In Silico Identification of Aminoadipate Semialdehyde Synthase ( AASS ) as a Novel Prognostic Biomarker in Triple-Negative Breast Cancer

2026 · doi:10.64898/2026.02.19.706804
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This study identified AASS as a favorable prognostic biomarker and metabolic tumor suppressor in triple-negative breast cancer through integrated network biology and machine learning analysis.

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This study utilized network biology and machine learning techniques to identify prognostic biomarkers for triple-negative breast cancer by analyzing expression cohorts. The researchers identified differentially expressed genes and performed protein-protein interaction analysis alongside feature selection methods like LASSO regression to pinpoint key candidates. Results indicated that Aminoadipate Semialdehyde Synthase (AASS) serves as a favorable prognostic marker, functioning as a metabolic tumor suppressor in this aggressive cancer subtype. The paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.

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Abstract

Triple-negative breast cancer (TNBC) is an aggressive subtype that lacks effective targeted therapies. This study aimed to identify robust prognostic biomarkers by integrating network biology with machine learning (ML) approaches. TNBC expression cohorts were analysed to identify differentially expressed genes (DEGs) and crucial gene clusters via limma and Weighted Gene Co-expression Network Analysis (WGCNA). In results, 579 DEGs were identified, and network analysis revealed two TNBC-associated modules. Overlapping determined 208 genes enriched in cell-cycle and mitotic-regulation pathways. To identify candidate biomarkers, protein-protein interaction (PPI) networks and ML feature selection techniques, including support vector machine with recursive feature elimination module (SVM-RFE) and least absolute shrinkage and selection operator (LASSO) regression, were performed. The Kaplan-Meier (KM) analysis revealed AASS and CCNA2 were favourable prognostic markers, whereas CXCL8, SPP1 , and CCNB1 were poor prognostic markers. Multi-level validation and immune-subtype analysis were carried out, revealing AASS as a novel TNBC-associated metabolic tumour suppressor.
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Abstract Triple-negative breast cancer (TNBC) is an aggressive subtype that lacks effective targeted therapies. This study aimed to identify robust prognostic biomarkers by integrating network biology with machine learning (ML) approaches. TNBC expression cohorts were analysed to identify differentially expressed genes (DEGs) and crucial gene clusters via limma and Weighted Gene Co-expression Network Analysis (WGCNA). In results, 579 DEGs were identified, and network analysis revealed two TNBC-associated modules. Overlapping determined 208 genes enriched in cell-cycle and mitotic-regulation pathways. To identify candidate biomarkers, protein-protein interaction (PPI) networks and ML feature selection techniques, including support vector machine with recursive feature elimination module (SVM-RFE) and least absolute shrinkage and selection operator (LASSO) regression, were performed. The Kaplan-Meier (KM) analysis revealed AASS and CCNA2 were favourable prognostic markers, whereas CXCL8, SPP1, and CCNB1 were poor prognostic markers. Multi-level validation and immune-subtype analysis were carried out, revealing AASS as a novel TNBC-associated metabolic tumour suppressor. Competing Interest Statement The authors have declared no competing interest.

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