Pan-Cancer Analysis Identifies MNX1 and Associated Antisense Transcripts as Biomarkers for Cancer

The identification of diagnostic and prognostic biomarkers is a major objective in improving clinical outcomes in cancer, which has been facilitated by the availability of high-throughput gene expression data. A growing interest in non-coding genomic regions has identified dysregulation of long non-coding RNAs (lncRNAs) in several malignancies, suggesting a potential use as biomarkers. In this study, we leveraged data from large-scale sequencing projects to uncover the expression patterns of the MNX1 gene and its associated lncRNAs MNX1-AS1 and MNX1-AS2 in solid tumours. Despite many reports describing MNX1 overexpression in several cancers, limited studies exist on MNX1-AS1 and MNX1-AS2 and their potential as biomarkers. By employing clustering methods to visualise multi-gene relationships, we identified a discriminative power of the three genes in distinguishing tumour vs. normal samples in several cancers of the gastrointestinal tract and reproductive systems, as well as in discerning oesophageal and testicular cancer histological subtypes. Notably, the expressions of MNX1 and its antisenses also correlated with clinical features and endpoints, uncovering previously unreported associations. This work highlights the advantages of using combinatory expression patterns of non-coding transcripts of differentially expressed genes as clinical evaluators and identifies MNX1, MNX1-AS1, and MNX1-AS2 expressions as robust candidate biomarkers for clinical applicationsD.R. is the recipient of a Kidscan funded PhD studentship and partly supported by Brunel University Londo

In Silico Study to predict the structural and functional consequences of SNPs on biomarkers of ovarian cancer (OC) and BPA exposure-associated OC

Supplementary Materials: The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ijms23031725/s1. Data Availability Statement: Data can be available upon reasonable request.Copyright: © 2021 by the authors. Background: Recently, we have shown that seven genes, namely GBP5, IRS2, KRT4, LINCOO707, MRPL55, RRS1 and SLC4A11, have prognostic power for the overall survival in ovarian cancer (OC). Methods: We present an analysis on the association of these genes with any phenotypes and mutations indicative of involvement in female cancers and predict the structural and functional consequences of those SNPS using in silico tools. Results: These seven genes present with 976 SNPs/mutations that are associated with human cancers, out of which 284 related to female cancers. We have then analysed the mutation impact on amino acid polarity, charge and water affinity, leading to the identification of 30 mutations in gynaecological cancers where amino acid (aa) changes lead to opposite polarity, charges and water affinity. Out of these 30 mutations identified, only a missense mutation (i.e., R831C/R804C in uterine corpus endometrial carcinomas, UCEC) was suggestive of structural damage on the SLC4A11 protein. Conclusions: We demonstrate that the R831C/R804C mutation is deleterious and the predicted ΔΔG values suggest that the mutation reduces the stability of the protein. Future in vitro studies should provide further insight into the role of this transporter protein in UCEC.Isambard Kingdom Brunel Research Scholarshiphttps://www.mdpi.com/article/10.3390/ijms23031725/s

Impact of environmentally relevant concentrations of Bisphenol A (BPA) on the gene expression profile in an in vitro model of the normal human ovary

