Identification of the key genes associated with chemotherapy sensitivity in ovarian cancer patients

Novel players in the development of chemoresistance in ovarian cancer: ovarian cancer stem cells, non-coding RNA and nuclear receptors

Ovarian cancer (OC) ranks as the fifth leading factor for female mortality globally, with a substantial burden of new cases and mortality recorded annually. Survival rates vary significantly based on the stage of diagnosis, with advanced stages posing significant challenges to treatment. OC is primarily categorized as epithelial, constituting approximately 90% of cases, and correct staging is essential for tailored treatment. The debulking followed by chemotherapy is the prevailing treatment, involving platinum-based drugs in combination with taxanes. However, the efficacy of chemotherapy is hindered by the development of chemoresistance, both acquired during treatment (acquired chemoresistance) and intrinsic to the patient (intrinsic chemoresistance). The emergence of chemoresistance leads to increased mortality rates, with many advanced patients experiencing disease relapse shortly after initial treatment. This review delves into the multifactorial nature of chemoresistance in OC, addressing mechanisms involving transport systems, apoptosis, DNA repair, and ovarian cancer stem cells (OCSCs). While previous research has identified genes associated with these mechanisms, the regulatory roles of non-coding RNA (ncRNA) and nuclear receptors in modulating gene expression to confer chemoresistance have remained poorly understood and underexplored. This comprehensive review aims to shed light on the genes linked to different chemoresistance mechanisms in OC and their intricate regulation by ncRNA and nuclear receptors. Specifically, we examine how these molecular players influence the chemoresistance mechanism. By exploring the interplay between these factors and gene expression regulation, this review seeks to provide a comprehensive mechanism driving chemoresistance in OC.

bestDEG: a web-based application automatically combines various tools to precisely predict differentially expressed genes (DEGs) from RNA-Seq data

Background

Differential gene expression analysis using RNA sequencing technology (RNA-Seq) has become the most popular technique in transcriptome research. Although many R packages have been developed to analyze differentially expressed genes (DEGs), several evaluations have shown that no single DEG analysis method outperforms all others. The validity of DEG identification could be increased by using multiple methods and producing the consensus results. However, DEG analysis methods are complex and most of them require prior knowledge of a programming language or command-line shell. Users who do not have this knowledge need to invest time and effort to acquire it.

Methods

We developed a novel web application called "bestDEG" to automatically analyze DEGs with different tools and compare the results. A differential expression (DE) analysis pipeline was created combining the edgeR, DESeq2, NOISeq, and EBSeq packages; selected because they use different statistical methods to identify DEGs. bestDEG was evaluated on human datasets from the MicroArray Quality Control (MAQC) project.

Results

The performance of the bestDEG web application with the human datasets showed excellent results, and the consensus method outperformed the other DE analysis methods in terms of precision (94.71%) and specificity (97.01%). bestDEG is a rapid and efficient tool to analyze DEGs. With bestDEG, users can select DE analysis methods and parameters in the user-friendly web interface. bestDEG also provides a Venn diagram and a table of results. Moreover, the consensus method of this tool can maximize the precision or minimize the false discovery rate (FDR), which reduces the cost of gene expression validation by minimizing wet-lab experiments.

The role of distinct BRD4 isoforms and their contribution to high-grade serous ovarian carcinoma pathogenesis

High-grade serous ovarian carcinoma (HGSOC) is the most aggressive type of ovarian cancer, often diagnosed at advanced stages. Molecularly, HGSOC shows high degree of genomic instability associated with large number of genetic alterations. BRD4 is the 4th most amplified gene in HGSOC, which correlates with poor patients' prognosis. BRD4 is constitutively expressed and generates two proteins, BRD4 long (BRD4-L) and BRD4 short (BRD4-S). Both isoforms contain bromodomains that bind to lysine-acetylated histones. Amongst other functions, BRD4 participates in chromatin organization, acetylation of histones, transcriptional control and DNA damage repair. In cancer patients with amplified BRD4, the increased activity of BRD4 is associated with higher expression of oncogenes, such as MYC, NOTCH3 and NRG1. BRD4-driven oncogenes promote increased tumor cells proliferation, genetic instability, epithelial-mesenchymal transition, metastasis and chemoresistance. Ablation of BRD4 activity can be successfully achieved with bromodomain inhibitors (BETi) and degraders, and it has been applied in pre-clinical and clinical settings. Inhibition of BRD4 function has an effective anti-cancer effect, reducing tumor growth whether ablated by single agents or in combination with other drugs. When combined with standard chemotherapy, BETi are capable of sensitizing highly resistant ovarian cancer cell lines to platinum drugs. Despite the evidence that BRD4 amplification in ovarian cancer contributes to poor patient prognosis, little is known about the specific mechanisms by which BRD4 drives tumor progression. In addition, newly emerging data revealed that BRD4 isoforms exhibit contradicting functions in cancer. Therefore, it is paramount to expand studies elucidating distinct roles of BRD4-L and BRD4-S in HGSOC, which has important implications on development of therapeutic approaches targeting BRD4.

