Identification of memory B-cell-associated miRNA signature to establish a prognostic model in gastric adenocarcinoma

Abstract

Background: Memory B cells and microRNAs (miRNAs) play important roles in the progression of gastric adenocarcinoma (GAC), also known as stomach adenocarcinoma (STAD). However, few studies have investigated the use of memory B-cell-associated miRNAs in predicting the prognosis of STAD.

Methods: We identified the marker genes of memory B cells by single-cell RNA sequencing (scRNA-seq) and identified the miRNAs associated with memory B cells by constructing an mRNA‒miRNA coexpression network. Then, univariate Cox, random survival forest (RSF), and stepwise multiple Cox regression (StepCox) algorithms were used to identify memory B-cell-associated miRNAs that were significantly related to overall survival (OS). A prognostic risk model was constructed and validated using these miRNAs, and patients were divided into a low-risk group and a high-risk group. In addition, the differences in clinicopathological features, tumour microenvironment, immune blocking therapy, and sensitivity to anticancer drugs in the two groups were analysed.

Results: Four memory B-cell-associated miRNAs (hsa-mir-145, hsa-mir-125b-2, hsa-mir-100, hsa-mir-221) with significant correlations to OS were identified and used to construct a prognostic model. Time-dependent receiver operating characteristic (ROC) curve analysis confirmed the feasibility of the model. Kaplan‒Meier (K‒M) survival curve analysis showed that the prognosis was poor in the high-risk group. Comprehensive analysis showed that patients in the high-risk group had higher immune scores, matrix scores, and immune cell infiltration and a poor immune response. In terms of drug screening, we predicted eight drugs with higher sensitivity in the high-risk group, of which CGP-60474 was associated with the greatest sensitivity.

Conclusions: In summary, we identified memory B-cell-associated miRNA prognostic features and constructed a novel risk model for STAD based on scRNA-seq data and bulk RNA-seq data. Among patients in the high-risk group, STAD showed the highest sensitivity to CGP-60474. This study provides prognostic insights into individualized and precise treatment for STAD patients.

Fig. 1

Flowchart of this study

Fig. 2

Identification of B-cell marker genes by scRNA-seq analysis. A After the first-level classification, seven cell types were identified by marker gene annotation. B UMAP plot showing the expression of the marker genes of the seven cell types. C After the second-level classification of B cells, four cell types were identified by marker gene annotation. D Violin plot showing the expression of the marker genes of the four cell types

Fig. 3

Enrichment analysis and the coexpression network of memory B-cell-associated genes. A GO enrichment analysis of memory B-cell-associated genes. B KEGG enrichment analysis of memory B-cell-associated genes. C Coexpression network of memory B-cell-associated genes and miRNAs. D Transcription factor enrichment analysis of memory B-cell-associated miRNAs

Fig. 4

Screening of memory B-cell-associated prognostic miRNAs. A Screening candidate memory B-cell-associated miRNAs by univariate Cox regression analysis. B–C Random survival forest error rate versus the number of classification trees and the relative importance of 24 miRNAs. D Multivariate stepwise Cox regression analysis screened five prognosis-associated miRNAs from candidate miRNAs. E Venn diagram showed four miRNAs screened by both RSF and StepCox(both)

Fig. 5

Construction and validation of the memory B-cell-associated miRNA prognostic signature. A–C RS distribution, survival status, and heatmap of the expression of four memory B-cell-associated miRNAs in the high-risk and low-risk groups of the training cohort, validation cohort 1, and validation cohort 2. D–F K‒M survival analysis of three cohorts. G–I Time-dependent ROC curve analysis of three cohorts

Fig. 6

RS-based clinical analysis and construction of the nomogram. A K‒M survival analysis of survival stratified by age, sex and stage. B–C The results of univariate Cox regression analysis and multivariate Cox regression analysis of clinical characteristics and risk scores. D Nomogram based on RS and clinical characteristics. E ROC curve analysis results. F Calibration curve analysis results. *** P<0.001

Fig. 7

RS-based analysis of the tumour immune microenvironment. A Differences in the estimate, immune, and stromal scores between the low-risk and high-risk groups. B Differential expression levels of 22 types of tumour-infiltrating immune cells between the low-risk and high-risk groups. * P<0.05, ** P<0.01, *** P<0.001, ns not significant

Fig. 8

RS-based analysis of ICB immunotherapy response. A Result of ICB immunotherapy response prediction in TCGA-STAD patients. B–D Differences in ICB immunotherapy response, EaSIeR immune score and TIDE score between the low-risk and high-risk groups. E–H Influence of immune cells, cell communication, transcription factors and ligand receptor pairs on ICB treatment response

Fig. 9

Screening of potential antineoplastic drugs based on prognostic miRNAs and risk group. A–C hsa-mir-100, hsa-mir-125b-2 and hsa-mir-145 were positively correlated with sensitivity to dromostanolone propionate, lovastatin and zoledronate, respectively. D hsa-mir-221 was negatively correlated with sensitivity to SR16157. E–L Eight drugs with higher sensitivity in the high-risk group