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Current Genomics

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ISSN (Print): 1389-2029
ISSN (Online): 1875-5488

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Research Article

Identification of Plausible Candidates in Prostate Cancer Using Integrated Machine Learning Approaches

Author(s): Bhumandeep Kour, Nidhi Shukla, Harshita Bhargava, Devendra Sharma, Amita Sharma, Anjuvan Singh, Jayaraman Valadi, Trilok Chand Sadasukhi, Sugunakar Vuree* and Prashanth Suravajhala*

Volume 24, Issue 5, 2023

Published on: 21 November, 2023

Page: [287 - 306] Pages: 20

DOI: 10.2174/0113892029240239231109082805

Price: $65

Abstract

Background: Currently, prostate-specific antigen (PSA) is commonly used as a prostate cancer (PCa) biomarker. PSA is linked to some factors that frequently lead to erroneous positive results or even needless biopsies of elderly people.

Objectives: In this pilot study, we undermined the potential genes and mutations from several databases and checked whether or not any putative prognostic biomarkers are central to the annotation. The aim of the study was to develop a risk prediction model that could help in clinical decision-making.

Methods: An extensive literature review was conducted, and clinical parameters for related comorbidities, such as diabetes, obesity, as well as PCa, were collected. Such parameters were chosen with the understanding that variations in their threshold values could hasten the complicated process of carcinogenesis, more particularly PCa. The gathered data was converted to semi-binary data (-1, -0.5, 0, 0.5, and 1), on which machine learning (ML) methods were applied. First, we cross-checked various publicly available datasets, some published RNA-seq datasets, and our whole-exome sequencing data to find common role players in PCa, diabetes, and obesity. To narrow down their common interacting partners, interactome networks were analysed using GeneMANIA and visualised using Cytoscape, and later cBioportal was used (to compare expression level based on Z scored values) wherein various types of mutation w.r.t their expression and mRNA expression (RNA seq FPKM) plots are available. The GEPIA 2 tool was used to compare the expression of resulting similarities between the normal tissue and TCGA databases of PCa. Later, top-ranking genes were chosen to demonstrate striking clustering coefficients using the Cytoscape- cytoHubba module, and GEPIA 2 was applied again to ascertain survival plots.

Results: Comparing various publicly available datasets, it was found that BLM is a frequent player in all three diseases, whereas comparing publicly available datasets, GWAS datasets, and published sequencing findings, SPFTPC and PPIMB were found to be the most common. With the assistance of GeneMANIA, TMPO and FOXP1 were found as common interacting partners, and they were also seen participating with BLM.

Conclusion: A probabilistic machine learning model was achieved to identify key candidates between diabetes, obesity, and PCa. This, we believe, would herald precision scale modeling for easy prognosis.

Keywords: cBioPortal, prostate cancer, biomarkers, machine learning, bioinformatics, Prostate-Specific Antigens (PSA).

