Cargando…

Integrated in silico analysis of LRP2 mutations to immunotherapy efficacy in pan-cancer cohort

PURPOSE: Immunotherapy has emerged as a novel therapy, while many patients are refractory. Although, several biomarkers have been identified as predictive biomarkers for immunotherapy, such as tumor specific genes, PD-1/PD-L1, tumor mutation burn (TMB), and microsatellite instability (MSI), results...

Descripción completa

Detalles Bibliográficos
Autores principales: Li, Chunbo, Ding, Yan, Zhang, Xuyin, Hua, Keqin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9283634/
https://www.ncbi.nlm.nih.gov/pubmed/35834061
http://dx.doi.org/10.1007/s12672-022-00528-8
_version_ 1784747366613516288
author Li, Chunbo
Ding, Yan
Zhang, Xuyin
Hua, Keqin
author_facet Li, Chunbo
Ding, Yan
Zhang, Xuyin
Hua, Keqin
author_sort Li, Chunbo
collection PubMed
description PURPOSE: Immunotherapy has emerged as a novel therapy, while many patients are refractory. Although, several biomarkers have been identified as predictive biomarkers for immunotherapy, such as tumor specific genes, PD-1/PD-L1, tumor mutation burn (TMB), and microsatellite instability (MSI), results remain unsatisfactory. The aim of this study is to evaluate the value of LRP2 mutations in predicating cancer immunotherapy. METHODS: We investigated the characteristics of low-density lipoprotein receptor-related protein 2 (LRP2) mutation in the cancer genome atlas (TCGA) and explored the potential association of LRP2 mutations with immunotherapy. Characteristics of LRP2 mutations in 33 cancer types were analyzed using large-scale public data. The association of LRP2 mutations with immune cell infiltration and immunotherapy efficacy was evaluated. Finally, a LPR2 mutation signature (LMS) was developed and validated by TCGA-UCEC and pan-cancer cohorts. Furthermore, we demonstrated the predictive power of LMS score in independent immunotherapy cohorts by performing a meta-analysis. RESULTS: Our results revealed that patients with LRP2 mutant had higher TMB and MSI compared with patients without LRP2 mutations. LRP2 mutations were associated with high levels of immune cells infiltration, immune-related genes expression and enrichment of immune related signaling pathways. Importantly, LRP2-mutated patients had a long overall survival (OS) after immunotherapy. In the endometrial cancer (EC) cohort, we found that patients with LRP2 mutations belonged to the POLE and MSI-H type and had a better prognosis. Finally, we developed a LRP2 mutations signature (LMS), that was significantly associated with prognosis in patients receiving immunotherapy. CONCLUSION: These results indicated that LRP2 mutations can serve as a biomarker for personalized tumor immunotherapy. Importantly, LMS is a potential predictor of patients’ prognosis after immunotherapy. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s12672-022-00528-8.
format Online
Article
Text
id pubmed-9283634
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Springer US
record_format MEDLINE/PubMed
spelling pubmed-92836342022-07-16 Integrated in silico analysis of LRP2 mutations to immunotherapy efficacy in pan-cancer cohort Li, Chunbo Ding, Yan Zhang, Xuyin Hua, Keqin Discov Oncol Research PURPOSE: Immunotherapy has emerged as a novel therapy, while many patients are refractory. Although, several biomarkers have been identified as predictive biomarkers for immunotherapy, such as tumor specific genes, PD-1/PD-L1, tumor mutation burn (TMB), and microsatellite instability (MSI), results remain unsatisfactory. The aim of this study is to evaluate the value of LRP2 mutations in predicating cancer immunotherapy. METHODS: We investigated the characteristics of low-density lipoprotein receptor-related protein 2 (LRP2) mutation in the cancer genome atlas (TCGA) and explored the potential association of LRP2 mutations with immunotherapy. Characteristics of LRP2 mutations in 33 cancer types were analyzed using large-scale public data. The association of LRP2 mutations with immune cell infiltration and immunotherapy efficacy was evaluated. Finally, a LPR2 mutation signature (LMS) was developed and validated by TCGA-UCEC and pan-cancer cohorts. Furthermore, we demonstrated the predictive power of LMS score in independent immunotherapy cohorts by performing a meta-analysis. RESULTS: Our results revealed that patients with LRP2 mutant had higher TMB and MSI compared with patients without LRP2 mutations. LRP2 mutations were associated with high levels of immune cells infiltration, immune-related genes expression and enrichment of immune related signaling pathways. Importantly, LRP2-mutated patients had a long overall survival (OS) after immunotherapy. In the endometrial cancer (EC) cohort, we found that patients with LRP2 mutations belonged to the POLE and MSI-H type and had a better prognosis. Finally, we developed a LRP2 mutations signature (LMS), that was significantly associated with prognosis in patients receiving immunotherapy. CONCLUSION: These results indicated that LRP2 mutations can serve as a biomarker for personalized tumor immunotherapy. Importantly, LMS is a potential predictor of patients’ prognosis after immunotherapy. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s12672-022-00528-8. Springer US 2022-07-14 /pmc/articles/PMC9283634/ /pubmed/35834061 http://dx.doi.org/10.1007/s12672-022-00528-8 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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/) .
spellingShingle Research
Li, Chunbo
Ding, Yan
Zhang, Xuyin
Hua, Keqin
Integrated in silico analysis of LRP2 mutations to immunotherapy efficacy in pan-cancer cohort
title Integrated in silico analysis of LRP2 mutations to immunotherapy efficacy in pan-cancer cohort
title_full Integrated in silico analysis of LRP2 mutations to immunotherapy efficacy in pan-cancer cohort
title_fullStr Integrated in silico analysis of LRP2 mutations to immunotherapy efficacy in pan-cancer cohort
title_full_unstemmed Integrated in silico analysis of LRP2 mutations to immunotherapy efficacy in pan-cancer cohort
title_short Integrated in silico analysis of LRP2 mutations to immunotherapy efficacy in pan-cancer cohort
title_sort integrated in silico analysis of lrp2 mutations to immunotherapy efficacy in pan-cancer cohort
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9283634/
https://www.ncbi.nlm.nih.gov/pubmed/35834061
http://dx.doi.org/10.1007/s12672-022-00528-8
work_keys_str_mv AT lichunbo integratedinsilicoanalysisoflrp2mutationstoimmunotherapyefficacyinpancancercohort
AT dingyan integratedinsilicoanalysisoflrp2mutationstoimmunotherapyefficacyinpancancercohort
AT zhangxuyin integratedinsilicoanalysisoflrp2mutationstoimmunotherapyefficacyinpancancercohort
AT huakeqin integratedinsilicoanalysisoflrp2mutationstoimmunotherapyefficacyinpancancercohort