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79 results on '"Ivshina A. V."'

1. Ensuring the Safety of a Quarry Distribution Network with a Voltage of 6–35 kV

Author

Boboev, Kh. D., Abdullozoda, R. T., Sayfiddinzoda, O. S., Abdullozoda, I. T., Ivshina, K. V., di Prisco, Marco, Series Editor, Chen, Sheng-Hong, Series Editor, Vayas, Ioannis, Series Editor, Kumar Shukla, Sanjay, Series Editor, Sharma, Anuj, Series Editor, Kumar, Nagesh, Series Editor, Wang, Chien Ming, Series Editor, Radionov, Andrey A., editor, Ulrikh, Dmitrii V., editor, Timofeeva, Svetlana S., editor, Alekhin, Vladimir N., editor, and Gasiyarov, Vadim R., editor

Published
2023
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3. KDM7A-DT induces genotoxic stress, tumorigenesis, and progression of p53 missense mutation-associated invasive breast cancer.

Author

Giannakakis, Antonis, Tsifintaris, Margaritis, Gouzouasis, Vasileios, Ghim Siong Ow, Mei Yee Aau, Papp, Csaba, Ivshina, Anna V., and Kuznetsov, Vladimir A.

Subjects
DNA repair, BREAST cancer, CANCER invasiveness, P53 protein, EPITHELIAL-mesenchymal transition, TUMOR suppressor genes
Abstract

Stress-induced promoter-associated and antisense lncRNAs (si-paancRNAs) originate from a reservoir of oxidative stress (OS)-specific promoters via RNAPII pausing-mediated divergent antisense transcription. Several studies have shown that the KDM7A divergent transcript gene (KDM7A-DT), which encodes a si-paancRNA, is overexpressed in some cancer types. However, the mechanisms of this overexpression and its corresponding roles in oncogenesis and cancer progression are poorly understood. We found that KDM7A-DT expression is correlated with highly aggressive cancer types and specific inherently determined subtypes (such as ductal invasive breast carcinoma (BRCA) basal subtype). Its regulation is determined by missense TP53 mutations in a subtype-specific context. KDM7A-DT transcribes several intermediate-sized ncRNAs and a full-length transcript, exhibiting distinct expression and localization patterns. Overexpression of KDM7A-DT upregulates TP53 protein expression and H2AX phosphorylation in nonmalignant fibroblasts, while in semi-transformed fibroblasts, OS superinduces KDM7A-DT expression in a TP53-dependent manner. KDM7A-DT knockdown and gene expression profiling in TP53-missense mutated luminal A BRCA variant, where it is abundantly expressed, indicate its significant role in cancer pathways. Endogenous over-expression of KDM7A-DT inhibits DNA damage response/repair (DDR/R) via the TP53BP1-mediated pathway, reducing apoptosis and promoting G2/M checkpoint arrest. Higher KDM7A-DTexpression in BRCA is associated with KDM7A-DT locus gain/amplification, higher histologic grade, aneuploidy, hypoxia, immune modulation scores, and activation of the c-myc pathway. Higher KDM7A-DT expression is associated with relatively poor survival outcomes in patients with luminal A or Basal subtypes. In contrast, it is associated with favorable outcomes in patients with HER2+ER- or luminal B subtypes. KDM7A-DT levels are coregulated with critical transcripts and proteins aberrantly expressed in BRCA, including those involved in DNA repair via non-homologous end joining and epithelial-to-mesenchymal transition pathway. In summary, KDM7A-DT and its si-lncRNA exhibit several intrinsic biological and clinical characteristics that suggest important roles in invasive BRCA and its subtypes. KDM7A-DT-defined mRNA and protein subnetworks offer resources for identifying clinically relevant RNA-based signatures and prospective targets for therapeutic intervention. [ABSTRACT FROM AUTHOR]

Published
2024
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6. Data from Genetic Reclassification of Histologic Grade Delineates New Clinical Subtypes of Breast Cancer

Author

Ivshina, Anna V., primary, George, Joshy, primary, Senko, Oleg, primary, Mow, Benjamin, primary, Putti, Thomas C., primary, Smeds, Johanna, primary, Lindahl, Thomas, primary, Pawitan, Yudi, primary, Hall, Per, primary, Nordgren, Hans, primary, Wong, John E.L., primary, Liu, Edison T., primary, Bergh, Jonas, primary, Kuznetsov, Vladimir A., primary, and Miller, Lance D., primary