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[1]
Rawla, P. Epidemiology of prostate cancer. World J. Oncol., 2019, 10(2), 63-89.
[http://dx.doi.org/10.14740/wjon1191] [PMID: 31068988]
[2]
Abate-Shen, C.; Shen, M.M. Molecular genetics of prostate cancer. Genes Dev., 2000, 14(19), 2410-2434.
[http://dx.doi.org/10.1101/gad.819500] [PMID: 11018010]
[3]
Pernar, C.H.; Ebot, E.M.; Wilson, K.M.; Mucci, L.A. The epidemiology of prostate cancer. Cold Spring Harb. Perspect. Med., 2018, 8(12), a030361.
[http://dx.doi.org/10.1101/cshperspect.a030361] [PMID: 29311132]
[4]
Velonas, V.; Woo, H.; Remedios, C.; Assinder, S. Current status of biomarkers for prostate cancer. Int. J. Mol. Sci., 2013, 14(6), 11034-11060.
[http://dx.doi.org/10.3390/ijms140611034] [PMID: 23708103]
[5]
Beck, T.; Hastings, R.K.; Gollapudi, S.; Free, R.C.; Brookes, A.J. GWAS Central: A comprehensive resource for the comparison and interrogation of genome-wide association studies. Eur. J. Hum. Genet., 2014, 22(7), 949-952.
[http://dx.doi.org/10.1038/ejhg.2013.274] [PMID: 24301061]
[6]
Kote-Jarai, Z.; Easton, D.F.; Stanford, J.L.; Ostrander, E.A.; Schleutker, J.; Ingles, S.A.; Schaid, D.; Thibodeau, S.; Dörk, T.; Neal, D.; Cox, A.; Maier, C.; Vogel, W.; Guy, M.; Muir, K.; Lophatananon, A.; Kedda, M-A.; Spurdle, A.; Steginga, S.; John, E.M.; Giles, G.; Hopper, J.; Chappuis, P.O.; Hutter, P.; Foulkes, W.D.; Hamel, N.; Salinas, C.A.; Koopmeiners, J.S.; Karyadi, D.M.; Johanneson, B.; Wahlfors, T.; Tammela, T.L.; Stern, M.C.; Corral, R.; McDonnell, S.K.; Schürmann, P.; Meyer, A.; Kuefer, R.; Leongamornlert, D.A.; Tymrakiewicz, M.; Liu, J.; O’Mara, T.; Gardiner, R.A.F.; Aitken, J.; Joshi, A.D.; Severi, G.; English, D.R.; Southey, M.; Edwards, S.M.; Al Olama, A.A.; Eeles, R.A.; Al Olama, A.A.; Eeles, R.A. Multiple novel prostate cancer predisposition loci confirmed by an international study: the PRACTICAL Consortium. Cancer Epidemiol. Biomarkers Prev., 2008, 17(8), 2052-2061.
[http://dx.doi.org/10.1158/1055-9965.EPI-08-0317] [PMID: 18708398]
[7]
Sharma, D.; Someshwar, S.; Kour, B.; Shukla, N.; Khilwani, B.; Vijay, M.; Gupta, A.; Ansari, A.; Vuree, S.; Kumar, A.; Singh, S. The CAPCI network: A CAncer Prostate Consortium of India for conducting next-generation genomic sequencing studies. Cancer Health Disparities, 2019.
[8]
Chen, N.; Zhou, Q. The evolving Gleason grading system. Chin. J. Cancer Res., 2016, 28(1), 58-64.
[PMID: 27041927]
[9]
Bulten, W.; Pinckaers, H.; van Boven, H.; Vink, R.; de Bel, T.; van Ginneken, B.; van der Laak, J.; Hulsbergen-van de Kaa, C.; Litjens, G. Automated deep-learning system for Gleason grading of prostate cancer using biopsies: A diagnostic study. Lancet Oncol., 2020, 21(2), 233-241.
[http://dx.doi.org/10.1016/S1470-2045(19)30739-9] [PMID: 31926805]
[10]
Iczkowski, K.A.; Lucia, M.S. Current perspectives on Gleason grading of prostate cancer. Curr. Urol. Rep., 2011, 12(3), 216-222.
[http://dx.doi.org/10.1007/s11934-011-0181-5] [PMID: 21424766]
