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Endometrial cancer (EC) is a fatal female reproductive tumor. Bioinformatic tools are increasingly developed to screen out molecular targets related to EC. In this study, http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE17025 and http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE40032 were obtained from Gene Expression Omnibus (GEO). “limma” package and Venn diagram tool were used to identify hub genes. FunRich was used for functional analysis. Retrieval of Interacting Genes Database (STRING) was used to analyze protein‐protein interaction (PPI) complex. Cancer Genome Atlas (TCGA), GEPIA, immunohistochemistry staining, and ROC curve analysis were carried out for validation. Univariate and multivariate regression analyses were performed to predict the risk score. Compound muscle action potential (CMap) was used to find potential drugs. GSEA was also done. We retrieved seven oncogenes which were upregulated and hypomethylated and 12 tumor suppressor genes (TSGs) which were downregulated and hypermethylated. The upregulated and hypomethylated genes were strikingly enriched in term “immune response” while the downregulated and hypermethylated genes were mainly focused on term “aromatic compound catabolic process.” TCGA and GEPIA were used to screen out EDNRB, CDO1, NDN, PLCD1, ROR2, ESPL1, PRAME, and PTTG1. Among them, ESPL1 and ROR2 were identified by Cox regression analysis and were used to construct prognostic risk model. The result showed that ESPL1 was a negative independent prognostic factor. Cmap identified aminoglutethimide, luteolin, sulfadimethoxine, and maprotiline had correlation with EC. GSEA results showed that “hedgehog signaling pathway” was enriched. This research inferred potential aberrantly methylated DEGs and dysregulated pathways may participate in EC development and firstly reported eight hub genes, including EDNRB, CDO1, NDN, PLCD1, ROR2, ESPL1, PRAME, and PTTG1 that could be used to predict EC prognosis. Aminoglutethimide and luteolin may be used to fight against EC., This research inferred potential aberrantly methylated DEGs and dysregulated pathways may participate in EC development and firstly reported 8 hub genes, including EDNRB, CDO1, NDN, PLCD1, ROR2, ESPL1, PRAME and PTTG1 that could be used to predict EC prognosis. Aminoglutethimide and luteolin may be used to fight against EC.

BackgroundCircular RNAs (circRNAs) are a new class of noncoding RNAs that have gained increased attention in human tumor research. However, the identification and function of circRNAs are largely unknown in the context of gastric cancer (GC). This study aims to identify novel circRNAs and determine their action networks in GC.MethodsA comprehensive strategy of data mining, reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and computational biology were conducted to discover novel circRNAs and to explore their potential mechanisms in GC. Promising therapeutic drugs for GC were determined by connectivity map (CMap) analysis.ResultsSix overlapped differentially expressed circRNAs (DECs) were screened from selected microarray and RNA-Seq datasets of GC, and the six DECs were then validated by sanger sequencing and RNase R treatment. Subsequent RT-qPCR analysis of GC samples confirmed decreased expressions of the six DECs (hsa_circ_0000390, hsa_circ_0000615, hsa_circ_0001438, hsa_circ_0002190, hsa_circ_0002449 and hsa_circ_0003120), all of which accumulated preferentially in the cytoplasm. MiRNA binding sites and AGO2 occupation of the six circRNAs were predicted using online databases, and circRNA–miRNA interactions including the six circRNAs and 33 miRNAs were determined. Then, 5320 target genes of the above 33 miRNAs and 1492 differently expressed genes (DEGs) from The Cancer Genome Atlas (TCGA) database were identified. After intersecting the miRNA target genes and the 889 downregulated DEGs, 320 overlapped target genes were acquired. The Kyoto Encyclopedia of Genes and Genomes enrichment analysis indicated that these target genes were related to two critical tumor-associated signaling pathways. A protein–protein interaction network with the 320 target genes was constructed using STRING, and fifteen hubgenes (ATF3, BTG2, DUSP1, EGR1, FGF2, FOSB, GNAO1, GNAI1, GNAZ, GNG7, ITPR1, ITPKB, JUND, NR4A3, PRKCB) in the network were identified. Finally, bioactive chemicals (including vorinostat, trichostatin A and astemizole) based on the fifteen hubgenes were identifed as therapeutic agents for GC through the CMap analysis.ConclusionsThis study provides a novel insight for further exploration of the pathogenesis and therapy of GC from the circRNA-miRNA-mRNA network perspective.

