Robust Identification of Differential Gene Expression Patterns from Multiple Transcriptomics Datasets for Early Diagnosis, Prognosis, and Therapies for Breast Cancer.

Background and Objectives: Breast cancer (BC) is one of the major causes of cancer-related death in women globally. Proper identification of BC-causing hub genes (HubGs) for prognosis, diagnosis, and therapies at an earlier stage may reduce such death rates. However, most of the previous studies detected HubGs through non-robust statistical approaches that are sensitive to outlying observations. Therefore, the main objectives of this study were to explore BC-causing potential HubGs from robustness viewpoints, highlighting their early prognostic, diagnostic, and therapeutic performance. Materials and Methods: Integrated robust statistics and bioinformatics methods and databases were used to obtain the required results. Results: We robustly identified 46 common differentially expressed genes (cDEGs) between BC and control samples from three microarrays (GSE26910, GSE42568, and GSE65194) and one scRNA-seq (GSE235168) dataset. Then, we identified eight cDEGs (COL11A1, COL10A1, CD36, ACACB, CD24, PLK1, UBE2C, and PDK4) as the BC-causing HubGs by the protein-protein interaction (PPI) network analysis of cDEGs. The performance of BC and survival probability prediction models with the expressions of HubGs from two independent datasets (GSE45827 and GSE54002) and the TCGA (The Cancer Genome Atlas) database showed that our proposed HubGs might be considered as diagnostic and prognostic biomarkers, where two genes, COL11A1 and CD24, exhibit better performance. The expression analysis of HubGs by Box plots with the TCGA database in different stages of BC progression indicated their early diagnosis and prognosis ability. The HubGs set enrichment analysis with GO (Gene ontology) terms and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways disclosed some BC-causing biological processes, molecular functions, and pathways. Finally, we suggested the top-ranked six drug molecules (Suramin, Rifaximin, Telmisartan, Tukysa Tucatinib, Lynparza Olaparib, and TG.02) for the treatment of BC by molecular docking analysis with the proposed HubGs-mediated receptors. Molecular docking analysis results also showed that these drug molecules may inhibit cancer-related post-translational modification (PTM) sites (Succinylation, phosphorylation, and ubiquitination) of hub proteins. Conclusions: This study's findings might be valuable resources for diagnosis, prognosis, and therapies at an earlier stage of BC.

Peptide-based targeted polymeric nanoparticles for siRNA delivery.

The development of polymer-based nanoparticulate delivery systems for siRNA is important for the clinical success of gene therapy. However, there are some major drawbacks that need to be overcome. Short interfering RNA (siRNA) has been investigated as a potential therapeutic drug to silence disease-associated genes, but its usage is limited due to the lack of effective and safe nanocarriers. In this study, DOPE-PEI, a nanoparticle consisting of the fusogenic lipid 1,2-dioleoyl-sn-glycero-3-phosphoethanolamine (DOPE) conjugated with low-molecular-weight, 600 Da, branched polyethylenimine (PEI) was produced and optimized for siRNA delivery. This delivery system was modified with other components such as 1,2-dioleoyl-sn-glycerol-3-phosphoethanolamine-N-[methoxy(polyethyleneglycol)2000] (DOPE-PEG2K), DOPE-PEG3.4K-bombesin and 1,2-dioleoyl-sn-glycerol-3-phosphoethanolamine/1,2-dioleoyl-3-trimethylammonium-propane (DOPE/DOTAP) and tested on PC-3 cells. The conjugation of DOPE to PEI polymer (DOPE-PEI) improved the efficiency of PEI to deliver siRNA into the cytosol and knockdown genes, but demonstrated high toxicity. The addition of DOPE-PEG2K reduced cellular toxicity by masking the surface positive charge of the DOPE-PEI/siRNA complex, with the incorporation of a gastrin-releasing peptide receptor (GRPR) targeting peptide and DOPE/DOTAP components improving the cellular uptake of siRNA into targeted cells and the siRNA knockdown efficiency.

Assessment of Drugs Toxicity and Associated Biomarker Genes Using Hierarchical Clustering.

