Online Prediction Tool Research Articles

Long noncoding RNA LUCAT1 promotes malignancy of ovarian cancer through regulation of miR-612/HOXA13 pathway

Emerging evidence shows that long noncoding RNA (lncRNA) is implicated in numerous kinds of malignant cancers, including ovarian cancer. In this study, we focused on the expression and function of long noncoding RNA lung cancer associated transcript 1 (LUCAT1) in ovarian cancer progression. We indicated that LUCAT1 expression was significantly upregulated in ovarian cancer tissues. Moreover, LUCAT1 expression was positively associated with tumor metastasis and clinical stage. Elevated expression of LUCAT1 decreased the survival rate of patients with ovarian cancer. In addition, we revealed that repression of LUCAT1 significantly suppressed the proliferation, migration and invasion, whereas promoted apoptotic rate. Through online predictive tools and functional experiments, we demonstrated that LUCAT1 and HOXA13 were targets of miR-612. We showed that LUCAT1 and miR-612 suppressed each other in a reciprocal way. Moreover, LUCAT1 promoted HOXA13 expression through inhibition of miR-612, eventually leading to ovarian cancer development. In conclusion, our findings revealed a novel molecular mechanism that LUCAT1/miR-612/HOXA13 pathway modulates ovarian cancer progression.

PO-352 The effects of lapatinib responsive miRNA on cell proliferation and cell invasion in HER2 +breast cancer cell line

IntroductionLapatinib is a dual receptor tyrosine kinase inhibitor that blocks EGFR (HER1) and HER2 activation in breast cancer. microRNAs (miRNA) are non-coding RNAs involved in the regulation of protein-coding genes and these regulatory effects address their emerging roles on the mechanism of action in targeted therapies. In this study, we aimed to identify the roles of lapatinib responsive miRNA (miR-X) by evaluating its regulatory effects on cell proliferation, cell invasion and lapatinib treatment in SKBR3 cell line.Material and methodsWe performed a microarray miRNA profiling in lapatinib treated and untreated SKBR3 cells to determine drug responsive miRNAs. Then, we investigated their expression profiles in TCGA breast cancer data via cBioPortal database. The lapatinib responsive miRNAs altered in TCGA data were selected for qRT-PCR validation. Following the inhibition or overexpression of the validated miRNA (miR-X), cell proliferation assay and cell invasion assay (xCELLigence system) were performed to define the functional roles in SKBR3 cells. Online target prediction (TargetScan) and pathway enrichment analysis (WebGestalt) tools were used to have a further insight for the involvement of lapatinib responsive miRNA in regulatory pathways.Results and discussionsWe identified 182 miRNAs to be differentially expressed in lapatinib treated SKBR3 cells (P-value<0.05). 15 of them were found to be altered in TCGA breast cancer data. 3 of them were selected for further validation. The rate of cell proliferation was defined to be decreased upon the inhibition of miR-X for 48 hours while there was no change after its overexpression. The inhibition of miR-X was repeated in a combination of 24 hour lapatinib treatment in SKBR3 cells. The anti-proliferative effects of lapatinib were found to be increased at the IC50 concentration level. The monitoring of cell invasion in real-time for 48 hours showed that the invasion of cells was decreased in the inhibition of miR-X when compared with the controls. Furthermore, MAPK signalling pathway, Notch signalling pathway and cell cycle were found to be significantly enriched in the pathway analysis.ConclusionOur study suggests that miRNAs are responsive to lapatinib treatment and they may increase the effectiveness of drug treatment by showing anti-proliferative effects in HER2 +cell line. We conclude that further functional validation may be useful to define the target genes in order to clarify the interaction between the miRNAs and signalling pathways in lapatinib treatment.

Biological role and clinical value of miR-99a-5p in head and neck squamous cell carcinoma (HNSCC): A bioinformatics-based study.

