scholarly journals Molecular Network-Based Drug Prediction in Thyroid Cancer

2019 ◽  
Vol 20 (2) ◽  
pp. 263 ◽  
Author(s):  
Xingyu Xu ◽  
Haixia Long ◽  
Baohang Xi ◽  
Binbin Ji ◽  
Zejun Li ◽  
...  
Keyword(s):  
Gene Expression ◽  
Thyroid Cancer ◽  
Hormone Secretion ◽  
The Cancer Genome Atlas ◽  
Expression Level ◽  
Literature Mining ◽  
Drug Selection ◽  
Exact Test ◽  
Cancer Data ◽  
Receptor Localization

As a common malignant tumor disease, thyroid cancer lacks effective preventive and therapeutic drugs. Thus, it is crucial to provide an effective drug selection method for thyroid cancer patients. The connectivity map (CMAP) project provides an experimental validated strategy to repurpose and optimize cancer drugs, the rationale behind which is to select drugs to reverse the gene expression variations induced by cancer. However, it has a few limitations. Firstly, CMAP was performed on cell lines, which are usually different from human tissues. Secondly, only gene expression information was considered, while the information about gene regulations and modules/pathways was more or less ignored. In this study, we first measured comprehensively the perturbations of thyroid cancer on a patient including variations at gene expression level, gene co-expression level and gene module level. After that, we provided a drug selection pipeline to reverse the perturbations based on drug signatures derived from tissue studies. We applied the analyses pipeline to the cancer genome atlas (TCGA) thyroid cancer data consisting of 56 normal and 500 cancer samples. As a result, we obtained 812 up-regulated and 213 down-regulated genes, whose functions are significantly enriched in extracellular matrix and receptor localization to synapses. In addition, a total of 33,778 significant differentiated co-expressed gene pairs were found, which form a larger module associated with impaired immune function and low immunity. Finally, we predicted drugs and gene perturbations that could reverse the gene expression and co-expression changes incurred by the development of thyroid cancer through the Fisher’s exact test. Top predicted drugs included validated drugs like baclofen, nevirapine, glucocorticoid, formaldehyde and so on. Combining our analyses with literature mining, we inferred that the regulation of thyroid hormone secretion might be closely related to the inhibition of the proliferation of thyroid cancer cells.

scholarly journals The BIRC Family Genes Expression in Patients with Triple Negative Breast Cancer

2021 ◽  
Vol 22 (4) ◽  
pp. 1820
Author(s):  
Anna Makuch-Kocka ◽  
Janusz Kocki ◽  
Anna Brzozowska ◽  
Jacek Bogucki ◽  
Przemysław Kołodziej ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Clinical Data ◽  
Triple Negative Breast Cancer ◽  
Triple Negative ◽  
Lymphatic Vessels ◽  
The Cancer Genome Atlas ◽  
Expression Level ◽  
Cancer Tissue ◽  
Expression Levels

The BIRC (baculoviral IAP repeat-containing; BIRC) family genes encode for Inhibitor of Apoptosis (IAP) proteins. The dysregulation of the expression levels of the genes in question in cancer tissue as compared to normal tissue suggests that the apoptosis process in cancer cells was disturbed, which may be associated with the development and chemoresistance of triple negative breast cancer (TNBC). In our study, we determined the expression level of eight genes from the BIRC family using the Real-Time PCR method in patients with TNBC and compared the obtained results with clinical data. Additionally, using bioinformatics tools (Ualcan and The Breast Cancer Gene-Expression Miner v4.5 (bc-GenExMiner v4.5)), we compared our data with the data in the Cancer Genome Atlas (TCGA) database. We observed diverse expression pattern among the studied genes in breast cancer tissue. Comparing the expression level of the studied genes with the clinical data, we found that in patients diagnosed with breast cancer under the age of 50, the expression levels of all studied genes were higher compared to patients diagnosed after the age of 50. We observed that in patients with invasion of neoplastic cells into lymphatic vessels and fat tissue, the expression levels of BIRC family genes were lower compared to patients in whom these features were not noted. Statistically significant differences in gene expression were also noted in patients classified into three groups depending on the basis of the Scarff-Bloom and Richardson (SBR) Grading System.

scholarly journals Leveraging collective regulatory effects of long-range DNA methylations to predict gene expressions and estimate their effects on phenotypes in cancer

