Whole transcriptome analysis reveals non-coding RNA's competing endogenous gene pairs as novel form of motifs in serous ovarian cancer.

The non-coding RNA (ncRNA) regulation appears to be associated to the diagnosis and targeted therapy of complex diseases. Motifs of non-coding RNAs and genes in the competing endogenous RNA (ceRNA) network would probably contribute to the accurate prediction of serous ovarian carcinoma (SOC). We conducted a microarray study profiling the whole transcriptomes of eight human SOCs and eight controls and constructed a ceRNA network including mRNAs, long ncRNAs, and circular RNAs (circRNAs). Novel form of motifs (mRNA-ncRNA-mRNA) were identified from the ceRNA network and defined as non-coding RNA's competing endogenous gene pairs (ceGPs), using a proposed method denoised individualized pair analysis of gene expression (deiPAGE). 18 cricRNA's ceGPs (cceGPs) were identified from multiple cohorts and were fused as an indicator (SOC index) for SOC discrimination, which carried a high predictive capacity in independent cohorts. SOC index was negatively correlated with the CD8+/CD4+ ratio in tumour-infiltration, reflecting the migration and growth of tumour cells in ovarian cancer progression. Moreover, most of the RNAs in SOC index were experimentally validated involved in ovarian cancer development. Our results elucidate the discriminative capability of SOC index and suggest that the novel competing endogenous motifs play important roles in expression regulation and could be potential target for investigating ovarian cancer mechanism or its therapy.

meGPS: a multi-omics signature for hepatocellular carcinoma detection integrating methylome and transcriptome data.

MOTIVATION: Hepatocellular carcinoma (HCC) is a primary malignancy with a poor prognosis. Recently, multi-omics molecular-level measurement enables HCC diagnosis and prognosis prediction, which is crucial for early intervention of personalized therapy to diminish mortality. Here, we introduce a novel strategy utilizing DNA methylation and RNA expression data to achieve a multi-omics gene pair signature (GPS) for HCC discrimination. RESULTS: The immune genes with negative correlations between expression and promoter methylation are enriched in the highly connected cancer-related pathway network, which are considered as the candidates for HCC detection. After that, we separately construct a methylation GPS (mGPS) and an expression GPS (eGPS), and then assemble them as a meGPS with five gene pairs, in which the significant methylation and expression changes occur between HCC tumor and non-tumor groups. Reliable performance has been validated by independent tissue (age, gender and etiology) and blood datasets. This study proposes a procedure for multi-omics GPS identification and develops a novel HCC signature using both methylome and transcriptome data, suggesting potential molecular targets for the detection and therapy of HCC. AVAILABILITY AND IMPLEMENTATION: Models are available at https://github.com/bioinformaticStudy/meGPS.git. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Improving bulk RNA-seq classification by transferring gene signature from single cells in acute myeloid leukemia.

The advances in single-cell RNA sequencing (scRNA-seq) technologies enable the characterization of transcriptomic profiles at the cellular level and demonstrate great promise in bulk sample analysis thereby offering opportunities to transfer gene signature from scRNA-seq to bulk data. However, the gene expression signatures identified from single cells are typically inapplicable to bulk RNA-seq data due to the profiling differences of distinct sequencing technologies. Here, we propose single-cell pair-wise gene expression (scPAGE), a novel method to develop single-cell gene pair signatures (scGPSs) that were beneficial to bulk RNA-seq classification to transfer knowledge across platforms. PAGE was adopted to tackle the challenge of profiling differences. We applied the method to acute myeloid leukemia (AML) and identified the scGPS from mouse scRNA-seq that allowed discriminating between AML and control cells. The scGPS was validated in bulk RNA-seq datasets and demonstrated better performance (average area under the curve [AUC] = 0.96) than the conventional gene expression strategies (average AUC$\le$ 0.88) suggesting its potential in disclosing the molecular mechanism of AML. The scGPS also outperformed its bulk counterpart, which highlighted the benefit of gene signature transfer. Furthermore, we confirmed the utility of scPAGE in sepsis as an example of other disease scenarios. scPAGE leveraged the advantages of single-cell profiles to enhance the analysis of bulk samples revealing great potential of transferring knowledge from single-cell to bulk transcriptome studies.

Discovery of a New CDK4/6 and PI3K/AKT Multiple Kinase Inhibitor Aminoquinol for the Treatment of Hepatocellular Carcinoma.

