Prognostic risk assessment model and drug sensitivity analysis of colon adenocarcinoma (COAD) based on immune-related lncRNA pairs

Abstract

Purpose: The aim of this study was to identify and screen long non-coding RNA (lncRNA) associated with immune genes in colon cancer, construct immune-related lncRNA pairs, establish a prognostic risk assessment model for colon adenocarcinoma (COAD), and explore prognostic factors and drug sensitivity.

Method: Our method was based on data from The Cancer Genome Atlas (TCGA). To begin, we obtained all pertinent demographic and clinical information on 385 patients with COAD. All lncRNAs significantly related to immune genes and with differential expression were identified to construct immune lncRNA pairs. Subsequently, least absolute shrinkage and selection operator and Cox models were used to screen out prognostic-related immune lncRNAs for the establishment of a prognostic risk scoring formula. Finally, We analysed the functional differences between subgroups and screened the drugs, and establish an individual prediction nomogram model.

Results: Our final analysis confirmed eight lncRNA pairs to construct prognostic risk assessment model. Results showed that the high-risk and low-risk groups had significant differences (training (n = 249): p < 0.001, validation (n = 114): p = 0.022). The prognostic model was certified as an independent prognosis model. Compared with the common clinicopathological indicators, the prognostic model had better predictive efficiency (area under the curve (AUC) = 0.805). Finally, We have analysed highly differentiated cellular pathways such as mucosal immune response, identified 9 differential immune cells, 10 sensitive drugs, and establish an individual prediction nomogram model (C-index = 0.820).

Conclusion: Our study verified that the eight lncRNA pairs mentioned can be used as biomarkers to predict the prognosis of COAD patients. Identified cells, drugs may have an positive effect on colon cancer prognosis.

Keywords: Colon adenocarcinoma; Drug sensitivity analysis; Immune-related lncRNA pairs; Prognostic risk assessment model.

Fig. 1

Establishment of a prognostic risk model for immune-associated lncRNA pairs. A Heatmap. B Volcano plot. Red areas represent upregulated lncRNAs, and green areas represent downregulated lncRNAs. C Eleven prognostic related lncRNA pairs screened out by univariate Cox analysis. D Tuning parameter (λ) selection in the LASSO model. The Partial Likelihood Deviance was plotted versus log (λ). Dotted vertical lines were drawn at the optimal values by using the minimum criteria and one standard error of the minimum criteria (the 1-SE criteria). A λ value of 0.062, with log (λ), − 4.006. E Lasso coefficient profiles of 11 lncRNA pairs. A coefficient profile is generated from the log (λ) sequence. The vertical line is plotted with the determined penalty value, resulting in nine non-zero coefficients. F Eight lncRNA pairs were obtained through multivariate Cox analysis for the establishment of a prognostic risk assessment model

Fig. 2

Differences in the high-risk group and the low-risk group. A Prognostic risk score distribution diagram of colon cancer patients. B Distribution of patients' survival status and survival time. C The training set. Kaplan–Meier survival curve of colon cancer patients from the low-risk group and the high-risk group. The high-risk group showed a poorer prognosis. D The validation set

Fig. 3

The relationships and independence between the risk score and different clinicopathological features. Relationships between the risk score and age (A), sex (B), stage (C), T-stage (D), M-stage (E), and N-stage (F). Univariate and multivariate Cox analyses to identify prognostic factors in patients with colon cancer. G Univariate Cox analysis. H Multivariate Cox analysis

Fig. 4

Prognostic risk model of colon cancer patients and prognostic prediction ROC curve of clinically relevant pathological information. A Training set prediction results and cut-off. B Prediction results of the training set for 1, 3, and 5 years. C Verification set prediction results. D Prognostic risk model of colon cancer patients and prognostic prediction results of clinically relevant pathological information

Fig. 5

Functional differences based on grouping. A GO analysis, Color represents Pvalue, and the size of the balls shows gene number. MF,Molecular Function; CC, Cellular Component; BP, Biological Process. B–J Immune cell differential analysis, in order of preference are Neutrophil, T cell CD4 + memory, T cell CD4 + memory resting, NK cell resting, Myeloid dendritic cell, Mast cell resting, Monocyte, T cell CD8 + and T cell regulatory

Fig. 6

Analysis of drug sensitivity. CCT007093, Embelin, PAC1, and Rapamycin were the most sensitive (p-value < 0.001). ABT.263, AZD.0530, IPA.3, Lenalidomide, Nilotinib, PLX4720 were relatively sensitive (p-value < 0.01)

Fig. 7

CCK-8 assay for the drug. A 24-h inhibitory capacity of CCT007093 drug. B 24-h inhibitory capacity of Embelin drug. C 24-h inhibitory capacity of the drug PAC-1. D 48-h inhibitory capacity of the CCT007093 drug. E 48-h inhibitory capacity of Embelin drug. F 48-h inhibitory capacity of PAC-1 drug. Where 0 mol/L is the control

Fig. 8

Nomogram individualized prediction model. C-index = 0.820 A Prediction of 1, 3 and 5 year prognostic survival probability of low-risk patients TCGA-A6-3807. B Prediction of 1, 3 and 5 year prognostic survival probability of high-risk patients TCGA-A6-2686. C Calibration curves of prognostic prediction models at 1, 3 and 5 years

Fig. 9

Construction of a prognostic model for COAD based on ncRNA pairs and the drug screening process. Based on gene expression data in FPKM format for COAD in TCGA, we screened eight prognosis-related ncRNA pairs, established a prognostic model risk score using cox and lasso, assessed the superiority of the model and analysed the functional differences between model subgroups. Among 138 drugs screened for potential use in the treatment of high-risk COAD and subjected to CCK-8 cellular assays, an individualised nomogram model was finally developed for clinical decision making