The identification and validation of drugs that promote health during aging (“geroprotectors”) are key to the retardation or prevention of chronic age‐related diseases. Here, we found that most of the established pro‐longevity compounds shown to extend lifespan in model organisms also alter extracellular matrix gene expression (i.e., matrisome) in human cell lines. To harness this observation, we used age‐stratified human transcriptomes to define the age‐related matreotype, which represents the matrisome gene expression pattern associated with age. Using a “youthful” matreotype, we screened in silico for geroprotective drug candidates. To validate drug candidates, we developed a novel tool using prolonged collagen expression as a non‐invasive and in‐vivo surrogate marker for Caenorhabditis elegans longevity. With this reporter, we were able to eliminate false‐positive drug candidates and determine the appropriate dose for extending the lifespan of C. elegans. We improved drug uptake for one of our predicted compounds, genistein, and reconciled previous contradictory reports of its effects on longevity. We identified and validated new compounds, tretinoin, chondroitin sulfate, and hyaluronic acid, for their ability to restore age‐related decline of collagen homeostasis and increase lifespan. Thus, our innovative drug screening approach—employing extracellular matrix homeostasis—facilitates the discovery of pharmacological interventions promoting healthy aging., We correlated extracellular matrix gene expression signatures (matreotypes) corresponding to young and old human tissues with drug‐treated expression profiles to predict longevity drugs. Then, we established Caenorhabditis elegans collagen homeostasis as a novel surrogate marker of longevity as the first pass for in‐vivo screening. We validated candidates via lifespan assays and identified new geroprotective drugs.

Background: Drug-induced liver injury (DILI) is an adverse reaction caused by the intake of drugs of common use that produces liver damage. The impact of DILI is estimated to affect around 20 in 100,000 inhabitants worldwide each year. Despite being one of the main causes of liver failure, the pathophysiology and mechanisms of DILI are poorly understood. In the present study, we developed an ensemble learning approach based on different features (CMap gene expression, chemical structures, drug targets) to predict drugs that might cause DILI and gain a better understanding of the mechanisms linked to the adverse reaction. Results: We searched for gene signatures in CMap gene expression data by using two approaches: phenotype-gene associations data from DisGeNET, and a non-parametric test comparing gene expression of DILI-Concern and No-DILI-Concern drugs (as per DILIrank definitions). The average accuracy of the classifiers in both approaches was 69%. We used chemical structures as features, obtaining an accuracy of 65%. The combination of both types of features produced an accuracy around 63%, but improved the independent hold-out test up to 67%. The use of drug-target associations as feature obtained the best accuracy (70%) in the independent hold-out test. Conclusions: When using CMap gene expression data, searching for a specific gene signature among the landmark genes improves the quality of the classifiers, but it is still limited by the intrinsic noise of the dataset. When using chemical structures as a feature, the structural diversity of the known DILI-causing drugs hampers the prediction, which is a similar problem as for the use of gene expression information. The combination of both features did not improve the quality of the classifiers but increased the robustness as shown on independent hold-out tests. The use of drug-target associations as feature improved the prediction, specially the specificity, and the results were comparable to previous research studies. The authors received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreements TransQST and eTRANSAFE (refs: 116030, 777365). This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation programme and EFPIA companies in kind contribution. The authors also received support from Spanish Ministry of Economy (MINECO, refs: BIO2017–85329-R (FEDER, EU), RYC-2015-17519) as well as EU H2020 Programme 2014–2020 under grant agreement No. 676559 (Elixir-Excelerate) and from Agència de Gestió D’ajuts Universitaris i de Recerca Generalitat de Catalunya (AGAUR, ref.: 2017SGR01020). L.I.F. received support from ISCIII-FEDER (ref: CPII16/00026). The Research Programme on Biomedical Informatics (GRIB) is a member of the Spanish National Bioinformatics Institute (INB), PRB2-ISCIII and is supported by grant PT13/0001/0023, of the PE I + D + i 2013–2016, funded by ISCIII and FEDER. The DCEXS is a “Unidad de Excelencia María de Maeztu”, funded by the MINECO (ref: MDM-2014-0370). J.A.P. received support from the CAMDA Travel Fellowship.

