scholarly journals Characterizing the Copy Number Variation of Non-Coding RNAs Reveals Potential Therapeutic Targets and Prognostic Markers of LUSC

2021 ◽  
Vol 12 ◽  
Author(s):  
Jinfeng Ning ◽  
Fengjiao Wang ◽  
Kaibin Zhu ◽  
Binxi Li ◽  
Qing Shu ◽  
...  
Keyword(s):  
Copy Number Variation ◽  
Copy Number ◽  
High Throughput Sequencing ◽  
Prognostic Markers ◽  
Therapeutic Targets ◽  
Lung Squamous Cell Carcinoma ◽  
Integrated Analysis ◽  
Clinical Prognosis ◽  
Number Variation ◽  
Non Coding Rnas

Lung squamous cell carcinoma (LUSC) has a poor clinical prognosis and a lack of available targeted therapies. Therefore, there is an urgent need to identify novel prognostic markers and therapeutic targets to assist in the diagnosis and treatment of LUSC. With the development of high-throughput sequencing technology, integrated analysis of multi-omics data will provide annotation of pathogenic non-coding variants and the role of non-coding sequence variants in cancers. Here, we integrated RNA-seq profiles and copy number variation (CNV) data to study the effects of non-coding variations on gene regulatory network. Furthermore, the 372 long non-coding RNAs (lncRNA) regulated by CNV were used as candidate genes, which could be used as biomarkers for clinical application. Nine lncRNAs including LINC00896, MCM8-AS1, LINC01251, LNX1-AS1, GPRC5D-AS1, CTD-2350J17.1, LINC01133, LINC01121, and AC073130.1 were recognized as prognostic markers for LUSC. By exploring the association of the prognosis-related lncRNAs (pr-lncRNAs) with immune cell infiltration, GPRC5D-AS1 and LINC01133 were highlighted as markers of the immunosuppressive microenvironment. Additionally, the cascade response of pr-lncRNA-CNV-mRNA-physiological functions was revealed. Taken together, the identification of prognostic markers and carcinogenic regulatory mechanisms will contribute to the individualized treatment for LUSC and promote the development of precision medicine.

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 Integrative analysis of prognostic long non-coding RNAs with copy number variation in bladder cancer

2021 ◽  
Vol 22 (8) ◽  
pp. 664-681
Author(s):  
Wenwen Zhong ◽  
Dejuan Wang ◽  
Bing Yao ◽  
Xiaoxia Chen ◽  
Zhongyang Wang ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Copy Number Variation ◽  
Copy Number ◽  
Integrative Analysis ◽  
Number Variation ◽  
Non Coding Rnas

scholarly journals Integrated Analysis of Copy Number Variation and Genome-Wide Expression Profiling in Colorectal Cancer Tissues

PLoS ONE ◽  
2014 ◽  
Vol 9 (4) ◽  
pp. e92553 ◽  
Author(s):  
Nur Zarina Ali Hassan ◽  
Norfilza Mohd Mokhtar ◽  
Teow Kok Sin ◽  
Isa Mohamed Rose ◽  
Ismail Sagap ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Copy Number Variation ◽  
Expression Profiling ◽  
Copy Number ◽  
Integrated Analysis ◽  
Genome Wide ◽  
Number Variation ◽  
Cancer Tissues ◽  
Genome Wide Expression

scholarly journals Multi-omics analysis based on integrated genomics, epigenomics and transcriptomics in pancreatic cancer

Epigenomics ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 507-524 ◽  
Author(s):  
Lingming Kong ◽  
Peng Liu ◽  
Mingjun Zheng ◽  
Busheng Xue ◽  
Keke Liang ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Copy Number Variation ◽  
Copy Number ◽  
Integrated Analysis ◽  
Molecular Subgroups ◽  
Dna Copy Number ◽  
Dna Copy Number Variation ◽  
New Genes ◽  
Omics Analysis ◽  
Number Variation

