Epigenetic alterations in creatine transporter deficiency: a new marker for dodecyl creatine ester therapeutic efficacy monitoring

Creatine transporter deficiency (CTD) is an X-linked disease caused by mutations in the Slc6a8 gene. The impaired creatine uptake in the brain leads to developmental delays with intellectual disability. We hypothesized that deficient creatine uptake in CTD cerebral cells impact methylation balance leading to alterations of genes and proteins expression by epigenetic mechanism. In this study, we determined the status of nucleic acid methylation in both Slc6a8 knockout mouse model and brain organoids derived from CTD patients’ cells. We also investigated the effect of dodecyl creatine ester (DCE), a promising prodrug that increases brain creatine content in the mouse model of CTD. The level of nucleic acid methylation was significantly reduced compared to healthy controls in both in vivo and in vitro CTD models. This hypo-methylation tended to be regulated by DCE treatment in vivo. These results suggest that increased brain creatine after DCE treatment restores normal levels of DNA methylation, unveiling the potential of using DNA methylation as a marker to monitor the drug efficacy

Molecular Pathogenesis of MALT lymphoma

The original dissertation included a number or articles which were excluded from the digital file for copyright reasons. This is a list of the articles:1) Hamoudi RA, Appert A, Ye H, Ruskone-Fourmestraux A, Streubel B, Chott A, Raderer M, Gong L, Wlodarska I, De Wolf-Peeters C, MacLennan KA, de Leval L, Isaacson PG, & Du MQ. Differential expression of NF-kappaB target genes in MALT lymphoma with and without chromosome translocation: insights into molecular mechanism. Leukemia. 2010 Aug;24(8):1487-1497 http://www.ncbi.nlm.nih.gov/pubmed/205206402) Ye H, Gong L, Liu H, Hamoudi RA, Shirali S, Ho L, Chott A, Streubel B, Siebert R, Gesk S, Martin-Subero JI, Radford JA, Banerjee S, Nicholson AG, Ranaldi R, Remstein ED, Gao Z, Zheng J, Isaacson PG, Dogan A & Du MQ. MALT lymphoma with t(14;18)(q32;q21)/IGH-MALT1 is characterized by strong cytoplasmic MALT1 and BCL10 expression. J Pathol. 2005 Feb;205(3):293-301. http://www.ncbi.nlm.nih.gov/pubmed/156824433) Liu H, Hamoudi RA, Ye H, Ruskone-Fourmestraux A, Dogan A, Isaacson PG & Du MQ. t(11;18)(q21;q21) of mucosa-associated lymphoid tissue lymphoma results from illegitimate non-homologous end joining following double strand breaks. Br J Haematol. 2004 May;125(3):318-329. http://www.ncbi.nlm.nih.gov/pubmed/150864124) Liu H, Ye H, Ruskone-Fourmestraux A, De Jong D, Pileri S, Thiede C, Lavergne A, Boot H, Caletti G, Wündisch T, Molina T, Taal BG, Elena S, Thomas T, Zinzani PL, Neubauer A, Stolte M, Hamoudi RA, Dogan A, Isaacson PG & Du MQ. T(11;18) is a marker for all stage gastric MALT lymphomas that will not respond to H. pylori eradication. Gastroenterology. 2002 May;122(5):1286-1294. http://www.ncbi.nlm.nih.gov/pubmed?term=119845155) Liu H, Ye H, Dogan A, Ranaldi R, Hamoudi RA, Bearzi I, Isaacson PG & Du MQ. T(11;18)(q21;q21) is associated with advanced mucosa-associated lymphoid tissue lymphoma that expresses nuclear BCL10. Blood. 2001 Aug 15;98(4):1182-1187. http://www.ncbi.nlm.nih.gov/pubmed?term=114934686) Liu H, Ruskon-Fourmestraux A, Lavergne-Slove A, Ye H, Molina T, Bouhnik Y, Hamoudi RA, Diss TC, Dogan A, Megraud F, Rambaud JC, Du MQ & Isaacson PG. Resistance of t(11;18) positive gastric mucosa-associated lymphoid tissue lymphoma to Helicobacter pylori eradication therapy. Lancet. 2001 Jan 6;357(9249):39-40. http://www.ncbi.nlm.nih.gov/pubmed?term=11197361Mucosa associated lymphoid tissue (MALT) lymphoma is characterized by t(11;18)(q21;q21)/API2-MALT1, t(1;14)(p22;q32)/BCL10-IGH and t(14;18)(q32;q21)/IGH-MALT1, which commonly activate the NF-κB pathway. Gastric MALT lymphomas harbouring such translocation do not respond to Helicobacter pylori eradication, while those without translocation can be cured by antibiotics. To understand the molecular mechanism of MALT lymphoma with and without chromosome translocation, 24 cases (15 translocation-positive and 9 translocation-negative) of MALT lymphomas together with 7 follicular lymphomas and 7 mantle cell lymphomas were analysed by Affymetrix gene expression microarray platform. Unsupervised clustering showed that cases of MALT lymphoma were clustered as a single branch. However, within the MALT lymphoma group, translocation-positive cases were intermingled with translocation-negative cases. Gene set enrichment analysis (GSEA) of the NF-κB target genes and 4394 additional gene sets covering various cellular pathways, biological processes and molecular functions showed that translocation-positive MALT lymphomas were characterized by an enhanced expression of NF-κB target genes, particularly TLR6, CCR2, CD69 and BCL2, while translocation-negative cases were featured by active inflammatory and immune responses, such as IL8, CD86, CD28 and ICOS. Separate analyses of the genes differentially expressed between translocation-positive and negative cases and measurement of gene ontology term in these differentially expressed genes by hypergeometric test reinforced the above findings by GSEA. The differential expression of these NF-κB target genes between MALT lymphoma with and without translocation was confirmed by quantitative RT-PCR and immunohistochemistry or Western blot. Expression of TLR6, in the presence of TLR2, enhanced both API2-MALT1 and BCL10 mediated NF-κB activation in vitro. In addition, there was cooperation between expression of BCL10, MALT1 or API2-MALT1, and stimulation of the antigen receptor or CD40 or TLR in NF-κB activation as shown by both reporter assay and IκBα degradation. Interestingly, expression of BCL10 but not API2-MALT1 and MALT1, in the presence of LPS stimulation, also triggered IκBβ degradation, suggesting activation of different NF-κB dimers between these oncogenic products. Study by co-immunoprecipitation showed that BCL10 directly interacts with MALT1. Sub-cellular localisation experiments in BJAB B-cells, showed that BCL10 localisation was affected by MALT1. When BCL10 was over-expressed, the protein was predominantly expressed in the nuclei, but when MALT1 was over-expressed, BCL10 was mainly localised in the cytoplasm. When both BCL10 and MALT1 were over-expressed, BCL10 was expressed in the cytoplasm in the early hours when the protein level was low, but in both the cytoplasm and nuclei after 9 hours when the protein level was high. Over-expression of API2-MALT1 did not shown any apparent effect on BCL10 sub-cellular localisation in vitro. Finally, comparison of MALT lymphoma expression microarray with other lymphomas showed lactoferrin to be highly expressed in MALT lymphoma. This was confirmed by qRT-PCR, showing lactoferrin to be significantly over-expressed in MALT lymphoma compared to FL and MCL. Thus lactoferrin may be a potential marker for MALT lymphoma

