scholarly journals TMPRSS2, a SARS-CoV-2 internalization protease is downregulated in head and neck cancer patients

2020 ◽  
Author(s):  
Andrea Sacconi ◽  
Sara Donzelli ◽  
Claudio Pulito ◽  
Stefano Ferrero ◽  
Aldo Morrone ◽  
...  
Keyword(s):  
Gene Expression ◽  
Oral Cavity ◽  
Cancer Patients ◽  
Expression Data ◽  
Wild Type ◽  
Sars Coronavirus ◽  
Lung Cancers ◽  
Normal Tissues ◽  
Link Type ◽  
Molecular Modulation

AbstractObjectivesTwo of the main target tissues of SARS-coronavirus 2 are the oral cavity pharynx-larynx epithelium, the main virus entry site, and the lung epithelium. The virus enters host cells through binding of the Spike protein to ACE2 receptor and subsequent S priming by the TMPRSS2 protease. Herein we aim to assess differences in both ACE2 and TMPRSS2 expression in normal tissues from oral cavity-pharynx-larynx and lung tissues as well as neoplastic tissues from the same histological areas. The information provided in this study may contribute to better understanding of SARS-coronavirus 2 ability to interact with different biological systems and contributes to cumulative knowledge on potential mechanisms to inhibit its diffusion.Materials and MethodsThe study has been conducted using The Cancer Genome Atlas (TCGA) and the Regina Elena Institute (IRE) databases and validated by experimental model in HNSCC and Lung cancer cells. Data from one COVID19 positive patient who was operated on for HNSCC was also included. We have analyzed 478 tumor samples and 44 normal samples from TCGA HNSCC cohort for whom both miRNA and mRNA sequencing was available. The dataset included 391 HPV- and 85 HPV+ cases, with 331 P53 mutated and 147 P53 wild type cases respectively. 352 out of 478 samples were male and 126 female. In IRE cohort we analyzed 66 tumor samples with matched normal sample for miRNA profiling and 23 tumor\normal matched samples for mRNA profiling. 45 out of 66 tumors from IRE cohort were male and 21 female, 38 were P53 mutated and 27 wild type. Most patients (63 of 66) in IRE cohort were HPV negative. Normalized TCGA HNSCC gene expression and miRNA expression data were obtained from Broad Institute TCGA Genome Data Analysis Center (http://gdac.broadinstitute.org/). mRNA expression data from IRE cohort used in this study has been deposited to NCBI’s Gene Expression Omnibus and is accessible through GEO series accession number GSE107591. In order to inference about potential molecular modulation of TMPRSS2, we also included miRNAs expression for the 66 IRE cohort matched tumor and normal samples from Agilent platform. DNA methylation data for TCGA tumors were obtained from Wanderer (http://maplab.imppc.org/wanderer/). We used miRWalk and miRNet web tools for miRNA-target interaction prediction and pathway enrichment analysis. The correlation and regression analyses as well as the miRNA and gene modulation and the survival analysis were conducted using Matlab R2019.ResultsTMPRSS2 expression in HNSCC was significantly reduced compared to the normal tissues and had a prognostic value in HNSCC patients. Reduction of TMPRSS2 expression was more evident in women than in men, in TP53 mutated versus wild TP53 tumors as well as in HPV negative patients compared to HPV positive counterparts. Functionally, we assessed the multivariate effect on TMPRSS2 in a single regression model. We observed that all variables had an independent effect on TMPRSS2 in HNSCC patients with HPV negative, TP53 mutated status and with elevated TP53-dependent Myc-target genes associated with low TMPRSS2 expression. Investigation of the molecular modulation of TMPRSS2 in both HNSCC and lung cancers revealed that expression of microRNAs targeting TMPRSS2 anti-correlated in both TCGA and IRE HNSCC datasets, while there was not evidence of TMPRSS2 promoter methylation in both tumor cohorts. Interestingly, the anti-correlation between microRNAs and TMPRSS2 expression was corroborated by testing this association in a SARS-CoV-2 positive HNSCC patient.ConclusionsCollectively, these findings suggest that tumoral tissues, herein exemplified by HNSCC and lung cancers might be more resistant to SARS-CoV-2 infection due to reduced expression of TMPRSS2. The protective mechanism might occur, at least partially, through the aberrant activation of TMPRSS2 targeting microRNAs; thereby providing strong evidence on the role of non-coding RNA molecule in host viral infection. These observations may help to better assess the frailty of SARS-CoV-2 positive cancer patients.

