Tuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis (M.tb.). Our integrative analysis aims to identify the transcriptional profiling and gene expression signature that distinguish individuals with active TB (ATB) disease, and those with latent tuberculosis infection (LTBI). In the present study, we reanalyzed a microarray dataset (GSE37250) from GEO database and explored the data for differential gene expression analysis between those with ATB and LTBI derived from Malawi and South African cohorts. We used BRB array tool to distinguish DEGs (differentially expressed genes) between ATB and LTBI. Pathway enrichment analysis of DEGs was performed using DAVID bioinformatics tool. The protein–protein interaction (PPI) network of most upregulated genes was constructed using STRING analysis. We have identified 375 upregulated genes and 152 downregulated genes differentially expressed between ATB and LTBI samples commonly shared among Malawi and South African cohorts. The constructed PPI network was significantly enriched with 76 nodes connected to 151 edges. The enriched GO term/pathways were mainly related to expression of IFN stimulated genes, interleukin-1 production, and NOD-like receptor signaling pathway. Downregulated genes were significantly enriched in the Wnt signaling, B cell development, and B cell receptor signaling pathways. The short-listed DEGs were validated in a microarray data from an independent cohort (GSE19491). ROC curve analysis was done to assess the diagnostic accuracy of the gene signature in discrimination of active and latent tuberculosis. Thus, we have derived a seven-gene signature, which included five upregulated genes FCGR1B, ANKRD22, CARD17, IFITM3, TNFAIP6 and two downregulated genes FCGBP and KLF12, as a biomarker for discrimination of active and latent tuberculosis. The identified genes have a sensitivity of 80–100% and specificity of 80–95%. Area under the curve (AUC) value of the genes ranged from 0.84 to 1. This seven-gene signature has a high diagnostic accuracy in discrimination of active and latent tuberculosis.

Background: We investigated the expression of TLR genes and the effects of CpG ODN in Estrogen Receptor positive, Progesterone Receptor positive breast cancer cell line (T47D) and a triple-negative breast cancer cell line (MDA-MB-468) followed by studying the immunostimulatory activity of CpG oligonucleotides in breast cancer cell lines T47D and MDA-MB-468. Materials and Methods: We evaluated the expression pattern of TLR genes (TLR1 to TLR9) in T47D and MDA-MB-468 cells using Real-time qPCR analysis. The intracellular TLR9 protein expression was studied by flow cytometry. The effect of CpG ODN on cell viability was tested using MTT assay. The relative expression of pro-inflammatory (IL6 and TNFα) and anti- inflammatory/immunosuppressive cytokines genes (IL10 and TGF beta1) were examined by Real-time qPCR. Results: We found that MDA-MB-468 cells expressed TLR2, TLR3, TLR6, TLR8 and TLR9 genes and T47D cells expressed TLR3, TRL5, TLR8 and TLR9 genes. Stimulation of TLR9 in vitro with CpG significantly reduced the cell viability of T47D and MDA-MB-468 cells. IL6 cytokine gene expression was significantly reduced in both CpG treated T47D cells and MDA-MB-468 cells. TNFα gene expression was significantly reduced after treatment with CpG in MDA-MB-468 cells but not in T47D cells. IL10 and TGFβ1 expression were downregulated in CpG treated T47D cells. Whereas, IL10 and TGFβ1 were elevated in CpG treated MDA-MB-468 cells. Conclusion: Our in vitro finding gives preliminary evidence that triggering TLR9 using CpG ODN decreases the cell proliferation and alters the pro-inflammatory cytokines in favour of inhibition of hormone receptor positive breast cancer cells T47D and triple negative breast cancer cells MDA-MB-468.

MDA‑MB‑231 cells represent malignant triple‑negative breast cancer, which overexpress epidermal growth factor receptor (EGFR) and two genes (AXL and VIM) associated with poor prognosis. The present study aimed to identify novel therapeutic targets and elucidate the functional networks for the AXL and VIM genes in MDA‑MB‑231 cells. We identified 71 genes upregulated in MDA‑MB‑231 vs. MCF7 cells using BRB‑Array tool to re‑analyse microarray data from six GEO datasets. Gene ontology and STRING analysis showed that 43/71 genes upregulated in MDA‑MB‑231 compared with MCF7 cells, regulate cell survival and migration. Another 19 novel genes regulate migration, metastases, senescence, autophagy and chemoresistance. The Pattern Miner systems biology tool uses specific genes as inputs or 'baits' to identify outputs from the NCI‑60 database. Using five genes regulating cancer cell migration (AXL, VIM, EGFR, CAPN2, and COL4A1) as input 'baits', we used pattern miner to identify statistically significant, co‑expressed genes from the list of 71 genes upregulated in MDA‑MB‑231 compared with MCF7 cells. Outputs were subsets of the 71 genes, which showed significant co‑expression with one or more of the five input genes. These outputs were used to develop functional networks for AXL and VIM. Analysis of these networks verified known properties of AXL and VIM, and suggested novel functions for these two genes. Thus, genes in the AXL network promote migration, metastasis and chemoresistance, whereas the VIM gene network regulates novel tumorigenic processes, such as lipogenesis, senescence and autophagy. Notably, these two networks contain 12 genes not reported for TNBC.