scholarly journals Mining the plasma-proteome associated genes in patients with gastro-esophageal cancers for biomarker discovery

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Frederick S. Vizeacoumar ◽  
Hongyu Guo ◽  
Lynn Dwernychuk ◽  
Adnan Zaidi ◽  
Andrew Freywald ◽  
...  
Keyword(s):  
Biomarker Discovery ◽  
Predictive Accuracy ◽  
Area Under The Curve ◽  
Cancer Recurrence ◽  
The Cancer Genome Atlas ◽  
Svm Classifier ◽  
Plasma Proteome ◽  
Cancer Genome Atlas ◽  
Novel Biomarkers ◽  
Evaluation Metric

AbstractGastro-esophageal (GE) cancers are one of the major causes of cancer-related death in the world. There is a need for novel biomarkers in the management of GE cancers, to yield predictive response to the available therapies. Our study aims to identify leading genes that are differentially regulated in patients with these cancers. We explored the expression data for those genes whose protein products can be detected in the plasma using the Cancer Genome Atlas to identify leading genes that are differentially regulated in patients with GE cancers. Our work predicted several candidates as potential biomarkers for distinct stages of GE cancers, including previously identified CST1, INHBA, STMN1, whose expression correlated with cancer recurrence, or resistance to adjuvant therapies or surgery. To define the predictive accuracy of these genes as possible biomarkers, we constructed a co-expression network and performed complex network analysis to measure the importance of the genes in terms of a ratio of closeness centrality (RCC). Furthermore, to measure the significance of these differentially regulated genes, we constructed an SVM classifier using machine learning approach and verified these genes by using receiver operator characteristic (ROC) curve as an evaluation metric. The area under the curve measure was > 0.9 for both the overexpressed and downregulated genes suggesting the potential use and reliability of these candidates as biomarkers. In summary, we identified leading differentially expressed genes in GE cancers that can be detected in the plasma proteome. These genes have potential to become diagnostic and therapeutic biomarkers for early detection of cancer, recurrence following surgery and for development of targeted treatment.

scholarly journals Characterization of Leading Dysregulated Plasma-Proteome Associated Genes in Patients with Gastro-Esophageal Cancers.

2020 ◽  
Author(s):  
Frederick Vizeacoumar ◽  
Lynn Dwernychuk ◽  
Adnan Zaidi ◽  
Andrew Freywald ◽  
Franco Vizeacoumar ◽  
...  
Keyword(s):  
Esophageal Cancer ◽  
Differential Expression ◽  
Stomach Cancer ◽  
Cancer Recurrence ◽  
The Cancer Genome Atlas ◽  
Plasma Proteome ◽  
Tissue Samples ◽  
Esophageal Cancers ◽  
Expression Of Genes ◽  
Novel Biomarkers

Abstract Background: Gastro-esophageal cancers are one of the major causes of cancer-related death in the world. There is a need for novel biomarkers in the management of gastro-esophageal cancers to identify new therapeutic targets and to yield predictive response to the available therapies. Our study aims to identify leading genes that are dysregulated (upregulated or downregulated) in patients with gastro-esophageal cancers.Methods: We examined gene expression data for those genes whose protein products can be detected in the plasma in 600 independent tumor samples and 46 matching normal tissue samples using the Cancer Genome Atlas (TCGA) to identify leading genes that are dysregulated in patients with gastro-esophageal cancers. Non-parametric Mann-Whitney-U test was used to evaluate differential expression of genes using a cut-off of P< 0.05.Results: The comparison between tumors sample and healthy tissue showed BIRC5 (p=2.61 E-08), APOC2 (p=3.23E-08), CENPF (p=4.38E-08), STMN1 (p=5.74E-08), and HNRPC (p=8.21E-08) were the leading genes significantly overexpressed in esophageal cancer whereas CST1 (p=3.97 E-21), INHBA (p=9.22E-20), ACAN (p=1.08E-19), HSP90AB1 (p=2.62E-19), and HSPD1 (p=3.91E-19) were the leading genes that were overexpressed in stomach cancer. Conversely, C16orf89 (9.78E-08), AR (1.01E-07), CKB (1.17E-07), ADH1B (1.79E-07), and NCAM1 (2.15E-07) were the leading gene that were significantly downregulated in esophageal cancer whereas GPX3 (1.65E-19), CLEC3B (5.70E-19), CFD (5.68E-18), GSN (4.5IE-17), and CCL14 (1.12E-16) were significantly downregulated in stomach cancer. Furthermore, Stage-based examination showed stage-specific differential expression of various genes as well as stage-wise increasing or decreasing up-regulation or down-regulation of selected genes, respectively. Conclusions: The present study identified leading upregulated and downregulated genes in gastro-esophageal cancers that can be detected in the plasma proteome. These genes have potential to become diagnostic and therapeutic biomarkers for early detection of cancer, recurrence following surgery and for development of targeted-treatment.

