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Selective poly adenylation predicts the efficacy of immunotherapy in patients with lung adenocarcinoma by multiple omics research

The aim of this study was to find the application value of selective polyadenylation in immune cell infiltration, biological transcription function and risk assessment of survival and prognosis in lung adenocarcinoma (LUAD). The processed original mRNA expression data of LUAD were downloaded, and th...

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Autores principales: Wu, Liusheng, Zhong, Yanfeng, Yu, Xiaoya, Wu, Dingwang, Xu, Pengcheng, Lv, Le, Ruan, Xin, Liu, Qi, Feng, Yu, Liu, Jixian, Li, Xiaoqiang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Lippincott Williams & Wilkins 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9481295/
https://www.ncbi.nlm.nih.gov/pubmed/35946526
http://dx.doi.org/10.1097/CAD.0000000000001319
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author Wu, Liusheng
Zhong, Yanfeng
Yu, Xiaoya
Wu, Dingwang
Xu, Pengcheng
Lv, Le
Ruan, Xin
Liu, Qi
Feng, Yu
Liu, Jixian
Li, Xiaoqiang
author_facet Wu, Liusheng
Zhong, Yanfeng
Yu, Xiaoya
Wu, Dingwang
Xu, Pengcheng
Lv, Le
Ruan, Xin
Liu, Qi
Feng, Yu
Liu, Jixian
Li, Xiaoqiang
author_sort Wu, Liusheng
collection PubMed
description The aim of this study was to find the application value of selective polyadenylation in immune cell infiltration, biological transcription function and risk assessment of survival and prognosis in lung adenocarcinoma (LUAD). The processed original mRNA expression data of LUAD were downloaded, and the expression profiles of 594 patient samples were collected. The (APA) events in TCGA-NA-SEQ data were evaluated by polyadenylation site use Index (PDUI) values, and the invasion of stromal cells and immune cells and tumor purity were calculated to group and select the differential genes. Lasso regression and stratified analysis were used to examine the role of risk scores in predicting patient outcomes. The study also used the GDSC database to predict the chemotherapeutic sensitivity of each tumor sample and used a regression method to obtain an IC50 estimate for each specific chemotherapeutic drug treatment. Then CIBERSORT algorithm was used to conduct Spearman correlation analysis, immune regulatory factor analysis and TIDE immune system function analysis for gene expression level and immune cell content. Finally, the Kaplan–Meier curve was used to analyze the correlation between stromal score and the immune score of LUAD. In this study, APA’s LUAD risk score prognostic model was constructed. KM survival analysis showed that immune score affected the prognosis of LUAD patients (P = 0.027) but the matrix score was not statistically significant (P = 0.1). We extracted 108 genes with APA events from 827 different genes and based on PUDI clustering and heat map, the survival rate of patients in the four groups was significantly different (P = 0.05). Multiple omics studies showed that risk score was significantly positively correlated with Macrophages M0, T cells Follicular helper, B cells naive and NK cells resting. It is significantly negatively correlated with dendritic cells resting, mast cells resting, monocyte, T cells CD4 memory resting and B cells memory. We further explored the relationship between the expression of immunosuppressor genes and risk score and found that ADORA2A, BTLA, CD160, CD244, CD274, CD96, CSF1R and CTLA4 genes were highly correlated with the risk score. Selective poly adenylation plays an important role in the development and progression of LUAD, immune invasion, tumor cell invasion and metastasis and biological transcription, and affects the survival and prognosis of LUAD patients.
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spelling pubmed-94812952022-09-21 Selective poly adenylation predicts the efficacy of immunotherapy in patients with lung adenocarcinoma by multiple omics research Wu, Liusheng Zhong, Yanfeng Yu, Xiaoya Wu, Dingwang Xu, Pengcheng Lv, Le Ruan, Xin Liu, Qi Feng, Yu Liu, Jixian Li, Xiaoqiang Anticancer Drugs Original Studies The aim of this study was to find the application value of selective polyadenylation in immune cell infiltration, biological transcription function and risk assessment of survival and prognosis in lung adenocarcinoma (LUAD). The processed original mRNA expression data of LUAD were downloaded, and the expression profiles of 594 patient samples were collected. The (APA) events in TCGA-NA-SEQ data were evaluated by polyadenylation site use Index (PDUI) values, and the invasion of stromal cells and immune cells and tumor purity were calculated to group and select the differential genes. Lasso regression and stratified analysis were used to examine the role of risk scores in predicting patient outcomes. The study also used the GDSC database to predict the chemotherapeutic sensitivity of each tumor sample and used a regression method to obtain an IC50 estimate for each specific chemotherapeutic drug treatment. Then CIBERSORT algorithm was used to conduct Spearman correlation analysis, immune regulatory factor analysis and TIDE immune system function analysis for gene expression level and immune cell content. Finally, the Kaplan–Meier curve was used to analyze the correlation between stromal score and the immune score of LUAD. In this study, APA’s LUAD risk score prognostic model was constructed. KM survival analysis showed that immune score affected the prognosis of LUAD patients (P = 0.027) but the matrix score was not statistically significant (P = 0.1). We extracted 108 genes with APA events from 827 different genes and based on PUDI clustering and heat map, the survival rate of patients in the four groups was significantly different (P = 0.05). Multiple omics studies showed that risk score was significantly positively correlated with Macrophages M0, T cells Follicular helper, B cells naive and NK cells resting. It is significantly negatively correlated with dendritic cells resting, mast cells resting, monocyte, T cells CD4 memory resting and B cells memory. We further explored the relationship between the expression of immunosuppressor genes and risk score and found that ADORA2A, BTLA, CD160, CD244, CD274, CD96, CSF1R and CTLA4 genes were highly correlated with the risk score. Selective poly adenylation plays an important role in the development and progression of LUAD, immune invasion, tumor cell invasion and metastasis and biological transcription, and affects the survival and prognosis of LUAD patients. Lippincott Williams & Wilkins 2022-08-09 2022-10 /pmc/articles/PMC9481295/ /pubmed/35946526 http://dx.doi.org/10.1097/CAD.0000000000001319 Text en Copyright © 2022 The Author(s). Published by Wolters Kluwer Health, Inc. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND) (https://creativecommons.org/licenses/by-nc-nd/4.0/) , where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal.
spellingShingle Original Studies
Wu, Liusheng
Zhong, Yanfeng
Yu, Xiaoya
Wu, Dingwang
Xu, Pengcheng
Lv, Le
Ruan, Xin
Liu, Qi
Feng, Yu
Liu, Jixian
Li, Xiaoqiang
Selective poly adenylation predicts the efficacy of immunotherapy in patients with lung adenocarcinoma by multiple omics research
title Selective poly adenylation predicts the efficacy of immunotherapy in patients with lung adenocarcinoma by multiple omics research
title_full Selective poly adenylation predicts the efficacy of immunotherapy in patients with lung adenocarcinoma by multiple omics research
title_fullStr Selective poly adenylation predicts the efficacy of immunotherapy in patients with lung adenocarcinoma by multiple omics research
title_full_unstemmed Selective poly adenylation predicts the efficacy of immunotherapy in patients with lung adenocarcinoma by multiple omics research
title_short Selective poly adenylation predicts the efficacy of immunotherapy in patients with lung adenocarcinoma by multiple omics research
title_sort selective poly adenylation predicts the efficacy of immunotherapy in patients with lung adenocarcinoma by multiple omics research
topic Original Studies
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9481295/
https://www.ncbi.nlm.nih.gov/pubmed/35946526
http://dx.doi.org/10.1097/CAD.0000000000001319
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