scholarly journals Potential lncRNA Biomarkers for HBV-Related Hepatocellular Carcinoma Diagnosis Revealed by Analysis on Coexpression Network

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Shunqiang Nong ◽  
Xiaohao Chen ◽  
Zechen Wang ◽  
Guidan Xu ◽  
Wujun Wei ◽  
...  
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Hepatocellular Carcinoma ◽  
Functional Annotation ◽  
Expression Profiles ◽  
Functional Enrichment ◽  
Coexpression Network ◽  
Support Vector ◽  
Immune Microenvironment ◽  
Tumor Immune Microenvironment

Background. Increasing evidence demonstrated that long noncoding RNA (lncRNA) could affect inflammatory tumor immune microenvironment by modulating gene expression and could be used as a biomarker for HBC-related hepatocellular carcinoma (HCC) but still needs further research. The aim of the present study was to determine an lncRNA signature for the diagnosis of HBV-related HCC. Methods. HBV-related HCC expression profiles (GSE55092, GSE19665, and GSE84402) were abstracted from the GEO (Gene Expression Omnibus) data resource, and R package limma and RobustRankAggreg were employed to identify common differentially expressed genes (DEGs). Using machine learning, optimal diagnostic lncRNA molecular markers for HBV-related HCC were identified. The expression of candidate lncRNAs was cross-validated in GSE121248, and an ROC (receiver operating characteristic) curve of lncRNA biomarkers was carried out. Additionally, a coexpression network and functional annotation was built, after which a PPI (protein-protein interaction) network along with module analysis were conducted with the Cytoscape open source software. Result. A total of 38 DElncRNAs and 543 DEmRNAs were identified with a fold change larger than 2.0 and a P value < 0.05. By machine learning, AL356056.2, AL445524.1, TRIM52-AS1, AC093642.1, EHMT2-AS1, AC003991.1, AC008040.1, LINC00844, and LINC01018 were screened out as optional diagnostic lncRNA biosignatures for HBV-related HCC. The AUC (areas under the curve) of the SVM (support vector machine) model and random forest model were 0.957 and 0.904, respectively, and the specificity and sensitivity were 95.7 and 100% and 94.3 and 86.5%, respectively. The results of functional enrichment analysis showed that the integrated coexpressed DEmRNAs shared common cascades in the p53 signaling pathway, retinol metabolism, PI3K-Akt signaling cascade, and chemical carcinogenesis. The integrated DEmRNA PPI network complex was found to be comprised of 87 nodes, and two vital modules with a high degree were selected with the MCODE app. Conclusion. The present study identified nine potential diagnostic biomarkers for HBV-related HCC, all of which could potentially modulated gene expression related to inflammatory conditions in the tumor immune microenvironment. The functional annotation of the target DEmRNAs yielded novel evidence in evaluating the precise functions of lncRNA in HBV-related HCC.

scholarly journals Predictive models for stage and risk classification in head and neck squamous cell carcinoma (HNSCC)

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e9656
Author(s):  
Sugandh Kumar ◽  
Srinivas Patnaik ◽  
Anshuman Dixit
Keyword(s):  
Machine Learning ◽  
Expression Profiles ◽  
Disease Process ◽  
Penalized Regression ◽  
Functional Enrichment ◽  
Machine Learning Techniques ◽  
Gradient Boosting ◽  
Support Vector ◽  
Sequencing Data ◽  
Therapeutic Modalities

Machine learning techniques are increasingly used in the analysis of high throughput genome sequencing data to better understand the disease process and design of therapeutic modalities. In the current study, we have applied state of the art machine learning (ML) algorithms (Random Forest (RF), Support Vector Machine Radial Kernel (svmR), Adaptive Boost (AdaBoost), averaged Neural Network (avNNet), and Gradient Boosting Machine (GBM)) to stratify the HNSCC patients in early and late clinical stages (TNM) and to predict the risk using miRNAs expression profiles. A six miRNA signature was identified that can stratify patients in the early and late stages. The mean accuracy, sensitivity, specificity, and area under the curve (AUC) was found to be 0.84, 0.87, 0.78, and 0.82, respectively indicating the robust performance of the generated model. The prognostic signature of eight miRNAs was identified using LASSO (least absolute shrinkage and selection operator) penalized regression. These miRNAs were found to be significantly associated with overall survival of the patients. The pathway and functional enrichment analysis of the identified biomarkers revealed their involvement in important cancer pathways such as GP6 signalling, Wnt signalling, p53 signalling, granulocyte adhesion, and dipedesis. To the best of our knowledge, this is the first such study and we hope that these signature miRNAs will be useful for the risk stratification of patients and the design of therapeutic modalities.

