scholarly journals In silico analysis and verification of S100 gene expression in gastric cancer

BMC Cancer ◽  
2008 ◽  
Vol 8 (1) ◽  
Author(s):  
Ji Liu ◽  
Xue Li ◽  
Guang-Long Dong ◽  
Hong-Wei Zhang ◽  
Dong-Li Chen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
In Silico ◽  
In Silico Analysis ◽  
Silico Analysis ◽  
S100 Gene

scholarly journals Characterization and risk association of polymorphisms in Aurora kinases A, B and C with genetic susceptibility to gastric cancer development

BMC Cancer ◽  
2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Aner Mesic ◽  
Marija Rogar ◽  
Petra Hudler ◽  
Nurija Bilalovic ◽  
Izet Eminovic ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Transcription Factors ◽  
In Silico ◽  
In Silico Analysis ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
Mitotic Kinases ◽  
Genes Encoding ◽  
Silico Analysis ◽  
Control Study

Abstract Background Single nucleotide polymorphisms (SNPs) in genes encoding mitotic kinases could influence development and progression of gastric cancer (GC). Methods Case-control study of nine SNPs in mitotic genes was conducted using qPCR. The study included 116 GC patients and 203 controls. In silico analysis was performed to evaluate the effects of polymorphisms on transcription factors binding sites. Results The AURKA rs1047972 genotypes (CT vs. CC: OR, 1.96; 95% CI, 1.05–3.65; p = 0.033; CC + TT vs. CT: OR, 1.94; 95% CI, 1.04–3.60; p = 0.036) and rs911160 (CC vs. GG: OR, 5.56; 95% CI, 1.24–24.81; p = 0.025; GG + CG vs. CC: OR, 5.26; 95% CI, 1.19–23.22; p = 0.028), were associated with increased GC risk, whereas certain rs8173 genotypes (CG vs. CC: OR, 0.60; 95% CI, 0.36–0.99; p = 0.049; GG vs. CC: OR, 0.38; 95% CI, 0.18–0.79; p = 0.010; CC + CG vs. GG: OR, 0.49; 95% CI, 0.25–0.98; p = 0.043) were protective. Association with increased GC risk was demonstrated for AURKB rs2241909 (GG + AG vs. AA: OR, 1.61; 95% CI, 1.01–2.56; p = 0.041) and rs2289590 (AC vs. AA: OR, 2.41; 95% CI, 1.47–3.98; p = 0.001; CC vs. AA: OR, 6.77; 95% CI, 2.24–20.47; p = 0.001; AA+AC vs. CC: OR, 4.23; 95% CI, 1.44–12.40; p = 0.009). Furthermore, AURKC rs11084490 (GG + CG vs. CC: OR, 1.71; 95% CI, 1.04–2.81; p = 0.033) was associated with increased GC risk. A combined analysis of five SNPs, associated with an increased GC risk, detected polymorphism profiles where all the combinations contribute to the higher GC risk, with an OR increased 1.51-fold for the rs1047972(CT)/rs11084490(CG + GG) to 2.29-fold for the rs1047972(CT)/rs911160(CC) combinations. In silico analysis for rs911160 and rs2289590 demonstrated that different transcription factors preferentially bind to polymorphic sites, indicating that AURKA and AURKB could be regulated differently depending on the presence of particular allele. Conclusions Our results revealed that AURKA (rs1047972 and rs911160), AURKB (rs2241909 and rs2289590) and AURKC (rs11084490) are associated with a higher risk of GC susceptibility. Our findings also showed that the combined effect of these SNPs may influence GC risk, thus indicating the significance of assessing multiple polymorphisms, jointly. The study was conducted on a less numerous but ethnically homogeneous Bosnian population, therefore further investigations in larger and multiethnic groups and the assessment of functional impact of the results are needed to strengthen the findings.

