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Comprehensive analysis of the exocytosis pathway genes in cervical cancer
Sangavi Eswaran1, Divya Adiga1, Nadeem Khan G1
1Department of Cell and Molecular Biology, Manipal School of Life Sciences, Manipal Academy of Higher Education, Karnataka, India.
Background:
Cervical cancer (CC) is the fourth most common gynecological malignancy globally. This suggests the need for improved markers for prognosis, better understanding of the molecular mechanism, and targets for therapy. The defective exocytosis pathway is proposed as bona fide drivers of carcinogenesis. This study aimed to identify the exocytosis pathway network and its contribution to CC.
Methods:
We screened exocytosis genes from the The Cancer Genome Atlas Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma (TCGA-CESC) dataset and performed differential expression and methylation, Kaplan-Meier survival, and pathway enrichment analysis. We constructed the protein-protein interaction networks (PPIN), predicted the possible metastatic genes, and identified FDA approved drugs to target the exocytosis network in CC.
Results:
Integrated bioinformatics analysis identified 245 differentially methylated genes, including 153 hypermethylated and 92 hypomethylated genes. Further, 89 exocytosis pathway genes were differentially expressed, including 60 downregulated and 29 upregulated genes in CC. The overlapping analysis identified 39 genes as methylation regulated genes and showed an inverse correlation between methylation and expression. The HCMDB database identified nine of the identified genes (GRIK5, PTPN6, GAB2, ATP8B4, HTR2A, SPARC, CLEC3B, VWF, and S100A11) were linked with metastasis in CC. Moreover, the Kaplan-Meier survival analysis identified that high expression of PTPN6 and low expression of CLEC3B were significantly linked with poor overall survival (OS) in patients with CC. The KEGG pathway enrichment analysis identified differentially expressed genes that were mainly involved with proteoglycans in cancer, TGF-beta signaling, PI3K-Akt signaling, MAPK signaling pathway, and others. The PPIN identified 89 nodes, 192 edges with VWF, MMP9, THBS1, IGF1, CLU, A2M, IGF2, SPARC, VAMP2, and FIGF as top 10 hub genes. The drug-gene interaction analysis identified 188 FDA approved drugs targeting 32 genes, including 5 drugs that are already in use for treating CC.
Conclusions:
In summary, we have identified the exocytosis pathway networks, candidate genes, and novel drugs for better management of CC.
