Computational Analysis of Gastric Canceromics Data to Identify Putative Biomarkers

EBSCOhost | 138949569 | Gastric Cancer - A Review., Available from: https://web.p.ebscohost.com/abstract?site=ehost&scope= site&jrnl=09757619&AN=138949569&h=rzG9fmTAy6FtZBG%2brB8uS6Sshp5u1k%2fdU7snZN8XnxZCHA0EmlwlJHgkJlMr%2bkBUqxCpqm%2f3EeI9El7TTVPKQg%3d%3d&crl=c&resultLocal=ErrCrlNoResults&resultNs=Ehost&crlhashurl=login.aspx%3fdirect%3dtrue%26profile%3dehost%26scope%3dsite%26authtype%3dcrawler%26jrnl%3d09757619%26AN%3d138949569 (Accessed on: April 20, 2023).

Maconi, G.; Manes, G.; Porro, G.B. Role of symptoms in diagnosis and outcome of gastric cancer. World J. Gastroenterol., 2008, 14(8), 1149-1155.
[http://dx.doi.org/10.3748/wjg.14.1149] [PMID: 18300338]

Hunt, R.; Quigley, E.; Abbas, Z.; Eliakim, A.; Emmanuel, A.; Goh, K.L.; Guarner, F.; Katelaris, P.; Smout, A.; Umar, M.; Whorwell, P.; Johanson, J.; Saenz, R.; Besançon, L.; Ndjeuda, E.; Horn, J.; Hungin, P.; Jones, R.; Krabshuis, J.; LeMair, A. Coping with common gastrointestinal symptoms in the community: A global perspective on heartburn, constipation, bloating, and abdominal pain/discomfort May 2013. J. Clin. Gastroenterol., 2014, 48(7), 567-578.
[http://dx.doi.org/10.1097/MCG.0000000000000141] [PMID: 25000344]

Humphrys, E.; Walter, F.M.; Rubin, G.; Emery, J.D.; Johnson, M.; Richards, A.; Fitzgerald, R.C.; Viswanath, Y.K.S.; Burt, J. Patient symptom experience prior to a diagnosis of oesophageal or gastric cancer: A multi-methods study. BJGP Open, 2020, 4(1), bjgpopen20X101001.
[http://dx.doi.org/10.3399/bjgpopen20X101001] [PMID: 31911419]

Graham, D.Y. History of Helicobacter pylori, duodenal ulcer, gastric ulcer and gastric cancer. World J. Gastroenterol., 2014, 20(18), 5191-5204.
[http://dx.doi.org/10.3748/wjg.v20.i18.5191] [PMID: 24833849]

Black, C.J.; Drossman, D.A.; Talley, N.J.; Ruddy, J.; Ford, A.C. Functional gastrointestinal disorders: Advances in understanding and management. Lancet, 2020, 396(10263), 1664-1674.
[http://dx.doi.org/10.1016/S0140-6736(20)32115-2] [PMID: 33049221]

Daniel, D.A.P.; Thangavel, K. Breathomics for gastric cancer classification using back-propagation neural network. J. Med. Signals Sens., 2016, 6(3), 172-182.
[http://dx.doi.org/10.4103/2228-7477.186879] [PMID: 27563574]

Rawla, P.; Barsouk, A. Epidemiology of gastric cancer: Global trends, risk factors and prevention. Prz Gastroenterol., 2018, 14(1), 26-38.
[http://dx.doi.org/10.5114/pg.2018.80001]

Tan, M.C.; Balakrishnan, M.; Graham, D.Y. Gastric cancer worldwide except Japan. In: Gastric Cancer: With Special Focus on Studies from Japan; Shiotani, A., Ed.; Springer: Singapore, 2019; pp. 17-28.
[http://dx.doi.org/10.1007/978-981-13-1120-8_2]

Thrift, A.P.; El-Serag, H.B. Burden of gastric cancer. Clin. Gastroenterol. Hepatol., 2020, 18(3), 534-542.
[http://dx.doi.org/10.1016/j.cgh.2019.07.045] [PMID: 31362118]

Forman, D.; Burley, V.J. Gastric cancer: Global pattern of the disease and an overview of environmental risk factors. Best Pract. Res. Clin. Gastroenterol., 2006, 20(4), 633-649.
[http://dx.doi.org/10.1016/j.bpg.2006.04.008] [PMID: 16997150]

Strong, V.E. Progress in gastric cancer. Updates Surg., 2018, 70(2), 157-159.
[http://dx.doi.org/10.1007/s13304-018-0543-3] [PMID: 29869781]

Cao, W.; Chen, H.D.; Yu, Y.W.; Li, N.; Chen, W.Q. Changing profiles of cancer burden worldwide and in China: A secondary analysis of the global cancer statistics 2020. Chin. Med. J., 2021, 134(7), 783-791.
[http://dx.doi.org/10.1097/CM9.0000000000001474] [PMID: 33734139]

Ferlay, J.; Colombet, M.; Soerjomataram, I.; Parkin, D.M.; Piñeros, M.; Znaor, A.; Bray, F. Cancer statistics for the year 2020: An overview. Int. J. Cancer, 2021, 149(4), 778-789.
[http://dx.doi.org/10.1002/ijc.33588] [PMID: 33818764]

Zhu, H.; Wang, Z.; Deng, B.; Mo, M.; Wang, H.; Chen, K.; Wu, H.; Ye, T.; Wang, B.; Ai, D.; Hao, S.; Tseng, I.; Zhao, K. Epidemiological landscape of esophageal cancer in Asia: Results from GLOBOCAN 2020. Thorac. Cancer, 2023, 14(11), 992-1003.
[http://dx.doi.org/10.1111/1759-7714.14835] [PMID: 36918204]

Mathew, A.; Gangadharan, P.; Varghese, C.; Nair, M.K. Diet and stomach cancer. Eur. J. Cancer Prev., 2000, 9(2), 89-98.
[http://dx.doi.org/10.1097/00008469-200004000-00004] [PMID: 10830575]

Sinha, R.; Anderson, D.E.; McDonald, S.S.; Greenwald, P. Cancer risk and diet in India. J. Postgrad. Med., 2003, 49(3), 222-228.
[PMID: 14597785]

Gajalakshmi, C.K.; Shanta, V. Diet and risk of stomach cancer: A case-control study in Madras, India. J. Cancer Prev., 1996, 2(3), 97-109.
[http://dx.doi.org/10.3727/108399896792195455]

Jayalekshmi, P.A.; Hassani, S.; Nandakumar, A.; Koriyama, C.; Sebastian, P.; Akiba, S. Gastric cancer risk in relation to tobacco use and alcohol drinking in Kerala, India - Karunagappally cohort study. World J. Gastroenterol., 2015, 21(44), 12676-12685.
[http://dx.doi.org/10.3748/wjg.v21.i44.12676] [PMID: 26640345]

Hebert, J.R.; Gupta, P.C.; Bhonsle, R.B.; Mehta, H.; Zheng, W.; Sanderson, M.; Teas, J. Dietary exposures and oral precancerous lesions in Srikakulam District, Andhra Pradesh, India. Public Health Nutr., 2002, 5(2), 303-312.
[http://dx.doi.org/10.1079/PHN2002249] [PMID: 12020382]

Sharma, A.; Radhakrishnan, V. Gastric cancer in India. Indian J. Med. Paediatr. Oncol., 2011, 32(1), 12-16.
[http://dx.doi.org/10.4103/0971-5851.81884] [PMID: 21731210]

Ibrahim, M.; Gilbert, K. Management of gastric cancer in Indian population. Transl. Gastroenterol. Hepatol., 2017, 2(8), 64.
[http://dx.doi.org/10.21037/tgh.2017.07.02] [PMID: 28905005]

Bhayal, A.C.; Krishnaveni, D.; Rao, K.P.R.; Kumar, A.R.; Jyothy, A.; Nallari, P.; Venkateshwari, A. Significant association of interleukin4 intron 3 VNTR Polymorphism with susceptibility to gastric cancer in a south indian population from Telangana. PLoS One, 2015, 10(9), e0138442.
[http://dx.doi.org/10.1371/journal.pone.0138442] [PMID: 26383107]

