Search

Breast cancer (BC) is a complex disease, which causes of high mortality rate in women. Early diagnosis and therapeutic improvements may reduce the mortality rate. There were more than 74 individual studies that have suggested BC-causing hub-genes (HubGs) in the literature. However, we observed that their HubG sets are not so consistent with each other. It may be happened due to the regional and environmental variations with the sample units. Therefore, it was required to explore hub of the HubG (hHubG) sets that might be more representative for early diagnosis and therapies of BC in different country regions and their environments. In this study, we selected top-ranked 10 HubGs (CCNB1, CDK1, TOP2A, CCNA2, ESR1, EGFR, JUN, ACTB, TP53, and CCND1) as the hHubG set by the protein-protein interaction network analysis based on all of 74 individual HubG sets. The hHubG set enrichment analysis detected some crucial biological processes, molecular functions, and pathways that are significantly associated with BC progressions. The expression analysis of hHubGs by box plots in different stages of BC progression and BC prediction models indicated that the proposed hHubGs can be considered as the early diagnostic and prognostic biomarkers. Finally, we suggested hHubGs-guided top-ranked 10 candidate drug molecules (SORAFENIB, AMG-900, CHEMBL1765740, ENTRECTINIB, MK-6592, YM201636, masitinib, GSK2126458, TG-02, and PAZOPANIB) by molecular docking analysis for the treatment against BC. We investigated the stability of top-ranked 3 drug-target complexes (SORAFENIB vs ESR1, AMG-900 vs TOP2A, and CHEMBL1765740 vs EGFR) by computing their binding free energies based on 100-ns molecular dynamic (MD) simulation based Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA) approach and found their stable performance. The literature review also supported our findings much more for BC compared with the results of individual studies. Therefore, the findings of this study may be useful resources for early diagnosis, prognosis, and therapies of BC. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Objective: This research aimed to assess the prevalence of caprine pasteurellosis, isolate and identify pasteurellosis (Mannheimia haemolytica and Pasteurella multocida) in pneumonic goats, and discover the main bacterial cause of pneumonia. Materials and Methods: One hundred and five samples (94 nasal swabs and 11 lung tissues) from goats suspected of having pneumonia were taken and transferred aseptically to the laboratory. Following the processing of the collected samples, Pasteurella spp. was isolated with the aid of plate culture methods. Biochemical characteristics were used to identify all bacterial isolates, which were then verified by polymerase chain reaction (PCR). Antimicrobial susceptibility testing was also carried out to evaluate the sensitivity profiles of various antibiotics. The Pasteurella haemolytica serotype-specific antigen (PHSSA) gene was used to identify isolates of M. haemolytica, and the KMT1 gene was used to identify isolates of P multocida. Results: From the 105 clinically suspicious samples, 51 (48.57%) were identified to be Pasteurella spp. through bacteriological testing and also by PCR targeting the 16S rRNA gene. Of these, 47.87% (45/94) were nasal swabs, and 54.55% (6/11) were lung tissues. Among confirmed samples, 70.59% (36/51) were identified as M. haemolytica, and 29.41% (15/51) were identified as P. multocida. Resistance to tetracycline, streptomycin, oxytetracycline, gentamicin, and ceftriaxone was found in 50%-83% of the isolates. In addition, PCR identified the PHSSA and KMT1 genes from isolates of P. multocida and M. haemolytica, respectively. Conclusion: The present study revealed that M. haemolytica and P multocida primarily caused pasteurellosis in pneumonic goats in Bangladesh. However, when treating these animals, the proper choice of antimicrobials should be made to control this disease. [ABSTRACT FROM AUTHOR]