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 Analysis of potential genetic biomarkers and molecular mechanism of smoking-related postmenopausal osteoporosis using weighted gene co-expression network analysis and machine learning

PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0257343
Author(s):  
Shaoshuo Li ◽  
Baixing Chen ◽  
Hao Chen ◽  
Zhen Hua ◽  
Yang Shao ◽  
...  
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Network Analysis ◽  
Postmenopausal Osteoporosis ◽  
Molecular Mechanism ◽  
Molecular Mechanisms ◽  
Functional Enrichment ◽  
Support Vector ◽  
Hub Genes ◽  
Genetic Biomarkers

Objectives Smoking is a significant independent risk factor for postmenopausal osteoporosis, leading to genome variations in postmenopausal smokers. This study investigates potential biomarkers and molecular mechanisms of smoking-related postmenopausal osteoporosis (SRPO). Materials and methods The GSE13850 microarray dataset was downloaded from Gene Expression Omnibus (GEO). Gene modules associated with SRPO were identified using weighted gene co-expression network analysis (WGCNA), protein-protein interaction (PPI) analysis, and pathway and functional enrichment analyses. Feature genes were selected using two machine learning methods: support vector machine-recursive feature elimination (SVM-RFE) and random forest (RF). The diagnostic efficiency of the selected genes was assessed by gene expression analysis and receiver operating characteristic curve. Results Eight highly conserved modules were detected in the WGCNA network, and the genes in the module that was strongly correlated with SRPO were used for constructing the PPI network. A total of 113 hub genes were identified in the core network using topological network analysis. Enrichment analysis results showed that hub genes were closely associated with the regulation of RNA transcription and translation, ATPase activity, and immune-related signaling. Six genes (HNRNPC, PFDN2, PSMC5, RPS16, TCEB2, and UBE2V2) were selected as genetic biomarkers for SRPO by integrating the feature selection of SVM-RFE and RF. Conclusion The present study identified potential genetic biomarkers and provided a novel insight into the underlying molecular mechanism of SRPO.

scholarly journals Integrated analysis of microbiome and host transcriptome reveals correlations between gut microbiota and clinical outcomes in HBV-related hepatocellular carcinoma

2020 ◽  
Vol 12 (1) ◽  
Author(s):  
Hechen Huang ◽  
Zhigang Ren ◽  
Xingxing Gao ◽  
Xiaoyi Hu ◽  
Yuan Zhou ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Clinical Outcome ◽  
Gut Microbiota ◽  
Bile Acids ◽  
Clinical Characteristics ◽  
Tumor Burden ◽  
Support Vector ◽  
Immune Microenvironment ◽  
Serum Bile Acids ◽  
Tumor Immune Microenvironment

Abstract Background The gut-liver axis plays a pivotal role in the pathogenesis of hepatocellular carcinoma (HCC). However, the correlations between the gut microbiome and the liver tumor transcriptome in patients with HCC and the impact of the gut microbiota on clinical outcome are less well-understood. Methods Fecal samples collected from HBV-related HCC patients (n = 113) and healthy volunteers (n = 100) were subjected to 16S rRNA sequencing of the microbiome. After a rigorous selection process, 32 paired tumor and adjacent non-tumor liver tissues from the HCC group were subjected to next-generation sequencing (NGS) RNA-seq. The datasets were analyzed individually and integrated with clinical characteristics for combined analysis using bioinformatics approaches. We further verified the potential of the gut microbiota to predict clinical outcome by a random forest model and a support vector machine model. Results We found that Bacteroides, Lachnospiracea incertae sedis, and Clostridium XIVa were enriched in HCC patients with a high tumor burden. By integrating the microbiome and transcriptome, we identified 31 robust associations between the above three genera and well-characterized genes, indicating possible mechanistic relationships in tumor immune microenvironment. Clinical characteristics and database analysis suggested that serum bile acids may be important communication mediators between these three genera and the host transcriptome. Finally, among these three genera, six important microbial markers associated with tumor immune microenvironment or bile acid metabolism showed the potential to predict clinical outcome (AUC = 81%). Conclusions This study revealed that changes in tumor immune microenvironment caused by the gut microbiota via serum bile acids may be important factors associated with tumor burden and adverse clinical outcome. Gut microbes can be used as biomarkers of clinical features and outcomes, and the microbe-associated transcripts of host tumors can partly explain how gut microbiota promotes HCC pathogenesis.