Artificial Neural Network Analysis of Gene Expression Data Predicted Non-Hodgkin Lymphoma Subtypes with High Accuracy

Predictive analytics using artificial intelligence is a useful tool in cancer research. A multilayer perceptron neural network used gene expression data to predict the lymphoma subtypes of 290 cases of non-Hodgkin lymphoma (GSE132929). The input layer included both the whole array of 20,863 genes and a cancer transcriptome panel of 1769 genes. The output layer was lymphoma subtypes, including follicular lymphoma, mantle cell lymphoma, diffuse large B-cell lymphoma, Burkitt lymphoma, and marginal zone lymphoma. The neural networks successfully classified the cases consistent with the lymphoma subtypes, with an area under the curve (AUC) that ranged from 0.87 to 0.99. The most relevant predictive genes were LCE2B, KNG1, IGHV7_81, TG, C6, FGB, ZNF750, CTSV, INGX, and COL4A6 for the whole set; and ARG1, MAGEA3, AKT2, IL1B, S100A7A, CLEC5A, WIF1, TREM1, DEFB1, and GAGE1 for the cancer panel. The characteristic predictive genes for each lymphoma subtypes were also identified with high accuracy (AUC = 0.95, incorrect predictions = 6.2%). Finally, the topmost relevant 30 genes of the whole set, which belonged to apoptosis, cell proliferation, metabolism, and antigen presentation pathways, not only predicted the lymphoma subtypes but also the overall survival of diffuse large B-cell lymphoma (series GSE10846, n = 414 cases), and most relevant cancer subtypes of The Cancer Genome Atlas (TCGA) consortium including carcinomas of breast, colorectal, lung, prostate, and gastric, melanoma, etc. (7441 cases). In conclusion, neural networks predicted the non-Hodgkin lymphoma subtypes with high accuracy, and the highlighted genes also predicted the survival of a pan-cancer series