Background Ovarian cancer (OC) is the most malignant tumor in the female reproductive system. About 75% of OC in complete remission of clinical symptoms still develop a recurrence. Therefore, searching for new treatment methods plays an important role in improving the prognosis of OC. Methods We downloaded the MAF files, RNA-seq data and clinical information from the TCGA database. The “maftools” package in R software was used to visualize the OC mutation data. We calculated the tumor mutation burden (TMB) of OC and analyzed its correlation with clinicopathological parameters and prognostic value. Tumor mutation burden related signature model was constructed to predict the overall survival (OS) of OC. Results The results revealed that there was a statistical correlation between TMB and FIGO stage, grade and tumor residual size of ovarian cancer patients. The Kaplan–Meier curve indicated that a high TMB is associated with better clinical outcomes of OC. The difference analysis indicated 24 upregulated genes and 619 downregulated genes in the high-TMB group compared with the low-TMB group. Besides, the TMBRS model based on five hub genes (RBMS3, PLA2G5, CDH2, AMHR2 and ADAMTS8) was constructed to predict the OS of OC. The ROC curve and validation data sets all revealed that the TMBRS model was reliable in predicting recurrence risk. Immune microenvironment analysis indicated the correlations between TMB and infiltrating immune cells. Conclusions Our results suggest that TMB plays an important role in the prognosis and guiding immunotherapy of OC. By detecting the TMB of OC, clinicians can more accurately treat patients with immunotherapy, thereby improving their survival rate.

Currently, coronavirus disease 2019 (COVID-19), has posed an imminent threat to global public health. Although some current therapeutic agents have showed potential prevention or treatment, a growing number of associated adverse events have occurred on patients with COVID-19 in the course of medical treatment. Therefore, a comprehensive assessment of the safety profile of therapeutic agents against COVID-19 is urgently needed. In this study, we proposed a network-based framework to identify the potential side effects of current COVID-19 drugs in clinical trials. We established the associations between 116 COVID-19 drugs and 30 kinds of human tissues based on network proximity and gene-set enrichment analysis (GSEA) approaches. Additionally, we focused on four types of drug-induced toxicities targeting four tissues, including hepatotoxicity, renal toxicity, lung toxicity, and neurotoxicity, and validated our network-based predictions by preclinical and clinical evidence available. Finally, we further performed pharmacovigilance analysis to validate several drug-tissue toxicities via data mining adverse event reporting data, and we identified several new drug-induced side effects without labeling in Food and Drug Administration (FDA) drug instructions. Overall, this study provides forceful approaches to assess potential side effects on COVID-19 drugs, which will be helpful for their safe use in clinical practice and promoting the discovery of antiviral therapeutics against SARS-CoV-2., Highlights • Network-based framework identifies the potential side effects of current COVID-19 drugs. • Network proximity and GSEA approaches predict the associations between COVID-19 drugs and human tissues. • Pharmacovigilance analysis validates several drug-tissue toxicities via data mining adverse event reporting data. • Several tissue toxicities of drugs that hadn't been labeled by FDA drug instructions were uncovered.