scholarly journals Upgrading the Repertoire of miRNAs in Gastric Adenocarcinoma to Provide a New Resource for Biomarker Discovery

2019 ◽  
Vol 20 (22) ◽  
pp. 5697 ◽  
Author(s):  
Michelle E. Pewarchuk ◽  
Mateus C. Barros-Filho ◽  
Brenda C. Minatel ◽  
David E. Cohn ◽  
Florian Guisier ◽  
...  
Keyword(s):  
Gastric Adenocarcinoma ◽  
Biomarker Discovery ◽  
The Cancer Genome Atlas ◽  
Small Rna Sequencing ◽  
Sequencing Data ◽  
Specific Expression ◽  
Novel Mirna ◽  
Cancer Genome Atlas ◽  
Context Specific ◽  
Independent Cohort

Recent studies have uncovered microRNAs (miRNAs) that have been overlooked in early genomic explorations, which show remarkable tissue- and context-specific expression. Here, we aim to identify and characterize previously unannotated miRNAs expressed in gastric adenocarcinoma (GA). Raw small RNA-sequencing data were analyzed using the miRMaster platform to predict and quantify previously unannotated miRNAs. A discovery cohort of 475 gastric samples (434 GA and 41 adjacent nonmalignant samples), collected by The Cancer Genome Atlas (TCGA), were evaluated. Candidate miRNAs were similarly assessed in an independent cohort of 25 gastric samples. We discovered 170 previously unannotated miRNA candidates expressed in gastric tissues. The expression of these novel miRNAs was highly specific to the gastric samples, 143 of which were significantly deregulated between tumor and nonmalignant contexts (p-adjusted < 0.05; fold change > 1.5). Multivariate survival analyses showed that the combined expression of one previously annotated miRNA and two novel miRNA candidates was significantly predictive of patient outcome. Further, the expression of these three miRNAs was able to stratify patients into three distinct prognostic groups (p = 0.00003). These novel miRNAs were also present in the independent cohort (43 sequences detected in both cohorts). Our findings uncover novel miRNA transcripts in gastric tissues that may have implications in the biology and management of gastric adenocarcinoma.

scholarly journals A Novel m6A Gene Signature Associated With Regulatory Immune Function for Prognosis Prediction in Clear-Cell Renal Cell Carcinoma

2021 ◽  
Vol 8 ◽  
Author(s):  
Siteng Chen ◽  
Ning Zhang ◽  
Encheng Zhang ◽  
Tao Wang ◽  
Liren Jiang ◽  
...  
Keyword(s):  
Risk Score ◽  
Clear Cell ◽  
Area Under The Curve ◽  
The Cancer Genome Atlas ◽  
Survival Prediction ◽  
Cell Renal Cell Carcinoma ◽  
Training Cohort ◽  
Rna Methylation ◽  
Cancer Genome Atlas ◽  
Genome Atlas

The important role of N6-methyladenosine (m6A) RNA methylation regulator in carcinogenesis and progression of clear-cell renal cell carcinoma (ccRCC) is poorly understood by now. In this study, we performed comprehensive analyses of m6A RNA methylation regulators in 975 ccRCC samples and 332 adjacent normal tissues and identified ccRCC-related m6A regulators. Moreover, the m6A diagnostic score based on ccRCC-related m6A regulators could accurately distinguish ccRCC from normal tissue in the Meta-cohort, which was further validated in the independent GSE-cohort and The Cancer Genome Atlas-cohort, with an area under the curve of 0.924, 0.867, and 0.795, respectively. Effective survival prediction of ccRCC by m6A risk score was also identified in the Cancer Genome Atlas training cohort and verified in the testing cohort and the independent GSE22541 cohort, with hazard ratio values of 3.474, 1.679, and 2.101 in the survival prognosis, respectively. The m6A risk score was identified as a risk factor of overall survival in ccRCC patients by the univariate Cox regression analysis, which was further verified in both the training cohort and the independent validation cohort. The integrated nomogram combining m6A risk score and predictable clinicopathologic factors could accurately predict the survival status of the ccRCC patients, with an area under the curve values of 85.2, 82.4, and 78.3% for the overall survival prediction in 1-, 3- and 5-year, respectively. Weighted gene co-expression network analysis with functional enrichment analysis indicated that m6A RNA methylation might affect clinical prognosis through regulating immune functions in patients with ccRCC.

