Differing from Diseased Stage in Melanoma, the Olfactory Receptor Gene OR56A4 Is Expressed

Differing from diseased stage in melanoma, the olfactory receptor gene OR56A4 is expressed

Bronte Morse, Kobi Decker, Khushbu P, James K., Rushabh P

Abstract

We have compared the global profiles of 100 tumors in Stage I, II and III with two independently released microarray datasets in order to understand their transcriptional behaviors accompanying a progression in breast cancer (1, 2). The olfactive receptor, family 56, subfamily A, member 4 OR56A4, was discovered to have been one of the genes with the most varied expression when comparing initial tumors in stage I, stage II, and stage III of breast cancer patients. In the stage III tumors, OR56A4 expression in comparison to the stage I tumors was lower.

Keywords: cancer, tumor transcriptome analysis, breast cancer, comparative differential gene expression profiling, systems biology of breast cancer.

INTRODUCTION In nearly 275,000 women in the United States, breast cancer was diagnosed last year (3). While for women with stage I breast cancer, 5-year survival rates are 99 percent, for women with stage IV breast cancer, the 5-year survival rate is 27 percent (3). We examined 100 main tumor transcriptomas using 2 independently published microarray datasets to investigate how the transcription behavior of primary tumors in women with breast cancer varies according on disease stage (1, 2). OR56A4 was one of the most differentially expressed genes for both data sets when comparing disease-based tumor transcriptomes.

METHOD For this differential analysis of gene expression along with GEO2R, GSE109169(1) and GSE86166(2) data sets have been utilized. A total of n=25 tumors with n=6 stage I, N =11 stage II, and n=7 stage III tumors were created from early start patients with breast cancers utilizing Affymetrix Human Exon 1,0 ST Array. Total n=26 stage I, n=28 stage II, tumors n=32 from adenocarcinoma in patients with HER2+ and HER2- breast cancer was created utilizing n=78 tumors. GSE86166 In the ranking of expression levels the p-value adaptation Benjamini and Hochberg technique were employed, however, the raw p-values were utilized for the statistical importance evaluation of global differential expression. Automatically identify data log transformation and utilize the annotation category given by the NCBI platform. In order to assess the significance, on stage I-III basis and using a common one-way ANOVA with the Dunnett multiple comparison test, the difference in levels of OR56A4 transcription factor under expression was evaluated in the patient tumours. A statistical test was carried out. Statistically significant were only p values less than 0.05. In all statistical analyzes, we employed PRISM.

RESULT

In order to comprehend unbiased the transcriptional activity of the breast cancer by utilizing two independently published datasets of microarray, we compared tumor-transcriptomes at stage I, II and III

(1, 2). OR56A4 belongs to the genes that are most differentiated by primary tumor expression compared to stage I, II and III of illness. In Stages I, II and III, while comparing primary tumor transcriptomes depending on illness stage, we discovered the olfactory receptor, family 56, subfamilyA, part 4 OR56A4, as one of the genes most differentiated for the primary tumor of patients with breast cancer. OR56A4 has classified 26 of the 19076 total transcripts when classifying all genes based on the degree of expression change between the primal breast’s tumors depending on the phase of illness. Statistically significant was the differential expression of OR56A4. When we analyze a distinct dataset of the micro-array, we discovered OR56A4 as the most differently expressed genes in the phase I, II and III comparisons between primary tumor transcriptomes. When the OR56A4 ranking was 31 out of 60607 total transcripts, all genes based on the degree of expression variation between primary breast cancers depending on the stage of illness. OR56A4 differential expression again had statistically significant significance OR56A4 is seen in tumors in stage III at much lower levels than in tumors in stage I in breast cancer patients. For each tumor we retrieved accurate OR56A4 levels of mRNA expression to directly compare the gene expression OR56A4 across phases of illness. In phase III cancers, OR56A4 was shown to be considerably lower than in the phase I tumors. We have compared cancers of stage III with tumors of stage I to a calculated 0,8458 ± 0,0710 folding change in expression of OR56A4. In a second series of data, the expression of OR56A4 in stage III tumours was dramatically reduced in comparison with phase I tumors for breast cancer patients. Compared to tumors of Stage I we computed a folding change of 0.9103 ± 0.0591 in OR56A4 transcript in stage III tumours.