MicroRNAs (miRNAs) are confirmed to be tumor promoters or suppressors in multiple squamous cell carcinomas (SCCs). miR‐99a‐5p has been demonstrated to be downregulated in cancerous tissues, but its functional role in head and neck SCC (HNSCC) and its mechanism of action have not been fully elucidated. Here, we studied the expression of miR‐99a‐5p in HNSCC and performed a clinical value assessment and then extracted mature expression data from The Cancer Genome Atlas (TCGA) and microarrays from Gene Expression Omnibus (GEO). Furthermore, biological analysis was constructed via online prediction tools. The results revealed that miR‐99a‐5p expression was markedly lower in HNSCC tissues than in normal tissues, which also showed significance in the prognosis of HNSCC. However, its diagnostic value could not be verified due to the lack of body fluid samples. Additionally, miR‐99a‐5p was expressed at higher levels in patients with low histological grade neoplasms than those with high histological grade neoplasms. The age of the patient might also be a possible clinical parameter affecting miR‐99a‐5p expression. Furthermore, miR‐99a‐5p significantly influenced HNSCC progression by regulating the PI3K‐Akt signaling pathway, in which the key target genes were upregulated in 519 HNSCC tissues compared to 44 normal tissues, as determined by the Gene Expression Profiling Interactive Analysis (GEPIA). In conclusion, our study may provide insights into the expression and mechanism of miR‐99a‐5p in HNSCC. Further studies are required to elucidate the role of miR‐99a‐5p and its potential clinical applications for HNSCC.

Personalized weight change prediction in the first week of life

Almost all neonates show physiological weight loss and consecutive weight gain after birth. The resulting weight change profiles are highly variable as they depend on multiple neonatal and maternal factors. This limits the value of weight nomograms for the early identification of neonates at risk for excessive weight loss and related morbidities. The objective of this study was to characterize weight changes and the effect of supplemental feeding in late preterm and term neonates during the first week of life, to identify and quantify neonatal and maternal influencing factors, and to provide an educational online prediction tool. Longitudinal weight data from 3638 healthy term and late preterm neonates were prospectively recorded up to 7 days of life. Two-thirds (n=2425) were randomized to develop a semi-mechanistic model characterizing weight change as a balance between time-dependent rates of weight gain and weight loss. The dose-dependent effect of supplemental feeding on weight gain was characterized. A population analysis applying nonlinear mixed-effects modeling was performed using NONMEM 7.3. The model was evaluated on the remaining third of neonates (n=1213). Key population characteristics (median [range]) of the whole sample were gestational age 39.9 [34.4-42.4] weeks, birth weight 3400 [1980-5580] g, maternal age 32 [15-51] years, cesarean section 26%, and girls 50%. The model demonstrated good predictive performance (bias 0.01%, precision 0.56%), and is able to accurately predict individual weight change (bias 0.15%, precision 1.43%) and the dose-dependent effects of supplemental feeding up to 1 week after birth based on weight measurements during the first 3 days of life, including birth weight, and the following characteristics: gestational age, gender, delivery mode, type of feeding, maternal age, and parity. We present the first mathematical model not only to describe weight change in term and late preterm neonates but also to provide an educational online tool for personalized weight prediction in the first week of life.

Arsenic trioxide-mediated suppression of miR-182-5p is associated with potent anti-oxidant effects through up-regulation of SESN2.

