Targeting of topoisomerases for prognosis and drug resistance in ovarian cancer

Targeted Drug Nanodelivery and Immunotherapy for Combating Tumor Resistance

Chemotherapy resistance is a common cause of tumor treatment failure. Various molecular responses, such as increased expression of efflux transporter proteins, including Pglycoprotein (P-gp), changes in the tumor microenvironment (TME), the role of platelets, and the effects of cancer stem cells (CSCs), can lead to drug resistance. Through extensive research on the mechanisms of drug resistance, more effective anti-resistance drugs and therapeutic approaches are being developed. This review explores drug resistance mechanisms and summarizes relevant anti-resistance drugs. In addition, due to the therapeutic limitations of the aforementioned treatments, new advances in nanocarrier-based combination immunotherapy to address the challenge of drug resistance have been described. Nanocarriers combined with immunotherapy can not only target tumor sites for targeted drug release but also modulate the autoimmune system and enhance immune efficacy, thereby overcoming tumor drug resistance. This review suggests new strategies for overcoming tumor drug resistance and is expected to inform tumor treatment and prognosis.

TOP2A modulates signaling via the AKT/mTOR pathway to promote ovarian cancer cell proliferation

Ovarian cancer (OC) is a form of gynecological malignancy that is associated with worse patient outcomes than any other cancer of the female reproductive tract. Topoisomerase II α (TOP2A) is commonly regarded as an oncogene that is associated with malignant disease progression in a variety of cancers, its mechanistic functions in OC have yet to be firmly established. We explored the role of TOP2A in OC through online databases, clinical samples, in vitro and in vivo experiments. And initial analyses of public databases revealed high OC-related TOP2A expression in patient samples that was related to poorer prognosis. This was confirmed by clinical samples in which TOP2A expression was elevated in OC relative to healthy tissue. Kaplan-Meier analyses further suggested that higher TOP2A expression levels were correlated with worse prognosis in OC patients. In vitro, TOP2A knockdown resulted in the inhibition of OC cell proliferation, with cells entering G1 phase arrest and undergoing consequent apoptotic death. In rescue assays, TOP2A was confirmed to regulate cell proliferation and cell cycle through AKT/mTOR pathway activity. Mouse model experiments further affirmed the key role that TOP2A plays as a driver of OC cell proliferation. These data provide strong evidence supporting TOP2A as an oncogenic mediator and prognostic biomarker related to OC progression and poor outcomes. At the mechanistic level, TOP2A can control tumor cell growth via AKT/mTOR pathway modulation. These preliminary results provide a foundation for future research seeking to explore the utility of TOP2A inhibitor-based combination treatment regimens in platinum-resistant recurrent OC patients.

Discovering novel prognostic biomarkers of hepatocellular carcinoma using eXplainable Artificial Intelligence

Hepatocellular carcinoma (HCC) remains a global health challenge with high mortality rates, largely due to late diagnosis and suboptimal efficacy of current therapies. With the imperative need for more reliable, non-invasive diagnostic tools and novel therapeutic strategies, this study focuses on the discovery and application of novel genetic biomarkers for HCC using explainable artificial intelligence (XAI). Despite advances in HCC research, current biomarkers like Alpha-fetoprotein (AFP) exhibit limitations in sensitivity and specificity, necessitating a shift towards more precise and reliable markers. This paper presents an innovative multi-model XAI and a probabilistic causal inference framework to identify and validate key genetic biomarkers for HCC prognosis. Our methodology involved analyzing clinical and gene expression data to identify potential biomarkers with prognostic significance. The study utilized robust AI models validated against extensive gene expression datasets, demonstrating not only the predictive accuracy but also the clinical relevance of the identified biomarkers through explainable metrics. The findings highlight the importance of biomarkers such as TOP3B, SSBP3, and COX7A2L, which were consistently influential across multiple models, suggesting their role in improving the predictive accuracy for HCC prognosis beyond AFP. Notably, the study also emphasizes the relevance of these biomarkers to the Hispanic population, aligning with the larger goal of demographic-specific research. The application of XAI in biomarker discovery represents a significant advancement in HCC research, offering a more nuanced understanding of the disease and laying the groundwork for improved diagnostic and therapeutic strategies.