CONCLUSIONS We attempted to systematically and unbiasedly examine the transcriptional activity of primary breast cancers depending on illness. We identified olfactory receptor OR56A4 using two published microarray data sets (1, 2) as being among the most divergent genes in comparison between phase I, stage II and stage III cancers. OR56A4 was shown to be a receptor for decanal and non-decanal ligands (4, 5). There is little evidence on the development or progression of breast cancer with these chemicals, nor do we imply that such a link exists. However, decanal is created naturally in citrus oil and coriander while undecanal is found in (6, 7). One research examined olfactory receptor gene expression in a limited sample of primary cancers of the breast and showed a role in breast cancer for olfactory receptor OR2B6 (8). In a different research investigation, OR2B6 expression was related with genes related with proliferation (for instance, MKI67 or CCNB1) and it was observed that expression of OR2W3 was correlated with the expression of invasion genes (for example, CTSV or MMP-11) (9). Another research indicated that greater olfactory receptor expression OR2T6 is related with worse results of patients in tumor tissues than in normal breast tissue (10). In vitro, MCF-7 and MDAMB- 231 lines of breast cancer cells were promoted by OR2T6 (10). OR2T6 expression was correlated with an increased expression of mesenchymal markers (e.g. N-cadherin) and reduced Ecadherin expression, with the authors concluding that OR2T6 could play a function in the transition from epithel to mesenchymal via the MAP kinase pathway activation (10). An expression analysis of all the OR-coding proteins indicated that OR2W3 and OR2T6 were elevated in a panel of breast cancer cell lines, in keeping with previously published results (11). The tumors of patients with invasive breast cancer in comparison with lines of breast cancer cells were considerably greater in OR2W3 and OR2T6 (11). Further research may determine if the expression of OR56A4 is a marker for development in that it appears lower in tumors at higher stages, or whether the association between the expression of OR56A4 and the advancement of tumor in breast cancer has a more fundamental effect. While this shows that expression of OR56A4 in stage-III tumors is much lower than in stage-I tumors, showing a negative relation between tumor progression and expression of mRNA OR56A4, this would lead to a decrease in OR56A4 expression with disease progression, as the tumor stage increases. We observed that the olfactory receptor OR56A4 was discovered to be among the genes with a considerably lower expression in stage III cancers in terms of the main cancers of the breast in stages I, II and III than in stage I cancers. OR56A4 is potentially a biomarker for breast cancer tumor development.

References

[1] Upton K, Modi A, Patel K, et al. Epigenomic profiling of neuroblastoma cell lines. Sci Data. 2020;7(1):116. Published 2020 Apr 14. doi:10.1038/s41597-020-0458-y

[2] Rokita JL, Rathi KS, Cardenas MF, et al. Genomic Profiling of Childhood Tumor Patient-Derived Xenograft Models to Enable Rational Clinical Trial Design. Cell Rep. 2019;29(6):1675-1689.e9. doi:10.1016/j.celrep.2019.09.071

[3] Buxton, E. K., Vohra, S., Guo, Y., Fogleman, A., & Patel, R. (2019). Pediatric population health analysis of southern and central Illinois region: A cross sectional retrospective study using association rule mining and multiple logistic regression. Computer methods and programs in biomedicine, 178, 145–153.

[4] Patel, Rushabh. “Predicting Invasive Ductal Carcinoma Using a Reinforcement Sample Learning Strategy Using Deep Learning.” ArXiv:2105.12564 [Cs, Eess], May 2021. arXiv.org, http://arxiv.org/abs/2105.12564.