Background The annual incidence of Bell’s palsy (BP) is 15 to 20 per 100,000 with 40,000 new cases each year, and the lifetime risk is 1 in 60. For decades, clinicians have searched the prognostic tests of sufficient accuracy for acute facial paralysis. Objective The present study was designed to verify in BP which clinical or electrophysiological parameters could be considered as predictive of the degree of recovery of normal facial muscle function. Methods Sixty-three patients with BP were initially assessed according to the House and Brackmann facial function scoring system “HB system”. All patients were followed for 3 months, the functional recovery then reassessed according to HB system. Nerve conduction studies were measured on the affected side via a bipolar surface stimulator placed over the stylomastoid foramen. Results We could not find statistically significant differences between BP with good and poor prognosis as regard age, sex, onset, diabetes, hypertension, dyslipidemia, or the initial HB Score. Compound motor action potential amplitude (CMAP) detected during the initial electroneurography (ENoG) was statistically significant between BP with good and poor prognosis. Conclusions The initial ENoG is more predictive of recovery of Bell’s palsy than the initial clinical grading using the HB system. Age, sex, hypertension, diabetes, and dyslipidemia do not seem to correlate with the degree of recovery in Bell’s palsy.

Background: The major limitation of targeted cancer therapy is development of acquired resistance. Intratumoral heterogeneity and coexist of multiple resistance mechanisms make combination therapies targeting one specific mechanism inefficient. Methods: Transcriptional signature obtained from GEO was used to reposition FDA-approved drugs to treat lung and breast cancers as well as overcome acquired resistance to EGFR TKIs in lung cancer and to tamoxifen in breast cancer via CMap. In vitro and in vivo models were used to examine candidate drugs for their anti-cancer and anti-resistance efficacy and underlying mechanisms. Results: We found that aspirin, the most commonly used drug, not only inhibited proliferation and promoted apoptosis of cancer cells, but also delayed and overcame acquired resistance to targeted therapy using in vitro and in vivo models. The underlying mechanism could be attributed to enhanced cancer stemness and activated NF-κB signaling in acquired resistant tumors, both of which were suppressed by aspirin and rendered resistant tumors more sensitive to aspirin. Conclusions: Our data identify aspirin as a potential candidate for combination therapy for lung and breast cancers.

Inhibitors of the 20S proteasome such as bortezomib (Velcade((R))) and carfilzomib (Kypriolis((R))) are in clinical use for the treatment of patients with multiple myeloma and mantle cell lymphoma. In an attempt to identify novel inhibitors of the ubiquitin-proteasome system (UPS) we used the connectivity map (CMap) resource, based on alterations of gene expression profiles by perturbagens, and performed COMPARE analyses of drug sensitivity patterns in the NCI60 panel. Cmap analysis identified a large number of small molecules with strong connectivity to proteasome inhibition, including both well characterized inhibitors of the 20S proteasome and molecules previously not described to inhibit the UPS. A number of these compounds have been reported to be cytotoxic to tumor cells and were tested for their ability to decrease processing of proteasome substrates. The antibiotic thiostrepton and the natural products celastrol and curcumin induced strong accumulation of polyubiquitinated proteasome substrates in exposed cells. Other compounds elicited modest increases of proteasome substrates, including the protein phosphatase inhibitor BCI-Cl and the farnesyltransferase inhibitor manumycin A, suggesting that these compounds inhibit proteasome function. Induction of chaperone expression in the absence of proteasome inhibition was observed by a number of compounds, suggesting other effects on the UPS. We conclude that the combination of bioinformatic analyses and cellular assays resulted in the identification of compounds with potential to inhibit the UPS. Funding Agencies|CancerfondenSwedish Cancer Society; VetenskapsradetSwedish Research Council; Radiumhemmets forskningsfonder; Knut och Alice Wallenbergs StiftelseKnut & Alice Wallenberg Foundation