Bhaskar, S.; Kumari, P.; Sweta; Sinha, D.K. Incidence of malignancy in gastric/antral perforation. Int. Surg. J., 2019, 6(9), 3347-3352.
[http://dx.doi.org/10.18203/2349-2902.isj20194077]

Leung, W.K.; Wu, M.; Kakugawa, Y.; Kim, J.J.; Yeoh, K.; Goh, K.L.; Wu, K.; Wu, D.; Sollano, J.; Kachintorn, U.; Gotoda, T.; Lin, J.; You, W.; Ng, E.K.W.; Sung, J.J.Y. Screening for gastric cancer in Asia: current evidence and practice. Lancet Oncol., 2008, 9(3), 279-287.
[http://dx.doi.org/10.1016/S1470-2045(08)70072-X] [PMID: 18308253]

Scartozzi, M.; Galizia, E.; Freddari, F.; Berardi, R.; Cellerino, R.; Cascinu, S. Molecular biology of sporadic gastric cancer: Prognostic indicators and novel therapeutic approaches. Cancer Treat. Rev., 2004, 30(5), 451-459.
[http://dx.doi.org/10.1016/j.ctrv.2004.01.001] [PMID: 15245777]

Oliveira, C.; Seruca, R.; Carneiro, F. Hereditary gastric cancer. Best Pract. Res. Clin. Gastroenterol., 2009, 23(2), 147-157.
[http://dx.doi.org/10.1016/j.bpg.2009.02.003] [PMID: 19414142]

Dicken, B.J.; Bigam, D.L.; Cass, C.; Mackey, J.R.; Joy, A.A.; Hamilton, S.M. Gastric adenocarcinoma. Ann. Surg., 2005, 241(1), 27-39.
[http://dx.doi.org/10.1097/01.sla.0000149300.28588.23] [PMID: 15621988]

Rustgi, S.D.; McKinley, M.; McBay, B.; Zylberberg, H.M.; Gomez, S.L.; Hur, C.; Kastrinos, F.; Gupta, S.; Kim, M.K.; Itzkowitz, S.H.; Shah, S.C. Epidemiology of gastric malignancies 2000–2018 according to histology: A population-based analysis of incidence and temporal trends. Clin. Gastroenterol. Hepatol., 2023, S1542-3565(23)00104-0.
[http://dx.doi.org/10.1016/j.cgh.2023.01.037] [PMID: 36792000]

Shang, J.; Peña, A.S. Multidisciplinary approach to understand the pathogenesis of gastric cancer. World J. Gastroenterol., 2005, 11(27), 4131-4139.
[http://dx.doi.org/10.3748/wjg.v11.i27.4131] [PMID: 16015679]

Guilford, P.; Humar, B.; Blair, V. Hereditary diffuse gastric cancer: Translation of CDH1 germline mutations into clinical practice. Gastric Cancer, 2010, 13(1), 1-10.
[http://dx.doi.org/10.1007/s10120-009-0531-x] [PMID: 20373070]

van der Post, R.S.; Vogelaar, I.P.; Manders, P.; van der Kolk, L.E.; Cats, A.; van Hest, L.P.; Sijmons, R.; Aalfs, C.M.; Ausems, M.G.E.M.; Gómez García, E.B.; Wagner, A.; Hes, F.J.; Arts, N.; Mensenkamp, A.R.; van Krieken, J.H.; Hoogerbrugge, N.; Ligtenberg, M.J.L. Accuracy of hereditary diffuse gastric cancer testing criteria and outcomes in patients with a germline mutation in CDH1. Gastroenterology, 2015, 149(4), 897-906.e19.
[http://dx.doi.org/10.1053/j.gastro.2015.06.003] [PMID: 26072394]

Matsukuma, K.E.; Mullins, F.M.; Dietz, L.; Zehnder, J.L.; Ford, J.M.; Chun, N.M.; Schrijver, I. Hereditary diffuse gastric cancer due to a previously undescribed CDH1 splice site mutation. Hum. Pathol., 2010, 41(8), 1200-1203.
[http://dx.doi.org/10.1016/j.humpath.2010.01.022] [PMID: 20624523]

Frontiers | CDH1 Gene and Hereditary Diffuse Gastric Cancer Syndrome: Molecular and Histological Alterations and Implications for Diagnosis And Treatment., https://www.frontiersin.org/articles/10.3389/fphar.2018.01421/full

Funakoshi, T.; Miyamoto, S.; Kakiuchi, N.; Nikaido, M.; Setoyama, T.; Yokoyama, A.; Horimatsu, T.; Yamada, A.; Torishima, M.; Kosugi, S.; Yamada, H.; Sugimura, H.; Haga, H.; Sakai, Y.; Ogawa, S.; Seno, H.; Muto, M.; Chiba, T. Genetic analysis of a case of Helicobacter pylori-uninfected intramucosal gastric cancer in a family with hereditary diffuse gastric cancer. Gastric Cancer, 2019, 22(4), 892-898.
[http://dx.doi.org/10.1007/s10120-018-00912-w] [PMID: 30542785]

Salama, N.R.; Hartung, M.L.; Müller, A. Life in the human stomach: Persistence strategies of the bacterial pathogen Helicobacter pylori. Nat. Rev. Microbiol., 2013, 11(6), 385-399.
[http://dx.doi.org/10.1038/nrmicro3016] [PMID: 23652324]

Andersen, L.P. Colonization and infection by Helicobacter pylori in humans. Helicobacter, 2007, 12(S2), 12-15.
[http://dx.doi.org/10.1111/j.1523-5378.2007.00574.x] [PMID: 17991171]

Shibagaki, K.; Itawaki, A.; Miyaoka, Y.; Kishimoto, K.; Takahashi, Y.; Kotani, S.; Mishiro, T.; Oshima, N.; Kawashima, K.; Ishimura, N.; Onuma, H.; Nagasaki, M.; Nagase, M.; Araki, A.; Kadota, K.; Kushima, R.; Ishihara, S. Intestinal-type gastric dysplasia in Helicobacter pylori-naïve patients. Virchows Arch., 2022, 480(4), 783-792.
[http://dx.doi.org/10.1007/s00428-021-03237-9] [PMID: 34787713]

Tüzün, Y.; Keskin, S.; Kote, E. The role of Helicobacter pylori infection in skin diseases: Facts and controversies. Clin. Dermatol., 2010, 28(5), 478-482.
[http://dx.doi.org/10.1016/j.clindermatol.2010.03.002] [PMID: 20797505]

Kim, S.S.; Ruiz, V.E.; Carroll, J.D.; Moss, S.F. Helicobacter pylori in the pathogenesis of gastric cancer and gastric lymphoma. Cancer Lett., 2011, 305(2), 228-238.
[http://dx.doi.org/10.1016/j.canlet.2010.07.014] [PMID: 20692762]

Kuboki, Y.; Yamashita, S.; Niwa, T.; Ushijima, T.; Nagatsuma, A.; Kuwata, T.; Yoshino, T.; Doi, T.; Ochiai, A.; Ohtsu, A. Comprehensive analyses using next-generation sequencing and immunohistochemistry enable precise treatment in advanced gastric cancer. Ann. Oncol., 2016, 27(1), 127-133.
[http://dx.doi.org/10.1093/annonc/mdv508] [PMID: 26489445]

Verma, R.; Sharma, P.C. Next generation sequencing-based emerging trends in molecular biology of gastric cancer. Am. J. Cancer Res., 2018, 8(2), 207-225.
[PMID: 29511593]

Yamamoto, H.; Watanabe, Y.; Maehata, T.; Morita, R.; Yoshida, Y.; Oikawa, R.; Ishigooka, S.; Ozawa, S.; Matsuo, Y.; Hosoya, K.; Yamashita, M.; Taniguchi, H.; Nosho, K.; Suzuki, H.; Yasuda, H.; Shinomura, Y.; Itoh, F. An updated review of gastric cancer in the next-generation sequencing era: Insights from bench to bedside and vice versa. World J. Gastroenterol., 2014, 20(14), 3927-3937.
[http://dx.doi.org/10.3748/wjg.v20.i14.3927] [PMID: 24744582]