scholarly journals THU0008 DEVELOPMENT OF A NOVEL TRANSLATIONAL IN SILICO INDICATION DISCOVERY FRAMEWORK: EXEMPLIFIED BY THE CLINICAL COMPOUND CENERIMOD

2020 ◽  
Vol 79 (Suppl 1) ◽  
pp. 216.2-217
Author(s):  
D. Hartl ◽  
M. Keller ◽  
A. Klenk ◽  
M. Murphy ◽  
M. Martinic ◽  
...  
Keyword(s):  
Gene Expression ◽  
Animal Models ◽  
Clinical Response ◽  
Gene Expression Data ◽  
In Silico ◽  
Drug Candidate ◽  
Expression Data ◽  
Computer Algorithms ◽  
Clinical Patient ◽  
Link Type

Background:To explore the full therapeutic spectrum of a drug it is crucial to consider its potential effectiveness in all diseases. Serendipitous clinical observations have often shown that approved drugs and those in development to be efficacious in indications different to those originally tested for. Traditional approaches to match a drug candidate with possible indications are mostly based on matching drug mechanistic knowledge with disease pathophysiology. Proof-of-concept trials or elaborate pre-clinical studies in animal models do not allow for a broad assessment due to high costs and slow progress. Gene expression changes in patients or animal models represent a good proxy to comprehensively assess both disease and drug effects. Furthermore, this data type can be integrated with a plethora of publicly available data.Objectives:Generation of a novel in silico framework to support the selection and expansion of potential indications which associate with a compound or approved drug. The framework was exemplified by the clinical compound cenerimod, a potent, selective, and orally active sphingosine-1-phosphate receptor 1 modulator (Piali et al., 2017).Methods:A total of ~13’000 public patient gene expression datasets from ~140 diseases were evaluated against cenerimod gene expression data generated in mouse disease models. To improve comparability of studies across platforms and species, computer algorithms (neural networks) were trained and employed to reduce noise within the data sets and improve signal. The predicted response to cenerimod for individual patients was contrasted against clinical patient characteristics.Results:The neural network algorithm efficiently reduced experimental noise and improved sensitivity in the gene expression data. The results predicted cenerimod to be efficacious in several auto-immune diseases foremost SLE. Additionally, focused analysis on individual patients rather than disease cohorts revealed potential determinants predictive of maximal clinical response, with the highest predicted clinical response for cenerimod in patients with severe inflammatory endotype and/or high SLE Disease Activity Index (SLEDAI).Conclusion:Combining preclinical compound data with the wealth of public disease gene expression data, provides great potential to support indication selection. The novel in silico framework identified SLE as a prime potential indication for cenerimod and supported the cenerimod phase 2b clinical trial in patients with SLE (CARE study,NCT03742037).References:[1]Piali, L., Birker-Robaczewska, M., Lescop, C., Froidevaux, S., Schmitz, N., Morrison, K., … Nayler, O. (2017). Cenerimod, a novel selective S1P1 receptor modulator with unique signaling properties. Pharmacology Research & Perspectives, 5(6), 1–12.https://doi.org/10.1002/prp2.370Disclosure of Interests:Dominik Hartl Shareholder of: Idorsia shares, Employee of: Idorsia employee, Marcel Keller Shareholder of: Idorsia options/shares, Employee of: Idorsia employee, Axel Klenk Shareholder of: Idorsia option/shares, Employee of: Idorsia employee, Mark Murphy Shareholder of: Idorsia shares and stock options, Employee of: Idorsia employee, Marianne Martinic Shareholder of: Idorsia options/shares, Employee of: Idorsia employee, Gabin Pierlot Shareholder of: Idorsia options/shares, Employee of: Idorsia employee, Peter Groenen Shareholder of: Idorsia options/shares, Employee of: Idorsia employee, Daniel Strasser Shareholder of: Idorsia options/shares, Employee of: Idorsia employee

scholarly journals A method of gene expression data transfer from cell lines to cancer patients for machine-learning prediction of drug efficiency

