A 13-Gene Signature Based on Estrogen Response Pathway for Predicting Survival and Immune Responses of Patients With UCEC

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Abstract

Background: Accumulating evidence suggests that anti-estrogens have been effective against multiple gynecological diseases, especially advanced uterine corpus endometrial carcinoma (UCEC), highlighting the contribution of the estrogen response pathway in UCEC progression. This study aims to identify a reliable prognostic signature for potentially aiding in the comprehensive management of UCEC.

Methods: Firstly, univariate Cox and LASSO regression were performed to identify a satisfying UCEC prognostic model quantifying patients’ risk, constructed from estrogen-response-related genes and verified to be effective by Kaplan-Meier curves, ROC curves, univariate and multivariate Cox regression. Additionally, a nomogram was constructed integrating the prognostic model and other clinicopathological parameters. Next, UCEC patients from the TCGA dataset were divided into low- and high-risk groups according to the median risk score. To elucidate differences in biological characteristics between the two risk groups, pathway enrichment, immune landscape, genomic alterations, and therapeutic responses were evaluated to satisfy this objective. As for treatment, effective responses to anti-PD-1 therapy in the low-risk patients and sensitivity to six chemotherapy drugs in the high-risk patients were demonstrated.

Results: The low-risk group with a relatively favorable prognosis was marked by increased immune cell infiltration, higher expression levels of HLA members and immune checkpoint biomarkers, higher tumor mutation burden, and lower copy number alterations. This UCEC prognostic signature, composed of 13 estrogen-response-related genes, has been identified and verified as effective.

Conclusion: Our study provides molecular signatures for further functional and therapeutic investigations of estrogen-response-related genes in UCEC and represents a potential systemic approach to characterize key factors in UCEC pathogenesis and therapeutic responses.

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Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries

Hyuna Sung, Jacques Ferlay, Rebecca Siegel … (2021)

This article provides an update on the global cancer burden using the GLOBOCAN 2020 estimates of cancer incidence and mortality produced by the International Agency for Research on Cancer. Worldwide, an estimated 19.3 million new cancer cases (18.1 million excluding nonmelanoma skin cancer) and almost 10.0 million cancer deaths (9.9 million excluding nonmelanoma skin cancer) occurred in 2020. Female breast cancer has surpassed lung cancer as the most commonly diagnosed cancer, with an estimated 2.3 million new cases (11.7%), followed by lung (11.4%), colorectal (10.0 %), prostate (7.3%), and stomach (5.6%) cancers. Lung cancer remained the leading cause of cancer death, with an estimated 1.8 million deaths (18%), followed by colorectal (9.4%), liver (8.3%), stomach (7.7%), and female breast (6.9%) cancers. Overall incidence was from 2-fold to 3-fold higher in transitioned versus transitioning countries for both sexes, whereas mortality varied <2-fold for men and little for women. Death rates for female breast and cervical cancers, however, were considerably higher in transitioning versus transitioned countries (15.0 vs 12.8 per 100,000 and 12.4 vs 5.2 per 100,000, respectively). The global cancer burden is expected to be 28.4 million cases in 2040, a 47% rise from 2020, with a larger increase in transitioning (64% to 95%) versus transitioned (32% to 56%) countries due to demographic changes, although this may be further exacerbated by increasing risk factors associated with globalization and a growing economy. Efforts to build a sustainable infrastructure for the dissemination of cancer prevention measures and provision of cancer care in transitioning countries is critical for global cancer control.

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clusterProfiler: an R package for comparing biological themes among gene clusters.

Guangchuang Yu, Li-Gen Wang, Yanyan Han … (2012)

Increasing quantitative data generated from transcriptomics and proteomics require integrative strategies for analysis. Here, we present an R package, clusterProfiler that automates the process of biological-term classification and the enrichment analysis of gene clusters. The analysis module and visualization module were combined into a reusable workflow. Currently, clusterProfiler supports three species, including humans, mice, and yeast. Methods provided in this package can be easily extended to other species and ontologies. The clusterProfiler package is released under Artistic-2.0 License within Bioconductor project. The source code and vignette are freely available at http://bioconductor.org/packages/release/bioc/html/clusterProfiler.html.