scholarly journals A Six-lncRNA Signature for Immunophenotype Prediction of Glioblastoma Multiforme

2021 ◽  
Vol 11 ◽  
Author(s):  
Ming Gao ◽  
Xinzhuang Wang ◽  
Dayong Han ◽  
Enzhou Lu ◽  
Jian Zhang ◽  
...  
Keyword(s):  
Glioblastoma Multiforme ◽  
Immune Cell ◽  
Area Under The Curve ◽  
Principal Component ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Non Coding Rna ◽  
Cancer Genome Atlas ◽  
The Central Nervous System

Glioblastoma multiforme (GBM) is the most aggressive primary tumor of the central nervous system. As biomedicine advances, the researcher has found the development of GBM is closely related to immunity. In this study, we evaluated the GBM tumor immunoreactivity and defined the Immune-High (IH) and Immune-Low (IL) immunophenotypes using transcriptome data from 144 tumors profiled by The Cancer Genome Atlas (TCGA) project based on the single-sample gene set enrichment analysis (ssGSEA) of five immune expression signatures (IFN-γ response, macrophages, lymphocyte infiltration, TGF-β response, and wound healing). Next, we identified six immunophenotype-related long non-coding RNA biomarkers (im-lncRNAs, USP30-AS1, HCP5, PSMB8-AS1, AL133264.2, LINC01684, and LINC01506) by employing a machine learning computational framework combining minimum redundancy maximum relevance algorithm (mRMR) and random forest model. Moreover, the expression level of identified im-lncRNAs was converted into an im-lncScore using the normalized principal component analysis. The im-lncScore showed a promising performance for distinguishing the GBM immunophenotypes with an area under the curve (AUC) of 0.928. Furthermore, the im-lncRNAs were also closely associated with the levels of tumor immune cell infiltration in GBM. In summary, the im-lncRNA signature had important clinical implications for tumor immunophenotyping and guiding immunotherapy in glioblastoma patients in future.

scholarly journals A Deep Learning Approach for Rapid Mutational Screening in Melanoma

2019 ◽  
Author(s):  
Randie H. Kim ◽  
Sofia Nomikou ◽  
Nicolas Coudray ◽  
George Jour ◽  
Zarmeena Dawood ◽  
...  
Keyword(s):  
Deep Learning ◽  
Structural Characteristics ◽  
Tumor Biology ◽  
Model Performance ◽  
Area Under The Curve ◽  
The Cancer Genome Atlas ◽  
Clinical Settings ◽  
Mutational Screening ◽  
Cancer Genome Atlas ◽  
Whole Slide Images

AbstractImage-based analysis as a rapid method for mutation detection can be advantageous in research or clinical settings when tumor tissue is limited or unavailable for direct testing. Here, we applied a deep convolutional neural network (CNN) to whole slide images of melanomas from 256 patients and developed a fully automated model that first selects for tumor-rich areas (Area Under the Curve AUC=0.96) then predicts for the presence of mutated BRAF in our test set (AUC=0.72) Model performance was cross-validated on melanoma images from The Cancer Genome Atlas (AUC=0.75). We confirm that the mutated BRAF genotype is linked to phenotypic alterations at the level of the nucleus through saliency mapping and pathomics analysis, which reveal that cells with mutated BRAF exhibit larger and rounder nuclei. Not only do these findings provide additional insights on how BRAF mutations affects tumor structural characteristics, deep learning-based analysis of histopathology images have the potential to be integrated into higher order models for understanding tumor biology, developing biomarkers, and predicting clinical outcomes.