Background: Hepatocellular carcinoma (HCC) is a lethal malignancy lacking effective treatment. The Cyclin-dependent kinases 4/6 (CDK4/6) and PI3K/AKT signal pathways play pivotal roles in carcinogenesis and are promising therapeutic targets for HCC. Here we identified a new CDK4/6 and PI3K/AKT multi-kinase inhibitor for the treatment of HCC. Methods: Using a repurposing and ensemble docking methodology, we screened a library of worldwide approved drugs to identify candidate CDK4/6 inhibitors. By MTT, apoptosis, and flow cytometry analysis, we investigated the effects of candidate drug in reducing cell-viability,inducing apoptosis, and causing cell-cycle arrest. The drug combination and thermal proteomic profiling (TPP) method were used to investigate whether the candidate drug produced antagonistic effect. The in vivo anti-cancer effect was performed in BALB/C nude mice subcutaneously xenografted with Huh7 cells. Results: We demonstrated for the first time that the anti-plasmodium drug aminoquinol is a new CDK4/6 and PI3K/AKT inhibitor. Aminoquinol significantly decreased cell viability, induced apoptosis, increased the percentage of cells in G1 phase. Drug combination screening indicated that aminoquinol could produce antagonistic effect with the PI3K inhibitor LY294002. TPP analysis confirmed that aminoquinol significantly stabilized CDK4, CDK6, PI3K and AKT proteins. Finally, in vivo study in Huh7 cells xenografted nude mice demonstrated that aminoquinol exhibited strong anti-tumor activity, comparable to that of the leading cancer drug 5-fluorouracil with the combination treatment showed the highest therapeutic effect. Conclusion: The present study indicates for the first time the discovery of a new CDK4/6 and PI3K/AKT multi-kinase inhibitor aminoquinol. It could be used alone or as a combination therapeutic strategy for the treatment of HCC.

Direct Measurement of B Lymphocyte Gene Expression Biomarkers in Peripheral Blood Transcriptomics Enables Early Prediction of Vaccine Seroconversion.

Peripheral blood transcriptome is a highly promising area for biomarker development. However, transcript abundances (TA) in these cell mixture samples are confounded by proportions of the component leukocyte subpopulations. This poses a challenge to clinical applications, as the cell of origin of any change in TA is not known without prior cell separation procedure. We developed a framework to develop a cell-type informative TA biomarkers which enable determination of TA of a single cell-type (B lymphocytes) directly in cell mixture samples of peripheral blood (e.g., peripheral blood mononuclear cells, PBMC) without the need for subpopulation separation. It is applicable to a panel of genes called B cell informative genes. Then a ratio of two B cell informative genes (a target gene and a stably expressed reference gene) obtained in PBMC was used as a new biomarker to represent the target gene expression in purified B lymphocytes. This approach, which eliminates the tedious procedure of cell separation and directly determines TA of a leukocyte subpopulation in peripheral blood samples, is called the Direct LS-TA method. This method is applied to gene expression datasets collected in influenza vaccination trials as early predictive biomarkers of seroconversion. By using TNFRSF17 or TXNDC5 as the target genes and TNFRSF13C or FCRLA as the reference genes, the Direct LS-TA B cell biomarkers were determined directly in the PBMC transcriptome data and were highly correlated with TA of the corresponding target genes in purified B lymphocytes. Vaccination responders had almost a 2-fold higher Direct LS-TA biomarker level of TNFRSF17 (log 2 SMD = 0.84, 95% CI = 0.47-1.21) on day 7 after vaccination. The sensitivity of these Direct LS-TA biomarkers in the prediction of seroconversion was greater than 0.7 and area-under curves (AUC) were over 0.8 in many datasets. In this paper, we report a straightforward approach to directly estimate B lymphocyte gene expression in PBMC, which could be used in a routine clinical setting. Moreover, the method enables the practice of precision medicine in the prediction of vaccination response. More importantly, seroconversion could now be predicted as early as day 7. As the acquired immunology pathway is common to vaccination against influenza and COVID-19, these biomarkers could also be useful to predict seroconversion for the new COVID-19 vaccines.

Evaluating the Consistency of Gene Methylation in Liver Cancer Using Bisulfite Sequencing Data.