Arsenic trioxide (ATO) is a traditional Chinese medicine that can induce oxidative stress for treatment of cancer cells. However, ATO may generate anti-oxidative responses to compromise the cytotoxic effect, but the underlying mechanisms remain unclear. Here we found that ATO could inhibit miR-182-5p expression in patient-derived primary S1 glioblastoma (GBM) cells accompanied by up-regulation of Sestrin-2 (SESN2) mRNA, a known anti-oxidant molecule. This phenomenon was also detected in a U87MG glioma cell line, human lung adenocarcinoma H1299 cell line and A549 cell line. Pretreatment with a free radical scavenger N-acetylcysteine (NAC) reduced the oxidative stress induced by ATO. Concomitantly, ATO mediated suppression of miR-182-5p and enhancement of SESN2 expression were also compromised. The MTT assay further showed that ATO induced cytotoxicity was enhanced by transfection of miR-182-5p mimics. Overexpression of miR-182-5p mimics significantly suppressed the expression of SENS2 and a firefly luciferase reporter gene fused to 3’- untranslated region (UTR) of SESN2 mRNA. Use of ribonucleoprotein immunoprecipitation (RNP-IP), ATO mediated suppression of miR-182-5p led to the stabilization of SESN2 mRNA as a result of Argonaute-2 (AGO2) dependent gene silencing. Furthermore, high expression of miR-182-5p and low expression of SESN2 mRNA tend to be associated with longer survival of glioma or lung cancer patients using public available gene expression datasets and online tools for prediction of clinical outcomes. Taken together, current data suggest that the miR-182-5p/SENS2 pathway is involved in ATO induced anti-oxidant responses, which may be important for the design of novel strategy for cancer treatment.

Individualized prediction of seizure relapse and outcomes following antiepileptic drug withdrawal after pediatric epilepsy surgery.

The objective of this study was to create a clinically useful tool for individualized prediction of seizure outcomes following antiepileptic drug withdrawal after pediatric epilepsy surgery. We used data from the European retrospective TimeToStop study, which included 766 children from 15 centers, to perform a proportional hazard regression analysis. The 2 outcome measures were seizure recurrence and seizure freedom in the last year of follow-up. Prognostic factors were identified through systematic review of the literature. The strongest predictors for each outcome were selected through backward selection, after which nomograms were created. The final models included 3 to 5 factors per model. Discrimination in terms of adjusted concordance statistic was 0.68 (95% confidence interval [CI] 0.67-0.69) for predicting seizure recurrence and 0.73 (95% CI 0.72-0.75) for predicting eventual seizure freedom. An online prediction tool is provided on www.epilepsypredictiontools.info/ttswithdrawal. The presented models can improve counseling of patients and parents regarding postoperative antiepileptic drug policies, by estimating individualized risks of seizure recurrence and eventual outcome.

Abstract P3-08-02: Validation of the online prediction tool PREDICT v. 2.0: A large population-based study in the Netherlands

Abstract Background This study aimed to validate the online prediction tool PREDICT version 2.0 on a large population-based cohort in the Netherlands. Separate analyses were performed to study its validity in specific prognostic subgroups. Methods All women diagnosed with non-metastasised primary invasive breast cancer in 2005, who received surgery as part of their treatment, were selected from the nationwide Netherlands Cancer Registry (NCR). The predicted 5-year and 10-year overall survival (OS) from PREDICT were compared with the observed OS from the NCR, for the overall cohort (separated by ER- and ER+ patients) and for predefined subgroups based on age, T stage, N stage, presence of micrometastases, grade, HER2 status, type of surgery, use and type of adjuvant systemic therapy and generation chemotherapy. Our a priori assumption was that PREDICT accurately predicted OS whenever the differences between predicted and observed outcomes were within a range of 5%, since a difference of 5% or more was considered as clinically relevant. The discriminatory accuracy and goodness-of-fit were determined using the area under the receiver operating characteristic curve (AUC) and the Chi2-test, respectively. Results Our study population consisted of 8,834 patients. Discriminatory accuracy for 5-year OS was good with an AUC of 0.80. For ER- and ER+ patients, the AUCs were 0.75 and 0.79, respectively. Predicted 5-year OS differed from observed by -1.4% (p=0.14) in the entire cohort, by -4.9% (p=0.02) in ER- patients and -0.7% (p=0.53) in ER+ patients. In all predefined subgroups no statistically significant differences between predicted and observed events were observed. Discriminatory accuracy for 10-year OS was good with an AUC of 0.78 for the overall cohort, 0.76 for ER- patients and 0.78 for ER+ patients. Predicted 10-year OS differed from observed by -1.0% (p=0.27) in the entire cohort, by -5.3 (p=0.01) in ER- patients and -0.1% (p=0.92) in ER+ patients. Ten-year OS was overestimated in patients ≥75 years (6.3%, p=0.00), and underestimated in T3 stage (-13.%, p=0.00) and in patients treated with both endocrine therapy and chemotherapy (-6.6%, p=0.02). Conclusion PREDICT accurately predicts 5-year OS in the entire Dutch validation population, and in all predefined subgroups. However, 10-year OS was overestimated for patients ≥75 years, and underestimated for T3 stage and patients treated with both endocrine therapy and chemotherapy. Given PREDICT's intentions to guide treatment decision-making, PREDICT may serve as a reliable prediction tool for the Dutch breast cancer population, but especially in patients ≥75 years, 10-year OS predictions should be interpreted with care. Citation Format: van Maaren MC, van Steekbeek CD, Pharoah PDP, Witteveen A, Sonke GS, Strobbe LJA, Poortmans PMP, Siesling S. Validation of the online prediction tool PREDICT v. 2.0: A large population-based study in the Netherlands [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr P3-08-02.

