Evidence map›Paper›PMID 39627792›Full record

ArticleCancer cell international2024

Identification of a novel immune-related gene signature by single-cell and bulk sequencing for the prediction of the immune landscape and prognosis of breast cancer.

Yanlin Gu, Zhengyang Feng, Xiaoyan Xu, Liyan Jin

Abstract read
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Article in Cancer cell international, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
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1 · What the graph read from it

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

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3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Yanlin Gu *Department of Thyroid and Breast Surgery, The Second Affiliated Hospital of Soochow University, Jiangsu, China.
Zhengyang Feng *Department of Oncology, The Second Affiliated Hospital of Soochow University, Jiangsu, China.
Xiaoyan Xu *Department of Operating Room, Traditional Chinese Medicine Hospital of Kunshan, Jiangsu, China.
Liyan JinDepartment of Thyroid and Breast Surgery, The Second Affiliated Hospital of Soochow University, Jiangsu, China. jinyuliangyuan1985@sina.com.

Funding

Beijing City Technology Innovation Fund KC2021-JF-0167-8State Key Laboratory of Radiation Medicine and Protection GZK12024047Suzhou Health Young Key Talents"National Tutorial system" training project Qngg 2023006Suzhou Health Young Key Talents"National Tutorial system" training project Qngg2024008Suzhou Science and Technology Bureau KJXW2022011
6 · The paper itself

Abstract

backgroundAs a common cause of cancer-related deaths in women, BRCA (breast cancer) shows complexity and requires precise biomarkers and treatment methods. This study delves into the molecular makeup of BRCA, focusing on immune profiles, molecular subtypes, gene expression and single-cell analysis.

methodsXCell was used to assess immune infiltration based on TCGA (the Cancer Genome Atlas) data and the clustering analysis was made. Differentially expressed genes were examined in distinct clusters, and the WGCNA (weighted correlation network analysis) was made to establish co-expression networks. The prognostic models were developed by Cox and LASSO-Cox regression. The clustering analysis, GSEA (Gene set enrichment analysis), GSVA (gene set variation analysis) and communication analysis of the single-cell dataset GSE161529 were performed to investigate the functional relevance. Real-time polymerase chain reaction (RT-PCR) was employed for evaluating gene expression.

resultsThe results revealed significant differences in immune cell infiltration between two clusters (C1 and C2). C2 had poorer survival outcomes, which was associated with higher expression of immune checkpoints PD1 and PD-L1. The gene modules identified via WGCNA were correlated with the immune-based subtypes. Then, a prognostic model comprising seven genes (ACSL1, ABCB5, XG, ADH4, OPN4, NPR3, NLGN1) was used to divide patients into high- and low-risk subgroups. The high-risk group had worse prognosis and higher scores of TIDE (Tumor Immune Dysfunction and Exclusion). The single-cell analysis depicted the immune landscape. Macrophages and endothelial cells exhibited higher AUCell scores. In cellular communication analysis, notably significant ligand-receptor interactions of HLA-DRA-> CD4 and TNFSF13B-> HLA-DPB1 were observed. The proportion of endothelial cells was correlated with risk scores. Finally, RT-PCR results illustrated the expression of seven genes in BRCA specimens.

conclusionThe integrative analysis provides new insights into molecular complexities of BRCA. Immune profiles and gene signatures hold potential for improving stratification of BRCA patients and guiding the development of personalized immunotherapy strategies.

Indexed as

BioinformaticsBreast cancerImmune infiltrationMolecular subtypingPrognostic modelSingle-cell analysis

Identifiers

PMID39627792
PMCPMC11613745

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