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. 2024 Apr 12;29(1):231.
doi: 10.1186/s40001-024-01814-7.

Functional analysis and validation of oncodrive gene AP3S1 in ovarian cancer through filtering of mutation data from whole-exome sequencing

Deshui Kong #  1   2 , Yu Wu #  1   2 , Qiyu Liu  1   2 , Cuiyu Huang  1   2 , Tongxia Wang  1   2 , Zongyao Huang  1   2 , Yan Gao  1   2 , Yuan Li  3   4 , Hongyan Guo  5   6
Affiliations

Functional analysis and validation of oncodrive gene AP3S1 in ovarian cancer through filtering of mutation data from whole-exome sequencing

Deshui Kong et al. Eur J Med Res. .

Abstract

Background: High-grade serous ovarian carcinoma (HGSOC) is the most aggressive and prevalent subtype of ovarian cancer and accounts for a significant portion of ovarian cancer-related deaths worldwide. Despite advancements in cancer treatment, the overall survival rate for HGSOC patients remains low, thus highlighting the urgent need for a deeper understanding of the molecular mechanisms driving tumorigenesis and for identifying potential therapeutic targets. Whole-exome sequencing (WES) has emerged as a powerful tool for identifying somatic mutations and alterations across the entire exome, thus providing valuable insights into the genetic drivers and molecular pathways underlying cancer development and progression.

Methods: Via the analysis of whole-exome sequencing results of tumor samples from 90 ovarian cancer patients, we compared the mutational landscape of ovarian cancer patients with that of TCGA patients to identify similarities and differences. The sequencing data were subjected to bioinformatics analysis to explore tumor driver genes and their functional roles. Furthermore, we conducted basic medical experiments to validate the results obtained from the bioinformatics analysis.

Results: Whole-exome sequencing revealed the mutational profile of HGSOC, including BRCA1, BRCA2 and TP53 mutations. AP3S1 emerged as the most weighted tumor driver gene. Further analysis of AP3S1 mutations and expression demonstrated their associations with patient survival and the tumor immune response. AP3S1 knockdown experiments in ovarian cancer cells demonstrated its regulatory role in tumor cell migration and invasion through the TGF-β/SMAD pathway.

Conclusion: This comprehensive analysis of somatic mutations in HGSOC provides insight into potential therapeutic targets and molecular pathways for targeted interventions. AP3S1 was identified as being a key player in tumor immunity and prognosis, thus providing new perspectives for personalized treatment strategies. The findings of this study contribute to the understanding of HGSOC pathogenesis and provide a foundation for improved outcomes in patients with this aggressive disease.

Keywords: AP3S1; EMT; Immune infiltration; Ovarian cancer; Somatic mutation; TGF-β pathway.

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Conflict of interest statement

The authors have no competing interests.