Development and Validation of an Empiric Tool to Predict Favorable Neurologic Outcomes Among PICU Patients.

To create a novel tool to predict favorable neurologic outcomes during ICU stay among children with critical illness. Logistic regression models using adaptive lasso methodology were used to identify independent factors associated with favorable neurologic outcomes. A mixed effects logistic regression model was used to create the final prediction model including all predictors selected from the lasso model. Model validation was performed using a 10-fold internal cross-validation approach. Virtual Pediatric Systems (VPS, LLC, Los Angeles, CA) database. Patients less than 18 years old admitted to one of the participating ICUs in the Virtual Pediatric Systems database were included (2009-2015). None. A total of 160,570 patients from 90 hospitals qualified for inclusion. Of these, 1,675 patients (1.04%) were associated with a decline in Pediatric Cerebral Performance Category scale by at least 2 between ICU admission and ICU discharge (unfavorable neurologic outcome). The independent factors associated with unfavorable neurologic outcome included higher weight at ICU admission, higher Pediatric Index of Morality-2 score at ICU admission, cardiac arrest, stroke, seizures, head/nonhead trauma, use of conventional mechanical ventilation and high-frequency oscillatory ventilation, prolonged hospital length of ICU stay, and prolonged use of mechanical ventilation. The presence of chromosomal anomaly, cardiac surgery, and utilization of nitric oxide were associated with favorable neurologic outcome. The final online prediction tool can be accessed at https://soipredictiontool.shinyapps.io/GNOScore/. Our model predicted 139,688 patients with favorable neurologic outcomes in an internal validation sample when the observed number of patients with favorable neurologic outcomes was among 139,591 patients. The area under the receiver operating curve for the validation model was 0.90. This proposed prediction tool encompasses 20 risk factors into one probability to predict favorable neurologic outcome during ICU stay among children with critical illness. Future studies should seek external validation and improved discrimination of this prediction tool.

Validation of the online prediction tool PREDICT v. 2.0 in the Dutch breast cancer population