[11]
Okotie, O.T.; Roehl, K.A.; Han, M.; Loeb, S.; Gashti, S.N.; Catalona, W.J. Characteristics of prostate cancer detected by digital rectal examination only. Urology, 2007, 70(6), 1117-1120.
[http://dx.doi.org/10.1016/j.urology.2007.07.019] [PMID: 18158030]
[12]
Hu, Y.; Liu, W.; Chen, Y.; Zhang, M.; Wang, L.; Zhou, H.; Wu, P.; Teng, X.; Dong, Y.; Zhou, J.; Xu, H.; Zheng, J.; Li, S.; Tao, T.; Hu, Y.; Jia, Y. Combined use of fasting plasma glucose and glycated hemoglobin A1c in the screening of diabetes and impaired glucose tolerance. Acta Diabetol., 2010, 47(3), 231-236.
[http://dx.doi.org/10.1007/s00592-009-0143-2] [PMID: 19760291]
[13]
Schnedl, W.J.; Liebminger, A.; Roller, R.E.; Lipp, R.W.; Krejs, G.J. Hemoglobin variants and determination of glycated hemoglobin (HbA1c). Diabetes Metab. Res. Rev., 2001, 17(2), 94-98.
[http://dx.doi.org/10.1002/dmrr.186] [PMID: 11307174]
[14]
Donadon, V.; Balbi, M.; Valent, F.; Avogaro, A. Glycated hemoglobin and antidiabetic strategies as risk factors for hepatocellular carcinoma. World J. Gastroenterol., 2010, 16(24), 3025-3032.
[http://dx.doi.org/10.3748/wjg.v16.i24.3025] [PMID: 20572306]
[15]
Kumar, P.R.; Bhansali, A.; Ravikiran, M.; Bhansali, S.; Dutta, P.; Thakur, J.S.; Sachdeva, N.; Bhadada, S.K.; Walia, R. Utility of glycated hemoglobin in diagnosing type 2 diabetes mellitus: A community-based study. J. Clin. Endocrinol. Metab., 2010, 95(6), 2832-2835.
[http://dx.doi.org/10.1210/jc.2009-2433] [PMID: 20371663]
[16]
Lippi, G.; Targher, G. Glycated hemoglobin (HbA1c): Old dogmas, a new perspective? cclm, 2010, 48(5), 609-614.
[http://dx.doi.org/10.1515/CCLM.2010.144] [PMID: 20464776]
[17]
Peila, R.; Rohan, T.E. Diabetes, glycated hemoglobin, and risk of cancer in the UK biobank study. Cancer Epidemiol. Biomarkers Prev., 2020, 29(6), 1107-1119.
[http://dx.doi.org/10.1158/1055-9965.EPI-19-1623] [PMID: 32179703]
[18]
Twig, G.; Afek, A.; Shamiss, A.; Derazne, E.; Tzur, D.; Gordon, B.; Tirosh, A. White blood cells count and incidence of type 2 diabetes in young men. Diabetes Care, 2013, 36(2), 276-282.
[http://dx.doi.org/10.2337/dc11-2298] [PMID: 22961572]
[19]
Vozarova, B.; Weyer, C.; Lindsay, R.S.; Pratley, R.E.; Bogardus, C.; Tataranni, P.A. High white blood cell count is associated with a worsening of insulin sensitivity and predicts the development of type 2 diabetes. Diabetes, 2002, 51(2), 455-461.
[http://dx.doi.org/10.2337/diabetes.51.2.455] [PMID: 11812755]
[20]
Blix, K.; Jensvoll, H.; Brækkan, S.K.; Hansen, J.B. White blood cell count measured prior to cancer development is associated with future risk of venous thromboembolism--the Tromsø study. PLoS One, 2013, 8(9), e73447.
[http://dx.doi.org/10.1371/journal.pone.0073447] [PMID: 24023876]
[21]
Nencioni, A.; Caffa, I.; Cortellino, S.; Longo, V.D. Fasting and cancer: Molecular mechanisms and clinical application. Nat. Rev. Cancer, 2018, 18(11), 707-719.
[http://dx.doi.org/10.1038/s41568-018-0061-0] [PMID: 30327499]
