In silico drug repositioning based on the integration of chemical, genomic and pharmacological spaces.

BACKGROUND: Drug repositioning refers to the identification of new indications for existing drugs. Drug-based inference methods for drug repositioning apply some unique features of drugs for new indication prediction. Complementary information is provided by these different features. It is therefore necessary to integrate these features for more accurate in silico drug repositioning. RESULTS: In this study, we collect 3 different types of drug features (i.e., chemical, genomic and pharmacological spaces) from public databases. Similarities between drugs are separately calculated based on each of the features. We further develop a fusion method to combine the 3 similarity measurements. We test the inference abilities of the 4 similarity datasets in drug repositioning under the guilt-by-association principle. Leave-one-out cross-validations show the integrated similarity measurement IntegratedSim receives the best prediction performance, with the highest AUC value of 0.8451 and the highest AUPR value of 0.2201. Case studies demonstrate IntegratedSim produces the largest numbers of confirmed predictions in most cases. Moreover, we compare our integration method with 3 other similarity-fusion methods using the datasets in our study. Cross-validation results suggest our method improves the prediction accuracy in terms of AUC and AUPR values. CONCLUSIONS: Our study suggests that the 3 drug features used in our manuscript are valuable information for drug repositioning. The comparative results indicate that integration of the 3 drug features would improve drug-disease association prediction. Our study provides a strategy for the fusion of different drug features for in silico drug repositioning.

CircRNA NALCN acts as an miR-493-3p sponge to regulate PTEN expression and inhibit glioma progression.

BACKGROUND: An increasing number of studies have shown that circular RNAs (circRNAs) play important roles in the regulation of tumor progression. Therefore, we explored the expression characteristics, function, and related mechanism of the newly identified circNALCN in glioma. METHODS: RNA sequencing was used to analyze the expression profiles of circRNAs in brain tissue from five glioma cases and four normal controls. Quantitative real-time polymerase chain reaction was implemented to examine the levels of circNALCN, miR-493-3p, and phosphatase and tensin homolog (PTEN). Cell counting kit 8 assays were performed to analyze cell proliferation, and cell migration was assessed by the wound healing test and Transwell assay. Dual-luciferase reporter, fluorescence in situ hybridization, and RNA pulldown assays were performed to confirm the role of circNALCN as an miR-493-3p sponge, weakening the inhibitory effect of miR-493-3p on target PTEN expression. RESULTS: The downregulated expression of circNALCN was observed in both glioma tissues and cell lines. CircNALCN expression was negatively correlated with World Health Organization grade and overall survival in patients with glioma. Functionally, the overexpression of circNALCN significantly inhibited the proliferation and migration of glioma cells, whereas miR-493-3p mimics counteracted these effects. The mechanistic analysis demonstrated that circNALCN acted as a competing endogenous RNA for miR-493-3p to relieve the repressive effects of miR-493-3p on its target, PTEN, suppressing glioma tumorigenesis. CONCLUSIONS: CircNALCN inhibits the progression of glioma through the miR-493-3p/PTEN axis, providing a developable biomarker and therapeutic target for glioma patients.

Prediction of the Age at Onset of Spinocerebellar Ataxia Type 3 with Machine Learning.

BACKGROUND: In polyglutamine (polyQ) disease, the investigation of the prediction of a patient's age at onset (AAO) facilitates the development of disease-modifying intervention and underpins the delay of disease onset and progression. Few polyQ disease studies have evaluated AAO predicted by machine-learning algorithms and linear regression methods. OBJECTIVE: The objective of this study was to develop a machine-learning model for AAO prediction in the largest spinocerebellar ataxia type 3/Machado-Joseph disease (SCA3/MJD) population from mainland China. METHODS: In this observational study, we introduced an innovative approach by systematically comparing the performance of 7 machine-learning algorithms with linear regression to explore AAO prediction in SCA3/MJD using CAG expansions of 10 polyQ-related genes, sex, and parental origin. RESULTS: Similar prediction performance of testing set and training set in each models were identified and few overfitting of training data was observed. Overall, the machine-learning-based XGBoost model exhibited the most favorable performance in AAO prediction over the traditional linear regression method and other 6 machine-learning algorithms for the training set and testing set. The optimal XGBoost model achieved mean absolute error, root mean square error, and median absolute error of 5.56, 7.13, 4.15 years, respectively, in testing set 1, with mean absolute error (4.78 years), root mean square error (6.31 years), and median absolute error (3.59 years) in testing set 2. CONCLUSION: Machine-learning algorithms can be used to predict AAO in patients with SCA3/MJD. The optimal XGBoost algorithm can provide a good reference for the establishment and optimization of prediction models for SCA3/MJD or other polyQ diseases. © 2020 International Parkinson and Movement Disorder Society.

