Comprehensive analysis of the exocytosis pathway genes in cervical cancer.

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.

Analysis of Nuclear Encoded Mitochondrial Gene Networks in Cervical Cancer.

BACKGROUND: Cervical cancer (CC) is one of the most common female cancers in many developing and underdeveloped countries. High incidence, late presentation, and mortality suggested the need for molecular markers. Mitochondrial defects due to abnormal expression of nuclear-encoded mitochondrial genes (NEMG) have been reported during cancer progression. Nevertheless, the application of NEMG for the prognosis of CC is still elusive. Herein, we aimed to investigate the associations between NEMG and CC prognosis. MATERIALS AND METHODS: The differentially expressed genes (DEG) in the TCGA-CESC dataset and NEMGs were retrieved from TACCO and Mitocarta2.0 databases, respectively. The impact of methylation on NEMG expression were predicted using DNMIVD and UALCAN tools. HCMDB tool was used to predict genes having metastatic potential. The prognostic models were constructed using DNMIVD, TACCO, GEPIA2, and SurvExpress. The functional enrichment analysis (FEA) was performed using clusterProfiler. The protein-protein interaction network (PPIN) was constructed to identify the hub genes (HG) using String and CytoHubba tools. Independent validation of the HG was performed using Oncomine and Human Protein Atlas databases. The druggable genes were predicted using DGIdb. RESULTS: Among the 52 differentially expressed NEMG, 15 were regulated by DNA methylation. The expression level of 16, 10, and 7 has the potential for CC staging, prediction of metastasis, and prognosis. Moreover, 1 driver gene and 16 druggable genes were also identified. The FEA identified the enrichment of cancer-related pathways, including AMPK and carbon metabolism in cancer. The combined expression of 10 HG has been shown to affect patient survival. CONCLUSION: Our findings suggest that the abnormal expression of NEMGs may play a critical role in CC development and progression. The genes identified in our study may serve as a prognostic indicator and therapeutic target in CC.
.

Integrated bioinformatic analysis of miR-15a/16-1 cluster network in cervical cancer.

The miR-15a/16-1 cluster is abnormally expressed in cervical cancer (CC) tissues and plays a vital role in cervical carcinogenesis. We aimed to evaluate the miR-15a/16-1 expression in healthy and cancerous cervical tissues, identify the associated networks, and to test its prognostic significance. miR-15a/16-1-MC expressions were analyzed in TCGA-CESC datasets by UALCAN, GEPIA2, and Datasetviewer. miR-15a/16-1 validated targets were extracted from mirTarBase and in silico functional analysis of the target genes were performed using WebGestalt. The interaction networks were constructed by the miRNet, STRING, and NetworkAnalyst tools. The prognostic significance and metastatic potential of the target genes were predicted using UALCAN and HCMDB. The FDA approved drugs to target miR-15a/16-1 and target gene network in CC were performed using DGIdb, STITCH and PanDrugs. TCGA-CESC and GEO data analysis suggested significant overexpression of miR-15a/16-1 in CC samples. The Kaplan-Meier survival analysis showed that miR-15a and its four target genes (BCL2, CCNE1, NUP50, and RBPJ) influence the overall survival of CC patients. Among the 66 differentially expressed target genes, 12 of them are linked to head, neck, or lung metastasis. Functional enrichment analysis predicted the association of this cluster with p53 signaling, human papillomavirus infection, PI3-AKT signaling pathway, and pathways in cancer. Drug-gene interaction analysis showed 52 potential FDA approved drugs to interact with the miR-15a/16-1 target genes. Nine of the 52 drugs are currently used as a chemotherapeutic agent for the treatment of CC patients. The present study shows that miR-15a/16-1 expression can be used as a clinical marker and target for therapy in CC.