Artificial Intelligence Predicted Overall Survival and Classified Mature B-Cell Neoplasms Based on Immuno-Oncology and Immune Checkpoint Panels

Artificial intelligence (AI) can identify actionable oncology biomarkers. This research integrates our previous analyses of non-Hodgkin lymphoma. We used gene expression and immunohistochemical data, focusing on the immune checkpoint, and added a new analysis of macrophages, including 3D rendering. The AI comprised machine learning (C5, Bayesian network, C&R, CHAID, discriminant analysis, KNN, logistic regression, LSVM, Quest, random forest, random trees, SVM, tree-AS, and XGBoost linear and tree) and artificial neural networks (multilayer perceptron and radial basis function). The series included chronic lymphocytic leukemia, mantle cell lymphoma, follicular lymphoma, Burkitt, diffuse large B-cell lymphoma, marginal zone lymphoma, and multiple myeloma, as well as acute myeloid leukemia and pan-cancer series. AI classified lymphoma subtypes and predicted overall survival accurately. Oncogenes and tumor suppressor genes were highlighted (MYC, BCL2, and TP53), along with immune microenvironment markers of tumor-associated macrophages (M2-like TAMs), T-cells and regulatory T lymphocytes (Tregs) (CD68, CD163, MARCO, CSF1R, CSF1, PD-L1/CD274, SIRPA, CD85A/LILRB3, CD47, IL10, TNFRSF14/HVEM, TNFAIP8, IKAROS, STAT3, NFKB, MAPK, PD-1/PDCD1, BTLA, and FOXP3), apoptosis (BCL2, CASP3, CASP8, PARP, and pathway-related MDM2, E2F1, CDK6, MYB, and LMO2), and metabolism (ENO3, GGA3). In conclusion, AI with immuno-oncology markers is a powerful predictive tool. Additionally, a review of recent literature was made

Artificial Intelligence Analysis of Gene Expression Predicted the Overall Survival of Mantle Cell Lymphoma and a Large Pan-Cancer Series

Mantle cell lymphoma (MCL) is a subtype of mature B-cell non-Hodgkin lymphoma characterized by a poor prognosis. First, we analyzed a series of 123 cases (GSE93291). An algorithm using multilayer perceptron artificial neural network, radial basis function, gene set enrichment analysis (GSEA), and conventional statistics, correlated 20,862 genes with 28 MCL prognostic genes for dimensionality reduction, to predict the patients’ overall survival and highlight new markers. As a result, 58 genes predicted survival with high accuracy (area under the curve = 0.9). Further reduction identified 10 genes: KIF18A, YBX3, PEMT, GCNA, and POGLUT3 that associated with a poor survival; and SELENOP, AMOTL2, IGFBP7, KCTD12, and ADGRG2 with a favorable survival. Correlation with the proliferation index (Ki67) was also made. Interestingly, these genes, which were related to cell cycle, apoptosis, and metabolism, also predicted the survival of diffuse large B-cell lymphoma (GSE10846, n = 414), and a pan-cancer series of The Cancer Genome Atlas (TCGA, n = 7289), which included the most relevant cancers (lung, breast, colorectal, prostate, stomach, liver, etcetera). Secondly, survival was predicted using 10 oncology panels (transcriptome, cancer progression and pathways, metabolic pathways, immuno-oncology, and host response), and TYMS was highlighted. Finally, using machine learning, C5 tree and Bayesian network had the highest accuracy for prediction and correlation with the LLMPP MCL35 proliferation assay and RGS1 was made. In conclusion, artificial intelligence analysis predicted the overall survival of MCL with high accuracy, and highlighted genes that predicted the survival of a large pan-cancer series

In silico Analysis of Publicly Available Transcriptomics Data Identifies Putative Prognostic and Therapeutic Molecular Targets for Papillary Thyroid Carcinoma