Comparative analysis of similarity measurements in miRNAs with applications to miRNA-disease association predictions.

BACKGROUND: As regulators of gene expression, microRNAs (miRNAs) are increasingly recognized as critical biomarkers of human diseases. Till now, a series of computational methods have been proposed to predict new miRNA-disease associations based on similarity measurements. Different categories of features in miRNAs are applied in these methods for miRNA-miRNA similarity calculation. Benchmarking tests on these miRNA similarity measures are warranted to assess their effectiveness and robustness. RESULTS: In this study, 5 categories of features, i.e. miRNA sequences, miRNA expression profiles in cell-lines, miRNA expression profiles in tissues, gene ontology (GO) annotations of miRNA target genes and Medical Subject Heading (MeSH) terms of miRNA-associated diseases, are collected and similarity values between miRNAs are quantified based on these feature spaces, respectively. We systematically compare the 5 similarities from multi-statistical views. Furthermore, we adopt a rule-based inference method to test their performance on miRNA-disease association predictions with the similarity measurements. Comprehensive comparison is made based on leave-one-out cross-validations and a case study. Experimental results demonstrate that the similarity measurement using MeSH terms performs best among the 5 measurements. It should be noted that the other 4 measurements can also achieve reliable prediction performance. The best-performed similarity measurement is used for new miRNA-disease association predictions and the inferred results are released for further biomedical screening. CONCLUSIONS: Our study suggests that all the 5 features, even though some are restricted by data availability, are useful information for inferring novel miRNA-disease associations. However, biased prediction results might be produced in GO- and MeSH-based similarity measurements due to incomplete feature spaces. Similarity fusion may help produce more reliable prediction results. We expect that future studies will provide more detailed information into the 5 feature spaces and widen our understanding about disease pathogenesis.

Prediction and interpretation of miRNA-disease associations based on miRNA target genes using canonical correlation analysis.

BACKGROUND: It has been shown that the deregulation of miRNAs is associated with the development and progression of many human diseases. To reduce time and cost of biological experiments, a number of algorithms have been proposed for predicting miRNA-disease associations. However, the existing methods rarely investigated the cause-and-effect mechanism behind these associations, which hindered further biomedical follow-ups. RESULTS: In this study, we presented a CCA-based model in which the possible molecular causes of miRNA-disease associations were comprehensively revealed by extracting correlated sets of genes and diseases based on the co-occurrence of miRNAs in target gene profiles and disease profiles. Our method directly suggested the underlying genes involved, which could be used for experimental tests and confirmation. The inference of associated diseases of a new miRNA was made by taking into account the weight vectors of the extracted sets. We extracted 60 pairs of correlated sets from 404 miRNAs with two profiles for 2796 target genes and 362 diseases. The extracted diseases could be considered as possible outcomes of miRNAs regulating the target genes which appeared in the same set, some of which were supported by independent source of information. Furthermore, we tested our method on the 404 miRNAs under the condition of 5-fold cross validations and received an AUC value of 0.84606. Finally, we extensively inferred miRNA-disease associations for 100 new miRNAs and some interesting prediction results were validated by established databases. CONCLUSIONS: The encouraging results demonstrated that our method could provide a biologically relevant prediction and interpretation of associations between miRNAs and diseases, which were of great usefulness when guiding biological experiments for scientific research.

Ontological function annotation of long non-coding RNAs through hierarchical multi-label classification.

Motivation: Long non-coding RNAs (lncRNAs) are an enormous collection of functional non-coding RNAs. Over the past decades, a large number of novel lncRNA genes have been identified. However, most of the lncRNAs remain function uncharacterized at present. Computational approaches provide a new insight to understand the potential functional implications of lncRNAs. Results: Considering that each lncRNA may have multiple functions and a function may be further specialized into sub-functions, here we describe NeuraNetL2GO, a computational ontological function prediction approach for lncRNAs using hierarchical multi-label classification strategy based on multiple neural networks. The neural networks are incrementally trained level by level, each performing the prediction of gene ontology (GO) terms belonging to a given level. In NeuraNetL2GO, we use topological features of the lncRNA similarity network as the input of the neural networks and employ the output results to annotate the lncRNAs. We show that NeuraNetL2GO achieves the best performance and the overall advantage in maximum F-measure and coverage on the manually annotated lncRNA2GO-55 dataset compared to other state-of-the-art methods. Availability and implementation: The source code and data are available at http://denglab.org/NeuraNetL2GO/. Contact: leideng@csu.edu.cn. Supplementary information: Supplementary data are available at Bioinformatics online.

