A Five-gene Signature based on MicroRNA for Predicting Prognosis and Immunotherapy in Stomach Adenocarcinoma


Дәйексөз келтіру

Толық мәтін

Аннотация

Aims:We aimed to classify molecular subtypes and establish a prognostic gene signature based on miRNAs for the prognostic prediction and therapeutic response in Stomach adenocarcinoma (STAD).

Background:STAD is a common diagnosed gastrointestinal malignancy and its heterogeneity is a big challenge that influences prognosis and precision therapies. Present study was designed to classify molecular subtypes and construct a prognostic gene signature based on miRNAs for the prognostic prediction and therapeutic response in STAD.

Objective:The objective of this study is to investigate the molecular subtypes and prognostic model for STAD.

Methods:A STAD specific miRNA-messenger RNA (mRNA) competing endogenous RNA (ceRNA) network was generated using the RNA-Seq and miRNA expression profiles from The Cancer Genome Atlas (TCGA) database, in which miRNA-related mRNAs were screened. Molecular subtypes were then determined using miRNA-related genes. Through univariate Cox analysis and multivariate regression analysis, a prognostic model was established in GSE84437 Train dataset and validated in GSE84437 Test, TCGA, GSE84437 and GSE66229 datasets. Immunotherapy datasets were employed for assessing the performance of the risk model. Finally, quantitative reverse transcription-polymerase chain reaction (qRT-PCR) was applied to validate the expression of hub genes used for the risk score signature.

Results:We constructed a ceRNA network containing 84 miRNAs and 907 mRNAs and determined two molecular subtypes based on 26 genes from the intersection of TCGASTAD and GSE84437 datasets. Subtype S2 had poor prognosis, lower tumor mutational burden, higher immune score and lower response to immunotherapy. Subtype S1 was more sensitive to Sorafenib, Pyrimethamine, Salubrinal, Gemcitabine, Vinorelbine and AKT inhibitor VIII. Next, a five-gene signature was generated and its robustness was validated in Test and external datasets. This risk model also had a good prediction performance in immunotherapy datasets.

Conclusion:This study promotes the underlying mechanisms of miRNA-based genes in STAD and offers directions for classification. A five-gene signature accurately predicts the prognosis and helps therapeutic options.

Авторлар туралы

Tianwei Wang

Department of Radiology, China-Japan Union Hospital of Jilin University

Email: info@benthamscience.net

Piji Chen

Department of Clinical Laboratory,, Yantian People’s Hospital of Southern University of Science and Technology,

Email: info@benthamscience.net

Tingting Li

Department of Oncology, Northern Theater Command General Hospital

Email: info@benthamscience.net

Jianong Li

Department of Oncology, Northern Theater Command General Hospital

Email: info@benthamscience.net

Dong Zhao

Department of Oncology, Northern Theater Command General Hospital

Email: info@benthamscience.net

Fanfei Meng

Department of Translational Medicine, YuceBio Technology Co., Ltd

Email: info@benthamscience.net

Yujie Zhao

Shenzhen Engineering Center for Translational Medicine of Precision Cancer Immunodiagnosis,, YuceBio Technology Co., Ltd