Sadikovic, B.; Al-Romaih, K.; Squire, J.; Zielenska, M. Cause and consequences of genetic and epigenetic alterations in human cancer. Curr. Genomics, 2008, 9(6), 394-408.
[http://dx.doi.org/10.2174/138920208785699580] [PMID: 19506729]

Huang, Y.W.; Huang, T.H.M.; Wang, L.S. Profiling DNA methylomes from microarray to genome-scale sequencing. Technol. Cancer Res. Treat., 2010, 9(2), 139-147.
[http://dx.doi.org/10.1177/153303461000900203] [PMID: 20218736]

Olova, N.; Krueger, F.; Andrews, S.; Oxley, D.; Berrens, R.V.; Branco, M.R.; Reik, W. Comparison of whole-genome bisulfite sequencing library preparation strategies identifies sources of biases affecting DNA methylation data. Genome Biol., 2018, 19(1), 33.
[http://dx.doi.org/10.1186/s13059-018-1408-2] [PMID: 29544553]

Neidhart, M. DNA Methylation and Complex Human Disease; Academic Press, 2015.

Qu, Y.; Dang, S.; Hou, P. Gene methylation in gastric cancer. Clin. Chim. Acta, 2013, 424, 53-65.
[http://dx.doi.org/10.1016/j.cca.2013.05.002] [PMID: 23669186]

Gardiner-Garden, M.; Frommer, M. CpG Islands in vertebrate genomes. J. Mol. Biol., 1987, 196(2), 261-282.
[http://dx.doi.org/10.1016/0022-2836(87)90689-9] [PMID: 3656447]

Xin, Y.; O’Donnell, A.H.; Ge, Y.; Chanrion, B.; Milekic, M.; Rosoklija, G.; Stankov, A.; Arango, V.; Dwork, A.J.; Gingrich, J.A.; Haghighi, F.G. Role of CpG context and content in evolutionary signatures of brain DNA methylation. Epigenetics, 2011, 6(11), 1308-1318.
[http://dx.doi.org/10.4161/epi.6.11.17876] [PMID: 22048252]

Shi, J.; Zhang, G.; Yao, D.; Liu, W.; Wang, N.; Ji, M.; He, N.; Shi, B.; Hou, P. Prognostic significance of aberrant gene methylation in gastric cancer. Am. J. Cancer Res., 2012, 2(1), 116-129.
[PMID: 22206050]

Baba, H.; Shigaki, H.; Baba, Y.; Harada, K.; Yoshida, N.; Watanabe, M. Epigenetic changes in gastrointestinal cancers. J. Cancer Metastasis Treat., 2015, 1(3), 113-122.
[http://dx.doi.org/10.4103/2394-4722.166991]

Soumyakrishnan, S.; Nagesh, R.; Peela, S.; Sreepriya, M. Epigenetic biomarkers for the detection of gastrointestinal cancers. In: Novel therapeutic approaches for gastrointestinal malignancies. Diagnostics and Therapeutic Advances in GI Malignancies; Nagaraju, G.P.; Peela, S., Eds.; Springer: Singapore, 2020.
[http://dx.doi.org/10.1007/978-981-15-5471-1_5]

Manne, A.; Woods, E.; Tsung, A.; Mittra, A. Biliary tract cancers: Treatment updates and future directions in the era of precision medicine and immuno-oncology. Front. Oncol., 2021, 11, 768009.
[http://dx.doi.org/10.3389/fonc.2021.768009] [PMID: 34868996]

Baba, Y.; Ishimoto, T.; Kurashige, J.; Iwatsuki, M.; Sakamoto, Y.; Yoshida, N.; Watanabe, M.; Baba, H. Epigenetic field cancerization in gastrointestinal cancers. Cancer Lett., 2016, 375(2), 360-366.
[http://dx.doi.org/10.1016/j.canlet.2016.03.009] [PMID: 26971491]

Tahara, T.; Arisawa, T. DNA methylation as a molecular biomarker in gastric cancer. Epigenomics, 2015, 7(3), 475-486.
[http://dx.doi.org/10.2217/epi.15.4] [PMID: 26077432]

Joo, J.K.; Kim, S.H.; Kim, H.G.; Kim, D.Y.; Ryu, S.Y.; Lee, K.H.; Lee, J.H. CpG methylation of transcription factor 4 in gastric carcinoma. Ann. Surg. Oncol., 2010, 17(12), 3344-3353.
[http://dx.doi.org/10.1245/s10434-010-1131-z] [PMID: 20585880]

Hu, X.T.; He, C. Recent progress in the study of methylated tumor suppressor genes in gastric cancer. Chin. J. Cancer, 2013, 32(1), 31-41.
[http://dx.doi.org/10.5732/cjc.011.10175] [PMID: 22059906]

Yates, D.R.; Rehman, I.; Abbod, M.F.; Meuth, M.; Cross, S.S.; Linkens, D.A.; Hamdy, F.C.; Catto, J.W.F. Promoter hypermethylation identifies progression risk in bladder cancer. Clin. Cancer Res., 2007, 13(7), 2046-2053.
[http://dx.doi.org/10.1158/1078-0432.CCR-06-2476] [PMID: 17404085]

Yoshida, S.; Yamashita, S.; Niwa, T.; Mori, A.; Ito, S.; Ichinose, M.; Ushijima, T. Epigenetic inactivation of FAT4 contributes to gastric field cancerization. Gastric Cancer, 2017, 20(1), 136-145.
[http://dx.doi.org/10.1007/s10120-016-0593-5] [PMID: 26792292]

Ulazzi, L.; Sabbioni, S.; Miotto, E.; Veronese, A.; Angusti, A.; Gafà, R.; Manfredini, S.; Farinati, F.; Sasaki, T.; Lanza, G.; Negrini, M. Nidogen 1 and 2 gene promoters are aberrantly methylated in human gastrointestinal cancer. Mol. Cancer, 2007, 6(1), 17.
[http://dx.doi.org/10.1186/1476-4598-6-17] [PMID: 17328794]

Mayne, S.T.; Navarro, S.A. Diet, obesity and reflux in the etiology of adenocarcinomas of the esophagus and gastric cardia in humans. J. Nutr., 2002, 132(S11), 3467S-3470S.
[http://dx.doi.org/10.1093/jn/132.11.3467S] [PMID: 12421872]

Kim, S.R.; Kim, K.; Lee, S.A.; Kwon, S.O.; Lee, J-K.; Keum, N.; Park, S.M. Effect of red, processed, and white meat consumption on the risk of gastric cancer: An overall and dose–response meta-analysis. Nutrients, 2019, 11(4), 826.
[http://dx.doi.org/10.3390/nu11040826] [PMID: 30979076]

Lu, Y.; Chen, J.; Ding, Y.; Jin, G.; Wu, J.; Huang, H.; Deng, B.; Hua, Z.; Zhou, Y.; Shu, Y.; Liu, P.; Hu, Z.; Shen, J.; Xu, Y.; Shen, H. Genetic variation of PSCA gene is associated with the risk of both diffuse- and intestinal-type gastric cancer in a Chinese population. Int. J. Cancer, 2010, 127(9), 2183-2189.
[http://dx.doi.org/10.1002/ijc.25228] [PMID: 20131315]

Nagata, M.; Muto, S.; Horie, S. Molecular biomarkers in bladder cancer: Novel potential indicators of prognosis and treatment outcomes. Dis. Markers, 2016, 2016, 1-5.
[http://dx.doi.org/10.1155/2016/8205836] [PMID: 26924873]

Verma, M.; Manne, U. Genetic and epigenetic biomarkers in cancer diagnosis and identifying high risk populations. Crit. Rev. Oncol. Hematol., 2006, 60(1), 9-18.
[http://dx.doi.org/10.1016/j.critrevonc.2006.04.002] [PMID: 16829121]

