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Updated: Jun 27, 2025
Integration of Bioinformatics Approaches and Experimental Validations to Understand the Role of Notch Signaling in Ovarian Cancer
Published on: January 12, 2020
Comprehensive profiling of endocrine metabolism identifies a novel signature with robust predictive value in ovarian
1Department of General Medicine, Changzheng Hospital, The Naval Military Medical University, Shanghai, China.
Background:
The cell endocrine pathway is a critical physiological process composed of the endoplasmic reticulum, Golgi apparatus and associated vesicles. Loss of enzymes or proteins can cause dysfunction of endoplasmic reticulum and Golgi apparatus and affect secretion pathways leading to a variety of human diseases, including cancer.
Methods:
The single-cell RNA sequencing and single nucleotide variant principal component analysis data of ovarian cancer were retrieved from The Cancer Genome Atlas and Gene Expression Omnibus (GEO) datasets. Eighty-four genes from SECRETORY_PATHWAYs were obtained from the gene set enrichment analysis (GSEA) website. Univariate cox regression analyses and ConsensusClusterPlus were used to identify prognostic genes and molecular subtypes, which were validated using the tumor immune dysfunction and exclusion (i.e. TIDE) analysis and gene mutation analysis. A prognosis model was established by randomForestSRC. Abundant infiltrated immune cells and pathway enrichment analyses were carried out, respectively, through ssGSEA, ESTIMATE, MCP-counter and GSEA. The drug sensitive analysis was performed using pRRophetic package. Immunotherapy datasets and pan-carcinoma analysis were used to examine the performance of prognostic model.
Results:
Eighteen prognostic genes from SECRETORY_PATHWAYs were found in both TCGA and GEO datasets. Next, two clusters (C1 and C2) were determined, for which C1 with a poor prognosis had higher immune infiltration. Tumor-related pathways, such as PATHWAYS_IN_CANCER and B_CELL_RECEPTOR_SIGNALING_PATHWAY, were enriched in C1. Moreover, C2 was suitable for immunotherapy. A four-gene (DNAJA1, NDRG3, LUZP1 and ZCCHC24) signature was developed and successfully validated. RiskScore of higher levels were significantly associated with worse prognoses. An enhanced immune infiltration, increased pathways score and inappropriate immunotherapy were observed in the high RiskScore group. The high- and low-RiskScore groups had different drug sensitivities. Immunotherapy datasets and pan-carcinoma analysis indicated that the low RiskScore group may benefit from immunotherapy.
Conclusions:
Based on the perspective of the secretory signaling pathway, a robust prognostic signature with great performances was determined, which may provide clues for clinical precision treatment of ovarian cancer.
Insights
A new four-gene signature from the secretory pathway can predict ovarian cancer prognosis and immunotherapy response. This discovery offers potential for personalized cancer treatments.
Area of Science:
- Oncology
- Molecular Biology
- Bioinformatics
Background:
- The secretory pathway, involving the endoplasmic reticulum and Golgi apparatus, is crucial for cell function.
- Dysfunction in this pathway can lead to diseases, including cancer.
Purpose of the Study:
- To identify prognostic genes within the secretory pathway in ovarian cancer.
- To develop a predictive model for patient outcomes and immunotherapy response.
Main Methods:
- Utilized single-cell RNA sequencing and variant PCA on TCGA and GEO ovarian cancer datasets.
- Identified prognostic genes and molecular subtypes using Cox regression and ConsensusClusterPlus.
- Validated findings with TIDE analysis, gene mutation analysis, and randomForestSRC for model development.
- Performed immune cell infiltration and pathway enrichment analyses (ssGSEA, ESTIMATE, MCP-counter, GSEA).
Main Results:
- Identified 18 prognostic genes from secretory pathways, leading to two distinct molecular subtypes (C1 and C2).
- Developed and validated a four-gene signature (DNAJA1, NDRG3, LUZP1, ZCCHC24) where higher risk scores correlated with poorer prognosis and higher immune infiltration.
- Observed distinct drug sensitivities and immunotherapy responses between high and low risk score groups, with the low risk group potentially benefiting from immunotherapy.
Conclusions:
- A robust four-gene prognostic signature derived from the secretory pathway was established for ovarian cancer.
- This signature demonstrates strong performance in predicting prognosis and immunotherapy response, paving the way for precision treatment strategies.
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