[22]
Jayedi, A.; Djafarian, K.; Rezagholizadeh, F.; Mirzababaei, A.; Hajimohammadi, M.; Shab-Bidar, S. Fasting blood glucose and risk of prostate cancer: A systematic review and meta-analysis of dose-response. Diabetes Metab., 2018, 44(4), 320-327.
[http://dx.doi.org/10.1016/j.diabet.2017.09.004] [PMID: 29074328]
[23]
Ghazanfari, Z.; Haghdoost, A.A.; Alizadeh, S.M.; Atapour, J.; Zolala, F. A comparison of HbA1c and fasting blood sugar tests in general population. Int. J. Prev. Med., 2010, 1(3), 187-194.
[PMID: 21566790]
[24]
Cheng, L.; Zhuang, H.; Ju, H.; Yang, S.; Han, J.; Tan, R.; Hu, Y. Exposing the causal effect of body mass index on the risk of type 2 diabetes mellitus: a mendelian randomization study. Front. Genet., 2019, 10, 94.
[http://dx.doi.org/10.3389/fgene.2019.00094] [PMID: 30891058]
[25]
Khandekar, M.J.; Cohen, P.; Spiegelman, B.M. Molecular mechanisms of cancer development in obesity. Nat. Rev. Cancer, 2011, 11(12), 886-895.
[http://dx.doi.org/10.1038/nrc3174] [PMID: 22113164]
[26]
Bianchini, F.; Kaaks, R.; Vainio, H. Overweight, obesity, and cancer risk. Lancet Oncol., 2002, 3(9), 565-574.
[http://dx.doi.org/10.1016/S1470-2045(02)00849-5] [PMID: 12217794]
[27]
Garg, S.K.; Maurer, H.; Reed, K.; Selagamsetty, R. Diabetes and cancer: Two diseases with obesity as a common risk factor. Diabetes Obes. Metab., 2014, 16(2), 97-110.
[http://dx.doi.org/10.1111/dom.12124] [PMID: 23668396]
[28]
Hopkins, B.D.; Goncalves, M.D.; Cantley, L.C. Obesity and cancer mechanisms: Cancer metabolism. J. Clin. Oncol., 2016, 34(35), 4277-4283.
[http://dx.doi.org/10.1200/JCO.2016.67.9712] [PMID: 27903152]
[29]
Liang, Y.; Ketchum, N.S.; Goodman, P.J.; Klein, E.A.; Thompson, I.M., Jr Is there a role for body mass index in the assessment of prostate cancer risk on biopsy? J. Urol., 2014, 192(4), 1094-1099.
[http://dx.doi.org/10.1016/j.juro.2014.04.015] [PMID: 24747090]
[30]
Taghizadeh, N.; Boezen, H.M.; Schouten, J.P.; Schröder, C.P.; Vries, E.G.E.; Vonk, J.M. BMI and lifetime changes in BMI and cancer mortality risk. PLoS One, 2015, 10(4), e0125261.
[http://dx.doi.org/10.1371/journal.pone.0125261] [PMID: 25881129]
[31]
Jamnagerwalla, J.; Howard, L.E.; Allott, E.H.; Vidal, A.C.; Moreira, D.M.; Castro-Santamaria, R.; Andriole, G.L.; Freeman, M.R.; Freedland, S.J. Serum cholesterol and risk of high-grade prostate cancer: Results from the REDUCE study. Prostate Cancer Prostatic Dis., 2018, 21(2), 252-259.
[http://dx.doi.org/10.1038/s41391-017-0030-9] [PMID: 29282360]
[32]
Saxena, A.; Mathur, N.; Tiwari, P.; Mathur, S.K. Whole transcriptome RNA-seq reveals key regulatory factors involved in type 2 diabetes pathology in peripheral fat of Asian Indians. Sci. Rep., 2021, 11(1), 10632.
[http://dx.doi.org/10.1038/s41598-021-90148-z] [PMID: 34017037]
[33]