Background and objectives: Assessment of drugs toxicity and associated biomarker genes is one of the most important tasks in the pre-clinical phase of drug development pipeline as well as in toxicogenomic studies. There are few statistical methods for the assessment of doses of drugs (DDs) toxicity and their associated biomarker genes. However, these methods consume more time for computation of the model parameters using the EM (expectation-maximization) based iterative approaches. To overcome this problem, in this paper, an attempt is made to propose an alternative approach based on hierarchical clustering (HC) for the same purpose. Methods and materials: There are several types of HC approaches whose performance depends on different similarity/distance measures. Therefore, we explored suitable combinations of distance measures and HC methods based on Japanese Toxicogenomics Project (TGP) datasets for better clustering/co-clustering between DDs and genes as well as to detect toxic DDs and their associated biomarker genes. Results: We observed that Word's HC method with each of Euclidean, Manhattan, and Minkowski distance measures produces better clustering/co-clustering results. For an example, in the case of the glutathione metabolism pathway (GMP) dataset LOC100359539/Rrm2, Gpx6, RGD1562107, Gstm4, Gstm3, G6pd, Gsta5, Gclc, Mgst2, Gsr, Gpx2, Gclm, Gstp1, LOC100912604/Srm, Gstm4, Odc1, Gsr, Gss are the biomarker genes and Acetaminophen_Middle, Acetaminophen_High, Methapyrilene_High, Nitrofurazone_High, Nitrofurazone_Middle, Isoniazid_Middle, Isoniazid_High are their regulatory (associated) DDs explored by our proposed co-clustering algorithm based on the distance and HC method combination Euclidean: Word. Similarly, for the peroxisome proliferator-activated receptor signaling pathway (PPAR-SP) dataset Cpt1a, Cyp8b1, Cyp4a3, Ehhadh, Plin5, Plin2, Fabp3, Me1, Fabp5, LOC100910385, Cpt2, Acaa1a, Cyp4a1, LOC100365047, Cpt1a, LOC100365047, Angptl4, Aqp7, Cpt1c, Cpt1b, Me1 are the biomarker genes and Aspirin_Low, Aspirin_Middle, Aspirin_High, Benzbromarone_Middle, Benzbromarone_High, Clofibrate_Middle, Clofibrate_High, WY14643_Low, WY14643_High, WY14643_Middle, Gemfibrozil_Middle, Gemfibrozil_High are their regulatory DDs. Conclusions: Overall, the methods proposed in this article, co-cluster the genes and DDs as well as detect biomarker genes and their regulatory DDs simultaneously consuming less time compared to other mentioned methods. The results produced by the proposed methods have been validated by the available literature and functional annotation.

Bombesin/oligoarginine fusion peptides for gastrin releasing peptide receptor (GRPR) targeted gene delivery.

The development of non-viral gene delivery systems, with the capacity to overcome most of the biological barriers facing gene delivery, is challenging. We have developed peptide-based, multicomponent, non-viral delivery systems, incorporating: a bombesin peptide ligand (BBN(6-14)), to selectively target the gastrin releasing peptide receptor (GRPR); oligoarginine peptides (hexa- (R6) and nona-arginine (R9)), for plasmid DNA (pDNA) condensation; and GALA, to facilitate endosome escape. The uptake and endosome escape efficiency of bombesin/oligoarginine and bombesin/oligoarginine/GALA fusion peptides for oligonucleotide delivery was evaluated in terms of their complex size, cellular uptake, endosome escape, and cellular toxicity. Complex size and cell uptake studies demonstrated that the nona-arginine/bombesin delivery system was more efficient at condensing and delivering pDNA into PC-3 prostate cancer cells compared to the hexa-arginine/bombesin delivery system. Further, competition with free bombesin peptide, and comparative uptake studies in Caco-2 cells, which express GRPR at a lower level, suggested that GRPR contributes to the targeted uptake of this system. The addition of GALA into the nona-arginine/bombesin-based system further increased the pDNA cellular uptake at all tested N/P ratios; facilitated endosomal pDNA release; and had limited effects on cell viability. In conclusion, the delivery system combining BBN(6-14) with nona-arginine and GALA had optimal characteristics for the delivery of pDNA into the GRPR overexpressing cell line PC-3.

A newly developed tool for measuring the availability of human resources for emergency obstetric and newborn care services: prospective analytic study in two district-level public facilities in Bangladesh.

BACKGROUND: In Bangladesh, while the infrastructure of public health facilities to provide maternal and newborn care services is adequate, services are not always available due to insufficient staffing. A human resource availability index for health facilities is needed for monitoring and advocacy. This study aimed to develop indices for measuring the availability of different types of human resources to provide round-the-clock emergency obstetric and newborn care (EmONC) service at district-level public facilities. METHODS: As part of a larger intervention study, 30 days of prospective observation of providers was done at a district hospital (DH) and a mother and child welfare centre (MCWC) in one district of Bangladesh using checklists. A scoring system was developed to create an index to quantify the availability of providers for maternal and newborn care. RESULTS: Based on the newly developed index, medical doctors in the emergency department of the DH were 100% available, but ranged from 27 to 41% availability in the obstetrics/gynecology (ob/gyn) and pediatric wards. In MCWC, the corresponding indices ranged from 32 to 36%. In the DH, the availability of nurses in the ob/gyn ward (96%) was relatively better than in the pediatric ward (65%) but that in operation theatre was only 31%. In the MCWC, the index for the presence of a paramedic or nursing aid was 82% in the ob/gyn ward and 63% in the operation theatre. However, the availability scores of facility support staff for maintenance and security were generally high (over 90%) in both facilities. CONCLUSIONS: Our newly developed index on availability of providers demonstrated huge gaps in availability of providers in evening and night shifts in most of the disciplines in the study facilities. This provider availability index is easy to create and can be used as a meaningful tool to quantify gaps in human resources by type in various types of district-level health facilities. Further studies are needed for adaptation of this tool in different types of health facilities and to assess its implication as an advocacy tool.