Cell Cycle ◽  
2018 ◽  
Vol 17 (4) ◽  
pp. 486-491 ◽  
Author(s):  
Nicolas Borisov ◽  
Victor Tkachev ◽  
Maria Suntsova ◽  
Olga Kovalchuk ◽  
Alex Zhavoronkov ◽  
...  
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Cancer Patients ◽  
Cell Lines ◽  
Gene Expression Data ◽  
Data Transfer ◽  
Expression Data ◽  
Drug Efficiency

scholarly journals BBCancer: an expression atlas of blood-based biomarkers in the early diagnosis of cancers

2019 ◽  
Author(s):  
Zhixiang Zuo ◽  
Huanjing Hu ◽  
Qingxian Xu ◽  
Xiaotong Luo ◽  
Di Peng ◽  
...  
Keyword(s):  
Gene Expression ◽  
Early Detection ◽  
Cancer Patients ◽  
High Throughput ◽  
Early Detection Of Cancer ◽  
Circular Rnas ◽  
Expression Data ◽  
Blood Samples ◽  
Cancer Types ◽  
High Throughput Gene Expression

Abstract The early detection of cancer holds the key to combat and control the increasing global burden of cancer morbidity and mortality. Blood-based screenings using circulating DNAs (ctDNAs), circulating RNA (ctRNAs), circulating tumor cells (CTCs) and extracellular vesicles (EVs) have shown promising prospects in the early detection of cancer. Recent high-throughput gene expression profiling of blood samples from cancer patients has provided a valuable resource for developing new biomarkers for the early detection of cancer. However, a well-organized online repository for these blood-based high-throughput gene expression data is still not available. Here, we present BBCancer (http://bbcancer.renlab.org/), a web-accessible and comprehensive open resource for providing the expression landscape of six types of RNAs, including messenger RNAs (mRNAs), long noncoding RNAs (lncRNAs), microRNAs (miRNAs), circular RNAs (circRNAs), tRNA-derived fragments (tRFRNAs) and Piwi-interacting RNAs (piRNAs) in blood samples, including plasma, CTCs and EVs, from cancer patients with various cancer types. Currently, BBCancer contains expression data of the six RNA types from 5040 normal and tumor blood samples across 15 cancer types. We believe this database will serve as a powerful platform for developing blood biomarkers.

scholarly journals SFARI Genes and where to find them; classification modelling to identify genes associated with Autism Spectrum Disorder from RNA-seq data

2021 ◽  
Author(s):  
Magdalena Navarro ◽  
T Ian Simpson
Keyword(s):  
Gene Expression ◽  
Autism Spectrum Disorder ◽  
Differential Gene Expression ◽  
Gene Expression Data ◽  
Gene List ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Expression Data ◽  
Link Type ◽  
Differential Gene

AbstractMotivationAutism spectrum disorder (ASD) has a strong, yet heterogeneous, genetic component. Among the various methods that are being developed to help reveal the underlying molecular aetiology of the disease, one that is gaining popularity is the combination of gene expression and clinical genetic data. For ASD, the SFARI-gene database comprises lists of curated genes in which presumed causative mutations have been identified in patients. In order to predict novel candidate SFARI-genes we built classification models combining differential gene expression data for ASD patients and unaffected individuals with a gene’s status in the SFARI-gene list.ResultsSFARI-genes were not found to be significantly associated with differential gene expression patterns, nor were they enriched in gene co-expression network modules that had a strong correlation with ASD diagnosis. However, network analysis and machine learning models that incorporate information from the whole gene co-expression network were able to predict novel candidate genes that share features of existing SFARI genes and have support for roles in ASD in the literature. We found a statistically significant bias related to the absolute level of gene expression for existing SFARI genes and their scores. It is essential that this bias be taken into account when studies interpret ASD gene expression data at gene, module and whole-network levels.AvailabilitySource code is available from GitHub (https://doi.org/10.5281/zenodo.4463693) and the accompanying data from The University of Edinburgh DataStore (https://doi.org/10.7488/ds/2980)[email protected]

scholarly journals Bioinformatic Analysis of Peripheral Blood miRNA of Breast Cancer Patients in Relation With Anthracycline Cardiotoxicity