Reyimu, A.; Chen, Y.; Song, X.; Zhou, W.; Dai, J.; Jiang, F. Identification of latent biomarkers in connection with progression and prognosis in oral cancer by comprehensive bioinformatics analysis. World J. Surg. Oncol., 2021, 19(1), 240.
[http://dx.doi.org/10.1186/s12957-021-02360-w] [PMID: 34384424]

Veera, B.P.; Rao, R.N.N. Biomarkers as the promising tools for early detection of gastrointestinal cancer. In: Recent Advancements in Biomarkers and Early Detection of Gastrointestinal Cancers; Springer, 2020; pp. 15-25.
[http://dx.doi.org/10.1007/978-981-15-4431-6_2]

Matsuoka, T.; Yashiro, M. Biomarkers of gastric cancer: Current topics and future perspective. World J. Gastroenterol., 2018, 24(26), 2818-2832.
[http://dx.doi.org/10.3748/wjg.v24.i26.2818] [PMID: 30018477]

Liu, L.; Pang, H.; He, Q.; Pan, B.; Sun, X.; Shan, J.; Wu, L.; Wu, K.; Yao, X.; Guo, Y. A novel strategy to identify candidate diagnostic and prognostic biomarkers for gastric cancer. Cancer Cell Int., 2021, 21(1), 335.
[http://dx.doi.org/10.1186/s12935-021-02007-6] [PMID: 34215253]

Zhou, C.; Zhong, X.; Song, Y.; Shi, J.; Wu, Z.; Guo, Z.; Sun, J.; Wang, Z. Prognostic biomarkers for gastric cancer: An umbrella review of the evidence. Front. Oncol., 2019, 9, 1321.
[http://dx.doi.org/10.3389/fonc.2019.01321] [PMID: 31850212]

Ye, D.; Xu, G.; Ma, W.; Li, Y.; Luo, W.; Xiao, Y.; Liu, Y.; Zhang, Z. Significant function and research progress of biomarkers in gastric cancer (Review). Oncol. Lett., 2019, 19(1), 17-29.
[http://dx.doi.org/10.3892/ol.2019.11078] [PMID: 31897111]

Herrera-Pariente, C.; Montori, S.; Llach, J.; Bofill, A.; Albeniz, E.; Moreira, L. Biomarkers for gastric cancer screening and early diagnosis. Biomedicines, 2021, 9(10), 1448.
[http://dx.doi.org/10.3390/biomedicines9101448] [PMID: 34680565]

Giordano, T.J. The cancer genome atlas research network: A sight to behold. Endocr. Pathol., 2014, 25(4), 362-365.
[http://dx.doi.org/10.1007/s12022-014-9345-4] [PMID: 25367656]

Tang, W.; Fu, K.; Sun, H.; Rong, D.; Wang, H.; Cao, H. CircRNA microarray profiling identifies a novel circulating biomarker for detection of gastric cancer. Mol. Cancer, 2018, 17(1), 137.
[http://dx.doi.org/10.1186/s12943-018-0888-8] [PMID: 30236115]

