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Triple-negative breast cancer (TNBC) poses a significant global health challenge due to its highly aggressive nature and invasive characteristics. Dysregulation of the Hippo pathway, a key regulator of various biological processes, is observed in TNBC, and its inhibition holds promise for impeding cancer growth. This in-silico analysis investigates the role of Transcriptional Enhanced Associate Domain 4 (TEAD4) in TNBC and its interaction with Yes Associated Protein (YAP) in cancer progression. Our results demonstrate that TEAD4 upregulation is linked to poor prognosis in TNBC, emphasizing its critical role in the disease. Moreover, we identify CID44521006, an analog of Flufenamic acid, as a potential therapeutic compound capable of disrupting the TEAD4-YAP interaction by binding to the YAP-binding domain of TEAD4. These findings underscore the significance of TEAD4 in TNBC and propose CID44521006 as a promising candidate for therapeutic intervention. The study contributes valuable insights to advance treatment options for TNBC, offering a potential avenue for the development of targeted therapies against this aggressive form of breast cancer. Supplementary Information: The online version contains supplementary material available at 10.1007/s40203-024-00239-8.

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Background: Breast cancer is the leading cause of frequent malignancy and morbidity among women across the globe, with an increment of 0.5% incidences every year. The deleterious effects of traditional treatment on off-target surrounding cells make it difficult to win the battle against breast cancer. Hence, an advancement in the therapeutic approach is crucial. Nanotechnology is one of the emerging methods for precise, targeted, and efficient drug delivery in cells. The previous study has demonstrated the cytotoxic effect of Ipomoea turpethum extract on breast cancer cells delivered via NIPAAM-VP-AA nanoparticles (NVA-IT). Manipulating the tumor microenvironment (TME) to inhibit cancer progression, invasion, and metastasis seems to be very insightful for researchers these days. With the help of secretome analysis of breast cancer cells after treatment with NVA-IT, we have tried to find out the possible TME manipulation achieved to favor a better prognosis of the disease. Method: MCF-7 and MDA MB-231 cells were treated with the IC50 value of NVA-IT, and the medium was separated from the cells after 24 h of the treatment. Nano LCMS/MS analysis was performed to identify the secretory proteins in the media. Further bioinformatics tools like GENT2, GSCA, GeneCodis 4, and STRING were used to identify the key proteins and their interactions. Result: From the nano LCMS/MS analysis, 70 differentially expressed secretory proteins in MCF-7 and 191 in MDA MB-231 were identified in the cell's media. Fifteen key target proteins were filtered using bioinformatics analysis, and the interaction of proteins involved in vesicular trafficking, cell cycle checkpoints, and oxidative stress-related proteins was prominent. Conclusion: This study concluded that I. turpethum extract-loaded NIPAAM-VP-AA nanoparticles alter the secretory proteins constituting the TME to cease cancer cell growth and metastasis.

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Introduction: Artemisia absinthium (wormwood) exhibits anticancer properties by inhibiting proliferation and causing cell death in breast cancer. Targeted drug delivery of A. absinthium nanoformulation using N-isopropyl acrylamide, N-vinyl pyrrolidone, and acrylic acid-based polymeric nanoparticles (NVA-AA NPs) was ensured by utilizing features of the tumor microenvironment, although their mechanism of action involved in cytotoxicity remains unknown. Methods: The present study employed nano LC-MS/MS to identify differences in secretory protein expression associated with the treatment of breast cancer cell lines (MCF-7; MDA-MB-231) by NVA-AA NPs for the determination of affected pathways and easily accessible therapeutic targets. Different bioinformatics tools were used to identify signature differentially expressed proteins (DEPs) using survival analysis by GENT2 and correlation analysis between their mRNA expressions and sensitivity toward small-molecule drugs as well as immune cell infiltration by GSCA. Results: Analysis by GENT2 revealed 22 signature DEPs with the most significant change in their expression regulation, namely, gelsolin, alpha-fetoprotein, complement component C3, C7, histone H2B type 1-K, histone H2A.Z, H2AX, heat shock cognate 71 kDa protein, heat shock 70 kDa protein 1-like, cytochrome c somatic, GTP-binding nuclear protein Ran, tubulin beta chain, tubulin alpha-1B chain, tubulin alpha-1C chain, phosphoglycerate mutase 1, kininogen 1, carboxypeptidase N catalytic chain, fibulin-1, peroxiredoxins 4, lactate dehydrogenase C, SPARC, and SPARC-like protein 1. Correlation analysis between their mRNA expressions versus immune cell infiltrates showed a positive correlation with antitumor immune response elicited by these NPs as well as a correlation with drug response shown by the GDSC and CTRP drugs in different cancer cells. Discussion: Our results suggest that NVA-AA NPs were able to invade the tumor microenvironment; transformed the communication network between the cancer cells; affected potential drivers of microtubular integrity, nucleosome assembly, and cell cycle; and eventually caused cell death.