2019 ◽  
Author(s):  
Wang Yadi ◽  
Chen Shurui ◽  
Zhang Tong ◽  
Chen Suxian ◽  
Tong Qing ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Cancer Patients ◽  
Cancer Chemotherapy ◽  
Bioinformatic Analysis ◽  
Expression Data ◽  
Breast Cancer Patients ◽  
Related Gene ◽  
Anthracycline Cardiotoxicity ◽  
Breast Cancer Chemotherapy

Abstract The current diagnostic methods and treatments still fail to lower the incidence of anthracycline-induced cardiotoxicity effectively. In this study, we aimed to (1) analyze the cardiotoxicity-related genes after breast cancer chemotherapy in gene expression database and (2) carry out bioinformatic analysis to identify cardiotoxicity-related abnormal expressions, the biomarkers of such abnormal expressions, and the key regulatory pathways after breast cancer chemotherapy. Cardiotoxicity-related gene expression data (GSE40447) after breast cancer chemotherapy was acquired from the GEO database. The biomarker expression data of women with chemotherapy-induced cardiotoxicity (group A), chemotherapy history but no cardiotoxicity (group B), and confirmatory diagnosis of breast cancer but normal ejection fraction before chemotherapy (group C) were analyzed to obtain the mRNA with differential expressions and predict the miRNAs regulating the differential expressions. The miRanda formula and functional enrichment analysis were used to screen abnormal miRNAs. Then, the gene ontology (GO) analysis was adapted to further screen the miRNAs related to cardiotoxicity after breast cancer chemotherapy. The data of differential analysis of biomarker expression of groups A, B, and C using the GSE40447-related gene expression profile database showed that there were 30 intersection genes. The differentially expressed mRNAs were predicted using the miRanda and TargetScan software, and a total of 2978 miRNAs were obtained by taking the intersections. Further, the GO analysis and targeted regulatory relationship between miRNA and target genes were used to establish miRNA-gene interaction network to screen and obtain 7 cardiotoxicity-related miRNAs with relatively high centrality, including hsa-miR-4638-3p, hsa-miR-5096, hsa-miR-4763-5p, hsa-miR-1273g-3p, hsa-miR6192, hsa-miR-4726-5p and hsa-miR-1273a. Among them, hsa-miR-4638-3p and hsa-miR-1273g-3p had the highest centrality. The PCR verification results were consistent with those of the chip data. There are differentially expressed miRNAs in the peripheral blood of breast cancer patients with anthracycline cardiotoxicity. Among them, hsa-miR-4638-3p and hsa-miR-1273g-3p are closely associated with the onset of anthracycline cardiotoxicity in patients with breast cancer. Mining, integrating, and validating effective information resources of biological gene chips can provide a new direction for further studies on the molecular mechanism of anthracycline cardiotoxicity.

scholarly journals Diagnostic Value of Insulinoma-Associated Protein 1 (INSM1) and Comparison With Established Neuroendocrine Markers in Pulmonary Cancers

2020 ◽  
Vol 144 (9) ◽  
pp. 1075-1085 ◽  
Author(s):  
Johan Staaf ◽  
Lena Tran ◽  
Linnea Söderlund ◽  
Björn Nodin ◽  
Karin Jirström ◽  
...  
Keyword(s):  
Gene Expression ◽  
Chromogranin A ◽  
Diagnostic Value ◽  
Small Cell ◽  
High Grade ◽  
Expression Data ◽  
Small Cell Lung ◽  
Lung Cancers ◽  
Pulmonary Pathology ◽  
Pulmonary Tumors