Epigenetically controlled Six3 expression regulates glioblastoma cell proliferation and invasion alongside modulating the activation levels of WNT pathway members.

Glioma is the most common primary brain tumor in adults. Six3 is a human homologue of the highly conserved sine oculis gene family and essential transcription regulatory factor in process of eye and fetal forebrain development. However, little is known about the role of Six3 in human tumorigenesis. The aim of this study is to investigate the methylation/expression of Six3 and reveal its function and action mechanism in glioma. Our results showed that Six3 was down-regulated in human glioma tissues and human glioma SHG-44, U251, SF126 and U373-MG cells compared with the normal tissues. And the down-regulation of Six3 was associated with the methylation of its promoter. Glioma U251 cells lacked endogenous Six3. Treatment with demethylating agent (5-aza-2'-deoxycytidine) or exogenous expression of Six3 restored Six3 production and resulted in suppression of cell cycle G1/S transition, proliferation and invasion and down-regulation of the expression of Wnt1, p-GSK3-ß, ß-catenin and cyclin D1 in glioma U251 cells. However, knockdown of Six3 in SHG-44 cells, which have relative higher baseline level of Six3, resulted in an opposite action. These results demonstrate that Six3 silence or loss in glioma is induced by its promoter hypermethylation and Six3 down-regulation contributes to proliferation and invasion of glioma. And this process is involved in activation of Wnt/ß-catenin pathway. Six3 play a suppressor role in the initiation and progression of human glioma and potentially serve as a target for the diagnosis and treatment of human glioma.

The Transmembrane Domains of ß and IX Subunits Mediate the Localization of the Platelet Glycoprotein Ib-IX Complex to the Glycosphingolipid-enriched Membrane Domain.

We have previously reported that the structural elements of the GP Ib-IX complex required for its localization to glycosphingolipid-enriched membranes (GEMs) reside in the Ibß and IX subunits. To identify them, we generated a series of cell lines expressing mutant GP Ibß and GP IX where 1) the cytoplasmic tails (CTs) of either or both GP Ibß and IX are truncated, and 2) the transmembrane domains (TMDs) of GP Ibß and GP IX were swapped with the TMD of a non-GEMs associating molecule, human transferrin receptor. Sucrose density fractionation analysis showed that the removal of either or both of the CTs from GP Ibß and GP IX does not alter GP Ibα-GEMs association when compared with the wild type. In contrast, swapping of the TMDs of either GP Ibß or GP IX with that of transferrin receptor results in a significant loss (∼ 50%) of GP Ibα from the low density GEMs fractions, with the largest effect seen in the dual TMD-replaced cells (> 80% loss) when compared with the wild type cells (100% of GP Ibα present in the GEMs fractions). Under high shear flow, the TMD-swapped cells adhere poorly to a von Willebrand factor-immobilized surface to a much lesser extent than the previously reported disulfide linkage dysfunctional GP Ibα-expressing cells. Thus, our data demonstrate that the bundle of GP Ibß and GP IX TMDs instead of their individual CTs is the structural element that mediates the ß/IX complex localization to the membrane GEMs, which through the α/ß disulfide linkage brings GP Ibα into the GEMs.

Membrane skeleton orchestrates the platelet glycoprotein (GP) Ib-IX complex clustering and signaling.

Platelet glycoprotein Ib-IX complex is affixed to the membrane skeleton through interaction with actin binding protein 280 (ABP-280). We find that removal of the ABP-280 binding sites in GP Ibα cytoplasmic tail has little impact on the complex clustering induced by antibody crosslinking. However, large truncation of the GP Ibα cytoplasmic tail allows the formation of larger patches of the complex, suggesting that an ABP-280 independent force may exist. Besides, we observe that the signaling upon GP Ib-IX clustering is elicited in both membrane lipid domain dependent and independent manner, a choice that relies on how the membrane skeleton interacts with the complex. Our findings suggest a more complex mechanism for how the membrane skeleton regulates the GP Ib-IX function. © 2016 IUBMB Life, 68(10):823-829, 2016.

