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. 2022 Feb;13(1):102-116.
doi: 10.21037/jgo-21-833.

Identification of lncRNAs based on different patterns of immune infiltration in gastric cancer

Shujia Chen #  1 , Xinyu Ben #  2 , Lianyi Guo  1 , Xiaofei Li  1
Affiliations

Identification of lncRNAs based on different patterns of immune infiltration in gastric cancer

Shujia Chen et al. J Gastrointest Oncol. 2022 Feb.

Abstract

Background: Gastric cancer is one of the most common malignant tumors in the world, which brings great challenges to people's life and health. The purpose of this study was to investigate immune related-lncRNAs and identify new biomarkers for the prognosis of gastric cancer (GC).

Methods: We downloaded data from The Cancer Genome Atlas (TCGA) and used R software to determine the ESTIMATEScore, ImmuneScore, and StromalScore of each tumor sample. We performed prognostic analysis and identified the differentially expressed lnRNAs, which were then used to construct a prognostic model. Among the 44 hub genes in the competitive endogenous RNA (ceRNA) network, 3 differentially expressed genes were verified by qPCR.

Results: Based on the degree of immune infiltration, cluster A had a higher ESTIMATEScore, ImmuneScore, and StromalScore and higher expression levels of PD-L1 (CD274) and CTLA4 than cluster B. Univariate Cox analysis was conducted for these differential lncRNAs, and 57 lncRNAs were found to have prognostic value (P<0.05). gene cluster A had a worse prognosis than gene cluster B (P=0.021). Then, a prognostic model was constructed. The low-risk group had a significantly higher survival rate. Finally, the qPCR results showed that the expression levels of BMPER, PRUNE2, and RBPMS2 were low in GC cell lines.

Conclusions: We identified a risk score of 19 lncRNAs as a prognostic marker of GC. There was a relationship between these 19 prognostic-related lncRNAs and the subtypes of infiltrating immune cells. An approach for predicting the prognosis of GC was therefore provided in this study.

Keywords: Gastric cancer (GC); The Cancer Genome Atlas (TCGA); immune infiltration.

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Conflict of interest statement

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jgo.amegroups.com/article/view/10.21037/jgo-21-833/coif). The authors have no conflicts of interest to declare.

Figures

Figure 1
Figure 1
Immune cell typing and grouping of gastric cancer patients. The crossover between stomach adenocarcinoma (STAD) samples was minimal (A-C). Gastric cancer samples were divided into a high immune cell infiltration cluster (n=215) and a low immune cell infiltration cluster (n=160) according to immune cell infiltration (D). CDF, cumulative distribution function.
Figure 2
Figure 2
Different lncRNAs were found among different clusters. The enriched fractions of different immune cell sets obtained by single-sample gene set enrichment analysis (ssGSEA) were significantly different between the 2 subtypes (A). ESTIMATE, immune, and stromal scores of the 2 clusters (B-D; all P<0.05). Significant differences were found in the expression of PD-L1 (CD274) and CTLA4 between the 2 subtypes (E). ***, P<0.001; ****, P<0.0001.
Figure 3
Figure 3
Different lncRNAs were found among different clusters. A total of 425 lncRNAs were determined as the characteristic lncRNAs of different immune subtypes (A). A total of 57 genes were found to have prognostic value (B). gene cluster B had the best prognosis between the 2 groups (C). The correlations between different clusters and clinical characteristics (D). The enrichment of different immune cell gene sets obtained by single-sample gene set enrichment analysis (ssGSEA) was significantly different between the 2 gene clusters (E). *, P<0.05; **, P<0.01; ***, P<0.001; ****, P<0.0001. ns, no significance.
Figure 4
Figure 4
Construction of a ceRNA network, functional annotation, and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. The 1601 differential mRNAs as determined by FC >2 (R software, EDGR) (A). The ceRNA network was constructed by 44 mRNAs, 27 lncRNAs, and 25 miRNAs (B). The major signaling pathways associated with these genes (C).
Figure 5
Figure 5
Different expression levels of BMPER, PRUNE2, and RBPMS2 in stomach adenocarcinoma (STAD) cell lines and bioinformatics analysis (normal and tumor samples). (A-C) The mRNA expression levels of BMPER, PRUNE2, and RBPMS2 in STAD cell lines (MKNA, AGS, MGC803) and the normal cell line (GES-1) were detected by quantitative real-time PCR (RT-qPCR). (D-F) The expression of BMPER, PRUNE2, RBPMS2 in STAD (normal and tumor samples) determined by bioinformatics analysis. **, P<0.01; ***, P<0.001.
Figure 6
Figure 6
LncRNA signature construction and survival analysis. Kaplan-Meier survival curve (A). Receiver operating characteristic (ROC) curve (B). The prediction model calibration curve (5 years) was constructed (C). Correlation charts demonstrated that the observed versus predicted rates of 5-year overall survival (OS) had ideal consistency (D).
Figure 7
Figure 7
Survival analysis of different clinicopathological features based on LncRNA signature. The survival probability between the 2 groups (age >65 and <65 years) (A, P<0.001; B, P<0.001). Compared with the high-risk group, the survival rate of females in the low-risk group was significantly higher (C, P=0.004), which was also the case for men (D, P<0.001). Compared with the low-risk group, the survival rate was significantly lower in the high-risk group (E,F, P<0.001). T1-2 (G, P=0.135) showed no significant difference. Compared with the low-risk group, the survival rate of the high-risk group was lower for T3-4 patients (H, P<0.001).
Figure 8
Figure 8
The differences among different immune subtypes and genotypes. The differences in risk scores between different immune subtypes (A) and gene clusters (B). An alluvial diagram was used to visualize the attribute changes of individual patients (C). ****, P<0.0001.
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