Published
2023
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7. Supplementary File 3 from Genetic Reclassification of Histologic Grade Delineates New Clinical Subtypes of Breast Cancer

Author

Ivshina, Anna V., primary, George, Joshy, primary, Senko, Oleg, primary, Mow, Benjamin, primary, Putti, Thomas C., primary, Smeds, Johanna, primary, Lindahl, Thomas, primary, Pawitan, Yudi, primary, Hall, Per, primary, Nordgren, Hans, primary, Wong, John E.L., primary, Liu, Edison T., primary, Bergh, Jonas, primary, Kuznetsov, Vladimir A., primary, and Miller, Lance D., primary

Published
2023
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8. Supplementary File 1 from Genetic Reclassification of Histologic Grade Delineates New Clinical Subtypes of Breast Cancer

Author

Ivshina, Anna V., primary, George, Joshy, primary, Senko, Oleg, primary, Mow, Benjamin, primary, Putti, Thomas C., primary, Smeds, Johanna, primary, Lindahl, Thomas, primary, Pawitan, Yudi, primary, Hall, Per, primary, Nordgren, Hans, primary, Wong, John E.L., primary, Liu, Edison T., primary, Bergh, Jonas, primary, Kuznetsov, Vladimir A., primary, and Miller, Lance D., primary

Published
2023
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9. Supplementary File 2 from Genetic Reclassification of Histologic Grade Delineates New Clinical Subtypes of Breast Cancer

Author

Ivshina, Anna V., primary, George, Joshy, primary, Senko, Oleg, primary, Mow, Benjamin, primary, Putti, Thomas C., primary, Smeds, Johanna, primary, Lindahl, Thomas, primary, Pawitan, Yudi, primary, Hall, Per, primary, Nordgren, Hans, primary, Wong, John E.L., primary, Liu, Edison T., primary, Bergh, Jonas, primary, Kuznetsov, Vladimir A., primary, and Miller, Lance D., primary

Published
2023
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11. Pareto-Gamma Statistic Reveals Global Rescaling in Transcriptomes of Low and High Aggressive Breast Cancer Phenotypes

Author

Chua, Alvin L. -S., Ivshina, Anna V., Kuznetsov, Vladimir A., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Istrail, Sorin, editor, Pevzner, Pavel, editor, Waterman, Michael, editor, Rajapakse, Jagath C., editor, Wong, Limsoon, editor, and Acharya, Raj, editor

Published
2006
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15. Discovery of widespread transcription initiation at microsatellites predictable by sequence-based deep neural network