Email: info@benthamscience.net

Zhendong Zheng

Department of Oncology, Northern Theater Command General Hospital

Хат алмасуға жауапты Автор.
Email: info@benthamscience.net

Xuefei Liu

Department of Oncology, Northern Theater Command General Hospital

Хат алмасуға жауапты Автор.
Email: info@benthamscience.net

Әдебиет тізімі

  1. Chen, L.; Lu, L.; Gong, X.; Xu, Y.; Chu, X.; Huang, G. Gastric cancer with bone marrow invasion and disseminated intravascular coagulation: A case report. Oncologie, 2022, 24(3), 599-604. doi: 10.32604/oncologie.2022.023310
  2. Sung, H.; Ferlay, J.; Siegel, R.L.; Laversanne, M.; Soerjomataram, I.; Jemal, A.; Bray, F. Global cancer statistics 2020: Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin., 2021, 71(3), 209-249. doi: 10.3322/caac.21660 PMID: 33538338
  3. Qiu, H.; Cao, S.; Xu, R. Cancer incidence, mortality, and burden in China: A time-trend analysis and comparison with the United States and United Kingdom based on the global epidemiological data released in 2020. Cancer Commun., 2021, 41(10), 1037-1048. doi: 10.1002/cac2.12197 PMID: 34288593
  4. Sun, D.; Cao, M.; Li, H.; He, S.; Chen, W. Cancer burden and trends in China: A review and comparison with Japan and South Korea. Chin. J. Cancer Res., 2020, 32(2), 129-139. doi: 10.21147/j.issn.1000-9604.2020.02.01 PMID: 32410791
  5. Balakrishnan, M.; George, R.; Sharma, A.; Graham, D.Y. Changing trends in stomach cancer throughout the world. Curr. Gastroenterol. Rep., 2017, 19(8), 36. doi: 10.1007/s11894-017-0575-8 PMID: 28730504
  6. Poorolajal, J.; Moradi, L.; Mohammadi, Y.; Cheraghi, Z.; Gohari-Ensaf, F. Risk factors for stomach cancer: A systematic review and meta-analysis. Epidemiol. Health, 2020, 42, e2020004. doi: 10.4178/epih.e2020004 PMID: 32023777
  7. Jafari-Sales, A.; Shariat, A.; Bannazadeh Baghi, H.; Baradaran, B.; Jafari, B. The presence of human papillomavirus and epstein-barr virus infection in gastric cancer: A systematic study. Oncologie, 2022, 24(3), 413-426. doi: 10.32604/oncologie.2022.024161
  8. Wittekind, C. The development of the TNM classification of gastric cancer. Pathol. Int., 2015, 65(8), 399-403. doi: 10.1111/pin.12306 PMID: 26036980
  9. Li, M.; Wei, J.; Xu, G.; Liu, Y.; Zhu, J. Surgery combined with molecular targeted therapy successfully treated giant esophageal gastrointestinal stromal tumor. Oncologie, 2022, 24(2), 349-356. doi: 10.32604/oncologie.2022.022436
  10. Zhang, M.; Hu, S.; Min, M.; Ni, Y.; Lu, Z.; Sun, X.; Wu, J.; Liu, B.; Ying, X.; Liu, Y. Dissecting transcriptional heterogeneity in primary gastric adenocarcinoma by single cell RNA sequencing. Gut, 2021, 70(3), 464-475. doi: 10.1136/gutjnl-2019-320368 PMID: 32532891
  11. Ali Syeda, Z.; Langden, S.S.S.; Munkhzul, C.; Lee, M.; Song, S.J. Regulatory mechanism of MicroRNA expression in cancer. Int. J. Mol. Sci., 2020, 21(5), 1723. doi: 10.3390/ijms21051723 PMID: 32138313