2018 ◽  
Author(s):  
Soyeon Kim ◽  
Hyun Jung Park ◽  
Xiangqin Cui ◽  
Degui Zhi
Keyword(s):  
Gene Expression ◽  
Long Range ◽  
Estrogen Receptor Status ◽  
The Cancer Genome Atlas ◽  
Collective Effects ◽  
Statistical Machine Learning ◽  
Promoter Regions ◽  
Gene Expressions ◽  
Cancer Data ◽  
Clinical Phenotypes

ABSTRACTDNA methylation of various genomic regions plays an important role in regulating gene expression in diverse biological contexts. However, most genome-wide studies have focused on the effect of 1) methylation in cis, not in trans and 2) a single CpG, not the collective effects of multiple CpGs, on gene expression. In this study, we developed a statistical machine learning model, geneEXPLORER (geneexpression prediction by long-range epigenetic regulation), that quantifies the collective effects of both cis- and trans- methylations on gene expression. By applying geneEXPLORER to The Cancer Genome Atlas (TCGA) breast and lung cancer data, we found that most genes are affected by methylations of as much as 10Mb from promoter regions or more, and the long-range methylation explains 50% of the variation in gene expression on average, far greater than cis-methylation. The highly predictive genes are related to breast cancer, especially oncogenes and suppressor genes. Further, the predicted gene expressions could predict clinical phenotypes such as breast tumor status and estrogen receptor status (AUC=0.999, 0.94 respectively) as accurately as the measured gene expression levels. These results suggest that geneEXPLORER provides a means for accurate imputation of gene expression, which can be further used to predict clinical phenotypes.

scholarly journals Inferring Aberrant Signal Transduction Pathways in Ovarian Cancer from TCGA Data

2014 ◽  
Vol 13s1 ◽  
pp. CIN.S13881 ◽  
Author(s):  
Richard Neapolitan ◽  
Xia Jiang
Keyword(s):  
Gene Expression ◽  
Signal Transduction ◽  
Ovarian Carcinoma ◽  
Impact Analysis ◽  
Current Knowledge ◽  
The Cancer Genome Atlas ◽  
Gene Expression Level ◽  
Expression Level ◽  
Signal Transduction Pathways ◽  
Significance Level

This paper concerns a new method for identifying aberrant signal transduction pathways (STPs) in cancer using case/control gene expression-level datasets, and applying that method and an existing method to an ovarian carcinoma dataset. Both methods identify STPs that are plausibly linked to all cancers based on current knowledge. Thus, the paper is most appropriate for the cancer informatics community. Our hypothesis is that STPs that are altered in tumorous tissue can be identified by applying a new Bayesian network (BN)-based method (causal analysis of STP aberration (CASA)) and an existing method (signaling pathway impact analysis (SPIA)) to the cancer genome atlas (TCGA) gene expression-level datasets. To test this hypothesis, we analyzed 20 cancer-related STPs and 6 randomly chosen STPs using the 591 cases in the TCGA ovarian carcinoma dataset, and the 102 controls in all 5 TCGA cancer datasets. We identified all the genes related to each of the 26 pathways, and developed separate gene expression datasets for each pathway. The results of the two methods were highly correlated. Furthermore, many of the STPs that ranked highest according to both methods are plausibly linked to all cancers based on current knowledge. Finally, CASA ranked the cancer-related STPs over the randomly selected STPs at a significance level below 0.05 ( P = 0.047), but SPIA did not ( P = 0.083).

scholarly journals Overexpression of B7-H3 Is Associated With Poor Prognosis in Laryngeal Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Yixuan Li ◽  
Qian Cai ◽  
Ximing Shen ◽  
Xiaoting Chen ◽  
Zhong Guan
Keyword(s):  
Gene Expression ◽  
Laryngeal Cancer ◽  
Poor Prognosis ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Expression Level ◽  
Normal Tissues ◽  
Qrt Pcr ◽  
Node Metastasis ◽  
B7 Family

