Identification of ribosomal stress related signature genes and immune microenvironment analysis in ovarian cancer based on multi-machine learning
Year: 2026
Type: article
Abstract: Ovarian cancer (OC) is a highly aggressive malignancy with poor prognosis and limited response to immunotherapy. Ribosomal stress, a cellular response to disrupted ribosome biogenesis, has been increasingly implicated in tumorigenesis and immune regulation, yet its contribution to OC remains unclear. We integrated four GEO transcriptomic datasets and identified ribosomal stress related signature genes (RSRGs) through differential expression and functional enrichment analyses. To construct a robust diagnostic model, three machine learning algorithms: LASSO regression, support vector machine recursive feature elimination (SVM-RFE), and random forest were combined. Immune infiltration patterns were evaluated using CIBERSORT, and interpretability analysis was performed using SHAP to determine feature importance. Functional validation of BMP6 was performed in ovarian cancer cell lines by RT-qPCR, Western blot, CCK-8, colony formation, and Transwell assays to evaluate its effects on proliferation, migration, and invasion. A total of 117 differentially expressed RSRGs were identified, mainly enriched in cytoskeletal regulation, lipid metabolism, proteoglycan signaling, and IL-17 mediated inflammatory pathways. The integrated machine learning approach identified six feature genes (SPP1, MAPK13, LCN2, JUP, DSP, and BMP6). SHAP analysis revealed that SPP1 and DSP had the greatest contributions to the predictive model. Immune profiling revealed increased macrophage M0/M2 and decreased CD8 + T cell infiltration in high-risk samples, with SPP1, MAPK13, and DSP positively correlated with macrophage abundance. Functional assays demonstrated that BMP6 was downregulated in ovarian cancer cells and that its overexpression significantly inhibited proliferation, migration, and invasion. This study identifies a six-gene ribosomal stress signature linking tumor intrinsic pathways and immune remodeling in OC. BMP6 exerts tumor-suppressive effects, supporting the potential of targeting ribosomal stress and its immune axis as a therapeutic strategy in ovarian cancer. Machine learning, based integration of multi-dataset analysis identified six ribosomal stress related genes (SPP1, MAPK13, LCN2, JUP, DSP, and BMP6) as diagnostic biomarkers for ovarian cancer. Functional validation of BMP6 demonstrates its tumor-suppressive role by inhibiting ovarian cancer cell proliferation, migration, and invasion. Ribosomal stress pathways intersect with IL-17 signaling and metabolic remodeling, linking nucleolar dysfunction to immune evasion and tumor progression in ovarian cancer.
Source: Discover Oncology
Authors: Xuechuan Han, Yan Yu, Yang Fan, Miao Zhang
Language: English
Subfield: Reproductive Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $1,690
OpenAlex ID: W7160845924