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

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 i...

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Autores principales: Liu, Ruquan, Huang, Biaojie, Shao, Yongzhao, Cai, Yongming, Liu, Xi, Ren, Zhonglu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10515266/
https://www.ncbi.nlm.nih.gov/pubmed/37735667
http://dx.doi.org/10.1186/s12967-023-04366-2
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author Liu, Ruquan
Huang, Biaojie
Shao, Yongzhao
Cai, Yongming
Liu, Xi
Ren, Zhonglu
author_facet Liu, Ruquan
Huang, Biaojie
Shao, Yongzhao
Cai, Yongming
Liu, Xi
Ren, Zhonglu
author_sort Liu, Ruquan
collection PubMed
description 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12967-023-04366-2.
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spelling pubmed-105152662023-09-23 Identification of memory B-cell-associated miRNA signature to establish a prognostic model in gastric adenocarcinoma Liu, Ruquan Huang, Biaojie Shao, Yongzhao Cai, Yongming Liu, Xi Ren, Zhonglu J Transl Med Research 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12967-023-04366-2. BioMed Central 2023-09-21 /pmc/articles/PMC10515266/ /pubmed/37735667 http://dx.doi.org/10.1186/s12967-023-04366-2 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Liu, Ruquan
Huang, Biaojie
Shao, Yongzhao
Cai, Yongming
Liu, Xi
Ren, Zhonglu
Identification of memory B-cell-associated miRNA signature to establish a prognostic model in gastric adenocarcinoma
title Identification of memory B-cell-associated miRNA signature to establish a prognostic model in gastric adenocarcinoma
title_full Identification of memory B-cell-associated miRNA signature to establish a prognostic model in gastric adenocarcinoma
title_fullStr Identification of memory B-cell-associated miRNA signature to establish a prognostic model in gastric adenocarcinoma
title_full_unstemmed Identification of memory B-cell-associated miRNA signature to establish a prognostic model in gastric adenocarcinoma
title_short Identification of memory B-cell-associated miRNA signature to establish a prognostic model in gastric adenocarcinoma
title_sort identification of memory b-cell-associated mirna signature to establish a prognostic model in gastric adenocarcinoma
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10515266/
https://www.ncbi.nlm.nih.gov/pubmed/37735667
http://dx.doi.org/10.1186/s12967-023-04366-2
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