Construction of a new immune-related lncRNA model and prediction of treatment and survival prognosis of human colon cancer

Abstract

Background: An increasing number of studies have shown that immune-related long noncoding RNAs (lncRNAs) do not require a unique expression level. This finding may help predict the survival and drug sensitivity of patients with colon cancer.

Methods: We retrieved original transcriptome and clinical data from The Cancer Genome Atlas (TCGA), sorted the data, differentiated mRNAs and lncRNAs, and then downloaded immune-related genes. Coexpression analysis predicted immune-related lncRNAs (irlncRNAs) and univariate analysis identified differentially expressed irlncRNAs (DEirlncRNAs). We have also amended the lasso pending region. Next, we compared the areas under the curve (AUCs), counted the Akaike information standard (AIC) value of the 3-year receiver operating characteristic (ROC) curve, and determined the cutoff point to establish the best model to differentiate the high or low disease risk group of colon cancer patients.

Results: We reevaluated the patients regarding the survival rate, clinicopathological features, tumor-infiltrating immune cells, immunosuppressive biomarkers, and chemosensitivity. A total of 155 irlncRNA pairs were confirmed, 31 of which were involved in the Cox regression model. After the colon cancer patients were regrouped according to the cutoff point, we could better distinguish the patients based on adverse survival outcomes, invasive clinicopathological features, the specific tumor immune cell infiltration status, high expression of immunosuppressive biomarkers, and low chemosensitivity.

Conclusions: In this study, we established a characteristic model by pairing irlncRNAs to better predict the survival rate, chemotherapy efficacy, and prognostic value of patients with colon cancer.

Keywords: Colon cancer; Immunotherapy; Tumor-infiltrating immune cell; lncRNA.

Fig. 1

Flow chart of this study

Fig. 2

Extract has the difference of immune-related genes. Using the TCGA data set and immune-related Ensembl annotation to identify differentially expressed genes. Shows the heat map figure (A) and the volcano (B). (C) The forest figure shows the COX proportional hazards regression method of stepwise regression method and the DEirlncRNAs

Fig. 3

Immune-related genes were used to establish a risk assessment model. (A) Draw 155 DEirlncRNA each generated on the model of ROC AUC value curve, and the highest point of the AUC was determined. ROC ideal DEirlncRNA model associated with the AUC value of maximum. Of 426 patients with colon cancer risk score, the biggest inflection point is made by AIC cutoff point. (B) 3 years compared with other common clinical features of ROC curve shows the superiority of risk score. (C) The optimal model of the ROC 1 year, 2 years, and 3 years shows that all the AUC values are greater than 0.91

Fig. 4

Risk assessment model of survival prognosis. (A) and (B) shows risk score and survival results of each case. (C) By the Kaplan-Meier test, high-risk group of patients’ survival time is short

Fig. 5

clinical correlation analysis based on risk assessment model. (A–G) Bar graph (A) and scatter diagram show that T (B), M (C), N (D), and (E) clinical stage were associated with a significant risk score. (F) Single-variable Cox regression to prove the clinical stage (p < 0.001, HR = 2.208, 95% CI [1.722–2.831]), the risk score (p < 0.001, HR = 1.004, 95% CI [1.003–1.005]) showed statistical significance, and multivariate Cox regression analysis prove that age (G) (p < 0.001, HR = 1.035, 95% letter interval [1.015–1.056]) and the clinical stage (p < 0.001, HR = 2.260, 95% CI [1.750–2.918], HR = 1.003, 95% CI [1.002–1.005])

Fig. 6

risk assessment model of tumor-infiltrating cells, immune checkpoint inhibitory molecules, and drug sensitivity analysis. (A) Spearman’s correlation analysis showed that the high-risk group of patients with CD4 T cells, monocytes, and tumor-infiltrating immune cells were positively correlated, and negative correlation with hematopoietic stem cells and neutrophils. (B) Using the risk model to predict tumor-infiltrating immune cells representative results. (C and D) The risk model is associated with immune checkpoint inhibitor-related biomarkers and found that high-risk score and PLD2 (p < 0.05) and high expression were positively related to (C), and MLH1 (p < 0.05) in the lower expression of negative correlation (D). (E) The high-risk score with chemotherapy drugs such as oxaliplatin into (p = 0.00089) the lower IC50, and the high-risk score is related to the high half-inhibitory concentration (IC50) of the protease inhibitors MG.132 (p = 0.025) and NVP-TAE684 (p = 0.01)