Bisulfite sequencing is considered as the gold standard approach for measuring DNA methylation, which acts as a pivotal part in regulating a variety of biological processes without changes in DNA sequences. In this study, we introduced the most prevalent methods for processing bisulfite sequencing data and evaluated the consistency of the data acquired from different measurements in liver cancer. Firstly, we introduced three commonly used bisulfite sequencing assays, i.e., reduced-representation bisulfite sequencing (RRBS), whole-genome bisulfite sequencing (WGBS), and targeted bisulfite sequencing (targeted BS). Next, we discussed the principles and compared different methods for alignment, quality assessment, methylation level scoring, and differentially methylated region identification. After that, we screened differential methylated genes in liver cancer through the three bisulfite sequencing assays and evaluated the consistency of their results. Ultimately, we compared bisulfite sequencing to 450 k beadchip and assessed the statistical similarity and functional association of differentially methylated genes (DMGs) among the four assays. Our results demonstrated that the DMGs measured by WGBS, RRBS, targeted BS and 450 k beadchip are consistently hypo-methylated in liver cancer with high functional similarity.

Long non-coding RNA pairs to assist in diagnosing sepsis.

BACKGROUND: Sepsis is the major cause of death in Intensive Care Unit (ICU) globally. Molecular detection enables rapid diagnosis that allows early intervention to minimize the death rate. Recent studies showed that long non-coding RNAs (lncRNAs) regulate proinflammatory genes and are related to the dysfunction of organs in sepsis. Identifying lncRNA signature with absolute abundance is challenging because of the technical variation and the systematic experimental bias. RESULTS: Cohorts (n = 768) containing whole blood lncRNA profiling of sepsis patients in the Gene Expression Omnibus (GEO) database were included. We proposed a novel diagnostic strategy that made use of the relative expressions of lncRNA pairs, which are reversed between sepsis patients and normal controls (eg. lncRNAi > lncRNAj in sepsis patients and lncRNAi < lncRNAj in normal controls), to identify 14 lncRNA pairs as a sepsis diagnostic signature. The signature was then applied to independent cohorts (n = 644) to evaluate its predictive performance across different ages and normalization methods. Comparing to common machine learning models and existing signatures, SepSigLnc consistently attains better performance on the validation cohorts from the same age group (AUC = 0.990 & 0.995 in two cohorts) and across different groups (AUC = 0.878 on average), as well as cohorts processed by an alternative normalization method (AUC = 0.953 on average). Functional analysis demonstrates that the lncRNA pairs in SepsigLnc are functionally similar and tend to implicate in the same biological processes including cell fate commitment and cellular response to steroid hormone stimulus. CONCLUSION: Our study identified 14 lncRNA pairs as signature that can facilitate the diagnosis of septic patients at an intervenable point when clinical manifestations are not dramatic. Also, the computational procedure can be generalized to a standard procedure for discovering diagnostic molecule signatures.

Discovery of vanoxerine dihydrochloride as a CDK2/4/6 triple-inhibitor for the treatment of human hepatocellular carcinoma.

BACKGROUND: Cyclin-dependent kinases 2/4/6 (CDK2/4/6) play critical roles in cell cycle progression, and their deregulations are hallmarks of hepatocellular carcinoma (HCC). METHODS: We used the combination of computational and experimental approaches to discover a CDK2/4/6 triple-inhibitor from FDA approved small-molecule drugs for the treatment of HCC. RESULTS: We identified vanoxerine dihydrochloride as a new CDK2/4/6 inhibitor, and a strong cytotoxicdrugin human HCC QGY7703 and Huh7 cells (IC50: 3.79 µM for QGY7703and 4.04 µM for Huh7 cells). In QGY7703 and Huh7 cells, vanoxerine dihydrochloride treatment caused G1-arrest, induced apoptosis, and reduced the expressions of CDK2/4/6, cyclin D/E, retinoblastoma protein (Rb), as well as the phosphorylation of CDK2/4/6 and Rb. Drug combination study indicated that vanoxerine dihydrochloride and 5-Fu produced synergistic cytotoxicity in vitro in Huh7 cells. Finally, in vivo study in BALB/C nude mice subcutaneously xenografted with Huh7 cells, vanoxerine dihydrochloride (40 mg/kg, i.p.) injection for 21 days produced significant anti-tumor activity (p < 0.05), which was comparable to that achieved by 5-Fu (10 mg/kg, i.p.), with the combination treatment resulted in synergistic effect. Immunohistochemistry staining of the tumor tissues also revealed significantly reduced expressions of Rb and CDK2/4/6in vanoxerinedihydrochloride treatment group. CONCLUSIONS: The present study isthe first report identifying a new CDK2/4/6 triple inhibitor vanoxerine dihydrochloride, and demonstrated that this drug represents a novel therapeutic strategy for HCC treatment.

