Germline genetic variants were interactively associated with somatic alterations in gastric cancer

Zhang, Xu; Wang, Yuzhuo; Tian, Tian; Zhou, Gangqiao; Jin, Guangfu

doi:10.1002/cam4.1612

Cited by 4 publications

(2 citation statements)

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“…In lung cancer, SNVsassociatewith somatic driver gene mutations or copy number alterations in ARID1A, CDKN2Aand genes linked to the cell cycle and MAPK pathways [14](Table 2). In gastric cancer, several germline SNVs associatewith somatic alterations in SOS1and other genes in the PDGF and DNA mismatch repair pathways [50](Table 2). In a cohort of ER-positive breast cancer patients, GxM analysis found associations of two SNVs, rs252913and rs331499(5q11.2)withsomatic PIK3CAvariants(3q26.32) [51](Table 2), which results in MAP3K1gene overexpression.…”

Section: Germline Variant By Somatic Mutation (Gxm) Associationin Transmentioning

confidence: 99%

Germline Variants Impact Somatic Events during Tumorigenesis

2019

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Cancer is characterized by diverse genetic alterations in both germline and somatic genomes that disrupt normal biology and providea selective advantage to cells during tumorigenesis. Germline and somatic genomes have been extensively studied independently, leading to numerous biological insights. Analyses integrating data from both genomes have identified genetic variants impacting somatic events in tumors, including hotspot driver mutations. Interactions among specific germline variants and somatic eventsinfluence cancer subtypes, treatment response, and clinical outcomes. Investigation of these complex interactions is increasing our understanding of aberrant pathwaysin tumorsthat may uncover novel therapeutic targets. Here, we review the literature describing the role germline genetic variantsplay in promoting selection of and generation of specific mutations during tumorigenesis.

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Section: Germline Variant By Somatic Mutation (Gxm) Associationin Transmentioning

confidence: 99%

Germline Variants Impact Somatic Events during Tumorigenesis

2019

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“…Accumulating evidence has revealed that both genetic alterations ( Zhang et al, 2018 ) and epigenetic alterations ( Fu, 2015 ) play key regulatory roles in GC pathogenesis. For example, the molecular subtypes of GC were classified by Kim Y. et al (2017) through the comprehensive analysis of genetic alterations in Korean GC patients, which provided a critical starting point for the design of more appropriate clinical trials.…”

Section: Introductionmentioning

confidence: 99%

Systematic Analyses of the Differentially Expressed Alternative Splicing Events in Gastric Cancer and Its Clinical Significance

Lin

Zhang

et al. 2020

Front. Genet.

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Accumulation of evidence has indicated a close relationship between alternative splicing (AS) and gastric cancer (GC), whereas systematic analyses of the differentially expressed AS events (DEAS) between GC and normal tissues are lacking. RNA-Seq data and the corresponding clinical information were downloaded from TCGA GC cohort. The percent spliced-in (PSI) value calculated in the GC tissues and normal tissues was employed to quantify the DEAS. Further, survival-associated DEAS and DEAS signatures were identified by univariate and multivariate cox regression analyses. To evaluate the association between DEAS and patients’ clinical features, Kaplan-Meier analysis, receiver operator characteristic (ROC) curve, Cox proportional regression and nomograms incorporating the DEAS signatures were performed. DEAS and their splicing networks were finally analyzed by bioinformatics methods. In addition, we use the method of random grouping to divide the samples into the training group and the test group. The final results of the two groups are consistent. After strict filtering, a total of 44,935 AS events were identified, among which 11,141 DEAS were preliminarily screened from 5032 genes. A total of 454 DEAS was associated with OS, and 872 DEAS were associated with DFS. The final prognostic signatures were constructed from the survival-associated DEAS with an area under the receiver operating characteristic (ROC) curve (AUC) greater than 0.6. Only ES in ABI1 was simultaneously associated with OS and DFS. Finally, we identified the splicing correlation network between the prognostic splicing factors (SF) and DEAS in GC. Our study provided a systematic portrait of survival-associated DEAS in GC and uncovered splicing networks that are valuable in deciphering the underlying mechanisms of AS in GC.

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