scholarly journals Identification of Flap endonuclease 1 as a potential core gene in hepatocellular carcinoma by integrated bioinformatics analysis

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7619 ◽  
Author(s):  
Chuanfei Li ◽  
Feng Qin ◽  
Hao Hong ◽  
Hui Tang ◽  
Xiaoling Jiang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Liver Cancer ◽  
Expression Profiles ◽  
Core Gene ◽  
Functional Enrichment ◽  
Potential Core ◽  
Potential Biomarker ◽  
Cancer Tissues ◽  
Core Genes

Hepatocellular carcinoma (HCC) is a common yet deadly form of malignant cancer. However, the specific mechanisms involved in HCC diagnosis have not yet fully elucidated. Herein, we screened four publically available Gene Expression Omnibus (GEO) expression profiles (GSE14520, GSE29721, GSE45267 and GSE60502), and used them to identify 409 differentially expressed genes (DEGs), including 142 and 267 up- and down-regulated genes, respectively. The DAVID database was used to look for functionally enriched pathways among DEGs, and the STRING database and Cytoscape platform were used to generate a protein-protein interaction (PPI) network for these DEGs. The cytoHubba plug-in was utilized to detect 185 hub genes, and three key clustering modules were constructed with the MCODE plug-in. Gene functional enrichment analyses of these three key clustering modules were further performed, and nine core genes including BIRC5, DLGAP5, DTL, FEN1, KIAA0101, KIF4A, MCM2, MKI67, and RFC4, were identified in the most critical cluster. Subsequently, the hierarchical clustering and expression of core genes in TCGA liver cancer tissues were analyzed using the UCSC Cancer Genomics Browser, and whether elevated core gene expression was linked to a poor prognosis in HCC patients was assessed using the GEPIA database. The PPI of the nine core genes revealed an interaction between FEN1, MCM2, RFC4, and BIRC5. Furthermore, the expression of FEN1 was positively correlated with that of three other core genes in TCGA liver cancer tissues. FEN1 expression in HCC and other tumor types was assessed with the FIREBROWSE and ONCOMINE databases, and results were verified in HCC samples and hepatoma cells. FEN1 levels were also positively correlated with tumor size, distant metastasis and vascular invasion. In conclusion, we identified nine core genes associated with HCC development, offering novel insight into HCC progression. In particular, the aberrantly elevated FEN1 may represent a potential biomarker for HCC diagnosis and treatment.

scholarly journals Integrated weighted gene coexpression network analysis identifies Frizzled 2 (FZD2) as a key gene in invasive malignant pleomorphic adenoma

2022 ◽  
Vol 20 (1) ◽  
Author(s):  
Zhenyuan Han ◽  
Huiping Ren ◽  
Jingjing Sun ◽  
Lihui Jin ◽  
Qin Wang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Network Analysis ◽  
Pleomorphic Adenoma ◽  
Expression Profiles ◽  
Functional Enrichment ◽  
Coexpression Network ◽  
Low Grade ◽  
Gene Coexpression Network ◽  
Gene Coexpression ◽  
Coexpression Network Analysis