Targeting TOP2B as a vulnerability in aging and aging-related diseases

The ongoing trend of rapid aging of the global population has unavoidably resulted in an increase in aging-related diseases. There is an immense amount of interest in the scientific community for the identification of molecular targets that may effectively mitigate the process of aging and aging-related diseases. The enzyme Topoisomerase IIβ (TOP2B) plays a crucial role in resolving the topological challenges that occur during DNA-related processes. It is believed that the disruption of TOP2B function contributes to the aging of cells and tissues, as well as the development of age-related diseases. Consequently, targeting TOP2B appears to be a promising approach for interventions aimed at mitigating the effects of aging. This review focuses on recent advancements in the understanding of the role of TOP2B in the processing of aging and aging-related disorders, thus providing a novel avenue for the development of anti-aging strategies.

Discovering Novel Prognostic Biomarkers of Hepatocellular Carcinoma using eXplainable Artificial Intelligence

Hepatocellular carcinoma (HCC) remains a global health challenge with high mortality rates, largely due to late diagnosis and suboptimal efficacy of current therapies. With the imperative need for more reliable, non-invasive diagnostic tools and novel therapeutic strategies, this study focuses on the discovery and application of novel genetic biomarkers for HCC using explainable artificial intelligence (XAI). Despite advances in HCC research, current biomarkers like Alpha-fetoprotein (AFP) exhibit limitations in sensitivity and specificity, necessitating a shift towards more precise and reliable markers. This paper presents an innovative XAI framework to identify and validate key genetic biomarkers for HCC prognosis. Our methodology involved analyzing clinical and gene expression data to identify potential biomarkers with prognostic significance. The study utilized robust AI models validated against extensive gene expression datasets, demonstrating not only the predictive accuracy but also the clinical relevance of the identified biomarkers through explainable metrics. The findings highlight the importance of biomarkers such as TOP3B, SSBP3, and COX7A2L, which were consistently influential across multiple models, suggesting their role in improving the predictive accuracy for HCC prognosis beyond AFP. Notably, the study also emphasizes the relevance of these biomarkers to the Hispanic population, aligning with the larger goal of demographic-specific research. The application of XAI in biomarker discovery represents a significant advancement in HCC research, offering a more nuanced understanding of the disease and laying the groundwork for improved diagnostic and therapeutic strategies.

Integration of bioinformatics and machine learning strategies identifies APM-related gene signatures to predict clinical outcomes and therapeutic responses for breast cancer patients

BACKGROUND

Tumor antigenicity and efficiency of antigen presentation jointly influence tumor immunogenicity, which largely determines the effectiveness of immune checkpoint blockade (ICB). However, the role of altered antigen processing and presentation machinery (APM) in breast cancer (BRCA) has not been fully elucidated.

METHODS

A series of bioinformatic analyses and machine learning strategies were performed to construct APM-related gene signatures to guide personalized treatment for BRCA patients. A single-sample gene set enrichment analysis (ssGSEA) algorithm and weighted gene co-expression network analysis (WGCNA) were combined to screen for BRCA-specific APM-related genes. The non-negative matrix factorization (NMF) algorithm was used to divide the cohort into different clusters and the fgsea algorithm was applied to investigate the altered signaling pathways. Random survival forest (RSF) and the least absolute shrinkage and selection operator (Lasso) Cox regression analysis were combined to construct an APM-related risk score (APMrs) signature to predict overall survival. Furthermore, a nomogram and decision tree were generated to improve predictive accuracy and risk stratification for individual patients. Based on Tumor Immune Dysfunction and Exclusion (TIDE) method, random forest (RF) and Lasso logistic regression model were combined to establish an APM-related immunotherapeutic response score (APMis). Finally, immune infiltration, immunomodulators, mutational patterns, and potentially applicable drugs were comprehensively analyzed in different APM-related risk groups. IHC staining was used to assess the expression of APM-related genes in clinical samples.