The immune checkpoint molecule, B7-H3, which belongs to the B7 family, has been shown to be overexpressed in various cancers. Its role in tumors is not well defined, and many studies suggest that it is associated with poor clinical outcomes. The effect of B7-H3 on laryngeal cancer has not been reported. This study investigated the expression of B7-H3 in laryngeal squamous cell carcinoma (LSCC), and its relationship with clinicopathological factors and prognosis of LSCC patients. The gene expression quantification data and clinical data of LSCC retrieved from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database were analyzed to determine the diagnostic and prognostic roles of B7-H3. Quantitative real-time polymerase chain reaction (qRT-PCR) was then performed to determine the gene expression level of B7-H3 between LSCC tissues and paired normal adjacent tissues. In addition, TCGA RNA-seq data was analyzed to evaluate the expression level of B7 family genes. Next, the protein expression of B7-H3 and CD8 in LSCC was determined using immunohistochemistry and immunofluorescence. qRT-PCR results showed that the expression level of B7-H3 mRNA was significantly higher in LSCC tissues than in adjacent normal tissues. Similar results were obtained from the TCGA analysis. The expression of B7-H3 was significantly associated with T stage, lymph node metastasis, and pathological tumor node metastasis (TNM) stage, and it was also an independent factor influencing the overall survival time (OS) of patients with LSCC. In addition, B7-H3 was negatively correlated with CD8+T cells. These results show that B7-H3 is upregulated in LSCC. Therefore, B7-H3 may serve as a biomarker of poor prognosis and a promising therapeutic target in LSCC.

scholarly journals Identification of Deregulated Transcription Factors Involved in Specific Bladder Cancer Subtypes

10.29007/v7qj ◽  
2020 ◽  
Author(s):  
Magali Champion ◽  
Julien Chiquet ◽  
Pierre Neuvial ◽  
Mohamed Elati ◽  
François Radvanyi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Bladder Cancer ◽  
Transcription Factor ◽  
Transcription Factors ◽  
Target Genes ◽  
The Cancer Genome Atlas ◽  
Reference Network ◽  
Data Set ◽  
Cancer Subtypes ◽  
Cancer Data

Comparison between tumoral and healthy cells may reveal abnormal regulation behaviors between a transcription factor and the genes it regulates, without exhibiting differential expression of the former genes. We propose a methodology for the identification of transcription factors involved in the deregulation of genes in tumoral cells. This strategy is based on the inference of a reference gene regulatory network that connects transcription factors to their downstream targets using gene expression data. Gene expression levels in tumor samples are then carefully compared to this reference network to detect deregulated target genes. A linear model is finally used to measure the ability of each transcription factor to explain these deregulations. We assess the performance of our method by numerical experiments on a public bladder cancer data set derived from the Cancer Genome Atlas project. We identify genes known for their implication in the development of specific bladder cancer subtypes as well as new potential biomarkers.

scholarly journals Systematic Pan-Cancer Analysis of KLRB1 with Prognostic Value and Immunological Activity across Human Tumors

2022 ◽  
Vol 2022 ◽  
pp. 1-21
Author(s):  
Xin Cheng ◽  
Yucheng Cao ◽  
Xiaowei Wang ◽  
Lin Cheng ◽  
Yaqiong Liu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Nk Cells ◽  
Cancer Patients ◽  
Molecular Mechanism ◽  
The Cancer Genome Atlas ◽  
T Cell Subsets ◽  
Expression Level ◽  
Clinical Prognosis ◽  
Chemotherapy Drugs ◽  
Pan Cancer

Introduction. KLRB1 is a gene encoding CD161 expressed in NK cells and some T cell subsets. At present, KLRB1 is believed to affect tumorigenesis and development by regulating the cytotoxicity of NK cells in several cancers. However, there is a lack of systematic reviews of KLRB1 in a variety of malignancies. Objectives. Hence, our research is aimed at providing a relatively comprehensive understanding of the role of KLRB1 in different types of cancer, paving the way for further research on the molecular mechanism and immunotherapy potential of KLRB1. Methods. In this study, we used relevant public databases, including TCGA (The Cancer Genome Atlas), GEO (Gene Expression Omnibus), CCLE (Cancer Cell Line Encyclopedia), GTEx (Genotype Tissue-Expression), and HPA (Human Protein Atlas), to perform a pan-cancer analysis of KLRB1 across 33 types of cancer. We explored the potential molecular mechanism of KLRB1 in clinical prognosis and tumor immunity from the aspects of gene expression, survival status, clinical phenotype, immune infiltration, immunotherapy response, and chemotherapeutic drug sensitivity. Results. KLRB1 was downregulated in 13 cancers while upregulated in kidney cancer. Patients with high expression of KLRB1 have a better prognosis in most types of cancer. Moreover, the KLRB1 expression level is related to TMB and MSI and related to various immune signatures of tumor. The expression of KLRB1 can affect tumor immune cell infiltration. KLRB1 expression level can also affect the sensitivity of chemotherapy drugs. Conclusions. KLRB1 may be a prognostic and immunological biomarker across tumors. At the same time, KLRB1 expression can reflect the sensitivity of cancer patients to chemotherapy drugs. KLRB1 may become a new target for immunotherapy.

