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Identification of STEAP3-based molecular subtype and risk model in ovarian cancer

BACKGROUND: Ovarian cancer (OC) is one of the most common malignancies in women. It has a poor prognosis owing to its recurrence and metastasis. Unfortunately, reliable markers for early diagnosis and prognosis of OC are lacking. Our research aimed to investigate the value of the six-transmembrane e...

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Autores principales: Zhao, Zouyu, Sun, Chongfeng, Hou, Jishuai, Yu, Panpan, Wei, Yan, Bai, Rui, Yang, Ping
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10308644/
https://www.ncbi.nlm.nih.gov/pubmed/37386521
http://dx.doi.org/10.1186/s13048-023-01218-x
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author Zhao, Zouyu
Sun, Chongfeng
Hou, Jishuai
Yu, Panpan
Wei, Yan
Bai, Rui
Yang, Ping
author_facet Zhao, Zouyu
Sun, Chongfeng
Hou, Jishuai
Yu, Panpan
Wei, Yan
Bai, Rui
Yang, Ping
author_sort Zhao, Zouyu
collection PubMed
description BACKGROUND: Ovarian cancer (OC) is one of the most common malignancies in women. It has a poor prognosis owing to its recurrence and metastasis. Unfortunately, reliable markers for early diagnosis and prognosis of OC are lacking. Our research aimed to investigate the value of the six-transmembrane epithelial antigen of prostate family member 3 (STEAP3) as a prognostic predictor and therapeutic target in OC using bioinformatics analysis. METHODS: STEAP3 expression and clinical data were acquired from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and Gene Expression Omnibus (GEO). Unsupervised clustering was used to identify molecular subtypes. Prognosis, tumor immune microenvironment (TIME), stemness indexes, and functional enrichment analysis were compared between two definite clusters. Through the least absolute shrinkage and selection operator (LASSO) regression analysis, a STEAP3-based risk model was developed, and the predictive effectiveness of this signature was confirmed using GEO datasets. A nomogram was used to predict the survival possibility of patients. Additionally, TIME, tumor immune dysfunction and exclusion (TIDE), stemness indexes, somatic mutations, and drug sensitivity were evaluated in different risk groups with OC. STEAP3 protein expression was detected using immunohistochemistry (IHC). RESULTS: STEAP3 displayed marked overexpression in OC. STEAP3 is an independent risk factor for OC. Based on the mRNA levels of STEAP3-related genes (SRGs), two distinct clusters were identified. Patients in the cluster 2 (C2) subgroup had a considerably worse prognosis, higher immune cell infiltration, and lower stemness scores. Pathways involved in tumorigenesis and immunity were highly enriched in the C2 subgroup. A prognostic model based on 13 SRGs was further developed. Kaplan-Meier analysis indicated that the overall survival (OS) of high-risk patients was poor. The risk score was significantly associated with TIME, TIDE, stemness indexes, tumor mutation burden (TMB), immunotherapy response, and drug sensitivity. Finally, IHC revealed that STEAP3 protein expression was noticeably elevated in OC, and overexpression of STEAP3 predicted poor OS and relapse-free survival (RFS) of patients. CONCLUSION: In summary, this study revealed that STEAP3 reliably predicts patient prognosis and provides novel ideas for OC immunotherapy. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13048-023-01218-x.
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spelling pubmed-103086442023-06-30 Identification of STEAP3-based molecular subtype and risk model in ovarian cancer Zhao, Zouyu Sun, Chongfeng Hou, Jishuai Yu, Panpan Wei, Yan Bai, Rui Yang, Ping J Ovarian Res Research BACKGROUND: Ovarian cancer (OC) is one of the most common malignancies in women. It has a poor prognosis owing to its recurrence and metastasis. Unfortunately, reliable markers for early diagnosis and prognosis of OC are lacking. Our research aimed to investigate the value of the six-transmembrane epithelial antigen of prostate family member 3 (STEAP3) as a prognostic predictor and therapeutic target in OC using bioinformatics analysis. METHODS: STEAP3 expression and clinical data were acquired from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and Gene Expression Omnibus (GEO). Unsupervised clustering was used to identify molecular subtypes. Prognosis, tumor immune microenvironment (TIME), stemness indexes, and functional enrichment analysis were compared between two definite clusters. Through the least absolute shrinkage and selection operator (LASSO) regression analysis, a STEAP3-based risk model was developed, and the predictive effectiveness of this signature was confirmed using GEO datasets. A nomogram was used to predict the survival possibility of patients. Additionally, TIME, tumor immune dysfunction and exclusion (TIDE), stemness indexes, somatic mutations, and drug sensitivity were evaluated in different risk groups with OC. STEAP3 protein expression was detected using immunohistochemistry (IHC). RESULTS: STEAP3 displayed marked overexpression in OC. STEAP3 is an independent risk factor for OC. Based on the mRNA levels of STEAP3-related genes (SRGs), two distinct clusters were identified. Patients in the cluster 2 (C2) subgroup had a considerably worse prognosis, higher immune cell infiltration, and lower stemness scores. Pathways involved in tumorigenesis and immunity were highly enriched in the C2 subgroup. A prognostic model based on 13 SRGs was further developed. Kaplan-Meier analysis indicated that the overall survival (OS) of high-risk patients was poor. The risk score was significantly associated with TIME, TIDE, stemness indexes, tumor mutation burden (TMB), immunotherapy response, and drug sensitivity. Finally, IHC revealed that STEAP3 protein expression was noticeably elevated in OC, and overexpression of STEAP3 predicted poor OS and relapse-free survival (RFS) of patients. CONCLUSION: In summary, this study revealed that STEAP3 reliably predicts patient prognosis and provides novel ideas for OC immunotherapy. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13048-023-01218-x. BioMed Central 2023-06-29 /pmc/articles/PMC10308644/ /pubmed/37386521 http://dx.doi.org/10.1186/s13048-023-01218-x Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This 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/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Zhao, Zouyu
Sun, Chongfeng
Hou, Jishuai
Yu, Panpan
Wei, Yan
Bai, Rui
Yang, Ping
Identification of STEAP3-based molecular subtype and risk model in ovarian cancer
title Identification of STEAP3-based molecular subtype and risk model in ovarian cancer
title_full Identification of STEAP3-based molecular subtype and risk model in ovarian cancer
title_fullStr Identification of STEAP3-based molecular subtype and risk model in ovarian cancer
title_full_unstemmed Identification of STEAP3-based molecular subtype and risk model in ovarian cancer
title_short Identification of STEAP3-based molecular subtype and risk model in ovarian cancer
title_sort identification of steap3-based molecular subtype and risk model in ovarian cancer
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10308644/
https://www.ncbi.nlm.nih.gov/pubmed/37386521
http://dx.doi.org/10.1186/s13048-023-01218-x
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