Breast cancer adaptability to the drug environment reduces the chemotherapeutic response and facilitates acquired drug resistance. Cancer-specific therapeutics can be more effective against advanced-stage cancer than standard chemotherapeutics. To extend the paradigm of cancer-specific therapeutics, clinically relevant acquired tamoxifen-resistant MCF-7 proteome was deconstructed to identify possible druggable targets (N = 150). Twenty-eight drug inhibitors were used against identified druggable targets to suppress non-resistant (NC) and resistant cells (RC). First, selected drugs were screened using growth-inhibitory response against NC and RC. Seven drugs were shortlisted for their time-dependent (10–12 days) cytotoxic effect and further narrowed to three effective drugs (e.g., cisplatin, doxorubicin, and hydroxychloroquine). The growth-suppressive effectiveness of selected drugs was validated in the complex spheroid model (progressive and regressive). In the progressive model, doxorubicin (RC: 83.64 %, NC: 54.81 %), followed by cisplatin (RC: 76.66 %, NC: 68.94 %) and hydroxychloroquine (RC: 68.70 %, NC: 61.78 %) showed a significant growth-suppressive effect. However, in fully grown regressive spheroid, after 4th drug treatment, cisplatin significantly suppressed RC (84.79 %) and NC (40.21 %), while doxorubicin and hydroxychloroquine significantly suppressed only RC (76.09 and 76.34 %). Our in-depth investigation effectively integrated the expression data with the cancer-specific therapeutic investigation. Furthermore, our three-step sequential drug-screening approach unbiasedly identified cisplatin, doxorubicin, and hydroxychloroquine as an efficacious drug to target heterogeneous cancer cell populations.
Hormonal-positive BC grows slowly, and hormonal-inhibitors effectively suppress the oncogenesis. However, development of drug-resistance not only reduces the drug-response but also increases the chance of BC aggressiveness. Further, alternative chemotherapeutics are widely used to control advanced-stage BC. In contrast, we hypothesized that, compared to standard chemotherapeutics, cancer-specific drugs can be more effective against resistant-cancer. Although cancer-specific treatment identification is an uphill battle, our work shows proteome data can be used for drug selection. We identified multiple druggable targets and, using ex-vivo methods narrowed multiple drugs to disease-condition-specific therapeutics. We consider that our investigation successfully interconnected the expression data with the functional disease-specific therapeutic investigation and selected drugs can be used for effective resistant treatment with higher therapeutic response.
This study was performed to investigate the proteomic basis underlying the interaction between vitamin D3 (VD) and insulin (I) within ovarian follicle using the pig as a model. Porcine antral follicles were incubated in vitro for 12 h with VD alone and I alone or in combination (VD + I) or with no treatment as the control (C). In total, 7690 and 7467 proteins were identified in the granulosa and theca interna compartments, respectively. Comparative proteomic analysis revealed 97 differentially abundant proteins (DAPs) within the granulosa layer and 11 DAPs within the theca interna layer. In the granulosa compartment, VD affected proteome leading to the promotion of cell proliferation, whereas I influenced mainly proteins related to cellular adhesion. The VD + I treatment induced granulosa cell proliferation probably via the DAPs involved in DNA synthesis and the cell cycle regulation. In the theca interna layer, VD alone or in co-treatment with I affected DAPs associated with cholesterol transport and lipid and steroid metabolic processes that was further confirmed by diminished lipid droplet accumulation.
The application of quantitative proteomics demonstrated for the first time the complexity of VD and I interactions in porcine ovarian follicle, providing a framework for understanding the molecular mechanisms underlying their cross-talk. Although identified DAPs were related to crucial ovarian processes, including the granulosa cell proliferation and cholesterol transport in the theca interna layer, novel molecular pathways underlying these processes have been proposed. The identified unique proteins may serve as indicators of VD and I interactions in both follicle layers, and could be useful biomarkers of ovarian pathologies characterized by impaired VD and I levels, such as polycystic ovary syndrome.
Colon cancer is a significant public health issue, and a deeper understanding of the molecular fundamentals [16] ehind is required to improve sensitivity and curability. This research explored the gene NDUFAF4 as a target of concern due to its link to a mitochondrial function and protein “Relatively of liver tumorigenesis”, which remains unclear is attributable to its inclusion into the complex I (CI) pathway. The gene ontology analysis, in turn, showed that NDUFAF4 is a key player in several critical biological phases linked to mitochondrial function and energy metabolism. Furthermore, survival analysis displayed that there was a strong correlation between NDUFAF4 expression and the patients' longevity suggesting that this factor may be important in colon cancer prognosis as well. The TCGA data proved that NDUFAF4 is elevated in colon cancer making the results of the analysis reported credible. All of the above justified the understanding of the role and importance of NDUFAF4 in treating each colon cancer patient as a molecular target. The findings help in understanding the colon cancer pathogenesis and suggest ways for developing more efficient diagnosis and treatment of the disease.
This research explored the gene NDUFAF4 as a target of concern due to its link to a mitochondrial function and protein “Relatively of liver tumorigenesis”, which remains unclear is attributable to its inclusion into the complex I (CI) pathway. Using a comprehensive approach to Gene Ontology analysis, Protein-Protein Interaction network modelling, survival analysis, KEGG pathway analysis, and validation using TCGA data, we identified the activities of NDUFAF4 in colon cancer. The Gene Ontology analysis, in turn, showed that NDUFAF4 is a key player in several critical biological phases linked to mitochondrial function and energy metabolism. The construction of the PPI network illustrates the interactors of NDUFAF4, the functional association protein within the cellular regulatory networks. In addition, survival analysis indicated that there was a considerable relationship between the expression of NDUFAF4 and patient survival, indicating its potential role as a prognostic factor in colon cancer. KEGG pathway analysis suggested that NDUFAF4 plays a role in thermogenesis and mitochondrial biogenesis, biological processes that should be targeted due to their implication in cellular metabolism and cancer onset. The use of TCGA information confirmed the upregulation of NDUFAF4 in colon cancer, thus making the findings of the analysis reported dependable. Overall, our study provided necessary information on the role and significance of NDUFAF4, a potential molecular target in colon cancer cases. These present findings enhance our knowledge of the pathogenesis of colon cancer and open new opportunities for designing novel diagnostic and therapeutic approaches to improve patient outcomes.
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