Cargando…

Tumor Microenvironment Heterogeneity-Based Score System Predicts Clinical Prognosis and Response to Immune Checkpoint Blockade in Multiple Colorectal Cancer Cohorts

Despite immune checkpoint blockade (ICB) therapy contributed to significant advances in cancer therapy, only a small percentage of patients with colorectal cancer (CRC) respond to it. Identification of these patients will facilitate ICB application in CRC. In this study, we integrated multiple CRC c...

Descripción completa

Detalles Bibliográficos
Autores principales: Wang, Hufei, Li, Zhi, Ou, Suwen, Song, Yanni, Luo, Kangjia, Guan, Zilong, Zhao, Lei, Huang, Rui, Yu, Shan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9274205/
https://www.ncbi.nlm.nih.gov/pubmed/35836930
http://dx.doi.org/10.3389/fmolb.2022.884839
_version_ 1784745257028550656
author Wang, Hufei
Li, Zhi
Ou, Suwen
Song, Yanni
Luo, Kangjia
Guan, Zilong
Zhao, Lei
Huang, Rui
Yu, Shan
author_facet Wang, Hufei
Li, Zhi
Ou, Suwen
Song, Yanni
Luo, Kangjia
Guan, Zilong
Zhao, Lei
Huang, Rui
Yu, Shan
author_sort Wang, Hufei
collection PubMed
description Despite immune checkpoint blockade (ICB) therapy contributed to significant advances in cancer therapy, only a small percentage of patients with colorectal cancer (CRC) respond to it. Identification of these patients will facilitate ICB application in CRC. In this study, we integrated multiple CRC cohorts (2,078 samples) to construct tumor microenvironment (TME) subtypes using TME indices calculated by CIBERSORT and ESTIMATE algorithms. Furthermore, a surrogate quantitative indicator, a tumor microenvironment immune gene (TMEIG) score system, was established using the key immune genes between TME clusters 1 and 2. The subsequent analysis demonstrated that TME subtypes and the TMEIG score system correlated with clinical outcomes of patients in multiple CRC cohorts and exhibited distinct immune statuses. Furthermore, Tumor Immune Dysfunction and Exclusion (TIDE) analysis indicated that patients with low TMEIG scores were more likely to benefit from ICB therapy. A study on two ICB cohorts (GSE78220 and IMvigor210) also validated that patients with low TMEIG scores exhibited higher ICB response rates and better prognoses after ICB treatment. The biomarker evaluation module on the TIDE website revealed that the TMEIG score was a robust predictive biomarker. Moreover, differential expression analysis, immunohistochemistry, qPCR experiments, and gene set prioritization module on the TIDE website demonstrated that the five genes that constitute the TMEIG score system (SERPINE1, FABP4, SCG2, CALB2, and HOXC6) were closely associated with tumorigenesis, immune cells, and ICB response indices. Finally, TMEIG scores could accurately predict the prognosis and ICB response of patients with CRC. SERPINE1, FABP4, SCG2, CALB2, and HOXC6 might be potential targets related to ICB treatment. Furthermore, our study provided new insights into precision ICB therapy in CRC.
format Online
Article
Text
id pubmed-9274205
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-92742052022-07-13 Tumor Microenvironment Heterogeneity-Based Score System Predicts Clinical Prognosis and Response to Immune Checkpoint Blockade in Multiple Colorectal Cancer Cohorts Wang, Hufei Li, Zhi Ou, Suwen Song, Yanni Luo, Kangjia Guan, Zilong Zhao, Lei Huang, Rui Yu, Shan Front Mol Biosci Molecular Biosciences Despite immune checkpoint blockade (ICB) therapy contributed to significant advances in cancer therapy, only a small percentage of patients with colorectal cancer (CRC) respond to it. Identification of these patients will facilitate ICB application in CRC. In this study, we integrated multiple CRC cohorts (2,078 samples) to construct tumor microenvironment (TME) subtypes using TME indices calculated by CIBERSORT and ESTIMATE algorithms. Furthermore, a surrogate quantitative indicator, a tumor microenvironment immune gene (TMEIG) score system, was established using the key immune genes between TME clusters 1 and 2. The subsequent analysis demonstrated that TME subtypes and the TMEIG score system correlated with clinical outcomes of patients in multiple CRC cohorts and exhibited distinct immune statuses. Furthermore, Tumor Immune