scholarly journals Comprehensive Analysis of the Functions and Prognostic Value of RNA-Binding Proteins in Thyroid Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Yue Ma ◽  
Shi Yin ◽  
Xiao-feng Liu ◽  
Jing Hu ◽  
Ning Cai ◽  
...  
Keyword(s):  
Gene Expression ◽  
Thyroid Cancer ◽  
Gene Expression Data ◽  
Binding Proteins ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed ◽  
Integrated Analysis ◽  
Expression Data

RNA binding proteins (RBPs) have been proved to play pivotal roles in a variety types of tumors. However, there is no convincible evidence disclosing the functions of RBPs in thyroid cancer (THCA) thoroughly and systematically. Integrated analysis of the functional and prognostic effect of RBPs help better understanding tumorigenesis and development in thyroid and may provide a novel therapeutic method for THCA. In this study, we obtained a list of human RBPs from Gerstberger database, which covered 1,542 genes encoding RBPs. Gene expression data of THCA was downloaded from The Cancer Genome Atlas (TCGA, n = 567), from which we extracted 1,491 RBPs’ gene expression data. We analyzed differentially expressed RBPs using R package “limma”. Based on differentially expressed RBPs, we constructed protein-protein interaction network and the GO and KEGG pathway enrichment analyses were carried out. We found six RBPs (AZGP1, IGF2BP2, MEX3A, NUDT16, NUP153, USB1) independently associated with prognosis of patients with thyroid cancer according to univariate and multivariate Cox proportional hazards regression models. The survival analysis and risk score analysis achieved good performances from this six-gene prognostic model. Nomogram was constructed to guide clinical decision in practice. Finally, biological experiments disclosed that NUP153 and USB1 can significantly impact cancer cell proliferation and migration. In conclusion, our research provided a new insight of thyroid tumorigenesis and development based on analyses of RBPs. More importantly, the six-gene model may play an important role in clinical practice in the future.

scholarly journals Over-Expression and Prognostic Significance of FN1, Correlating with Immune Infiltrates in Thyroid Cancer

2021 ◽  
Author(s):  
Qi-Shun Geng ◽  
Zhi-Bo Shen ◽  
Li-Feng Li ◽  
Jie Zhao
Keyword(s):  
Gene Expression ◽  
Thyroid Cancer ◽  
Prognostic Significance ◽  
Interaction Network ◽  
Progression Free Survival ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Clinical Prognosis ◽  
Network Analyses

Abstract Background. Thyroid cancer (THCA) is a malignancy affecting the endocrine system, which currently has no effective treatment due to a limited number of suitable drugs and prognostic markers. Methods. Three Gene Expression Omnibus (GEO) datasets were selected to identify differentially expressed genes (DEGs) between THCA and normal thyroid samples using GEO2R tools of National Center for Biotechnology Information. We identified hub gene FN1 using functional enrichment and protein–protein interaction network analyses. Subsequently, we evaluated the importance of gene expression on clinical prognosis using The Cancer Genome Atlas (TCGA) database and GEO datasets. MEXPRESS was used to investigate the correlation between gene expression and DNA methylation; the correlations between FN1 and cancer immune infiltrates were investigated using CIBERSORT. In addition, we assessed the effect of silencing FN1 expression, using an in vitro cellular model of THCA. Immunohistochemical(IHC) was used to elevate the correlation between CD276 and FN1. Results. FN1 expression was highly correlated with progression-free survival and moderately to strongly correlated with the infiltration levels of M2 macrophages and resting memory CD4 + T cells, as well as with CD276 expression. We suggest promoter hypermethylation as the mechanism underlying the observed changes in FN1 expression, as 20 CpG sites in 507 THCA cases in TCGA database showed a negative correlation with FN1 expression. In addition, silencing FN1 expression suppressed clonogenicity, motility, invasiveness, and expression of CD276 in vitro. The correlation between FN1 and CD276 was further confirmed by immunohistochemical. Conclusion. Our findings show that FN1 expression levels correlate with prognosis and immune infiltration levels in THCA, suggesting that FN1 expression be used as immunity-related biomarker and therapeutic target in THCA.