Investigation of bombesin peptide as a targeting ligand for the gastrin releasing peptide (GRP) receptor.

Gastrin releasing peptide (GRP) receptor (GRPR), a bombesin family receptor, is overexpressed in many cancers including breast, prostate, pancreatic and lung. The targeting of therapeutics to GRPR can be achieved using the full-length (14 amino acid) GRP analogue Bombesin (BBN) or the truncated BBN(6-14) sequence, both of which bind GRPR with high affinity and specificity. In this study, we have investigated the level of GRPR expression in various cancerous (Caco-2, HeLa, LNCap, MDA-MB-231, and PC-3) and non-cancerous (WPMY-1) cell lines using a western blotting approach. Such information is currently lacking in the literature, and is therefore of importance for the in vitro assessment of GRPR targeted therapeutics. Of the cell lines assessed, the PC-3 (prostate cancer) and Caco-2 (colon cancer) cell lines demonstrated the highest and lowest levels of GRPR expression respectively. Using this information, we further investigated the cellular uptake of carboxyfluorescein-labelled BBN and BBN(6-14) peptides by flow cytometry and confocal microscopy using cell lines that express GRPR (Caco-2, HeLa, PC-3). The uptake of each of these peptides was similar, suggesting that the shorter BBN(6-14) peptide is sufficient for GRPR targeting. Further, the uptake of these peptides could be inhibited by competition with unlabelled BBN peptides, suggesting their cellular uptake is GRPR-mediated, while the level of BBN uptake (as measured by flow cytometry) was found to be directly proportional to the level of GRPR expression. Overall, the information obtained from these studies provides useful information for the in vitro assessment of GRPR targeted therapeutics.

Robust identification of significant interactions between toxicogenomic biomarkers and their regulatory chemical compounds using logistic moving range chart.

Identification of significant interactions between genes and chemical compounds/drugs is an important issue in toxicogenomic studies as well as in drug discovery and development. There are some online and offline computational tools for toxicogenomic data analysis to identify the biomarker genes and their regulatory chemical compounds/drugs. However, none of the researchers has considered yet the identification of significant interactions between genes and compounds. Therefore, in this paper, we have discussed two approaches namely moving range chart (MRC) and logistic moving range chart (LMRC) for the identification of significant up-regulatory (UpR) and down-regulatory (DnR) gene-compound interactions as well as toxicogenomic biomarkers and their regulatory chemical compounds/drugs. We have investigated the performance of both MRC and LMRC approaches using simulated datasets. Simulation results show that both approaches perform almost equally in absence of outliers. However, in presence of outliers, the LMRC shows much better performance than the MRC. In case of real life toxicogenomic data analysis, the proposed LMRC approach detected some important down-regulated biomarker genes those were not detected by other approaches. Therefore, in this paper, our proposal is to use LMRC for robust identification of significant interactions between genes and chemical compounds/drugs.

Toxic Dose prediction of Chemical Compounds to Biomarkers using an ANOVA based Gene Expression Analysis.

The aim of toxicogenomic studies is to optimize the toxic dose levels of chemical compounds (CCs) and their regulated biomarker genes. This is also crucial in drug discovery and development. There are popular online computational tools such as ToxDB and Toxygates to identify toxicogenomic biomarkers using t-test. However, they are not suitable for the identification of biomarker gene regulatory dose of corresponding CCs. Hence, we describe a one-way ANOVA model together with Tukey's HSD test for the identification of toxicogenomic biomarker genes and their influencing CC dose with improved efficiency. Glutathione metabolism pathway data analysis shows high and middle dose for acetaminophen, and nitrofurazone as well as high dose for methapyrilene as significant toxic CC dose. The corresponding regulated top seven toxicogenomic biomarker genes found in this analysis is Gstp1, Gsr, Mgst2, Gclm, G6pd, Gsta5 and Gclc.

Robust Co-clustering to Discover Toxicogenomic Biomarkers and Their Regulatory Doses of Chemical Compounds Using Logistic Probabilistic Hidden Variable Model.