MicroRNAs 10a and 10b Regulate the Expression of Human Platelet Glycoprotein Ibα for Normal Megakaryopoiesis.

MicroRNAs are a class of small non-coding RNAs that bind to the three prime untranslated region (3'-UTR) of target mRNAs. They cause a cleavage or an inhibition of the translation of target mRNAs, thus regulating gene expression. Here, we employed three prediction tools to search for potential miRNA target sites in the 3'-UTR of the human platelet glycoprotein (GP) 1BA gene. A luciferase reporter assay shows that miR-10a and -10b sites are functional. When miR-10a or -10b mimics were transfected into the GP Ibß/GP IX-expressing cells, along with a DNA construct harboring both the coding and 3'-UTR sequences of the human GP1BA gene, we found that they inhibit the transient expression of GP Ibα on the cell surface. When the miR-10a or -10b mimics were introduced into murine progenitor cells, upon megakaryocyte differentiation, we found that GP Ibα mRNA expression was markedly reduced, suggesting that a miRNA-induced mRNA degradation is at work. Thus, our study identifies GP Ibα as a novel target of miR-10a and -10b, suggesting that a drastic reduction in the levels of miR-10a and -10b in the late stage of megakaryopoiesis is required to allow the expression of human GP Ibα and the formation of the GP Ib-IX-V complex.

Knockdown of long noncoding RNA urothelial cancer-associated 1 enhances cisplatin chemosensitivity in tongue squamous cell carcinoma cells.

Cisplatin-based chemotherapy has been found to improve the prognosis of patients with tongue squamous cell carcinoma (TSCC), the most common oral cancer with a poor prognosis. Chemoresistance to cisplatin appears to be an important clinical problem for cisplatin-based TSCC chemotherapy. Long noncoding RNAs (lncRNAs) play important roles in regulating tumor cells' sensitivity to chemotherapeutic agents. A recent study has shown that the expression of lncRNA UCA1 is significantly enhanced in TSCCs, suggesting that UCA1 may play a role in TSCC progression. In the present study, we explored the effects and the underlying mechanisms of UCA1 on cisplatin chemosensitivity/chemoresistance and apoptosis in TSCC cells. Transient transfection of siRNA was used to knock down UCA1 in human TSCC cell lines CAL 27 and SCC-9, where UCA1 was highly overexpressed compared to normal human tongue tissues. Knockdown of UCA1 markedly increased cisplatin-induced caspase 3 activity and apoptosis in CAL 27 and SCC-9 cells. On the other hand, it decreased cisplatin-induced phosphatidylinositol 3-kinase (PI3K) activity and activation phosphorylation of Akt. UCA1 knockdown resulted in one magnitude decrease in the half maximal inhibitory concentration (IC50) of cisplatin in CAL 27 and SCC-9 cells, from 8.9 mM and 20.7 mM down to 0.6 mM and 1.7 mM, respectively. In conclusion, this study has shown that UCA1 knockdown markedly increased cisplatin-induced apoptosis and chemosensitivity in TSCC cells, likely through inhibiting cisplatin-activated PI3K/Akt signaling. It provides new insights into the functional role of UCA1 in cancer cells and suggests that UCA1 knockdown could be a new strategy to increase cisplatin chemosensitivity and thereby improve the therapeutic outcomes of cisplatin-based chemotherapy for TSCC.

Therapeutic Potentials of Medicinal Leech in Chinese Medicine.

The use of medicinal leeches in clinical therapy has been employed for a long time, as it was originally recognized for exerting antithrombin effects. These effects were due to the ability of the leech to continuously suck blood while attached to human skin. According to Chinese Pharmacopoei, leeches used in traditional Chinese medicine mainly consist of Whitmania pigra Whitman, Hirudo nipponia Whitman, and Whitmania acranulata, but the latter two species are relatively scarce. The main constituents of leeches are protein and peptide macromolecules. They can be categorized into two categories based on their pharmacological effects. One group consists of active ingredients that directly target the coagulation system, such as hirudin, heparin, and histamine, which are widely known. The other group comprises protease inhibitor components like Decorsin and Hementin. Among these, hirudin secreted by the salivary glands of the leech is the most potent thrombin inhibitor and served as the sole remedy for preventing blood clotting until the discovery of heparin. Additionally, leeches play a significant role in various traditional Chinese medicine formulations. In recent decades, medicinal leeches have been applied in fields including anti-inflammatory treatment, cardiovascular disease management, antitumor treatment, and many other medical conditions. In this review, we present a comprehensive overview of the historical journey and medicinal applications of leeches in various medical conditions, emphasizing their pharmaceutical significance within traditional Chinese medicine. This review offers valuable insights for exploring additional therapeutic opportunities involving the use of leeches in various diseases and elucidating their underlying mechanisms for future research.