Background Acute myeloid leukemia can develop as myoblasts infiltrate into organs and tissues anywhere other than the bone marrow, which called extramedullary infiltration (EMI), indicating a poor prognosis. Circular RNAs (circRNAs) are a novel class of non-coding RNAs that feature covalently closed continuous loops, suggesting their potential as micro RNA (miRNA) “sponges” that can participate in biological processes and pathogenesis. However, investigations on circRNAs in EMI were conducted rarely. In this study, the overall alterations of circRNAs and their regulatory network between EMI and non-EMI AML were delineated. Methods CircRNA and whole genome microarrays derived from EMI and non-EMI AML bone marrow mononuclear cells were carried out. Functional analysis was performed via Gene Ontology and KEGG test methods. The speculated functional roles of circRNAs were based on mRNAs and predicted miRNAs that played intermediate roles. Integrated bioinformatic analysis was conducted to further characterize the circRNA/miRNA/mRNA regulatory network and identify the functions of distinct circRNAs. The Cancer Genome Atlas (TCGA) data were acquired to evaluate the poor prognosis of distinct target genes of circRNAs. Reverse transcription-quantitative polymerase chain reaction was conducted to identify the expression of has_circRNA_0004520. Connectivity map (CMap) analysis was further performed to predict potential therapeutic agents for EMI. Results 253 circRNAs and 663 genes were upregulated and 259 circRNAs and 838 genes were downregulated in EMI compared to non-EMI AML samples. GO pathways were enriched in progress including cell adhesion (GO:0030155; GO:0007155), migration (GO:0016477; GO:0030334), signal transduction (GO:0009966; GO:0007165) and cell–cell communication. Overlapping circRNAs envolved in pathways related to regulate cell–cell crosstalk, 17 circRNAs were chosen based on their putative roles. 7 target genes of 17 circRNAs (LRRK1, PLXNB2, OLFML2A, LYPD5, APOL3, ZNF511, and ASB2) indicated a poor prognosis, while overexpression of PAPLN and NRXN3 indicated a better one based on data from TCGA. LY-294002, trichostatin A and SB-202190 were identified as therapeutic candidates for EMI by the CMap analysis. Conclusion Taken together, this study reveals the overall alterations of circRNA and mRNA involved in EMI and suggests potential circRNAs may act as biomarkers and targets for early diagnosis and treatment of EMI. Electronic supplementary material The online version of this article (10.1186/s12967-018-1726-x) contains supplementary material, which is available to authorized users.

Background Circular RNAs (circRNAs) have received increasing attention in human tumor research. However, there are still a large number of unknown circRNAs that need to be deciphered. The aim of this study is to unearth novel circRNAs as well as their action mechanisms in hepatocellular carcinoma (HCC). Methods A combinative strategy of big data mining, reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and computational biology was employed to dig HCC-related circRNAs and to explore their potential action mechanisms. A connectivity map (CMap) analysis was conducted to identify potential therapeutic agents for HCC. Results Six differently expressed circRNAs were obtained from three Gene Expression Omnibus microarray datasets (GSE78520, GSE94508 and GSE97332) using the RobustRankAggreg method. Following the RT-qPCR corroboration, three circRNAs (hsa_circRNA_102166, hsa_circRNA_100291 and hsa_circRNA_104515) were selected for further analysis. miRNA response elements of the three circRNAs were predicted. Five circRNA–miRNA interactions including two circRNAs (hsa_circRNA_104515 and hsa_circRNA_100291) and five miRNAs (hsa-miR-1303, hsa-miR-142-5p, hsa-miR-877-5p, hsa-miR-583 and hsa-miR-1276) were identified. Then, 1424 target genes of the above five miRNAs and 3278 differently expressed genes (DEGs) on HCC were collected. By intersecting the miRNA target genes and the DEGs, we acquired 172 overlapped genes. A protein–protein interaction network based on the 172 genes was established, with seven hubgenes (JUN, MYCN, AR, ESR1, FOXO1, IGF1 and CD34) determined from the network. The Gene Oncology, Kyoto Encyclopedia of Genes and Genomes and Reactome enrichment analyses revealed that the seven hubgenes were linked with some cancer-related biological functions and pathways. Additionally, three bioactive chemicals (decitabine, BW-B70C and gefitinib) based on the seven hubgenes were identified as therapeutic options for HCC by the CMap analysis. Conclusions Our study provides a novel insight into the pathogenesis and therapy of HCC from the circRNA–miRNA–mRNA network view.