Author

Grapotte M., Saraswat M., Bessiere C., Menichelli C., Ramilowski J. A., Severin J., Hayashizaki Y., Itoh M., Tagami M., Murata M., Kojima-Ishiyama M., Noma S., Noguchi S., Kasukawa T., Hasegawa A., Suzuki H., Nishiyori-Sueki H., Frith M. C., Abugessaisa I., Aitken S., Aken B. L., Alam I., Alam T., Alasiri R., Alhendi A. M. N., Alinejad-Rokny H., Alvarez M. J., Andersson R., Arakawa T., Araki M., Arbel T., Archer J., Archibald A. L., Arner E., Arner P., Asai K., Ashoor H., Astrom G., Babina M., Baillie J. K., Bajic V. B., Bajpai A., Baker S., Baldarelli R. M., Balic A., Bansal M., Batagov A. O., Batzoglou S., Beckhouse A. G., Beltrami A. P., Beltrami C. A., Bertin N., Bhattacharya S., Bickel P. J., Blake J. A., Blanchette M., Bodega B., Bonetti A., Bono H., Bornholdt J., Bttcher M., Bougouffa S., Boyd M., Breda J., Brombacher F., Brown J. B., Bult C. J., Burroughs A. M., Burt D. W., Busch A., Caglio G., Califano A., Cameron C. J., Cannistraci C. V., Carbone A., Carlisle A. J., Carninci P., Carter K. W., Cesselli D., Chang J. -C., Chen J. C., Chen Y., Chierici M., Christodoulou J., Ciani Y., Clark E. L., Coskun M., Dalby M., Dalla E., Daub C. O., Davis C. A., de Hoon M. J. L., de Rie D., Denisenko E., Deplancke B., Detmar M., Deviatiiarov R., Di Bernardo D., Diehl A. D., Dieterich L. C., Dimont E., Djebali S., Dohi T., Dostie J., Drablos F., Edge A. S. B., Edinger M., Ehrlund A., Ekwall K., Elofsson A., Endoh M., Enomoto H., Enomoto S., Faghihi M., Fagiolini M., Farach-Carson M. C., Faulkner G. J., Favorov A., Fernandes A. M., Ferrai C., Forrest A. R. R., Forrester L. M., Forsberg M., Fort A., Francescatto M., Freeman T. C., Frith M., Fukuda S., Funayama M., Furlanello C., Furuno M., Furusawa C., Gao H., Gazova I., Gebhard C., Geier F., Geijtenbeek T. B. H., Ghosh S., Ghosheh Y., Gingeras T. R., Gojobori T., Goldberg T., Goldowitz D., Gough J., Greco D., Gruber A. J., Guhl S., Guigo R., Guler R., Gusev O., Gustincich S., Ha T. J., Haberle V., Hale P., Hallstrom B. M., Hamada M., Handoko L., Hara M., Harbers M., Harrow J., Harshbarger J., Hase T., Hashimoto K., Hatano T., Hattori N., Hayashi R., Herlyn M., Hettne K., Heutink P., Hide W., Hitchens K. J., Sui S. H., 't Hoen P. A. C., Hon C. C., Hori F., Horie M., Horimoto K., Horton P., Hou R., Huang E., Huang Y., Hugues R., Hume D., Ienasescu H., Iida K., Ikawa T., Ikemura T., Ikeo K., Inoue N., Ishizu Y., Ito Y., Ivshina A. V., Jankovic B. R., Jenjaroenpun P., Johnson R., Jorgensen M., Jorjani H., Joshi A., Jurman G., Kaczkowski B., Kai C., Kaida K., Kajiyama K., Kaliyaperumal R., Kaminuma E., Kanaya T., Kaneda H., Kapranov P., Kasianov A. S., Katayama T., Kato S., Kawaguchi S., Kawai J., Kawaji H., Kawamoto H., Kawamura Y. I., Kawasaki S., Kawashima T., Kempfle J. S., Kenna T. J., Kere J., Khachigian L., Kiryu H., Kishima M., Kitajima H., Kitamura T., Kitano H., Klaric E., Klepper K., Klinken S. P., Kloppmann E., Knox A. J., Kodama Y., Kogo Y., Kojima M., Kojima S., Komatsu N., Komiyama H., Kono T., Koseki H., Koyasu S., Kratz A., Kukalev A., Kulakovskiy I., Kundaje A., Kunikata H., Kuo R., Kuo T., Kuraku S., Kuznetsov V. A., Kwon T. J., Larouche M., Lassmann T., Law A., Le-Cao K. -A., Lecellier C. -H., Lee W., Lenhard B., Lennartsson A., Li K., Li R., Lilje B., Lipovich L., Lizio M., Lopez G., Magi S., Mak G. K., Makeev V., Manabe R., Mandai M., Mar J., Maruyama K., Maruyama T., Mason E., Mathelier A., Matsuda H., Medvedeva Y. A., Meehan T. F., Mejhert N., Meynert A., Mikami N., Minoda A., Miura H., Miyagi Y., Miyawaki A., Mizuno Y., Morikawa H., Morimoto M., Morioka M., Morishita S., Moro K., Motakis E., Motohashi H., Mukarram A. K., Mummery C. L., Mungall C. J., Murakawa Y., Muramatsu M., Nagasaka K., Nagase T., Nakachi Y., Nakahara F., Nakai K., Nakamura K., Nakamura Y., Nakazawa T., Nason G. P., Nepal C., Nguyen Q. H., Nielsen L. K., Nishida K., Nishiguchi K. M., Nishiyori H., Nitta K., Notredame C., Ogishima S., Ohkura N., Ohno H., Ohshima M., Ohtsu T., Okada Y., Okada-Hatakeyama M., Okazaki Y., Oksvold P., Orlando V., Ow G. S., Ozturk M., Pachkov M., Paparountas T., Parihar S. P., Park S. -J., Pascarella G., Passier R., Persson H., Philippens I. H., Piazza S., Plessy C., Pombo A., Ponten F., Poulain S., Poulsen T. M., Pradhan S., Prezioso C., Pridans C., Qin X. -Y., Quackenbush J., Rackham O., Ramilowski J., Ravasi T., Rehli M., Rennie S., Rito