BackgroundPREDICT version 2.0 is increasingly used to estimate prognosis in breast cancer. This study aimed to validate this tool in specific prognostic subgroups in the Netherlands. MethodsAll operated women with non-metastatic primary invasive breast cancer, diagnosed in 2005, were selected from the nationwide Netherlands Cancer Registry (NCR). Predicted and observed 5- and 10-year overall survival (OS) were compared for the overall cohort, separated by oestrogen receptor (ER) status, and predefined subgroups. A >5% difference was considered as clinically relevant. Discriminatory accuracy and goodness-of-fit were determined using the area under the receiver operating characteristic curve (AUC) and the Chi-squared-test. ResultsWe included 8834 patients. Discriminatory accuracy for 5-year OS was good (AUC 0.80). For ER-positive and ER-negative patients, AUCs were 0.79 and 0.75, respectively. Predicted 5-year OS differed from observed by −1.4% in the entire cohort, −0.7% in ER-positive and −4.9% in ER-negative patients. Five-year OS was accurately predicted in all subgroups.Discriminatory accuracy for 10-year OS was good (AUC 0.78). For ER-positive and ER-negative patients AUCs were 0.78 and 0.76, respectively. Predicted 10-year OS differed from observed by −1.0% in the entire cohort, −0.1% in ER-positive and −5.3 in ER-negative patients. Ten-year OS was overestimated (6.3%) in patients ≥75 years and underestimated (−13.%) in T3 tumours and patients treated with both endocrine therapy and chemotherapy (−6.6%). ConclusionsPREDICT predicts OS reliably in most Dutch breast cancer patients, although results for both 5-year and 10-year OS should be interpreted carefully in ER-negative patients. Furthermore, 10-year OS should be interpreted cautiously in patients ≥75 years, T3 tumours and in patients considering endocrine therapy and chemotherapy.

A bioinformatics approach for identification of miR-100 targets implicated in breast cancer.

MicroRNAs (miRNAs) are small endogenous non-coding RNAs with principal roles in regulation of protein expression via translation repression and mRNA degradation. Based on these roles they are implicated in tumourigenesis processes as well. Among them is miR-100 which can exert both tumor suppressor and oncogenic functions in various cancer types. In breast cancer, it has been shown to affect apoptosis, epithelial-mesenchymal transition as well as tumor-related signaling pathways. In the present study, we introduce a novel approach for identification of miR-100 target genes which are possibly implicated in breast cancer pathogenesis. We applied 14 online tools for prediction of miR-100 target genes and used gene expression data produced by DNA microarray technology. By combining these two sets of data we proposed a list of miR-100 target genes with possible involvement in breast cancer. Considering the role of miR-100 as a context-dependent chief regulator of the cancer-related signaling pathways and a potential target for therapeutic modalities, identification of its targets would pave the way for designing new approaches for cancer treatment or sensitization of cancer cells to standard treatments.

IRSpotH-TNCPseAAC: Identifying Recombination Spots in Human by Using Pseudo Trinucleotide Composition With an Ensemble of Support Vector Machine Classifiers

Background: For the formation of human gametes, meiotic recombination is crucial. Meanwhile, it has played an important role in the process that generates genetic diversity for that it is a defining event in the formation of human sperm and eggs. However, the recombination isn't a random occurrence across a genome, it usually occurs in some genomic regions, the so-called “hotspots”, with higher probability, while in the so-called “coldspots” with lower probability. Research has shown that new combinations of genetic variations can be provided by recombination. Therefore, the useful insights for in-depth studying of the genome evolution process and the mechanism of recombination would be provided based on the information of the coldspots and hotspots. Currently, the recombination regions would be determined by experiments, but it's a tedious job, which generally requires precious instruments and takes a long time. So in the study the work is starting to be studied by computational predicting models to address the above problems. Method: In this paper, a new predictor, called ‘iRSpotH-TNCPseAAC’ was developed to identify the human recombination coldspots and hotspots. In the new discrete predictive model, a feature vector called ‘pseudo trinucleotide composition’ or PseTNC is proposed to formulate the given DNA segment with its sequence-order information as complete as possible. Results: In this study, based on the rigorous jackknife test the overall success rate obtained by iRSpotH- TNCPseAAC is higher than 93% in identifying human’s recombination spots, and with mean success rate is 76.07% of the concerned 18 chromosomes. It means that our predictor can become a useful complementary tool in this area. Not only that, the PseTNC method can be used to further explore many other DNA-related problems. Finally, a web- server called iRSpotH-TNCPseAAC, which has the advantages of easy operation and convenient for using, is built and freely accessible at http://www.jci-bioinfo.cn/iRSpotH-TNCPseAAC. Conclusion: To timely acquire the information of recombination spots in DNA sequence is very significant to make in-depth study on epigenetic inheritance and analyze human diseases. Furthermore, it will facilitate drug development. A certain conclusion is that the iRSpotH-TNCPseAAC predictor may become a very practical online predictive high throughput tools in identifying recombination spots. Keywords: Pseudo amino acid composition, support vector machine, web-server, iRSpotH-TNCPseAAC, meiosis, coldspots, hotspots.

