In silico and molecular validation of identified putative genes and functional analysis of a N K G2D ligand as a breast cancer biomarkers
Bankole, Habeeb Adebodun
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The current diagnostic, prognostic, predictive and therapeutic monitoring methods used for breast cancer are limited. Thus, research into more specific, sensitive and effective strategies is required. Breast cancer is the most prevalent form of cancer in women worldwide and accounts for the most common cause of death in women every year. Cancer development is characterized by a wide spread of genetic abnormalities of gene sequences that can be used in detecting and monitoring treatment of the disease as a result of altered gene expression patterns which leave a trail of biomarkers. Seven candidate genes (Gene 1-7) were identified from a previous in silico study and their gene products (BRG 1-7) were annotated to be good candidate breast cancer biomarkers. Differential gene expression analysis using quantitative real-time PCR (qRT-PCR) validated the over-expression of Gene 3, Gene 4 and Gene 7 in a breast cancer cell line (MCF7), of which Gene 7, annotated as a Natural killer group 2, member D (NKG2D) ligand, was observed to be the most over-expressed gene. The innate immune system is the first line of the body's physiological defense against diseases and the natural killer (NK) cells, are central to mediating this type of immunity. NK cells are activated when a specific surface receptor such as the NKG2D receptor binds its ligands expressed by tumor cells. To evade being detected by the immune system, cancer cells are reported to shed off the NKG2D ligands and are expected to be present in the bodily fluids of cancer patients. Also, chemotherapeutics have been reported to suppress the natural anti-tumour immune response, thus should be taken into account when designing optimal therapy for cancer patients. The aim of this research was to validate these candidate genes as effective breast cancer biomarkers using several in silico methods as well as molecular techniques and study the effect of Gene 7 on modulating the effect of several pro-apoptotic compounds. The in silico part of the study investigated the functional, protein interaction, pathways, and tissue expression specificity of the candidate biomarkers using computational software such as DAVID, STRING, KEGG, Genecards and GEA. Also an in silico validation of the prognostic/predictive values of the genes was analysed using SurvExpress, KMplot, and GOBO. Protein expression of selected genes was analysed by Western blot, and immunofluorescence analysis. BRG 7 gene was cloned into pcDNA3.1 vector using recombinant DNA technology while commercial shRNA construct was used to 'knock-down' Gene 7 expression. The two constructs were used to transfect MCF-7 and MCF-12A cells. Over-expression and 'knock down' Gene 7 in transfected cells was confirmed using western blot analysis. Stably transfected cells were then treated with three pro-apoptotic compounds (Camptothecin, Doxorubicin and DMSO) for 24 hours. The apoptotic cells were stained with 3, 4, 5, 6-tetrachloro-2', 4', 5', 7' tetraiodofluorescein (TCTF) and then analysed using flow cytometry. Functional analysis linked Gene 1, Gene 2, Gene 4, Gene 6 and Gene 7 to different cancer related processes. The pathway analysis showed Gene 1, Gene 2, Gene 4 and Gene 7 were involved in pathways that can be linked to cancer modulation. The protein-protein interaction analysis showed only BRG 2 was directly linked to two major hallmarks of cancer (Apoptosis and Autophagy). Breast cancer associated Transcription factors were shown to regulate these genes. Gene 1 and Gene 5 as well as the three genes observed to be highly expressed in the qRT-PCR study were validated to differentially express in breast cancer. An additional protein (BRG 8) was identified and postulated to be a good biomarker candidate for breast cancer based on its direct interaction with BRG 7 and estrogen receptor protein (ESR). The prognostic value of the candidate genes were monitored in two datasets (DATA1 and DATA2) in SurvExpress. DATA1 showed that Gene 6 and Gene 8 while DATA2 showed that Gene 3, Gene 6 and Gene 7 were valuable candidate genes in breast cancer prognosis. The survival curves from the two datasets showed the combined genes could predict the outcome of breast cancer patients undergoing treatments. A plot box output from SurvExpress showed most of the genes were differentially expressed comparing two risk groups. The Kaplan Meier plotter confirmed, Gene 1, Gene 3, Gene 4 and Gene 7 have a significant P-value in predicting the survival outcome based on gene differential expression value. GOBO analysis showed the genes may accurately predict the survival outcome of estrogen positive subtype, ERBB2 subtype of estrogen receptor negative and lymph node negative subtype of ER- tumours, but not all subtype of ER- tumours. Western blot analysis showed BRG 7 may be highly expressed in MCF-7 as compared to MCF-12A, BRG 8 was found to be expressed in all cancer cell types analyzed except for MCF-7 and HT29. BRG 2 was found to be expressed in all cancer types analyzed. immunofluorescence analysis showed BRG 3, BRG 4 and BRG 7 are differentially expressed in breast cancer cell line and are more localized on the cell membrane when compared to the breast non-cancer cell line. Over-expression and gene knock down in cells were successfully confirmed with Western blot analysis. Stably transfected MCF-12A cell for over-expression of BRG7 protein, resulted in cell senescent and the cell stopped growing while stably transfected MCF-7 over-expressing BRG7 did not show any morphological changes. Apoptosis was enhanced in cells treated with camptothecin, doxorubicin and DMSO overexpressing BRG7. Apoptosis was reduced in camptothecin and DMSO treated gene 'knock-down' cells but not doxorucin treated. BRG7 gene 'knock down' in transfected cells showed varying response to all three pro-apoptotic compounds. From this study Gene 3, 5, 7 and 8 and their protein levels were confirmed to be differentially expressed in breast cancer cells and could serve as putative biomarkers for breast cancer. However the variance in the effectiveness of individual genes suggests that the set of genes would perform better than individual gene. The modulating role of BRG7 in drug induced apoptosis, suggest it could probably play an important role in personalised medicine and could serve as a biomarker to monitor the prognosis and/or therapeutic outcome of pro-apoptotic drugs in breast cancer patients. These findings will be further investigated in human breast tissues to validate these data.