T., Rizzu P., Robert C., Roos M., Rost B., Roudnicky F., Roy R., Rye M. B., Sachenkova O., Saetrom P., Sai H., Saiki S., Saito M., Saito A., Sakaguchi S., Sakai M., Sakaue S., Sakaue-Sawano A., Sandelin A., Sano H., Sasamoto Y., Sato H., Saxena A., Saya H., Schafferhans A., Schmeier S., Schmidl C., Schmocker D., Schneider C., Schueler M., Schultes E. A., Schulze-Tanzil G., Semple C. A., Seno S., Seo W., Sese J., Sheng G., Shi J., Shimoni Y., Shin J. W., SimonSanchez J., Sivertsson A., Sjostedt E., Soderhall C., Laurent G. S., Stoiber M. H., Sugiyama D., Summers K. M., Suzuki A. M., Suzuki K., Suzuki M., Suzuki N., Suzuki T., Swanson D. J., Swoboda R. K., Taguchi A., Takahashi H., Takahashi M., Takamochi K., Takeda S., Takenaka Y., Tam K. T., Tanaka H., Tanaka R., Tanaka Y., Tang D., Taniuchi I., Tanzer A., Tarui H., Taylor M. S., Terada A., Terao Y., Testa A. C., Thomas M., Thongjuea S., Tomii K., Triglia E. T., Toyoda H., Tsang H. G., Tsujikawa M., Uhlen M., Valen E., van de Wetering M., van Nimwegen E., Velmeshev D., Verardo R., Vitezic M., Vitting-Seerup K., von Feilitzen K., Voolstra C. R., Vorontsov I. E., Wahlestedt C., Wasserman W. W., Watanabe K., Watanabe S., Wells C. A., Winteringham L. N., Wolvetang E., Yabukami H., Yagi K., Yamada T., Yamaguchi Y., Yamamoto M., Yamamoto Y., Yamanaka Y., Yano K., Yasuzawa K., Yatsuka Y., Yo M., Yokokura S., Yoneda M., Yoshida E., Yoshida Y., Yoshihara M., Young R., Young R. S., Yu N. Y., Yumoto N., Zabierowski S. E., Zhang P. G., Zucchelli S., Zwahlen M., Chatelain C., Brehelin L., Grapotte, M., Saraswat, M., Bessiere, C., Menichelli, C., Ramilowski, J. A., Severin, J., Hayashizaki, Y., Itoh, M., Tagami, M., Murata, M., Kojima-Ishiyama, M., Noma, S., Noguchi, S., Kasukawa, T., Hasegawa, A., Suzuki, H., Nishiyori-Sueki, H., Frith, M. C., Abugessaisa, I., Aitken, S., Aken, B. L., Alam, I., Alam, T., Alasiri, R., Alhendi, A. M. N., Alinejad-Rokny, H., Alvarez, M. J., Andersson, R., Arakawa, T., Araki, M., Arbel, T., Archer, J., Archibald, A. L., Arner, E., Arner, P., Asai, K., Ashoor, H., Astrom, G., Babina, M., Baillie, J. K., Bajic, V. B., Bajpai, A., Baker, S., Baldarelli, R. M., Balic, A., Bansal, M., Batagov, A. O., Batzoglou, S., Beckhouse, A. G., Beltrami, A. P., Beltrami, C. A., Bertin, N., Bhattacharya, S., Bickel, P. J., Blake, J. A., Blanchette, M., Bodega, B., Bonetti, A., Bono, H., Bornholdt, J., Bttcher, M., Bougouffa, S., Boyd, M., Breda, J., Brombacher, F., Brown, J. B., Bult, C. J., Burroughs, A. M., Burt, D. W., Busch, A., Caglio, G., Califano, A., Cameron, C. J., Cannistraci, C. V., Carbone, A., Carlisle, A. J., Carninci, P., Carter, K. W., Cesselli, D., Chang, J. -C., Chen, J. C., Chen, Y., Chierici, M., Christodoulou, J., Ciani, Y., Clark, E. L., Coskun, M., Dalby, M., Dalla, E., Daub, C. O., Davis, C. A., de Hoon, M. J. L., de Rie, D., Denisenko, E., Deplancke, B., Detmar, M., Deviatiiarov, R., Di Bernardo, D., Diehl, A. D., Dieterich, L. C., Dimont, E., Djebali, S., Dohi, T., Dostie, J., Drablos, F., Edge, A. S. B., Edinger, M., Ehrlund, A., Ekwall, K., Elofsson, A., Endoh, M., Enomoto, H., Enomoto, S., Faghihi, M., Fagiolini, M., Farach-Carson, M. C., Faulkner, G. J., Favorov, A., Fernandes, A. M., Ferrai, C., Forrest, A. R. R., Forrester, L. M., Forsberg, M., Fort, A., Francescatto, M., Freeman, T. C., Frith, M., Fukuda, S., Funayama, M., Furlanello, C., Furuno, M., Furusawa, C., Gao, H., Gazova, I., Gebhard, C., Geier, F., Geijtenbeek, T. B. H., Ghosh, S., Ghosheh, Y., Gingeras, T. R., Gojobori, T., Goldberg, T., Goldowitz, D., Gough, J., Greco, D., Gruber, A. J., Guhl, S., Guigo, R., Guler, R., Gusev, O., Gustincich, S., Ha, T. J., Haberle, V., Hale, P., Hallstrom, B. M., Hamada, M., Handoko, L., Hara, M., Harbers, M., Harrow, J., Harshbarger, J., Hase, T., Hashimoto, K., Hatano, T., Hattori, N., Hayashi, R., Herlyn, M., Hettne, K., Heutink, P., Hide, W., Hitchens, K. J., Sui, S. H., 't