ISSN: 1837-9664Journal of Cancer
Global reach, higher impact
J Cancer 2024; 15(2):383-400. doi:10.7150/jca.88359 This issue Cite
Research Paper
Comprehensive analysis based on glycolytic and glutaminolytic pathways signature for predicting prognosis and immunotherapy in ovarian cancer
1. Department of Gynecology and Obstetrics, Renmin Hospital of Wuhan University, Wuhan, Hubei Province, People's Republic of China.
2. Department of Gynecology and Obstetrics, Xiangyang No. 1 People's Hospital, Hubei University of Medicine, Xiangyang, Hubei Province, People's Republic of China.
# These authors contributed equally to this work.
Abstract
Background: Our study attempts to develop and identify an aerobic glycolysis and glutamine-related genes (AGGRGs) signature for estimating prognostic effectively of ovarian cancer (OV) patients.
Materials & methods: OV related data were extracted from the multiple public databases, including TCGA-OV, GSE26193, GSE63885, and ICGC-OV. A consistent clustering approach was used to characterize the subtypes associated with AGGRGs. LASSO Cox regressions was utilized to construct the prognosis signatures of AGGRGs. In addition, GSE26193, GSE63885 and ICGC-OV served as independent external cohorts to assess the reliability of the model. In vitro and in vivo experiments were conducted to study the role of AAK1 in the malignant progression and glutamine metabolism of OV, and assessed its therapeutic potential for treating OV patients.
Results: OV patients could be separated into four subtypes (quiescent, glycolysis, glutaminolytic, and mixed subtypes). The survival outcome of glutaminolytic subtype was notably worse than the glycolytic subtype. Besides, we identified eight AGGRGs (AAK1, GJB6, HMGN5, LPIN3, INTS6L, PPOX, SPAG4, and ZNF316) to establish a prognostic signature for OV patients. Comprehensive analysis revealed that the signature risk score served as an independent prognostic factor for OV. Additionally, high-risk OV patients were less sensitive to platinum and, conversely, were proved to be more responsive to immunotherapy than low-risk score. In cytological experiments, we found that AAK1 could promote cancer progression and glutamine metabolism via activating the Notch3 pathway in OV cells. Furthermore, knockdown of AAK1 significantly inhibited tumor growth and weight, decreased lung metastases, and ultimately extended the survival time of the nude mice.
Conclusions: The prognostic signature of AGGRGs constructed could efficiently estimate the prognosis and immunotherapy effectiveness of OV patients. In addition, AAK1 may represent a promising therapeutic target for OV.
Keywords: Aerobic glycolysis, Glutaminolytic, Ovarian cancer, Prognosis, Immunotherapy.
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