ObjectiveCombined with long non-coding RNA (lncRNA) to find a regression model that can be used to predict the survival rate of patients with colon cancer before operation.MethodsThe clinical information and gene expression information of patients with colon cancer were downloaded by using TCGA database. The differentially expressed lncRNAs in tumor and paracancerous tissues were screened out, and then combined with the clinical information of patients to construct Cox proportional hazard regression model.ResultsA total of 26 kinds of lncRNAs with statistical difference in gene expression between paracancerous tissues and tumor tissues were selected (P<0.05). Through repeated screening and comparison of prediction efficiency, the prediction model was finally selected, which was constructed by patients’ age, M stage, N stage, and three kinds of lncRNAs (ZFAS1, SNHG25, and SNHG7) gene expression level: age [HR=4.00, 95%CI: (1.48, 10.84), P=0.006], M stage [HR=3.96, 95%CI: (2.23, 7.04), P<0.001], N stage [HR=1.87, 95%CI: (1.24, 2.84), P=0.003], ZFAS1 gene expression level [HR=0.60, 95%CI: (0.41, 0.86), P=0.006], SNHG25 gene expression level [HR=0.85, 95%CI: (0.73, 1.00), P=0.045], and SNHG7 gene expression level [HR=2.32, 95%CI: (1.53, 3.52), P<0.001] were all independent risk factors for postoperative survival of patients with colon cancer. The area under the ROC curves for predicting 1, 3, and 5-year overall survival were 0.802, 0.828, and 0.771, respectiely, which had a good prediction ability.ConclusionThe predictive model constructed by the combination of ZFAS1, SNHG25, SNHG7 genes expression level with M stage, N stage, and age can better predict the overall survival rate of patients before operation, which can effectively guide clinical decision-making and choose the most suitable treatment method for patients.
ObjectiveTo explore the effect of La-related protein 6 (LARP6) gene on the survival of postoperative patients with gastric cancer, and to explore its relationship with immune cell infiltration.MethodsThe clinical survival information and gene expression information of gastric cancer patients were downloaded from The Cancer Genome Atlas (TCGA) database. The relationship between LARP6 gene expression and clinical characteristics of patients were analyzed. Cox proportion hazard regression model was used to find out the prognostic risk factors of gastric cancer patients, and then Kaplan-Meier plotter database was used to verify. Then the correlation between LARP6 gene expression and immunity was proved by Tumor IMmune Estimation Resource (TIMER) immune database.ResultsIn gastric cancer patients, the expression of LARP6 gene was related to pathological stage, T stage, and N stage (P<0.05), but not related to M stage and sex (P>0.05). Multivariate Cox proportion hazard regression analysis showed that age [HR=2.022, 95%CI was (1.287, 3.176), P=0.002] and LARP6 gene expression [HR=1.176, 95%CI was (1.070, 1.293), P<0.001] were prognostic factors. Further verified by Kaplan Meier plotter database, the results also showed that the overall survival (OS) and progression-free survival (PFS) of gastric cancer patients with high expression of LAPR6 gene were worse than those with low expression of LARP6 gene (P<0.001). TIMER database was used to explore the correlation between the expression level of LARP6 gene and immune cell infiltration in patients with gastric cancer, and the results showed that the expression level of LARP6 gene in gastric cancer patients was positively correlated with the infiltration number of CD4+ T cells and macrophage cell (P<0.001). Log-rank results showed that infiltration number of macrophage cell and LARP6 gene expression were risk factors for clinical prognosis of gastric cancer patients (P<0.05).ConclusionsMacrophage cell andcell and LARP6 gene expression are risk factors for gastric cancer patients. LARP6 may be a new target for the treatment of gastric cancer.
ObjectiveTo construct a new model for predicting the overall survival rate of gastric cancer and to guide the clinical work.MethodsThe clinical information and gene expression information of patients with gastric cancer were downloaded through The Cancer Genome Atlas (TCGA) database. The clinicopathologic characteristics and gene expression information affecting the overall survival rate of gastric cancer patients were screened by univariate COX regression and Lasson regression, then the predictive model was constructed by multiple COX regression model, and the predictive model was tested by receiver operating characteristic curve, calibration curve and decision curve analysis curve. The effect of genes included in the predictive model on the overall survival rate of patients with gastric cancer was discussed, and the predictive model diagram was drawn.ResultsThrough repeated screening and comparison of the model, the patient’s age, T stage, N stage, M stage and 12 genes (INCENP, IGHD3-16, ITFG1-AS1, NEK5, MATN3, YWHABP2, SYT12, LINC01210, ZNF385C, LINC01980, CYMP-AS1 and FAT3) were included in the predictive model. The prediction ability of this model was close to or more than 80%, which was significantly higher than that of the traditional TNM staging prediction system. All the indexes included in the model were significantly different by univariate and multivariate COX regression analysis（P<0.05）, and the 12 genes included were the risk factors affecting the overall survival rate of gastric cancer.ConclusionThe gastric cancer prediction model constructed by combining clinical characteristics and genomics has good predictive ability and can guide clinical work.