scholarly journals Integrative Analysis and Identification of an Excellent lncRNA Signature to Predict Prognosis in Patients with COAD

2021 ◽  
Author(s):  
ZhiHua Chen ◽  
YiLin Lin ◽  
SuYong Lin ◽  
Ji Gao ◽  
Shao-Qin Chen
Keyword(s):  
Copy Number ◽  
Cox Regression ◽  
Gene Expression Omnibus ◽  
Copy Number Variations ◽  
Integrative Analysis ◽  
Transcriptome Data ◽  
Cox Regression Analysis ◽  
Number Variation ◽  
Cancer Genome Atlas ◽  
Internal Mechanism

Abstract Backgroud: Tumour recurrence and metastasis lead to poor prognosis incolon cancer(COAD). Therefore We aimed to identify a lncRNA signature through an integrative analysis of copy number variation, mutation and transcriptome data to predict prognosis and explore its internal mechanism.Methods: The lncRNA expression profile were collected fromThe Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). TCGA data was randomly divided 3:1 intotraining andtesting cohort. In the training, weperformed integrated analyses of three candidate lncRNA sets that correlated with prognosis, copy number variations and mutations to establish a signature through Cox regression analysis. The robustness was determined in the testing and GEO.Results: An 11-lncRNA signature that was significantly associated with prognosiswas constructed in the training (P<0.0001, HR=2.014) , And this signature was validated in the testing(P=0.0019, HR=3.374) and GSE17536(P=0.0076, HR=1.864). The signature is significantly related to MSI status and clinical prognostic factors. The prognostic-relatedrisk scores were significantly excellent than the other five models have been reported. Furthermore, GSEA suggested that the signature was involved in COAD development and metastasis-related pathways.Conclusions: We identifiedansignature has strong robustness and can stably predict the prognosis of COAD in different platformsand may be implicated in COAD pathogenesis and metastasis and applied clinically as a prognostic marker.

scholarly journals Integrative Analysis and Identification of an Excellent lncRNA Signature to Predict Prognosis in Patients With Colon Cancer

2021 ◽  
Author(s):  
ZhiHua Chen ◽  
YiLin Lin ◽  
SuYong Lin ◽  
YiSu Liu ◽  
Yan Zheng ◽  
...  
Keyword(s):  
Colon Cancer ◽  
Copy Number ◽  
Cox Regression ◽  
Gene Expression Omnibus ◽  
Copy Number Variations ◽  
Integrative Analysis ◽  
The Cancer Genome Atlas ◽  
Risk Scores ◽  
Cox Regression Analysis ◽  
Number Variation

Abstract Backgroud: Tumour recurrence and metastasis lead to poor prognosis in colon cancer (COAD). Therefore We aimed to identify a lncRNA signature through an integrative analysis of copy number variation, mutation and transcriptome data to predict prognosis and explore its internal mechanism.Methods: The lncRNA expression profile were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). TCGA data was randomly divided 3:1 into training and testing cohort. In the training, we performed integrated analyses of three candidate lncRNA sets that correlated with prognosis, copy number variations and mutations to establish a signature through Cox regression analysis. The robustness was determined in the testing and GEO. Results: An 11-lncRNA signature that was significantly associated with prognosis was constructed in the training (P<0.0001, HR=2.014) , And this signature was validated in the testing (P=0.0019, HR=3.374) and GSE17536 (P=0.0076, HR=1.864). The signature is significantly related to MSI status and clinical prognostic factors. The prognostic-related risk scores were significantly excellent than the other five models have been reported. Furthermore, GSEA suggested that the signature was involved in COAD development and metastasis-related pathways.Conclusions: We identified an signature has strong robustness and can stably predict the prognosis of COAD in different platforms and may be implicated in COAD pathogenesis and metastasis and applied clinically as a prognostic marker.

scholarly journals Integrative Analysis of ceRNA Network Reveals Functional lncRNAs in Intrahepatic Cholangiocarcinoma

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Dongkai Zhou ◽  
Bingqiang Gao ◽  
Qifan Yang ◽  
Yang Kong ◽  
Weilin Wang
Keyword(s):  
Intrahepatic Cholangiocarcinoma ◽  
Cox Regression ◽  
Expression Profiles ◽  
Adaptive Immune Response ◽  
Gene Expression Omnibus ◽  
Integrative Analysis ◽  
The Cancer Genome Atlas ◽  
Messenger Rnas ◽  
Cox Regression Analysis ◽  
Cerna Network

