SNP mutation‐related genes in breast cancer for monitoring and prognosis of patients: A study based on the TCGA database

Gao, Chundi; Zhuang, Jing; Zhou, Chao; Li, Huayao; Liu, Cun; Liu, Lijuan; Feng, Fubin; Liu, Ruijuan; Sun, Changgang

doi:10.1002/cam4.2065

Cited by 35 publications

(33 citation statements)

References 37 publications

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“…Other two survival-related genes RAD21 and CKS2 are microRNA protein coding host genes (van't Veer et al, 2002). In addition, CDH1 is from CD molecule (Gao et al, 2019). Of the 68 cancer survival-related gene sets in Varn…”

Section: System Analysis Survival-related Biomarker Gene Sets In 12 Dmentioning

confidence: 99%

Detecting Cancer Survival Related Gene Markers Based on Rectified Factor Network

Liu

Wang

et al. 2020

Front. Bioeng. Biotechnol.

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Detecting gene sets that serve as biomarkers for differentiating patient survival groups may help diagnose diseases robustly and develop multi-gene targeted therapies. However, due to the exponential growth of search space imposed by gene combinations, the performance of existing methods is still far from satisfactory. In this study, we developed a new method called BISG (BIclustering based Survival-related Gene sets detection) based on a rectified factor network (RFN) model, which allows efficiently biclustering gene subsets. By correlating genes in each significant bicluster with patient survival outcomes using a log-rank test and multi-sampling strategy, multiple survival-related gene sets can be detected. We applied BISG on three different cancer types, and the resulting gene sets were tested as biomarkers for survival analyses. Secondly, we systematically analyzed 12 different cancer datasets. Our analysis shows that the genes in all the survival-related gene sets are mainly from five gene families: microRNA protein coding host genes, zinc fingers C2H2-type, solute carriers, CD (cluster of differentiation) molecules, and ankyrin repeat domain containing genes. Moreover, we found that they are mainly enriched in heme metabolism, apoptosis, hypoxia and inflammatory response-related pathways. We compared BISG with two other methods, GSAS and IPSOV. Results show that BISG can better differentiate patient survival groups in different datasets. The identified biomarkers suggested by our study provide useful hypotheses for further investigation. BISG is publicly available with open source at https:// github.com/LingtaoSu/BISG.

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Section: System Analysis Survival-related Biomarker Gene Sets In 12 Dmentioning

confidence: 99%

Detecting Cancer Survival Related Gene Markers Based on Rectified Factor Network

Liu

Wang

et al. 2020

Front. Bioeng. Biotechnol.

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“…Single-nucleotide polymorphisms (SNPs) are the most widespread source of genetic variation between individuals 1 . A single base substitution can induce profound changes in a protein’s structure, altering its enzymatic function 2 , cellular trafficking 3 , or solubility 4 .…”

Section: Introductionmentioning

confidence: 99%

Competitive SNP-LAMP probes for rapid and robust single-nucleotide polymorphism detection

Romero

2021

Preprint

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Single-nucleotide polymorphisms (SNPs) are the most common source of genetic variation between individuals and have implications in human disease, pathogen drug resistance, and agriculture. SNPs are typically detected using DNA sequencing, which requires advanced sample preparation and instrumentation, and thus cannot be deployed for on-site testing or in low-resource settings. In this work we have developed a simple and robust assay to rapidly detect SNPs in nucleic acid samples. Our approach combines LAMP-based target amplification with fluorescent probes to detect SNPs with high specificity in a one-pot reaction format. A competitive "sink" strand preferentially binds to off-target products and shifts the free energy landscape to favor specific activation by SNP products. We demonstrated the broad utility and reliability of our SNP-LAMP method by detecting three distinct SNPs across the human genome. We also designed an assay to rapidly detect highly transmissible SARS-CoV-2 variants. This work demonstrates that competitive SNP-LAMP is a powerful and universal method that could be applied in point-of-care settings to detect any target SNP with high specificity and sensitivity.

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“…Moreover, the TCGA database can be used to extract data to perform high‐throughput genomic analyses and better understand the genetic basis of a particular disease through the genome sequencing and bioinformatics analysis of gene mutations that may be responsible for the occurrence of cancer 11 . Based on the TCGA database, Gao et al investigated SNP‐mutated genes for the diagnosis of breast cancer 12 . Previously, several studies have investigated the possible molecular mechanisms of the initiation and development of LUAD in order to identify novel therapeutic targets.…”

Section: Introductionmentioning

confidence: 99%

Screening and identification of biomarkers associated with the diagnosis and prognosis of lung adenocarcinoma

Wang

Zhang²,

Chen³

et al. 2020

Clinical Laboratory Analysis

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Background In this study, we aimed to identify the pathogenesis and prognostic biomarkers of lung adenocarcinoma (LUAD). Methods Differentially expressed mRNAs (DEmRNAs) and single nucleotide polymorphism (SNP) mutant genes were screened. In addition, enrichment and protein‐protein interaction (PPI) network analyses of the SNP‐mutated genes were performed. Thereafter, the correlation between gene mutation and expression was analyzed. Finally, the mutated genes associated with LUAD prognosis were validated on the basis of The Cancer Genome Atlas (TCGA) database. Results A total of 2502 DEmRNAs were initially screened in this study. We identified 756 SNP‐mutated genes from more than 30 cases. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis revealed that the mutated genes involved in LUAD were mainly associated with the ECM‐receptor interaction, focal adhesion, and calcium signaling pathways. Tumor protein p53 (TP53) and neurexin 1 (NRXN1) with the higher degree were chosen as the hub genes in the PPI network. In addition, the correlation analysis revealed six genes, including assembly factor for spindle microtubules (ASPM), centromere protein F (CENPF), contactin 3 (CNTN3), catenin delta 2 (CTNND2), PKHD1 like 1 (PKHD1L1), and semaphorin 6D (SEMA6D), and three SNP mutations at ASPM rs368020495, CENPF rs762653487, and PKHD1L1 rs768349010 sites that were found to be associated with LUAD prognosis. Further validation showed that among the aforementioned six mutated genes, CENPF was upregulated and SEMA6D was downregulated. Conclusion CENPF, SEMA6D, TP53, and NRXN1 were found to be closely associated with the development of LUAD.

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SNP mutation‐related genes in breast cancer for monitoring and prognosis of patients: A study based on the TCGA database

Cited by 35 publications

References 37 publications

Detecting Cancer Survival Related Gene Markers Based on Rectified Factor Network

Detecting Cancer Survival Related Gene Markers Based on Rectified Factor Network

Competitive SNP-LAMP probes for rapid and robust single-nucleotide polymorphism detection

Screening and identification of biomarkers associated with the diagnosis and prognosis of lung adenocarcinoma

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