Edge-relational window-attentional graph neural network for gene expression prediction in spatial transcriptomics analysis.

Spatial transcriptomics (ST), containing gene expression with fine-grained (i.e., different windows) spatial location within tissue samples, has become vital in developing innovative treatments. Traditional ST technology, however, rely on costly specialized commercial equipment. Addressing this, our article aims to creates a cost-effective, virtual ST approach using standard tissue images for gene expression prediction, eliminating the need for expensive equipment. Conventional approaches in this field often overlook the long-distance spatial dependencies between different sample windows or need prior gene expression data. To overcome these limitations, we propose the Edge-Relational Window-Attentional Network (ErwaNet), enhancing gene prediction by capturing both local interactions and global structural information from tissue images, without prior gene expression data. ErwaNet innovatively constructs heterogeneous graphs to model local window interactions and incorporates an attention mechanism for global information analysis. This dual framework not only provides a cost-effective solution for gene expression predictions but also obviates the necessity of prior knowledge gene expression information, a significant advantage in the field of cancer research where it enables a more efficient and accessible analytical paradigm. ErwaNet stands out as a prior-free and easy-to-implement Graph Convolution Network (GCN) method for predicting gene expression from tissue images. Evaluation of the two public breast cancer datasets shows that ErwaNet, without additional information, outperforms the state-of-the-art (SOTA) methods. Code is available at https://github.com/biyecc/ErwaNet.

ScRNA-seq reveals novel immune-suppressive T cells and investigates CMV-TCR-T cells cytotoxicity against GBM.

BACKGROUND: Glioblastoma (GBM) is a fatal primary brain malignancy in adults. Previous studies have shown that cytomegalovirus (CMV) is a risk factor for tumorigenesis and aggressiveness for glioblastoma. However, little is known about how CMV infection affects immune cells in the tumor microenvironment of GBM. Furthermore, there has been almost no engineered T-cell receptor (TCR)-T targeting CMV for GBM research to date. METHODS: We evaluated the CMV infection status of patients with GBM's tumor tissue by immune electron microscopy, immunofluorescence, and droplet digital PCR. We performed single-cell RNA sequencing for CMV-infected GBM to investigate the effects of CMV on the GBM immune microenvironment. CellChat was applied to analyze the interaction between cells in the GBM tumor microenvironment. Additionally, we conducted single-cell TCR/B cell receptor (BCR) sequencing and Grouping of Lymphocyte Interactions with Paratope Hotspots 2 algorithms to acquire specific CMV-TCR sequences. Genetic engineering was used to introduce CMV-TCR into primary T cells derived from patients with CMV-infected GBM. Flow cytometry was used to measure the proportion and cytotoxicity status of T cells in vitro. RESULTS: We identified two novel immune cell subpopulations in CMV-infected GBM, which were bipositive CD68+SOX2+ tumor-associated macrophages and FXYD6+ T cells. We highlighted that the interaction between bipositive TAMs or cancer cells and T cells was predominantly focused on FXYD6+ T cells rather than regulatory T cells (Tregs), whereas, FXYD6+ T cells were further identified as a group of novel immunosuppressive T cells. CMV-TCR-T cells showed significant therapeutic effects on the human-derived orthotopic GBM mice model. CONCLUSIONS: These findings provided an insight into the underlying mechanism of CMV infection promoting the GBM immunosuppression, and provided a novel potential immunotherapy strategy for patients with GBM.

Prediction of associations between OMIM diseases and microRNAs by random walk on OMIM disease similarity network.