Liu, J.; Lichtenberg, T.; Hoadley, K.A.; Poisson, L.M.; Lazar, A.J.; Cherniack, A.D.; Kovatich, A.J.; Benz, C.C.; Levine, D.A.; Lee, A.V.; Omberg, L.; Wolf, D.M.; Shriver, C.D.; Thorsson, V.; Hu, H.; Caesar-Johnson, S.J.; Demchok, J.A.; Felau, I.; Kasapi, M.; Ferguson, M.L.; Hutter, C.M.; Sofia, H.J.; Tarnuzzer, R.; Wang, Z.; Yang, L.; Zenklusen, J.C.; Zhang, J.J.; Chudamani, S.; Liu, J.; Lolla, L.; Naresh, R.; Pihl, T.; Sun, Q.; Wan, Y.; Wu, Y.; Cho, J.; DeFreitas, T.; Frazer, S.; Gehlenborg, N.; Getz, G.; Heiman, D.I.; Kim, J.; Lawrence, M.S.; Lin, P.; Meier, S.; Noble, M.S.; Saksena, G.; Voet, D.; Zhang, H.; Bernard, B.; Chambwe, N.; Dhankani, V.; Knijnenburg, T.; Kramer, R.; Leinonen, K.; Liu, Y.; Miller, M.; Reynolds, S.; Shmulevich, I.; Thorsson, V.; Zhang, W.; Akbani, R.; Broom, B.M.; Hegde, A.M.; Ju, Z.; Kanchi, R.S.; Korkut, A.; Li, J.; Liang, H.; Ling, S.; Liu, W.; Lu, Y.; Mills, G.B.; Ng, K-S.; Rao, A.; Ryan, M.; Wang, J.; Weinstein, J.N.; Zhang, J.; Abeshouse, A.; Armenia, J.; Chakravarty, D.; Chatila, W.K.; de Bruijn, I.; Gao, J.; Gross, B.E.; Heins, Z.J.; Kundra, R.; La, K.; Ladanyi, M.; Luna, A.; Nissan, M.G.; Ochoa, A.; Phillips, S.M.; Reznik, E.; Sanchez-Vega, F.; Sander, C.; Schultz, N.; Sheridan, R.; Sumer, S.O.; Sun, Y.; Taylor, B.S.; Wang, J.; Zhang, H.; Anur, P.; Peto, M.; Spellman, P.; Benz, C.; Stuart, J.M.; Wong, C.K.; Yau, C.; Hayes, D.N.; Parker, J.S.; Wilkerson, M.D.; Ally, A.; Balasundaram, M.; Bowlby, R.; Brooks, D.; Carlsen, R.; Chuah, E.; Dhalla, N.; Holt, R.; Jones, S.J.M.; Kasaian, K.; Lee, D.; Ma, Y.; Marra, M.A.; Mayo, M.; Moore, R.A.; Mungall, A.J.; Mungall, K.; Robertson, A.G.; Sadeghi, S.; Schein, J.E.; Sipahimalani, P.; Tam, A.; Thiessen, N.; Tse, K.; Wong, T.; Berger, A.C.; Beroukhim, R.; Cherniack, A.D.; Cibulskis, C.; Gabriel, S.B.; Gao, G.F.; Ha, G.; Meyerson, M.; Schumacher, S.E.; Shih, J.; Kucherlapati, M.H.; Kucherlapati, R.S.; Baylin, S.; Cope, L.; Danilova, L.; Bootwalla, M.S.; Lai, P.H.; Maglinte, D.T.; Van Den Berg, D.J.; Weisenberger, D.J.; Auman, J.T.; Balu, S.; Bodenheimer, T.; Fan, C.; Hoadley, K.A.; Hoyle, A.P.; Jefferys, S.R.; Jones, C.D.; Meng, S.; Mieczkowski, P.A.; Mose, L.E.; Perou, A.H.; Perou, C.M.; Roach, J.; Shi, Y.; Simons, J.V.; Skelly, T.; Soloway, M.G.; Tan, D.; Veluvolu, U.; Fan, H.; Hinoue, T.; Laird, P.W.; Shen, H.; Zhou, W.; Bellair, M.; Chang, K.; Covington, K.; Creighton, C.J.; Dinh, H.; Doddapaneni, H.V.; Donehower, L.A.; Drummond, J.; Gibbs, R.A.; Glenn, R.; Hale, W.; Han, Y.; Hu, J.; Korchina, V.; Lee, S.; Lewis, L.; Li, W.; Liu, X.; Morgan, M.; Morton, D.; Muzny, D.; Santibanez, J.; Sheth, M.; Shinbro, E.; Wang, L.; Wang, M.; Wheeler, D.A.; Xi, L.; Zhao, F.; Hess, J.; Appelbaum, E.L.; Bailey, M.; Cordes, M.G.; Ding, L.; Fronick, C.C.; Fulton, L.A.; Fulton, R.S.; Kandoth, C.; Mardis, E.R.; McLellan, M.D.; Miller, C.A.; Schmidt, H.K.; Wilson, R.K.; Crain, D.; Curley, E.; Gardner, J.; Lau, K.; Mallery, D.; Morris, S.; Paulauskis, J.; Penny, R.; Shelton, C.; Shelton, T.; Sherman, M.; Thompson, E.; Yena, P.; Bowen, J.; Gastier-Foster, J.M.; Gerken, M.; Leraas, K.M.; Lichtenberg, T.M.; Ramirez, N.C.; Wise, L.; Zmuda, E.; Corcoran, N.; Costello, T.; Hovens, C.; Carvalho, A.L.; de Carvalho, A.C.; Fregnani, J.H.; Longatto-Filho, A.; Reis, R.M.; Scapulatempo-Neto, C.; Silveira, H.C.S.; Vidal, D.O.; Burnette, A.; Eschbacher, J.; Hermes, B.; Noss, A.; Singh, R.; Anderson, M.L.; Castro, P.D.; Ittmann, M.; Huntsman, D.; Kohl, B.; Le, X.; Thorp, R.; Andry, C.; Duffy, E.R.; Lyadov, V.; Paklina, O.; Setdikova, G.; Shabunin, A.; Tavobilov, M.; McPherson, C.; Warnick, R.; Berkowitz, R.; Cramer, D.; Feltmate, C.; Horowitz, N.; Kibel, A.; Muto, M.; Raut, C.P.; Malykh, A.; Barnholtz-Sloan, J.S.; Barrett, W.; Devine, K.; Fulop, J.; Ostrom, Q.T.; Shimmel, K.; Wolinsky, Y.; Sloan, A.E.; De Rose, A.; Giuliante, F.; Goodman, M.; Karlan, B.Y.; Hagedorn, C.H.; Eckman, J.; Harr, J.; Myers, J.; Tucker, K.; Zach, L.A.; Deyarmin, B.; Hu, H.; Kvecher, L.; Larson, C.; Mural, R.J.; Somiari, S.; Vicha, A.; Zelinka, T.; Bennett, J.; Iacocca, M.; Rabeno, B.; Swanson, P.; Latour, M.; Lacombe, L.; Têtu, B.; Bergeron, A.; McGraw, M.; Staugaitis, S.M.; Chabot, J.; Hibshoosh, H.; Sepulveda, A.; Su, T.; Wang, T.; Potapova, O.; Voronina, O.; Desjardins, L.; Mariani, O.; Roman-Roman, S.; Sastre, X.; Stern, M-H.; Cheng, F.; Signoretti, S.; Berchuck, A.; Bigner, D.; Lipp, E.; Marks, J.; McCall, S.; McLendon, R.; Secord, A.; Sharp, A.; Behera, M.; Brat, D.J.; Chen, A.; Delman, K.; Force, S.; Khuri, F.; Magliocca, K.; Maithel, S.; Olson, J.J.; Owonikoko, T.; Pickens, A.; Ramalingam, S.; Shin, D.M.; Sica, G.; Van Meir, E.G.; Zhang, H.; Eijckenboom, W.; Gillis, A.; Korpershoek, E.; Looijenga, L.; Oosterhuis, W.; Stoop, H.; van Kessel, K.E.; Zwarthoff, E.C.; Calatozzolo, C.; Cuppini, L.; Cuzzubbo, S.; DiMeco, F.; Finocchiaro, G.; Mattei, L.; Perin, A.; Pollo, B.; Chen, C.; Houck, J.; Lohavanichbutr, P.; Hartmann, A.; Stoehr, C.; Stoehr, R.; Taubert, H.; Wach, S.; Wullich, B.; Kycler, W.; Murawa, D.; Wiznerowicz, M.; Chung, K.; Edenfield, W.J.; Martin, J.; Baudin, E.; Bubley, G.; Bueno, R.; De Rienzo, A.; Richards, W.G.; Kalkanis, S.; Mikkelsen, T.; Noushmehr, H.; Scarpace, L.; Girard, N.; Aymerich, M.; Campo, E.; Giné, E.; Guillermo, A.L.; Van Bang, N.; Hanh, P.T.; Phu, B.D.; Tang, Y.; Colman, H.; Evason, K.; Dottino, P.R.; Martignetti, J.A.; Gabra, H.; Juhl, H.; Akeredolu, T.; Stepa, S.; Hoon, D.; Ahn, K.; Kang, K.J.; Beuschlein, F.; Breggia, A.; Birrer, M.; Bell, D.; Borad, M.; Bryce, A.H.; Castle, E.; Chandan, V.; Cheville, J.; Copland, J.A.; Farnell, M.; Flotte, T.; Giama, N.; Ho, T.; Kendrick, M.; Kocher, J-P.; Kopp, K.; Moser, C.; Nagorney, D.; O’Brien, D.; O’Neill, B.P.; Patel, T.; Petersen, G.; Que, F.; Rivera, M.; Roberts, L.; Smallridge, R.; Smyrk, T.; Stanton, M.; Thompson, R.H.; Torbenson, M.; Yang, J.D.; Zhang, L.; Brimo, F.; Ajani, J.A.; Angulo Gonzalez, A.M.; Behrens, C.; Bondaruk, J.; Broaddus, R.; Czerniak, B.; Esmaeli, B.; Fujimoto, J.; Gershenwald, J.; Guo, C.; Lazar, A.J.; Logothetis, C.; Meric-Bernstam, F.; Moran, C.; Ramondetta, L.; Rice, D.; Sood, A.; Tamboli, P.; Thompson, T.; Troncoso, P.; Tsao, A.; Wistuba, I.; Carter, C.; Haydu, L.; Hersey, P.; Jakrot, V.; Kakavand, H.; Kefford, R.; Lee, K.; Long, G.; Mann, G.; Quinn, M.; Saw, R.; Scolyer, R.; Shannon, K.; Spillane, A.; Stretch, J.; Synott, M.; Thompson, J.; Wilmott, J.; Al-Ahmadie, H.; Chan, T.A.; Ghossein, R.; Gopalan, A.; Levine, D.A.; Reuter, V.; Singer, S.; Singh, B.; Tien, N.V.; Broudy, T.; Mirsaidi, C.; Nair, P.; Drwiega, P.; Miller, J.; Smith, J.; Zaren, H.; Park, J-W.; Hung, N.P.; Kebebew, E.; Linehan, W.M.; Metwalli, A.R.; Pacak, K.; Pinto, P.A.; Schiffman, M.; Schmidt, L.S.; Vocke, C.D.; Wentzensen, N.; Worrell, R.; Yang, H.; Moncrieff, M.; Goparaju, C.; Melamed, J.; Pass, H.; Botnariuc, N.; Caraman, I.; Cernat, M.; Chemencedji, I.; Clipca, A.; Doruc, S.; Gorincioi, G.; Mura, S.; Pirtac, M.; Stancul, I.; Tcaciuc, D.; Albert, M.; Alexopoulou, I.; Arnaout, A.; Bartlett, J.; Engel, J.; Gilbert, S.; Parfitt, J.; Sekhon, H.; Thomas, G.; Rassl, D.M.; Rintoul, R.C.; Bifulco, C.; Tamakawa, R.; Urba, W.; Hayward, N.; Timmers, H.; Antenucci, A.; Facciolo, F.; Grazi, G.; Marino, M.; Merola, R.; de Krijger, R.; Gimenez-Roqueplo, A-P.; Piché, A.; Chevalier, S.; McKercher, G.; Birsoy, K.; Barnett, G.; Brewer, C.; Farver, C.; Naska, T.; Pennell, N.A.; Raymond, D.; Schilero, C.; Smolenski, K.; Williams, F.; Morrison, C.; Borgia, J.A.; Liptay, M.J.; Pool, M.; Seder, C.W.; Junker, K.; Omberg, L.; Dinkin, M.; Manikhas, G.; Alvaro, D.; Bragazzi, M.C.; Cardinale, V.; Carpino, G.; Gaudio, E.; Chesla, D.; Cottingham, S.; Dubina, M.; Moiseenko, F.; Dhanasekaran, R.; Becker, K-F.; Janssen, K-P.; Slotta-Huspenina, J.; Abdel-Rahman, M.H.; Aziz, D.; Bell, S.; Cebulla, C.M.; Davis, A.; Duell, R.; Elder, J.B.; Hilty, J.; Kumar, B.; Lang, J.; Lehman, N.L.; Mandt, R.; Nguyen, P.; Pilarski, R.; Rai, K.; Schoenfield, L.; Senecal, K.; Wakely, P.; Hansen, P.; Lechan, R.; Powers, J.; Tischler, A.; Grizzle, W.E.; Sexton, K.C.; Kastl, A.; Henderson, J.; Porten, S.; Waldmann, J.; Fassnacht, M.; Asa, S.L.; Schadendorf, D.; Couce, M.; Graefen, M.; Huland, H.; Sauter, G.; Schlomm, T.; Simon, R.; Tennstedt, P.; Olabode, O.; Nelson, M.; Bathe, O.; Carroll, P.R.; Chan, J.M.; Disaia, P.; Glenn, P.; Kelley, R.K.; Landen, C.N.; Phillips, J.; Prados, M.; Simko, J.; Smith-McCune, K.; VandenBerg, S.; Roggin, K.; Fehrenbach, A.; Kendler, A.; Sifri, S.; Steele, R.; Jimeno, A.; Carey, F.; Forgie, I.; Mannelli, M.; Carney, M.; Hernandez, B.; Campos, B.; Herold-Mende, C.; Jungk, C.; Unterberg, A.; von Deimling, A.; Bossler, A.; Galbraith, J.; Jacobus, L.; Knudson, M.; Knutson, T.; Ma, D.; Milhem, M.; Sigmund, R.; Godwin, A.K.; Madan, R.; Rosenthal, H.G.; Adebamowo, C.; Adebamowo, S.N.; Boussioutas, A.; Beer, D.; Giordano, T.; Mes-Masson, A-M.; Saad, F.; Bocklage, T.; Landrum, L.; Mannel, R.; Moore, K.; Moxley, K.; Postier, R.; Walker, J.; Zuna, R.; Feldman, M.; Valdivieso, F.; Dhir, R.; Luketich, J.; Mora Pinero, E.M.; Quintero-Aguilo, M.; Carlotti, C.G., Jr; Dos Santos, J.S.; Kemp, R.; Sankarankuty, A.; Tirapelli, D.; Catto, J.; Agnew, K.; Swisher, E.; Creaney, J.; Robinson, B.; Shelley, C.S.; Godwin, E.M.; Kendall, S.; Shipman, C.; Bradford, C.; Carey, T.; Haddad, A.; Moyer, J.; Peterson, L.; Prince, M.; Rozek, L.; Wolf, G.; Bowman, R.; Fong, K.M.; Yang, I.; Korst, R.; Rathmell, W.K.; Fantacone-Campbell, J.L.; Hooke, J.A.; Kovatich, A.J.; Shriver, C.D.; DiPersio, J.; Drake, B.; Govindan, R.; Heath, S.; Ley, T.; Van Tine, B.; Westervelt, P.; Rubin, M.A.; Lee, J.I.; Aredes, N.D.; Mariamidze, A. An integrated TCGA pan-cancer clinical data resource to drive high-quality survival outcome analytics. Cell, 2018, 173(2), 400-416.e11.
[http://dx.doi.org/10.1016/j.cell.2018.02.052] [PMID: 29625055]

