Evidence map›Paper›PMID 41325182›Full record

ArticleTechnology in cancer research & treatment

Immunological and Prognostic Profiling of Triple-Negative Breast Cancer Based on Single-Cell and Bulk RNA Sequencing of T-Cell Exhaustion.

Han Xiao, Qingling Mo, Li Wang, Xiaoling Leng, Guofu Huang

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Article in Technology in cancer research & treatment. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Han XiaoDepartment of Oncology, Fifth Affiliated Hospital, Xinjiang Medical University, Urumqi, China.ORCID 0009-0001-7637-4194
Qingling MoDongguan Institute of Clinical Cancer Research, Dongguan Key Laboratory of Precision Diagnosis and Treatment for Tumors, The Tenth Affiliated Hospital of Southern Medical University (Dongguan People's Hospital), Dongguan, China.
Li WangDepartment of Oncology, Fifth Affiliated Hospital, Xinjiang Medical University, Urumqi, China.
Xiaoling LengDongguan Institute of Clinical Cancer Research, Dongguan Key Laboratory of Precision Diagnosis and Treatment for Tumors, The Tenth Affiliated Hospital of Southern Medical University (Dongguan People's Hospital), Dongguan, China.
Guofu HuangDongguan Institute of Clinical Cancer Research, Dongguan Key Laboratory of Precision Diagnosis and Treatment for Tumors, The Tenth Affiliated Hospital of Southern Medical University (Dongguan People's Hospital), Dongguan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

IntroductionTriple-Negative Breast Cancer (TNBC) is a subtype of breast cancer characterized by the absence of estrogen receptors, progesterone receptors, and HER2 expression, accounting for approximately 15% of all breast cancer cases. TNBC is associated with poor prognosis and lacks effective targeted therapies. T cell exhaustion (TEX) refers to the functional decline of T cells within the tumor microenvironment, which adversely affects immune responses. This study investigates the role of TEX in TNBC and its relationship with patient prognosis. The objective of this research is to evaluate the impact of TEX characteristics on the response to immunotherapy in TNBC patients through single-cell RNA sequencing and various bioinformatics analyses, aiming to identify potential biomarkers and therapeutic targets.MethodsWe performed a comprehensive analysis of the GSE161529 dataset using 10× scRNA-seq, identifying 12,477 high-quality cells and seven distinct cell subpopulations. Differential expression analysis and co-expression network construction revealed 154 candidate TEX-related genes, from which five prognostically significant genes were selected to construct a risk scoring model.ResultsOur findings indicate that TEX characteristics are crucial for prognostic assessment in TNBC patients, with significant correlations between risk scores and tumor-infiltrating immune cells. High-risk patients exhibited elevated expression levels of immune checkpoint genes, suggesting potential implications for immunotherapy.ConclusionsThis study underscores the clinical significance of TEX features in TNBC prognosis and highlights the urgent need for therapeutic strategies targeting TEX to improve patient outcomes.

Indexed as

Biomarkers, TumorT-LymphocytesTriple Negative Breast NeoplasmsComputational BiologyFemaleGene Expression ProfilingGene Expression Regulation, NeoplasticHumansLymphocytes, Tumor-InfiltratingPrognosisSequence Analysis, RNASingle-Cell AnalysisT-Cell ExhaustionTranscriptomeTumor MicroenvironmentBiomarkers, Tumorimmune responseprognostic biomarkers.single-cell RNA sequencingT cell exhaustion (TEX)Triple-Negative Breast Cancer (TNBC)

Identifiers

PMID41325182
PMCPMC12669502

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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.