Increasing evidence has revealed that microRNAs (miRNAs) play important roles in the development and progression of human diseases. However, efforts made to uncover OMIM disease-miRNA associations are lacking and the majority of diseases in the OMIM database are not associated with any miRNA. Therefore, there is a strong incentive to develop computational methods to detect potential OMIM disease-miRNA associations. In this paper, random walk on OMIM disease similarity network is applied to predict potential OMIM disease-miRNA associations under the assumption that functionally related miRNAs are often associated with phenotypically similar diseases. Our method makes full use of global disease similarity values. We tested our method on 1226 known OMIM disease-miRNA associations in the framework of leave-one-out cross-validation and achieved an area under the ROC curve of 71.42%. Excellent performance enables us to predict a number of new potential OMIM disease-miRNA associations and the newly predicted associations are publicly released to facilitate future studies. Some predicted associations with high ranks were manually checked and were confirmed from the publicly available databases, which was a strong evidence for the practical relevance of our method.

[Screening and analysis of peptides specifically binding to the schistosomulum tegument of Schistosoma japonicum].

OBJECTIVE: To screen and analyze the peptides in 12 phage-display peptide library specifically binding to the schistosomulum, not cercaria, tegument of Schistosoma japonicum. METHODS: A 12 phage-display peptide library was screened with the S. japonicum schistosomula and cercariae as the target cells for biopanning by degrees, 15 positive clones were picked randomly and deduced by DNA sequencing. According the sequencing result, ELISA test, elution recovery test and immunohistochemical staining were performed to determine the specificity of the phages to the tegument. To further examine its binding properties, the positive peptide conjugated to RhB and recombinant pEGFP-C2 plasmid were similarly synthesized. RESULTS: After 3 rounds of biopanning, the phage recovery rate increased from 3.50 x 10(-5)% to 3.20 x 10(-2)%, indicating that the phage library was successfully enriched in the tegument of schistosomula. The analyzed sequences were identical with 3 peptide sequence of ZL6, ZL4 and ZL1. ELISA showed that the P/N value of MppZL4, MppZL6 and MppZL binding the schistosomulum membrane protein was 6.72, 3.65 and 2.22, while 1.58, 5.15 and 1.20 of binding the membrane protein of cercariae, respectively. Elution recovery test showed that the elution recovery rate of MppZL4 [(4.60 +/- 0.27) x 10(-2)%] was much higher than that of MppZL6 [(2.10 +/- 0.23) x 10(-3)%], MppZL1 [(1.20 +/- 0.28) x 10(-3)%] and M13KE [(1.30 +/- 0.60) x 10(-7)%] (P<0.01). Immunohistochemical staining showed that MppZL4 specifically bound to the tegument of schistosomula with a positive rate of 83.0% (83/100). Fluorescent microscopy revealed that the synthesized RhB-ZL4 bound to the tegument of schistosomula. The ZL4/pEGFP-C2 plasmid was introduced into juvenile S. japonicum and expressed in the parasite. CONCLUSION: The peptide of ZL4 specifically binds to the schistosomulum tegument but not to that of cercaria.

F10 gene hypomethylation, a putative biomarker for glioma prognosis.

Tumors are usually characterized by an imbalance in cytosine methylation, including hypomethylation of CpG islands. In this study, bisulfite sequencing PCR was used to assess the promoter methylation status of coagulation factor X (F10) gene in tumors of 96 glioma patients and in glioma cells U251, SF767, and SF126, and the effect of promoter hypomethylation on protein expression was evaluated immunohistochemically. The study showed that the demethylation ratio of F10 in SF126, SF767, and U251 cells was 38.6, 26.4, and 24.3% respectively. Hypomethylation of F10 was detected in 82.3% of glioma specimens and in no normal brain tissues, with significant correlation with its protein expression. However there was no remarkable relationship between F10 hypomethylation and sex, age, and advanced tumor grade. The correlation between F10 hypomethylation, protein expression, and overall survival (OS) was statistically significant. Hypomethylation of F10 promoter in gliomas accounted for F10 encoding protein FX overexpression and aggressive biological behavior in a subset of patients. Furthermore, in the F10 hypomethylation group, OS was shorter for patients with F10 overexpression than for those without. Detection of these epigenetic changes in tumors may provide important information regarding prognosis.

[Pathogen identification of 10 suspected cases of sparganosis mansoni].