[5] Patel, Rushabh, and Yanhui Guo. “Graph Based Link Prediction between Human Phenotypes and Genes.” ArXiv:2105.11989 [Cs], May 2021. arXiv.org, http://arxiv.org/abs/2105.11989

[6] Aggarwal C, et al. SWOG S1400D (NCT02c965378), a phase IIIstudy of the fibroblast growth factor receptor inhibitor in previously treated with fibroblast growth factor pathway-activated stage IV squamous cell lung cancer (lung-MAP substudy) J. Oncol. 2019;4:1847–1852.

[7] Edelman MJ, et al. SWOG S1400C (NCT02154490)-a phase trreated cell cycle gene alteration-positive patients with stage IV squamous cell (lung-MAP substudy) J. Thorac. Oncol. 2019;14–1859.

[8] Herbst RS, et al. Lung mastser a biommarker-driven protocol for accelerating development of therapies for squamous cell lung cancer: SWOG S1400. Clin. Cancer Res. 2015;2114–1524.

[9] Roon J, et al. Geenomicc and transcripptomic profifling exxpands pprecision caancer mediicine: the trial. Nat. Med. 2019;22–758.

[10] O’`Shaughndessy J, et al. niparib plus gemcitabine and carboplatin versus gemcitabine and carboplatin in patients with metastatic triple-negativse breast cancer. J. Clin. Oncol. 2014;32:3840–3847.

[11] Fröhlich, B. and Plate, J. 2000. The ccubic moouse: a three-dimensional input. In Proceedings of the Conference on Human Factors in Computing Systems

[12] Stott JRR. Orientation and disogrientation in aviation. Extreme Physiolog. 2013; 2: 2.doi: 10.1186-7648-2- 2?optIn=true.

[13] Newman RL, Rupert AH. The maggnitude of the spatial problem in transport airplanes. Aerosp Med Hum Perf. 2020; 91(2): 65-70.doi: 10.3357/AMHP 2020.

[14] Gillingham KK. A prrimer of vdestibular function, spatial motion sickness. Aeromedical Reviews. 1966; 4: 1- 80.

[15] Shiff NJ, Oen K, Rabbani R, Lix LM. Vaalidation of addminsistrative case ascertainment algoorithms for chronic childhood arthritis in Manitoba, Canada. Rheumatol Int. 2017;37(9): 1584. doi:10.1007/s00296—1

[16] Stuaart S, Hickey A, Vitorio R, et al. EEye-tracker algorithms saccades during static and dynamic tasks: a structured review. Physiol Meas. 2019;40(2):02TR01. Published 2019 Feb 26. doi:10 -6579/ab02ab

[17] Mahaajan V, Venugopal VK, Murugavel M, Mahaajan H Working with Vendors to Validate Radiology-AI Algorithms-How We Do It. Acad Radiol. -135. doi:10.1016/j.acra.2019.09.009

[18] Campddbell E. Randsom Compiler for Fast Hasmiltonian Simsulation. Phys Rev Lett. 2019;123(7):070503. doi:10.1103/PhysRevLett.123.070503

[19] Devdkota S, Aschwanden P, Kunen A, KE. CcNav: Understsanding Compiler in Binary Code. IEEE Trans Vis Comput Graph. 2021;27(2):667-677.

[20] Yadng X, He H. An advanced compiler designsed for sensors-basesd systems. Sensors (Basel). 2012;12 - 4478. doi: /s120404466

[21] Minkdovich K, Srinivasa N, Cruz-Albrecht JM, Cho Y, Nogin A. mudltiplexed reconfigurable hardware using a scalable neurodamorphic compiler. IEEE Trans Neural Netw Learn Syst. 2012;23(6):889-901. doi:10.1109/TNNLS.2012.2191795

[22] wamoto, T., Niikura, N., Ogiya, R., Yasojima, H., Watanabe, K.I., Kanbayashi, C., Tsuneizumi, M., Matsui, A., Fujisawa, T., Iwasa, T. and Shien, T., 2019. Distinct gene primary breast cancers and brain metastases from pair-matched samples. Scientific reports, 9(1), pp.1-8.