Dysfunction and Exclusion (TIDE) analysis indicated that patients with low TMEIG scores were more likely to benefit from ICB therapy. A study on two ICB cohorts (GSE78220 and IMvigor210) also validated that patients with low TMEIG scores exhibited higher ICB response rates and better prognoses after ICB treatment. The biomarker evaluation module on the TIDE website revealed that the TMEIG score was a robust predictive biomarker. Moreover, differential expression analysis, immunohistochemistry, qPCR experiments, and gene set prioritization module on the TIDE website demonstrated that the five genes that constitute the TMEIG score system (SERPINE1, FABP4, SCG2, CALB2, and HOXC6) were closely associated with tumorigenesis, immune cells, and ICB response indices. Finally, TMEIG scores could accurately predict the prognosis and ICB response of patients with CRC. SERPINE1, FABP4, SCG2, CALB2, and HOXC6 might be potential targets related to ICB treatment. Furthermore, our study provided new insights into precision ICB therapy in CRC. Frontiers Media S.A. 2022-06-28 /pmc/articles/PMC9274205/ /pubmed/35836930 http://dx.doi.org/10.3389/fmolb.2022.884839 Text en Copyright © 2022 Wang, Li, Ou, Song, Luo, Guan, Zhao, Huang and Yu. 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 Molecular Biosciences
Wang, Hufei
Li, Zhi
Ou, Suwen
Song, Yanni
Luo, Kangjia
Guan, Zilong
Zhao, Lei
Huang, Rui
Yu, Shan
Tumor Microenvironment Heterogeneity-Based Score System Predicts Clinical Prognosis and Response to Immune Checkpoint Blockade in Multiple Colorectal Cancer Cohorts
title Tumor Microenvironment Heterogeneity-Based Score System Predicts Clinical Prognosis and Response to Immune Checkpoint Blockade in Multiple Colorectal Cancer Cohorts
title_full Tumor Microenvironment Heterogeneity-Based Score System Predicts Clinical Prognosis and Response to Immune Checkpoint Blockade in Multiple Colorectal Cancer Cohorts
title_fullStr Tumor Microenvironment Heterogeneity-Based Score System Predicts Clinical Prognosis and Response to Immune Checkpoint Blockade in Multiple Colorectal Cancer Cohorts
title_full_unstemmed Tumor Microenvironment Heterogeneity-Based Score System Predicts Clinical Prognosis and Response to Immune Checkpoint Blockade in Multiple Colorectal Cancer Cohorts
title_short Tumor Microenvironment Heterogeneity-Based Score System Predicts Clinical Prognosis and Response to Immune Checkpoint Blockade in Multiple Colorectal Cancer Cohorts
title_sort tumor microenvironment heterogeneity-based score system predicts clinical prognosis and response to immune checkpoint blockade in multiple colorectal cancer cohorts
topic Molecular Biosciences
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9274205/
https://www.ncbi.nlm.nih.gov/pubmed/35836930
http://dx.doi.org/10.3389/fmolb.2022.884839
work_keys_str_mv AT wanghufei tumormicroenvironmentheterogeneitybasedscoresystempredictsclinicalprognosisandresponsetoimmunecheckpointblockadeinmultiplecolorectalcancercohorts
AT lizhi tumormicroenvironmentheterogeneitybasedscoresystempredictsclinicalprognosisandresponsetoimmunecheckpointblockadeinmultiplecolorectalcancercohorts
AT ousuwen tumormicroenvironmentheterogeneitybasedscoresystempredictsclinicalprognosisandresponsetoimmunecheckpointblockadeinmultiplecolorectalcancercohorts
AT songyanni tumormicroenvironmentheterogeneitybasedscoresystempredictsclinicalprognosisandresponsetoimmunecheckpointblockadeinmultiplecolorectalcancercohorts
AT luokangjia tumormicroenvironmentheterogeneitybasedscoresystempredictsclinicalprognosisandresponsetoimmunecheckpointblockadeinmultiplecolorectalcancercohorts
AT guanzilong tumormicroenvironmentheterogeneitybasedscoresystempredictsclinicalprognosisandresponsetoimmunecheckpointblockadeinmultiplecolorectalcancercohorts
AT zhaolei tumormicroenvironmentheterogeneitybasedscoresystempredictsclinicalprognosisandresponsetoimmunecheckpointblockadeinmultiplecolorectalcancercohorts
AT huangrui tumormicroenvironmentheterogeneitybasedscoresystempredictsclinicalprognosisandresponsetoimmunecheckpointblockadeinmultiplecolorectalcancercohorts
AT yushan tumormicroenvironmentheterogeneitybasedscoresystempredictsclinicalprognosisandresponsetoimmunecheckpointblockadeinmultiplecolorectalcancercohorts