The study utilized data from The Cancer Genome Atlas Breast Cancer (TCGA-BRCA) cohort, which included 1,113 tumor samples and 113 normal breast tissue samples. To identify genes whose expression patterns are significantly altered during tumor development, the researchers employed weighted gene co-expression network analysis (WGCNA). This computational technique constructs a network where genes are nodes and their co-expression correlations are edges, allowing for the identification of modules of highly interconnected genes. By using normal breast tissue as a reference network, the team assessed how these modules were preserved or disrupted across different stages of breast cancer, from stage I to stage IV.

Among the ten co-expression modules identified, the turquoise module demonstrated the weakest preservation pattern across tumor-derived networks, indicating that the genes within this module undergo significant regulatory changes during cancer progression. Functional enrichment analysis revealed that this module is heavily involved in epithelial development, cell adhesion, and extracellular matrix interactions. Key pathways included ECM-receptor interaction and the PI3K-Akt signaling pathway, both of which are critical in tumor progression and metastasis. The disruption of these networks suggests a loss of normal tissue architecture and communication, a hallmark of aggressive malignancy.

To pinpoint specific biomarkers, the researchers constructed a protein-protein interaction network from the genes in the least-preserved module and used eleven different algorithms to rank hub genes. From 120 candidate genes, six were selected based on their independent association with overall survival in multivariable Cox regression analysis: CLDN9, SDC1, IL2, CLIC6, KRT5, and MMP25. These genes were used to construct a risk score model, where each gene’s expression level is weighted by its regression coefficient. Patients with higher risk scores, driven largely by the expression of CLDN9 and SDC1, exhibited significantly poorer survival outcomes compared to those with lower scores.

The prognostic performance of the six-gene signature was rigorously validated in independent external cohorts, including three datasets from the Gene Expression Omnibus (GSE20685, GSE42568, and GSE58812) and the large METABRIC cohort. The model demonstrated consistent discriminative ability across these diverse populations, with area under the curve (AUC) values ranging from approximately 0.53 to 0.72 depending on the time point and cohort. In the TCGA training set, the model achieved AUC values of 0.641, 0.723, and 0.689 for 1-, 3-, and 5-year overall survival, respectively. The robustness of the model was further confirmed by its ability to predict disease-specific survival, disease-free interval, and progression-free interval, indicating that the signature captures broad aspects of tumor aggressiveness.

Beyond survival prediction, the study investigated the relationship between the risk score and the tumor immune microenvironment. Single-sample Gene Set Enrichment Analysis (ssGSEA) and the ESTIMATE algorithm revealed that patients in the low-risk group exhibited a more immune-enriched profile. Specifically, the low-risk group showed higher infiltration of T cells, natural killer cells, and antigen-presenting cells, along with elevated scores for immune-related functional signatures such as antigen presentation and cytotoxic activity. In contrast, the high-risk group was characterized by a more immunosuppressive microenvironment, with reduced immune cell infiltration and higher expression of proliferation-associated pathways such as E2F targets and G2M checkpoint.

The distinct immune phenotypes observed between the risk groups prompted an evaluation of potential immunotherapy response using computational tools such as the Immunophenoscore (IPS) and Tumor Immune Dysfunction and Exclusion (TIDE) algorithms. The low-risk group displayed higher IPS values, suggesting a more immunogenic phenotype and potentially greater sensitivity to immune checkpoint blockade. However, TIDE analysis presented a more complex picture, with the low-risk group showing higher dysfunction scores, which might indicate a less favorable predicted response in some contexts. This discordance between IPS and TIDE scores highlights the complexity of the tumor immune landscape and suggests that the six-gene signature captures distinct immune-related characteristics that may not uniformly predict immunotherapy benefit.

Further analysis of the individual genes revealed their diverse biological roles in cancer. CLDN9, a tight junction protein, has been linked to tumor invasion and metastasis in other cancers, while SDC1, a transmembrane proteoglycan, is associated with tumor progression and brain metastasis in breast cancer. IL2, a key cytokine in immune regulation, showed favorable prognostic associations, consistent with its role in T-cell proliferation. KRT5, a keratin protein, and MMP25, a matrix metalloproteinase, were also found to be significantly associated with survival, reflecting the interplay between epithelial structure, immune signaling, and extracellular matrix remodeling in breast cancer biology.

The study also evaluated the diagnostic potential of the six-gene signature in an independent microarray cohort (GSE86374). The combined logistic regression model achieved an AUC of 0.927 for distinguishing breast tumor tissues from normal tissues, demonstrating strong diagnostic accuracy. Individual genes such as SDC1 and KRT5 showed significant expression differences between tumor and normal samples, further supporting their utility as biomarkers. The integration of these genes into a single score provides a comprehensive molecular fingerprint that reflects both the intrinsic biology of the tumor and its interaction with the host immune system.

While the six-gene signature shows promise for risk stratification and molecular characterization, the authors note that prospective validation in larger multicenter cohorts is necessary to confirm its clinical applicability. Additionally, functional studies are required to elucidate the precise mechanisms by which these genes influence tumor progression and immune evasion. Nevertheless, this work represents a significant step toward integrating network-based bioinformatics with clinical outcomes, offering a potential tool for personalized medicine in breast cancer. By linking gene expression patterns to immune microenvironment characteristics, the study provides new insights into the heterogeneity of breast cancer and the factors that drive patient outcomes.

Subject of Research: Development and validation of a six-gene prognostic signature associated with tumor microenvironment characteristics in breast cancer

Article Title: Identification and validation of a six-gene prognostic signature associated with tumor microenvironment characteristics in breast cancer

Article References: Keshavarzi, F., Akhoundi, F., & Hosseini, A. (2026). Identification and validation of a six-gene prognostic signature associated with tumor microenvironment characteristics in breast cancer. Heliyon, 12(16), Article e45601. https://doi.org/10.1016/j.heliyon.2026.e45601

Image Credits: AI Generated

DOI: 10.1016/j.heliyon.2026.e45601

Keywords: breast cancer, prognostic signature, tumor microenvironment, WGCNA, gene expression, immunotherapy, bioinformatics, survival analysis, Identification, validation, six-gene, prognostic