MemBrain: An Easy-to-Use Online Webserver for Transmembrane Protein Structure Prediction

Membrane proteins are an important kind of proteins embedded in the membranes of cells and play crucial roles in living organisms, such as ion channels, transporters, receptors. Because it is difficult to determinate the membrane protein’s structure by wet-lab experiments, accurate and fast amino acid sequence-based computational methods are highly desired. In this paper, we report an online prediction tool called MemBrain, whose input is the amino acid sequence. MemBrain consists of specialized modules for predicting transmembrane helices, residue–residue contacts and relative accessible surface area of α-helical membrane proteins. MemBrain achieves a prediction accuracy of 97.9% of ATMH, 87.1% of AP, 3.2 ± 3.0 of N-score, 3.1 ± 2.8 of C-score. MemBrain-Contact obtains 62%/64.1% prediction accuracy on training and independent dataset on top L/5 contact prediction, respectively. And MemBrain-Rasa achieves Pearson correlation coefficient of 0.733 and its mean absolute error of 13.593. These prediction results provide valuable hints for revealing the structure and function of membrane proteins. MemBrain web server is free for academic use and available at www.csbio.sjtu.edu.cn/bioinf/MemBrain/.

Escherichia coli PagP Enzyme-Based De Novo Design and In Vitro Activity of Antibacterial Peptide LL-37.

BackgroundThe aim of this study was to investigate the antimicrobial property of peptide LL-37 sequences.Material/MethodsHumanized antibacterial peptide LL-37 and the mutant were prepared by chemical synthesis. The physicochemical properties of antibacterial peptide LL-37 were analyzed by SWISS-MODEL online prediction tool. Molecular docking between antibacterial peptide LL-37 fragments and palmitoyl transferase PagP was made with Lamarckian genetic algorithm by AutoDock1.5.6.ResultsThe systems contacted each other at 8.75 picosec. After 20 picsec, the system had no trend of dissociation, and the bond energy of weak bond -C-O-H…NH2-CH2- was calculated. The hydrophobic groups were important factors that led to contact and merged the two parts. The contacted weak bond -C-O-H…NH2-CH2- was the bridge for contacting LL-37 with palmitoyl transferase PagP. The binding sites of antibacterial peptide LL-37 and palmitoyl transferase PagP mainly included LYS8, GLU11, LEU28, LYS12, PHE27, ILE13, and PHE6 of antibacterial peptide LL-37 and ARG94, TRP89, ASN65, SER3, GLU90, GLU90, ASN100, HIS102, and THR92 of palmitoyl transferase PagP.ConclusionsAntibacterial peptide LL-37 had stronger antibacterial effect via inhibition of activity of PagP.

