Cargando…

Identification of TGF-β signaling-related molecular patterns, construction of a prognostic model, and prediction of immunotherapy response in gastric cancer

Background: TGF-β signaling pathway plays an essential role in tumor progression and immune responses. However, the link between TGF-β signaling pathway-related genes (TSRGs) and clinical prognosis, tumor microenvironment (TME), and immunotherapy in gastric cancer is unclear. Methods: Transcriptome...

Descripción completa

Detalles Bibliográficos
Autores principales: Zeng, Cheng, He, Rong, Dai, Yuyang, Lu, Xiaohuan, Deng, Linghui, Zhu, Qi, Liu, Yu, Liu, Qian, Lu, Wenbin, Wang, Yue, Jin, Jianhua
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9715605/
https://www.ncbi.nlm.nih.gov/pubmed/36467074
http://dx.doi.org/10.3389/fphar.2022.1069204
_version_ 1784842488539774976
author Zeng, Cheng
He, Rong
Dai, Yuyang
Lu, Xiaohuan
Deng, Linghui
Zhu, Qi
Liu, Yu
Liu, Qian
Lu, Wenbin
Wang, Yue
Jin, Jianhua
author_facet Zeng, Cheng
He, Rong
Dai, Yuyang
Lu, Xiaohuan
Deng, Linghui
Zhu, Qi
Liu, Yu
Liu, Qian
Lu, Wenbin
Wang, Yue
Jin, Jianhua
author_sort Zeng, Cheng
collection PubMed
description Background: TGF-β signaling pathway plays an essential role in tumor progression and immune responses. However, the link between TGF-β signaling pathway-related genes (TSRGs) and clinical prognosis, tumor microenvironment (TME), and immunotherapy in gastric cancer is unclear. Methods: Transcriptome data and related clinical data of gastric cancer were downloaded from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, and 54 TSRGs were obtained from the Molecular Signatures Database (MSigDB). We systematically analyzed the expression profile characteristics of 54 TSRGs in 804 gastric cancer samples and examined the differences in prognosis, clinicopathological features, and TME among different molecular subtypes. Subsequently, TGF-β-related prognostic models were constructed using univariate and least absolute shrinkage and selection operator (LASSO) Cox regression analysis to quantify the degree of risk in each patient. Patients were divided into two high- and low-risk groups based on the median risk score. Finally, sensitivity to immune checkpoint inhibitors (ICIs) and anti-tumor agents was assessed in patients in high- and low-risk groups. Results: We identified two distinct TGF-β subgroups. Compared to TGF-β cluster B, TGF-β cluster A exhibits an immunosuppressive microenvironment with a shorter overall survival (OS). Then, a novel TGF-β-associated prognostic model, including SRPX2, SGCE, DES, MMP7, and KRT17, was constructed, and the risk score was demonstrated as an independent prognostic factor for gastric cancer patients. Further studies showed that gastric cancer patients in the low-risk group, characterized by higher tumor mutation burden (TMB), the proportion of high microsatellite instability (MSI-H), immunophenoscore (IPS), and lower tumor immune dysfunction and exclusion (TIDE) score, had a better prognosis, and linked to higher response rate to immunotherapy. In addition, the risk score and anti-tumor drug sensitivity were strongly correlated. Conclusion: These findings highlight the importance of TSRGs, deepen the understanding of tumor immune microenvironment, and guide individualized immunotherapy for gastric cancer patients.
format Online
Article
Text
id pubmed-9715605
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-97156052022-12-03 Identification of TGF-β signaling-related molecular patterns, construction of a prognostic model, and prediction of immunotherapy response in gastric cancer Zeng, Cheng He, Rong Dai, Yuyang Lu, Xiaohuan Deng, Linghui Zhu, Qi Liu, Yu Liu, Qian Lu, Wenbin Wang, Yue Jin, Jianhua Front Pharmacol Pharmacology Background: TGF-β signaling pathway plays an essential role in tumor progression and immune responses. However, the link between TGF-β signaling pathway-related genes (TSRGs) and clinical prognosis, tumor microenvironment (TME), and immunotherapy in gastric cancer is unclear. Methods: Transcriptome data and related clinical data of gastric cancer were downloaded from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, and 54 TSRGs were obtained from the Molecular Signatures Database (MSigDB). We systematically analyzed the expression profile characteristics of 54 TSRGs in 804 gastric cancer samples and examined the differences in prognosis, clinicopathological features, and TME among different molecular subtypes. Subsequently, TGF-β-related prognostic models were constructed using univariate and least absolute shrinkage and selection operator (LASSO) Cox regression analysis