Genome-Wide Search for SNP Interactions in GWAS Data: Algorithm, Feasibility, Replication Using Schizophrenia Datasets.

In this study, we looked for potential gene-gene interaction in susceptibility to schizophrenia by an exhaustive searching for SNP-SNP interactions in 3 GWAS datasets (phs000021:phg000013, phs000021:phg000014, phs000167) using our recently published algorithm. The search space for SNP-SNP interaction was confined to 8 biologically plausible ways of interaction under dominant-dominant or recessive-recessive modes. First, we performed our search of all pair-wise combination of 729,454 SNPs after filtering by SNP genotype quality. All possible pairwise interactions of any 2 SNPs (5 × 1011) were exhausted to search for significant interaction which was defined by p-value of chi-square tests. Nine out the top 10 interactions, protein coding genes were partnered with non-coding RNA (ncRNA) which suggested a new alternative insight into interaction biology other than the frequently sought-after protein-protein interaction. Therefore, we extended to look for replication among the top 10,000 interaction SNP pairs and high proportion of concurrent genes forming the interaction pairs were found. The results indicated that an enrichment of signals over noise was present in the top 10,000 interactions. Then, replications of SNP-SNP interaction were confirmed for 14 SNPs-pairs in both replication datasets. Biological insight was highlighted by a potential binding between FHIT (protein coding gene) and LINC00969 (lncRNA) which showed a replicable interaction between their SNPs. Both of them were reported to have expression in brain. Our study represented an early attempt of exhaustive interaction analysis of GWAS data which also yield replicated interaction and new insight into understanding of genetic interaction in schizophrenia.

Predicting associations among drugs, targets and diseases by tensor decomposition for drug repositioning.

BACKGROUND: Development of new drugs is a time-consuming and costly process, and the cost is still increasing in recent years. However, the number of drugs approved by FDA every year per dollar spent on development is declining. Drug repositioning, which aims to find new use of existing drugs, attracts attention of pharmaceutical researchers due to its high efficiency. A variety of computational methods for drug repositioning have been proposed based on machine learning approaches, network-based approaches, matrix decomposition approaches, etc. RESULTS: We propose a novel computational method for drug repositioning. We construct and decompose three-dimensional tensors, which consist of the associations among drugs, targets and diseases, to derive latent factors reflecting the functional patterns of the three kinds of entities. The proposed method outperforms several baseline methods in recovering missing associations. Most of the top predictions are validated by literature search and computational docking. Latent factors are used to cluster the drugs, targets and diseases into functional groups. Topological Data Analysis (TDA) is applied to investigate the properties of the clusters. We find that the latent factors are able to capture the functional patterns and underlying molecular mechanisms of drugs, targets and diseases. In addition, we focus on repurposing drugs for cancer and discover not only new therapeutic use but also adverse effects of the drugs. In the in-depth study of associations among the clusters of drugs, targets and cancer subtypes, we find there exist strong associations between particular clusters. CONCLUSIONS: The proposed method is able to recover missing associations, discover new predictions and uncover functional clusters of drugs, targets and diseases. The clustering of drugs, targets and diseases, as well as the associations among the clusters, provides a new guiding framework for drug repositioning.

Improving prediction of phenotypic drug response on cancer cell lines using deep convolutional network.