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Sporadic cases of breast cancer being more prevalent than the hereditary cases, can be largely attributed to environmental pollutants like polycyclic aromatic hydrocarbons (PAHs). The aim of the present study was to identify gene dysregulations and the associations in DMBA (a PAH) induced breast cancer. A breast cancer model was developed in Wistar rats (n = 40), using DMBA. Serum proteomics (2D electrophoresis and MALDI-TOF MS) followed by relative gene expression analysis in mammary glands were conducted to reach to the differential gene signatures. The candidate genes were subjected to survival analysis (by GEPIA2 and KM plotter) and infiltration analysis (by ImmuCellAI) for correlating gene expression with patient survival and immune cell infiltration respectively. Further, the regulatory network investigation (by Cytoscape) was performed to find out the transcription factors (TFs) and miRNAs of the concerned genes. A gene trio (ANXA5, MTG1, PPP2R5B), expressing differentially in early mammary carcinogenesis at 4 months (precancerous stage) till full-fledged cancerous stage (post 6 months) was identified. The altered gene expression was associated with less survival among breast cancer patients (n = 4019). The dysregulated expression also has a correlation with enhanced mammary infiltration of immune cells. Moreover, a regulatory network (comprising of 77 transcription factors and 50 miRNAs) involved in the regulation of candidate genes was also deciphered. The deregulated target genes can therefore be explored for reregulation via identified TFs and miRNAs, and survival thereby improved.

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Ovarian cancer (OC) is second most common form of gynaecological cancer world wide . In this study, we collected and analyzed three ovarian cancer microarray raw datasets from Gene Expression Omnibus, NCBI, and identified a total of 1806 significant DEGs (Differentially expressed genes). The functional analysis of the DEGs showed that the 885 upregulated DEGs were mostly enriched in protein-binding activity, while the downregulated 796 genes were mostly enriched in retinal dehydrogenase activity and GABA receptor binding. We then constructed a protein-protein interaction network of the DEGs DEGs in ovarian cancer datasetsand analyzed the network to find cluster subnets, using molecular complex detection (MCODE). Common genes among top hub gene list, bottleneck gene list and maximum clique centrality (MCC) gene lists were identified as key driver genes, After analyzing the network. The following genes, STK12 (Serine threonine protein kinase), UBE2C (Ubiquitin-conjugating enzyme E2 C), CENPA (Centromere protein A), CCNB1 (Cyclin B1), POLD1 (polymerase delta 1) and KIF11 (Kinesin Family Member 11) were finally identified as driver genes. Higher expression of the key driver genes, STK12, UBE2C, CENPA, CCNB1, POLD1 and KIF11, was associated with lower overall survival (OS) among ovarian cancer patients. Therefore, the identified driver genes could be important diagnostic and prognostic biomarkers for predicting ovarian cancer progression and understanding the mechanism of tumour formation and recurrence.

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Network medicine provides network theoretical tools, methods and properties to study underlying laws governing human interactome to identify disease states and disease complexity leading to drug discovery. Within this framework, we investigated the topological properties of ovarian cancer network (OCN) and the roles of hubs to understand OCN organization to address disease states and complexity. The OCN constructed from the experimentally verified genes exhibits fractal nature in the topological properties with deeply rooted functional communities indicating self-organizing behavior. The network properties at all levels of organization obey one parameter scaling law which lacks centrality lethality rule. We showed that $\langle k\rangle $ can be taken as a scaling parameter, where, power law exponent can be estimated from the ratio of network diameters. The betweenness centrality $C_B$ shows two distinct behaviors one shown by high degree hubs and the other by segregated low degree nodes. The $C_B$ power law exponent is found to connect the exponents of distributions of high and low degree nodes. OCN showed the absence of rich-club formation which leads to the missing of a number of attractors in the network causing formation of weakly tied diverse functional modules to keep optimal network efficiency. In OCN, provincial and connector hubs, which includes identified key regulators, take major responsibility to keep the OCN integrity and organization. Further, most of the key regulators are found to be over expressed and positively correlated with immune infiltrates. Finally, few potential drugs are identified related to the key regulators.