Hoen, P. A. C., Hon, C. C., Hori, F., Horie, M., Horimoto, K., Horton, P., Hou, R., Huang, E., Huang, Y., Hugues, R., Hume, D., Ienasescu, H., Iida, K., Ikawa, T., Ikemura, T., Ikeo, K., Inoue, N., Ishizu, Y., Ito, Y., Ivshina, A. V., Jankovic, B. R., Jenjaroenpun, P., Johnson, R., Jorgensen, M., Jorjani, H., Joshi, A., Jurman, G., Kaczkowski, B., Kai, C., Kaida, K., Kajiyama, K., Kaliyaperumal, R., Kaminuma, E., Kanaya, T., Kaneda, H., Kapranov, P., Kasianov, A. S., Katayama, T., Kato, S., Kawaguchi, S., Kawai, J., Kawaji, H., Kawamoto, H., Kawamura, Y. I., Kawasaki, S., Kawashima, T., Kempfle, J. S., Kenna, T. J., Kere, J., Khachigian, L., Kiryu, H., Kishima, M., Kitajima, H., Kitamura, T., Kitano, H., Klaric, E., Klepper, K., Klinken, S. P., Kloppmann, E., Knox, A. J., Kodama, Y., Kogo, Y., Kojima, M., Kojima, S., Komatsu, N., Komiyama, H., Kono, T., Koseki, H., Koyasu, S., Kratz, A., Kukalev, A., Kulakovskiy, I., Kundaje, A., Kunikata, H., Kuo, R., Kuo, T., Kuraku, S., Kuznetsov, V. A., Kwon, T. J., Larouche, M., Lassmann, T., Law, A., Le-Cao, K. -A., Lecellier, C. -H., Lee, W., Lenhard, B., Lennartsson, A., Li, K., Li, R., Lilje, B., Lipovich, L., Lizio, M., Lopez, G., Magi, S., Mak, G. K., Makeev, V., Manabe, R., Mandai, M., Mar, J., Maruyama, K., Maruyama, T., Mason, E., Mathelier, A., Matsuda, H., Medvedeva, Y. A., Meehan, T. F., Mejhert, N., Meynert, A., Mikami, N., Minoda, A., Miura, H., Miyagi, Y., Miyawaki, A., Mizuno, Y., Morikawa, H., Morimoto, M., Morioka, M., Morishita, S., Moro, K., Motakis, E., Motohashi, H., Mukarram, A. K., Mummery, C. L., Mungall, C. J., Murakawa, Y., Muramatsu, M., Nagasaka, K., Nagase, T., Nakachi, Y., Nakahara, F., Nakai, K., Nakamura, K., Nakamura, Y., Nakazawa, T., Nason, G. P., Nepal, C., Nguyen, Q. H., Nielsen, L. K., Nishida, K., Nishiguchi, K. M., Nishiyori, H., Nitta, K., Notredame, C., Ogishima, S., Ohkura, N., Ohno, H., Ohshima, M., Ohtsu, T., Okada, Y., Okada-Hatakeyama, M., Okazaki, Y., Oksvold, P., Orlando, V., Ow, G. S., Ozturk, M., Pachkov, M., Paparountas, T., Parihar, S. P., Park, S. -J., Pascarella, G., Passier, R., Persson, H., Philippens, I. H., Piazza, S., Plessy, C., Pombo, A., Ponten, F., Poulain, S., Poulsen, T. M., Pradhan, S., Prezioso, C., Pridans, C., Qin, X. -Y., Quackenbush, J., Rackham, O., Ramilowski, J., Ravasi, T., Rehli, M., Rennie, S., Rito, T., Rizzu, P., Robert, C., Roos, M., Rost, B., Roudnicky, F., Roy, R., Rye, M. B., Sachenkova, O., Saetrom, P., Sai, H., Saiki, S., Saito, M., Saito, A., Sakaguchi, S., Sakai, M., Sakaue, S., Sakaue-Sawano, A., Sandelin, A., Sano, H., Sasamoto, Y., Sato, H., Saxena, A., Saya, H., Schafferhans, A., Schmeier, S., Schmidl, C., Schmocker, D., Schneider, C., Schueler, M., Schultes, E. A., Schulze-Tanzil, G., Semple, C. A., Seno, S., Seo, W., Sese, J., Sheng, G., Shi, J., Shimoni, Y., Shin, J. W., Simonsanchez, J., Sivertsson, A., Sjostedt, E., Soderhall, C., Laurent, G. S., Stoiber, M. H., Sugiyama, D., Summers, K. M., Suzuki, A. M., Suzuki, K., Suzuki, M., Suzuki, N., Suzuki, T., Swanson, D. J., Swoboda, R. K., Taguchi, A., Takahashi, H., Takahashi, M., Takamochi, K., Takeda, S., Takenaka, Y., Tam, K. T., Tanaka, H., Tanaka, R., Tanaka, Y., Tang, D., Taniuchi, I., Tanzer, A., Tarui, H., Taylor, M. S., Terada, A., Terao, Y., Testa, A. C., Thomas, M., Thongjuea, S., Tomii, K., Triglia, E. T., Toyoda, H., Tsang, H. G., Tsujikawa, M., Uhlen, M., Valen, E., van de Wetering, M., van Nimwegen, E., Velmeshev, D., Verardo, R., Vitezic, M., Vitting-Seerup, K., von Feilitzen, K., Voolstra, C. R., Vorontsov, I. E., Wahlestedt, C., Wasserman, W. W., Watanabe, K., Watanabe, S., Wells, C. A., Winteringham, L. N., Wolvetang, E., Yabukami, H., Yagi, K., Yamada, T., Yamaguchi, Y., Yamamoto, M., Yamamoto, Y., Yamanaka, Y., Yano, K., Yasuzawa, K., Yatsuka, Y., Yo, M., Yokokura, S., Yoneda, M., Yoshida, E., Yoshida, Y., Yoshihara, M., Young, R., Young, R. S., Yu, N. Y., Yumoto, N., Zabierowski, S. E., Zhang, P. G., Zucchelli, S., Zwahlen, M., Chatelain, C., Brehelin, L., Institute of Biotechnology, Biosciences, Institut de Génétique Moléculaire de Montpellier (IGMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Institut de Biologie Computationnelle (IBC), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Méthodes et Algorithmes pour la Bioinformatique (MAB), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), RIKEN Center for Integrative Medical Sciences [Yokohama] (RIKEN IMS), RIKEN - Institute of Physical and Chemical Research [Japon] (RIKEN), National Institute of Advanced Industrial Science and Technology (AIST), SANOFI Recherche, University of British Columbia (UBC), Experimental Immunology, Infectious diseases, AII - Infectious diseases, Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), and Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Montpellier (UM)

