Evidence map›Paper›PMID 33400208›Full record

ArticleBreast cancer (Tokyo, Japan)2021

An immune-related risk gene signature predicts the prognosis of breast cancer.

Wenning Cao, Yike Jiang, Xiang Ji, Lan Ma

Abstract read
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Article in Breast cancer (Tokyo, Japan), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 · What the graph read from it

What it found

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Wenning CaoDepartment of Chemistry, Tsinghua University, Beijing, 100084, People's Republic of China.
Yike JiangInstitute of Biomedical Health Technology and Engineering, Shenzhen Bay Laboratory, Shenzhen, 518132, People's Republic of China.
Xiang JiState Key Laboratory of Chemical Oncogenomics, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, 518055, People's Republic of China.
Lan MaState Key Laboratory of Chemical Oncogenomics, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, 518055, People's Republic of China. malan@sz.tsinghua.edu.cn.ORCID http://orcid.org/0000-0002-6610-5871

Funding

Technology R & D Funds of Shenzhen, China GJHZ20170314164935502
6 · The paper itself

Abstract

backgroundAccurate prediction of the outcome of breast cancer remains as a challenge due to its heterogeneous nature. We aimed to construct an immune-related risk signature to predict the overall outcome of breast cancer using bioinformatic approaches.

methodsIn this study, transcriptome and survival data obtained from The Cancer Genome Atlas database and the Gene Expression Omnibus database were used to identify differentially expressed genes between breast cancer and normal samples. A regulatory network was constructed based on the immune-related prognostic genes and transcription factors screened from the differently expressed genes. The immune-related risk gene signature was obtained using the least absolute shrinkage and selection operator (LASSO) method and Cox regression model. The immune-related prognostic scores of breast cancer (IPSBC) calculated from the risk signature were used to group breast cancer patients by risk levels. The accuracy of IPSBC was evaluated by survival analysis and receiver operating characteristic curve analysis. The independency and the relationship of IPSBC with clinicopathological characteristics and abundance of tumor-infiltrated immune cells were also investigated.

resultsA total of 4296 differentially expressed genes between breast cancer and normal samples were identified, and a total of 13 prognostic immune-related genes were eventually selected as the risk gene signature, which was an independent prognostic factor of the overall survival of breast cancer. The IPSBC stratified breast cancer patients into low- and high-risk groups. Breast cancer patients in the high-risk group were associated with worse overall outcomes, more advanced stage and less abundance of tumor-infiltrated immune cells, including B cells, CD4

conclusionIn this study, an immune-related gene signature of breast cancer was identified, which could be used as potential prognostic and therapeutic targets of breast cancer.

Indexed as

AgedBiomarkers, TumorBreast NeoplasmsDatabases, GeneticDisease-Free SurvivalFemaleGene Expression ProfilingGene Expression Regulation, NeoplasticHumansMiddle AgedRisk AssessmentSurvival AnalysisTranscription FactorsTranscriptomeBiomarkers, TumorTranscription FactorsBreast cancerImmune-related genesPrognostic modelSurvival analysisTumor-infiltrated immune cells

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.