Multiomics analysis identifies key genes and pathways related to N6-methyladenosine RNA modification in ovarian cancer

Aims: To explore the pathways and target genes related to N6-methyladenosine (m⁶A) methylation in ovarian cancer and their effect on patient prognosis. Methods & materials: The Cancer Genome Atlas was used to screen genes related to m⁶A regulators in terms of gene expression, mutation and copy number variation. These genes were subjected to pathway enrichment analysis. Prognosis-related genes were screened and involved in risk signature construction. Immunohistochemistry was used for verification. Results: We obtained 1408 genes dysregulated in parallel to m⁶A regulators, which were mainly involved in the platelet activation pathway. The m⁶A-related signature was constructed based on the expression of four prognosis-related genes (RPS6KA2, JUNB, HNF4A and P2RX1). Conclusion: This work provides new insights into the mechanism of m⁶A methylation in ovarian cancer.

Mitogen signal-associated pathways, energy metabolism regulation, and mediation of tumor immunogenicity play essential roles in the cellular response of malignant pleural mesotheliomas to platinum-based treatment: a retrospective study

Background

Malignant pleural mesothelioma (MPM) is a rare malignant tumor associated with asbestos exposure, with infaust prognosis and overall survival below 20 months in treated patients. Platinum is still the backbone of the chemotherapy protocols, and the reasons for the rather poor efficacy of platinum compounds in MPM remain largely unknown. Therefore, we aimed to analyze differences in key signaling pathways and biological mechanisms in therapy-naïve samples and samples after chemotherapy in order to evaluate the effect of platinum-based chemotherapy.

Methods

The study cohort comprised 24 MPM tumor specimens, 12 from therapy-naïve and 12 from patients after platinum-based therapy. Tumor samples were screened using the NanoString nCounter platform for digital gene expression analysis with an appurtenant custom-designed panel comprising a total of 366 mRNAs covering the most important tumor signaling pathways. Significant pathway associations were identified by gene set enrichment analysis using the WEB-based GEne SeT AnaLysis Toolkit (WebGestalt)

Results

We have found reduced activity of TNF (normalized enrichment score: 2.03), IL-17 (normalized enrichment score: 1.93), MAPK (normalized enrichment score: 1.51), and relaxin signaling pathways (normalized enrichment score: 1.42) in the samples obtained after platinum-based therapy. In contrast, AMPK (normalized enrichment score: –1.58), mTOR (normalized enrichment score: –1.50), Wnt (normalized enrichment score: –1.38), and longevity regulating pathway (normalized enrichment score: –1.31) showed significantly elevated expression in the same samples.

Conclusions

We could identify deregulated signaling pathways due to a directed cellular response to platinum-induced cell stress. Our results are paving the ground for a better understanding of cellular responses and escape mechanisms, carrying a high potential for improved clinical management of patients with MPM.

Identification of the key genes associated with chemotherapy sensitivity in ovarian cancer patients

Background

Ovarian cancer (OC) is the deadliest gynecological cancer. The absence of biomarkers in early detection and chemotherapy resistance is a principal cause of treatment failure in OC.

Methods

In this study, next generation sequencing (NGS) was used to sequence the mRNA of 44 OC patients including 14 chemotherapy insensitive and 18 sensitive patients. Differentially expressed genes (DEGs) from OC patients (compared with healthy controls) and chemotherapy sensitive patients (compared with chemotherapy insensitive patients) were identified by edgeR v3.12.0 in R v3.2.2, which were enriched using Gene Ontology (GO) database and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG). The common DEGs in cancer occurring and chemotherapy sensitivity were further screened. Among them, genes participating in chemotherapy sensitivity associated pathways were regarded as chemotherapy sensitivity‐related key genes. Quantitative real‐time PCR (qPCR) and immunohistochemistry (IHC) were used to verify the expression of the key genes.

Results

We found 1588 DEGs between OC patients and healthy controls (HCs), which were mainly enriched in cell cycle pathway. Meanwhile, 249 DEGs were identified between chemotherapy sensitive and insensitive OC patients, which were mainly enriched in MAPK signaling pathway, ERBB signaling pathway, TNF signaling pathway, and IL‐17 signaling pathway. Thirty‐five DEGs were shared in chemotherapy sensitivity group and cancer occurring group. Among them, there are five genes (JUND, JUNB, MUC5B, NRG1, and NR4A1) participating in the above four chemotherapy sensitivity‐related pathways. It is remarkable that JUND is in the upstream of MUC5B in IL‐17 signaling pathway and their expressions were verified by qPCR and IHC.

Conclusions

The expression levels of the key genes related to chemotherapy sensitivity might be used as biomarkers to predict the treatment outcome and as a target to improve prognosis.