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. 2022 Jun 2;12(1):9209.
doi: 10.1038/s41598-022-13200-6.

Analysis of immune related gene expression profiles and immune cell components in patients with Barrett esophagus

Lin Shi  1 , Renwei Guo  1 , Zhuo Chen  1 , Ruonan Jiao  1 , Shuangshuang Zhang  1 , Xuanxuan Xiong  2
Affiliations

Analysis of immune related gene expression profiles and immune cell components in patients with Barrett esophagus

Lin Shi et al. Sci Rep. .

Abstract

Barrett's esophagus (BE) is a well-known precancerous condition of esophageal adenocarcinoma. However, the immune cells and immune related genes involved in BE development and progression are not fully understood. Therefore, our study attempted to investigate the roles of immune cells and immune related genes in BE patients. The raw gene expression data were downloaded from the GEO database. The limma package in R was used to screen differentially expressed genes (DEGs). Then we performed the least absolute shrinkage and selection operator (LASSO) and random forest (RF) analyses to screen key genes. The proportion of infiltrated immune cells was evaluated using the CIBERSORT algorithm between BE and normal esophagus (NE) samples. The spearman index was used to show the correlations of immune genes and immune cells. Receiver operating characteristic (ROC) curves were used to assess the diagnostic value of key genes in BE. A total of 103 differentially expressed immune-related genes were identified between BE samples and normal samples. Then, 7 genes (CD1A, LTF, FABP4, PGC, TCF7L2, INSR,SEMA3C) were obtained after Lasso analysis and RF modeling. CIBERSORT analysis revealed that resting CD4 T memory cells and gamma delta T cells were present at significantly lower levels in BE samples. Moreover, plasma cell and regulatory T cells were present at significantly higher levels in BE samples than in NE samples. INSR had the highest AUC values in ROC analysis. We identified 7 immune related genes and 4 different immune cells in our study, that may play vital roles in the occurrence and development of BE. Our findings improve the understanding of the molecular mechanisms of BE.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Identification of DEGs form GEO dataset. (A) The volcano plot of DEGs between BE and NE samples. (B) The heatmap plot of DEGs between BE and NE samples.
Figure 2
Figure 2
Identification of DEGs form GEO dataset. (A) The volcano plot of immune related DEGs between BE and NE samples. (B) The heatmap plot of immune related DEGs between BE and NE samples.
Figure 3
Figure 3
GO function analysis and KEGG pathway analysis. (A) GO functional enrichment analysis of the immune-related DEGs. (B) KEGG functional enrichment analysis of the immune-related DEGs.
Figure 4
Figure 4
Identification of the optimal immune-related biomarkers. (A,B) LASSO regression analysis. (C) Top 20 genes by RF model sort by accuracy. (D) Venn diagram of overlapping.
Figure 5
Figure 5
Distribution of immune cells between BE and NE samples. (A) Percentage of immune cells in each sample. (B) Heatmap. (C) Violin plot.
Figure 6
Figure 6
Spearman correlation of the key genes and the immune cells. (A) Correlation in BE samples. (B) Correlation in NE samples.
Figure 7
Figure 7
The ROC curve of each key genes.
See this image and copyright information in PMC

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