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Current Signal Transduction Therapy

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ISSN (Print): 1574-3624
ISSN (Online): 2212-389X

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Research Article

Robust Predictive Model for Different Cancers using Biomarker Proteins

Author(s): Shruti Jain* and Ayodeji Olalekan Salau

Volume 18, Issue 3, 2023

Published on: 06 October, 2023

Article ID: e061023221868 Pages: 14

DOI: 10.2174/0115743624257352230920091046

Price: $65

Abstract

Background: When analyzing multivariate data, it can be challenging to quantify and pinpoint relationships between a collection of consistent characteristics. Reliable computational prediction of cancer patient's response to treatment based on their clinical and molecular profiles is essential in this era of precision medicine. This is essential in helping doctors choose the least contaminated and most potent restorative therapies that are now available. Better patient monitoring and selection are now possible in clinical trials.

Methods: This research proposes a novel robust model to aid in the diagnosis of various cancers induced by biomarker proteins (Protein Kinase B, MAPK, and mammalian Target of Rapamycin). Later, various medications (Perifosine, Wortmannin, and Rapamycin) were proposed to cure cancer. Various studies were carried out to obtain all of the results, which aid in the identification of various types of cancer. The drugs mentioned in this essay help to ward off cancer. Scaling and normalization were carried out using parallel coordinates plots and correlation tests, respectively. The boosted tree method and kNN with multiple distance approaches were also used to generate a solid model. The medical diagnosis system was enhanced by training the boosted tree technique to identify various tumors. A robust model was validated by predicting various values that were displayed against the observed value and agreed with the advised strategy to locate biomarkers to show the value of our method.

Results: The results show that the predicted and observed values agree with each other, especially for MAPK pathways. The observed correlation coefficient (r2) is 0.9847 without intercept and 0.9849 with intercept. The precise computational prediction of the reaction of cancer patients to treatment based on the patient's clinical and molecular profiles is vital in the period of exactitude medicine.

Conclusion: A robust model was validated by predicting the different values that were plotted with the observed value, which agrees with the results of the proposed technique to uncover biomarkers and shows the effectiveness of our technique.