B553 In Silico Analysis of Global Gene Expression in Myeloma CCL: In Search of Stem Cell Genes

2009 ◽  
Vol 9 ◽  
pp. S148
Author(s):  
HE Johnsen ◽  
T Urup ◽  
AD Hoejfeldt ◽  
KB Fogd ◽  
KS Bergkvist ◽  
...  
Keyword(s):  
Gene Expression ◽  
Stem Cell ◽  
In Silico ◽  
In Silico Analysis ◽  
Global Gene Expression ◽  
Silico Analysis

KCNE2, a down-regulated gene identified by in silico analysis, suppressed proliferation of gastric cancer cells

2007 ◽  
Vol 246 (1-2) ◽  
pp. 129-138 ◽  
Author(s):  
Pan Yanglin ◽  
Zhao Lina ◽  
Liu Zhiguo ◽  
Liu Na ◽  
Jin Haifeng ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Cancer Cells ◽  
In Silico ◽  
In Silico Analysis ◽  
Gastric Cancer Cells ◽  
Silico Analysis

Double hit viral parasitism, polymicrobial CNS residency and perturbed proteostasis in Alzheimer’s disease: A data driven, in silico analysis of gene expression data

2020 ◽  
Vol 127 ◽  
pp. 124-135
Author(s):  
George D. Vavougios ◽  
Christiane Nday ◽  
Sygliti-Henrietta Pelidou ◽  
Sotirios G. Zarogiannis ◽  
Konstantinos I. Gourgoulianis ◽  
...  
Keyword(s):  
Gene Expression ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Gene Expression Data ◽  
In Silico ◽  
In Silico Analysis ◽  
Data Driven ◽  
Expression Data ◽  
Double Hit ◽  
Silico Analysis

scholarly journals In Silico analysis of Gastric carcinoma Serial Analysis of Gene Expression libraries reveals different profiles associated with ethnicity

2008 ◽  
Vol 7 (1) ◽  
pp. 22 ◽  
Author(s):  
Francisco J Ossandon ◽  
Cynthia Villarroel ◽  
Francisco Aguayo ◽  
Eudocia Santibanez ◽  
Naohide Oue ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gastric Carcinoma ◽  
In Silico ◽  
In Silico Analysis ◽  
Expression Libraries ◽  
Silico Analysis

scholarly journals The pattern of gene copy number alteration (CNAs) in hepatocellular carcinoma: an in silico analysis

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Arman Shahrisa ◽  
Maryam Tahmasebi-Birgani ◽  
Hossein Ansari ◽  
Zahra Mohammadi ◽  
Vinicio Carloni ◽  
...  
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
In Silico ◽  
Copy Number ◽  
Copy Number Alteration ◽  
Gene Copy Number ◽  
In Silico Analysis ◽  
Gene Copy ◽  
Sequencing Data ◽  
Silico Analysis

Abstract Background Hepatocellular carcinoma (HCC) is the most common type of liver cancer that occurs predominantly in patients with previous liver conditions. In the absence of an ideal screening modality, HCC is usually diagnosed at an advanced stage. Recent studies show that loss or gain of genomic materials can activate the oncogenes or inactivate the tumor suppressor genes to predispose cells toward carcinogenesis. Here, we evaluated both the copy number alteration (CNA) and RNA sequencing data of 361 HCC samples in order to locate the frequently altered chromosomal regions and identify the affected genes. Results Our data show that the chr1q and chr8p are two hotspot regions for genomic amplifications and deletions respectively. Among the amplified genes, YY1AP1 (chr1q22) possessed the largest correlation between CNA and gene expression. Moreover, it showed a positive correlation between CNA and tumor grade. Regarding deleted genes, CHMP7 (chr8p21.3) possessed the largest correlation between CNA and gene expression. Protein products of both genes interact with other cellular proteins to carry out various functional roles. These include ASH1L, ZNF496, YY1, ZMYM4, CHMP4A, CHMP5, CHMP2A and CHMP3, some of which are well-known cancer-related genes. Conclusions Our in-silico analysis demonstrates the importance of copy number alterations in the pathology of HCC. These findings open a door for future studies that evaluate our results by performing additional experiments.

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