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Identification and Quantification of Iron Metabolism Landscape on Therapy and Prognosis in Bladder Cancer

The morbidity of bladder cancer (BLCA) is high and has gradually elevated in recent years. BLCA is also characterized by high recurrence and high invasiveness. Due to the drug resistance and lack of effective prognostic indicators, the prognosis of patients with BLCA is greatly affected. Iron metabo...

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Autores principales: Song, Xiaodong, Xin, Sheng, Zhang, Yucong, Mao, Jiaquan, Duan, Chen, Cui, Kai, Chen, Liang, Li, Fan, Liu, Zheng, Wang, Tao, Liu, Jihong, Liu, Xiaming, Song, Wen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8899848/
https://www.ncbi.nlm.nih.gov/pubmed/35265613
http://dx.doi.org/10.3389/fcell.2022.810272
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author Song, Xiaodong
Xin, Sheng
Zhang, Yucong
Mao, Jiaquan
Duan, Chen
Cui, Kai
Chen, Liang
Li, Fan
Liu, Zheng
Wang, Tao
Liu, Jihong
Liu, Xiaming
Song, Wen
author_facet Song, Xiaodong
Xin, Sheng
Zhang, Yucong
Mao, Jiaquan
Duan, Chen
Cui, Kai
Chen, Liang
Li, Fan
Liu, Zheng
Wang, Tao
Liu, Jihong
Liu, Xiaming
Song, Wen
author_sort Song, Xiaodong
collection PubMed
description The morbidity of bladder cancer (BLCA) is high and has gradually elevated in recent years. BLCA is also characterized by high recurrence and high invasiveness. Due to the drug resistance and lack of effective prognostic indicators, the prognosis of patients with BLCA is greatly affected. Iron metabolism is considered to be a pivot of tumor occurrence, progression, and tumor microenvironment (TME) in tumors, but there is little research in BLCA. Herein, we used univariate COX regression analysis to screen 95 prognosis-related iron metabolism-related genes (IMRGs) according to transcription RNA sequencing and prognosis information of the Cancer Genome Atlas (TCGA) database. TCGA-BLCA cohort was clustered into four distinct iron metabolism patterns (C1, C2, C3, and C4) by the non-negative matrix factorization (NMF) algorithm. Survival analysis showed that C1 and C3 patterns had a better prognosis. Gene set variant analysis (GSVA) revealed that C2 and C4 patterns were mostly enriched in carcinogenic and immune activation pathways. ESTIMATE and single sample gene set enrichment analysis (ssGSEA) also confirmed the level of immune cell infiltration in C2 and C4 patterns was significantly elevated. Moreover, the immune checkpoint genes in C2 and C4 patterns were observably overexpressed. Studies on somatic mutations showed that the tumor mutation burden (TMB) of C1 and C4 patterns was the lowest. Chemotherapy response assessment revealed that C2 pattern was the most sensitive to chemotherapy, while C3 pattern was the most insensitive. Then we established the IMRG prognosis signature (IMRGscore) by the least absolute shrinkage and selection operator (LASSO), including 13 IMRGs (TCIRG1, CTSE, ATP6V0A1, CYP2C8, RNF19A, CYP4Z1, YPEL5, PLOD1, BMP6, CAST, SCD, IFNG, and ASIC3). We confirmed IMRGscore could be utilized as an independent prognostic indicator. Therefore, validation and quantification of iron metabolism landscapes will help us comprehend the formation of the BLCA immunosuppressive microenvironment, guide the selection of chemotherapeutic drugs and immunotherapy, and predict the prognosis of patients.
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spelling pubmed-88998482022-03-08 Identification and Quantification of Iron Metabolism Landscape on Therapy and Prognosis in Bladder Cancer Song, Xiaodong Xin, Sheng Zhang, Yucong Mao, Jiaquan Duan, Chen Cui, Kai Chen, Liang Li, Fan Liu, Zheng Wang, Tao Liu, Jihong Liu, Xiaming Song, Wen Front Cell Dev Biol Cell and Developmental Biology The morbidity of bladder cancer (BLCA) is high and has gradually elevated in recent years. BLCA is also characterized by high recurrence and high invasiveness. Due to the drug resistance and lack of effective prognostic indicators, the prognosis of patients with BLCA is greatly affected. Iron metabolism is considered to be a pivot of tumor occurrence, progression, and tumor microenvironment (TME) in tumors, but there is little research in BLCA. Herein, we used univariate COX regression analysis to screen 95 prognosis-related iron metabolism-related genes (IMRGs) according to transcription RNA sequencing and prognosis information of the Cancer Genome Atlas (TCGA) database. TCGA-BLCA cohort was clustered into four distinct iron metabolism patterns (C1, C2, C3, and C4) by the non-negative matrix factorization (NMF) algorithm. Survival analysis showed that C1 and C3 patterns had a better prognosis. Gene set variant analysis (GSVA) revealed that C2 and C4 patterns were mostly enriched in carcinogenic and immune activation pathways. ESTIMATE and single sample gene set enrichment analysis (ssGSEA) also confirmed the level of immune cell infiltration in C2 and C4 patterns was significantly elevated. Moreover, the immune checkpoint genes in C2 and C4 patterns were observably overexpressed. Studies on somatic mutations showed that the tumor mutation burden (TMB) of C1 and C4 patterns was the lowest. Chemotherapy response assessment revealed that C2 pattern was the most sensitive to chemotherapy, while C3 pattern was the most insensitive. Then we established the IMRG prognosis signature (IMRGscore) by the least absolute shrinkage and selection operator (LASSO), including 13 IMRGs (TCIRG1, CTSE, ATP6V0A1, CYP2C8, RNF19A, CYP4Z1, YPEL5, PLOD1, BMP6, CAST, SCD, IFNG, and ASIC3). We confirmed IMRGscore could be utilized as an independent prognostic indicator. Therefore, validation and quantification of iron metabolism landscapes will help us comprehend the formation of the BLCA immunosuppressive microenvironment, guide the selection of chemotherapeutic drugs and immunotherapy, and predict the prognosis of patients. Frontiers Media S.A. 2022-02-21 /pmc/articles/PMC8899848/ /pubmed/35265613 http://dx.doi.org/10.3389/fcell.2022.810272 Text en Copyright © 2022 Song, Xin, Zhang, Mao, Duan, Cui, Chen, Li, Liu, Wang, Liu, Liu and Song. 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 Cell and Developmental Biology
Song, Xiaodong
Xin, Sheng
Zhang, Yucong
Mao, Jiaquan
Duan, Chen
Cui, Kai
Chen, Liang
Li, Fan
Liu, Zheng
Wang, Tao
Liu, Jihong
Liu, Xiaming
Song, Wen
Identification and Quantification of Iron Metabolism Landscape on Therapy and Prognosis in Bladder Cancer
title Identification and Quantification of Iron Metabolism Landscape on Therapy and Prognosis in Bladder Cancer
title_full Identification and Quantification of Iron Metabolism Landscape on Therapy and Prognosis in Bladder Cancer
title_fullStr Identification and Quantification of Iron Metabolism Landscape on Therapy and Prognosis in Bladder Cancer
title_full_unstemmed Identification and Quantification of Iron Metabolism Landscape on Therapy and Prognosis in Bladder Cancer
title_short Identification and Quantification of Iron Metabolism Landscape on Therapy and Prognosis in Bladder Cancer
title_sort identification and quantification of iron metabolism landscape on therapy and prognosis in bladder cancer
topic Cell and Developmental Biology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8899848/
https://www.ncbi.nlm.nih.gov/pubmed/35265613
http://dx.doi.org/10.3389/fcell.2022.810272
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