Context.— The diagnostic distinction of pulmonary neuroendocrine (NE) tumors from non–small cell lung carcinomas (NSCLCs) is clinically relevant for prognostication and treatment. Diagnosis is based on morphology and immunohistochemical staining. Objective.— To determine the diagnostic value of insulinoma-associated protein 1 (INSM1), in comparison with established NE markers, in pulmonary tumors. Design.— Fifty-four pulmonary NE tumors and 632 NSCLCs were stained for INSM1, CD56, chromogranin A, and synaptophysin. In a subset, gene expression data were available for analysis. Also, 419 metastases to the lungs were stained for INSM1. A literature search identified 39 additional studies with data on NE markers in lung cancers from the last 15 years. Seven of these included data on INSM1. Results.— A positive INSM1 staining was seen in 39 of 54 NE tumors (72%) and 6 of 623 NSCLCs (1%). The corresponding numbers were 47 of 54 (87%) and 14 of 626 (2%) for CD56, 30 of 54 (56%) and 6 of 629 (1%) for chromogranin A, and 46 of 54 (85%) and 49 of 630 (8%) for synaptophysin, respectively. Analysis of literature data revealed that CD56 and INSM1 were the best markers for identification of high-grade NE pulmonary tumors when considering both sensitivity and specificity, while synaptophysin also showed good sensitivity. INSM1 gene expression was clearly associated with NE histology. Conclusions.— The solid data of both our and previous studies confirm the diagnostic value of INSM1 as a NE marker in pulmonary pathology. The combination of CD56 with INSM1 and/or synaptophysin should be the first-hand choice to confirm pulmonary high-grade NE tumors. INSM1 gene expression could be used to predict NE tumor histology.

Deep learning-based classification and interpretation of gene expression data from cancer and normal tissues

2020 ◽  
Vol 24 (2) ◽  
pp. 121
Author(s):  
Taesung Park ◽  
Sangick Park ◽  
Kyullhee Han ◽  
SungMin Kim ◽  
Chan Hee Lee ◽  
...  
Keyword(s):  
Gene Expression ◽  
Deep Learning ◽  
Gene Expression Data ◽  
Expression Data ◽  
Normal Tissues

scholarly journals Supervised Classification by Filter Methods and Recursive Feature Elimination Predicts Risk of Radiotherapy-Related Fatigue in Patients with Prostate Cancer

2014 ◽  
Vol 13 ◽  
pp. CIN.S19745 ◽  
Author(s):  
Leorey N. Saligan ◽  
Juan Luis Fernández-Martínez ◽  
Enrique J. deAndrés-Galiana ◽  
Stephen Sonis
Keyword(s):  
Gene Expression ◽  
Prostate Cancer ◽  
Cancer Patients ◽  
Gene Expression Data ◽  
Fold Change ◽  
Learning Phase ◽  
Small Scale ◽  
Specific Gene ◽  
Expression Data ◽  
Validation Phase

Background Fatigue is a common side effect of cancer (CA) treatment. We used a novel analytical method to identify and validate a specific gene cluster that is predictive of fatigue risk in prostate cancer patients (PCP) treated with radiotherapy (RT). Methods A total of 44 PCP were categorized into high-fatigue (HF) and low-fatigue (LF) cohorts based on fatigue score change from baseline to RT completion. Fold-change differential and Fisher's linear discriminant analyses (LDA) from 27 subjects with gene expression data at baseline and RT completion generated a reduced base of most discriminatory genes (learning phase). A nearest-neighbor risk (k-NN) prediction model was developed based on small-scale prognostic signatures. The predictive model validity was tested in another 17 subjects using baseline gene expression data (validation phase). Result The model generated in the learning phase predicted HF classification at RT completion in the validation phase with 76.5% accuracy. Conclusion The results suggest that a novel analytical algorithm that incorporates fold-change differential analysis, LDA, and a k-NN may have applicability in predicting regimen-related toxicity in cancer patients with high reliability, if we take into account these results and the limited amount of data that we had at disposal. It is expected that the accuracy will be improved by increasing data sampling in the learning phase.

Sign in / Sign up

Export Citation Format

Share Document