Intrahepatic cholangiocarcinoma (ICC) is the second most common lethal liver cancer worldwide. Currently, despite the latest developments in genomics and transcriptomics for ICC in recent years, the molecular pathogenesis promoting ICC remains elusive, especially in regulatory mechanisms of long noncoding RNAs (lncRNAs), which acts as competing endogenous RNA (ceRNA). In order to elucidate the molecular mechanism of functional lncRNA, expression profiles of lncRNAs, microRNAs (miRNAs), and messenger RNAs (mRNAs) were obtained from The Cancer Genome Atlas (TCGA) database and an integrative analysis of the ICC-associated ceRNA network was performed. Moreover, gene oncology enrichment analyses for the genes in the ceRNA network were implemented and novel prognostic biomarker lncRNA molecules were identified. In total, 6,738 differentially expressed mRNAs (DEmRNAs), 2,768 lncRNAs (DElncRNAs), and 173 miRNAs (DEmiRNAs) were identified in tumor tissues and adjacent nontumor ICC tissues with the thresholds of adjusted P<0.01 and logFC>2. An ICC-specific ceRNA network was successfully constructed with 30 miRNAs, 16 lncRNAs, and 80 mRNAs. Gene oncology enrichment analyses revealed that they were associated with the adaptive immune response, T cell selection and positive regulation of GTPase activity categories. Among the ceRNA networks, DElncRNAs ARHGEF26-AS1 and MIAT were found to be hub genes in underexpressed and overexpressed networks, respectively. Notably, univariate Cox regression analysis indicated that DElncRNAs HULC significantly correlated with overall survival (OS) in ICC patients (P value < 0.05), and an additional survival analysis for HULC was reconfirmed in an independent ICC cohort from the Gene Expression Omnibus (GEO) database. These findings contribute to a more comprehensive understanding of the ICC-specific ceRNA network and provide novel strategies for subsequent functional studies of lncRNAs in ICC.

scholarly journals TEAD4 is a novel independent predictor of prognosis in LGG patients with IDH mutation

2021 ◽  
Vol 16 (1) ◽  
pp. 323-335
Author(s):  
Hai-Yan Yuan ◽  
Ya-Juan Lv ◽  
Yi Chen ◽  
Dan Li ◽  
Xi Li ◽  
...  
Keyword(s):  
Cox Regression ◽  
Disease Free Survival ◽  
Predictive Biomarkers ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Lower Grade ◽  
Cox Regression Analysis ◽  
Study Results ◽  
Number Variation ◽  
Genome Atlas

Abstract TEA domain family members (TEADs) play important roles in tumor progression. Till now, the genomic status of TEADs in patients with glioma has not been well investigated. To confirm whether the genomic status of TEADs could affect the prognosis of patients with glioma, the copy number variation (CNV), mutation and expression data of glioma cohorts in The Cancer Genome Atlas, Gene Expression Omnibus and Chinese Glioma Genome Atlas were comprehensively analyzed. Results showed that TEAD CNV frequency in lower grade gliomas (LGGs) was higher than in glioblastoma multiforme (GBM). Multivariate cox regression analysis showed that TEAD4 CNV increase was significantly associated with overall survival (OS) and disease-free survival (DFS) in LGGs (OS p = 0.022, HR = 1.444, 95% CI: 1.054–1.978; DFS p = 0.005, HR = 1.485, 95% CI: 1.124–1.962), while not in GBM. Patients with TEAD4 CNV increase showed higher expression level of TEAD4 gene. In LGG patients with IDH mutation, those with higher TEAD4 expression levels had shorter OS and DFS. Integrating TEAD4 CNV increase, IDH mutations, TP53 mutation, ATRX mutation and 1p19q co-deletion would separate patients with LGG into four groups with significant differences in prognosis. These study results suggested that TEAD4 variations were independent predictive biomarkers for the prognosis in patients with LGG with IDH mutation.