Norditerpenoids and dinorditerpenoids represent diterpenoids widely distributed in the genus Podocarpus with notable chemical structures and biological activities. We previously reported that nagilactone E (NLE), a dinorditerpenoid isolated from Podocarpus nagi, possessed anticancer effects against lung cancer cells in vitro. In this study we investigated the in vivo effect of NLE against lung cancer as well as the underlying mechanisms. We administered NLE (10 mg·kg−1·d−1, ip) to CB-17/SCID mice bearing human lung cancer cell line A549 xenograft for 3 weeks. We found that NLE administration significantly suppressed the tumor growth without obvious adverse effects. Thereafter, RNA sequencing (RNA-seq) analysis was performed to study the mechanisms of NLE. The effects of NLE on A549 cells have been illustrated by GO and pathway enrichment analyses. CMap dataset analysis supported NLE to be a potential protein synthesis inhibitor. The inhibitory effect of NLE on synthesis of total de novo protein was confirmed in Click-iT assay. Using the pcDNA3-RLUC-POLIRES-FLUC luciferase assay we further demonstrated that NLE inhibited both cap-dependent and cap-independent translation. Finally, molecular docking revealed the low-energy binding conformations of NLE and its potential target RIOK2. In conclusion, NLE is a protein synthesis inhibitor with anticancer activity.

Epithelial ovarian cancer (EOC) is one of the malignancies in women, which has the highest mortality. However, the microlevel mechanism has not been discussed in detail. The expression profiles GSE27651, GSE38666, GSE40595, and GSE66957 including 188 tumor and 52 nontumor samples were downloaded from the Gene Expression Omnibus database. The differentially expressed genes (DEGs) were filtered using R software, and we performed functional analysis using the clusterProfiler. Cytoscape software, the molecular complex detection plugin and database STRING analyzed DEGs to construct protein-protein interaction network. We identified 116 DEGs including 81 upregulated and 35 downregulated DEGs. Functional analysis revealed that they were significantly enriched in the extracellular region and biosynthesis of amino acids. We next identified four bioactive compounds (vorinostat, LY-294002,trichostatin A, and tanespimycin) based on ConnectivityMap. Then 114 nodes were obtained in protein–protein interaction. The three most relevant modules were detected. In addition, according to degree ≥ 10, 14 core genes including FOXM1, CXCR4, KPNA2, NANOG, UBE2C, KIF11, ZWINT, CDCA5, DLGAP5, KIF15, MCM2, MELK, SPP1, and TRIP13 were identified. Kaplan–Meier analysis, Oncomine, and Gene Expression Profiling Interactive Analysis showed that overexpression of FOXM1, SPP1, UBE2C, KIF11, ZWINT, CDCA5, UBE2C, and KIF15 was related to bad prognosis of EOC patients. CDCA5, FOXM1, KIF15, MCM2, and ZWINT were associated with stage. Receiver operating characteristic (ROC) curve showed that messenger RNA levels of these five genes exhibited better diagnostic efficiency for normal and tumor tissues. The Human Protein Atlas database was performed. The protein levels of these five genes were significantly higher in tumor tissues compared with normal tissues. Functional enrichment analysis suggested that all the hub genes played crucial roles in citrate cycle tricarboxylic acid cycle. Furthermore, the univariate and multivariate Cox proportional hazards regression showed that ZWINT was independent prognostic indictor among EOC patients. The genes and pathways discovered in the above studies may open a new direction for EOC treatment.

