Identification of Potential Breast Cancer Stem Cell Biomarkers in the Secretome Using a Network Interaction Approach Analysis.

BACKGROUND: Breast cancer stem cells (BCSCs) play a role in the high rates of resistance, recurrence, and metastasis. The precise biomarkers of BCSCs can assist effectively in identifying cancer, assessing prognosis, diagnosing, and monitoring therapy. The aim of this study was to give a complete analysis for predicting specific biomarkers of BCSCs. METHODS: We aggregated profile datasets in this work to shed light on the underlying critical genes and pathways of BCSCs. We obtained two expression profiling by array datasets (GSE7513 and GSE7515) from the Gene Expression Omnibus (GEO) database to identify biomarkers in BCSCs. Enrichr was used to do functional analysis, including gene ontology (GO) and reactome pathway. Furthermore, the protein-protein interaction (PPI) of these differential expression genes (DEGs) was visualized using Cytoscape with the search tool for the retrieval of interacting genes (STRING). The hub genes in the PPI network were chosen for further investigation. RESULTS: We identified 65 up-regulated and 190 down- regulated DEGs and the GO enrichment analysis revealed that these DEGs were enriched in biological process related to tumorigenesis and stemness, including alter the extracellular matrix's physicochemical properties, cytoskeletal reorganisation, adhesion, motility, migration, growth, and survival. The Reactome analysis indicated that these DEGs were also involved in modulating function of ECM, regulation cancer metabolism and angiogenesis, tumor growth, proliferation, and metastasis. CONCLUSION: Our bioinformatic study revealed that FYN, INADL, OCLN, F11R, and TOP2A were potential biomarker panel of BCSCs from secretome.

Prediction of the Signaling Pathway in Polycystic Ovary Syndrome Using an Integrated Bioinformatics Approach.

Objectives The purpose of this study was to define the underlying biological mechanisms of PCOS utilizing the protein-protein interaction networks that were constructed based on the putative disease-causing genes for PCOS. Design No animals were used in this research because this study is an In-Silico studies which mainly uses softwares and online analysis tools. Participants/Materials, Settings Genes datasets related with PCOS were obtained from Genecard. Methods The protein-protein interaction networks (PPIN) of PCOS were created using the String Database after genes related with PCOS were obtained from Genecard. After that, we performed an analysis of the hub-gene clusters extracted from the PPIN using the ShinyGO algorithm. In the final step of this research project, functional enrichment analysis was used to investigate the primary biological activities and signaling pathways that were associated with the hub clusters. Results The Genecard database provided the source for the identification of a total of 1072 potential genes related with PCOS. The PPIN that was generated by using the genes that we collected above contained a total of 82 genes and three different types of cluster interaction interactions. In addition, after conducting research on the PPIN with the shinyGO plug-in, 19 of the most important gene clusters were discovered. The primary biological functions that were enriched in the key clusters that were developed were ovarian stereoidogenesis, breast cancer pathway, regulation of lipid and glucose metabolism by AMPK pathway, and ovarian stereoidogenesis. The integrated analysis that was performed in the current study demonstrated that these hub clusters and their connected genes are closely associated to the pathogenesis of PCOS. Limitations Several of the significant genes that were identified in this study, such as ACVR1, SMAD5, BMP6, SMAD3, SMAD4 and AMH. It is necessary to do additional research using large samples, several centers, and multiple ethnicities in order to verify these findings. Conclusions The integrated analysis that was performed in the current study demonstrated that these hub clusters and their connected genes are closely associated to the pathogenesis of PCOS. This information may possibly bring unique insights for the treatment of PCOS as well as the investigation of its underlying pathogenic mechanism.

Synthesis and in vitro Activity of Eugenyl Benzoate Derivatives as BCL-2 Inhibitor in Colorectal Cancer with QSAR and Molecular Docking Approach.

OBJECTIVE: This study is aimed to acquiring new compounds of Eugenyl benzoate (2-methoxy-4-(prop-2-en-1-yl)phenyl benzoate) derivatives that can inhibit HT29 colorectal cancer cells. METHODS: In this research, we used several chemical reactions to synthesize novel compounds, such as Esterification, Demethylation, Halohydrin, and Sharpless reaction. Cytotoxicity assays were performed to test the inhibitory activity of compounds against HT29 colon cancer cells. QSAR analysis were carried out to analyse the relationship of chemical structure of the novel compounds with their cytotoxic activity. RESULT: Ten novel compounds were successfully synthesized and tested in vitro against the HT29 cell. The IC50 of the novel compounds were between 26.56 µmol/ml - 286.81 µmol/ml which compound 4-[(2S)-2,3-dihydroxypropyl]-2-methoxyphenyl 2-hydroxybenzoate (9) showed as best active compound as BCL-2 inhibitors better than other synthesized compounds and Eugenol as well. QSAR analysis of the in vitro results gave a Log equation: 1/IC50 = -0.865-0.210 (LogP)2 + 1,264 (logP)-0.994 CMR (n = 10; r = 0.706; SE: 0.21; F = 0.497, sig = 7.86). The equation shows the log variable P and CMR affect IC50. The properties of hydrophobicity (log P) are more instrumental than the ones of steric (CMR). CONCLUSION: The active compound (9) given best activities as BCL-2 inhibitors than other eugenol derivatives. QSAR indicates the logP and CMR have effect on its colorectal cytotoxic activity which the hydrophobicity parameter (logP) plays more role than the steric parameter (CMR).