Data Availability Statement: All data is publicly available from online repositories as indicated in the materials and methods section. Supplementary Materials: The following supporting information can be downloaded at: https: //www.mdpi.com/article/10.3390/ijms23105334/s1.Copyright © 2022 by the authors. Endocrine-disrupting chemicals (EDCs), including the xenoestrogen Bisphenol A (BPA), can interfere with hormonal signalling. Despite increasing reports of adverse health effects associated with exposure to EDCs, there are limited data on the effect of BPA in normal human ovaries. In this paper, we present a detailed analysis of the transcriptomic landscape in normal Human Epithelial Ovarian Cells (HOSEpiC) treated with BPA (10 and 100 nM). Gene expression profiles were determined using high-throughput RNA sequencing, followed by functional analyses using bioinformatics tools. In total, 272 and 454 differentially expressed genes (DEGs) were identified in 10 and 100 nM BPA-treated HOSEpiCs, respectively, compared to untreated controls. Biological pathways included mRNA surveillance pathways, oocyte meiosis, cellular senescence, and transcriptional misregulation in cancer. BPA exposure has a considerable impact on 10 genes: ANAPC2, AURKA, CDK1, CCNA2, CCNB1, PLK1, BUB1, KIF22, PDE3B, and CCNB3, which are also associated with progesterone-mediated oocyte maturation pathways. Future studies should further explore the effects of BPA and its metabolites in the ovaries in health and disease, making use of validated in vitro and in vivo models to generate data that will address existing knowledge gaps in basic biology, hazard characterisation, and risk assessment associated with the use of xenoestrogens such as BPA.Brunel University London Isambard Kingdom Brunel Research Scholarship (grant 10418139)

Effect of MYC and PARP Inhibitors in Ovarian Cancer Using an In Vitro Model

Figures and Data: https://ar.iiarjournals.org/content/44/5/1817/tab-figures-dataBackground/Aim: The 8q24 chromosomal region, which contains the MYC and PVT1 candidate oncogenes, is amplified in carcinomas. Both genes have been involved in the etiopathogenesis of ovarian cancer (OC). In this study, we used an in vitro OC model with a known 8q24 copy number increase and in silico tools to investigate the expression of MYC/PVT1 loci and copy number variation in OC. We also assessed the effects of rucaparib (a PARP inhibitor) in the presence or absence of 10058F4 (a MYC inhibitor) on the expression of MYC/linear PVT1/circular PVT1. Materials and Methods: Tissue culture, chromosome preparation, RNA extraction, RT-qPCR, FISH, and wound healing assays were employed. OncoDB, cBioportal, UALKAN, and ROC Plotter in silico tools were also utilized. Results: Although PVT1 and MYC expression levels remained unaltered in OC, putative copy number alterations across all cancers showed a marked difference between the two genes, particularly in gain and amplification for MYC. PVT1 expression demonstrated prognostic value for the treatment of patients with serous and endometrioid OC. Both genes correlated with PARP10, FAM83H, and DEPTOR. The use of rucaparib in the presence or absence of the MYC inhibitor (10058F4) in vitro, led to a significant down-regulation in the expression of MYC, linear, and circular PVT1. Conclusion: Our data provide a novel insight into the potential interactions of MYC and PVT1 with other genes. Moreover, we identified a new PARP inhibition mechanism down-regulating MYC, as well as the linear and circular PVT1 transcripts. Future work should expand on clinical studies to better understand the prognostic role of PVT1 in OC.Cancer Treatment & Research Trust (CTRT); Global Thesis Program 2017-2018 (University of Bari Aldo Moro)

In silico and in vitro analysis of lncRNA XIST reveals a panel of possible lung cancer regulators and a five-gene diagnostic signature

© 2020 by the authors. Long non-coding RNAs (lncRNAs) perform a wide functional repertoire of roles in cell biology, ranging from RNA editing to gene regulation, as well as tumour genesis and tumour progression. The lncRNA X-inactive specific transcript (XIST) is involved in the aetiopathogenesis of non-small cell lung cancer (NSCLC). However, its role at the molecular level is not fully elucidated. The expression of XIST and co-regulated genes TSIX, hnRNPu, Bcl-2, and BRCA1 analyses in lung cancer (LC) and controls were performed in silico. Differentially expressed genes (DEGs) were determined using RNA-seq in H1975 and A549 NSCLC cell lines following siRNA for XIST. XIST exhibited sexual dimorphism, being up-regulated in females compared to males in both control and LC patient cohorts. RNA-seq revealed 944 and 751 DEGs for A549 and H1975 cell lines, respectively. These DEGs are involved in signal transduction, cell communication, energy pathways, and nucleic acid metabolism. XIST expression associated with TSIX, hnRNPu, Bcl-2, and BRCA1 provided a strong collective feature to discriminate between controls and LC, implying a diagnostic potential. There is a much more complex role for XIST in lung cancer. Further studies should concentrate on sex-specific changes and investigate the signalling pathways of the DEGs following silencing of this lncRNA

Non-redundant functions of H2A.Z.1 and H2A.Z.2 in chromosome segregation and cell cycle progression.

