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Updated: Jun 16, 2025
Optimization of a Multiplex RNA-based Expression Assay Using Breast Cancer Archival Material
Published on: August 1, 2018
Introducing effective genes in lymph node metastasis of breast cancer patients using SHAP values based on the mRNA
Sepideh Zununi Vahed1, Seyed Mahdi Hosseiniyan Khatibi1,2, Yalda Rahbar Saadat1
1Kidney Research Center, Tabriz University of Medical Sciences, Tabriz, Iran.
Objective:
Breast cancer, a global concern predominantly impacting women, poses a significant threat when not identified early. While survival rates for breast cancer patients are typically favorable, the emergence of regional metastases markedly diminishes survival prospects. Detecting metastases and comprehending their molecular underpinnings are crucial for tailoring effective treatments and improving patient survival outcomes.
Methods:
Various artificial intelligence methods and techniques were employed in this study to achieve accurate outcomes. Initially, the data was organized and underwent hold-out cross-validation, data cleaning, and normalization. Subsequently, feature selection was conducted using ANOVA and binary Particle Swarm Optimization (PSO). During the analysis phase, the discriminative power of the selected features was evaluated using machine learning classification algorithms. Finally, the selected features were considered, and the SHAP algorithm was utilized to identify the most significant features for enhancing the decoding of dominant molecular mechanisms in lymph node metastases.
Results:
In this study, five main steps were followed for the analysis of mRNA expression data: reading, preprocessing, feature selection, classification, and SHAP algorithm. The RF classifier utilized the candidate mRNAs to differentiate between negative and positive categories with an accuracy of 61% and an AUC of 0.6. During the SHAP process, intriguing relationships between the selected mRNAs and positive/negative lymph node status were discovered. The results indicate that GDF5, BAHCC1, LCN2, FGF14-AS2, and IDH2 are among the top five most impactful mRNAs based on their SHAP values.
Conclusion:
The prominent identified mRNAs including GDF5, BAHCC1, LCN2, FGF14-AS2, and IDH2, are implicated in lymph node metastasis. This study holds promise in elucidating a thorough insight into key candidate genes that could significantly impact the early detection and tailored therapeutic strategies for lymph node metastasis in patients with breast cancer.
Insights
This study identifies key messenger RNAs (mRNAs) like GDF5 and BAHCC1 that are crucial for detecting lymph node metastasis in breast cancer. These findings can improve early detection and guide personalized treatment strategies for patients.
Area of Science:
- Oncology
- Genomics
- Bioinformatics
Background:
- Breast cancer poses a global health challenge, with survival rates significantly decreasing upon the development of regional metastases.
- Early detection of metastases and understanding their molecular basis are critical for effective treatment and improved patient outcomes.
Purpose of the Study:
- To identify key molecular markers associated with lymph node metastasis in breast cancer using artificial intelligence.
- To enhance the understanding of molecular mechanisms driving metastasis for improved diagnostic and therapeutic strategies.
Main Methods:
- Employed artificial intelligence, including Particle Swarm Optimization (PSO) and SHAP algorithms, for feature selection and analysis of mRNA expression data.
- Utilized hold-out cross-validation, data cleaning, normalization, and machine learning classification (Random Forest) to evaluate feature discriminative power.
Main Results:
- Identified five impactful messenger RNAs (mRNAs): GDF5, BAHCC1, LCN2, FGF14-AS2, and IDH2, significantly associated with lymph node metastasis status.
- The Random Forest classifier achieved 61% accuracy and 0.6 AUC in differentiating metastatic status based on candidate mRNAs.
- The SHAP algorithm revealed significant relationships between these mRNAs and lymph node positivity.
Conclusions:
- The identified mRNAs (GDF5, BAHCC1, LCN2, FGF14-AS2, IDH2) are implicated in lymph node metastasis in breast cancer.
- These findings offer potential for early detection and the development of targeted therapeutic strategies for lymph node metastasis.
