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. 2023 Jan 4:12:1019909.
doi: 10.3389/fonc.2022.1019909. eCollection 2022.

The value of metabolic LncRNAs in predicting prognosis and immunotherapy efficacy of gastric cancer

Peizhun Du  1 , Pengcheng Liu  1 , Rajan Patel  2 , Shiyu Chen  3 , Cheng'en Hu  1 , Guangjian Huang  1 , Yi Liu  4
Affiliations

The value of metabolic LncRNAs in predicting prognosis and immunotherapy efficacy of gastric cancer

Peizhun Du et al. Front Oncol. .

Abstract

Introduction: As a unique feature of malignant tumors, abnormal metabolism can regulate the immune microenvironment of tumors. However, the role of metabolic lncRNAs in predicting the prognosis and immunotherapy of gastric cancer (GC) has not been explored.

Methods: We downloaded the metabolism-related genes from the GSEA website and identified the metabolic lncRNAs. Co-expression analysis and Lasso Cox regression analysis were utilized to construct the risk model. To value the reliability and sensitivity of the model, Kaplan-Meier analysis and receiver operating characteristic curves were applied. The immune checkpoints, immune cell infiltration and tumor mutation burden of low- and high-risk groups were compared. Tumor Immune Dysfunction and Exclusion (TIDE) score was conducted to evaluate the response of GC patients to immunotherapy.

Results: Twenty-three metabolic lncRNAs related to the prognosis of GC were obtained. Three cluster patterns based on metabolic lncRNAs could distinguish GC patients with different overall survival time (OS) effectively (p<0.05). The risk score model established by seven metabolic lncRNAs was verified as an independent prognostic indicator for predicting the OS of GC. The AUC value of the risk model was higher than TNM staging. The high-risk patients were accompanied by significantly increased expression of immune checkpoint molecules (including PD-1, PD-L1 and CTLA4) and increased tumor tolerant immune cells, but significantly decreased tumor mutation burden (TMB). Consistently, TIDE values of low-risk patients were significantly lower than that of high-risk patients.

Discussion: The metabolic lncRNAs risk model can reliably and independently predict the prognosis of GC. The feature that simultaneously map the immune status of tumor microenvironment and TMB gives risk model great potential to serve as an indicator of immunotherapy.

Keywords: gastric cancer; immune microenviroment; lncRNA; metabolism; prognosis.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Identification of metabolic lncRNAs. (A) Metabolism-related genes significantly associated with the prognosis of GC patients. (B) Interaction network diagram for relationship between metabolism-related genes and their relationship with lncRNAs. (C) Forest plot of lncRNAs expression by one-way Cox analysis, where red represented high risk lncRNAs and green represented low risk lncRNAs. (D) Heatmap of metabolic lncRNAs expression in normal and tumor samples. Red represented upregulated expression, and blue represented downregulated expression. *p<0.05,**p < 0.01 and ***p < 0.001.
Figure 2
Figure 2
Construction of metabolic lncRNAs patterns. (A) Consensus clustering matrix for k = 3. (B) Kaplan–Meier analysis of patients in three metabolic lncRNAs patterns. (C) The clinicopathological differences among cluster 1, cluster 2 and cluster 3. **p < 0.01.
Figure 3
Figure 3
Immune cells infiltration of three clusters in GC. (A–D) Immune genes (LILRB1, NR4A1, BTLA) or oncogene (PVT1) expression of three clusters in GC. (E) Differences levels of infiltration of the 22 immune cells in three metabolic lncRNAs patterns. (F–H) The comparsion of ESTIMATEScore, ImmuneScore, and StromalScore in cluster 1, cluster 2 and cluster 3. *p<0.05,**p < 0.01 and ***p < 0.001.
Figure 4
Figure 4
Establishment of risk score model. (A) The distribution of lambda and the best options in Lasso analysis (B) The weight of each candidate gene in the model. (C, D) Kaplan-Meier survival curves of the OS of patients between the high- and low-risk groups in the training (C) and testing (D) set. (E, F) The ROC curves of the risk score model in training (E) and testing (F) group.
Figure 5
Figure 5
Prognostic value of the risk score model. (A, B) Patterns of survival status and survival time in the training and testing group. (C, D) Distribution of metabolic lncRNAs risk score model in the high- and low-risk groups plotted in training and testing set. (E, F) Heatmap showed the expression standards of the seven prognostic lncRNAs of training and testing set.
Figure 6
Figure 6
Metabolic lncRNAs risk score model was an independent prognostic factor. Univariate (A, B) and multivariate (C, D) Cox regression analysis of the association between clinicopathological features (including risk score) and OS of patients in the training and testing group. (E) The ROC curves of the risk score model and clinicopathological parameters (F) The ROC curves of the risk score model at 1-,3-,5-years.
Figure 7
Figure 7
The prognostic risk model was applied to clinical features and immune characteristics of GC patients (A–D) Boxplot of relationship analysis of risk score and clinical features. (E) The clinicopathological differences between the high- and low-risk groups. ***p < 0.001.
Figure 8
Figure 8
Kaplan-Meier curves of OS differences stratified by tumor invasion depth (T), lymph node matestasis (N), distinal metastasis (M) and TNM staging between the high- and low-risk groups.
Figure 9
Figure 9
Metabolic lncRNAs risk model reflected the immune microenvironment (A–D) The relationship between immune checkpoint molecules and risk scores. (E) Heatmap of differences in immune function between high- and low-risk group. (F–J) The relationship between immune cell types and risk scores. (K) Boxplot of relationship analysis of risk score and ImmuneScore. *p < 0.05,**p < 0.01, ***p < 0.001.
Figure 10
Figure 10
Metabolic lncRNAs risk model predicted immunotherapy efficacy. (A, B) Waterfall diagram of high- low-risk GC patients. (C) Tumor mutation burden analysis to compare the TMB of GC patients in two groups. (D,E) Kaplan-Meier analysis to compare the OS of GC patients in different groups. (F) Violin diagram showed the TIDE values of GC patients in two groups. (G,H) Sensitivity of patients with different risk scores to cisplatin treatment.
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