Immunoinformatics Approach for Epitope-Based Vaccine Design: Key Steps for Breast Cancer Vaccine.

Vaccines are an upcoming medical intervention for breast cancer. By targeting the tumor antigen, cancer vaccines can be designed to train the immune system to recognize tumor cells. Therefore, along with technological advances, the vaccine design process is now starting to be carried out with more rational methods such as designing epitope-based peptide vaccines using immunoinformatics methods. Immunoinformatics methods can assist vaccine design in terms of antigenicity and safety. Common protocols used to design epitope-based peptide vaccines include tumor antigen identification, protein structure analysis, T cell epitope prediction, epitope characterization, and evaluation of protein-epitope interactions. Tumor antigen can be divided into two types: tumor associated antigen and tumor specific antigen. We will discuss the identification of tumor antigens using high-throughput technologies. Protein structure analysis comprises the physiochemical, hydrochemical, and antigenicity of the protein. T cell epitope prediction models are widely available with various prediction parameters as well as filtering tools for the prediction results. Epitope characterization such as allergenicity and toxicity can be done in silico as well using allergenicity and toxicity predictors. Evaluation of protein-epitope interactions can also be carried out in silico with molecular simulation. We will also discuss current and future developments of breast cancer vaccines using an immunoinformatics approach. Finally, although prediction models have high accuracy, the opposite can happen after being tested in vitro and in vivo. Therefore, further studies are needed to ensure the effectiveness of the vaccine to be developed. Although epitope-based peptide vaccines have the disadvantage of low immunogenicity, the addition of adjuvants can be a solution.

Targeted Sequencing of Germline Breast Cancer Susceptibility Genes for Discovering Pathogenic/Likely Pathogenic Variants in the Jakarta Population.

Germline predisposition plays an important role in breast cancer. Different ethnic populations need respective studies on cancer risks pertinent to germline variants. We aimed to discover the pathogenic and likely pathogenic variants (P/LP-Vs) of germline breast cancer susceptibility genes and to evaluate their correlation with the clinical characteristics in Jakarta populations. The pure DNA was extracted from the blood buffy coat, using reagents from the QIAamp DNA Mini Kit® (Qiagen, Hilden, Germany). The DNA libraries were prepared using the TargetRich™ Hereditary Cancer Panel (Kailos Genetics®, Huntsville, AL, USA). The barcoded DNA libraries were sequenced using the Illumina NextSeq 500 platform. In-house bioinformatics pipelines were used to analyze the gene variants. We identified 35 pathogenic and likely pathogenic (P/LP-Vs) variants (28 frameshift, 5 nonsense, and 2 splice-site variants). The P/LP-Vs group was statistically significantly different in luminal B status (p < 0.05) compared with the non-P/LP-Vs group. The P/LP-Vs found both in BRCA1/2 genes and non-BRCA genes may increase the risk of breast cancer and alter drug responses. The screening of multigene variants is suggested, rather than BRCA testing only. Prior knowledge of the germline variants status is important for optimal breast cancer diagnosis and optimal therapy.

Virtual screening of Indonesian herbal compounds as COVID-19 supportive therapy: machine learning and pharmacophore modeling approaches.

BACKGROUND: The number of COVID-19 cases continues to grow in Indonesia. This phenomenon motivates researchers to find alternative drugs that function for prevention or treatment. Due to the rich biodiversity of Indonesian medicinal plants, one alternative is to examine the potential of herbal medicines to support COVID therapy. This study aims to identify potential compound candidates in Indonesian herbal using a machine learning and pharmacophore modeling approaches. METHODS: We used three classification methods that had different decision-making processes: support vector machine (SVM), multilayer perceptron (MLP), and random forest (RF). For the pharmacophore modeling approach, we performed a structure-based analysis on the 3D structure of the main protease SARS-CoV-2 (3CLPro) and repurposed SARS, MERS, and SARS-CoV-2 drugs identified from the literature as datasets in the ligand-based method. Lastly, we used molecular docking to analyze the interactions between the 3CLpro and 14 hit compounds from the Indonesian Herbal Database (HerbalDB), with lopinavir as a positive control. RESULTS: From the molecular docking analysis, we found six potential compounds that may act as the main proteases of the SARS-CoV-2 inhibitor: hesperidin, kaempferol-3,4'-di-O-methyl ether (Ermanin); myricetin-3-glucoside, peonidin 3-(4'-arabinosylglucoside); quercetin 3-(2G-rhamnosylrutinoside); and rhamnetin 3-mannosyl-(1-2)-alloside. CONCLUSIONS: Our layered virtual screening with machine learning and pharmacophore modeling approaches provided a more objective and optimal virtual screening and avoided subjective decision making of the results. Herbal compounds from the screening, i.e. hesperidin, kaempferol-3,4'-di-O-methyl ether (Ermanin); myricetin-3-glucoside, peonidin 3-(4'-arabinosylglucoside); quercetin 3-(2G-rhamnosylrutinoside); and rhamnetin 3-mannosyl-(1-2)-alloside are potential antiviral candidates for SARS-CoV-2. Moringa oleifera and Psidium guajava that consist of those compounds, could be an alternative option as COVID-19 herbal preventions.

