells. FASN staining in the IMDC cohort is ongoing to better investigate the obesity paradox in mRCC.

scholarly journals miRNA-144-5p/ITGA3 Suppressed the Tumor-Promoting Behaviors of Thyroid Cancer Cells by Downregulating ITGA3

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Jizong Zhang ◽  
Yan Zhong ◽  
Yiming Sang ◽  
Guanghui Ren
Keyword(s):  
Thyroid Cancer ◽  
Expression Profiles ◽  
The Cancer Genome Atlas ◽  
Luciferase Assay ◽  
Cell Behaviors ◽  
Protein Levels ◽  
Related Cell ◽  
Cancer Genome Atlas ◽  
Thyroid Cancer Cells ◽  
Insight Into

Objective. To ascertain the mechanism of miRNA-144-5p and ITGA3 in thyroid cancer (TC). Methods. From The Cancer Genome Atlas (TCGA), RNA expression profiles were obtained for the expression analysis of miRNAs and mRNAs in TC. qRT-PCR and western blot were utilized to measure the expression of miRNA-144-5p and ITGA3 at RNA and protein levels, respectively. The association between miRNA-144-5p and ITGA3 was validated by the dual-luciferase assay. CCK-8, scratch healing, transwell, and flow cytometry assays were employed to evaluate tumor-related cell behaviors. Results. Low-expressed miRNA-144-5p and high-expressed ITGA3 were found in TC cells relative to normal cells. miRNA-144-5p expression was positively associated with suppressive effects on proliferative, invasive, and migratory ability of TC cells while negatively associated with cell apoptosis. miRNA-144-5p inhibited ITGA3 expression in TC, and its overexpression remarkably reversed the tumor-promoting effects of overexpressed ITGA3 on the biological functions of TC. Conclusion. It is verified in our study that cell growth of TC is inhibited by the miRNA-144-5p/ITGA3 axis, which represents an underlying target for TC. This research proposed a new insight into the strategy of TC treatment.

scholarly journals Comparison of Target Features for Predicting Drug-Target Interactions by Deep Neural Network Based on Large-Scale Drug-Induced Transcriptome Data

Pharmaceutics ◽  
2019 ◽  
Vol 11 (8) ◽  
pp. 377 ◽  
Author(s):  
Hanbi Lee ◽  
Wankyu Kim
Keyword(s):  
Neural Network ◽  
Drug Target ◽  
Large Scale ◽  
Deep Neural Network ◽  
Structural Information ◽  
Drug Repositioning ◽  
Expression Profiles ◽  
Ppi Network ◽  
Drug Induced ◽  
Integrated Network

Uncovering drug-target interactions (DTIs) is pivotal to understand drug mode-of-action (MoA), avoid adverse drug reaction (ADR), and seek opportunities for drug repositioning (DR). For decades, in silico predictions for DTIs have largely depended on structural information of both targets and compounds, e.g., docking or ligand-based virtual screening. Recently, the application of deep neural network (DNN) is opening a new path to uncover novel DTIs for thousands of targets. One important question is which features for targets are most relevant to DTI prediction. As an early attempt to answer this question, we objectively compared three canonical target features extracted from: (i) the expression profiles by gene knockdown (GEPs); (ii) the protein–protein interaction network (PPI network); and (iii) the pathway membership (PM) of a target gene. For drug features, the large-scale drug-induced transcriptome dataset, or the Library of Integrated Network-based Cellular Signatures (LINCS) L1000 dataset was used. All these features are closely related to protein function or drug MoA, of which utility is only sparsely investigated. In particular, few studies have compared the three types of target features in DNN-based DTI prediction under the same evaluation scheme. Among the three target features, the PM and the PPI network show similar performances superior to GEPs. DNN models based on both features consistently outperformed other machine learning methods such as naïve Bayes, random forest, or logistic regression.

Bioinformatics analysis of prognostic value genes in colorectal cancer microenvironment

2019 ◽  
Author(s):  
Taohua Yue ◽  
Jing Zhu ◽  
Xin Wang ◽  
Yisheng Pan ◽  
Yucun Liu ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Large Scale ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Previous Literature ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Gastrointestinal Malignancies ◽  
Protein Protein Interaction ◽  
Cancer Genome Atlas

Abstract Colorectal cancer (CRC) is one of the most deadly gastrointestinal malignancies. The openness of the Cancer Genome Atlas (TCGA) allows us to perform correlation analysis between large-scale transcriptome data and overall survival (OS) of multiple malignancies. Previous literature reports that the infiltration of immune cells and stromal cells in the tumor microenvironment (TME) significantly associate with the prognosis of cancers. Based on the ESTIMATE algorithm, the immune and stromal components in TME can be quantified by immune and stromal scores. To determine the effects of immune and stromal cell associated genes on CRC prognosis, we divided the CRC cases into high- and low-groups based on the immune/stromal scores and identified 999 differentially expressed genes (DEGs). Heatmaps, functional enrichment analysis and protein‐protein interaction (PPIs) networks further indicated that 999 DEGs mainly participated in stromal composition and immune response. Finally, we obtained 56 genes that were significantly associated with CRC prognosis from 999 DEGs and identified the PPIs networks. The role of 41 genes in CRC has been reported in previous literature, and the other 15 genes have never been reported. Therefore, we found 15 novel TME genes associated with CRC prognosis waiting for more researches.

scholarly journals Genomically Driven Precision Medicine to Improve Outcomes in Anaplastic Thyroid Cancer

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Nicole Pinto ◽  
Morgan Black ◽  
Krupal Patel ◽  
John Yoo ◽  
Joe S. Mymryk ◽  
...  
Keyword(s):  
Thyroid Cancer ◽  
Surgical Resection ◽  
Standard Of Care ◽  
Anaplastic Thyroid Cancer ◽  
Genetic Alterations ◽  
The Cancer Genome Atlas ◽  
Genetic Landscape ◽  
Cancer Genome Atlas ◽  
Well Differentiated

Thyroid cancer is an endocrine malignancy with an incidence rate that has been increasing steadily over the past 30 years. While well-differentiated subtypes have a favorable prognosis when treated with surgical resection and radioiodine, undifferentiated subtypes, such as anaplastic thyroid cancer (ATC), are far more aggressive and have a poor prognosis. Conventional therapies (surgical resection, radiation, chemotherapy, and radioiodine) have been utilized for treatment of ATC, yet these treatments have not significantly improved the overall mortality rate. As cancer is a genetic disease, genetic alterations such as mutations, fusions, activation of oncogenes, and silencing of tumor suppressors contribute to its aggressiveness. With the use of next-generation sequencing and the Cancer Genome Atlas, mutation-directed therapy is recognized as the upcoming standard of care. In this review, we highlight the known genetic landscape of ATC and the need for a comprehensive genetic characterization of this disease in order to identify additional therapeutic targets to improve patient outcomes.

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