Prediction of a miRNA-mRNA functional synergistic network for cervical squamous cell carcinoma

miRNAs that regulate apoptosis in breast cancer and cervical cancer

In today's world, one of the main problems is cancer, which still has a long way to go to cure it, and it brings a lot of financial and emotional costs to the people of society and governments. Breast cancer (BC) and cervical cancer (CC), two of the most common cancers, are caused by several genetic and environmental factors in women. These two cancers' involvement rate is higher than other cancers in women. microRNAs (miRNAs) are non-coding RNA molecules with a length of 18 to 24 nucleotides, which play an important role in post-translational changes. miRNAs themselves are divided into two categories, oncomiRs and tumor suppressors. OncomiRs have a part in tumor expansion and tumor suppressors prevent tumor development and progress. miRNAs can control cellular processes by regulating various pathways including autophagy, apoptosis, and signaling. Apoptosis is a type of programmed cell death that includes intrinsic and extrinsic pathways and is different from other cell death pathways such as necrosis and ferroptosis. Apoptosis controls the growth, differentiation, and death of cells by regulating the death of damaged and old cells, and since miRNAs are one of the factors that regulate apoptosis, and divided into two categories: pro-apoptotic and anti-apoptotic. We decided in this study to investigate the relationship between miRNAs and apoptosis in the most common women's cancers, BC and CC.

Regulation and tumor-suppressive function of the miR-379/miR-656 (C14MC) cluster in cervical cancer

Cervical cancer (CC) is a key contributor to cancer-related mortality in several countries. The identification of molecular markers and the underlying mechanism may help improve CC management. We studied the regulation and biological function of the chromosome 14 microRNA cluster (C14MC; miR-379/miR-656) in CC. Most C14MC members exhibited considerably lower expression in CC tissues and cell lines in The Cancer Genome Atlas (TCGA) cervical squamous cell carcinoma and endocervical adenocarcinoma patient cohorts. Bisulfite Sanger sequencing revealed hypermethylation of the C14MC promoter in CC tissues and cell lines. 5-aza-2 deoxy cytidine treatment reactivated expression of the C14MC members. We demonstrated that C14MC is a methylation-regulated miRNA cluster via artificial methylation and luciferase reporter assays. C14MC downregulation correlated with poor overall survival and may promote metastasis. C14MC activation via the lentiviral-based CRISPRa approach inhibited growth, proliferation, migration, and invasion; enhanced G2/M arrest; and induced senescence. Post-transcriptional regulatory network analysis of C14MC transcriptomic data revealed enrichment of key cancer-related pathways, such as metabolism, the cell cycle, and phosphatidylinositol 3-kinase (PI3K)-AKT signaling. Reduced cell proliferation, growth, migration, invasion, and senescence correlated with the downregulation of active AKT, MYC, and cyclin E1 (CCNE1) and the overexpression of p16, p21, and p27. We showed that C14MC miRNA activation increases reactive oxygen species (ROS) levels, intracellular Ca2+ levels, and lipid peroxidation rates, and inhibits epithelial-mesenchymal transition (EMT). C14MC targets pyruvate dehydrogenase kinase-3 (PDK3) according to the luciferase reporter assay. PDK3 is overexpressed in CC and is inversely correlated with C14MC. Both miR-494-mimic transfection and C14MC activation inhibited PDK3 expression. Reduced glucose uptake and lactate production, and upregulation of PDK3 upon C14MC activation suggest the potential role of these proteins in metabolic reprogramming. Finally, we showed that C14MC activation may inhibit EMT signaling. Thus, C14MC is a tumor-suppressive and methylation-regulated miRNA cluster in CC. Reactivation of C14MC can be useful in the management of CC.

Metadata analysis to explore hub of the hub-genes highlighting their functions, pathways and regulators for cervical cancer diagnosis and therapies

Cervical cancer (CC) is considered as the fourth most common women cancer globally.that shows malignant features of local infiltration and invasion into adjacent organs and tissues. There are several individual studies in the literature that explored CC-causing hub-genes (HubGs), however, we observed that their results are not so consistent. Therefore, the main objective of this study was to explore hub of the HubGs (hHubGs) that might be more representative CC-causing HubGs compare to the single study based HubGs. We reviewed 52 published articles and found 255 HubGs/studied-genes in total. Among them, we selected 10 HubGs (CDK1, CDK2, CHEK1, MKI67, TOP2A, BRCA1, PLK1, CCNA2, CCNB1, TYMS) as the hHubGs by the protein-protein interaction (PPI) network analysis. Then, we validated their differential expression patterns between CC and control samples through the GPEA database. The enrichment analysis of HubGs revealed some crucial CC-causing biological processes (BPs), molecular functions (MFs) and cellular components (CCs) by involving hHubGs. The gene regulatory network (GRN) analysis identified four TFs proteins and three miRNAs as the key transcriptional and post-transcriptional regulators of hHubGs. Then, we identified hHubGs-guided top-ranked FDA-approved 10 candidate drugs and validated them against the state-of-the-arts independent receptors by molecular docking analysis. Finally, we investigated the binding stability of the top-ranked three candidate drugs (Docetaxel, Temsirolimus, Paclitaxel) by using 100 ns MD-based MM-PBSA simulations and observed their stable performance. Therefore the finding of this study might be the useful resources for CC diagnosis and therapies.

miR‑99a‑5p inhibits glycolysis and induces cell apoptosis in cervical cancer by targeting RRAGD