  12. Chen, X.; Li, T.H.; Zhao, Y.; Wang, C.C.; Zhu, C.C. Deep-belief network for predicting potential miRNA-disease associations. Brief. Bioinform., 2021, 22(3), bbaa186. doi: 10.1093/bib/bbaa186 PMID: 34020550
  13. Ha, J.; Park, C.; Park, C.; Park, S. IMIPMF: Inferring miRNA-disease interactions using probabilistic matrix factorization. J. Biomed. Inform., 2020, 102, 103358. doi: 10.1016/j.jbi.2019.103358 PMID: 31857202
  14. Ha, J.; Park, S. NCMD: Node2vec-based neural collaborative filtering for predicting MiRNA-Disease Association. IEEE/ACM Trans. Comput. Biol. Bioinform., 2023, 20(2), 1257-1268. doi: 10.1109/TCBB.2022.3191972
  15. Ha, J. MDMF: Predicting miRNA–Disease association based on matrix factorization with disease similarity constraint. J. Pers. Med., 2022, 12(6), 885. doi: 10.3390/jpm12060885 PMID: 35743670
  16. Ha, J. SMAP: Similarity-based matrix factorization framework for inferring miRNA-disease association. Knowl. Base. Syst., 2023, 263, 110295. doi: 10.1016/j.knosys.2023.110295
  17. Qi, X.; Lin, Y.; Chen, J.; Shen, B. Decoding competing endogenous RNA networks for cancer biomarker discovery. Brief. Bioinform., 2020, 21(2), 441-457. doi: 10.1093/bib/bbz006 PMID: 30715152
  18. Shannon, P.; Markiel, A.; Ozier, O.; Baliga, N.S.; Wang, J.T.; Ramage, D.; Amin, N.; Schwikowski, B.; Ideker, T. Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res., 2003, 13(11), 2498-2504. doi: 10.1101/gr.1239303 PMID: 14597658
  19. Liao, Y.; Wang, J.; Jaehnig, E.J.; Shi, Z.; Zhang, B. WebGestalt 2019: gene set analysis toolkit with revamped UIs and APIs. Nucleic Acids Res., 2019, 47(W1), W199-W205. doi: 10.1093/nar/gkz401 PMID: 31114916
  20. Wilkerson, M.; Waltman, P.; Wilkerson, M.M. Package ‘ConsensusClusterPlus’ a class discovery tool with confidence assessments and item tracking. Bioinformatics, 2013, 26(12), 1572-1573.
  21. Thorsson, V.; Gibbs, D.L.; Brown, S.D.; Wolf, D.; Bortone, D.S.; Ou, Yang T.H.; Porta-Pardo, E.; Gao, G.F.; Plaisier, C.L.; Eddy, J.A.; Ziv, E.; Culhane, A.C.; Paull, E.O.; Sivakumar, I.K.A.; Gentles, A.J.; Malhotra, R.; Farshidfar, F.; Colaprico, A.; Parker, J.S.; Mose, L.E.; Vo, N.S.; Liu, J.; Liu, Y.; Rader, J.; Dhankani, V.; Reynolds, S.M.; Bowlby, R.; Califano, A.; Cherniack, A.D.; Anastassiou, D.; Bedognetti, D.; Mokrab, Y.; Newman, A.M.; Rao, A.; Chen, K.; Krasnitz, A.; Hu, H.; Malta, T.M.; Noushmehr, H.; Pedamallu, C.S.; Bullman, S.; Ojesina, A.I.; Lamb, A.; Zhou, W.; Shen, H.; Choueiri, T.K.; Weinstein, J.N.; Guinney, J.; Saltz, J.; Holt, R.A.; Rabkin, C.S.; Lazar, A.J.; Serody, J.S.; Demicco, E.G.; Disis, M.L.; Vincent, B.G.; Shmulevich, I.; 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.; Shinbrot, 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.; Gonzalez, A.M.A.; 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; 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.; Pinero, E.M.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. The immune landscape of cancer. Immunity, 2019, 51(2), 411-412. doi: 10.1016/j.immuni.2019.08.004 PMID: 31433971