Amirmahani, F.; Ebrahimi, N.; Molaei, F.; Faghihkhorasani, F.; Jamshidi Goharrizi, K.; Mirtaghi, S.M.; Borjian-Boroujeni, M.; Hamblin, M.R. Approaches for the integration of big data in translational medicine: Single-cell and computational methods. Ann. N. Y. Acad. Sci., 2021, 1493(1), 3-28.
[http://dx.doi.org/10.1111/nyas.14544] [PMID: 33410160]

Gutman, D.A.; Cobb, J.; Somanna, D.; Park, Y.; Wang, F.; Kurc, T.; Saltz, J.H.; Brat, D.J.; Cooper, L.A.D.; Kong, J. Cancer digital slide archive: an informatics resource to support integrated in silico analysis of TCGA pathology data. J. Am. Med. Inform. Assoc., 2013, 20(6), 1091-1098.
[http://dx.doi.org/10.1136/amiajnl-2012-001469] [PMID: 23893318]

Shaikh, N.; Bapat, S.; Karthikeyan, M.; Vyas, R. A review on computational analysis of big data in breast cancer for predicting potential biomarkers. Curr. Top. Med. Chem., 2022, 22(21), 1793-1810.
[http://dx.doi.org/10.2174/1568026622666220907121942] [PMID: 36082858]

Tamborero, D.; Lopez-Bigas, N.; Gonzalez-Perez, A. Oncodrive-CIS: A method to reveal likely driver genes based on the impact of their copy number changes on expression. PLoS One, 2013, 8(2), e55489.
[http://dx.doi.org/10.1371/journal.pone.0055489] [PMID: 23408991]

Tomczak, Katarzyna; Czerwińska, Patrycja; Wiznerowicz, Maciej The Cancer Genome Atlas (TCGA): An immeasurable source of knowledge. Contemp Oncol, 2015, 19(1A), A68-A77.
[http://dx.doi.org/10.5114/wo.2014.47136]

Wang, Z.; Jensen, M.A.; Zenklusen, J.C. A practical guide to the cancer genome atlas (TCGA). Methods Mol Biol., 2016, 1418, 111-141.
[http://dx.doi.org/10.1007/978-1-4939-3578-9_6]

Hutter, C.; Zenklusen, J.C. The cancer genome atlas: Creating lasting value beyond its data. Cell, 2018, 173(2), 283-285.
[http://dx.doi.org/10.1016/j.cell.2018.03.042] [PMID: 29625045]

Zhu, Y.; Qiu, P.; Ji, Y. TCGA-Assembler: Open-source software for retrieving and processing TCGA data. Nat. Methods, 2014, 11(6), 599-600.
[http://dx.doi.org/10.1038/nmeth.2956] [PMID: 24874569]

Pruitt, K.D.; Tatusova, T.; Maglott, D.R. NCBI reference sequence (RefSeq): A curated non-redundant sequence database of genomes, transcripts and proteins. Nucleic Acids Res., 2005, 33(S1), D501-D504.
[http://dx.doi.org/10.1093/nar/gki025]

Geer, L.Y.; Marchler-Bauer, A.; Geer, R.C.; Han, L.; He, J.; He, S.; Liu, C.; Shi, W.; Bryant, S.H. The NCBI BioSystems database. Nucleic Acids Res., 2010, 38, D492-D496.
[http://dx.doi.org/10.1093/nar/gkp858]

Sharma, S.; Ciufo, S.; Starchenko, E.; Darji, D.; Chlumsky, L.; Karsch-Mizrachi, I.; Schoch, C.L. The NCBI biocollections database. Database., 2018, 2018, bay006.
[http://dx.doi.org/10.1093/database/bay006] [PMID: 29688360]

Federhen, S. The NCBI taxonomy database. Nucleic Acids Res., 2012, 40(D1), D136-D143.
[http://dx.doi.org/10.1093/nar/gkr1178] [PMID: 22139910]

Barrett, T.; Suzek, T.O.; Troup, D.B.; Wilhite, S.E.; Ngau, W.C.; Ledoux, P.; Rudnev, D.; Lash, A.E.; Fujibuchi, W.; Edgar, R. NCBI GEO: Mining millions of expression profiles—database and tools. Nucleic Acids Res., 2005, 33, D562-D566.
[http://dx.doi.org/10.1093/nar/gki022]

Benson, D.; Boguski, M.S.; Lipman, D.J.; Ostell, J.; Ouellette, B.F. GenBank. Nucleic Acids Res., 1998, 26(1), 1-7.
[http://dx.doi.org/10.1093/nar/26.1.1] [PMID: 9399790]

Rapp, B.A.; Wheeler, D.L. Bioinformatics resources from the National Center for Biotechnology Information: An integrated foundation for discovery. J. Am. Soc. Inf. Sci. Technol., 2005, 56(5), 538-550.
[http://dx.doi.org/10.1002/asi.20142]

Johnson, Mark; Zaretskaya, Irena; Raytselis, Yan; Merezhuk, Yuri; McGinnis, Scott; Madden, Thomas L. NCBI BLAST: A better web interface. Nucleic Acids Res., 2008, 36, W5-W9.
[http://dx.doi.org/10.1093/nar/gkn201]

Zhu, Z.; Li, L.; Xu, J.; Ye, W.; Chen, B.; Zeng, J.; Huang, Z. Comprehensive analysis reveals a metabolic ten-gene signature in hepatocellular carcinoma. PeerJ, 2020, 8, e9201.
[http://dx.doi.org/10.7717/peerj.9201] [PMID: 32518728]

Mohanty, V. The Role of Non-Oncogenic Variants in Cancers: Onco-Passengers and Germline Polymorphisms., 2018,

Survival Prediction via Partial Ordering in Feature Space and Sample Space - ProQuest. Thesis (M.S.): Bilkent University, Department of Computer Engineering, 2016.