Whole genome sequencing data of Candida krusei, the pathogen causing Candidaemia, from Department of Parasitology Culture Collection, Faculty of Medicine Universitas Indonesia.

Candida krusei is a Candida non-albicans species with a high prevalence, which causes candidaemia. Current treatment guidelines include fluconazole as a primary therapeutic option for the treatment of these infections; however, it is only a fungistatic against Candida spp., and both inherent and acquired resistance to fluconazole have been reported. C. krusei species is also reported as the only Candida sp. which has an intrinsic resistance factor to fluconazole. Therefore, in dealing with antifungal resistance, it is necessary to develop new antifungal agents that are efficient in the treatment of fungal infections, especially those caused by C. krusei. The purpose of this study was to investigate the genome of clinical C. krusei isolates and correlate the resistant phenotypes with mutations in resistance genes. A total of 16 samples of C. krusei from clinical samples from hospitals in Jakarta were used in the experiment. All colonies were extracted using the QIAamp DNA Mini Kit. The library was prepared using the Illumina DNA Prep Kit. The sequencing process was carried out on the Illumina MiSeq Platform using a 2x301 paired-end configuration. FASTQ raw files are available under the BioProject Accession Number PRJNA819536 and Sequence Read Archive Accession Numbers SRR18739949 and SRR18739964.

Genome-based characterization of Escherichia coli causing bloodstream infection through next-generation sequencing.

Escherichia coli are one of the commonest bacteria causing bloodstream infection (BSI). The aim of the research was to identify the serotypes, MLST (Multi Locus Sequence Type), virulence genes, and antimicrobial resistance of E. coli isolated from bloodstream infection hospitalized patients in Cipto Mangunkusumo National Hospital Jakarta. We used whole genome sequencing methods rather than the conventional one, to characterized the serotypes, MLST (Multi Locus Sequence Type), virulence genes, and antimicrobial resistance (AMR) of E. coli. The composition of E. coli sequence types (ST) was as follows: ST131 (n = 5), ST38 (n = 3), ST405 (n = 3), ST69 (n = 3), and other STs (ST1057, ST127, ST167, ST3033, ST349, ST40, ST58, ST6630). Enteroaggregative E. coli (EAEC) and Extra-intestinal pathogenic E. coli (ExPEC) groups were found dominant in our samples. Twenty isolates carried virulence genes for host cells adherence and 15 for genes that encourage E. coli immune evasion by enhancing survival in serum. ESBL-genes were present in 17 E. coli isolates. Other AMR genes also encoded resistance against aminoglycosides, quinolones, chloramphenicol, macrolides and trimethoprim. The phylogeny analysis showed that phylogroup D is dominated and followed by phylogroup B2. The E. coli isolated from 22 patients in Cipto Mangunkusumo National Hospital Jakarta showed high diversity in serotypes, sequence types, virulence genes, and AMR genes. Based on this finding, routinely screening all bacterial isolates in health care facilities can improve clinical significance. By using Whole Genome Sequencing for laboratory-based surveillance can be a valuable early warning system for emerging pathogens and resistance mechanisms.

Targeted genome sequencing data of young women breast cancer patients in Cipto Mangunkusumo national hospital, Jakarta.

Breast cancer is the most common cancer in women, accounting for approximately 25% of all cancer cases worldwide. Some breast cancer patients are genetically predisposed to genes involved in genomic stability. We report the targeted genome sequencing data of 24 young women (aged below 45 years) breast cancer patients admitted to Cipto Mangunkusumo National Hospital, Jakarta, Indonesia. These data will be useful in detecting the genome markers of breast cancer and in deciding the diagnostics and therapies. DNA sequences were obtained using the Illumina NextSeq 500 platform. FASTQ raw files are available under BioProject accession number PRJNA606794 and Sequence Read Archive accession numbers SRR11774092-SRR11774115.

Whole genome sequencing data of Escherichia coli isolated from bloodstream infection patients in Cipto Mangunkusumo National Hospital, Jakarta, Indonesia.

Bloodstream infections (BSIs) are some of the most devastating preventable complications in critical care units. Of the bacterial causes of BSIs, Escherichia coli is the most common among Enterobacteriaceae. Bacteria resistant to therapeutic antibiotics represent a significant global health challenge. In this study, we present whole genome sequence data of 22 E. coli isolates that were obtained from bloodstream infection patients admitted to Cipto Mangunkusumo National Hospital, Jakarta, Indonesia. These data will be useful for analysing the serotypes, virulence genes, and antimicrobial resistance genes of E. coli. DNA sequences of E. coli were obtained using the Illumina MiSeq platform. The FASTQ raw files of these sequences are available under BioProject accession number PRJNA596854 and Sequence Read Archive accession numbers SRR10761126-SRR10761147.