Systematic Analysis of the Therapy Resistance Genes and their Prognostic Relevance in Cervical Cancer.

INTRODUCTION: Critical issues in the therapeutic management of cervical cancer (CC) include therapy resistance and treatment failure. The development of therapy resistance is a multifaceted, progressive process, including genetic and epigenetic abnormalities. The present study aimed to identify genes that may contribute to therapy resistance in CC. MATERIALS AND METHODS: We have created an extensive list of the genes in cancer that are therapy-resistant using a text-mining approach. The list was compared with the TCGA-CESC dataset to identify the differentially expressed therapy resistance genes (DETRGs) in CC. We used online resources (UALCAN, DNMIVD, cBio- Portal, HCMDB, OncoDB, ShinyGO, HPA, KM Plotter, TIMER, and DGIdb) to determine the potential association between methylation and expression of therapy resistance genes with the prognosis and clinical outcomes in CC. RESULTS: The systematic analysis identified 71 out of 91 DETRGs showed aberrant DNA methylation. The overlapping analysis identified 25 genes to show an inverse correlation between methylation and expression. Further, differential expression or methylation could be helpful in CC staging, HPV association, prediction of metastasis and prognosis. The study identified seven driver genes in CC. The PPIN identifies ten hub genes (HGs) associated with CC staging, cancer hallmarks, and prognosis to affect long-term survival. CONCLUSION: Our thorough investigation uncovered several novel genes and pathways that might contribute to therapy resistance in CC. The genes identified in our study may serve as a biomarker, prognostic indicator, and therapeutic target in CC.

Integrated bioinformatic analysis to understand the association between phthalate exposure and breast cancer progression.

Phthalates have been extensively used as plasticizers while manufacturing plastic-based consumer products. Estradiol mimicking properties and association studies suggest phthalates may contribute to breast cancer (BC). We performed an in-silico analysis and functional studies to understand the association between phthalate exposure and BC progression. Search for phthalate-responsive genes using the comparative toxicogenomics database identified 20 genes as commonly altered in response to multiple phthalates exposure. Of the 20 genes, 12 were significantly differentially expressed between normal and BC samples. In BC samples, 9 out of 20 genes showed a negative correlation between promoter methylation and its expression. AHR, BAX, BCL2, CAT, ESR2, IL6, and PTGS2 expression differed significantly between metastatic and non-metastatic BC samples. Gene set enrichment analysis identified metabolism, ATP-binding cassette transporters, insulin signaling, and type II diabetes as highly enriched pathways. The diagnostic assessment based on 20 genes expression suggested a sensitivity and a specificity >0.91. The aberrantly expressed phthalate interactive gene influenced the overall survival of BC patients. Drug-gene interaction analysis identified 14 genes and 523 candidate drugs, including 19 BC treatment-approved drugs. Di(2-ethylhexyl) phthlate (DEHP) exposure increased the growth, proliferation, and migration of MCF-7 and MDA-MB-231 cells in-vitro. DEHP exposure induced morphological changes, actin cytoskeletal remodeling, increased ROS content, reduced basal level lipid peroxidation, and induced epithelial to mesenchymal transition (EMT). The present approach can help to explore the potentially damaging effects of environmental agents on cancer risk and understand the underlined pathways and molecular mechanisms.

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.
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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.

Molecular landscape of recurrent cervical cancer.

Cervical cancer (CC) is a major gynecological problem in developing and underdeveloped countries. Despite the significant advancement in early detection and treatment modalities, several patients recur. Moreover, the molecular mechanisms responsible for CC recurrence remains obscure. The patients with CC recurrence often show poor prognosis and significantly high mortality rates. The clinical management of recurrent CC depends on treatment history, site, and extent of the recurrence. Owing to poor prognosis and limited treatment options, recurrent CC often presents a challenge to the clinicians. Several in vitro, in vivo, and patient studies have led to the identification of the critical molecular changes responsible for CC recurrence. Both aberrant genetic and epigenetic modifications leading to altered cell signaling pathways have been reported to impact CC recurrence. Researchers are currently trying to dissect the molecular pathways in CC and translate these findings for better management of disease. This article attempts to review the existing knowledge of disease relapse, accompanying challenges, and associated molecular players in CC.