Application of Copy Number Variation Sequencing in Genetic Analysis of Miscarriages in Early and Middle Pregnancy

2020 ◽  
Vol 160 (11-12) ◽  
pp. 634-642
Author(s):  
Shiqiang Luo ◽  
Xingyuan Chen ◽  
Tizhen Yan ◽  
Jiaolian Ya ◽  
Zehui Xu ◽  
...  
Keyword(s):  
Copy Number Variation ◽  
High Throughput ◽  
Copy Number ◽  
High Throughput Sequencing ◽  
Chromosomal Abnormalities ◽  
Pregnancy Termination ◽  
Mendelian Inheritance ◽  
Copy Number Variations ◽  
Abnormal Chromosome ◽  
Number Variation

High-throughput sequencing based on copy number variation (CNV-seq) is commonly used to detect chromosomal abnormalities. This study identifies chromosomal abnormalities in aborted embryos/fetuses in early and middle pregnancy and explores the application value of CNV-seq in determining the causes of pregnancy termination. High-throughput sequencing was used to detect chromosome copy number variations (CNVs) in 116 aborted embryos in early and middle pregnancy. The detection data were compared with the Database of Genomic Variants (DGV), the Database of Chromosomal Imbalance and Phenotype in Humans using Ensemble Resources (DECIPHER), and the Online Mendelian Inheritance in Man (OMIM) database to determine the CNV type and the clinical significance. High-throughput sequencing results were successfully obtained in 109 out of 116 specimens, with a detection success rate of 93.97%. In brief, there were 64 cases with abnormal chromosome numbers and 23 cases with CNVs, in which 10 were pathogenic mutations and 13 were variants of uncertain significance. An abnormal chromosome number is the most important reason for embryo termination in early and middle pregnancy, followed by pathogenic chromosome CNVs. CNV-seq can quickly and accurately detect chromosome abnormalities and identify microdeletion and microduplication CNVs that cannot be detected by conventional chromosome analysis, which is convenient and efficient for genetic etiology diagnosis in miscarriage.

scholarly journals Carcinosarcoma of the prostate: case report with molecular and histological characterization

2018 ◽  
Vol 33 (4) ◽  
pp. 540-544 ◽  
Author(s):  
Samanta Salvi ◽  
Valentina Casadio ◽  
Filippo Martignano ◽  
Giorgia Gurioli ◽  
Maria Maddalena Tumedei ◽  
...  
Keyword(s):  
Copy Number Variation ◽  
Copy Number ◽  
Copy Number Variations ◽  
Molecular Characteristics ◽  
Glutathione S Transferase ◽  
Molecular Alterations ◽  
Positive Expression ◽  
Molecular Pattern ◽  
Programmed Death ◽  
Number Variation

Background: We report a case of prostatic carcinosarcoma, a rare variant of prostatic cancer, which is composed of a mixture of epithelial and mesenchymal components with a generally poor outcome. Aims and methods: We aim to identify molecular alterations, in particular copy number variations of AR and c -MYC genes, methylation and expression of glutathione S-transferase P1 (GSTP1), programmed death-ligand 1 (PD-L1), AR, and phosphorylated AR expression. Results: We found a distinct molecular pattern between adenocarcinoma and carcinosarcoma, which was characterized by high AR copy number variation gain; positive expression of PD-L1, AR, and phosphorylated AR; low espression of GSTP1 in epithelial component. The sarcomatoid component had a lower gain of the AR gene, and no expression of PD-L1, AR, phosphorylated AR, or GSTP1. Both components had a gain of c-MYC copy number variation. Conclusions: Our findings suggest that carcinosarcoma has specific molecular characteristics that could be indicative for early diagnosis and treatment selection.

scholarly journals Identification and validation of immunotypes for clear cell RCC prognosis

2021 ◽  
Author(s):  
Zhaolin Yang ◽  
Jiale Zhou ◽  
Yizheng Xue ◽  
Yu Zhang ◽  
Kaijun Zhou ◽  
...  
Keyword(s):  
Overall Survival ◽  
Cox Regression ◽  
Clear Cell ◽  
Survival Rates ◽  
Prognostic Index ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Cox Regression Analysis ◽  
Immune Genes ◽  
Metastasis Model

