Rucaparib antagonize multidrug resistance in cervical cancer cells through blocking the function of ABC transporters

Identification of miRNAs and target genes associated with lymph node metastasis in cervical cancer using bioinformatics analysis

This study was designed to identify the differentially expressed miRNAs (DEMs) and genes (DEGs) in metastatic cervical cancer using bioinformatic tools. In this study, fifty-seven DEMs (48 downregulated and 9 upregulated) were identified, among which miR-4459 and miR-3195 expression was negatively associated with overall survival of cervical cancer patients. Then, 476 target DEGs were determined, and protein-protein interaction (PPI) network was constructed. Seventeen hub genes (LONRF2, CCNE2, AURKA, SYT1, NEGR1, PPP1R12B, GABRP, RAD51, CDK1, FBLN5, PRKG1, CDC6, CACNA1C, MEOX2, ANLN, MYLK, and EDNRB) were finally selected to construct the miRNA-hub gene network. Overall, our study discovered the key miRNAs and mRNAs related to lymph node metastasis (LNM) in cervical cancer, which helps discover candidate therapeutic targets for cervical cancer.

A novel necroptosis-related lncRNA based signature predicts prognosis and response to treatment in cervical cancer

Background: Necroptosis has been demonstrated to play a crucial role in the prognosis prediction and assessment of treatment outcome in cancers, including cervical cancer. The purpose of this study was to explore the potential prognostic value of necroptosis-related lncRNAs and their relationship with immune microenvironment and response to treatment in cervical cancer. Methods: Data from The Cancer Genome Atlas (TCGA) were collected to obtain synthetic data matrices. Necroptosis-related lncRNAs were identified by Pearson Correlation analysis. Univariate Cox and multivariate Cox regression analysis and Lasso regression were used to construct a necroptosis-related LncRNAs signature. Kaplan-Meier analysis, univariate and multivariate Cox regression analyses, receiver operating characteristic (ROC) curve, nomogram, and calibration curves analysis were performed to validate this signature. Gene set enrichment analyses (GSEA), immunoassays, and the half-maximal inhibitory concentration (IC50) were also analyzed. Results: Initially, 119 necroptosis-related lncRNAs were identified based on necroptosis-related genes and differentially expressed lncRNAs between normal and cervical cancer samples. Then, a prognostic risk signature consisting of five necroptosis-related lncRNAs (DDN-AS1, DLEU1, RGS5, RUSC1-AS1, TMPO-AS1) was established by Cox regression analysis, and LASSO regression techniques. Based on this signature, patients with cervical cancer were classified into a low- or high-risk group. Cox regression confirmed this signature as an independent prognostic predictor with an AUC value of 0.789 for predicting 1-year OS. A nomogram including signature, age, and TNM stage grade was then established, and showed an AUC of 0.82 for predicting 1-year OS. Moreover, GSEA analysis showed that immune-related pathways were enriched in the low-risk group; immunoassays showed that most immune cells, ESTIMAT scores and immune scores were negatively correlated with risk score and that the expression of immune checkpoint-proteins (CD27, CD48, CD200, and TNFRSF14) were higher in the low-risk group. In addition, patients in the low-risk group were more sensitive to Rucaparib, Navitoclax and Crizotinib than those in the high-risk group. Conclusion: We established a novel necroptosis-related lncRNA based signature to predict prognosis, tumor microenvironment and response to treatment in cervical cancer. Our study provides clues to tailor prognosis prediction and individualized immunization/targeted therapy strategies.

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.

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.

Molecular Modeling Strategies of Cancer Multidrug Resistance

Cancer remains a leading cause of morbidity and mortality worldwide. Hence, the increase in cancer cases observed in the elderly population, as well as in children and adolescents, makes human malignancies a prime target for anticancer drug development. Although highly effective chemotherapeutic agents are continuously developed and approved for clinical treatment, the major impediment towards curative cancer therapy remains multidrug resistance (MDR). In recent years, intensive studies have been carried out on the identification of new therapeutic molecules to reverse MDR efflux transporters of the ATP-binding cassette (ABC) superfamily. Although a great deal of progress has been made in the development of specific inhibitors for certain MDR efflux pumps in experimental studies, advanced computational studies can accelerate this drug development process. In the literature, there are many experimental studies on the impact of natural products and synthetic small molecules on the reversal of cancer MDR. Molecular modeling methods provide an opportunity to explain the activity of these molecules on the ABC-transporter family with non-covalent interactions as well as it is possible to carry out studies for the discovery of new anticancer drugs specific to MDR with these methods. The coordinate file of the 3-dimensional (3D) structure of the target protein is indispensable for molecular modeling studies. In some cases where a 3D structure cannot be obtained by experimental methods, the homology modeling method can be applied to obtain the file containing the target protein's information including atomic coordinates, secondary structure assignments, and atomic connectivity. Homology modeling studies are of great importance for efflux transporter proteins that still lack 3D structures due to crystallization problems with multiple hydrophobic transmembrane domains. Quantum mechanics, molecular docking and molecular dynamics simulation applications are the most frequently used molecular modeling methods in the literature to investigate non-covalent interactions between the drug-ABC transporter superfamily. The quantitative structure-activity relationship (QSAR) model provides a relationship between the chemical properties of a compound and its biological activity. Determining the pharmacophore region for a new drug molecule by superpositioning a series of molecules according to their physicochemical properties using QSAR models is another method in which molecular modeling is used in computational drug development studies with ABC transporter proteins. There are also in silico absorption, distribution, metabolism, excretion, and toxicity (ADME/Tox) studies conducted to make a prediction about the pharmacokinetic properties, and drug-likeness of new molecules. Drug repurposing studies, which have become a trending topic in recent years, involve identifying possible new targets for an already approved drug molecule. There are few studies in the literature in which drug repurposing performed by molecular modelling methods has been applied on ABC transporter proteins. The aim of the current paper is to create a complete review of drug development studies including aforementioned molecular modeling methods carried out between the years 2019-2021. Furthermore, an intensive investigation is also conducted on licensed applications and free web servers used in in silico studies. The current review is an up-to-date guide for researchers who plan to conduct computational studies with MDR transporter proteins.