RESULTS

In this study, APMrs and APMis showed favorable performances in risk stratification and therapeutic prediction for BRCA patients. APMrs exhibited more powerful prognostic capacity and accurate survival prediction compared to conventional clinicopathological features. APMrs was closely associated with distinct mutational patterns, immune cell infiltration and immunomodulators expression. Furthermore, the two APM-related gene signatures were independently validated in external cohorts with prognosis or immunotherapeutic responses. Potential applicable drugs and targets were mined in the APMrs-high group. APM-related genes were further validated in our in-house samples.

CONCLUSION

The APM-related gene signatures established in our study could improve the personalized assessment of survival risk and guide ICB decision-making for BRCA patients.

The Curcumin Analog PAC Is a Potential Solution for the Treatment of Triple-Negative Breast Cancer by Modulating the Gene Expression of DNA Repair Pathways

Breast Cancer (BC) is one of the most common and challenging cancers among females worldwide. Conventional treatments for oral cancer rely on the use of radiology and surgery accompanied by chemotherapy. Chemotherapy presents many side effects, and the cells often develop resistance to this chemotherapy. It will be urgent to adopt alternative or complementary treatment strategies that are new and more effective without these negative effects to improve the well-being of patients. A substantial number of epidemiological and experimental studies reported that many compounds are derived from natural products such as curcumin and their analogs, which have a great deal of beneficial anti-BC activity by inducing apoptosis, inhibiting cell proliferation, migration, and metastasis, modulating cancer-related pathways, and sensitizing cells to radiotherapy and chemotherapy. In the present study, we investigated the effect of the curcumin-analog PAC on DNA repair pathways in MCF-7 and MDA-MB-231 human breast-cancer cell lines. These pathways are crucial for genome maintenance and cancer prevention. MCF-7 and MDA-MB-231 cells were exposed to PAC at 10 µM. MTT and LDH assays were conducted to evaluate the effects of PAC on cell proliferation and cytotoxicity. Apoptosis was assessed in breast cancer cell lines using flow cytometry with annexin/Pi assay. The expression of proapoptotic and antiapoptotic genes was determined by RT-PCR to see if PAC is active in programming cell death. Additionally, DNA repair signaling pathways were analyzed by PCR arrays focusing on genes being related and confirmed by quantitative PCR. PAC significantly inhibited breast-cancer cell proliferation in a time-dependent manner, more on MDA-MB-231 triple-negative breast cancer cells. The flow cytometry results showed an increase in apoptotic activity. These data have been established by the gene expression and indicate that PAC-induced apoptosis by an increased Bax and decreased Bcl-2 expression. Moreover, PAC affected multiple genes involved in the DNA repair pathways occurring in both cell lines (MCF-7 and MDA-MB231). In addition, our results suggest that PAC upregulated more than twice 16 genes (ERCC1, ERCC2, PNKP, POLL, MPG, NEIL2, NTHL1, SMUG1, RAD51D, RAD54L, RFC1, TOP3A, XRCC3, XRCC6BP1, FEN1, and TREX1) in MDA-MB-231, 6 genes (ERCC1, LIG1, PNKP, UNG, MPG, and RAD54L) in MCF-7, and 4 genes (ERCC1, PNKP, MPG, and RAD54L) in the two cell lines. In silico analysis of gene-gene interaction shows that there are common genes between MCF-7 and MDA-MB-321 having direct and indirect effects, among them via coexpression, genetic interactions, pathways, predicted and physical interactions, and shared protein domains with predicted associated genes indicating they are more likely to be functionally related. Our data show that PAC increases involvement of multiple genes in a DNA repair pathway, this certainly can open a new perspective in breast-cancer treatment.

