Evidence mapPaperPMID 41019288Full record

ArticleBiochemistry and biophysics reports2025

Identification and external validation of a prognostic signature based on myeloid-derived suppressor cells-related LncRNAs to evaluate survival prognosis and treatment efficacy in invasive breast carcinoma.

Yun Zhong, Tong Mu, Shunyao Zhang, Yuan Xiang, Lei Xie, Wenxiong Zhang, Kang Wang

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Article in Biochemistry and biophysics reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

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7 authors.

Yun ZhongDepartment of Thoracic Surgery, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, 330006, China.
Tong MuThe Second Clinical Medical School, Jiangxi Medical College, Nanchang University, Nanchang, 330088, China.
Shunyao ZhangThe Second Clinical Medical School, Jiangxi Medical College, Nanchang University, Nanchang, 330088, China.
Yuan XiangThe Second Clinical Medical School, Jiangxi Medical College, Nanchang University, Nanchang, 330088, China.
Lei XieDepartment of Thoracic Surgery, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, 330006, China.
Wenxiong ZhangDepartment of Thoracic Surgery, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, 330006, China.
Kang WangDepartment of Traditional Chinese Medicine, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, 330006, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Originating in the hematopoietic tissue, myeloid-derived suppressor cells (MDSCs) significantly contribute to tumor-related immunological processes. However, their relationship with long noncoding RNAs (lncRNAs) and breast cancer remains incompletely understood. In this study, we introduced MDSCs-associated lncRNAs as novel prognostic biomarkers to assess outcomes in patients with invasive breast carcinoma (BRCA). Methods: Information regarding BRCA cases, including clinical and genomic details, was obtained from the TCGA repository. Predictive indicators were discovered, and their reliability underwent thorough verification. A clinically useful nomogram was developed following application-based validation. Additional investigations encompassed functional analysis, TMB assessment, TME profiling, immunotherapy efficacy forecasting, and drug sensitivity testing along with target identification. Long non-coding RNA expression was measured using reverse transcription quantitative PCR. Results: A risk stratification model incorporating eight MDSCs-related lncRNAs effectively predicted patient outcomes. Kaplan-Meier (K-M) survival analysis clearly indicated a much worse prognosis among patients classified as high-risk (p < 0.001). The nomogram accurately forecasted overall survival (OS). Analysis of functional enrichment revealed that pathways associated with epithelial cells showed activity among patients at higher risk. Characterization of the tumor microenvironment showed increased immune cell presence in those classified as low-risk. Conversely, individuals with greater risk displayed higher tumor mutational burden. TIDE and IPS analyses indicated superior immunotherapy responsiveness in the low-risk BRCA subgroup. Among 47 drugs with notable IC50 variations, Ribociclib, PD173074, KU-55933, NU7441, and nutlin-3a exhibited lower IC50 values within the low-risk group, whereas Lapatinib demonstrated greater efficacy among the high-risk group. Moreover, 10 potential therapeutic agents and their targets were predicted for high-risk patients. RT-qPCR validation confirmed the robustness of the model. Conclusions: We successfully verified a new model of molecular markers of MDSCs-related lncRNAs, offering critical insights for predicting outcomes and guiding therapeutic decisions in BRCA cases.

Indexed as

BioinformaticsBreast carcinomaExternal validationLong noncoding RNAsMyeloid-derived suppressor cellsPrognostic signature

Identifiers

PMID41019288
PMCPMC12476114

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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 Socratic graph.