© 2021 The Authors. H2A.Z is a H2A-type histone variant essential for many aspects of cell biology, ranging from gene expression to genome stability. From deuterostomes, H2A.Z evolved into two paralogues, H2A.Z.1 and H2A.Z.2, that differ by only three amino acids and are encoded by different genes (H2AFZ and H2AFV, respectively). Despite the importance of this histone variant in development and cellular homeostasis, very little is known about the individual functions of each paralogue in mammals. Here, we have investigated the distinct roles of the two paralogues in cell cycle regulation and unveiled non-redundant functions for H2A.Z.1 and H2A.Z.2 in cell division. Our findings show that H2A.Z.1 regulates the expression of cell cycle genes such as Myc and Ki-67 and its depletion leads to a G1 arrest and cellular senescence. On the contrary, H2A.Z.2, in a transcription-independent manner, is essential for centromere integrity and sister chromatid cohesion regulation, thus playing a key role in chromosome segregation.The Wellcone Trust. Grant Number: 210742/Z/18/Z; Kidscan Children's Cancer Research (Kidscan

Transcriptional Landscape of 3D vs. 2D Ovarian Cancer Cell Models

Data Availability Statement: RNAseq and array data can be found via the following NCBI accession codes: PRJNA472611, PRJNA530150, PRJNA564843, PRJNA564843, PRJNA232817, and PRJNA318768. A full list of samples can be viewed in Supplementary Table S1 available online at https://www.mdpi.com/2072-6694/15/13/3350#app1-cancers-15-03350 .Simple Summary: Ovarian cancer is one of the most lethal female cancers. Numerous investigations into the development and progression of this disease have resulted in the creation of numerous three-dimensional culture models to better reflect the natural microenvironment of these tumours. In this study, we leverage the available transcriptomics and clinical and novel experimental data to evaluate the impact of the growth conditions on various cancer cells and examine whether they better approximate the behaviour of tumour cells compared to the classical two-dimensional models. Our results show that variability in the growth conditions can impact key genes and biological processes that are hallmarks of cancer, highlighting the need for future studies to identify which is the most appropriate in vitro/preclinical model to study tumour microenvironments.Supplementary Materials: The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/cancers15133350/s1, Figure S1: Top enriched gene sets for 2D vs. 3D OVCAR8; Table S1: Cell line information and associated accession codes; Figure S2: Growth of SKOV3 cells from Day 2 to Day 9 showing clear spheroid-like structures; Table S2: (A) Top 150 differentially expressed genes in OVCAR8 in all three grown media: grown on agarose, collagen, Matrigel. The differential expression in each of the growth media is with respect to 2D controls of the OVCAR8; (B) Top 150 differentially expressed genes across the cell lines A1847, A2780, C30, C70, OVCAR3, OVCAR4, OVCAR5, OVCAR8, OVCAR10, PEO1, SKOV-3, UPN275 grown on agarose vs. 2D controls; (C) Top 150 differentially expressed genes in the cell lines Kuramochi, OVCAR4, and OVCAR8, grown on collagen vs. 2D controls or the respective cell lines.Copyright © 2023 by the authors. Three-dimensional (3D) cancer models are revolutionising research, allowing for the recapitulation of an in vivo-like response through the use of an in vitro system, which is more complex and physiologically relevant than traditional monolayer cultures. Cancers such as ovarian (OvCa) are prone to developing resistance, are often lethal, and stand to benefit greatly from the enhanced modelling emulated by 3D cultures. However, the current models often fall short of the predicted response, where reproducibility is limited owing to the lack of standardised methodology and established protocols. This meta-analysis aims to assess the current scope of 3D OvCa models and the differences in the genetic profiles presented by a vast array of 3D cultures. An analysis of the literature (Pubmed.gov) spanning 2012–2022 was used to identify studies with paired data of 3D and 2D monolayer counterparts in addition to RNA sequencing and microarray data. From the data, 19 cell lines were found to show differential regulation in their gene expression profiles depending on the bio-scaffold (i.e., agarose, collagen, or Matrigel) compared to 2D cell cultures. The top genes differentially expressed in 2D vs. 3D included C3, CXCL1, 2, and 8, IL1B, SLP1, FN1, IL6, DDIT4, PI3, LAMC2, CCL20, MMP1, IFI27, CFB, and ANGPTL4. The top enriched gene sets for 2D vs. 3D included IFN-α and IFN-γ response, TNF-α signalling, IL-6-JAK-STAT3 signalling, angiogenesis, hedgehog signalling, apoptosis, epithelial–mesenchymal transition, hypoxia, and inflammatory response. Our transversal comparison of numerous scaffolds allowed us to highlight the variability that can be induced by these scaffolds in the transcriptional landscape and identify key genes and biological processes that are hallmarks of cancer cells grown in 3D cultures. Future studies are needed to identify which is the most appropriate in vitro/preclinical model to study tumour microenvironments.Cancer Treatment and Research Trust and the University Hospitals Coventry and Warwickshire NHS Trust (grant no. 12899)