Background: Thyroid cancer is the most common endocrine malignancy. However, the molecular mechanism involved in its pathogenesis is not well characterized. Purpose: The objective of this study is to identify key cellular pathways and differentially expressed genes along the thyroid cancer pathogenesis sequence as well as to identify potential prognostic and therapeutic targets. Methods: Publicly available transcriptomics data comprising a total of 95 samples consisting of 41 normal, 28 non-aggressive and 26 metastatic papillary thyroid carcinoma (PTC) cases were used. Transcriptomics data were normalized and filtered identifying 9394 differentially expressed genes. The genes identified were subjected to pathway analysis using absGSEA identifying PTC related pathways. Three of the genes identified were validated on 508 thyroid cancer biopsies using RNAseq and TNMplot. Results: Pathway analysis revealed a total of 2193 differential pathways among non-aggressive samples and 1969 among metastatic samples compared to normal tissue. Pathways for non-aggressive PTC include calcium and potassium ion transport, hormone signaling, protein tyrosine phosphatase activity and protein tyrosine kinase activity. Metastatic pathways include growth, apoptosis, activation of MAPK and regulation of serine threonine kinase activity. Genes for non-aggressive are KCNQ1, CACNA1D, KCNN4, BCL2, and PTK2B and metastatic PTC are EGFR, PTK2B, KCNN4 and BCL2. Three of the genes identified were validated using clinical biopsies showing significant overexpression in aggressive compared to non-aggressive PTC; EGFR (p < 0.05), KCNN4 (p < 0.001) and PTK2B (p < 0.001). DrugBank database search identified several FDA approved drug targets including anti-EGFR Vandetanib used to treat thyroid cancer in addition to others that may prove useful in treating PTC. Conclusion: Transcriptomics analysis identified putative prognostic targets including EGFR, PTK2B, BCL2, KCNQ1, KCNN4 and CACNA1D. EGFR, PTK2B and KCN44 were validated using thyroid cancer clinical biopsies. The drug search identified FDA approved drugs including Vandetanib in addition to others that may prove useful in treating the disease

Identification of novel prognostic markers in cervical intraepithelial neoplasia using LDMAS (LOH Data Management and Analysis Software).

BACKGROUND: Detection of Loss of Heterozygosity (LOH) is one of the most common molecular applications in the study of human diseases, in particular cancer. The technique is commonly used to examine whether a known tumour suppressor gene is inactivated or to map unknown tumour suppressor gene(s). However, with the increasing number of samples analysed using different software, no tool is currently available to integrate and facilitate the extensive and efficient data retrieval and analyses, such as correlation of LOH data with various clinical data sets. RESULTS: An algorithm to identify prognostic disease markers is devised and implemented as novel software called LDMAS. LDMAS is a software suite designed for data retrieval, management and integrated analysis of the clinico-pathological data and molecular results from independent databases. LDMAS is used in stratification of disease stages according to clinical stage or histological features and correlation of various clinico-pathological features with molecular findings to obtain relevant prognostic markers such as those used in predicting the outcome of cervical intraepithelial neoplasia (CIN). This approach lead to the identification of novel prognostic cervical cancer markers and extraction of useful clinical information such as correlation of Human Papilloma Virus (HPV) status with CIN lesions. CONCLUSIONS: A novel software called LDMAS is implemented and used to extract and identify prognostic disease markers. The software is used to successfully identify 4 novel prognostic markers that can be used to predict the outcome of CIN. LDMAS provides an essential platform for the extraction of useful information from large amount of data generated by LOH studies. LDMAS provides three unique and novel features for LOH analysis: (1) automatic extraction of relevant data from patient records and reports (2) correlation of LOH data with clinico-pathological data and (3) storage of complex data in flexible format. The first feature automates the creation of database of clinically relevant information from huge amount of data, the second feature extracts useful biomedical information such as prognostic markers in CIN and the third feature simplifies the statistical analyses of the data and allows non-statisticians to carry out the analysis. Additionally, LDMAS can be used to extract clinically useful markers from other diseases and interface to high throughput genotyping analysis software such as GDAS used to generate LOH data from Affymetrix GeneChip Mapping arrays.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

Artificial intelligence and classification of mature lymphoid neoplasms

Hematologists, geneticists, and clinicians came to a multidisciplinary agreement on the classification of lymphoid neoplasms that combines clinical features, histological characteristics, immunophenotype, and molecular pathology analyses. The current classification includes the World Health Organization (WHO) Classification of tumours of haematopoietic and lymphoid tissues revised 4th edition, the International Consensus Classification (ICC) of mature lymphoid neoplasms (report from the Clinical Advisory Committee 2022), and the 5th edition of the proposed WHO Classification of haematolymphoid tumours (lymphoid neoplasms, WHO-HAEM5). This article revises the recent advances in the classification of mature lymphoid neoplasms. Artificial intelligence (AI) has advanced rapidly recently, and its role in medicine is becoming more important as AI integrates computer science and datasets to make predictions or classifications based on complex input data. Summarizing previous research, it is described how several machine learning and neural networks can predict the prognosis of the patients, and classified mature B-cell neoplasms. In addition, new analysis predicted lymphoma subtypes using cell-of-origin markers that hematopathologists use in the clinical routine, including CD3, CD5, CD19, CD79A, MS4A1 (CD20), MME (CD10), BCL6, IRF4 (MUM-1), BCL2, SOX11, MNDA, and FCRL4 (IRTA1). In conclusion, although most categories are similar in both classifications, there are also conceptual differences and differences in the diagnostic criteria for some diseases. It is expected that AI will be incorporated into the lymphoma classification as another bioinformatics tool

