Triple negative breast cancer (TNBC) is an aggressive subtype of breast cancer with poor clinical outcomes and lack of approved targeted therapy. Dysregulated microRNAs (miRNAs) have been considered a promising biomarker, which plays an important role in the tumorigenesis of human cancer. Due to the increase in miRNA profiling datasets of TNBC, a proper analysis is required for studying. Therefore, this study used meta‐analysis to amalgamate ten miRNA profiling studies of TNBC. By the robust rank aggregation method, metasignatures of six miRNAs (4 upregulated: hsa‐miR‐135b‐5p, hsa‐miR‐18a‐5p, hsa‐miR‐9‐5p and hsa‐miR‐522‐3p; 2 downregulated: hsa‐miR‐190b and hsa‐miR‐449a) were obtained. The gene ontology analysis revealed that target genes regulated by miRNAs were associated with processes like the regulation of transcription, DNA dependent, and signal transduction. Also, it is noted from the pathway analysis that signaling and cancer pathways were associated with the progression of TNBC. A Naïve Bayes‐based classifier built with miRNA signatures discriminates TNBC and non‐TNBC samples in test data set with high diagnostic sensitivity and specificity. From the analysis carried out by the study, it is suggested that the identified miRNAs are of great importance in improving the diagnostics and therapeutics for TNBC.

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Triple‐negative breast cancer (TNBC) has attracted more attention compared with other breast cancer subtypes due to its aggressive nature, poor prognosis, and chemotherapy remains the mainstay of treatment with no other approved targeted therapy. Therefore, the study aimed to discover more promising therapeutic targets and investigating new insights of biological mechanism of TNBC. Six microarray data sets consisting of 463 non‐TNBC and 405 TNBC samples were mined from Gene Expression Omnibus. The data sets were integrated by meta‐analysis and identified 1075 differentially expressed genes. Protein‐protein interaction network was constructed which consists of 486 nodes and 1932 edges, where 29 hub genes were obtained with high topological measures. Further, 16 features (hub genes), 12 upregulated (AURKB, CCNB2, CDC20, DDX18, EGFR, ENO1, MYC, NUP88, PLK1, PML, POLR2F, and SKP2) and four downregulated ( CCND1, GLI3, SKP1, and TGFB3) were selected through machine learning correlation based feature selection method on training data set. A naïve Bayes based classifier built using the expression profiles of 16 features (hub genes) accurately and reliably classify TNBC from non‐TNBC samples in the validation test data set with a receiver operating curve of 0.93 to 0.98. Subsequently, Gene Ontology analysis revealed that the hub genes were enriched in mitotic cell cycle processes and Kyoto Encyclopedia of Genes and Genomes pathway analysis showed that they were enriched in cell cycle pathways. Thus, the identified key hub genes and pathways highlighted in the study would enhance the understanding of molecular mechanism of TNBC which may serve as potential therapeutic target.

Re-emergence of ZIKV has caused infections in more than 1.5 million people. The molecular mechanism and pathogenesis of ZIKV is not well explored due to unavailability of adequate model and lack of publically accessible resources to provide information of ZIKV-Human protein interactome map till today. This study made an attempt to curate the ZIKV-Human interaction proteins from published literatures and RNA-Seq data. 11 direct interaction, 12 associated genes are retrieved from literatures and 3742 Differentially Expressed Genes (DEGs) are obtained from RNA-Seq analysis. The genes have been analyzed to construct the ZIKV-Human Interactome Map. The importance of the study has been illustrated by the enrichment analysis and observed that direct interaction and associated genes are enriched in viral entry into host cell. Also, ZIKV infection modulates 32% signal and 27% immune system pathways. The integrated database, ZikaBase has been developed to help the virology research community and accessible at https://test5.bicpu.edu.in.

Breast cancer affects every 1 of 3000 pregnant women or in the first post-partum year is referred as Pregnancy Associated Breast Cancer (PABC) in mid 30s. Even-though rare disease, classified under hormone receptor negative status which metastasis quickly to other parts by extra cellular matrix degradation. Hence it is important to find an optimal treatment option for a PABC patient. Also additional care should be taken to choose the drug; in order to avoid fetal malformation and post-partum stage side-effects. The adaptation of target based therapy in the clinical practice may help to substitute the mastectomy treatment. Recent studies suggested that certain altered Post Translational Modifications (PTMs) may be an indicative of breast cancer progression; an attempt is made to consider the over represented PTM as a parameter for gene selection. The public dataset of PABC from GEO were examined to select Differentially Expressed Genes (DEG). The corresponding PTMs for DEG were collected and association between them was found using data mining technique. Usually clustering algorithm has been applied for the study of gene expression with drawback of clustering of gene products based on specified features. But association rule mining method overcome this shortcoming and determines the useful and in depth relationships. From the association, genes were selected to study the interactions and pathways. These studies emphasis that the genes KLF12, FEN1 MUC1 and SP110, can be chosen as target, which control cancer development, without any harm to pregnancy as well as fetal developmental process.