Subjects
0301 basic medicine, General Physics and Astronomy, Genome, Mice, 0302 clinical medicine, Transcription (biology), Promoter Regions, Genetic, Transcription Initiation, Genetic, 0303 health sciences, Multidisciplinary, 1184 Genetics, developmental biology, physiology, High-Throughput Nucleotide Sequencing, Neurodegenerative Diseases, 222 Other engineering and technologies, Genomics, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], humanities, Enhancer Elements, Genetic, Microsatellite Repeat, Transcription Initiation Site, Sequence motif, Transcription Initiation, Human, Enhancer Elements, Neural Networks, Science, 610 Medicine & health, Computational biology, Biology, Article, General Biochemistry, Genetics and Molecular Biology, Promoter Regions, 03 medical and health sciences, Computer, Deep Learning, Tandem repeat, Genetic, Clinical Research, [SDV.BBM.GTP]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], Machine learning, Genetics, Animals, Humans, Polymorphism, Enhancer, Transcriptomics, Gene, A549 Cell, 030304 developmental biology, Polymorphism, Genetic, Neurodegenerative Disease, Base Sequence, Animal, Genome, Human, Human Genome, Computational Biology, Promoter, General Chemistry, 113 Computer and information sciences, Cap analysis gene expression, 030104 developmental biology, [SDV.GEN.GH]Life Sciences [q-bio]/Genetics/Human genetics, Cardiovascular and Metabolic Diseases, A549 Cells, Minion, Generic health relevance, 3111 Biomedicine, Neural Networks, Computer, 610 Medizin und Gesundheit, 030217 neurology & neurosurgery, FANTOM consortium, Microsatellite Repeats
Abstract