The interactions between drugs and their target proteins induce altered expression of genes involved in complex intracellular networks. The properties of these functional network modules are critical for the identification of drug targets, for drug repurposing, and for understanding the underlying mode of action of the drug. The topological modules generated by a computational approach are defined as functional clusters. However, the functions inferred for these topological modules extracted from a large-scale molecular interaction network, such as a protein-protein interaction (PPI) network, could differ depending on different cluster detection algorithms. Moreover, the dynamic gene expression profiles among tissues or cell types causes differential functional interaction patterns between the molecular components. Thus, the connections in the PPI network should be modified by the transcriptomic landscape of specific cell lines before producing topological clusters. Here, we systematically investigated the clusters of a cell-based PPI network by using four cluster detection algorithms. We subsequently compared the performance of these algorithms for target gene prediction, which integrates gene perturbation data with the cell-based PPI network using two drug target prioritization methods, shortest path and diffusion correlation. In addition, we validated the proportion of perturbed genes in clusters by finding candidate anti-breast cancer drugs and confirming our predictions using literature evidence and cases in the ClinicalTrials.gov. Our results indicate that the Walktrap (CW) clustering algorithm achieved the best performance overall in our comparative study.

Multi-omics signatures of patients with bladder cancer (BC) can guide the identification of known de-risked therapeutic compounds through drug repurposing, an approach not extensively explored yet. In this study, we target drug repurposing in the context of BC, driven by tissue omics signatures. To identify compounds that can reverse aggressive high-risk Non-Muscle Invasive BC (NMIBC) to less aggressive low-risk molecular subtypes, the next generation Connectivity Map (CMap) was employed using as input previously published proteomics and transcriptomics respective signatures. Among the identified compounds, the ATP-competitive inhibitor of mTOR, WYE-354, showed a consistently very high score for reversing the aggressive BC molecular signatures. WYE-354 impact was assessed in a panel of eight multi-origin BC cell lines and included impaired colony growth and proliferation rate without any impact on apoptosis. Overall, with this study we introduce a promising pipeline for the repurposing of drugs for BC treatment, based on patients&rsquo, omics signatures.

Huntington’s disease (HD) is a genetic disease caused by a CAG trinucleotide repeat expansion encoding a polyglutamine tract in the huntingtin (HTT) protein, ultimately leading to neuronal loss and consequent cognitive decline and death. As no treatments for HD currently exist, several chemical screens have been performed using cell-based models of mutant HTT toxicity. These screens measured single disease-related endpoints, such as cell death, but had low ‘hit rates’ and limited dimensionality for therapeutic detection. Here, we have employed gene expression microarray analysis of HD samples—a snapshot of the expression of 25,000 genes—to define a gene expression signature for HD from publically available data. We used this information to mine a database for chemicals positively and negatively correlated to the HD gene expression signature using the Connectivity Map, a tool for comparing large sets of gene expression patterns. Chemicals with negatively correlated expression profiles were highly enriched for protective characteristics against mutant HTT fragment toxicity in in vitro and in vivo models. This study demonstrates the potential of using gene expression to mine chemical activity, guide chemical screening, and detect potential novel therapeutic compounds. Key messages Single-endpoint chemical screens have low therapeutic discovery hit-rates. In the context of HD, we guided a chemical screen using gene expression data. The resulting chemicals were highly enriched for suppressors of mutant HTT fragment toxicity. This study provides a proof of concept for wider usage in all chemical screening. Electronic supplementary material The online version of this article (doi:10.1007/s00109-015-1344-5) contains supplementary material, which is available to authorized users.

