Translational oncology · 2026
Integrative single-cell sequencing and machine learning analyses identify chronic inflammation and cancer-associated fibroblast subsets as drivers of adverse outcomes in ovarian cancer.
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Kuang Mingqin, Shen Changmei, Yang Chunping, Xie Shuchun, Luo Yiheng, Liao Baozhen, Chen Hailong
Аннотация
BACKGROUND: Ovarian cancer remains the deadliest gynecological malignancy, with neoadjuvant chemotherapy (NACT) often leaving residual fibroblast-enriched disease.
METHODS: Single-cell RNA sequencing data from 64,097 cells were analyzed using Seurat (v4.1.3) with Harmony batch correction. CellChat (v1.1.3) mapped intercellular communication, while Monocle3 constructed pseudotime trajectories. High-dimensional weighted gene co-expression network analysis (hdWGCNA) identified fibroblast subclusters with soft threshold β=12 (scale-free R²=0.8). Six machine learning algorithms screened hub genes across three bulk RNA-seq datasets. ROC curves evaluated diagnostic performance. In vitro validation included siRNA-mediated NHP2 knockdown in OVCAR-8 and A2780 cells, with proliferation assessed by CCK-8, migration, and invasion by Transwell assays.
RESULTS: Compared to pre-NACT, post-NACT tissues exhibited increased fibroblast and CD8+ T cell proportions, with reduced dendritic cells. CellChat analysis revealed selective communication between epithelial cells, monocytes, CD8+ T cells, and fibroblasts. hdWGCNA identified the M8 fibroblast subcluster as the predominant post-chemotherapy population, showing diffuse spatial distribution and strongest association with tumor epithelial cells. Differential expression analysis identified 15 upregulated genes post-NACT, with NHP2 showing optimal diagnostic value (AUC=0.758). SVM algorithm identified CALU, NHP2, ANXA5, CUTA, and TUBA1B as hub genes. High NHP2 expression correlated with poor overall survival (HR=1.26, 95% CI: 1.10-1.43, P = 0.00,057) and progression-free survival (HR=1.23, 95% CI: 1.08-1.40, P = 0.0018). NHP2 knockdown significantly inhibited proliferation (P < 0.001), invasion (P < 0.01), and migration (P < 0.001).
CONCLUSION: The M8 fibroblast subcluster and NHP2 represent critical mediators of chemotherapy resistance and immunosuppression in ovarian cancer, offering promising therapeutic targets to improve patient outcomes.
METHODS: Single-cell RNA sequencing data from 64,097 cells were analyzed using Seurat (v4.1.3) with Harmony batch correction. CellChat (v1.1.3) mapped intercellular communication, while Monocle3 constructed pseudotime trajectories. High-dimensional weighted gene co-expression network analysis (hdWGCNA) identified fibroblast subclusters with soft threshold β=12 (scale-free R²=0.8). Six machine learning algorithms screened hub genes across three bulk RNA-seq datasets. ROC curves evaluated diagnostic performance. In vitro validation included siRNA-mediated NHP2 knockdown in OVCAR-8 and A2780 cells, with proliferation assessed by CCK-8, migration, and invasion by Transwell assays.
RESULTS: Compared to pre-NACT, post-NACT tissues exhibited increased fibroblast and CD8+ T cell proportions, with reduced dendritic cells. CellChat analysis revealed selective communication between epithelial cells, monocytes, CD8+ T cells, and fibroblasts. hdWGCNA identified the M8 fibroblast subcluster as the predominant post-chemotherapy population, showing diffuse spatial distribution and strongest association with tumor epithelial cells. Differential expression analysis identified 15 upregulated genes post-NACT, with NHP2 showing optimal diagnostic value (AUC=0.758). SVM algorithm identified CALU, NHP2, ANXA5, CUTA, and TUBA1B as hub genes. High NHP2 expression correlated with poor overall survival (HR=1.26, 95% CI: 1.10-1.43, P = 0.00,057) and progression-free survival (HR=1.23, 95% CI: 1.08-1.40, P = 0.0018). NHP2 knockdown significantly inhibited proliferation (P < 0.001), invasion (P < 0.01), and migration (P < 0.001).
CONCLUSION: The M8 fibroblast subcluster and NHP2 represent critical mediators of chemotherapy resistance and immunosuppression in ovarian cancer, offering promising therapeutic targets to improve patient outcomes.