OBJECTIVE: To diagnose 10 cases of clinically suspected cases of sparganosis mansoni by pathogen identification. METHODS: In the period from August 2009 to August 2011, 10 biopsy specimens were obtained from 10 patients of four hospitals to identify the pathogen. Among the 10 cases, 4 cases showed abdominal subcutaneous mass, 3 showed eyelid swelling, 1 displayed brain lesions, 1 showed pulmonary mass, and 1 showed pleural effusion. There was one parasite each from three patients with eyelid swelling, and one patient with abdominal subcutaneous mass, which were observed by naked eye and microscope morphologically and histologically. Specimens from other six cases were examined by microscope after paraffin embedding, sectioning, and HE staining. For further identification, the parasite biopsy tissue specimens were detected by immunohistochemistry with Sparganum mansoni-immunized rabbit serum as the primary antibody. RESULTS: Three intact worms, from three patients with eyelid swelling, showed typical S. mansoni morphological characteristics. One residue parasite from the abdominal subcutaneous mass showed network structures and full of calcareous corpuscles in the body under microscope same as that of S. mansoni. The histological structure in three of the six sections showed typically the body wall with folds, which was dense, thick and deeply eosine stained, part of the tegument outside was covered by micro-hairs. In the worm body there was net-like loose structure and calcareous corpuscles without cavity. The structure of the other three worm sections was atypical. The six worm sections were positive by immunohistochemical detection. CONCLUSION: The 10 clinically suspected cases are diagnosed as sparganosis mansoni.

In silico drug repositioning based on integrated drug targets and canonical correlation analysis.

BACKGROUND: Besides binding to proteins, the most recent advances in pharmacogenomics indicate drugs can regulate the expression of non-coding RNAs (ncRNAs). The polypharmacological feature in drugs enables us to find new uses for existing drugs (namely drug repositioning). However, current computational methods for drug repositioning mainly consider proteins as drug targets. Meanwhile, these methods identify only statistical relationships between drugs and diseases. They provide little information about how drug-disease associations are formed at the molecular target level. METHODS: Herein, we first comprehensively collect proteins and two categories of ncRNAs as drug targets from public databases to construct drug-target interactions. Experimentally confirmed drug-disease associations are downloaded from an established database. A canonical correlation analysis (CCA) based method is then applied to the two datasets to extract correlated sets of targets and diseases. The correlated sets are regarded as canonical components, and they are used to investigate drug's mechanism of actions. We finally develop a strategy to predict novel drug-disease associations for drug repositioning by combining all the extracted correlated sets. RESULTS: We receive 400 canonical components which correlate targets with diseases in our study. We select 4 components for analysis and find some top-ranking diseases in an extracted set might be treated by drugs interfacing with the top-ranking targets in the same set. Experimental results from 10-fold cross-validations show integrating different categories of target information results in better prediction performance than only using proteins or ncRNAs as targets. When compared with 3 state-of-the-art approaches, our method receives the highest AUC value 0.8576. We use our method to predict new indications for 789 drugs and confirm 24 predictions in the top 1 predictions. CONCLUSIONS: To the best of our knowledge, this is the first computational effort which combines both proteins and ncRNAs as drug targets for drug repositioning. Our study provides a biologically relevant interpretation regarding the forming of drug-disease associations, which is useful for guiding future biomedical tests.

LncRNA PELATON, a Ferroptosis Suppressor and Prognositic Signature for GBM.

PELATON is a long noncoding RNA also known as long intergenic nonprotein coding RNA 1272 (LINC01272). The known reports showed that PELATON functions as an onco-lncRNA or a suppressor lncRNA by suppressing miRNA in colorectal cancer, gastric cancer and lung cancer. In this study, we first found that PELATON, as an onco-lncRNA, alleviates the ferroptosis driven by mutant p53 and promotes mutant p53-mediated GBM proliferation. We also first confirmed that PELATON is a new ferroptosis suppressor lncRNA that functions as a ferroptosis inhibitor mainly by mutant P53 mediating the ROS ferroptosis pathway, which inhibits the production of ROS, reduces the levels of divalent iron ions, promotes the expression of SLC7A11, and inhibits the expression of ACSL4 and COX2.PELATON can inhibit the expression of p53 in p53 wild-type GBM cells and regulate the expression of BACH1 and CD44, but it has no effect on p53, BACH1 and CD44 in p53 mutant GBM cells. PELATON and p53 can form a complex through the RNA binding protein EIF4A3. Knockdown of PELATON resulted in smaller mitochondria, increased mitochondrial membrane density, and enhanced sensitivity to ferroptosis inducers to inhibit GBM cell proliferation and invasion. In addition, we established a favourite prognostic model with NCOA4 and PELATON. PELATON is a promising target for the prognosis and treatment of GBM.