to quantify the degree of risk in each patient. Patients were divided into two high- and low-risk groups based on the median risk score. Finally, sensitivity to immune checkpoint inhibitors (ICIs) and anti-tumor agents was assessed in patients in high- and low-risk groups. Results: We identified two distinct TGF-β subgroups. Compared to TGF-β cluster B, TGF-β cluster A exhibits an immunosuppressive microenvironment with a shorter overall survival (OS). Then, a novel TGF-β-associated prognostic model, including SRPX2, SGCE, DES, MMP7, and KRT17, was constructed, and the risk score was demonstrated as an independent prognostic factor for gastric cancer patients. Further studies showed that gastric cancer patients in the low-risk group, characterized by higher tumor mutation burden (TMB), the proportion of high microsatellite instability (MSI-H), immunophenoscore (IPS), and lower tumor immune dysfunction and exclusion (TIDE) score, had a better prognosis, and linked to higher response rate to immunotherapy. In addition, the risk score and anti-tumor drug sensitivity were strongly correlated. Conclusion: These findings highlight the importance of TSRGs, deepen the understanding of tumor immune microenvironment, and guide individualized immunotherapy for gastric cancer patients. Frontiers Media S.A. 2022-11-18 /pmc/articles/PMC9715605/ /pubmed/36467074 http://dx.doi.org/10.3389/fphar.2022.1069204 Text en Copyright © 2022 Zeng, He, Dai, Lu, Deng, Zhu, Liu, Liu, Lu, Wang and Jin. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Pharmacology
Zeng, Cheng
He, Rong
Dai, Yuyang
Lu, Xiaohuan
Deng, Linghui
Zhu, Qi
Liu, Yu
Liu, Qian
Lu, Wenbin
Wang, Yue
Jin, Jianhua
Identification of TGF-β signaling-related molecular patterns, construction of a prognostic model, and prediction of immunotherapy response in gastric cancer
title Identification of TGF-β signaling-related molecular patterns, construction of a prognostic model, and prediction of immunotherapy response in gastric cancer
title_full Identification of TGF-β signaling-related molecular patterns, construction of a prognostic model, and prediction of immunotherapy response in gastric cancer
title_fullStr Identification of TGF-β signaling-related molecular patterns, construction of a prognostic model, and prediction of immunotherapy response in gastric cancer
title_full_unstemmed Identification of TGF-β signaling-related molecular patterns, construction of a prognostic model, and prediction of immunotherapy response in gastric cancer
title_short Identification of TGF-β signaling-related molecular patterns, construction of a prognostic model, and prediction of immunotherapy response in gastric cancer
title_sort identification of tgf-β signaling-related molecular patterns, construction of a prognostic model, and prediction of immunotherapy response in gastric cancer
topic Pharmacology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9715605/
https://www.ncbi.nlm.nih.gov/pubmed/36467074
http://dx.doi.org/10.3389/fphar.2022.1069204
work_keys_str_mv AT zengcheng identificationoftgfbsignalingrelatedmolecularpatternsconstructionofaprognosticmodelandpredictionofimmunotherapyresponseingastriccancer
AT herong identificationoftgfbsignalingrelatedmolecularpatternsconstructionofaprognosticmodelandpredictionofimmunotherapyresponseingastriccancer
AT daiyuyang identificationoftgfbsignalingrelatedmolecularpatternsconstructionofaprognosticmodelandpredictionofimmunotherapyresponseingastriccancer
AT luxiaohuan identificationoftgfbsignalingrelatedmolecularpatternsconstructionofaprognosticmodelandpredictionofimmunotherapyresponseingastriccancer
AT denglinghui identificationoftgfbsignalingrelatedmolecularpatternsconstructionofaprognosticmodelandpredictionofimmunotherapyresponseingastriccancer
AT zhuqi identificationoftgfbsignalingrelatedmolecularpatternsconstructionofaprognosticmodelandpredictionofimmunotherapyresponseingastriccancer
AT liuyu identificationoftgfbsignalingrelatedmolecularpatternsconstructionofaprognosticmodelandpredictionofimmunotherapyresponseingastriccancer
AT liuqian identificationoftgfbsignalingrelatedmolecularpatternsconstructionofaprognosticmodelandpredictionofimmunotherapyresponseingastriccancer
AT luwenbin identificationoftgfbsignalingrelatedmolecularpatternsconstructionofaprognosticmodelandpredictionofimmunotherapyresponseingastriccancer
AT wangyue identificationoftgfbsignalingrelatedmolecularpatternsconstructionofaprognosticmodelandpredictionofimmunotherapyresponseingastriccancer
AT jinjianhua identificationoftgfbsignalingrelatedmolecularpatternsconstructionofaprognosticmodelandpredictionofimmunotherapyresponseingastriccancer