Journal of Medical Molecular Biology ›› 2023, Vol. 20 ›› Issue (1): 34-39.doi: 10.3870/j.issn.1672-8009.2023.01.006

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Establishment of Immune Related lncRNA Risk Prediction Model for Colon Cancer

  

  1. 1 Department of Gastroenterology, the First Affiliated Hospital of Xi’ an Medical College, Xi’ an 710077, China  2 The Second Clinical Medical College of Shaanxi University of Traditional Chinese Medicine, Xianyang, Shaanxi, 712046, China
  • Online:2023-01-31 Published:2023-03-24

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Abstract: Objective To analyze the immune related lncRNAs that affect the prognosis of colon cancer, and construct a related prediction model for the prediction of the prognosis of colon cancer patients. Methods Download the lncRNA expression profile of colon cancer in the TCGA database, and the data was standardized by TPM to analyze the differential expression of all lncRNAs. The KNN method was used to supplement the missing values. Immune-related lncRNAs were extracted and identified of by co-expression method, and then LASSO regression analysis was performed on the top 100 differentially expressed lncRNAs. Then the single-factor and multi-factor COX regression analysis were performed. Finally, based on the relationship between the lncRNA risk score and the gene expression, the risk factor association chart, the KM curve and the ROC curve for the evaluation of the value of the predicted model were constructed by using the ggplot2 package of R 4. 0. 2 statistical software. Results Through the analysis of differencial expression, a total of 2 258 lncRNAs were found to be differentially expressed in the cancer and paracancerous tissues, of which 1 648 were up-regulated and 610 were down-regulated. The top 100 differentially expressed immune related lncRNAs were selected for LASSO regression analysis, and a total of 12 lncRNAs were screened out. Univariate and multivariate COX regression analysis showed that AC092723. 1, AC007182. 1 and AC004947. 1 were significantly related to the prognosis. Using R 4. 0. 2 statistical software to construct a prognostic risk factor association chart. The ROC curve showed that the predictive values for the prediction of 1, 3 and 5 years were high, and the AUC were 0. 79 (95 % CI: 0. 67-0. 91), 0. 78 (95 % CI: 0. 66-0. 9), 0. 7 (95 % CI: 0. 51-0. 9), respectively. Conclusion This study uses the TCGA public database for the bioinformatics analysis and constructs a prognostic model that shows high predictive values. In addition to certain clinical significance, it also provides certain directions for the future researches of lncRNAs in colon cancer.

Key words: TCGA database, colon cancer, immune related lncRNA

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