Combined PARP and Dual Topoisomerase Inhibition Potentiates Genome Instability and Cell Death in Ovarian Cancer

Although ovarian cancer is a rare disease, it constitutes the fifth leading cause of cancer death among women. It is of major importance to develop new therapeutic strategies to improve survival. Combining P8-D6, a novel dual topoisomerase inhibitor with exceptional anti-tumoral properties in ovarian cancer and compounds in preclinical research, and olaparib, a PARP inhibitor targeting DNA damage repair, is a promising approach. P8-D6 induces DNA damage that can be repaired by base excision repair or homologous recombination in which PARP plays a major role. This study analyzed benefits of combining P8-D6 and olaparib treatment in 2D and 3D cultures with ovarian cancer cells. Measurement of viability, cytotoxicity and caspase activity were used to assess therapy efficacy and to calculate the combination index (CI). Further DNA damage was quantified using the biomarkers RAD51 and γH2A.X. The combinational treatment led to an increased caspase activity and reduced viability. CI values partially show synergisms in combinations at 100 nM and 500 nM P8-D6. More DNA damage accumulated, and spheroids lost their membrane integrity due to the combinational treatment. While maintaining the same therapy efficacy as single-drug therapy, doses of P8-D6 and olaparib can be reduced in combinational treatments. Synergisms can be seen in some tested combinations. In summary, the combination therapy indicates benefits and acts synergistic at 100 nM and 500 nM P8-D6.

Novel Au Carbene Complexes as Promising Multi-Target Agents in Breast Cancer Treatment

Over the past decade, metal complexes based on N-heterocyclic carbenes (NHCs) have attracted great attention due to their wide and exciting applications in material sciences and medicinal chemistry. In particular, the gold-based complexes are the focus of research efforts for the development of new anticancer compounds. Literature data and recent results, obtained by our research group, reported the design, the synthesis and the good anticancer activity of some silver and gold complexes with NHC ligands. In particular, some of these complexes were active towards some breast cancer cell lines. Considering this evidence, here we report some new Au-NHC complexes prepared in order to improve solubility and biological activity. Among them, the compounds 1 and 6 showed an interesting anticancer activity towards the breast cancer MDA-MB-231 and MCF-7 cell lines, respectively. In addition, in vitro and in silico studies demonstrated that they were able to inhibit the activity of the human topoisomerases I and II and the actin polymerization reaction. Moreover, a downregulation of vimentin expression and a reduced translocation of NF-kB into the nucleus was observed. The interference with these vital cell structures induced breast cancer cells' death by triggering the extrinsic apoptotic pathway.

Machine-learning model derived gene signature predictive of paclitaxel survival benefit in gastric cancer: results from the randomised phase III SAMIT trial

OBJECTIVE

To date, there are no predictive biomarkers to guide selection of patients with gastric cancer (GC) who benefit from paclitaxel. Stomach cancer Adjuvant Multi-Institutional group Trial (SAMIT) was a 2×2 factorial randomised phase III study in which patients with GC were randomised to Pac-S-1 (paclitaxel +S-1), Pac-UFT (paclitaxel +UFT), S-1 alone or UFT alone after curative surgery.

DESIGN

The primary objective of this study was to identify a gene signature that predicts survival benefit from paclitaxel chemotherapy in GC patients. SAMIT GC samples were profiled using a customised 476 gene NanoString panel. A random forest machine-learning model was applied on the NanoString profiles to develop a gene signature. An independent cohort of metastatic patients with GC treated with paclitaxel and ramucirumab (Pac-Ram) served as an external validation cohort.

RESULTS

From the SAMIT trial 499 samples were analysed in this study. From the Pac-S-1 training cohort, the random forest model generated a 19-gene signature assigning patients to two groups: Pac-Sensitive and Pac-Resistant. In the Pac-UFT validation cohort, Pac-Sensitive patients exhibited a significant improvement in disease free survival (DFS): 3-year DFS 66% vs 40% (HR 0.44, p=0.0029). There was no survival difference between Pac-Sensitive and Pac-Resistant in the UFT or S-1 alone arms, test of interaction p<0.001. In the external Pac-Ram validation cohort, the signature predicted benefit for Pac-Sensitive (median PFS 147 days vs 112 days, HR 0.48, p=0.022).