Joly, Y.; Dove, E.S.; Knoppers, B.M.; Bobrow, M.; Chalmers, D. Data sharing in the post-genomic world: The experience of the international cancer genome consortium (ICGC) data access compliance office (DACO). PLOS Comput. Biol., 2012, 8(7), e1002549.
[http://dx.doi.org/10.1371/journal.pcbi.1002549] [PMID: 22807659]

Forbes, S.; Clements, J.; Dawson, E.; Bamford, S.; Webb, T.; Dogan, A.; Flanagan, A.; Teague, J.; Wooster, R.; Futreal, P.A.; Stratton, M.R. Cosmic 2005. Br. J. Cancer, 2006, 94(2), 318-322.
[http://dx.doi.org/10.1038/sj.bjc.6602928] [PMID: 16421597]

Edwards, N.J.; Oberti, M.; Thangudu, R.R.; Cai, S.; McGarvey, P.B.; Jacob, S.; Madhavan, S.; Ketchum, K.A. The CPTAC data portal: A resource for cancer proteomics research. J. Proteome Res., 2015, 14(6), 2707-2713.
[http://dx.doi.org/10.1021/pr501254j] [PMID: 25873244]

Cheng, W.C.; Chung, I.F.; Chen, C.Y.; Sun, H.J.; Fen, J.J.; Tang, W.C.; Chang, T.Y.; Wong, T.T.; Wang, H.W. DriverDB: An exome sequencing database for cancer driver gene identification. Nucleic Acids Res., 2014, 42(D1), D1048-D1054.
[http://dx.doi.org/10.1093/nar/gkt1025] [PMID: 24214964]

Pal, R.; Berlow, N.; Haider, S. Anticancer drug sensitivity analysis: An integrated approach applied to erlotinib sensitivity prediction in the CCLE database. Proceedings 2012 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS), , pp. 9-12.2012
[http://dx.doi.org/10.1109/GENSIPS.2012.6507714]

Clark, K.; Vendt, B.; Smith, K.; Freymann, J.; Kirby, J.; Koppel, P.; Moore, S.; Phillips, S.; Maffitt, D.; Pringle, M.; Tarbox, L.; Prior, F. The Cancer Imaging Archive (TCIA): Maintaining and operating a public information repository. J. Digit. Imaging, 2013, 26(6), 1045-1057.
[http://dx.doi.org/10.1007/s10278-013-9622-7] [PMID: 23884657]

Gentleman, R.C.; Carey, V.J.; Bates, D.M.; Bolstad, B.; Dettling, M.; Dudoit, S.; Ellis, B.; Gautier, L.; Ge, Y.; Gentry, J.; Hornik, K.; Hothorn, T.; Huber, W.; Iacus, S.; Irizarry, R.; Leisch, F.; Li, C.; Maechler, M.; Rossini, A.J.; Sawitzki, G.; Smith, C.; Smyth, G.; Tierney, L.; Yang, J.Y.H.; Zhang, J. Bioconductor: Open software development for computational biology and bioinformatics. Genome Biol., 2004, 5(10), R80.
[http://dx.doi.org/10.1186/gb-2004-5-10-r80] [PMID: 15461798]

Blankenberg, D.; Hillman-Jackson, J. Analysis of next-generation sequencing data using galaxy. In: Stem Cell Transcriptional Networks; , 2014; pp. 21-43.
[http://dx.doi.org/10.1007/978-1-4939-0512-6_2]

Gao, J.; Aksoy, B.A.; Dogrusoz, U.; Dresdner, G.; Gross, B.; Sumer, S.O.; Sun, Y.; Jacobsen, A.; Sinha, R.; Larsson, E.; Cerami, E.; Sander, C.; Schultz, N. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci. Signal., 2013, 6(269), pl1-pl1.
[http://dx.doi.org/10.1126/scisignal.2004088] [PMID: 23550210]

Behjati, S.; Tarpey, P.S. What is next generation sequencing? Arch. Dis. Child. Educ. Pract. Ed., 2013, 98(6), 236-238.
[http://dx.doi.org/10.1136/archdischild-2013-304340] [PMID: 23986538]

Grada, A.; Weinbrecht, K. Next-generation sequencing: Methodology and application. J. Invest. Dermatol., 2013, 133(8), 1-4.
[http://dx.doi.org/10.1038/jid.2013.248] [PMID: 23856935]

Morganti, S.; Tarantino, P.; Ferraro, E.; D’Amico, P.; Viale, G.; Trapani, D.; Duso, B.A.; Curigliano, G. Complexity of genome sequencing and reporting: Next generation sequencing (NGS) technologies and implementation of precision medicine in real life. Crit. Rev. Oncol. Hematol., 2019, 133, 171-182.
[http://dx.doi.org/10.1016/j.critrevonc.2018.11.008] [PMID: 30661654]

Cai, H.; Jing, C.; Chang, X.; Ding, D.; Han, T.; Yang, J.; Lu, Z.; Hu, X.; Liu, Z.; Wang, J.; Shang, L.; Wu, S.; Meng, P.; Lin, L.; Zhao, J.; Nie, M.; Yin, K. Mutational landscape of gastric cancer and clinical application of genomic profiling based on target next-generation sequencing. J. Transl. Med., 2019, 17(1), 189.
[http://dx.doi.org/10.1186/s12967-019-1941-0] [PMID: 31164161]

Businello, G.; Galuppini, F.; Fassan, M. The impact of recent next generation sequencing and the need for a new classification in gastric cancer. Best Pract Res Clin Gastroenterol., 2021, 50-51, 101730.
[http://dx.doi.org/10.1016/j.bpg.2021.101730]

Habibi, M.; Taheri, G. A new machine learning method for cancer mutation analysis. PLOS Comput. Biol., 2022, 18(10), e1010332.
[http://dx.doi.org/10.1371/journal.pcbi.1010332] [PMID: 36251702]

Kourou, K.; Exarchos, T.P.; Exarchos, K.P.; Karamouzis, M.V.; Fotiadis, D.I. Machine learning applications in cancer prognosis and prediction. Comput. Struct. Biotechnol. J., 2015, 13, 8-17.
[http://dx.doi.org/10.1016/j.csbj.2014.11.005] [PMID: 25750696]

Cruz, J.A.; Wishart, D.S. Applications of machine learning in cancer prediction and prognosis. Cancer Inform., 2006, 2
[http://dx.doi.org/10.1177/117693510600200030] [PMID: 19458758]

Ghods, A.; Cook, D.J. A survey of deep network techniques all classifiers can adopt. Data Min. Knowl. Discov., 2021, 35(1), 46-87.
[http://dx.doi.org/10.1007/s10618-020-00722-8] [PMID: 34584490]

Kadurin, A.; Aliper, A.; Kazennov, A.; Mamoshina, P.; Vanhaelen, Q.; Khrabrov, K.; Zhavoronkov, A. The cornucopia of meaningful leads: Applying deep adversarial autoencoders for new molecule development in oncology. Oncotarget, 2017, 8(7), 10883-10890.
[http://dx.doi.org/10.18632/oncotarget.14073] [PMID: 28029644]

