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. 2015 Nov 4:5:16066.
doi: 10.1038/srep16066.

Stroma-associated master regulators of molecular subtypes predict patient prognosis in ovarian cancer (VSports最新版本)

Shengzhe Zhang  1   2 , Ying Jing  1 , Meiying Zhang  3   4 , Zhenfeng Zhang  1   5 , Pengfei Ma  1 , Huixin Peng  1 , Kaixuan Shi  2 , Wei-Qiang Gao  1   2 , Guanglei Zhuang  1   4
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VSports手机版 - Stroma-associated master regulators of molecular subtypes predict patient prognosis in ovarian cancer

Shengzhe Zhang et al. Sci Rep. .

Abstract

High-grade serous ovarian carcinoma (HGS-OvCa) has the lowest survival rate among all gynecologic cancers and is hallmarked by a high degree of heterogeneity. The Cancer Genome Atlas network has described a gene expression-based molecular classification of HGS-OvCa into Differentiated, Mesenchymal, Immunoreactive and Proliferative subtypes. However, the biological underpinnings and regulatory mechanisms underlying the distinct molecular subtypes are largely unknown VSports手机版. Here we showed that tumor-infiltrating stromal cells significantly contributed to the assignments of Mesenchymal and Immunoreactive clusters. Using reverse engineering and an unbiased interrogation of subtype regulatory networks, we identified the transcriptional modules containing master regulators that drive gene expression of Mesenchymal and Immunoreactive HGS-OvCa. Mesenchymal master regulators were associated with poor prognosis, while Immunoreactive master regulators positively correlated with overall survival. Meta-analysis of 749 HGS-OvCa expression profiles confirmed that master regulators as a prognostic signature were able to predict patient outcome. Our data unraveled master regulatory programs of HGS-OvCa subtypes with prognostic and potentially therapeutic relevance, and suggested that the unique transcriptional and clinical characteristics of ovarian Mesenchymal and Immunoreactive subtypes could be, at least partially, ascribed to tumor microenvironment. .

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Figures (V体育2025版)

Figure 1
Figure 1. The TCGA subtypes are not associated with patient prognosis.
(A) Tumors from TCGA, Tothill and Crijns datasets were separated into four clusters on the basis of gene expression. (B) Kaplan Meier curves for four molecular subtypes in the TCGA, Tothill and Crijns datasets.
Figure 2
Figure 2. Tumor-associated stromal content contributes to defining Mesenchymal and Immunoreactive subtypes.
(A) Tumor purity estimated by ABSOLUTE analysis for four molecular subtypes of TCGA samples. (B) ESTIMATE scores for four molecular subtypes of TCGA samples. (C) GSEA for upregulation of Mesenchymal and Immunoreactive genes in microdissected tumor stroma versus epithelial tissues. (D) GSEA for downregulation of Mesenchymal and Immunoreactive genes in patient-derived xenografts (PDX) versus matched primary tumors.
Figure 3
Figure 3. Regulatory networks and master regulators of Mesenchymal and Immunoreactive subtypes.
(A) The Mesenchymal regulatory network showing the six MRs (square nodes) and all inferred targets (round nodes). (B) The Immunoreactive regulatory network showing the ten MRs (square nodes) and all inferred targets (round nodes). (C) The list of Mesenchymal and Immunoreactive MRs.
Figure 4
Figure 4. Mesenchymal and Immunoreactive MRs correlate with tumor stroma and patient survival.
(A) Heatmap of Mesenchymal and Immunoreactive MRs expression and ssGSEA scores for four molecular subtypes of TCGA samples. Binary scores were shown to indicate whether a tumor sample activated Mesenchymal or Immunoreactive MRs. Red, activated; black, not activated. (B) Mesenchymal and Immunoreactive MRs scores in microdissected tumor stroma (5 samples in GSE9890 and 31 samples in GSE40595) versus epithelial tissues (5 samples in GSE9890 and 32 samples in GSE40595). (C) Mesenchymal and Immunoreactive MRs scores in PDX versus matched primary tumors (9 samples). (D) Mesenchymal and Immunoreactive MRs scores in tumor metastasis versus primary tumors (9 paired samples). (E) Mesenchymal and Immunoreactive MRs scores in tumors treated with chemotherapy (34 samples) versus non-treated tumors (35 samples). (F) Kaplan Meier curves for three prognostic groups of TCGA samples classified by Mesenchymal and Immunoreactive MRs signatures. G. Kaplan Meier curves for meta-analysis of 749 HGS-OvCa expression profiles across five cohorts. H. Kaplan Meier curves for ‘immu + mese−’ and ‘immu + mese+’ patients.
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