Abstract Background Invasive malignant pleomorphic adenoma (IMPA) is a highly malignant neoplasm of the oral salivary glands with a poor prognosis and a considerable risk of recurrence. Many disease-causing genes of IMPA have been identified in recent decades (e.g., P53, PCNA and HMGA2), but many of these genes remain to be explored. Weighted gene coexpression network analysis (WGCNA) is a newly emerged algorithm that can cluster genes and form modules based on similar gene expression patterns. This study constructed a gene coexpression network of IMPA via WGCNA and then carried out multifaceted analysis to identify novel disease-causing genes. Methods RNA sequencing (RNA-seq) was performed for 10 pairs of IMPA and normal tissues to acquire the gene expression profiles. Differentially expressed genes (DEGs) were screened out with the cutoff criteria of |log2 Fold change (FC)|> 1 and adjusted p value  < 0.05. Then, WGCNA was applied to systematically identify the hidden diagnostic hub genes of IMPA. Results In this research, a total of 1970 DEGs were screened out in IMPA tissues, including 1056 upregulated DEGs and 914 downregulated DEGs. Functional enrichment analysis was performed for identified DEGs and revealed an enrichment of tumor-associated GO terms and KEGG pathways. We used WGCNA to identify gene module most relevant with the histological grade of IMPA. The gene FZD2 was then recognized as the hub gene of the selected module with the highest module membership (MM) value and intramodule connectivity in protein–protein interaction (PPI) network. According to immunohistochemistry (IHC) staining, the expression level of FZD2 was higher in low-grade IMPA than in high-grade IMPA. Conclusion FZD2 shows an expression dynamic that is negatively correlated with the clinical malignancy of IMPA and it plays a central role in the transcription network of IMPA. Thus, FZD2 serves as a promising histological indicator for the precise prediction of IMPA histological stages.

scholarly journals Transcriptome Network Analysis Identifies CXCL13-CXCR5 Signaling Modules in the Prostate Tumor Immune Microenvironment

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Adaugo Q. Ohandjo ◽  
Zongzhi Liu ◽  
Eric B. Dammer ◽  
Courtney D. Dill ◽  
Tiara L. Griffen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Network Analysis ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Gene Expression Signature ◽  
Cell Types ◽  
Immune Microenvironment ◽  
Tumor Immune Microenvironment ◽  
Quality Checks ◽  
And Function

Abstract The tumor immune microenvironment (TIME) consists of multiple cell types that contribute to the heterogeneity and complexity of prostate cancer (PCa). In this study, we sought to understand the gene-expression signature of patients with primary prostate tumors by investigating the co-expression profiles of patient samples and their corresponding clinical outcomes, in particular “disease-free months” and “disease reoccurrence”. We tested the hypothesis that the CXCL13-CXCR5 axis is co-expressed with factors supporting TIME and PCa progression. Gene expression counts, with clinical attributes from PCa patients, were acquired from TCGA. Profiles of PCa patients were used to identify key drivers that influence or regulate CXCL13-CXCR5 signaling. Weighted gene co-expression network analysis (WGCNA) was applied to identify co-expression patterns among CXCL13-CXCR5, associated genes, and key genetic drivers within the CXCL13-CXCR5 signaling pathway. The processing of downloaded data files began with quality checks using NOISeq, followed by WGCNA. Our results confirmed the quality of the TCGA transcriptome data, identified 12 co-expression networks, and demonstrated that CXCL13, CXCR5 and associated genes are members of signaling networks (modules) associated with G protein coupled receptor (GPCR) responsiveness, invasion/migration, immune checkpoint, and innate immunity. We also identified top canonical pathways and upstream regulators associated with CXCL13-CXCR5 expression and function.

scholarly journals Identification Of Hepatocellular Carcinoma Using Supervised Learning Algorithms

2014 ◽  
Vol 14 (3) ◽  
pp. 5535-5542
Author(s):  
Sagri Sharma ◽  
Sanjay Kadam ◽  
Hemant Darbari
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Supervised Learning ◽  
Dna Analysis ◽  
Learning Algorithm ◽  
Expression Profiles ◽  
Learning Algorithms ◽  
Support Vector ◽  
Unseen Data ◽  
Supervised Learning Algorithms

Analysis of diseases integrating multi-factors increases the complexity of the problem and therefore, development of frameworks for the analysis of diseases is an issue that is currently a topic of intense research. Due to the inter-dependence of the various parameters, the use of traditional methodologies has not been very effective. Consequently, newer methodologies are being sought to deal with the problem. Supervised Learning Algorithms are commonly used for performing the prediction on previously unseen data. These algorithms are commonly used for applications in fields ranging from image analysis to protein structure and function prediction and they get trained using a known dataset to come up with a predictor model that generates reasonable predictions for the response to new data. Gene expression profiles generated by DNA analysis experiments can be quite complex since these experiments can involve hypotheses involving entire genomes. The application of well-known machine learning algorithm - Support Vector Machine - to analyze the expression levels of thousands of genes simultaneously in a timely, automated and cost effective way is thus used. The objectives to undertake the presented work are development of a methodology to identify genes relevant to Hepatocellular Carcinoma (HCC) from gene expression dataset utilizing supervised learning algorithms & statistical evaluations along with development of a predictive framework that can perform classification tasks on new, unseen data

scholarly journals Cerebrospinal fluid cells immune landscape in multiple sclerosis

