As a type of programmed cell death, anoikis resistance plays an essential role in tumor metastasis, allowing cancer cells to survive in the systemic circulation and as a key pathway for regulating critical biological processes. We conducted an exploratory analysis to improve risk stratification and optimize adjuvant treatment choices for patients with breast cancer, and identify multigene features in mRNA and lncRNA transcriptome profiles associated with anoikis. First, the variance selection method filters low information content genes in RNA sequence and then extracts the mRNA and lncRNA expression data base on annotation files. Then, the top ten key mRNAs are screened out through the PPI network. Pearson analysis has been employed to identify lncRNAs related to anoikis, and the prognosis-related lncRNAs are selected using Univariate Cox regression and machine learning. Finally, we identified a group of RNAs (including ten mRNAs and six lncRNAs) and integrated the expression data of 16 genes to construct a risk-scoring system for BRCA prognosis and drug sensitivity analysis. The risk score's validity has been evaluated with the ROC curve, Kaplan-Meier survival curve analysis and decision curve analysis (DCA). For the methylation data, we have obtained 169 anoikis-related prognostic methylation sites, integrated these sites with 16 RNA features and further used the deep learning model to evaluate and predict the survival risk of patients. The developed anoikis feature is demonstrated a consistency index (C-index) of 0.778, indicating its potential to predict the survival probability of breast cancer patients using deep learning methods.
Citation: Huili Yang, Wangren Qiu, Zi Liu. Anoikis-related mRNA-lncRNA and DNA methylation profiles for overall survival prediction in breast cancer patients[J]. Mathematical Biosciences and Engineering, 2024, 21(1): 1590-1609. doi: 10.3934/mbe.2024069
As a type of programmed cell death, anoikis resistance plays an essential role in tumor metastasis, allowing cancer cells to survive in the systemic circulation and as a key pathway for regulating critical biological processes. We conducted an exploratory analysis to improve risk stratification and optimize adjuvant treatment choices for patients with breast cancer, and identify multigene features in mRNA and lncRNA transcriptome profiles associated with anoikis. First, the variance selection method filters low information content genes in RNA sequence and then extracts the mRNA and lncRNA expression data base on annotation files. Then, the top ten key mRNAs are screened out through the PPI network. Pearson analysis has been employed to identify lncRNAs related to anoikis, and the prognosis-related lncRNAs are selected using Univariate Cox regression and machine learning. Finally, we identified a group of RNAs (including ten mRNAs and six lncRNAs) and integrated the expression data of 16 genes to construct a risk-scoring system for BRCA prognosis and drug sensitivity analysis. The risk score's validity has been evaluated with the ROC curve, Kaplan-Meier survival curve analysis and decision curve analysis (DCA). For the methylation data, we have obtained 169 anoikis-related prognostic methylation sites, integrated these sites with 16 RNA features and further used the deep learning model to evaluate and predict the survival risk of patients. The developed anoikis feature is demonstrated a consistency index (C-index) of 0.778, indicating its potential to predict the survival probability of breast cancer patients using deep learning methods.
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Huili Yang, Wangren Qiu, Zi Liu. Anoikis-related mRNA-lncRNA and DNA methylation profiles for overall survival prediction in breast cancer patients[J]. Mathematical Biosciences and Engineering, 2024, 21(1): 1590-1609. doi: 10.3934/mbe.2024069
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