Using the Cap Analysis of Gene Expression (CAGE) technology, the FANTOM5 consortium provided one of the most comprehensive maps of transcription start sites (TSSs) in several species. Strikingly, ~72% of them could not be assigned to a specific gene and initiate at unconventional regions, outside promoters or enhancers. Here, we probe these unassigned TSSs and show that, in all species studied, a significant fraction of CAGE peaks initiate at microsatellites, also called short tandem repeats (STRs). To confirm this transcription, we develop Cap Trap RNA-seq, a technology which combines cap trapping and long read MinION sequencing. We train sequence-based deep learning models able to predict CAGE signal at STRs with high accuracy. These models unveil the importance of STR surrounding sequences not only to distinguish STR classes, but also to predict the level of transcription initiation. Importantly, genetic variants linked to human diseases are preferentially found at STRs with high transcription initiation level, supporting the biological and clinical relevance of transcription initiation at STRs. Together, our results extend the repertoire of non-coding transcription associated with DNA tandem repeats and complexify STR polymorphism., Nature Communications, 12 (1), ISSN:2041-1723

Published
2020
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22. Open resources and controlled from distance educational technologies are in engineering education

Author

Ivshina, G. V.

Subjects
СИСТЕМА ЭЛЕКТРОННОГО ОБУЧЕНИЯ, CDIO, ИНЖЕНЕРНОЕ ОБРАЗОВАНИЕ, ENGINEERING EDUCATION, CDIO-ПОДХОД, E-LEARNING, ЭЛЕКТРОННЫЕ КУРСЫ, E-LEARNING COURSES, OPEN EDUCATIONAL RESOURCES, ОТКРЫТЫЕ ОБРАЗОВАТЕЛЬНЫЕ РЕСУРСЫ
Abstract

Работа посвящена изучению опыта применения открытых образовательных ресурсов и дистанционных образовательных технологий в инженерном образовании за рубежом и в России. Описаны основные проблемы создания и внедрения системы электронного обучения в инженерное образование. The work is devoted to studying the experience of the application of open educational resources in engineering education abroad and in Russia. The basic a problem of the creation and implementation of e-learning in engineering education.

Published
2015

23. Distance learning technologies in open education: the experience of the Kazan federal university

Author

Ivshina, G. V.

Abstract

The paradigm of open education is considered from the practical point of view on the example of the implementation of the Kazan Federal University. Given the results of creation and introduction of the system of distance learning as the basis of open education. Парадигма открытого образования рассмотрена с практической точки зрения на примере реализации в Казанском федеральном университете. Приведены результаты создания и внедрения системы дистанционного обучения как основы открытого образования.

Published
2014

24. Открытые ресурсы и дистанционные образовательные технологии в инженерном образовании

Author

Ivshina, G. V., Ившина, Г. В., Ivshina, G. V., and Ившина, Г. В.

Abstract

Работа посвящена изучению опыта применения открытых образовательных ресурсов и дистанционных образовательных технологий в инженерном образовании за рубежом и в России. Описаны основные проблемы создания и внедрения системы электронного обучения в инженерное образование., The work is devoted to studying the experience of the application of open educational resources in engineering education abroad and in Russia. The basic a problem of the creation and implementation of e-learning in engineering education.

Published
2015

28. Contrasting expression patterns of coding and noncoding parts of the human genome upon oxidative stress

Author

Giannakakis, Antonis, primary, Zhang, Jingxian, additional, Jenjaroenpun, Piroon, additional, Nama, Srikanth, additional, Zainolabidin, Norliyana, additional, Aau, Mei Yee, additional, Yarmishyn, Aliaksandr A., additional, Vaz, Candida, additional, Ivshina, Anna V., additional, Grinchuk, Oleg V., additional, Voorhoeve, Mathijs, additional, Vardy, Leah A., additional, Sampath, Prabha, additional, Kuznetsov, Vladimir A., additional, Kurochkin, Igor V., additional, and Guccione, Ernesto, additional

Published
2015
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29. Transposon insertional mutagenesis in mice identifies human breast cancer susceptibility genes and signatures for stratification.

