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Luo, Qiu‐Yun; Di, Tian; Chen, Zhi‐Gang; Peng, Jian‐Hong; Sun, Jian; Xia, Zeng‐Fei; Pan, Wen‐Tao; Luo, Fan; Lu, Fei‐Teng; Sun, Yu‐Ting; Yang, Li‐Qiong; Zhang, Lin; Qiu, Miao‐Zhen; Yang, Da‐Jun
Cancer science, October 2022, Volume: 113, Issue: 10Journal Article
Colon cancer is the third most common cancer and the second leading cause of cancer‐related death worldwide. Dysregulated RNA splicing factors have been reported to be associated with tumorigenesis and development in colon cancer. In this study, we interrogated clinical and RNA expression data of colon cancer patients from The Cancer Genome Atlas (TCGA) dataset and the Gene Expression Omnibus (GEO) database. Genes regulating RNA splicing correlated with survival in colon cancer were identified and a risk score model was constructed using Cox regression analyses. In the risk model, RNA splicing factor peroxisome proliferator‐activated receptor‐γ coactivator‐1α (PPARGC1) is correlated with a good survival outcome, whereas Cdc2‐like kinase 1(CLK1), CLK2, and A‐kinase anchor protein 8‐like (AKAP8L) with a bad survival outcome. The risk model has a good performance for clinical prognostic prediction both in the TCGA cohort and the other two validation cohorts. In the tumor microenvironment (TME) analysis, the immune score was higher in the low‐risk group, and TME‐related pathway gene expression was also higher in low‐risk group. We further verified the mRNA and protein expression levels of these four genes in the adjacent nontumor, tumor, and liver metastasis tissues of colon cancer patients, which were consistent with bioinformatics analysis. In addition, knockdown of AKAP8L can suppress the proliferation and migration of colon cancer cells. Animal studies have also shown that AKAP8L knockdown can inhibit tumor growth in colon cancer in vivo. We established a prognostic risk model for colon cancer based on genes related to RNA splicing regulation and uncovered the role of AKAP8L in promoting colon cancer progression. we interrogated transcriptome and the corresponding clinical data of colon cancer patients from the The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) dataset to investigate prognosis associated splicing‐related signature.And our study established a prognostic risk model based on RNA splicing regulation related genes for colon cancer.
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