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Machine learning combined with multi-omics to identify immune-related LncRNA signature as biomarkers for predicting breast cancer prognosis

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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Machine learning combined with multi-omics to identify immune-related LncRNA signature as biomarkers for predicting breast cancer prognosis. / Liu, Yuxing; Chen, Jintao; Yang, Daifeng et al.
In: Scientific Reports, Vol. 15, No. 1, 23863, 31.12.2025.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Liu, Y, Chen, J, Yang, D, Liu, C, Tang, C, Cai, S & Huang, Y 2025, 'Machine learning combined with multi-omics to identify immune-related LncRNA signature as biomarkers for predicting breast cancer prognosis', Scientific Reports, vol. 15, no. 1, 23863. https://doi.org/10.1038/s41598-025-10186-9

APA

Liu, Y., Chen, J., Yang, D., Liu, C., Tang, C., Cai, S., & Huang, Y. (2025). Machine learning combined with multi-omics to identify immune-related LncRNA signature as biomarkers for predicting breast cancer prognosis. Scientific Reports, 15(1), Article 23863. Advance online publication. https://doi.org/10.1038/s41598-025-10186-9

Vancouver

Liu Y, Chen J, Yang D, Liu C, Tang C, Cai S et al. Machine learning combined with multi-omics to identify immune-related LncRNA signature as biomarkers for predicting breast cancer prognosis. Scientific Reports. 2025 Dec 31;15(1):23863. Epub 2025 Jul 4. doi: 10.1038/s41598-025-10186-9

Author

Liu, Yuxing ; Chen, Jintao ; Yang, Daifeng et al. / Machine learning combined with multi-omics to identify immune-related LncRNA signature as biomarkers for predicting breast cancer prognosis. In: Scientific Reports. 2025 ; Vol. 15, No. 1.

Bibtex

@article{035bd0d098934fec8fd7695aaa7263dd,
title = "Machine learning combined with multi-omics to identify immune-related LncRNA signature as biomarkers for predicting breast cancer prognosis",
abstract = "This study developed an immune-related long non-coding RNAs (lncRNAs)-based prognostic signature by integrating multi-omics data and machine learning algorithms to predict survival and therapeutic responses in breast cancer patients. Utilizing transcriptomic and gene expression data from TCGA and GEO databases, 72 immune-related lncRNAs were identified through weighted gene co-expression network analysis (WGCNA) and ImmuLncRNA algorithms. The model was further optimized using 101 combinations of 10 machine learning approaches, ultimately constructing an immune-related lncRNA signature(IRLS) scoring system comprising nine key lncRNAs. Validated across 17 independent cohorts, the model demonstrated that high-risk patients had significantly shorter overall survival (OS) (P < 0.05), with predictive performance surpassing 95 published models (P < 0.05). Additionally, the IRLS score predicted responses to paclitaxel chemotherapy, and the low-risk group exhibited higher immune cell infiltration (P < 0.05), showing significant negative correlations with CD8A, PD-L1, tumor mutational burden (TMB), and neoantigen load (NAL). In immune checkpoint inhibitor (ICI) treatment cohorts, low IRLS scores were associated with improved response rates to atezolizumab. Our findings suggest that the IRLS model serves as a novel biomarker for prognostic stratification and personalized therapeutic decision-making in breast cancer.",
keywords = "Treatment prediction, Immune microenvironment, Prognostic model, Breast cancer, Long non-coding RNA, Machine learning",
author = "Yuxing Liu and Jintao Chen and Daifeng Yang and Chenming Liu and Chunhui Tang and Shanshan Cai and Yingxuan Huang",
year = "2025",
month = jul,
day = "4",
doi = "10.1038/s41598-025-10186-9",
language = "English",
volume = "15",
journal = "Scientific Reports",
issn = "2045-2322",
publisher = "Nature Publishing Group",
number = "1",

}

RIS

TY - JOUR

T1 - Machine learning combined with multi-omics to identify immune-related LncRNA signature as biomarkers for predicting breast cancer prognosis

AU - Liu, Yuxing

AU - Chen, Jintao

AU - Yang, Daifeng

AU - Liu, Chenming

AU - Tang, Chunhui

AU - Cai, Shanshan

AU - Huang, Yingxuan

PY - 2025/7/4

Y1 - 2025/7/4

N2 - This study developed an immune-related long non-coding RNAs (lncRNAs)-based prognostic signature by integrating multi-omics data and machine learning algorithms to predict survival and therapeutic responses in breast cancer patients. Utilizing transcriptomic and gene expression data from TCGA and GEO databases, 72 immune-related lncRNAs were identified through weighted gene co-expression network analysis (WGCNA) and ImmuLncRNA algorithms. The model was further optimized using 101 combinations of 10 machine learning approaches, ultimately constructing an immune-related lncRNA signature(IRLS) scoring system comprising nine key lncRNAs. Validated across 17 independent cohorts, the model demonstrated that high-risk patients had significantly shorter overall survival (OS) (P < 0.05), with predictive performance surpassing 95 published models (P < 0.05). Additionally, the IRLS score predicted responses to paclitaxel chemotherapy, and the low-risk group exhibited higher immune cell infiltration (P < 0.05), showing significant negative correlations with CD8A, PD-L1, tumor mutational burden (TMB), and neoantigen load (NAL). In immune checkpoint inhibitor (ICI) treatment cohorts, low IRLS scores were associated with improved response rates to atezolizumab. Our findings suggest that the IRLS model serves as a novel biomarker for prognostic stratification and personalized therapeutic decision-making in breast cancer.

AB - This study developed an immune-related long non-coding RNAs (lncRNAs)-based prognostic signature by integrating multi-omics data and machine learning algorithms to predict survival and therapeutic responses in breast cancer patients. Utilizing transcriptomic and gene expression data from TCGA and GEO databases, 72 immune-related lncRNAs were identified through weighted gene co-expression network analysis (WGCNA) and ImmuLncRNA algorithms. The model was further optimized using 101 combinations of 10 machine learning approaches, ultimately constructing an immune-related lncRNA signature(IRLS) scoring system comprising nine key lncRNAs. Validated across 17 independent cohorts, the model demonstrated that high-risk patients had significantly shorter overall survival (OS) (P < 0.05), with predictive performance surpassing 95 published models (P < 0.05). Additionally, the IRLS score predicted responses to paclitaxel chemotherapy, and the low-risk group exhibited higher immune cell infiltration (P < 0.05), showing significant negative correlations with CD8A, PD-L1, tumor mutational burden (TMB), and neoantigen load (NAL). In immune checkpoint inhibitor (ICI) treatment cohorts, low IRLS scores were associated with improved response rates to atezolizumab. Our findings suggest that the IRLS model serves as a novel biomarker for prognostic stratification and personalized therapeutic decision-making in breast cancer.

KW - Treatment prediction

KW - Immune microenvironment

KW - Prognostic model

KW - Breast cancer

KW - Long non-coding RNA

KW - Machine learning

U2 - 10.1038/s41598-025-10186-9

DO - 10.1038/s41598-025-10186-9

M3 - Journal article

VL - 15

JO - Scientific Reports

JF - Scientific Reports

SN - 2045-2322

IS - 1

M1 - 23863

ER -