Warde-Farley, D.; Donaldson, S.L.; Comes, O.; Zuberi, K.; Badrawi, R.; Chao, P.; Franz, M.; Grouios, C.; Kazi, F.; Lopes, C.T.; Maitland, A.; Mostafavi, S.; Montojo, J.; Shao, Q.; Wright, G.; Bader, G.D.; Morris, Q. The GeneMANIA prediction server: Biological network integration for gene prioritization and predicting gene function. Nucleic Acids Res., 2010, 38(S2), W214-W220.
[http://dx.doi.org/10.1093/nar/gkq537] [PMID: 20576703]
[34]
Armenia, J.; Wankowicz, S.A.M.; Liu, D.; Gao, J.; Kundra, R.; Reznik, E.; Chatila, W.K.; Chakravarty, D.; Han, G.C.; Coleman, I.; Montgomery, B.; Pritchard, C.; Morrissey, C.; Barbieri, C.E.; Beltran, H.; Sboner, A.; Zafeiriou, Z.; Miranda, S.; Bielski, C.M.; Penson, A.V.; Tolonen, C.; Huang, F.W.; Robinson, D.; Wu, Y.M.; Lonigro, R.; Garraway, L.A.; Demichelis, F.; Kantoff, P.W.; Taplin, M.E.; Abida, W.; Taylor, B.S.; Scher, H.I.; Nelson, P.S.; de Bono, J.S.; Rubin, M.A.; Sawyers, C.L.; Chinnaiyan, A.M.; Schultz, N.; Van Allen, E.M. The long tail of oncogenic drivers in prostate cancer. Nat. Genet., 2018, 50(5), 645-651.
[http://dx.doi.org/10.1038/s41588-018-0078-z] [PMID: 29610475]
[35]
Abeshouse, A.; Ahn, J.; Akbani, R.; Ally, A.; Amin, S.; Andry, C.D.; Annala, M.; Aprikian, A.; Armenia, J.; Arora, A.; Auman, J.T.; Balasundaram, M.; Balu, S.; Barbieri, C.E.; Bauer, T.; Benz, C.C.; Bergeron, A.; Beroukhim, R.; Berrios, M.; Bivol, A.; Bodenheimer, T.; Boice, L.; Bootwalla, M.S.; Borges dos Reis, R.; Boutros, P.C.; Bowen, J.; Bowlby, R.; Boyd, J.; Bradley, R.K.; Breggia, A.; Brimo, F.; Bristow, C.A.; Brooks, D.; Broom, B.M.; Bryce, A.H.; Bubley, G.; Burks, E.; Butterfield, Y.S.N.; Button, M.; Canes, D.; Carlotti, C.G.; Carlsen, R.; Carmel, M.; Carroll, P.R.; Carter, S.L.; Cartun, R.; Carver, B.S.; Chan, J.M.; Chang, M.T.; Chen, Y.; Cherniack, A.D.; Chevalier, S.; Chin, L.; Cho, J.; Chu, A.; Chuah, E.; Chudamani, S.; Cibulskis, K.; Ciriello, G.; Clarke, A.; Cooperberg, M.R.; Corcoran, N.M.; Costello, A.J.; Cowan, J.; Crain, D.; Curley, E.; David, K.; Demchok, J.A.; Demichelis, F.; Dhalla, N.; Dhir, R.; Doueik, A.; Drake, B.; Dvinge, H.; Dyakova, N.; Felau, I.; Ferguson, M.L.; Frazer, S.; Freedland, S.; Fu, Y.; Gabriel, S.B.; Gao, J.; Gardner, J.; Gastier-Foster, J.M.; Gehlenborg, N.; Gerken, M.; Gerstein, M.B.; Getz, G.; Godwin, A.K.; Gopalan, A.; Graefen, M.; Graim, K.; Gribbin, T.; Guin, R.; Gupta, M.; Hadjipanayis, A.; Haider, S.; Hamel, L.; Hayes, D.N.; Heiman, D.I.; Hess, J.; Hoadley, K.A.; Holbrook, A.H.; Holt, R.A.; Holway, A.; Hovens, C.M.; Hoyle, A.P.; Huang, M.; Hutter, C.M.; Ittmann, M.; Iype, L.; Jefferys, S.R.; Jones, C.D.; Jones, S.J.M.; Juhl, H.; Kahles, A.; Kane, C.J.; Kasaian, K.; Kerger, M.; Khurana, E.; Kim, J.; Klein, R.J.; Kucherlapati, R.; Lacombe, L.; Ladanyi, M.; Lai, P.H.; Laird, P.W.; Lander, E.S.; Latour, M.; Lawrence, M.S.; Lau, K.; LeBien, T.; Lee, D.; Lee, S.; Lehmann, K-V.; Leraas, K.M.; Leshchiner, I.; Leung, R.; Libertino, J.A.; Lichtenberg, T.M.; Lin, P.; Linehan, W.M.; Ling, S.; Lippman, S.M.; Liu, J.; Liu, W.; Lochovsky, L.; Loda, M.; Logothetis, C.; Lolla, L.; Longacre, T.; Lu, Y.; Luo, J.; Ma, Y.; Mahadeshwar, H.S.; Mallery, D.; Mariamidze, A.; Marra, M.A.; Mayo, M.; McCall, S.; McKercher, G.; Meng, S.; Mes-Masson, A-M.; Merino, M.J.; Meyerson, M.; Mieczkowski, P.A.; Mills, G.B.; Shaw, K.R.M.; Minner, S.; Moinzadeh, A.; Moore, R.A.; Morris, S.; Morrison, C.; Mose, L.E.; Mungall, A.J.; Murray, B.A.; Myers, J.B.; Naresh, R.; Nelson, J.; Nelson, M.A.; Nelson, P.S.; Newton, Y.; Noble, M.S.; Noushmehr, H.; Nykter, M.; Pantazi, A.; Parfenov, M.; Park, P.J.; Parker, J.S.; Paulauskis, J.; Penny, R.; Perou, C.M.; Piché, A.; Pihl, T.; Pinto, P.A.; Prandi, D.; Protopopov, A.; Ramirez, N.C.; Rao, A.; Rathmell, W.K.; Rätsch, G.; Ren, X.; Reuter, V.E.; Reynolds, S.M.; Rhie, S.K.; Rieger-Christ, K.; Roach, J.; Robertson, A.G.; Robinson, B.; Rubin, M.A.; Saad, F.; Sadeghi, S.; Saksena, G.; Saller, C.; Salner, A.; Sanchez-Vega, F.; Sander, C.; Sandusky, G.; Sauter, G.; Sboner, A.; Scardino, P.T.; Scarlata, E.; Schein, J.E.; Schlomm, T.; Schmidt, L.S.; Schultz, N.; Schumacher, S.E.; Seidman, J.; Neder, L.; Seth, S.; Sharp, A.; Shelton, C.; Shelton, T.; Shen, H.; Shen, R.; Sherman, M.; Sheth, M.; Shi, Y.; Shih, J.; Shmulevich, I.; Simko, J.; Simon, R.; Simons, J.V.; Sipahimalani, P.; Skelly, T.; Sofia, H.J.; Soloway, M.G.; Song, X.; Sorcini, A.; Sougnez, C.; Stepa, S.; Stewart, C.; Stewart, J.; Stuart, J.M.; Sullivan, T.B.; Sun, C.; Sun, H.; Tam, A.; Tan, D.; Tang, J.; Tarnuzzer, R.; Tarvin, K.; Taylor, B.S.; Teebagy, P.; Tenggara, I.; Têtu, B.; Tewari, A.; Thiessen, N.; Thompson, T.; Thorne, L.B.; Tirapelli, D.P.; Tomlins, S.A.; Trevisan, F.A.; Troncoso, P.; True, L.D.; Tsourlakis, M.C.; Tyekucheva, S.; Van Allen, E.; Van Den Berg, D.J.; Veluvolu, U.; Verhaak, R.; Vocke, C.D.; Voet, D.; Wan, Y.; Wang, Q.; Wang, W.; Wang, Z.; Weinhold, N.; Weinstein, J.N.; Weisenberger, D.J.; Wilkerson, M.D.; Wise, L.; Witte, J.; Wu, C-C.; Wu, J.; Wu, Y.; Xu, A.W.; Yadav, S.S.; Yang, L.; Yang, L.; Yau, C.; Ye, H.; Yena, P.; Zeng, T.; Zenklusen, J.C.; Zhang, H.; Zhang, J.; Zhang, J.; Zhang, W.; Zhong, Y.; Zhu, K.; Zmuda, E. The molecular taxonomy of primary prostate cancer. Cell, 2015, 163(4), 1011-1025.
[http://dx.doi.org/10.1016/j.cell.2015.10.025] [PMID: 26544944]
[36]
Lonsdale, J.; Thomas, J.; Salvatore, M.; Phillips, R.; Lo, E.; Shad, S.; Hasz, R.; Walters, G.; Garcia, F.; Young, N.; Foster, B.; Moser, M.; Karasik, E.; Gillard, B.; Ramsey, K.; Sullivan, S.; Bridge, J.; Magazine, H.; Syron, J.; Fleming, J.; Siminoff, L.; Traino, H.; Mosavel, M.; Barker, L.; Jewell, S.; Rohrer, D.; Maxim, D.; Filkins, D.; Harbach, P.; Cortadillo, E.; Berghuis, B.; Turner, L.; Hudson, E.; Feenstra, K.; Sobin, L.; Robb, J.; Branton, P.; Korzeniewski, G.; Shive, C.; Tabor, D.; Qi, L.; Groch, K.; Nampally, S.; Buia, S.; Zimmerman, A.; Smith, A.; Burges, R.; Robinson, K.; Valentino, K.; Bradbury, D.; Cosentino, M.; Diaz-Mayoral, N.; Kennedy, M.; Engel, T.; Williams, P.; Erickson, K.; Ardlie, K.; Winckler, W.; Getz, G.; DeLuca, D.; MacArthur, D.; Kellis