Figures

Fig. 1
Fig. 1
Mutations in BRCA1/2 and TP53 at PUTH. A Top 30 mutated genes in the PUTH data. B Comparison of mutation rates of BRCA1 in PUTH and TCGA. C Mutation sites and frequencies of BRCA1 in PUTH and TCGA. D Comparison of mutation rates of BRCA2 in PUTH and TCGA. E Mutation sites and frequencies of BRCA2 in PUTH and TCGA. F Comparison of mutation rates of TP53 in PUTH and TCGA. G Mutation sites and frequencies of TP53 in PUTH and TCGA. H Mutation rates of BRCA1/2 and TP53 after combining TCGA and PUTH data. I Forest plot comparing the differences in mutated genes between TCGA and PUTH datasets
Fig. 2
Fig. 2
Demonstration of recurrence in patients with ovarian cancer. A Forest plot of clinical characteristics and recurrence outcomes in the PUTH data. B Drug strategies calculated based on mutation data. C Genes showing the most significant mutation differences between recurrence and non-recurrence samples in our follow-up patients. D Oncodrive genes identified from PUTH data
Fig. 3
Fig. 3
Mutational status of AP3S1 and survival analysis. A Lollipop plot of AP3S1 in recurrence and non-recurrence samples from the PUTH data. B Comparison of mutation rates and sites of AP3S1 between PUTH and TCGA data. C Patients with high AP3S1 expression in TCGA had worse overall survival (OS) than those with low expression. D Patients with high AP3S1 expression in TCGA had worse disease-free survival (DFS) than those with low expression. E Comparison of progression-free survival (PFS) between AP3S1-mutated and non-mutated samples from the PUTH follow-up data; patients with mutations had better PFS than wild-type patients
Fig. 4
Fig. 4
AP3S1 expression in ovarian cancer cells and tissues. A Subcellular localization of AP3S1 showed its predominant expression in the cytoplasm (green). B Immunohistochemical results indicate higher expression of AP3S1 in ovarian cancer tissues compared to normal ovarian tissues
Fig. 5
Fig. 5
AP3S1 exhibition in ovarian cancer single-cell sequencing results. A Single-cell sequencing results classifying cell subgroups in ovarian cancer tissues. B Cell subgroups identified as immune cell clusters. C Expression level of AP3S1 in immune cell clusters. D Proportions of various immune cells. E Proportions of different immune cells in various ovarian cancer samples. F Expression of AP3S1 in different immune cells. G Expression of DNA repair pathways in cell subgroups. H Expression of oxidative phosphorylation pathways in cell subgroups. I Expression of TGF-β pathways in cell subgroups
Fig. 6
Fig. 6
AP3S1 is involved in ovarian cancer immune infiltration. A TCGA samples divided into two groups based on AP3S1 expression, and the differences in infiltration of 24 immune cells between the two groups. B Significant correlation of AP3S1 expression in TCGA ovarian cancer samples with B cells and CD4 + T cell infiltration. C Significant differences in overall immune cell infiltration scores between high and low AP3S1 expression groups. D Significant differences in StromalScore, ImmuneScore and ESTIMATEScore between high and low AP3S1 expression groups
Fig. 7
Fig. 7
AP3S1 regulates immune cell infiltration and immune checkpoints. A Differential infiltration of various immune cells in different CNV types of AP3S1. B Comparison of AP3S1 expression groups in immune checkpoint
Fig. 8
Fig. 8
Enrichment analysis of AP3S1 in ovarian cancer. A Gene Ontology (GO) enrichment analysis of genes correlated with AP3S1 expression. B KEGG pathway enrichment analysis of genes correlated with AP3S1 expression. C GSEA analysis results of genes correlated with AP3S1 expression. D Results of Reactome, WikiPathways, CORUM, and Human Phenotype Ontology analysis of genes correlated with AP3S1 expression
Fig. 9
Fig. 9
AP3S1 is involved in regulating the migratory capacity of ovarian cancer. A WGCNA analysis of genes correlated with AP3S1 expression and ovarian cancer clinical characteristic. B Significant inhibition of migration and metastasis ability in ovarian cancer cells after AP3S1 knockdown. C Scratch assay confirms that low AP3S1 expression weakens the migration ability of ovarian tumor cells. D The proliferation ability of ovarian cancer cells was inhibited after knocking down AP3S1. All data are mean ± SD. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001. Representative data are from three independent experiments
Fig. 10
Fig. 10
AP3S1 regulates EMT in ovarian cancer through the TGF-β pathway. A Significant inhibition of TGF-β and EMT pathways in ovarian cancer cells after AP3S1 knockdown. B The TGF-β pathway activator SRI-011381 rescues the decreased expression of TGF-β pathway and EMT-related proteins caused by AP3S1 knockdown. C Pirfenidone further exacerbates the decreased expression of TGF-β pathway and EMT-related proteins caused by AP3S1 knockdown. All data are mean ± SD. Significance calculated using the unpaired t-test. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001. Representative data are from three independent experiments
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