Metabolic gene signature for predicting breast cancer recurrence using transcriptome analysis

2021 ◽  
Author(s):  
Juan Feng ◽  
Jun Ren ◽  
Qingfeng Yang ◽  
Lingxia Liao ◽  
Le Cui ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Recurrence ◽  
Recurrence Risk ◽  
Gene Signature ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Breast Cancer Patients ◽  
Metabolic Gene ◽  
Limma Package ◽  
Cancer Genome Atlas

Background: The study aimed at identifying a metabolic gene signature for stratifying the risk of recurrence in breast cancer. Materials & Methods: The data of patients were obtained from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. The limma package was used to identify differentially expressed metabolic genes, and a metabolic gene signature was constructed. Results: A five-gene metabolic signature was established that demonstrated satisfactory accuracy and predictive power in both training and validation cohorts. Also, a nomogram for predicting recurrence-free survival was established using a combination of the metabolism gene risk score and the clinicopathological features. Conclusions: The proposed metabolic gene signature and nomogram have a significant prognostic value and may improve the recurrence risk stratification for breast cancer patients.

lncRNA and mRNA signature for prognosis prediction of glioblastoma

2020 ◽  
Vol 16 (13) ◽  
pp. 837-848 ◽  
Author(s):  
Guohong Liu ◽  
Yunbao Pan ◽  
Yueying Li ◽  
Haibo Xu
Keyword(s):  
Cox Regression ◽  
Expression Profiles ◽  
Tissue Expression ◽  
The Cancer Genome Atlas ◽  
Normal Brain ◽  
Multiple Gene ◽  
Cox Regression Analysis ◽  
R Language ◽  
Cancer Genome Atlas ◽  
Novel Biomarkers

Aims: We aimed to find out potential novel biomarkers for prognosis of glioblastoma (GBM). Materials & methods: We downloaded mRNA and lncRNA expression profiles of 169 GBM and five normal samples from The Cancer Genome Atlas and 129 normal brain samples from genotype-tissue expression. We use R language to perform the following analyses: differential RNA expression analysis of GBM samples using ‘edgeR’ package, survival analysis taking count of single or multiple gene expression level using ‘survival’ package, univariate and multivariate Cox regression analysis using Cox function plugged in ‘survival’ package. Gene ontology and Kyoto encyclopedia of genes and genomes pathway analysis were performed using FunRich tool online. Results and conclusion: We obtained differentially DEmRNAs and DElncRNAs in GBM samples. Most prognostically relevant mRNAs and lncRNAs were filtered out. ‘GPCR ligand binding’ and ‘Class A/1’ are found to be of great significance. In short, our study provides novel biomarkers for prognosis of GBM.

scholarly journals A Simple Competing Endogenous RNA Network Identifies Novel mRNA, miRNA, and lncRNA Markers in Human Cholangiocarcinoma

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Cheng Zhang ◽  
Chunlin Ge
Keyword(s):  
Cox Regression ◽  
Clinical Information ◽  
The Cancer Genome Atlas ◽  
Primary Liver Tumor ◽  
Normal Tissues ◽  
Cerna Network ◽  
Tumorigenesis And Progression ◽  
Competing Endogenous Rna Network ◽  
Cancer Genome Atlas ◽  
Novel Biomarkers

Background. Cholangiocarcinoma (CCA) is the second most common malignant primary liver tumor and has shown an alarming increase in incidence over the last two decades. However, the mechanisms behind tumorigenesis and progression remain insufficient. The present study aimed to uncover the underlying regulatory mechanism on CCA and find novel biomarkers for the disease prognosis. Method. The RNA-sequencing (RNA-seq) datasets of lncRNAs, miRNAs, and mRNAs in CCA as well as relevant clinical information were obtained from the Cancer Genome Atlas (TCGA) database. After pretreatment, differentially expressed RNAs (DERNAs) were identified and further interrogated for their correlations with clinical information. Prognostic RNAs were selected using univariate Cox regression. Then, a ceRNA network was constructed based on these RNAs. Results. We identified a total of five prognostic DEmiRNAs, 63 DElncRNAs, and 90 DEmRNAs between CCA and matched normal tissues. Integrating the relationship between the different types of RNAs, an lncRNA-miRNA-mRNA network was established and included 28 molecules and 47 interactions. Screened prognostic RNAs involved in the ceRNA network included 3 miRNAs (hsa-mir-1295b, hsa-mir-33b, and hsa-mir-6715a), 7 lncRNAs (ENSG00000271133, ENSG00000233834, ENSG00000276791, ENSG00000241155, COL18A1-AS1, ENSG00000274737, and ENSG00000235052), and 18 mRNAs (ANO9, FUT4, MLLT3, ABCA3, FSCN2, GRID2IP, NCK2, MACC1, SLC35E4, ST14, SH2D3A, MOB3B, ACTL10, RAB36, ATP1B3, MST1R, SEMA6A, and SEL1L3). Conclusions. Our study identified novel prognostic makers and predicted a previously unknown ceRNA regulatory network in CCA and may provide novel insight into a further understanding of lncRNA-mediated ceRNA regulatory mechanisms in CCA.