CONCLUSION

Using machine-learning techniques on one of the largest GC trials (SAMIT), we identify a gene signature representing the first predictive biomarker for paclitaxel benefit.

TRIAL REGISTRATION NUMBER

UMIN Clinical Trials Registry: C000000082 (SAMIT); ClinicalTrials.gov identifier, 02628951 (South Korean trial).

A study of deregulated MMR pathways and anticancer potential of curcuma derivatives using computational approach

Plant derived products have steadily gained momentum in treatment of cancer over the past decades. Curcuma and its derivatives, in particular, have diverse medicinal properties including anticancer potential with proven safety as supported by numerous in vivo and in vitro studies. A defective Mis-Match Repair (MMR) is implicated in solid tumors but its role in haematologic malignancies is not keenly studied and the current literature suggests that it is limited. Nonetheless, there are multiple pathways interjecting the mismatch repair proteins in haematologic cancers that may have a direct or indirect implication in progression of the disease. Here, through computational analysis, we target proteins that are involved in rewiring of multiple signaling cascades via altered expression in cancer using various curcuma derivatives (Curcuma longa L. and Curcuma caesia Roxb.) which in turn, profoundly controls MMR protein function. These biomolecules were screened to identify their efficacy on selected targets (in blood-related cancers); aberrations of which adversely impacted mismatch repair machinery. The study revealed that of the 536 compounds screened, six of them may have the potential to regulate the expression of identified targets and thus revive the MMR function preventing genomic instability. These results reveal that there may be potential plant derived biomolecules that may have anticancer properties against the tumors driven by deregulated MMR-pathways.

Gene Expression Signature of Acquired Chemoresistance in Neuroblastoma Cells

Drug resistance of childhood cancer neuroblastoma is a serious clinical problem. Patients with relapsed disease have a poor prognosis despite intense treatment. In the present study, we aimed to identify chemoresistance gene expression signatures in vincristine resistant neuroblastoma cells. We found that vincristine-resistant neuroblastoma cells formed larger clones and survived under reduced serum conditions as compared with non-resistant parental cells. To identify the possible mechanisms underlying vincristine resistance in neuroblastoma cells, we investigated the expression profiles of genes known to be involved in cancer drug resistance. This specific gene expression patterns could predict the behavior of a tumor in response to chemotherapy and for predicting the prognosis of high-risk neuroblastoma patients. Our signature could help chemoresistant neuroblastoma patients in avoiding useless and harmful chemotherapy cycles.

Mining topoisomerase isoforms in gastric cancer

DNA topoisomerases essentially remove topological strains generated during DNA replication, transcription, DNA repair, and other cytogenetic processes. However, distinct expression level and prognostic significance of individual topoisomerase isoforms in gastric cancer (GC) remain largely unexplored. In this study, we utilized Oncomine and Kaplan-Meier plotter database to detect the mRNA expression level of individual topoisomerase isoforms as well as assess their prognostic significance in GC patients. With the exception of TOP3B and TOP2B, levels of all topoisomerase isoforms were found to be elevated in GC patients when compared to the normal tissues. Elevated expression of TOP1 and TOP1MT was relevant to longer overall survival (OS) in GC and gastric intestinal type adenocarcinoma (GITA) patients, but not in diffuse gastric adenocarcinoma (DFA) patients. Increased expression of TOP2A and TOP2B was related to better OS in GC, as well as in GITA and DFA patients. In contrast, increased expression TOP3A and TOP3B was associated with shorter OS in GC, as well as in GITA and DFA patients. We also applied the Tumor IMmune Estimation Resource (TIMER) tool to assess the correlations between distinct topoisomerase isoforms and the infiltrating immune cell landscape. Furthermore, we found that down-regulating the expression of TOP3A by shRNA significantly inhibited the proliferation and colony formation in GC cells compared to control shRNA treated cells. Thus our study lays the framework for utilizing topoisomerases in better understanding the complexity and heterogeneity of GC and for developing strategies for novel customized therapy in GC patients.