Cervical cancer (CC) is one of the most common gynecological malignancies that endangers women's health. A negative effect of glycolysis is that it contributes to abnormal tumor growth. MicroRNA (miR)-99a expression has been found to be decreased in CC. The present study aimed to investigate the role of miR-99a-5p in glycolysis in CC. For this purpose, the association between miR-99a and the prognosis of patients with CC from The Cancer Genome Atlas database was analyzed using Kaplan-Meier analysis. miR-99a-5p expression and Ras-related GTP binding D (RRAGD) expression were assessed using reverse transcription-quantitative PCR and western blot analysis. Cell proliferation and apoptosis were examined using an MTT assay and flow cytometry, respectively. Glucose uptake, lactate concentration and extracellular acidification rate were measured using a glucose uptake colorimetric assay, a lactate colorimetric assay and a Seahorse XFe96 extracellular flux analyzer, respectively. The association between miR-99a-5p and RRAGD was predicted using TargetScan 7.1, and was confirmed using dual luciferase reporter assay. The results revealed that miR-99a-5p expression was decreased and that of RRAGD was increased in CC tissues and cell lines. RRAGD was negatively regulated by miR-99a-5p. The overexpression of miR-99a-5p induced apoptosis and inhibited glycolysis in CC cells by targeting RRAGD. On the whole, the present study revealed a novel mechanism through which miR-99a-5p regulates cell apoptosis and glycolysis in CC, thus providing a potential therapeutic target for CC.

Bioinformatics Screening of Potential Biomarkers from mRNA Expression Profiles to Discover Drug Targets and Agents for Cervical Cancer

Bioinformatics analysis has been playing a vital role in identifying potential genomic biomarkers more accurately from an enormous number of candidates by reducing time and cost compared to the wet-lab-based experimental procedures for disease diagnosis, prognosis, and therapies. Cervical cancer (CC) is one of the most malignant diseases seen in women worldwide. This study aimed at identifying potential key genes (KGs), highlighting their functions, signaling pathways, and candidate drugs for CC diagnosis and targeting therapies. Four publicly available microarray datasets of CC were analyzed for identifying differentially expressed genes (DEGs) by the LIMMA approach through GEO2R online tool. We identified 116 common DEGs (cDEGs) that were utilized to identify seven KGs (AURKA, BRCA1, CCNB1, CDK1, MCM2, NCAPG2, and TOP2A) by the protein–protein interaction (PPI) network analysis. The GO functional and KEGG pathway enrichment analyses of KGs revealed some important functions and signaling pathways that were significantly associated with CC infections. The interaction network analysis identified four TFs proteins and two miRNAs as the key transcriptional and post-transcriptional regulators of KGs. Considering seven KGs-based proteins, four key TFs proteins, and already published top-ranked seven KGs-based proteins (where five KGs were common with our proposed seven KGs) as drug target receptors, we performed their docking analysis with the 80 meta-drug agents that were already published by different reputed journals as CC drugs. We found Paclitaxel, Vinorelbine, Vincristine, Docetaxel, Everolimus, Temsirolimus, and Cabazitaxel as the top-ranked seven candidate drugs. Finally, we investigated the binding stability of the top-ranked three drugs (Paclitaxel, Vincristine, Vinorelbine) by using 100 ns MD-based MM-PBSA simulations with the three top-ranked proposed receptors (AURKA, CDK1, TOP2A) and observed their stable performance. Therefore, the proposed drugs might play a vital role in the treatment against CC.

Recent development of bioinformatics tools for microRNA target prediction

MicroRNAs (miRNAs) are central players that regulate the post-transcriptional processes of gene expression. Binding of miRNAs to target mRNAs can repress their translation by inducing the degradation or by inhibiting the translation of the target mRNAs. High-throughput experimental approaches for miRNA target identification are costly and time-consuming, depending on various factors. It is vitally important to develop the bioinformatics methods for accurately predicting miRNA targets. With the increase of RNA sequences in the post-genomic era, bioinformatics methods are being developed for miRNA studies specially for miRNA target prediction. This review summarizes the current development of state-of-the-art bioinformatics tools for miRNA target prediction, points out the progress and limitations of the available miRNA databases, and their working principles. Finally, we discuss the caveat and perspectives of the next-generation algorithms for the prediction of miRNA targets.

Screening of genes related to survival prognosis of cervical squamous cell carcinoma and construction of prognosis prediction model

AIM

We aimed to screen for the genes related to survival prognosis of cervical squamous cell carcinoma (CSCC) and then constructed a prognosis prediction model.

METHODS

The GSE63514 dataset was obtained from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO). The CSCC gene dataset and the GSE44001 dataset were obtained from The Cancer Genome Atlas and NCBI GEO, respectively. The Kaplan-Meier (KM) curve was used to evaluate the association between high and low prognosis that was with the actual survival prognosis information. The Cox proportional hazards model was used to screen out the optimized prognostic-related signature differentially expressed gene (DEG) combinations. Gene set enrichment analysis was used to perform pathway enrichment annotation analysis for DEGs that were related to risk grouping.

RESULTS

In total, 16 399 DEGs were obtained and 23 gene ontology biological processes and 8 Kyoto Encyclopedia of Genes and Genomes pathways were screened. Nine optimized DEG groups related to independent prognosis were selected. The KM curves of pathologic N0 and N1 showed that low-risk group were associated with a better overall survival (p = 1.518e; p = 1.704e-01). The pathways related to risk grouping were cytokine-cytokine receptor interaction, JAK stat signaling pathway, and glycolysis-gluconeogenesis.

CONCLUSION

On the basis of this study, we established a prognostic risk model, which provided a reliable prognostic tool and was of great significance for locating the biomarkers related to survival prognosis in CSCC.