SAC3 domain containing 1 (SAC3D1) has been reported to be involved in numerous types of cancer. However, the role of SAC3D1 in GC has not yet been elucidated. In the present study, the mRNA expression level of SAC3D1 between GC and normal tissues were assessed with a continuous variable meta‑analysis based on multiple datasets from public databases. The protein expression level of SAC3D1 in GC and normal tissues was assessed by an in‑house immunohistochemistry (IHC). The association between SAC3D1 expression and some clinical parameters was assessed based on the TCGA and IHC data. Survival analysis was performed to assess the association between SAC3D1 expression and the survival of GC patients. The co‑expressed genes of SAC3D1 were determined by integrating three online tools, and the enrichment analyses were performed to determine SAC3D1‑related pathways and hub co‑expressed genes. SAC3D1 was significantly upregulated in GC tumor tissues in comparison to normal tissues with the SMD being 0.45 (0.12, 0.79). The IHC results also indicated that SAC3D1 protein expression in GC tissues was markedly higher than in normal tissues. The SMD following the addition of the IHC data was 0.59 (0.11, 1.07). The protein levels of SAC3D1 were positively associated with the histological grade, T stage and N stage of GC (P<0.001). The TCGA data also revealed that the SAC3D1 mRNA level was significantly associated with the N stage (P<0.001). Moreover, prognosis analysis indicated that SAC3D1 was closely associated with the prognosis of patients with GC. Moreover, 410 co‑expressed genes of SAC3D1 were determined, and these genes were mainly enriched in the cell cycle. In total, 4 genes (CDK1, CCNB1, CCNB2 and CDC20) were considered key co‑expressed genes. On the whole, these findings demonstrate that SAC3D1 is highly expressed in GC and may be associated with the progression of GC.
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http://dx.doi.org/10.3892/ijo.2020.5048 | DOI Listing |
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Ningxia Medical University, Xing Qing Block, Shengli Street No.1160, Yin Chuan City, 750004, Ningxia Province, People's Republic of China.
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This study aimed to identify splicing quantitative trait loci (cis-sQTL) in Nelore cattle muscle tissue and explore the involvement of spliced genes (sGenes) in immune system-related biological processes. Genotypic data from 80 intact male Nelore cattle were obtained using SNP-Chip technology, while RNA-Seq analysis was performed to measure gene expression levels, enabling the integration of genomic and transcriptomic datasets. The normalized expression levels of spliced transcripts were associated with single nucleotide polymorphisms (SNPs) through an analysis of variance using an additive linear model with the MatrixEQTL package.
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