  22. Kamarudin, A.N.; Cox, T. Kolamunnage-Dona, R. Time-dependent ROC curve analysis in medical research: Current methods and applications. BMC Med. Res. Methodol., 2017, 17(1), 53. doi: 10.1186/s12874-017-0332-6 PMID: 28388943
  23. Yu, G.; Wang, L.G.; Han, Y.; He, Q.Y. clusterProfiler: An R package for comparing biological themes among gene clusters. OMICS, 2012, 16(5), 284-287. doi: 10.1089/omi.2011.0118 PMID: 22455463
  24. Mariathasan, S. Turley, S.J.; Nickles, D.; Castiglioni, A.; Yuen, K.; Wang, Y.; Kadel, E.E., III; Koeppen, H.; Astarita, J.L.; Cubas, R.; Jhunjhunwala, S.; Banchereau, R.; Yang, Y.; Guan, Y.; Chalouni, C.; Ziai, J.; Şenbabaoğlu, Y.; Santoro, S.; Sheinson, D.; Hung, J.; Giltnane, J.M.; Pierce, A.A.; Mesh, K.; Lianoglou, S.; Riegler, J.; Carano, R.A.D.; Eriksson, P.; Höglund, M.; Somarriba, L.; Halligan, D.L.; van der Heijden, M.S.; Loriot, Y.; Rosenberg, J.E.; Fong, L.; Mellman, I.; Chen, D.S.; Green, M.; Derleth, C.; Fine, G.D.; Hegde, P.S.; Bourgon, R.; Powles, T. TGFβ attenuates tumour response to PD-L1 blockade by contributing to exclusion of T cells. Nature, 2018, 554(7693), 544-548. doi: 10.1038/nature25501 PMID: 29443960
  25. Balar, A.V.; Galsky, M.D.; Rosenberg, J.E.; Powles, T.; Petrylak, D.P.; Bellmunt, J.; Loriot, Y.; Necchi, A.; Hoffman-Censits, J.; Perez-Gracia, J.L.; Dawson, N.A.; van der Heijden, M.S.; Dreicer, R.; Srinivas, S.; Retz, M.M.; Joseph, R.W.; Drakaki, A.; Vaishampayan, U.N.; Sridhar, S.S.; Quinn, D.I.; Durán, I.; Shaffer, D.R.; Eigl, B.J.; Grivas, P.D.; Yu, E.Y.; Li, S.; Kadel, E.E., III; Boyd, Z.; Bourgon, R.; Hegde, P.S.; Mariathasan, S.; Thåström, A.; Abidoye, O.O.; Fine, G.D.; Bajorin, D.F. Atezolizumab as first-line treatment in cisplatin-ineligible patients with locally advanced and metastatic urothelial carcinoma: A single-arm, multicentre, phase 2 trial. Lancet, 2017, 389(10064), 67-76. doi: 10.1016/S0140-6736(16)32455-2 PMID: 27939400
  26. Geeleher, P.; Cox, N.; Huang, R.S. pRRophetic: An R package for prediction of clinical chemotherapeutic response from tumor gene expression levels. PLoS One, 2014, 9(9), e107468. doi: 10.1371/journal.pone.0107468 PMID: 25229481
  27. Kim, J.Y.; Choi, J.K.; Jung, H. Genome-wide methylation patterns predict clinical benefit of immunotherapy in lung cancer. Clin. Epigenetics, 2020, 12(1), 119. doi: 10.1186/s13148-020-00907-4 PMID: 32762727
  28. Hugo, W.; Zaretsky, J.M.; Sun, L.; Song, C.; Moreno, B.H.; Hu-Lieskovan, S.; Berent-Maoz, B.; Pang, J.; Chmielowski, B.; Cherry, G.; Seja, E.; Lomeli, S.; Kong, X.; Kelley, M.C.; Sosman, J.A.; Johnson, D.B.; Ribas, A.; Lo, R.S. Genomic and transcriptomic features of response to anti-PD-1 therapy in metastatic melanoma. Cell, 2016, 165(1), 35-44. doi: 10.1016/j.cell.2016.02.065 PMID: 26997480
  29. Ostrand-Rosenberg, S. Cross-talk between myeloid-derived suppressor cells (MDSC), macrophages, and dendritic cells enhances tumor-induced immune suppression. In: Seminars in cancer biology; Elsevier, 2012.
