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Comprehensive Analyses Identify APOBEC3A as a Genomic Instability-Associated Immune Prognostic Biomarker in Ovarian
Fangfang Xu1, Tingwei Liu1, Zhuonan Zhou2
1Department of Gynecology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, China.
Abstract:
Ovarian cancer (OC) is one of the most malignant tumors whose mortality rate ranks first in gynecological tumors. Although immunotherapy sheds new light on clinical treatments, the low response still restricts its clinical use because of the unique characteristics of OC such as immunosuppressive microenvironment and unstable genomes. Further exploration on determining an efficient biomarker to predict the immunotherapy response of OC patients is of vital importance. In this study, integrative analyses were performed systematically using transcriptome profiles and somatic mutation data from The Cancer Genome Atlas (TCGA) based on the immune microenvironment and genomic instability of OC patients. Firstly, intersection analysis was conducted to identify immune-related differentially expressed genes (DEGs) and genomic instability-related DEGs. Secondly, Apolipoprotein B MRNA Editing Enzyme Catalytic Subunit 3A (APOBEC3A) was recognized as a protective factor for OC, which was also verified through basic experiments such as quantitative reverse transcription PCR (RT-qPCR), immunohistochemistry (IHC), Cell Counting Kit-8 (CCK-8), and transwell assays. Thirdly, the correlation analyses of APOBEC3A expression with tumor-infiltrating immune cells (TICs), inhibitory checkpoint molecules (ICPs), Immunophenoscores (IPS), and response to anti-PD-L1 immunotherapy were further applied along with single-sample GSEA (ssGSEA), demonstrating APOBEC3A as a promising biomarker to forecast the immunotherapy response of OC patients. Last, the relationship between APOBEC3A expression with tumor mutation burden (TMB), DNA damage response (DDR) genes, and m6A-related regulators was also analyzed along with the experimental verification of immunofluorescence (IF) and RT-qPCR, comprehensively confirming the intimate association of APOBEC3A with genomic instability in OC. In conclusion, APOBEC3A was identified as a protective signature and a promising prognostic biomarker for forecasting the survival and immunotherapy effect of OC patients, which might accelerate the clinical application and improve immunotherapy effect.
Insights
Apolipoprotein B mRNA editing enzyme catalytic subunit 3A (APOBEC3A) is a protective factor in ovarian cancer. It shows promise as a biomarker for predicting immunotherapy response and patient survival.
Area of Science:
- Oncology
- Genomics
- Immunology
Background:
- Ovarian cancer (OC) has a high mortality rate, and current immunotherapies have limited efficacy due to the tumor's immunosuppressive microenvironment and genomic instability.
- Identifying reliable biomarkers is crucial for predicting immunotherapy response in OC patients.
Purpose of the Study:
- To identify a biomarker that predicts immunotherapy response in ovarian cancer patients.
- To investigate the role of APOBEC3A in ovarian cancer's immune microenvironment and genomic instability.
Main Methods:
- Integrative analysis of The Cancer Genome Atlas (TCGA) transcriptome and somatic mutation data.
- Identification of immune and genomic instability-related differentially expressed genes (DEGs).
- Experimental validation using RT-qPCR, IHC, CCK-8, transwell assays, ssGSEA, and immunofluorescence.
Main Results:
- APOBEC3A was identified as a protective factor in OC, validated through multiple experimental assays.
- APOBEC3A expression correlates with tumor-infiltrating immune cells, checkpoint molecules, and response to anti-PD-L1 immunotherapy.
- APOBEC3A is strongly associated with genomic instability markers, including tumor mutation burden and DNA damage response genes.
Conclusions:
- APOBEC3A serves as a protective signature and a promising prognostic biomarker for ovarian cancer.
- APOBEC3A can predict patient survival and immunotherapy effectiveness, potentially improving clinical applications.
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