Detection of biomarker genes and their regulatory doses of chemical compounds (DCCs) is one of the most important tasks in toxicogenomic studies as well as in drug design and development. There is an online computational platform "Toxygates" to identify biomarker genes and their regulatory DCCs by co-clustering approach. Nevertheless, the algorithm of that platform based on hierarchical clustering (HC) does not share gene-DCC two-way information simultaneously during co-clustering between genes and DCCs. Also it is sensitive to outlying observations. Thus, this platform may produce misleading results in some cases. The probabilistic hidden variable model (PHVM) is a more effective co-clustering approach that share two-way information simultaneously, but it is also sensitive to outlying observations. Therefore, in this paper we have proposed logistic probabilistic hidden variable model (LPHVM) for robust co-clustering between genes and DCCs, since gene expression data are often contaminated by outlying observations. We have investigated the performance of the proposed LPHVM co-clustering approach in a comparison with the conventional PHVM and Toxygates co-clustering approaches using simulated and real life TGP gene expression datasets, respectively. Simulation results show that the proposed method improved the performance over the conventional PHVM in presence of outliers; otherwise, it keeps equal performance. In the case of real life TGP data analysis, three DCCs (glibenclamide-low, perhexilline-low, and hexachlorobenzene-medium) for glutathione metabolism pathway dataset as well as two DCCs (acetaminophen-medium and methapyrilene-low) for PPAR signaling pathway dataset were incorrectly co-clustered by the Toxygates online platform, while only one DCC (hexachlorobenzene-low) for glutathione metabolism pathway was incorrectly co-clustered by the proposed LPHVM approach. Our findings from the real data analysis are also supported by the other findings in the literature.

Market Assessment and Product Evaluation of Probiotic Containing Dietary Supplements Available in Bangladesh Market.

Probiotics containing food supplements available in Bangladesh market were identified and collected for assessment. To assess their label claim, they were resuspended into sterile distilled water. Then, series dilutions of each sample solution were prepared and immediately plated out, in duplicate, into De Man Rogosa Sharpe (MRS) agar. These plates were then incubated at 37°C for 48 hours and colonies were counted. Viable cell numbers stated on the labels were compared with actual viable cell numbers. To assess the viability of the probiotics included in the products, probiotic strains were isolated from each of the four products and screened for inhibitory activity against six indicator strains. It was surprisingly found that although the viable cell numbers of all supplements were three to four log cycles lower than label claim of the products, however, this problem did not affect the inhibitory activity of the probiotic strains against indicator strains according to in vitro assessment. Legislation and regulation regarding prebiotic-probiotic containing products should be built up in Bangladesh to ensure quality products supply to the consumers. Moreover, manufacturers of probiotic containing products should take the responsibility for providing the consumer with scientifically and legally correct information.

Role of early serum beta human chorionic gonadotropin measurement in predicting multiple pregnancy and pregnancy wastage in an in vitro ET fertilization cycle.

OBJECTIVE: This study was performed to assess the prognostic value of serum beta human chorionic gonadotropin (ßhCG), measured on day 14 post embryo transfer (ET) for predicting multiple gestation and pregnancy wastage in women undergoing in vitro fertilization ET (IVF-ET). MATERIALS AND METHODS: This retrospective study was performed between May 2009 and November 2012. Out of the 181 women who conceived, 168 were included and the remaining 13 were excluded as their pregnancy was biochemical. Serum ßhCG was measured using a chemiluminescent enzyme immunometric assay. The predictive values of serum ßhCG for establishing multiple pregnancy and pregnancy wastages were calculated by receiver operating characteristic (ROC) curve analysis. Median values of serum ßhCG and outcome of all pregnancies were compared. RESULTS: Out of the 168 patients who conceived after IVF treatment, 114 (68%) were viable pregnancies (delivered/ongoing). Among the viable pregnancies, 97 (85%) had a successful pregnancy outcome and the remaining 17 patients are ongoing pregnancies. Median values of ßhCG (625 IU/L) among viable pregnancies was significantly (P < 0.05) higher than that of nonviable pregnancies (174 IU/L). The median values of ßhCG for singleton (502 IU/L), twins (1093 IU/L), and triplets (2160 IU/L) was statistically significant (P < 0.05). Using ROC curve it was predicted that for a value of ßhCG at 375 IU/L, the sensitivity of viable pregnancy was 65% and specificity of viable pregnancy was also 65%, with positive and negative predictive values of 65 and 68%, respectively. Similarly for multiple pregnancy and pregnancy wastage the predictive values of ßhCG were 808 and 375 IU/L, respectively; while the sensitivity and specificity is more than 65% each. CONCLUSION: ßhCG cutoff values determined on day 14 post ET by ROC curve analysis are useful in discriminating between multiple pregnancy and pregnancy losses. The cutoff value might aid in the prognosis, clinical management, and counseling of the IVF patients.