  30. Mullen, J.; Kato, S.; Sicklick, J.K.; Kurzrock, R. Targeting ARID1A mutations in cancer. Cancer Treat. Rev., 2021, 100, 102287. doi: 10.1016/j.ctrv.2021.102287 PMID: 34619527
  31. Kim, Y.B.; Ahn, J.M.; Bae, W.J.; Sung, C.O.; Lee, D. Functional loss of ARID1A is tightly associated with high PD-L1 expression in gastric cancer. Int. J. Cancer, 2019, 145(4), 916-926. doi: 10.1002/ijc.32140 PMID: 30664822
  32. Gu, Y. Somatic ARID1A mutation stratifies patients with gastric cancer to PD-1 blockade and adjuvant chemotherapy. Cancer Immunol. Immunother., 2022, 1-10. PMID: 36369379
  33. Kim, J.W.; Lee, H.S.; Nam, K.H.; Ahn, S.; Kim, J.W.; Ahn, S.H.; Park, D.J.; Kim, H.H.; Lee, K.W. PIK3CA mutations are associated with increased tumor aggressiveness and Akt activation in gastric cancer. Oncotarget, 2017, 8(53), 90948-90958. doi: 10.18632/oncotarget.18770 PMID: 29207615
  34. Yao, J.; You, Q.; Zhang, X.; Zhang, Y.; Xu, J.; Zhao, X.; Li, J.; Wang, X.; Gong, Z.; Zhang, D.; Wang, W. PIK3CA somatic mutations as potential biomarker for immunotherapy in elder orTP53 mutated gastric cancer patients. Clin. Genet., 2023, 103(2), 200-208. doi: 10.1111/cge.14260 PMID: 36346122
  35. Sobierajska, K. Endothelial cells in the tumor microenvironment. Tumor Microenvironment: Non-Hematopoietic Cells, , 2020, 71-86. doi: 10.1007/978-3-030-37184-5_6
  36. Nagl, L.; Horvath, L.; Pircher, A.; Wolf, D. Tumor endothelial cells (TECs) as potential immune directors of the tumor microenvironment–new findings and future perspectives. Front. Cell Dev. Biol., 2020, 8, 766. doi: 10.3389/fcell.2020.00766 PMID: 32974337
  37. Xue, X.; Huang, J.; Yu, K.; Chen, X.; He, Y.; Qi, D.; Wu, Y. YB-1 transferred by gastric cancer exosomes promotes angiogenesis via enhancing the expression of angiogenic factors in vascular endothelial cells. BMC Cancer, 2020, 20(1), 996. doi: 10.1186/s12885-020-07509-6 PMID: 33054752
  38. Sahai, E.; Astsaturov, I.; Cukierman, E.; DeNardo, D.G.; Egeblad, M.; Evans, R.M.; Fearon, D.; Greten, F.R.; Hingorani, S.R.; Hunter, T.; Hynes, R.O.; Jain, R.K.; Janowitz, T.; Jorgensen, C.; Kimmelman, A.C.; Kolonin, M.G.; Maki, R.G.; Powers, R.S.; Puré, E.; Ramirez, D.C.; Scherz-Shouval, R.; Sherman, M.H.; Stewart, S.; Tlsty, T.D.; Tuveson, D.A.; Watt, F.M.; Weaver, V.; Weeraratna, A.T.; Werb, Z. A framework for advancing our understanding of cancer-associated fibroblasts. Nat. Rev. Cancer, 2020, 20(3), 174-186. doi: 10.1038/s41568-019-0238-1 PMID: 31980749
  39. Grunberg, N.; Pevsner-Fischer, M.; Goshen-Lago, T.; Diment, J.; Stein, Y.; Lavon, H.; Mayer, S.; Levi-Galibov, O.; Friedman, G.; Ofir-Birin, Y.; Syu, L.J.; Migliore, C.; Shimoni, E.; Stemmer, S.M.; Brenner, B.; Dlugosz, A.A.; Lyden, D.; Regev-Rudzki, N.; Ben-Aharon, I.; Scherz-Shouval, R. Cancer-associated fibroblasts promote aggressive gastric cancer phenotypes via heat shock factor 1–mediated secretion of extracellular vesicles. Cancer Res., 2021, 81(7), 1639-1653. doi: 10.1158/0008-5472.CAN-20-2756 PMID: 33547159