BACKGROUND: Understanding the phenotypic drug response on cancer cell lines plays a vital role in anti-cancer drug discovery and re-purposing. The Genomics of Drug Sensitivity in Cancer (GDSC) database provides open data for researchers in phenotypic screening to build and test their models. Previously, most research in these areas starts from the molecular fingerprints or physiochemical features of drugs, instead of their structures. RESULTS: In this paper, a model called twin Convolutional Neural Network for drugs in SMILES format (tCNNS) is introduced for phenotypic screening. tCNNS uses a convolutional network to extract features for drugs from their simplified molecular input line entry specification (SMILES) format and uses another convolutional network to extract features for cancer cell lines from the genetic feature vectors respectively. After that, a fully connected network is used to predict the interaction between the drugs and the cancer cell lines. When the training set and the testing set are divided based on the interaction pairs between drugs and cell lines, tCNNS achieves 0.826, 0.831 for the mean and top quartile of the coefficient of determinant (R2) respectively and 0.909, 0.912 for the mean and top quartile of the Pearson correlation (Rp) respectively, which are significantly better than those of the previous works (Ammad-Ud-Din et al., J Chem Inf Model 54:2347-9, 2014), (Haider et al., PLoS ONE 10:0144490, 2015), (Menden et al., PLoS ONE 8:61318, 2013). However, when the training set and the testing set are divided exclusively based on drugs or cell lines, the performance of tCNNS decreases significantly and Rp and R2 drop to barely above 0. CONCLUSIONS: Our approach is able to predict the drug effects on cancer cell lines with high accuracy, and its performance remains stable with less but high-quality data, and with fewer features for the cancer cell lines. tCNNS can also solve the problem of outliers in other feature space. Besides achieving high scores in these statistical metrics, tCNNS also provides some insights into the phenotypic screening. However, the performance of tCNNS drops in the blind test.

MolTarPred: A web tool for comprehensive target prediction with reliability estimation.

Molecular target prediction can provide a starting point to understand the efficacy and side effects of phenotypic screening hits. Unfortunately, the vast majority of in silico target prediction methods are not available as web tools. Furthermore, these are limited in the number of targets that can be predicted, do not estimate which target predictions are more reliable and/or lack comprehensive retrospective validations. We present MolTarPred ( http://moltarpred.marseille.inserm.fr/), a user-friendly web tool for predicting protein targets of small organic compounds. It is powered by a large knowledge base comprising 607,659 compounds and 4,553 macromolecular targets collected from the ChEMBL database. In about 1 min, the predicted targets for the supplied molecule will be listed in a table. The chemical structures of the query molecule and the most similar compounds annotated with the predicted target will also be shown to permit visual inspection and comparison. Practical examples of the use of MolTarPred are showcased. MolTarPred is a new resource for scientists that require a more complete knowledge of the polypharmacology of a molecule. The introduction of a reliability score constitutes an attractive functionality of MolTarPred, as it permits focusing experimental confirmatory tests on the most reliable predictions, which leads to higher prospective hit rates.

Exploiting locational and topological overlap model to identify modules in protein interaction networks.

BACKGROUND: Clustering molecular network is a typical method in system biology, which is effective in predicting protein complexes or functional modules. However, few studies have realized that biological molecules are spatial-temporally regulated to form a dynamic cellular network and only a subset of interactions take place at the same location in cells. RESULTS: In this study, considering the subcellular localization of proteins, we first construct a co-localization human protein interaction network (PIN) and systematically investigate the relationship between subcellular localization and biological functions. After that, we propose a Locational and Topological Overlap Model (LTOM) to preprocess the co-localization PIN to identify functional modules. LTOM requires the topological overlaps, the common partners shared by two proteins, to be annotated in the same localization as the two proteins. We observed the model has better correspondence with the reference protein complexes and shows more relevance to cancers based on both human and yeast datasets and two clustering algorithms, ClusterONE and MCL. CONCLUSION: Taking into consideration of protein localization and topological overlap can improve the performance of module detection from protein interaction networks.

Discovering Genetic Factors for psoriasis through exhaustively searching for significant second order SNP-SNP interactions.

In this paper, we aim at discovering genetic factors of psoriasis through searching for statistically significant SNP-SNP interactions exhaustively from two real psoriasis genome-wide association study datasets (phs000019.v1.p1 and phs000982.v1.p1) downloaded from the database of Genotypes and Phenotypes. To deal with the enormous search space, our search algorithm is accelerated with eight biological plausible interaction patterns and a pre-computed look-up table. After our search, we have discovered several SNPs having a stronger association to psoriasis when they are in combination with another SNP and these combinations may be non-linear interactions. Among the top 20 SNP-SNP interactions being found in terms of pairwise p-value and improvement metric value, we have discovered 27 novel potential psoriasis-associated SNPs where most of them are reported to be eQTLs of a number of known psoriasis-associated genes. On the other hand, we have inferred a gene network after selecting the top 10000 SNP-SNP interactions in terms of improvement metric value and we have discovered a novel long distance interaction between XXbac-BPG154L12.4 and RNU6-283P which is not a long distance haplotype and may be a new discovery. Finally, our experiments with the synthetic datasets have shown that our pre-computed look-up table technique can significantly speed up the search process.