scholarly journals Identification and Potential Mechanisms of a 7-MicroRNA Signature That Predicts Prognosis in Patients with Lower-Grade Glioma

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Zhizheng Liu ◽  
Hongliang Meng ◽  
Miaoxian Fang ◽  
Wenlong Guo
Keyword(s):  
Signaling Pathway ◽  
Predictive Accuracy ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed ◽  
Lower Grade ◽  
Clinical Practices ◽  
Lower Grade Glioma ◽  
Cancer Genome Atlas ◽  
Genome Atlas

Background. Lower-grade glioma is an intracranial cancer that may develop into glioblastoma with high mortality. The main objective of our study is to develop microRNA for LGG patients which will provide novel prognostic biomarkers along with therapeutic targets. Methods. Clinicopathological data of LGG patients and their RNA expression profile were downloaded through The Cancer Genome Atlas Relevant expression profiles of RNA, and clinicopathological data of the LGG patients had been extracted from the database of “The Cancer Genome Atlas.” Differential expression analysis had been conducted for identification of the differentially expressed microRNAs as well as mRNAs in LGG samples and normal ones. ROC curves and K–M plots were plotted to confirm performance and for predictive accuracy. For the confirmation of microRNAs as an independent prognostic factor, an independent prognosis analysis was conducted. Moreover, target differentially expressed genes of these identified prognostic microRNAs that were extracted and protein-protein interaction networks were developed. Moreover, the biological functions of signature were determined through Genome Ontology analysis, genome pathway analysis, and Kyoto Encyclopedia of Genes. Results. 7-microRNA signature was identified that has the ability of categorization of individuals with LGG into high- and low-risk groups on the basis of significant difference in survival during training and testing cohorts (P < 0.001). The 7-microRNA signature had appeared to be robust in predictive accuracy (all AUC> 0.65). It was also approved with multivariate Cox regression along with some traditional clinical practices that we can use 7-microRNA signature for therapeutic purposes as a self-regulating predictive OS factor (P < 0.001). KEGG and Gene Ontology (GO) analyses reported that 7-microRNAs had mainly developed in important pathways related with glioma, e.g., the “cAMP signaling pathway,” “glutamatergic synapses,” and “calcium signaling pathway”. Conclusion. A newly discovered 7-microRNA signature could be a potential target for the diagnosis and treatment for LGG patients.

scholarly journals CpG-methylation-based risk score predicts progression in colorectal cancer

Epigenomics ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 605-615 ◽  
Author(s):  
Yao Deng ◽  
Hao Wan ◽  
Jianbo Tian ◽  
Xiang Cheng ◽  
Meilin Rao ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Risk Score ◽  
Area Under The Curve ◽  
The Cancer Genome Atlas ◽  
Free Interval ◽  
Future Clinical Practice ◽  
Risk Of Progression ◽  
Precision Therapy ◽  
Cancer Genome Atlas ◽  
Genome Atlas

Aim: To identify patients with colorectal cancer (CRC) who are at a truly higher risk of progression, which is key for individualized approaches to precision therapy. Materials & methods: We developed a predictor associated with progression-free interval (PFI) using The Cancer Genome Atlas CRC methylation data. Results: The risk score was associated with PFI in the whole cohort (p < 0.001). A nomogram consisting of the risk score and other significant clinical features was generated to predict the 3- and 5-year PFI in the whole set (area under the curve: 0.79 and 0.71, respectively). Conclusion: The risk score based on 23 DNA-methylation sites may serve as the basis for improved prediction of progression in patients with CRC in future clinical practice.

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