Genomic landscape and mutational impacts of recurrently mutated genes in cancers

Liu, Baolin; Hu, Feifei; Zhang, Qiong; Hu, Hui; Zheng, Yuxin; Tang, Qin; Guo, An‐Yuan

doi:10.1002/mgg3.458

Cited by 15 publications

(9 citation statements)

References 48 publications

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“…Annotation of acquired SNVs revealed a number of common mutated genes across tumor samples, containing several known cancer-related genes (Figure 1B). Several members of mucin (MUC16, MUC3A, MUC6, and MUC4) were among the most frequently mutated genes, which is consistence with previous reports (Chang et al, 2006;Pereira et al, 2016;Liu et al, 2018;Pareja et al, 2019). Other noteworthy genes included DSPP, PER3, MTCH2, and KRT18, all have been reported with important roles in tumor formation and development.…”

Section: Landscape Of Cca Genome and Transcriptomesupporting

confidence: 87%

Genomic and Transcriptomic Landscape of Tumor Clonal Evolution in Cholangiocarcinoma

et al. 2020

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Cholangiocarcinoma remained a severe threat to human health. Deciphering the genomic and/or transcriptomic profiles of tumor has been proved to be a promising strategy for exploring the mechanism of tumorigenesis and development, which could also provide valuable insights into Cholangiocarcinoma. However, little knowledge has been obtained regarding to how the alteration among different omics levels is connected. Here, using whole exome sequencing and transcriptome sequencing, we performed a thorough evaluation for the landscape of genome and transcriptome in cholangiocarcinoma and illustrate the alteration of tumor on different biological levels. Meanwhile, we also identified the clonal structure of each included tumor sample and discovered different clonal evolution patterns related to patients' survival. Furthermore, we extracted subnetworks that were greatly influenced by tumor clonal/subclonal mutations or transcriptome change. The topology relationship between genes affected by genomic/transcriptomic changes in biological interaction networks revealed that alteration of genome and transcriptome was highly correlated, and somatic mutations located on important genes might affect the expression of numerous genes in close range.

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Section: Landscape Of Cca Genome and Transcriptomesupporting

confidence: 87%

Genomic and Transcriptomic Landscape of Tumor Clonal Evolution in Cholangiocarcinoma

et al. 2020

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“…In addition, in KEGG enrichment results, ECM also regulated intercellular communication, cell connectivity plasticity, and cell adhesion molecules interacting with various cytokines/chemokines or growth factors [ 29 , 30 ]. There were 34% of the genes in the ECM-receptor interaction pathway mutated repeatedly in cancer [ 31 ]. Focal adhesion kinase (FAK) is highly expressed in thymic epithelial tumors and can be used as an independent prognostic biomarker [ 32 ].…”

Section: Discussionmentioning

confidence: 99%

Identification of Molecular Characteristics and New Prognostic Targets for Thymoma by Multiomics Analysis

Liu

Zhang

Zhao

et al. 2021

BioMed Research International

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Background. Thymoma is a heterogeneous tumor originated from thymic epithelial cells. The molecular mechanism of thymoma remains unclear. Methods. The expression profile, methylation, and mutation data of thymoma were obtained from TCGA database. The coexpression network was constructed using the variance of gene expression through WGCNA. Enrichment analysis using clusterProfiler R package and overall survival (OS) analysis by Kaplan-Meier method were carried out for the intersection of differential expression genes (DEGs) screened by limma R package and important module genes. PPI network was constructed based on STRING database for genes with significant impact on survival. The impact of key genes on the prognosis of thymoma was evaluated by ROC curve and Cox regression model. Finally, the immune cell infiltration, methylation modification, and gene mutation were calculated. Results. We obtained eleven coexpression modules, and three of them were higher positively correlated with thymoma. DEGs in these three modules mainly involved in MAPK cascade and PPAR pathway. LIPE, MYH6, ACTG2, KLF4, SULT4A1, and TF were identified as key genes through the PPI network. AUC values of LIPE were the highest. Cox regression analysis showed that low expression of LIPE was a prognostic risk factor for thymoma. In addition, there was a high correlation between LIPE and T cells. Importantly, the expression of LIPE was modified by methylation. Among all the mutated genes, GTF2I had the highest mutation frequency. Conclusion. These results suggested that the molecular mechanism of thymoma may be related to immune inflammation. LIPE may be the key genes affecting prognosis of thymoma. Our findings will help to elucidate the pathogenesis and therapeutic targets of thymoma.

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“…TNN gene encodes proteins involved in cell migration [33]. In tumors it stimulates angiogenesis of endothelial cells.…”

Section: Discussionmentioning

confidence: 99%

DEGs and Biological Process Profiling to screen the novel biomarkers associated with both Uterine Leiomyomas and Uterine leiomyosarcomas

Upadhyay

Gupta

Dubey

et al. 2021

Preprint

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Background Uterine Leiomyomas (ULM) or Uterine fibroid are benign lesion of unspecified aetiology and still there is dearth of prognostic biomarkers for diagnosis. The aim of this present study is to explore the novel biomarkers to be associated with Uterine Leiomyomas (ULM) and Uterine leiomyosarcomas (ULMS) that were responsible for their pathogenicity. Methods The microarray dataset (GSEID:GSE64763) was retrieved from the Gene Expression Omnibus database. Data preprocessing and differential gene expression analysis was performed. Principal Component Analysis (PCA) plot and heat map for ULM and ULMS were constructed for respective differentially expressed genes. The DEGs were further intersected to find the common DEGs in ULM and ULMS. Based upon STRING v 10.5, protein- protein interaction network was constructed. Further, Gene Ontology (GO) and KEGG pathway enrichment analysis were also performed to dissect out possible function and pathways. Results A total of 50 significant DEGs for ULM while 321 DEGs for ULMS have been identified with their official gene symbol. Between ULM and ULMS, total 14 common DEGs were identified of which 8 were up-regulated while 6 were down-regulated. Comparison of DEGs list with annotated gene list obtained from OMIM and Gene Cards, lead to identification of only 3 known disease genes (RAD51B, ESR1 and PDGFRA) while SHOX2, TNN and COL11A1 genes were found to be novel biomarkers in ULM and ULMS both. Gene ontology and KEGG pathway enrichment analysis of common novel and known candidate genes led to the identification of several important processes and pathways like ECM receptor interactions and Focal adhesion. Conclusions SHOX2, TNN and COL11A1 are the novel biomarkers related to both ULM and ULMS disease and have been found to be associated with ECM receptor interactions and Focal adhesion like pathways and hence can serve as novel diagnostic as well as therapeutic targets.

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Genomic landscape and mutational impacts of recurrently mutated genes in cancers

Cited by 15 publications

References 48 publications

Genomic and Transcriptomic Landscape of Tumor Clonal Evolution in Cholangiocarcinoma

Genomic and Transcriptomic Landscape of Tumor Clonal Evolution in Cholangiocarcinoma

Identification of Molecular Characteristics and New Prognostic Targets for Thymoma by Multiomics Analysis

DEGs and Biological Process Profiling to screen the novel biomarkers associated with both Uterine Leiomyomas and Uterine leiomyosarcomas

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