Abstract Purpose To develop an immunotype-based prognostic model for predicting the overall survival (OS) of patients with clear cell renal carcinoma (ccRCC). We explored novel immunotypes of patients with ccRCC, particularly those associated with overall survival. A risk-metastasis model was constructed by integrating the immunotypes with immune genes and used to test the accuracy of the immunotype model. Patients and Methods Patient cohort data were obtained from The Cancer Genome Atlas (TCGA) database, Gene Expression Omnibus (GEO) database, Renji database, and Surveillance, Epidemiology, and End Results (SEER) database. We employed the R software to select 3 immune cells and construct an immunotype-based prediction model. Immune genes selected using random Forest Algorithm were validated by immunohistochemistry (IHC). The H&L risk-metastasis model was constructed to assess the accuracy of the immunotype model through Multivariate COX regression analysis. Result Patients with ccRCC were categorized into immunotype H subgroup and immunotype L subgroup based on the overall survival rates. The immunotypes were found to be the independent prognostic index for ccRCC prognosis. As such, we constructed a new immunotypes-based SSIGN model. Three immune genes associated with difference between immunotype H and L were identified. An H&L risk-metastasis model was constructed to evaluate the accuracy of the immunotype model. Compared to the W-Risk-metastasis model which did not incorporate immunotypes, the H&L risk-metastasis model was more precise in predicting the survival of ccRCC patients. Conclusion The established immunotype model can effectively predict the survival of ccRCC patients. Except for mast cells, T cells and macrophages are positively associated with the overall survival of patients. The three immune genes identified, herein, can predict the survival rate of ccRCC patients, and expression of these immune genes is strongly linked to poor survival. The new SSIGN model provides an accurate tool for predicting the survival of ccRCC patients. H&L risk-metastasis model can effectively predict the risk of tumor metastasis.

scholarly journals Identification of prognostic values defined by copy number variation, mRNA and protein expression of LANCL2 and EGFR in IDH1/2-wild-type glioblastoma

2021 ◽  
Author(s):  
Hua-fu Zhao ◽  
Xiu-ming Zhou ◽  
Jing Wang ◽  
Fan-fan Chen ◽  
Chang-peng Wu ◽  
...  
Keyword(s):  
Protein Expression ◽  
Copy Number ◽  
Intracellular Localization ◽  
Methylation Status ◽  
Growth Factor Receptor ◽  
Copy Number Variations ◽  
Wild Type ◽  
Number Variation ◽  
Newly Diagnosed Glioblastoma ◽  
Mrna And Protein Expression

Abstract Background Epidermal growth factor receptor (EGFR) and lanthionine synthetase C-like 2 (LanCL2) genes locate in the same amplicon, and co-amplification of EGFR and LANCL2 is frequent in glioblastoma. However, the prognostic value of LANCL2 and EGFR co-amplification, and their mRNA and protein expression in glioblastoma remain unclear yet. Methods This study analyzed the prognostic values of the copy number variations (CNVs), mRNA and protein expression of LANCL2 and EGFR in glioblastoma specimens from TCGA database or our tumor banks. Results The amplification of LANCL2 or EGFR, and their co-amplification were frequent in glioblastoma of TCGA database and our tumor banks. CNVs of LANCL2 or EGFR were significantly correlated with IDH1/2 mutation but not MGMT promoter methylation status. LANCL2 or EGFR amplification, and their co-amplification were significantly associated with reduced overall survival (OS) of glioblastoma patients, rather than IDH1/2-wild-type glioblastoma patients. mRNA and protein overexpression of LANCL2 and EGFR was also frequently found in glioblastoma. LANCL2, rather than EGFR, was overexpressed in relapsing glioblastoma, compared with newly diagnosed glioblastoma. However, mRNA or protein expression of EGFR and LANCL2 was not significantly correlated with OS of glioblastoma patients. In addition, the intracellular localization of LanCL2, not EGFR, was associated with the grade of gliomas. Conclusions Taken together, amplification and mRNA overexpression of LANCL2 and EGFR, and their co-amplification and co-expression were frequent in glioblastoma patients. Our findings suggest that CNVs of LANCL2 and EGFR were the independent diagnostic and prognostic biomarkers for histological glioblastoma patients, but not for IDH1/2-wild-type glioblastoma patients.

scholarly journals A Novel Six-mRNA Signature Predicts Survival of Patients With Glioblastoma Multiforme

2021 ◽  
Vol 12 ◽  
Author(s):  
Zhentao Liu ◽  
Hao Zhang ◽  
Hongkang Hu ◽  
Zheng Cai ◽  
Chengyin Lu ◽  
...  
Keyword(s):  
Glioblastoma Multiforme ◽  
Cox Regression ◽  
Methylation Status ◽  
Survival Rates ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Survival Prediction ◽  
Lasso Regression ◽  
Cox Regression Analysis ◽  
Score Model