A Meta-analysis of 2D vs. 3D Ovarian Cancer Cellular Models

bioRxiv preprint doi: https://doi.org/10.1101/2022.12.05.519144; this version posted December 8, 2022. The copyright holder for this preprint (which was not certified by peer review, see: https://www.biorxiv.org/about/FAQ#unrefereed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.Data Availability Statement: RNAseq and Array data can be found via the following NCBI accession codes: PRJNA472611, PRJNA530150, PRJNA564843, PRJNA564843, PRJNA232817, PRJNA318768. A full list of samples can be viewed in Supplementary table 1.Acknowledgments: The authors acknowledge the Biocenter Finland (BF) and Tampere Imaging Facility (TIF) for the service.Supplementary Materials are available online at https://www.biorxiv.org/content/10.1101/2022.12.05.519144v1.full#F11Copyright © author/funder 2022. Three-dimensional (3D) cancer models are revolutionizing research, allowing for the recapitulation of in vivo like response through the use of an in vitro system, more complex and physiologically relevant than traditional mono-layer culture. Cancers such as ovarian (OvCa), are prone to developing resistance and are often lethal, and stand to benefit greatly from the enhanced modelling emulated by 3D culture. However current models often fall short of predicted response where reproducibility is limited owing to the lack of standardized methodology and established protocols. This meta-analysis aims to assess the current scope of 3D OvCa models and the differences in genetic profile presented by a vast array of 3D cultures. A meta-analysis of the literature (Pubmed.gov) spanning 2012 – 2022, was used to identify studies with comparable monolayer (2D) counterparts in addition to RNA sequencing and microarray data. From the data 19 cell lines were found to show differential regulation in their gene expression profiles depending on the bio-scaffold (i.e. agarose, collagen or Matrigel) compared to 2D cell cultures. Top genes differentially expressed 2D vs. 3D include C3, CXCL1, 2 and 8, IL1B, SLP1, FN1, IL6, DDIT4, PI3, LAMC2, CCL20, MMP1, IFI27, CFB, and ANGPTL4. Top Enriched Gene sets for 2D vs. 3D include IFN-α and IFN-γ Response, TNF-α signalling, IL-6-JAK-STAT3 signalling, angiogenesis, hedgehog signalling, apoptosis, epithelial mesenchymal transition, hypoxia, and inflammatory response. Our transversal comparison of numerous scaffolds allowed us to highlight the variability that can be induced by these scaffolds in the transcriptional landscape as well as identifying key genes and biological processes that are hallmarks of cancer cells grown in 3D cultures. Future studies are needed to identify which is the most appropriate in vitro/preclinical model to study tumour microenvironment.Cancer Treatment & Research Trust and University Hospitals Coventry and Warwickshire NHS Trust (grant no. 12899)