, M.; Thomson, A.; Young, T.; Gelfand, E.; Donovan, M.; Meng, Y.; Grant, G.; Mash, D.; Marcus, Y.; Basile, M.; Liu, J.; Zhu, J.; Tu, Z.; Cox, N.J.; Nicolae, D.L.; Gamazon, E.R. Im, H.K.; Konkashbaev, A.; Pritchard, J.; Stevens, M.; Flutre, T.; Wen, X.; Dermitzakis, E.T.; Lappalainen, T.; Guigo, R.; Monlong, J.; Sammeth, M.; Koller, D.; Battle, A.; Mostafavi, S.; McCarthy, M.; Rivas, M.; Maller, J.; Rusyn, I.; Nobel, A.; Wright, F.; Shabalin, A.; Feolo, M.; Sharopova, N.; Sturcke, A.; Paschal, J.; Anderson, J.M.; Wilder, E.L.; Derr, L.K.; Green, E.D.; Struewing, J.P.; Temple, G.; Volpi, S.; Boyer, J.T.; Thomson, E.J.; Guyer, M.S.; Ng, C.; Abdallah, A.; Colantuoni, D.; Insel, T.R.; Koester, S.E.; Little, A.R.; Bender, P.K.; Lehner, T.; Yao, Y.; Compton, C.C.; Vaught, J.B.; Sawyer, S.; Lockhart, N.C.; Demchok, J.; Moore, H.F. The genotype-tissue expression (GTEx) project. Nat. Genet., 2013, 45(6), 580-585.
[http://dx.doi.org/10.1038/ng.2653] [PMID: 23715323]
[37]
Tang, Z.; Li, C.; Kang, B.; Gao, G.; Li, C.; Zhang, Z. GEPIA: A web server for cancer and normal gene expression profiling and interactive analyses. Nucleic Acids Res., 2017, 45(W1), W98-W102.
[http://dx.doi.org/10.1093/nar/gkx247] [PMID: 28407145]
[38]
Shen, Y.; Liu, J.; Zhang, L.; Dong, S.; Zhang, J.; Liu, Y.; Zhou, H.; Dong, W. Identification of potential biomarkers and survival analysis for head and neck squamous cell carcinoma using bioinformatics strategy: A study based on TCGA and GEO datasets. BioMed Res. Int., 2019, 2019, 1-14.
[http://dx.doi.org/10.1155/2019/7376034] [PMID: 31485443]
[39]
Li, C.; Tang, Z.; Zhang, W.; Ye, Z.; Liu, F. GEPIA2021: integrating multiple deconvolution-based analysis into GEPIA. Nucleic Acids Res., 2021, 49(W1), W242-W246.
[http://dx.doi.org/10.1093/nar/gkab418] [PMID: 34050758]
[40]
Assenov, Y.; Ramírez, F.; Schelhorn, S.E.; Lengauer, T.; Albrecht, M. Computing topological parameters of biological networks. Bioinformatics, 2008, 24(2), 282-284.
[http://dx.doi.org/10.1093/bioinformatics/btm554] [PMID: 18006545]
[41]
Gollapalli, P. G, T.S.; H, M.; Shetty, P.; N, S.K. Network topology analysis of essential genes interactome of Helicobacter pylori to explore novel therapeutic targets. Microb. Pathog., 2021, 158, 105059.
[http://dx.doi.org/10.1016/j.micpath.2021.105059] [PMID: 34157412]
[42]
Saini, S. PSA and beyond: Alternative prostate cancer biomarkers. Cell. Oncol., 2016, 39(2), 97-106.
[http://dx.doi.org/10.1007/s13402-016-0268-6] [PMID: 26790878]
[43]
Duffy, M.J. Biomarkers for prostate cancer: Prostate-specific antigen and beyond. Clin. Chem. Lab. Med., 2020, 58(3), 326-339.
[http://dx.doi.org/10.1515/cclm-2019-0693] [PMID: 31714881]
[44]
Hatakeyama, S.; Yoneyama, T.; Tobisawa, Y.; Ohyama, C. Recent progress and perspectives on prostate cancer biomarkers. Int. J. Clin. Oncol., 2017, 22(2), 214-221.
[http://dx.doi.org/10.1007/s10147-016-1049-y] [PMID: 27730440]

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