Identification of high-risk cutaneous melanoma tumors is improved when combining the online American Joint Committee on Cancer Individualized Melanoma Patient Outcome Prediction Tool with a 31-gene expression profile–based classification

A significant proportion of patients with American Joint Committee on Cancer (AJCC)-defined early-stage cutaneous melanoma have disease recurrence and die. A 31-gene expression profile (GEP) that accurately assesses metastatic risk associated with primary cutaneous melanomas has been described. We sought to compare accuracy of the GEP in combination with risk determined using the web-based AJCC Individualized Melanoma Patient Outcome Prediction Tool. GEP results from 205 stage I/II cutaneous melanomas with sufficient clinical data for prognostication using the AJCC tool were classified as low (class 1) or high (class 2) risk. Two 5-year overall survival cutoffs (AJCC 79% and 68%), reflecting survival for patients with stage IIA or IIB disease, respectively, were assigned for binary AJCC risk. Cox univariate analysis revealed significant risk classification of distant metastasis-free and overall survival (hazard ratio range 3.2-9.4, P<.001) for both tools. In all, 43 (21%) cases had discordant GEP and AJCC classification (using 79% cutoff). Eleven of 13 (85%) deaths in that group were predicted as high risk by GEP but low risk by AJCC. Specimens reflect tertiary care center referrals; more effective therapies have been approved for clinical use after accrual. The GEP provides valuable prognostic information and improves identification of high-risk melanomas when used together with the AJCC online prediction tool.

Computational prediction of species-specific malonylation sites via enhanced characteristic strategy.

Protein malonylation is a novel post-translational modification (PTM) which orchestrates a variety of biological processes. Annotation of malonylation in proteomics is the first-crucial step to decipher its physiological roles which are implicated in the pathological processes. Comparing with the expensive and laborious experimental research, computational prediction can provide an accurate and effective approach to the identification of many types of PTMs sites. However, there is still no online predictor for lysine malonylation. By searching from literature and database, a well-prepared up-to-data benchmark datasets were collected in multiple organisms. Data analyses demonstrated that different organisms were preferentially involved in different biological processes and pathways. Meanwhile, unique sequence preferences were observed for each organism. Thus, a novel malonylation site online prediction tool, called MaloPred, which can predict malonylation for three species, was developed by integrating various informative features and via an enhanced feature strategy. On the independent test datasets, AUC (area under the receiver operating characteristic curves) scores are obtained as 0.755, 0.827 and 0.871 for Escherichia coli ( E.coli ), Mus musculus ( M.musculus ) and Homo sapiens ( H.sapiens ), respectively. The satisfying results suggest that MaloPred can provide more instructive guidance for further experimental investigation of protein malonylation. http://bioinfo.ncu.edu.cn/MaloPred.aspx . jdqiu@ncu.edu.cn. Supplementary data are available at Bioinformatics online.

Functional mechanisms for diabetic nephropathy-associated genetic variants

Diabetic nephropathy (DN) is one of the major complications of diabetes. A tremendous amount of genetic variations have been identified to be associated with DN. However, most of them only generate from statistical associations at the DNA level, generally without direct functional evidence regarding their association mechanisms underlying DN. Based on the publicly available datasets and resources, this study performed integrative analyses (expression quantitative trait loci analysis, differential gene expression analysis and functional prediction analysis) to detect the molecular functional mechanisms underlying the associations for DN. Among 150 selected (P < E-4) genetic associations that were archived in the public databases, two single nucleotide polymorphisms (SNPs) (rs3135377 and rs9469220) have been found to act as cis-effect regulators of the “identified” gene (HLA-DRA and HLA-DRB1). These eQTL genes have differential expression signals in the DN-associated cell groups. These SNPs were predicted as regulatory sites by utilizing online prediction tools. Our data suggest potential mechanistic links underlying the association between DN and two identified SNPs. These results could help us to have a deeper understanding of the functional relevance of genetic variants with susceptibility to DN, which is useful for pursuit of in-depth validation studies to dissect their involvements and molecular functional mechanisms in DN.

Let-7a and its target, insulin-like growth factor 1 receptor, are differentially expressed in recurrent prostate cancer.