Retinal vascular changes and arterial stiffness during 8-month isolation and confinement: the SIRIUS-21 space analog mission

IntroductionIsolation and confinement are significant stressors during space travel that can impact crewmembers’ physical and mental health. Space travel has been shown to accelerate vascular aging and increase the risk of cardiovascular and cerebrovascular disorders. However, the effect of prolonged isolation and confinement on microvascular function has not yet been thoroughly investigated.MethodsRetinal vascular imaging was conducted on four crewmembers during- and post-8-month SIRIUS-21 space analog mission. Central retinal arteriolar equivalent (CRAE), central retinal venular equivalent (CRVE), and arteriovenous ratio (AVR) were measured. Pulse wave velocity (PWV), an indicator of arterial stiffness, was also measured.ResultsData from 4 participants was analyzed. These participants had a mean age of 34.75 ± 5.44 years, height of 170.00 ± 2.00 cm, weight of 74.50 ± 12.53 kg, and average BMI of 25.47 ± 3.94 kg/m2. During- and post-isolation, average CRVE showed an upward trend (Pearson’s r 0.784, R-square 0.62), suggesting a dilation of retinal venules, while AVR showed a downward trend (Pearson’s r −0.238, R-square 0.057), which is suggestive of a higher risk of cardiovascular and cerebrovascular dysfunctions. But neither of these trends were statistically significant. Additionally, the average PWV showed an upward trend during- and after-isolation across all crew members.ConclusionIsolation and confinement appear to contribute towards retinal vascular damage and arterial stiffness. This cautiously suggests an increased risk of cardiovascular and cerebrovascular disorders due to the contribution of the isolation in space flight. Further studies are needed to confirm and expand on these results as we prepare for future manned missions to the Moon and Mars

Identification of p53-target genes in human papillomavirus-associated head and neck cancer by integrative bioinformatics analysis

IntroductionHead and neck cancer (HNC) is a highly prevalent and heterogeneous malignancy. Although extensive efforts have been made to advance its treatment, the prognosis remained poor with increased mortality. Human papillomaviruses (HPV) have been associated with high risk in HNC. TP53, a tumor suppressor, is the most frequently altered gene in HNC, therefore, investigating its target genes for the identification of novel biomarkers or therapeutic targets in HPV-related HNC progression is highly recommended.MethodsTranscriptomic profiles from three independent gene expression omnibus (GEO) datasets, including 44 HPV+ and 70 HPV- HNC patients, were subjected to integrative statistical and Bioinformatics analyses. For the top-selected marker, further in-silico validation in TCGA and GTEx databases and experimental validation in 65 (51 HPV- and 14 HPV+) subjects with histologically confirmed head and neck squamous cell carcinoma (HNSCC) have been performed.ResultsA total of 498 differentially expressed genes (DEGs) were identified including 291 up-regulated genes and 207 down-regulated genes in HPV+ compared to HPV- HNSCC patients. Functional annotations and gene set enrichment analysis (GSEA) showed that the up-regulated genes were significantly involved in p53-related pathways. The integrative analysis between the Hub-genes identified in the complex protein-protein network and the top frequent genes resulting from GSEA showed an intriguing correlation with five biomarkers which are EZH2, MDM2, PCNA, STAT5A and TYMS. Importantly, the MDM2 gene showed the highest gene expression difference between HPV+ and HPV- HNSCC (Average log2FC = 1.89). Further in-silico validation in a large HNSCC cohort from TCGA and GTEx databases confirmed the over-expression of MDM2 in HPV+ compared to HPV- HNSCC patients (p = 2.39E-05). IHC scoring showed that MDM2 protein expression was significantly higher in HPV+ compared to HPV- HNSCC patients (p = 0.031).DiscussionOur findings showed evidence that over-expression of MDM2, proto-oncogene, may affect the occurrence and proliferation of HPV-associated HNSCC by disturbing the p53-target genes and consequently the p53-related pathways