Glioblastoma multiforme (GBM) is a devastating brain tumor and displays divergent clinical outcomes due to its high degree of heterogeneity. Reliable prognostic biomarkers are urgently needed for improving risk stratification and survival prediction. In this study, we analyzed genome-wide mRNA profiles in GBM patients derived from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases to identify mRNA-based signatures for GBM prognosis with survival analysis. Univariate Cox regression model was used to evaluate the relationship between the expression of mRNA and the prognosis of patients with GBM. We established a risk score model that consisted of six mRNA (AACS, STEAP1, STEAP2, G6PC3, FKBP9, and LOXL1) by the LASSO regression method. The six-mRNA signature could divide patients into a high-risk and a low-risk group with significantly different survival rates in training and test sets. Multivariate Cox regression analysis confirmed that it was an independent prognostic factor in GBM patients, and it has a superior predictive power as compared with age, IDH mutation status, MGMT, and G-CIMP methylation status. By combining this signature and clinical risk factors, a nomogram can be established to predict 1-, 2-, and 3-year OS in GBM patients with relatively high accuracy.

Identification and validation of a hub gene prognostic index for hepatocellular carcinoma

2021 ◽  
Author(s):  
Q Shi ◽  
Z Meng ◽  
XX Tian ◽  
YF Wang ◽  
WH Wang
Keyword(s):  
Hepatocellular Carcinoma ◽  
Cox Regression ◽  
Prognostic Index ◽  
Interaction Network ◽  
Gene Signature ◽  
Gene Expression Omnibus ◽  
Cox Regression Analysis ◽  
Protein Protein Interaction ◽  
Normal Tissues ◽  
Significant Independent Prognostic Factor

Aims: We aim to provide new insights into the mechanisms of hepatocellular carcinoma (HCC) and identify key genes as biomarkers for the prognosis of HCC. Materials & methods: Differentially expressed genes between HCC tissues and normal tissues were identified via the Gene Expression Omnibus tool. The top ten hub genes screened by the degree of the protein nodes in the protein–protein interaction network also showed significant associations with overall survival in HCC patients. Results: A prognostic model containing a five-gene signature was constructed to predict the prognosis of HCC via multivariate Cox regression analysis. Conclusion: This study identified a novel five-gene signature ( CDK1, CCNB1, CCNB2, BUB1 and KIF11) as a significant independent prognostic factor.

Hadoop-CNV-RF: a clinically validated and scalable copy number variation detection tool for next-generation sequencing data

2020 ◽  
Author(s):  
Getiria Onsongo ◽  
Ham Ching Lam ◽  
Matthew Bower ◽  
Bharat Thyagarajan
Keyword(s):  
Copy Number ◽  
Copy Number Variations ◽  
Next Generation Sequencing Data ◽  
Sequencing Data ◽  
Large Gene ◽  
Data Framework ◽  
Number Variation ◽  
Targeted Capture ◽  
Objective Detection ◽  
Gene Panels

Abstract Objective : Detection of small copy number variations (CNVs) in clinically relevant genes is routinely being used to aid diagnosis. We recently developed a tool, CNV-RF , capable of detecting small clinically relevant CNVs. CNV-RF was designed for small gene panels and did not scale well to large gene panels. On large gene panels, CNV-RF routinely failed due to memory limitations. When successful, it took about 2 days to complete a single analysis, making it impractical for routinely analyzing large gene panels. We need a reliable tool capable of detecting CNVs in the clinic that scales well to large gene panels. Results : We have developed Hadoop-CNV-RF, a scalable implementation of CNV-RF . Hadoop-CNV-RF is a freely available tool capable of rapidly analyzing large gene panels. It takes advantage of Hadoop, a big data framework developed to analyze large amounts of data. Preliminary results show it reduces analysis time from about 2 days to less than 4 hours and can seamlessly scale to large gene panels. Hadoop-CNV-RF has been clinically validated for targeted capture data and is currently being used in a CLIA molecular diagnostics laboratory. Its availability and usage instructions are publicly available at: https://github.com/getiria-onsongo/hadoop-cnvrf-public .

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