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RETRACTED ARTICLE: Ensemble learning-based gene signature and risk model for predicting prognosis of triple-negative breast cancer

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This article was retracted on 13 May 2024

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Abstract

Although medical science has been fully developed, due to the high heterogeneity of triple-negative breast cancer (TNBC), it is still difficult to use reasonable and precise treatment. In this study, based on local optimization-feature screening and genomics screening strategy, we screened 25 feature genes. In multiple machine learning algorithms, feature genes have excellent discriminative diagnostic performance among samples composed of multiple large datasets. After screening at the single-cell level, we identified genes expressed substantially in myeloid cells (MCGs) that have a potential association with TNBC. Based on MCGs, we distinguished two types of TNBC patients who showed considerable differences in survival status and immune-related characteristics. Immune-related gene risk scores (IRGRS) were established, and their validity was verified using validation cohorts. A total of 25 feature genes were obtained, among which CXCL9, CXCL10, CCL7, SPHK1, and TREM1 were identified as the result after single-cell level analysis and screening. According to these entries, the cohort was divided into MCA and MCB subtypes, and the two subtypes had significant differences in survival status and tumor-immune microenvironment. After Lasso-Cox screening, IDO1, GNLY, IRF1, CTLA4, and CXCR6 were selected for constructing IRGRS. There were significant differences in drug sensitivity and immunotherapy sensitivity among high-IRGRS and low-IRGRS groups. We revealed the dynamic relationship between TNBC and TIME, identified a potential biomarker called Granulysin (GNLY) related to immunity, and developed a multi-process machine learning package called “MPMLearning 1.0” in Python.

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Availability of data and materials

All presented data in this study are available from the corresponding author upon reasonable request.

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Abbreviations

TNBC :

Triple-negative breast cancer

ER :

Estrogen receptor

PR :

Progesterone receptor

HER2 :

Human epidermal growth factor receptor 2

TIME :

Tumor-immune microenvironment

TAMs :

Tumor-associated macrophages

ML :

Machine learning

S RA :

Sequence read archive

GEO :

Gene expression omnibus

RF :

Random forest

Oob_score :

Out of bag score

GBDT :

Gradient boosting decision tree

LR :

Logistics regression

SVM :

Support vector machine

TCGA :

The Cancer Genome Atlas

AUC :

Area under curve

ScRNA-seq :

Single-cell RNA sequencing

CXCL9 :

C-X-C motif chemokine 9

CXCL10 :

C-X-C motif chemokine 10

CCL7 :

C-C motif chemokine ligand 7

SPHK1 :

Sphingosine kinase 1

TREM1 :

Triggering receptor expressed on myeloid cells 1

K-M :

Kaplan-Meier

OS :

Overall survival

RFS :

Relapse-free survival

ESTIMATE :

Estimation of stromal and immune cells in malignant tumor tissues using expression

HLA :

Human leukocyte antigen

GSEA :

Gene set enrichment analysis

KEGG :

Kyoto Encyclopedia of Genes and Genomes

NES :

Normalized enrichment score

FDR :

False discovery rate

IRGRS :

Immune-related genes risk score

ROC curve :

Receiver operating characteristic curve

TMB :

Tumor mutation burden

GDSC :

Genomics of drug sensitivity in cancer

CTRP :

The Cancer Therapeutics Response Portal

PRISM :

Profiling relative inhibition simultaneously in mixtures

ICs :

Immune checkpoints

ICIs :

Immune checkpoint inhibitors

PDCD1 :

Programmed cell death protein-1

CD274 :

Programmed cell death protein ligand-1

LAG3 :

Lymphocyte activation gene-3

TIGIT :

T cell immunoreceptor with Ig and ITIM domains

CTLA4 :

Cytotoxic T lymphocyte-associated antigen-4

GNLY :

Granulysin

References

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Acknowledgements

We acknowledge all authors participating in this study for their work and helpful comments.

Funding

This word was supported by the Key Project supported by the Scientific Research Foundation of the Education Bureau of Liaoning Province (2020LZD03); the Liaoning XingLiao Talents Project (XLYC2005014); and the National Natural Science Foundation of China (U1908215). I would like to thank all the teachers and students in the Research Center of Data and Information Science of the School of Medical Devices of Shenyang Pharmaceutical University for their efforts in this project.

Author information

Authors and Affiliations

  1. School of Pharmacy, Shenyang Pharmaceutical University, Shenyang, 110016, China

    Tiancheng Li & Kai Ma

  2. School of Medical Devices, Shenyang Pharmaceutical University, Shenyang, 110016, China

    Siqi Chen, Yuqi Zhang, Xiwei Jiang, Rongwu Xiang, Fei Zhai & Guixia Ling

  3. School of Life Sciences and Biopharmaceutical Science, Shenyang Pharmaceutical University, Shenyang, 110016, China

    Qianqian Zhao

  4. Liaoning Medical Big Data and Artificial Intelligence Engineering Technology Research Center, Shenyang, 110016, China

    Rongwu Xiang

Authors
  1. Tiancheng Li
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  2. Siqi Chen
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  3. Yuqi Zhang
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  4. Qianqian Zhao
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  5. Kai Ma
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  6. Xiwei Jiang
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  7. Rongwu Xiang
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  8. Fei Zhai
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  9. Guixia Ling
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Contributions

T.L. was responsible for bioinformatics analysis, prepared figures and tables, and designed and wrote the manuscript. S.C., Y.Z., Q.Z., and K.M. carried out data preprocessing. F.Z. also provided some basic code and revised this manuscript. X.J. proofread the manuscript and figures. R.X. and G.L. conceived the concept, instructed bioinformatics analysis, supervised results, and was responsible for its financial supports and the corresponding works.

Corresponding authors

Correspondence to Fei Zhai or Guixia Ling.

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Ethics approval and consent to participate

This study is based on public datasets and does not include new data that require ethical approval and consent.

Competing interests

The authors declare no competing interests.

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This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s10142-024-01370-7"

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Li, T., Chen, S., Zhang, Y. et al. RETRACTED ARTICLE: Ensemble learning-based gene signature and risk model for predicting prognosis of triple-negative breast cancer. Funct Integr Genomics 23, 81 (2023). https://doi.org/10.1007/s10142-023-01009-z

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  • DOI: https://doi.org/10.1007/s10142-023-01009-z

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