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Is Open Access

GSVA: gene set variation analysis for microarray and RNA-Seq data

Sonja Hänzelmann, Robert Castelo, Justin Guinney (2013)

Background Gene set enrichment (GSE) analysis is a popular framework for condensing information from gene expression profiles into a pathway or signature summary. The strengths of this approach over single gene analysis include noise and dimension reduction, as well as greater biological interpretability. As molecular profiling experiments move beyond simple case-control studies, robust and flexible GSE methodologies are needed that can model pathway activity within highly heterogeneous data sets. Results To address this challenge, we introduce Gene Set Variation Analysis (GSVA), a GSE method that estimates variation of pathway activity over a sample population in an unsupervised manner. We demonstrate the robustness of GSVA in a comparison with current state of the art sample-wise enrichment methods. Further, we provide examples of its utility in differential pathway activity and survival analysis. Lastly, we show how GSVA works analogously with data from both microarray and RNA-seq experiments. Conclusions GSVA provides increased power to detect subtle pathway activity changes over a sample population in comparison to corresponding methods. While GSE methods are generally regarded as end points of a bioinformatic analysis, GSVA constitutes a starting point to build pathway-centric models of biology. Moreover, GSVA contributes to the current need of GSE methods for RNA-seq data. GSVA is an open source software package for R which forms part of the Bioconductor project and can be downloaded at http://www.bioconductor.org.

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Author and article information

Contributors

Yimin Li: URI : https://loop.frontiersin.org/people/1595833/overview

Ruotong Tian: URI : https://loop.frontiersin.org/people/1595816/overview

Jiaxin Liu: URI : https://loop.frontiersin.org/people/1354024/overview

Chunlin Ou: URI : https://loop.frontiersin.org/people/679006/overview

Qihui Wu: URI : https://loop.frontiersin.org/people/1566699/overview

Xiaodan Fu: URI : https://loop.frontiersin.org/people/1569409/overview

Journal

Journal ID (nlm-ta): Front Mol Biosci

Journal ID (iso-abbrev): Front Mol Biosci

Journal ID (publisher-id): Front. Mol. Biosci.

Title: Frontiers in Molecular Biosciences

Publisher: Frontiers Media S.A.

ISSN (Electronic): 2296-889X

Publication date (Electronic): 26 April 2022

Publication date Collection: 2022

Volume: 9

Electronic Location Identifier: 833910

Affiliations

[1] ¹ Department of Pathology , Fudan University Shanghai Cancer Center , Shanghai, China

[2] ² Department of Oncology , Shanghai Medical College , Fudan University , Shanghai, China

[3] ³ Department of Pharmacology , School of Basic Medical Sciences , Shanghai Medical College , Fudan University , Shanghai, China

[4] ⁴ Department of Pathology , School of Basic Medical Sciences , Central South University , Changsha, China

[5] ⁵ Department of Pathology , Xiangya Hospital , Central South University , Changsha, China

[6] ⁶ National Clinical Research Center for Geriatric Disorders , Xiangya Hospital , Changsha, China

[7] ⁷ Department of Obstetrics and Gynecology , Xiangya Hospital , Central South University , Changsha, China

Author notes

Edited by: Xinwei Han, Zhengzhou University, China

Reviewed by: Xiaoyong Ge, Zhengzhou University, China

Pedro José Carlos Rondot Radío, , University of Buenos Aires, Argentina

*Correspondence: Xiaodan Fu, jessicafu0225@ 123456163.com ; Qihui Wu, 146511038@ 123456csu.edu.cn

[ † ]

These authors have contributed equally to this work and share first authorship

This article was submitted to Molecular Diagnostics and Therapeutics, a section of the journal Frontiers in Molecular Biosciences

Article

Publisher ID: 833910

DOI: 10.3389/fmolb.2022.833910

PMC ID: 9087353

PubMed ID: 35558564

SO-VID: 140e2a02-d203-41ea-bc39-dd95568ad06e

License:

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.

History

Date received : 12 December 2021

Date accepted : 11 April 2022

Funding

Funded by: National Natural Science Foundation of China , doi 10.13039/501100001809;

A 13-Gene Signature Based on Estrogen Response Pathway for Predicting Survival and Immune Responses of Patients With UCEC

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Most cited references 61

Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries

clusterProfiler: an R package for comparing biological themes among gene clusters.

GSVA: gene set variation analysis for microarray and RNA-Seq data

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