Prostate cancer (PCa) is the most common malignancy in males worldwide. Approximately 30% of those patients who received radical prostatectomy developed clinical recurrence accompanied by elevated serum prostate‑specific antigen levels. Although knowledge regarding the development of PCa has been significantly improved, the molecular mechanism underlying recurrence remains largely unknown. The objective of the present study was to identify the differentially expressed microRNAs (miRNAs) in recurrent PCa to explore the possible involvement of miRNAs in the relapse. The expression of 6 miRs that have been previously reported to be downregulated in PCa stem cells were examined in a total of 32 recurrent and 36 non‑recurrent PCa samples, and let‑7a was substantially decreased in the recurrent PCa. Using the online prediction tools, let‑7a was identified to virtually target insulin‑like growth factor 1 receptor (IGF1R). IGF1R as a target of let‑7a was subsequently validated using the luciferase assay. Exogenous expression of let‑7a suppressed the expression of IGF1R, and reduced the proliferation of PCa cells by introducing apoptosis to the cells. In conclusion, the present data demonstrated a possible involvement of let‑7a in the pathogenesis of recurrent PCa, and it may be a potential target of the disease.

Assessment of Latent Heat and Solid Fraction of Al-22Si Alloy Using Newtonian and Fourier Analysis Techniques

Computer aided cooling curve analysis (CACCA) is an online prediction tool for the determination of solidification characteristics of metals or alloys. The results of CACCA can be used to accurately determine latent heat and solid fraction needed for modeling of the solidification process. Newtonian and Fourier analysis techniques adopt a data base line fitting technique to the first derivative curve for calculation of the solid fraction and latent heat of solidification. This paper describes the theoretical and experimental procedures involved Newtonian and Fourier analysis techniques with reference to an Al-22% Si alloy. The correlations between the solid fraction and temperature/time for the alloy were determined.

SuccFind: a novel succinylation sites online prediction tool via enhanced characteristic strategy.

Lysine succinylation orchestrates a variety of biological processes. Annotation of succinylation in proteomes is the first-crucial step to decipher physiological roles of succinylation implicated in the pathological processes. In this work, we developed a novel succinylation site online prediction tool, called SuccFind, which is constructed to predict the lysine succinylation sites based on two major categories of characteristics: sequence-derived features and evolutionary-derived information of sequence and via an enhanced feature strategy for further optimizations. The assessment results obtained from cross-validation suggest that SuccFind can provide more instructive guidance for further experimental investigation of protein succinylation. A user-friendly server is freely available on the web at: http://bioinfo.ncu.edu.cn/SuccFind.aspx. jdqiu@ncu.edu.cn. Supplementary data are available at Bioinformatics online.

Cloning and characterization of a novel PE_PGRS60 protein (Rv3652) of Mycobacterium tuberculosis H37 Rv exhibit fibronectin-binding property.

The binding of pathogenic bacteria to extracellular matrix components enhances adhesion and invasion of host cells. The host receptor proteins such as fibronectin (Fn) targeted to pathogenic ligands that have clinical importance. In the present study, we cloned, expressed, purified, and identified a novel Fn-binding protein from PE_PGRS60 (Rv3652) of Mycobacterium tuberculosis H37 Rv. The protein product of Rv3652 showed optimum binding efficiency to 10 ng Fn at 0.2 µg purified protein of PE_PGRS60 and 20 ng Fn at 0.2 µg concentrations, respectively. PE_PGRS60 protein (primary sequences) of different pathogenic mycobacterium species retrieved from NCBI exhibited complete homology at the 104 residues on multiple sequence alignment. The primary sequence of protein from H37 Rv was further used to predict cleavage signals. The secondary structure prediction method revealed a number of residues responsible for alpha helices formation and percentage of residues participating in the random coils and extended strands. In addition, online prediction tools such as B- and T-cell epitopes showed the surface probability scale and antigenic propensity scale. The current finding opens new opportunity to mycobacterial survival and pathogenesis research of PE-polymorphic GC-rich repetitive sequences (PE-PGRS) family proteins.