Differential expression of mTOR components in endometriosis and ovarian cancer: Effects of rapalogues and dual kinase inhibitors on mTORC1 and mTORC2 stoichiometry

© Rogers‑Broadway et al. Endometriosis is a well-known risk factor for ovarian cancer. The genetic changes that characterise endometriosis are poorly understood; however, the mechanistic target of rapamycin (mTOR) pathway is involved. In this study, we investigated the expression of key mTOR components in endometriosis and the effects of rapalogues using an endometrioid ovarian carcinoma cell line (MdAH 2774) as an in vitro model. Gene expression of mTOR, dEPTOR, Rictor and Raptor was assessed by qPcR in 24 endometriosis patients and in silico in ovarian cancer patients. Furthermore, the effects of Rapamycin, Everolimus, deforolimus, Temsirolimus, Resveratrol, and BEZ235 (dactolisib, a dual kinase inhibitor) on mTOR signalling components was assessed. mTOR showed a significant increase in the expression in endometriosis and ovarian endometrioid adenocarcinoma patients compared to non-affected controls. dEPTOR, an inhibitor of mTOR, was downregulated in the advanced stages of ovarian cancer (III and IV) compared to earlier stages (I and II). Treatment of MdAH-2774 cells with the mTOR inhibitors resulted in the significant upregulation of dEPTOR mRNA, whereas treatment with rapamycin and BEZ-235 (100 nM) resulted in downregulation of the mTOR protein expression after 48 h of treatment. None of the treat ments resulted in translocation of mTOR from cytoplasm to nucleus. Upregulation of dEPTOR is a positive prognostic marker in ovarian cancer and is increased in response to mTOR pathway inhibition suggesting that it functions as a tumour suppressor gene in endometrioid ovarian carcinoma. collectively, our data suggest the mTOR pathway as a potential connection between endometriosis and ovarian cancer and may be a potential target in the treatment of both conditions

Comparative analysis of pseudogenes across three phyla

Pseudogenes are degraded fossil copies of genes. Here, we report a comparison of pseudogenes spanning three phyla, leveraging the completed annotations of the human, worm, and fly genomes, which we make available as an online resource. We find that pseudogenes are lineage specific, much more so than proteincoding genes, reflecting the different remodeling processes marking each organism’s genome evolution. The majority of human pseudogenes are processed, resulting from a retrotranspositional burst at the dawn of the primate lineage. This burst can be seen in the largely uniform distribution of pseudogenes across the genome, their preservation in areas with low recombination rates, and their preponderance in highly expressed gene families. In contrast, worm and fly pseudogenes tell a story of numerous duplication events. In worm, these duplications have been preserved through selective sweeps, so we see a large number of pseudogenes associated with highly duplicated families such as chemoreceptors. However, in fly, the large effective population size and high deletion rate resulted in a depletion of the pseudogene complement. Despite large variations between these species, we also find notable similarities. Overall, we identify a broad spectrum of biochemical activity for pseudogenes, with the majority in each organism exhibiting varying degrees of partial activity. In particular, we identify a consistent amount of transcription (∼15%) across all species, suggesting a uniform degradation process. Also, we see a uniform decay of pseudogene promoter activity relative to their coding counterparts and identify a number of pseudogenes with conserved upstream sequences and activity, hinting at potential regulatory roles