Gui, T.; Dong, X.; Li, R.; Li, Y.; Wang, Z. Identification of hepatocellular carcinoma-related genes with a machine learning and network analysis. J. Comput. Biol., 2015, 22(1), 63-71.
[http://dx.doi.org/10.1089/cmb.2014.0122] [PMID: 25247452]

Hirasawa, T.; Aoyama, K.; Tanimoto, T.; Ishihara, S.; Shichijo, S.; Ozawa, T.; Ohnishi, T.; Fujishiro, M.; Matsuo, K.; Fujisaki, J.; Tada, T. Application of artificial intelligence using a convolutional neural network for detecting gastric cancer in endoscopic images. Gastric Cancer, 2018, 21(4), 653-660.
[http://dx.doi.org/10.1007/s10120-018-0793-2] [PMID: 29335825]

Yue, W.; Wang, Z.; Chen, H.; Payne, A.; Liu, X. Machine learning with applications in breast cancer diagnosis and prognosis. Designs, 2018, 2(2), 13.
[http://dx.doi.org/10.3390/designs2020013]

Razzak, M.I.; Naz, S.; Zaib, A. Deep learning for medical image processing: overview, challenges and the future. In: Classification in BioApps; , 2018; pp. 323-350.
[http://dx.doi.org/10.1007/978-3-319-65981-7_12]

Gu, J.; Bourne, P.E. Structural Bioinformatics; John Wiley & Sons, 2009.

Srivastava, P.A.; Kalra, S.; Yennamalli, R.M. Structural bioinformatics and big data analytics: A mini-review. Int. J. Comput. Biol., 2017, 6(1), 25-30.

Brown, D.K.; Bishop, O.T. Role of structural bioinformatics in drug discovery by computational snp analysis: Analyzing variation at the protein level. Glob Heart., 2017, 12(2), 151-161.
[http://dx.doi.org/10.1016/j.gheart.2017.01.009]

Ryan, B.M.; Robles, A.I.; Harris, C.C. Genetic variation in microRNA networks: The implications for cancer research. Nat. Rev. Cancer, 2010, 10(6), 389-402.
[http://dx.doi.org/10.1038/nrc2867] [PMID: 20495573]

de Oliveira, S.; Deane, C. Co-evolution techniques are reshaping the way we do structural bioinformatics. F1000 Res., 2017, 6, 1224.
[http://dx.doi.org/10.12688/f1000research.11543.1] [PMID: 28781768]

Zhang, Y. Template-based modeling and free modeling by I-TASSER in CASP7. Proteins, 2007, 69(S8)(Suppl. 8), 108-117.
[http://dx.doi.org/10.1002/prot.21702] [PMID: 17894355]

Chou, K.C. Structural bioinformatics and its impact to biomedical science. Curr. Med. Chem., 2004, 11(16), 2105-2134.
[http://dx.doi.org/10.2174/0929867043364667] [PMID: 15279552]

Cavasotto, C.N.; Phatak, S.S. Homology modeling in drug discovery: Current trends and applications. Drug Discov. Today, 2009, 14(13-14), 676-683.
[http://dx.doi.org/10.1016/j.drudis.2009.04.006] [PMID: 19422931]

Peng, J.; Xu, J. A multiple-template approach to protein threading. Proteins, 2011, 79(6), 1930-1939.
[http://dx.doi.org/10.1002/prot.23016] [PMID: 21465564]

Al-Lazikani, B.; Jung, J.; Xiang, Z.; Honig, B. Protein structure prediction. Curr. Opin. Chem. Biol., 2001, 5(1), 51-56.
[http://dx.doi.org/10.1016/S1367-5931(00)00164-2] [PMID: 11166648]

Kuhlman, B.; Bradley, P. Advances in protein structure prediction and design. Nat. Rev. Mol. Cell Biol., 2019, 20(11), 681-697.
[http://dx.doi.org/10.1038/s41580-019-0163-x] [PMID: 31417196]

Roy, A.; Zhang, Y. Protein structure prediction. In: Encyclopedia of Life Sciences; Springer, 2012.
[http://dx.doi.org/10.1002/9780470015902.a0003031.pub2]

Eweas, A. Advances in molecular modeling and docking as a tool for modern drug discovery. Pharma Chem., 2014, 6, 211-228.

Shaikh, N.; Linthoi, R.K.; Swamy, K.V.; Karthikeyan, M.; Vyas, R. Comprehensive molecular docking and dynamic simulations for drug repurposing of clinical drugs against multiple cancer kinase targets. J. Biomol. Struct. Dyn., 2022, (0), 1-9.
[http://dx.doi.org/10.1080/07391102.2022.2124453] [PMID: 36134605]

Tomasiak, L.; Karch, R.; Schreiner, W. Long-term molecular dynamics simulations reveal flexibility properties of a free and TCR-bound PMHC-I system. 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), , pp. 1295-1302.2020
[http://dx.doi.org/10.1109/BIBM49941.2020.9313545]

De Vivo, M.; Masetti, M.; Bottegoni, G.; Cavalli, A. Role of molecular dynamics and related methods in drug discovery. J. Med. Chem., 2016, 59(9), 4035-4061.
[http://dx.doi.org/10.1021/acs.jmedchem.5b01684] [PMID: 26807648]

Knapp, B.; Frantal, S.; Cibena, M.; Schreiner, W.; Bauer, P. Is an intuitive convergence definition of molecular dynamics simulations solely based on the root mean square deviation possible? J. Comput. Biol., 2011, 18(8), 997-1005.
[http://dx.doi.org/10.1089/cmb.2010.0237] [PMID: 21702691]

Caves, L.S.D.; Evanseck, J.D.; Karplus, M. Locally accessible conformations of proteins: Multiple molecular dynamics simulations of crambin. Protein Sci., 1998, 7(3), 649-666.
[http://dx.doi.org/10.1002/pro.5560070314] [PMID: 9541397]

Papin, J.A.; Stelling, J.; Price, N.D.; Klamt, S.; Schuster, S.; Palsson, B.O. Comparison of network-based pathway analysis methods. Trends Biotechnol., 2004, 22(8), 400-405.
[http://dx.doi.org/10.1016/j.tibtech.2004.06.010] [PMID: 15283984]

Doncheva, N.T.; Klein, K.; Domingues, F.S.; Albrecht, M. Analyzing and visualizing residue networks of protein structures. Trends Biochem. Sci., 2011, 36(4), 179-182.
[http://dx.doi.org/10.1016/j.tibs.2011.01.002] [PMID: 21345680]

Grewal, R.; Roy, S. Modeling proteins as residue interaction networks. Protein Pept. Lett., 2015, 22(10), 923-933.
[http://dx.doi.org/10.2174/0929866522666150728115552] [PMID: 26216263]

Barthélemy, M. Betweenness centrality in large complex networks. Eur. Phys. J. B, 2004, 38(2), 163-168.
[http://dx.doi.org/10.1140/epjb/e2004-00111-4]

Sasaki, T. [New anti-cancer drugs for gastrointestinal cancers]. Gan To Kagaku Ryoho, 1997, 24(13), 1925-1931.
[PMID: 9350237]

Adhikari, A.; Mandal, D.; Rana, D.; Nath, J.; Bose, A.; Sonika; Orasugh, J.T.; De, S.; Chattopadhyay, D. COVID-19 mitigation: Nanotechnological intervention, perspective, and future scope. Materials Advances, 2023, 4(1), 52-78.
[http://dx.doi.org/10.1039/D2MA00797E]

Ina, K.; Kataoka, T.; Ando, T. The use of lentinan for treating gastric cancer. Anticancer Agents Med Chem., 2013, 13(5), 681-688.
[http://dx.doi.org/10.2174/1871520611313050002]

Xu, W.; Li, B.; Xu, M.; Yang, T.; Hao, X. Traditional Chinese medicine for precancerous lesions of gastric cancer: A review. Biomed. Pharmacother., 2022, 146, 112542.
[http://dx.doi.org/10.1016/j.biopha.2021.112542] [PMID: 34929576]