scholarly journals Identification of immune-related lncRNAs to improve the prognosis prediction for patients with papillary thyroid cancer

2021 ◽  
Vol 41 (2) ◽  
Author(s):  
Zhiyang Li ◽  
Weixun Lin ◽  
Jiehua Zheng ◽  
Weida Hong ◽  
Juan Zou ◽  
...  
Keyword(s):  
Gene Expression ◽  
Thyroid Cancer ◽  
Papillary Thyroid Cancer ◽  
Predictive Ability ◽  
Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Clinical Samples ◽  
Pathway Enrichment Analysis ◽  
Lasso Regression ◽  
Papillary Thyroid

Abstract Objective: To identify immune-related long non-coding RNAs (lncRNAs) in papillary thyroid cancer (PTC). Methods: The Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases were used to obtain the gene expression profile. Immune-related lncRNAs were screened from the Molecular Signatures Database v4.0 (MsigDB). We performed a survival analysis of critical lncRNAs. Further, the function of prognostic lncRNAs was inferred using the Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) to clarify the possible mechanisms underlying their predictive ability. The assessment was performed in clinical samples and PTC cells. Results: We obtained 4 immune-related lncRNAs, 15 microRNAs (miRNAs), and 375 mRNAs as the key mediators in the pathophysiological processes of PTC from the GEO database. Further, Lasso regression analysis identified seven prognostic markers (LINC02550, SLC26A4-AS1, ACVR2B-AS1, AC005479.2, LINC02454, and AL136366.1), most of which were related to tumor development. The KEGG pathway enrichment analysis showed different, changed genes mainly enriched in the cancer-related pathways, PI3K-Akt signaling pathway, and focal adhesion. Only SLC26A4-AS1 had an intersection in the results of the two databases. Conclusion: LncRNA SLC26A4-AS1, which is the most associated with prognosis, may play an oncogenic role in the development of PTC.

scholarly journals Pathway-Structured Predictive Model for Cancer Survival Prediction: A Two-Stage Approach

2016 ◽  
Author(s):  
Xinyan Zhang ◽  
Yan Li ◽  
Tomi Akinyemiju ◽  
Akinyemi Ojesina ◽  
Phillip Buckhaults ◽  
...  
Keyword(s):  
Gene Expression ◽  
Predictive Model ◽  
Cox Model ◽  
The Cancer Genome Atlas ◽  
Prognostic Scores ◽  
Survival Prediction ◽  
Two Stage ◽  
Pathway Information ◽  
Persistent Problem ◽  
Cancer Data

Heterogeneity in terms of tumor characteristics, prognosis, and survival among cancer patients has been a persistent problem for many decades. Currently, prognosis and outcome predictions are made based on clinical factors and/or by incorporating molecular profiling data. However, inaccurate prognosis and prediction may result by using only clinical or molecular information directly. One of the main shortcomings of past studies is the failure to incorporate prior biological information into the predictive model, given strong evidence of pathway-based genetic nature of cancer, i.e. the potential for oncogenes to be grouped into pathways based on biological functions such as cell survival, proliferation and metastatic dissemination. To address this problem, we propose a two-stage procedure to incorporate pathway information into the prognostic modeling using large-scale gene expression data. In the first stage, we fit all predictors within each pathway using penalized Cox model (Lasso, Ridge and Elastic Net) and Bayesian hierarchical Cox model. In the second stage, we combine the cross-validated prognostic scores of all pathways obtained in the first stage as new predictors to build an integrated prognostic model for prediction. We apply the proposed method to analyze breast cancer data from The Cancer Genome Atlas (TCGA), predicting overall survival using clinical data and gene expression profiling. The data includes ~20000 genes mapped into 109 pathways for 505 patients. The results show that the proposed approach not only improves survival prediction compared with the alternative analysis that ignores the pathway information, but also identifies significant biological pathways.

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