Nerve function assessments are crucial for surgical intervention during acoustic neuroma surgery. Cranial nerves such as acoustic and facial nerves, can be possibly damaged during tumor dissection. Proper surgical intervention should prevent neurological deficit and achieve total tumor removal. Conventionally, nerve function is qualitatively evaluated by surgeon and neurologist. Facial nerves can be preserved by monitoring the compound muscle action potential (CMAP) response. The differences in the amplitude and latency of CMAP are used as indicators during surgical interventions. However, baseline CMAPs cannot be recorded in the presence of large acoustic tumors. This paper presents a new way of estimating nerve function. Instead of a single CMAP examination, multi‐CMAP responses are obtained from a train of varied stimulus intensities and these are applied a mathematical model. Shifts in the mathematical model parameters reflect changes in facial nerve function. In this study, experiments conducted in frog revealed that shifts in the linear model parameters were related to the level of induced nerve injury. Significant differences in the slope parameter of the linear model were found between each nerve condition. The identification of healthy and severed nerves via a support vector machine (SVM) corresponded to 94% accuracy. This classification criterion could be used with surgical intervention to prevent severed facial nerve palsy in acoustic neuroma surgery. The proposed method could be used to estimate nerve outcomes without prior information of a CMAP baseline.

Marta Banach,1,* Jakub Antczak,1,* Rafał Rola21Department of Clinical Neurophysiology, 2First Department of Neurology, Institute of Psychiatry and Neurology, Warsaw, Poland *These authors contributed equally to this workBackground: Myotonic dystrophy (DM) type 1 and type 2 are inherited diseases characterized by myotonia and myopathy. Additional symptoms include, among others, peripheral neuropathy and sleep-related breathing disorders (SRBDs). There is growing evidence for a complex association between DM1 and DM2, which was described in patients with diabetes mellitus and in the general population. In this study, we investigated whether there is an association between peripheral neuropathy and SRBDs also in the population of patients with DM.Methods: The study included 16 patients with DM1 (mean age, 37.9±14.1 years; 20–69 years) and eight patients with DM2 (mean age, 47.6±14.1 years; 20–65 years), who underwent a sensory and motor nerve conduction study (NCS) and diagnostic screening for SRBDs. In both groups, the NCS parameters were correlated with respiratory parameters.Results: In both groups, the amplitude of the ulnar sensory nerve action potential (SNAP) correlated with the mean arterial oxygen saturation (SaO2). In addition, in the DM2 group, the median SNAP correlated with the mean SaO2. In the DM1 group, the median SNAP and the distal motor latency (DML) of the ulnar nerve correlated with the apnea–hypopnea index, while the oxygen desaturation index correlated with the DML of the tibial nerve and with conduction velocity in the sural nerve.Conclusion: Our results indicate a complex association between neuropathy and SRBDs in DM1 and DM2. Axonal degeneration may contribute to nocturnal hypoxemia and vice versa. Neuropathy may contribute to muscle weakness, which in turn may cause respiratory events.Keywords: myotonic dystrophy, SRBD and neuropathy with AHI, SNAP, CMAP

Existing computational methods for drug repositioning either rely only on the gene expression response of cell lines after treatment, or on drug-to-disease relationships, merging several information levels. However, the noisy nature of the gene expression and the scarcity of genomic data for many diseases are important limitations to such approaches. Here we focused on a drug-centered approach by predicting the therapeutic class of FDA-approved compounds, not considering data concerning the diseases. We propose a novel computational approach to predict drug repositioning based on state-of-the-art machine-learning algorithms. We have integrated multiple layers of information: i) on the distances of the drugs based on how similar are their chemical structures, ii) on how close are their targets within the protein-protein interaction network, and iii) on how correlated are the gene expression patterns after treatment. Our classifier reaches high accuracy levels (78%), allowing us to re-interpret the top misclassifications as re-classifications, after rigorous statistical evaluation. Efficient drug repurposing has the potential to significantly impact the whole field of drug development. The results presented here can significantly accelerate the translation into the clinics of known compounds for novel therapeutic uses.