Nucleolin Promotes Cisplatin Resistance in Cervical Cancer by the YB1-MDR1 Pathway

Purpose

Cervical cancer is the fourth most common cancer in women worldwide and is the main cause of cancer-related deaths in women. Cisplatin (DDP) is one of the major chemotherapeutic drugs for cervical cancer patients. But, drug resistance limits the effectiveness of cancer therapy. Nucleolin (NCL) is a nucleocytoplasmic multifunctional protein involved in the development of cancer. It has been reported that NCL may be a potential target for modulation of drug resistance. However, the precise molecular mechanisms are poorly understood.

Materials and Methods

Human cervical cancer Hela cells and their cisplatin-resistant cell line Hela/DDP were used in this study. The protein level of NCL in cervical cancer cells was measured by western blot analysis. Hela cells and Hela/DDP cells were transfected with NCL overexpression plasmid or NCL siRNA separately. MTT and EdU assay were performed to evaluate the cell viability and sensitivity to cisplatin. The drug efflux function of MDR1 protein was assessed by intracellular rhodamine-123 accumulation assay.The promoter activity of MDR1 was assessed by using a dual-luciferase reporter assay.

Results

We found that the protein level of NCL was elevated in Hela/DDP cells. Overexpression of NCL increased cervical cancer cell proliferation and attenuated the sensitivity to cisplatin. Overexpression of NCL increased Multidrug resistance (MDR1) gene expression and drug efflux. Our results demonstrated that NCL was highly related with cisplatin resistance in cervical cancer. NCL played an important role in MDR1 gene transcription through regulation of the transcription factor YB1.

Conclusion

Our findings revealed the novel role of NCL in cisplatin-resistant cervical cancer and NCL may be a potential therapeutic target for chemoresistance.

Novel 4,5-dihydrospiro[benzo[c]azepine-1,1'-cyclohexan]-3(2H)-one derivatives as PARP-1 inhibitors: Design, synthesis and biological evaluation

To further explore the research of novel PARP-1 inhibitors, we designed and synthesized a series of novel amide PARP-1 inhibitors based on our previous research. Most compounds displayed certain antitumor activities against four tumor cell lines (A549, HepG2, HCT-116, and MCF-7). Specifically, the candidate compound R8e possessed strong anti-proliferative potency toward A549 cells with the IC₅₀ value of 2.01 μM. Compound R8e had low toxicity to lung cancer cell line. And the in vitro enzyme inhibitory activity of compound R8e was better than rucaparib. Molecular docking studies provided a rational binding model of compound R8e in complex with rucaparib. The following cell cycle and apoptosis assays revealed that compound R8e could arrest cell cycle in the S phase and induce cell apoptosis. Western blot analysis further showed that compound R8e could effectively inhibit the PAR's biosynthesis and was more effective than rucaparib. Overall, based on the biological activity evaluation, compound R8e could be a potential lead compound for further developing novel amide PARP-1 inhibitors.

Involvement of ABC transporters in the detoxification of non-substrate nanoparticles in lung and cervical cancer cells

This paper aimed to systemically investigate the role of adenosine triphosphate-binding cassette (ABC transporters) in the detoxification of non-substrate nanoparticles including titanium dioxide (n-TiO₂, 5-10 nm) and gold (AuNPs, 3 nm, 15 nm, and 80 nm, named as Au-3, Au-15 and Au-80) in human lung cancer (A549) and human cervical cancer (HeLa) cells. All these nanoparticles were of larger hydrophilic diameters than the channel sizes of ABC transporters, thus should not be the substrates of membrane proteins. After 24-h treatment, they induced significant cytotoxicity as reflected by the reduction in cell viability and glutathione (GSH) contents, as well as the increase in reactive oxygen species (ROS) level. At median-lethal concentrations (10 mg/L n-TiO₂, 2 mg/L Au-3, 5 mg/L Au-15, and 10 mg/L Au-80 for A549 cells; 20 mg/L n-TiO₂, 2 mg/L Au-3, 5 mg/L Au-15, and 10 mg/L Au-80 for Hela cells), all the nanoparticles significantly induced the gene expressions and activities of ABC transporters including P-glycoprotein (PGP) and multidrug resistance associated protein 1 (MRP1). Addition of transporter inhibitors enhanced the ROS levels produced by nanoparticles, but didn't alter their death-inducing effects and intracellular accumulations. With specific suppressors, transcription factors like nuclear factor-erythroid 2-related factor-2 (NRF2) and pregnane X receptor (PXR) were proved to be important in the induction of ABC transporters by nanoparticles. After all, this paper revealed a damage-dependent modulation of ABC transporters by non-substrate nanoparticles. The up-regulated ABC transporters could help in reducing the oxidative stress produced by nanoparticles. Such information should be useful in assessing the environmental risk of nanoparticles, as well as their interactions with other chemical toxicants or drugs.