A Humanized Yeast Phenomic Model of Deoxycytidine Kinase to Predict Genetic Buffering of Nucleoside Analog Cytotoxicity

Knowledge about synthetic lethality can be applied to enhance the efficacy of anticancer therapies in individual patients harboring genetic alterations in their cancer that specifically render it vulnerable. We investigated the potential for high-resolution phenomic analysis in yeast to predict such genetic vulnerabilities by systematic, comprehensive, and quantitative assessment of drug–gene interaction for gemcitabine and cytarabine, substrates of deoxycytidine kinase that have similar molecular structures yet distinct antitumor efficacy. Human deoxycytidine kinase (dCK) was conditionally expressed in the Saccharomyces cerevisiae genomic library of knockout and knockdown (YKO/KD) strains, to globally and quantitatively characterize differential drug–gene interaction for gemcitabine and cytarabine. Pathway enrichment analysis revealed that autophagy, histone modification, chromatin remodeling, and apoptosis-related processes influence gemcitabine specifically, while drug–gene interaction specific to cytarabine was less enriched in gene ontology. Processes having influence over both drugs were DNA repair and integrity checkpoints and vesicle transport and fusion. Non-gene ontology (GO)-enriched genes were also informative. Yeast phenomic and cancer cell line pharmacogenomics data were integrated to identify yeast–human homologs with correlated differential gene expression and drug efficacy, thus providing a unique resource to predict whether differential gene expression observed in cancer genetic profiles are causal in tumor-specific responses to cytotoxic agents.

Topoisomerase 1B poisons: Over a half-century of drug leads, clinical candidates, and serendipitous discoveries

Topoisomerases are DNA processing enzymes that relieve supercoiling (torsional strain) in DNA, are necessary for normal cellular division, and act by nicking (and then religating) DNA strands. Type 1B topoisomerase (Top1) is overexpressed in certain tumors, and the enzyme has been extensively investigated as a target for cancer chemotherapy. Various chemical agents can act as "poisons" of the enzyme's religation step, leading to Top1-DNA lesions, DNA breakage, and eventual cellular death. In this review, agents that poison Top1 (and have thus been investigated for their anticancer properties) are surveyed, including natural products (such as camptothecins and indolocarbazoles), semisynthetic camptothecin and luotonin derivatives, and synthetic compounds (such as benzonaphthyridines, aromathecins, and indenoisoquinolines), as well as targeted therapies and conjugates. Top1 has also been investigated as a therapeutic target in certain viral and parasitic infections, as well as autoimmune, inflammatory, and neurological disorders, and a summary of literature describing alternative indications is also provided. This review should provide both a reference for the medicinal chemist and potentially offer clues to aid in the development of new Top1 poisons.

Prognostic Characteristics of MACC1 Expression in Epithelial Ovarian Cancer

Recent studies have shown that overexpression of metastasis-associated in colon cancer 1 (MACC1) is significantly associated with adverse prognoses of patients with different kinds of cancer. However, the exact survival effect of MACC1 on epithelial ovarian cancer (EOC) patients has not yet been established. Thus, the objective of this study was to explore the prognostic role of MACC1 mRNA in EOC by using Kaplan-Meier (KM) plotter and ONCOMINE database. Our results indicated that MACC1 mRNA high expression was significantly associated with unfavorable overall survival (hazard ratio (HR) = 1.51 (95% confidence interval (CI): 1.21 - 1.88), P = 0.00025) and progression-free survival (HR = 1.53 (95% CI: 1.24 - 1.89), P = 5.8e-05) in EOC patients. We also found that the expression of MACC1 mRNA in EOC was 2.5 times higher than that in normal surface ovarian epithelium, which was statistically significant (P = 2.86e-7). Our results suggest that MACC1 expression might be a biomarker for poor prognosis in individual EOC patients.

