Hypoxia is a salient feature of most solid tumors, and hypoxic adaptation of cancer cells has crucial implications in propagation of malignant clonal cell population. Osteopontin (OPN) has been identified as a hypoxia-responsive gene, but the mechanistic and regulatory role of OPN under hypoxia is less characterized. The present study identifies the existence of a positive inter-regulatory loop between hypoxia and OPN. We have shown that hypoxia induces OPN expression in breast cancer cells; however, the expression was found to be HIF1α independent. OPN enabled transcriptional upregulation of HIF1α expression both under normoxia and hypoxia, whereas stability of HIF1α protein in breast cancer cells remained unaffected. Moreover, we have shown that OPN induces integrin-linked kinase (ILK)/Akt-mediated nuclear factor (NF)-κB p65 activation leading to HIF1α-dependent vascular endothelial growth factor (VEGF) expression and angiogenesis in response to hypoxia. These in vitro data are biologically important as OPN expressing cells induce greater tumor growth and angiogenesis through enhanced expressions of proangiogenic molecules as compared with control. Immunohistochemical analysis of human breast cancer specimens revealed significant correlation between OPN and HIF1α but not HIF2α. Elevated expression of HIF1α and OPN was observed in pre-neoplastic and early stage infiltrating ductal carcinoma implicating the role of these proteins in neoplastic progression of breast cancer. Together, our results substantiate the prime role of OPN in cellular adaptation through ILK and NF-κB-mediated HIF1α-dependent VEGF expression in response to hypoxia that ultimately controls breast cancer progression and angiogenesis. Our study reinforces the fact that targeting OPN and its regulated signaling network hold important therapeutic implications.

Invasive ductal carcinoma (IDC) is the most common type of breast cancer and the leading cause of breast cancer related mortality. In the present study, metabolomic profiles of 72 tissue samples and 146 serum samples were analysed using targeted liquid chromatography multiple reaction monitoring mass spectrometry (LC-MRM/MS) and untargeted gas chromatography mass spectrometry (GC-MS) approaches. Combination of univariate and multivariate statistical treatment identified significant alterations of 42 and 32 metabolites in tissue and serum samples of IDC, respectively when compared to control. Some of the metabolite changes from tissue were also reflected in serum, indicating a bi-directional interaction of metabolites in IDC. Additionally, 8 tissue metabolites and 9 serum metabolites showed progressive change from control to benign to IDC suggesting their possible role in malignant transformation. We have identified a panel of three metabolites viz. tryptophan, tyrosine, and creatine in tissue and serum, which could be useful in screening of IDC subjects from both control and benign. The metabolomic alterations in IDC showed perturbations in purine and pyrimidine metabolism, amino sugar metabolism, amino acid metabolism, fatty acid biosynthesis etc. Comprehensively, this study provides valuable insights into metabolic adaptations of IDC, which can help to identify diagnostic markers as well as potential therapeutic targets.

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Globally, breast cancer is the second most common cancer among women. Although biomarker discoveries through various proteomic approaches of tissue and serum samples have been studied in breast cancer, urinary proteome alterations in breast cancer are least studied. Urine being a noninvasive biofluid and a significant source of proteins, it has the potential in early diagnosis of breast cancer. This study used complementary quantitative gel-based and gel-free proteomic approaches to find a panel of urinary protein markers that could discriminate HER2 enriched (HE) subtype breast cancer from the healthy controls. A total of 183 differentially expressed proteins were identified using three complementary approaches, namely 2D-DIGE, iTRAQ, and sequential window acquisition of all theoretical mass spectra. The differentially expressed proteins were subjected to various bioinformatics analyses for deciphering the biological context of these proteins using protein analysis through evolutionary relationships, database for annotation, visualization and integrated discovery, and STRING. Multivariate statistical analysis was undertaken to identify the set of most significant proteins, which could discriminate HE breast cancer from healthy controls. Immunoblotting and MRM-based validation in a separate cohort testified a panel of 21 proteins such as zinc-alpha2-glycoprotein, A2GL, retinol-binding protein 4, annexin A1, SAP3, SRC8, gelsolin, kininogen 1, CO9, clusterin, ceruloplasmin, and α1-antitrypsin could be a panel of candidate markers that could discriminate HE breast cancer from healthy controls.