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Glycogen synthase kinase 3 (GSK3) is a serine/threonine kinase which in the presence of ATP in its ATP-binding pocket transfers a phosphate to a primed substrate. GSK3ß is an isoform of GSK3 which has been projected as a potent therapeutic target in human diseases including cancers and metabolic syndrome. Incidentally, cardiovascular disease is a common cause of non-cancer related deaths in prostate cancer (PCa) patients, mainly due to the effects of androgen-deprivation therapy (ADT), a mainstay for PCa treatment. Several small molecular inhibitors of GSK3 are either ATP-competitive (bind to the ATP-binding pocket), or non-ATP-competitive inhibitors (binding to the substrate-binding site of the enzyme). In this study, 2-ß-D-glucopyranosyl-1,3,6,7-tetrahydroxy-9H-xanthen-9-one (ßDGT), a natural xanthonoid present in many plant species, is reported to bind to the ATP-binding pocket of GSK3ß and inhibit its activity, as demonstrated by the molecular docking and molecular dynamics simulation analysis and experimental validation in vitro. A comparison of the binding affinities with five known ATP-competitive inhibitors of GSK3ß suggested similarity in binding site residues in the ATP-binding pocket of the enzyme. The optimum inhibitory concentration of the xanthonoid as determined by the luminescent kinase assay was 200 µM. The study envisages the use of ßDGT as a natural ATP-competitive inhibitor of GSK3ß and implicates its use in PCa patients on ADT, a cardiovascular disease risk, and other pathological conditions where GSK3 inhibition may be clinically important. HighlightsGSK3ß is a multifaceted kinase known for its role in cancers, cardiovascular, and other diseases.In this study, ßDGT, a xanthonoid, is reported to bind to the ATP-binding pocket of GSK3ß.A comparison of ßDGT binding with 5 known ATP-competitive inhibitors of GSK3ß suggested the involvement of residues at the ATP binding site.The binding site analysis suggested an ATP-competitive mechanism of enzyme inhibition.Study envisages the use of ßDGT as a natural ATP-competitive inhibitor of GSK3ß and implicates its use in prostate cancer patients on androgen-deprivation therapy, a cardiovascular disease risk, and other pathological conditions.Communicated by Ramaswamy H. Sarma.

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Complex disease networks can be studied successfully using network theoretical approach which helps in finding key disease genes and associated disease modules. We studied prostate cancer (PCa) protein-protein interaction (PPI) network constructed from patients' gene expression datasets and found that the network exhibits hierarchical scale free topology which lacks centrality lethality rule. Knockout experiments of the sets of leading hubs from the network leads to transition from hierarchical (HN) to scale free (SF) topology affecting network integration and organization. This transition, HN â†’ SF, due to removal of significant number of the highest degree hubs, leads to relatively decrease in information processing efficiency, cost effectiveness of signal propagation, compactness, clustering of nodes and energy distributions. A systematic transition from a diassortative PCa PPI network to assortative networks after the removal of top 50 hubs then again reverting to disassortativity nature on further removal of the hubs was also observed indicating the dominance of the largest hubs in PCa network intergration. Further, functional classification of the hubs done by using within module degrees and participation coefficients for PCa network, and leading hubs knockout experiments indicated that kinless hubs serve as the basis of establishing links among constituting modules and heterogeneous nodes to maintain network stabilization. We, then, checked the essentiality of the hubs in the knockout experiment by performing Fisher's exact test on the hubs, and showed that removal of kinless hubs corresponded to maximum lethality in the network. However, excess removal of these hubs essentially may cause network breakdown.

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Identification of key regulators and regulatory pathways is an important step in the discovery of genes involved in cancer. Here, we propose a method to identify key regulators in prostate cancer (PCa) from a network constructed from gene expression datasets of PCa patients. Overexpressed genes were identified using BioXpress, having a mutational status according to COSMIC, followed by the construction of PCa Interactome network using the curated genes. The topological parameters of the network exhibited power law nature indicating hierarchical scale-free properties and five levels of organization. Highest degree hubs (k ≥ 65) were selected from the PCa network, traced, and 19 of them was identified as novel key regulators, as they participated at all network levels serving as backbone. Of the 19 hubs, some have been reported in literature to be associated with PCa and other cancers. Based on participation coefficient values most of these are connector or kinless hubs suggesting significant roles in modular linkage. The observation of non-monotonicity in the rich club formation suggested the importance of intermediate hubs in network integration, and they may play crucial roles in network stabilization. The network was self-organized as evident from fractal nature in topological parameters of it and lacked a central control mechanism.

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Globally only 5-10% of people encountering Mycobacterium tuberculosis have a lifetime risk of active disease indicating a strong host genetic bias towards development of tuberculosis. In the current study we investigated genotype variants pertaining to five cytokine genes namely IFNG, TNFA, IL4, IL10 and IL12 in the north Indian population with active pulmonary tuberculosis (APTB) and correlated the serum cytokine levels with the corresponding genotypes. Twenty five single nucleotide polymorphisms (SNPs) including six loci examined for the first time in tuberculosis were selected for genotyping in 108 patients with APTB from north India and 48 healthy regional controls (HC). Applying exclusion criteria 12 SNPs passed all the filters and were analysed further. The serum cytokine concentrations were measured by ELISA. Compared to HC mean serum IFN-γ, IL-12, IL-4, and IL-10 levels were higher in APTB (p = 0.3661, p = 0.0186, p = 0.003, p = 0.7, respectively). In contrast the mean serum TNF-α level was higher in HC (p = 0.007). Comparison of genotypes and serum levels of the corresponding cytokine genes reveal that though IFN-γ and IL-4 levels were higher in APTB the genotype variants showed no difference between HC and APTB. In contrast the genotypes of the selected rsIDs in the TNFA, IL12 and IL10 genes showed significant association with the varying serum levels of corresponding cytokines. The variant of the TNFA gene at rs3093662, the IL12 gene at rs3213094 and rs3212220 and the IL10 gene at rs3024498 did show a strong indication to be of relevance to the immunity to tuberculosis. To our knowledge this is the first report from this region relating genotypes and serum cytokine levels in north Indian population.

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