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Zijian Li ◽  
Yongchao Liu ◽  
Aili Jia ◽  
Yueran Cui ◽  
Juan Feng
Keyword(s):  
Gene Expression ◽  
Multiple Sclerosis ◽  
Cerebrospinal Fluid ◽  
Molecular Mechanisms ◽  
Immune Cell ◽  
Expression Profiles ◽  
Functional Enrichment ◽  
Control Group ◽  
Immune Microenvironment ◽  
Derivation Cohort

Abstract Background Multiple Sclerosis (MS) is a potentially devastating autoimmune neurological disorder, which characteristically induces demyelination of white matter in the brain and spinal cord. Methods In this study, three characteristics of the central nervous system (CNS) immune microenvironment occurring during MS onset were explored; immune cell proportion alteration, differential gene expression profile, and related pathways. The raw data of two independent datasets were obtained from the ArrayExpress database; E-MTAB-69, which was used as a derivation cohort, and E-MTAB-2374 which was used as a validation cohort. Differentially expressed genes (DEGs) were identified by the false discovery rate (FDR) value of < 0.05 and |log2 (Fold Change)|> 1, for further analysis. Then, functional enrichment analyses were performed to explore the pathways associated with MS onset. The gene expression profiles were analyzed using CIBERSORT to identify the immune type alterations involved in MS disease. Results After verification, the proportion of five types of immune cells (plasma cells, monocytes, macrophage M2, neutrophils and eosinophils) in cerebrospinal fluid (CSF) were revealed to be significantly altered in MS cases compared to the control group. Thus, the complement and coagulation cascades and the systemic lupus erythematosus (SLE) pathways may play critical roles in MS. We identified NLRP3, LILRB2, C1QB, CD86, C1QA, CSF1R, IL1B and TLR2 as eight core genes correlated with MS. Conclusions Our study identified the change in the CNS immune microenvironment of MS cases by analysis of the in silico data using CIBERSORT. Our data may assist in providing directions for further research as to the molecular mechanisms of MS and provide future potential therapeutic targets in treatment.

scholarly journals Integrated Bioinformatic Analysis Revealed the Regulation of Angiogenesis by Tumor Cells in Hepatocellular Carcinoma

2021 ◽  
Author(s):  
Meisi Huo ◽  
Kangkang Yu ◽  
Yahui Zheng ◽  
Lu Liu ◽  
Hao Zhao ◽  
...  
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Endothelial Cells ◽  
Tumor Cells ◽  
Expression Profiles ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Hcc Cells

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer mortality, metastasis accounts for most of the cases. Angiogenesis plays an important role in cancer metastasis, but how tumor cells affect the function of endothelial cells by dictating their miRNA expression remains largely unknown.Differentially expressed miRNAs (DEMs) were identified through dataset downloaded from the Gene Expression Omnibus (GEO) database and analyzed by GEO2R. We then use online tools to obtain potential targets of candidate microRNAs(miRNAs) and functional enrichment analysis，as well as the protein-protein interaction (PPI). Finally, the function of miR-302c-3p was validated through in vitro assay.In the current study, we found that HCC cells altered miRNAs expression profiles of human umbilical vein endothelial cells (HUVECs) and miR-302c-3p was the most downregulated miRNA in HUVECs when they were co-cultured with HCC-LM3 cells. Functional enrichment analysis of the candidate targets revealed that these genes were involved in epigenetic regulation of gene expression, in particular, histone methylation. In addition, PPI network demonstrated distinct roles of genes targeted by miR-302c-3p. Importantly, inhibition of angiogenesis, migration and permeability by the most downregulated miR-302c-3p in HUVECs was confirmed in vitro. These findings brought us novel insight into the regulation of angiogenesis by HCC cells and provided potential targets for the development of therapeutic strategies.

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