Comprehensive profiling of endocrine metabolism identifies a novel signature with robust predictive value in ovarian cancer

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.