Analysis and prognostic significance of tumour immune infiltrates and immune microenvironment of m6A-related lncRNAs in patients with gastric cancer View Full Text


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Article Info

DATE

2022-07-25

AUTHORS

Jiarong Huang, Jinxuan Song, xiangyu Li, Shuangfei Liu, Wentao Huang, Ziyi Shen, Yan Cheng, Shien Kou, Zhenguo Gao, Yunhong Tian, Jiani Hu

ABSTRACT

BackgroundStudies have shown that long noncoding RNAs and N6-methyladenosine play important roles in gastric cancer. The purpose of this study was to determine the correlation and prognostic value of m6A-related lncRNAs and immune infiltration in gastric cancer.MethodsWe downloaded the clinically related information and RNA-Seq transcriptome data of gastric cancer patients from the TCGA database. Univariate Cox regression analysis and Pearson analysis were used to screen out m6A-related lncRNAs. Consensus cluster analysis was used to divide the sample into two clusters, and LASSO analysis and Cox regression analysis were used to construct a risk scoring model.ResultsA total of 25 lncRNA expression profiles were screened, and gastric cancer patients were divided into different subtypes. Cluster 2 had a better prognosis, but its stromal score, ESTIMATE score and immune score were low. Cluster 1 was rich in resting memory CD4 T cells, regulatory T cells, monocytes, and resting mast cells, and Cluster 2 was rich in activated memory CD4 T cells and follicular helper T cells. Thirteen lncRNAs were selected to construct a risk model, and the prognosis of gastric cancer patients in the high-risk group was poor. The expression of PD-L1 in tumours is significantly higher than that in normal tissues. Univariate and multivariate Cox regression analysis results showed that the overall survival rate was significantly related to stage and the risk score, which can be used as an independent prognostic factor. The results of the heatmap and scatter plot showed that clusters (P = 0.0045) and grade (G1–2, G3, P = 0.0037) were significantly related to prognosis. The relationship between the risk score and immune cell infiltration showed that memory B cells, resting dendritic cells, M0 macrophages, and M2 macrophages were positively correlated with the risk score, while resting mast cells, monocytes, activated NK cells, and follicular helper T cells were negatively correlated with the risk score.ConclusionThe results of this study indicate that m6A-related lncRNAs may play an important role in the prognosis of gastric cancer patients and the tumour immune microenvironment and may provide help for the treatment of gastric cancer patients. More... »

PAGES

164

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/s12920-022-01318-5

DOI

http://dx.doi.org/10.1186/s12920-022-01318-5

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PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/35879790


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27 BackgroundStudies
28 CD4 T cells
29 Cox regression analysis
30 Cox regression analysis results
31 ESTIMATE scores
32 LASSO analysis
33 M0 macrophages
34 M2 macrophages
35 MethodsWe
36 N6-methyladenosine
37 NK cells
38 PD-L1
39 Pearson analysis
40 RNA-seq transcriptome data
41 RNAs
42 ResultsA total
43 T cells
44 TCGA database
45 analysis
46 analysis results
47 better prognosis
48 cancer
49 cancer patients
50 cell infiltration
51 cells
52 cluster 1
53 cluster 2
54 cluster analysis
55 clusters
56 consensus cluster analysis
57 correlation
58 data
59 database
60 dendritic cells
61 different subtypes
62 expression
63 expression profiles
64 factors
65 follicular helper T cells
66 gastric cancer
67 gastric cancer patients
68 grade
69 group
70 heatmaps
71 help
72 helper T cells
73 high-risk group
74 immune cell infiltration
75 immune infiltrates
76 immune infiltration
77 immune microenvironment
78 immune score
79 important role
80 independent prognostic factor
81 infiltrates
82 infiltration
83 information
84 lncRNA expression profiles
85 lncRNAs
86 m6A
87 macrophages
88 mast cells
89 memory B cells
90 memory CD4 T cells
91 microenvironment
92 model
93 monocytes
94 normal tissues
95 overall survival rate
96 patients
97 plots
98 profile
99 prognosis
100 prognostic factors
101 prognostic significance
102 prognostic value
103 purpose
104 rate
105 regression analysis
106 regression analysis results
107 regulatory T cells
108 relationship
109 results
110 risk
111 risk model
112 risk score
113 role
114 samples
115 scatter plots
116 scores
117 significance
118 stage
119 stromal scores
120 study
121 subtypes
122 survival rate
123 tissue
124 total
125 transcriptome data
126 treatment
127 tumor immune infiltrates
128 tumor immune microenvironment
129 tumors
130 values
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