Annexin A2 could enhance multidrug resistance by regulating NF-κB signaling pathway in pediatric neuroblastoma

Background

Chemotherapy is one of major therapeutic regimens for neuroblastoma (NB) in children. However, recurrence and metastasis associated with poor prognosis caused by acquired multidrug resistance remains a challenge. There is a great need to achieve new insight into the molecular mechanism of drug resistance in NB. The aim of this study is to identify novel drug sensitivity-related biomarkers as well as new therapeutic targets to overcome chemoresistance.

Methods

We proteome-wide quantitatively compared protein expression of two NB cell lines with different drug sensitivities, isolated from the same patient prior to and following chemotherapy. Annexin A2 (ANXA2) emerged as a key factor contributing to drug resistance in NB. Then, we assessed the correlation of ANXA2 expression and clinical characteristics using a tissue microarray. Further, the roles of ANXA2 in chemoresistance for NB and the underlying mechanisms were studied by using short hairpin RNA (shRNA) in vitro and vivo.

Results

First in total, over 6000 proteins were identified, and there were about 460 significantly regulated proteins which were up- or down-regulated by greater than two folds. We screened out ANXA2 which was upregulated by more than 12-fold in the chemoresistant NB cell line, and it might be involved in the drug resistance of NB. Then, using a tissue chip containing 42 clinical NB samples, we found that strong expression of ANXA2 was closely associated with advanced stage, greater number of chemotherapy cycles, tumor metastasis and poor prognosis. Following knockdown of ANXA2 in NB cell line SK-N-BE(2) using shRNA, we demonstrate enhanced drug sensitivity for doxorubicin (2.77-fold) and etoposide (7.87-fold) compared with control. Pro-apoptotic genes such as AIF and cleaved-PARP were upregulated. Inhibiting ANXA2 expression attenuated transcriptional activity of NF-κB via down-regulated nuclear translocation of subunit p50. Finally, simulated chemotherapy in a xenograft NB nude mouse model suggests that ANXA2 knockdown could improve clinical results in vivo.

Conclusion

Our profiling data provided a rich source for further study of the molecular mechanisms of acquired drug resistance in NB. Further study may determine the role of ANXA2 as a prognostic biomarker and a potential therapeutic target for patients with multidrug-resistant NB.

Electronic supplementary material

The online version of this article (doi:10.1186/s13046-017-0581-6) contains supplementary material, which is available to authorized users.

Exploration of the sequential gene changes in epithelial ovarian cancer induced by carboplatin via microarray analysis

The purpose of the current study was to explore the carboplatin-induced sequential changes in gene expression and screen out key genes, which were associated with effects of carboplatin on epithelial ovarian cancer (EOC). The microarray dataset GSE13525 was downloaded from the Gene Expression Omnibus database, including 6 EOC cell samples separately treated with carboplatin at 24, 30 and 36 h (case group), and 6 samples treated with phosphate-buffered saline at the same time points (control group). A total of 3 sets of differentially expressed genes (DEGs) were respectively identified in case samples at 24, 30 and 36 h compared with the control group via the Limma package, and separately recorded as DEG-24, DEG-30 and DEG-36. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of the overlapped DEGs were performed via the Database for Annotation, Visualization and Integrated Discovery. The protein-protein interaction (PPI) network was constructed and analyzed by Cytoscape software. In addition, the survival curves were drawn to illustrate the association between the expression levels of certain critical genes and the prognosis of EOC. A total of 170, 605 and 1043 DEGs were separately obtained in DEG-24 DEG-30 and DEG-36, and 110 overlaps were identified. The overlaps were enriched in 77 GO terms and 3 KEGG pathways. A total of 152 pairs were involved in the PPI network, and the abnormal expression levels (high or low) of c-Jun and cyclin B1 (CCNB1) would reduce the survival time of patients with EOC. The study indicated that c-Jun and CCNB1 may be the prognostic biomarkers of EOC treated with carboplatin, and certain pathways (such as p53 signaling pathway, cell cycle and mitogen-activation protein kinase signaling pathway) may be involved in carboplatin-resistant EOC.