Discovery of rafoxanide as a dual CDK4/6 inhibitor for the treatment of skin cancer.

Since cyclin­dependent kinases 4/6 (CDK4/6) play pivotal roles in cell cycle regulation and are overexpressed in human skin cancers, CDK4/6 inhibitors are potentially effective drugs for skin cancer. In the present study, we present a mixed computational and experimental study attempting to repurpose approved small­molecule drugs as dual CDK4/6 inhibitors for skin cancer treatment. We performed structure­based virtual screening using the docking software idock, targeting an ensemble of CDK4/6 structures. We identified and selected nine compounds with significant predicted scores, and evaluated their cytotoxic effects in vitro in A375 and A431 human skin cancer cell lines. Rafoxanide was found to exhibit the highest cytotoxic effects (IC50: 1.09 µM for A375 and 1.31 µM for A431 cells). Consistent with the expected properties of CDK4/6 inhibitors, rafoxanide significantly increased the G1 phase population. Notably, we revealed that rafoxanide specifically decreased the expression of CDK4/6, cyclin D, retinoblastoma protein (Rb) and the phosphorylation of CDK4/6 and Rb. Furthermore, the anticancer effect of rafoxanide was demonstrated in vivo in BALB/C nude mice subcutaneously xenografted with human skin cancer A375 cells. Rafoxanide (40 mg/kg, i.p.) exhibited significant antitumor activity, comparable to that of oxaliplatin (5 mg/kg, i.p.). The combined administration of rafoxanide and oxaliplatin produced a synergistic therapeutic effect. To the best of our knowledge, the present study is the first to indicate that rafoxanide inhibits CDK4/6 activity and is a potential candidate drug for the treatment of human skin cancer.

Identification and characterization of moonlighting long non-coding RNAs based on RNA and protein interactome.

Motivation: Moonlighting proteins are a class of proteins having multiple distinct functions, which play essential roles in a variety of cellular and enzymatic functioning systems. Although there have long been calls for computational algorithms for the identification of moonlighting proteins, research on approaches to identify moonlighting long non-coding RNAs (lncRNAs) has never been undertaken. Here, we introduce a novel methodology, MoonFinder, for the identification of moonlighting lncRNAs. MoonFinder is a statistical algorithm identifying moonlighting lncRNAs without a priori knowledge through the integration of protein interactome, RNA-protein interactions and functional annotation of proteins. Results: We identify 155 moonlighting lncRNA candidates and uncover that they are a distinct class of lncRNAs characterized by specific sequence and cellular localization features. The non-coding genes that transcript moonlighting lncRNAs tend to have shorter but more exons and the moonlighting lncRNAs have a variable localization pattern with a high chance of residing in the cytoplasmic compartment in comparison to the other lncRNAs. Moreover, moonlighting lncRNAs and moonlighting proteins are rather mutually exclusive in terms of both their direct interactions and interacting partners. Our results also shed light on how the moonlighting candidates and their interacting proteins implicated in the formation and development of cancers and other diseases. Availability and implementation: The code implementing MoonFinder is supplied as an R package in the supplementary material. Supplementary information: Supplementary data are available at Bioinformatics online.

Quantification of non-coding RNA target localization diversity and its application in cancers.

Subcellular localization is pivotal for RNAs and proteins to implement biological functions. The localization diversity of protein interactions has been studied as a crucial feature of proteins, considering that the protein-protein interactions take place in various subcellular locations. Nevertheless, the localization diversity of non-coding RNA (ncRNA) target proteins has not been systematically studied, especially its characteristics in cancers. In this study, we provide a new algorithm, non-coding RNA target localization coefficient (ncTALENT), to quantify the target localization diversity of ncRNAs based on the ncRNA-protein interaction and protein subcellular localization data. ncTALENT can be used to calculate the target localization coefficient of ncRNAs and measure how diversely their targets are distributed among the subcellular locations in various scenarios. We focus our study on long non-coding RNAs (lncRNAs), and our observations reveal that the target localization diversity is a primary characteristic of lncRNAs in different biotypes. Moreover, we found that lncRNAs in multiple cancers, differentially expressed cancer lncRNAs, and lncRNAs with multiple cancer target proteins are prone to have high target localization diversity. Furthermore, the analysis of gastric cancer helps us to obtain a better understanding that the target localization diversity of lncRNAs is an important feature closely related to clinical prognosis. Overall, we systematically studied the target localization diversity of the lncRNAs and uncovered its association with cancer.