  40. Tam, S.Y.; Wu, V.W.C.; Law, H.K.W. Hypoxia-induced epithelial-mesenchymal transition in cancers: HIF-1α and beyond. Front. Oncol., 2020, 10, 486. doi: 10.3389/fonc.2020.00486 PMID: 32322559
  41. Tandon, V.; de la Vega, L.; Banerjee, S. Emerging roles of DYRK2 in cancer. J. Biol. Chem., 2021, 296, 100233. doi: 10.1074/jbc.REV120.015217 PMID: 33376136
  42. Zhang, X.; Xiao, R.; Lu, B.; Wu, H.; Jiang, C.; Li, P.; Huang, J. Kinase DYRK2 acts as a regulator of autophagy and an indicator of favorable prognosis in gastric carcinoma. Colloids Surf. B Biointerfaces, 2022, 209(Pt 1), 112182. doi: 10.1016/j.colsurfb.2021.112182 PMID: 34749023
  43. Evangelisti, C.; Rusciano, I.; Mongiorgi, S.; Ramazzotti, G.; Lattanzi, G.; Manzoli, L.; Cocco, L.; Ratti, S. The wide and growing range of lamin B-related diseases: From laminopathies to cancer. Cell. Mol. Life Sci., 2022, 79(2), 126. doi: 10.1007/s00018-021-04084-2 PMID: 35132494
  44. Liu, M.; Li, H.; Zhang, H.; Zhou, H.; Jiao, T.; Feng, M.; Na, F.; Sun, M.; Zhao, M.; Xue, L.; Xu, L. RBMS1 promotes gastric cancer metastasis through autocrine IL-6/JAK2/STAT3 signaling. Cell Death Dis., 2022, 13(3), 287. doi: 10.1038/s41419-022-04747-3 PMID: 35361764
  45. Yue, T.; Li, J.; Liang, M.; Yang, J.; Ou, Z.; Wang, S.; Ma, W.; Fan, D. Identification of the KCNQ1OT1/miR-378a-3p/RBMS1 axis as a novel prognostic biomarker associated with immune cell infiltration in gastric cancer. Front. Genet., 2022, 13, 928754. doi: 10.3389/fgene.2022.928754 PMID: 35910231
  46. Zeng, X.; Wang, H.Y.; Wang, Y.P.; Bai, S.Y.; Pu, K.; Zheng, Y.; Guo, Q.H.; Guan, Q.L.; Ji, R.; Zhou, Y.N. COL4A family: Potential prognostic biomarkers and therapeutic targets for gastric cancer. Transl. Cancer Res., 2020, 9(9), 5218-5232. doi: 10.21037/tcr-20-517 PMID: 35117889
  47. Si, W.; Shen, J.; Zheng, H.; Fan, W. The role and mechanisms of action of microRNAs in cancer drug resistance. Clin. Epigenetics, 2019, 11(1), 25. doi: 10.1186/s13148-018-0587-8 PMID: 30744689
  48. Huang, R.; Gu, W.; Sun, B.; Gao, L. Identification of COL4A1 as a potential gene conferring trastuzumab resistance in gastric cancer based on bioinformatics analysis. Mol. Med. Rep., 2018, 17(5), 6387-6396. doi: 10.3892/mmr.2018.8664 PMID: 29512712
  49. Ding, F.; Gao, F.; Zhang, S.; Lv, X.; Chen, Y.; Liu, Q. A review of the mechanism of DDIT4 serve as a mitochondrial related protein in tumor regulation. Sci. Prog., 2021, 104(1) doi: 10.1177/0036850421997273 PMID: 33729069
  50. Li, N.; Ouyang, Y.; Chen, S.; Peng, C.; He, C.; Hong, J.; Yang, X.; Zhu, Y.; Lu, N.H. Integrative analysis of differential lncRNA/mRNA expression profiling in Helicobacter pylori infection-associated gastric carcinogenesis. Front. Microbiol., 2020, 11, 880. doi: 10.3389/fmicb.2020.00880 PMID: 32457731

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