Graphical Abstract

[1]
Bartl R, Frisch B. Osteoporosis: Diagnosis, prevention, therapy. Heidelberg: Springer 2009.
[http://dx.doi.org/10.1007/978-3-540-79527-8]
[2]
Lee SK, Lo CS, Wang CM, et al. A computer-aided design mammography screening system for detection and classification of microcalci-fications. Int J Med Inform 2000; 60(1): 29-57.
[http://dx.doi.org/10.1016/S1386-5056(00)00067-8] [PMID: 10974640]
[3]
World Health Organization. Breast cancer awareness month in October. 2012.2014. Available From: http://www.who.int/cancer/events/breast_cancer_month/en/
[4]
Cancer Research UK. Breast cancer statistics 2015. Available From: https://www.cancerresearchuk.org/health-professional/cancer-statistics/statistics-by-cancer-type/breast-cancer
[5]
Cancer Research UK. Available From: http://www.cancer.org/acs/groups/content/@research/documents/document/acspc-046381.pdf
[6]
Cancer Research UK. How cancer starts 2020. Available From: https://www.cancerresearchuk.org/about-cancer/what-is-cancer/how-cancer-starts
[7]
Janes KA, Albeck JG, Gaudet S, Sorger PK, Lauffenburger DA, Yaffe MB. A systems model of signaling identifies a molecular basis set for cytokine-induced apoptosis. Science 2005; 310(5754): 1646-53.
[http://dx.doi.org/10.1126/science.1116598] [PMID: 16339439]
[8]
National Cancer Registry Program. Indian Council for Medical Research, Consolidated report of the population based cancer registries 1990-96. New Delhi: Indian Council of Medical Research 2009.
[9]
GBD 2015 Risk Factors Collaborators. Global, regional, and national comparative risk assessment of 79 behavioral, environmental and occupational, and metabolic risks or clusters of risks, 1990-2015: A systematic analysis for the Global Burden of Disease Study 2015. Lancet 2016; 388(10053): 1659-724.
[http://dx.doi.org/10.1016/S0140-6736(16)31679-8] [PMID: 27733284]
[10]
de Bruin EC, Medema JP. Apoptosis and non-apoptotic deaths in cancer development and treatment response. Cancer Treat Rev 2008; 34(8): 737-49.
[http://dx.doi.org/10.1016/j.ctrv.2008.07.001] [PMID: 18722718]
[11]
Jain S. Communication of signals and responses leading to cell survival/cell death using Engineered Regulatory Networks 2012.
[12]
Gaudet S, Kevin JA, John AG, Emily PA, Douglas LA, Peter SK. A compendium of signals and responses triggered by pro-death and pro-survival cytokines. Mol Cell Proteomics 2005; 4(10): 1569-90.
[13]
Jain SPK, Naik PK, Bhooshan SV. Mathematical modelling deciphering balance between cell survival and cell death using insulin. New Biol 2011; 1(1): 46-58.
[14]
Jain S. Parametric and non parametric distribution analysis of AkT for cell survival/death. Int J Artif Intell Soft Comput 2017; 6(1): 43-55.
[http://dx.doi.org/10.1504/IJAISC.2017.084232]
[15]
Schenck A, Goto-Silva L, Collinet C, et al. The endosomal protein Appl1 mediates Akt substrate specificity and cell survival in vertebrate development. Cell 2008; 133(3): 486-97.
[http://dx.doi.org/10.1016/j.cell.2008.02.044] [PMID: 18455989]
[16]
Jain S. System modeling of AKT using linear and robust regression analysis. Curr Trends Biotechnol Pharm 2018; 12(2): 177-86.
[17]
Portt L, Norman G, Clapp C, Greenwood M, Greenwood MT. Anti-apoptosis and cell survival: A review. Biochim Biophys Acta Mol Cell Res 2011; 1813(1): 238-59.
[http://dx.doi.org/10.1016/j.bbamcr.2010.10.010] [PMID: 20969895]
[18]
Kerr J F R, Wyllie AH, Currie AR. Apoptosis: A basic biological phenomenon with wide-ranging implications in tissue kinetics. Br J Cancer 1972; 26(4): 239-57.
[http://dx.doi.org/10.1038/bjc.1972.33] [PMID: 4561027]
[19]
Vishrutha V, Ravi Shankar M. Early Detection and Classification of Breast Cancer In: Satapathy S, Biswal B, Udgata S, Mandal J, Eds. Proceedings of the 3rd International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA). 327
[20]
Salau AO. Adaptive Diagnostic Machine Learning Technique for Classification of Cell Decisions for AKT Protein. Informatics in Medicine Unlocked 2021; 23: 100511.
[21]
Farrell PJ, Rogers-Stewart K. Comprehensive study of tests for normality and symmetry: Extending the Spiegelhalter test. J Stat Comput Simul 2006; 76(9): 803-16.
[http://dx.doi.org/10.1080/10629360500109023]
[22]
Oztuna D, Elhan AH, Tuccar E. Investigation of four different normality tests in terms of Type I error rate and power under different distributions. Turk J Med Sci 2006; 36(3): 171-6.
[23]
STATISTICA Software Link. STATISTICA 13.3. 2023. Available from: https://statistica.software.informer.com/
[24]
Fogh J, Trempe G. New Human Tumor Cell LinesHuman Tumor Cells in Vitro. Boston, MA: Springer 1975.
[http://dx.doi.org/10.1007/978-1-4757-1647-4_5]
[25]
Inselberg A. The plane with parallel coordinates. Vis Comput 1985; 1(2): 69-91.
[http://dx.doi.org/10.1007/BF01898350]
[26]
Miller JJ, Wegman EJ. Construction of line densities for parallel coordinate plots.Computing and Graphics in Statistics. New York. Spring-er-Verlag. 1991; Vol. 36: pp. 107-23.
[http://dx.doi.org/10.1007/978-1-4613-9154-8_8]
[27]
Salau AO, Jain S. Feature extraction: A survey of the types, techniques, and applications International Conference on Signal Processing and Communication (ICSC). Noida, India. 2019; p. 158-64.
[http://dx.doi.org/10.1109/ICSC45622.2019.8938371]
[28]
Jain S, Paul S. Determination of Risk Prediction using Random forest technique for Breast Cancer 2022 IEEE Delhi Section Conference (DELCON). New Delhi, India 2022; pp. 1-4.
[http://dx.doi.org/10.1109/DELCON54057.2022.9752841]
[29]
Jain S, Chauhan DS. Instance-based learning of marker proteins of carcinoma cells for cell death/survival. Comput Methods Biomech Biomed Eng Imaging Vis 2019.
[30]
Jain S, Salau AO. An image feature selection approach for dimensionality reduction based on kNN and SVM for AkT proteins. Cogent Eng 2019; 6(1): 1599537.
[http://dx.doi.org/10.1080/23311916.2019.1599537]

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