Identification of Clinically Approved Drugs Indacaterol and Canagliflozin for Repurposing to Treat Epidermal Growth Factor Tyrosine Kinase Inhibitor-Resistant Lung Cancer.

In advanced lung cancer, epidermal growth factor tyrosine kinase inhibitors (EGFR TKIs) have extraordinary clinical efficacy. However, their usefulness is severely compromised by drug resistance mediated by various mechanisms, the most important of which is the secondary EGFR T790M mutation. The mutation blocks the binding of EGFR TKIs to the receptor kinase, thereby abolishing the therapeutic efficacy. In this study, we used our free and open-source protein-ligand docking software idock to screen worldwide approved small-molecule drugs against EGFR T790M. The computationally selected drug candidates were evaluated in vitro in resistant non-small cell lung cancer (NSCLC) cell lines. The specificity of the drugs toward the mutant EGFR was demonstrated by cell-free kinase inhibition assay. The inhibition of EGFR kinase activity and its downstream signaling pathways in NSCLC cells was shown by immunoblot analysis. The positive hints were revealed to be indacaterol, canagliflozin, and cis-flupenthixol, all of which were shown to induce apoptosis in NSCLC cells harboring the EGFR T790M mutation. Moreover, the combination of indacaterol with gefitinib was also found to produce synergistic anticancer effect in NSCLC cells bearing EGFR T790M. The observed synergistic effect was likely contributed by the enhanced inhibition of EGFR and its downstream signaling molecules.

Econazole nitrate inhibits PI3K activity and promotes apoptosis in lung cancer cells.

The phosphatidylinositol-3-kinase (PI3K)/AKT signaling pathway plays a pivotal role in many cellular processes, including the proliferation, survival and differentiation of lung cancer cells. Thus, PI3K is a promising therapeutic target for lung cancer treatment. In this study, we applied free and open-source protein-ligand docking software, screened 3167 FDA-approved small molecules, and identified putative PI3Kα inhibitors. Among them, econazole nitrate, an antifungal agent, exhibited the highest activity in decreasing cell viability in pathological types of NSCLC cell lines, including H661 (large cell lung cancer) and A549 (adenocarcinoma). Econazole decreased the protein levels of p-AKT and Bcl-2, but had no effect on the phosphorylation level of ERK. It inhibited cell growth and promote apoptosis in a dose-dependent manner. Furthermore, the combination of econazole and cisplatin exhibited additive and synergistic effects in the H661 and A549 lung cancer cell lines, respectively. Finally, we demonstrated that econazole significantly suppressed A549 tumor growth in nude mice. Our findings suggest that econazole is a new PI3K inhibitor and a potential drug that can be used in lung cancer treatment alone or in combination with cisplatin.

In silico prediction and in vitro and in vivo validation of acaricide fluazuron as a potential inhibitor of FGFR3 and a candidate anticancer drug for bladder carcinoma.

Bladder carcinoma (BC) is the ninth most common cause of cancer worldwide. Surgical resection and conventional chemotherapy and radiotherapy will ultimately fail due to tumor recurrence and resistance. Thus, the development of novel treatment is urgently needed. Fibroblast growth factor receptor 3 (FGFR3) is an important and well-established target for BC treatment. In this study, we utilized the free and open-source protein-ligand docking software idock to prospectively identify potential inhibitors of FGFR3 from 3,167 worldwide approved small-molecule drugs using a repositioning strategy. Six high-scoring compounds were purchased and tested in vitro. Among them, the acaricide drug fluazuron exhibited the highest antiproliferative effect in human BC cell lines RT112 and RT4. We further demonstrated that fluazuron treatment significantly increased the percentage of apoptosis cells, and decreased the phosphorylation level of FGFR3 and its downstream proteins FRS2-α, AKT, and ERK. We also investigated the anticancer effect of fluazuron in vivo in BALB/C nude mice subcutaneously xenografted with RT112 cells. Our results showed that oral treatment with fluazuron (80 mg/kg) significantly inhibited tumor growth. These results suggested for the first time that fluazuron is a potential inhibitor of FGFR3 and a candidate anticancer drug for the treatment of BC.