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Exploring Prognostic Gene Factors in Breast Cancer via Machine Learning

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Abstract

Breast cancer remains the most prevalent cancer in women. To date, its underlying molecular mechanisms have not been fully uncovered. The determination of gene factors is important to improve our understanding on breast cancer, which can correlate the specific gene expression and tumor staging. However, the knowledge in this regard is still far from complete. Thus, this study aimed to explore these knowledge gaps by analyzing existing gene expression profile data from 3149 breast cancer samples, where each sample was represented by the expression of 19,644 genes and classified into Nottingham histological grade (NHG) classes (Grade 1, 2, and 3). To this end, a machine learning–based framework was designed. First, the profile data were analyzed by using seven feature ranking algorithms to evaluate the importance of features (genes). Seven feature lists were generated, each of which sorted features in accordance with feature importance evaluated from a special aspect. Then, the incremental feature selection method was applied to each list to determine essential features for classification and building efficient classifiers. Consequently, overlapping genes, such as AURKA, CBX2, and MYBL2, were deemed as potentially related to breast cancer malignancy and prognosis, indicating that such genes were identified to be important by multiple feature ranking algorithms. In addition, the study formulated classification rules to reflect special gene expression patterns for three NHG classes. Some genes and rules were analyzed and supported by recent literature, providing new references for studying breast cancer.

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Data Availability

The datasets analysed during the current study are available in the Gene Expression Omnibus repository, [https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE202203].

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Funding

This work was supported by the National Key R&D Program of China (2022YFF1203202), Strategic Priority Research Program of Chinese Academy of Sciences (XDA26040304, XDB38050200), the Fund of the Key Laboratory of Tissue Microenvironment and Tumor of Chinese Academy of Sciences (202002), Shandong Provincial Natural Science Foundation (ZR2022MC072).

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Author notes

  1. QingLan Ma and Lei Chen have contributed equally to this work.

Authors and Affiliations

  1. School of Life Sciences, Shanghai University, Shanghai, 200444, China

    QingLan Ma & Yu-Dong Cai

  2. College of Information Engineering, Shanghai Maritime University, Shanghai, 201306, China

    Lei Chen

  3. Department of Computer Science, Guangdong AIB Polytechnic College, Guangzhou, 510507, China

    KaiYan Feng

  4. Key Laboratory of Stem Cell Biology, Shanghai Jiao Tong University School of Medicine (SJTUSM) & Shanghai Institutes for Biological Sciences (SIBS), Chinese Academy of Sciences (CAS), Shanghai, 200030, China

    Wei Guo

  5. Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China

    Tao Huang

  6. CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China

    Tao Huang

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  1. QingLan Ma
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by QLM, LC, WG and TH. The first draft of the manuscript was written by QLM and LC and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Tao Huang or Yu-Dong Cai.

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Supplementary Information

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10528_2024_10712_MOESM1_ESM.xlsx

Supplementary file1 (XLSX 1189 KB)—Feature ranking results obtained using LASSO, LightGBM, MCFS, mRMR, RF_ZL, CATboost and XGBoost methods.

10528_2024_10712_MOESM2_ESM.xlsx

Supplementary file2 (XLSX 504 KB)—Performance of IFS with four different classification algorithms on LASSO, LightGBM, MCFS, mRMR, RF_ZL, CATboost and XGBoost feature lists.

10528_2024_10712_MOESM3_ESM.xlsx

Supplementary file3 (XLSX 17 KB)—Intersection of seven critical gene sets extracted from the LASSO, LightGBM, MCFS, mRMR, RF_ZL, CATboost and XGBoost feature lists.

10528_2024_10712_MOESM4_ESM.xlsx

Supplementary file4 (XLSX 634 KB)—Classification rules generated by decision tree using its optimal features on seven feature lists.

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Ma, Q., Chen, L., Feng, K. et al. Exploring Prognostic Gene Factors in Breast Cancer via Machine Learning. Biochem Genet (2024). https://doi.org/10.1007/s10528-024-10712-w

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