Aim: Integrated analysis of genomics, epigenomics, transcriptomics and clinical information contributes to identify specific molecular subgroups and find novel biomarkers for pancreatic cancer. Materials & methods: The DNA copy number variation, the simple nucleotide variation, methylation and mRNA data of pancreatic cancer patients were obtained from The Cancer Genome Atlas. Four molecular subgroups (iC1, iC2, iC3 and iC4) of pancreatic cancer were identified by integrating analysis. Results: The iC1 subgroup harbors better prognosis, higher immune score, lesser DNA copy number variation mutations and better genomic stability compared with iC2, iC3 and iC4 subgroups. Three new genes ( GRAP2, ICAM3 and A2ML1) correlated with prognosis were identified. Conclusion: Integrated multi-omics analysis provides fresh insight into molecular classification of pancreatic cancer, which may help discover new prognostic biomarkers and reveal the underlying mechanism of pancreatic cancer.

scholarly journals Copy number variation in female infertility and candidate gene screening for common infertility-related diseases

2021 ◽  
Vol 103 (3) ◽  
pp. 73-79
Author(s):  
Zhainagul Kozhabek ◽  
◽  
Min Pang ◽  
Qiongzhen Zhao ◽  
Jiangyan Yi ◽  
...  
Keyword(s):  
Copy Number Variation ◽  
High Frequency ◽  
Copy Number ◽  
High Throughput Sequencing ◽  
Female Infertility ◽  
Functional Genes ◽  
Genome Copy ◽  
Sequencing Technologies ◽  
Number Variation ◽  
Genome Copy Number

To investigate the correlation between the genome copy number variation and female infertility we collected 3962 female infertility samples and analyzed copy number variation (CNV) using high-throughput sequencing technologies. In this study 269 CNVs were found in 246 samples, 17 of which were new CNVs. The occurrence of CNVs was mostly found in X chromosome, and some candidate genes related to female infertility were screened. We also found some high frequency CNVs, which contain important functional genes. This study filled the blank of CNV research on female infertility and discovered the characteristics of CNV (CNV preference, recurrent CNV), which provided genetic reference for female infertility.

scholarly journals Integrated Analysis of Copy Number Variation, Microsatellite Instability, and Tumor Mutation Burden Identifies an 11-Gene Signature Predicting Survival in Breast Cancer

2021 ◽  
Vol 9 ◽  
Author(s):  
Xin Jin ◽  
Junfeng Yan ◽  
Chuanzhi Chen ◽  
Yi Chen ◽  
Wen-Kuan Huang
Keyword(s):  
Breast Cancer ◽  
Copy Number Variation ◽  
Genetic Variants ◽  
Copy Number ◽  
Risk Model ◽  
Gene Signature ◽  
Integrated Analysis ◽  
Tumor Mutation Burden ◽  
Mutation Burden ◽  
Number Variation

Genetic variants such as copy number variation (CNV), microsatellite instability (MSI), and tumor mutation burden (TMB) have been reported to associate with the immune microenvironment and prognosis of patients with breast cancer. In this study, we performed an integrated analysis of CNV, MSI, and TMB data obtained from The Cancer Genome Atlas, thereby generating two genetic variants-related subgroups. We characterized the differences between the two subgroups in terms of prognosis, MSI burden, TMB, CNV, mutation landscape, and immune landscape. We found that cluster 2 was marked by a worse prognosis and lower TMB. According to these groupings, we identified 130 differentially expressed genes, which were subjected to univariate and least absolute shrinkage and selection operator-penalized multivariate modeling. Consequently, we constructed an 11-gene signature risk model called the genomic variation-related prognostic risk model (GVRM). Using ROC analysis and a calibration plot, we estimated the prognostic prediction of this GVRM. We confirmed the predictive efficiency of this GVRM by validating it in another independent International Cancer Genome Consortium cohort. Our results conclude that an 11-gene signature developed by integrated analysis of CNV, MSI, and TMB has a high potential to predict breast cancer prognosis, which provided a strong rationale for further investigating molecular mechanisms and guiding clinical decision-making in breast cancer.

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