Author

Liming Chen, Jenjaroenpun, Piroon, Ching Pillai, Andrea Mun, Ivshina, Anna V., Ghim Siong Ow, Efthimios, Motakis, Tang Zhiqun, Tuan Zea Tan, Song-Choon Lee, Rogers, Keith, Ward, Jerrold M., Mori, Seiichi, Adams, David J., Jenkins, Nancy A., Copeland, Neal G., Kenneth Hon-Kim Ban, Kuznetsov, Vladimir A., and Thiery, Jean Paul

Subjects
MUTAGENESIS, BREAST cancer, GENE expression, GENETIC testing, NEUROFIBROMIN
Abstract

Robust prognostic gene signatures and therapeutic targets are difficult to derive from expression profiling because of the significant heterogeneity within breast cancer (BC) subtypes. Here, we performed forward genetic screening in mice using Sleeping Beauty transposon mutagenesis to identify candidate BC driver genes in an unbiased manner, using a stabilized N-terminal truncated β-catenin gene as a sensitizer. We identified 134 mouse susceptibility genes from 129 common insertion sites within 34 mammary tumors. Of these, 126 genes were orthologous to protein-coding genes in the human genome (hereafter, human BC susceptibility genes, hBCSGs), 70% of which are previously reported cancer-associated genes, and ~16% are known BC suppressor genes. Network analysis revealed a gene hub consisting of E1A binding protein P300 (EP300), CD44 molecule (CD44), neurofibromin (NF1) and phosphatase and tensin homolog (PTEN), which are linked to a significant number of mutated hBCSGs. From our survival prediction analysis of the expression of human BC genes in 2,333 BC cases, we isolated a sixgene- pair classifier that stratifies BC patients with high confidence into prognostically distinct low-, moderate-, and high-risk subgroups. Furthermore, we proposed prognostic classifiers identifying three basal and three claudin-low tumor subgroups. Intriguingly, our hBCSGs are mostly unrelated to cell cycle/mitosis genes and are distinct from the prognostic signatures currently used for stratifying BC patients. Our findings illustrate the strength and validity of integrating functional mutagenesis screens in mice with human cancer transcriptomic data to identify highly prognostic BC subtyping biomarkers. [ABSTRACT FROM AUTHOR]

Published
2017
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30. Дистанционные технологии обучения в открытом образовании: опыт Казанского федерального университета

Author

Ivshina, G. V., Ившина, Г. В., Ivshina, G. V., and Ившина, Г. В.

Abstract

The paradigm of open education is considered from the practical point of view on the example of the implementation of the Kazan Federal University. Given the results of creation and introduction of the system of distance learning as the basis of open education., Парадигма открытого образования рассмотрена с практической точки зрения на примере реализации в Казанском федеральном университете. Приведены результаты создания и внедрения системы дистанционного обучения как основы открытого образования.

Published
2014

34. Genetic reclassification of histologic grade delineates new clinical subtypes of breast cancer

Author

Ivshina, Anna V., George, Joshy, Senko, Oleg, Mow, Benjamin, Putti, Thomas C., Smeds, Johanna, Lindahl, Thomas, Pawitan, Yudi, Hall, Per, Nordgren, Hans, Wong, John E. L., Liu, Edison T., Bergh, Jonas, Kuznetsov, Vladimir A., Miller, Lance D., Ivshina, Anna V., George, Joshy, Senko, Oleg, Mow, Benjamin, Putti, Thomas C., Smeds, Johanna, Lindahl, Thomas, Pawitan, Yudi, Hall, Per, Nordgren, Hans, Wong, John E. L., Liu, Edison T., Bergh, Jonas, Kuznetsov, Vladimir A., and Miller, Lance D.

Abstract

Histologic grading of breast cancer defines morphologic subtypes informative of metastatic potential, although not without considerable interobserver disagreement and clinical heterogeneity particularly among the moderately differentiated grade 2 (G2) tumors. We posited that a gene expression signature capable of discerning tumors of grade 1 (G1) and grade 3 (W) histology might provide a more objective measure of grade with prognostic benefit for patients with G2 disease. To this end, we studied the expression profiles of 347 primary invasive breast tumors analyzed on Affymetrix microarrays. Using class prediction algorithms, we identified 264 robust grade-associated markers, six of which could accurately classify G1 and G3 tumors, and separate G2 tumors into two highly discriminant classes (termed G2a and G2b genetic grades) with patient survival outcomes highly similar to those with G1 and G3 histology, respectively. Statistical analysis of conventional clinical variables further distinguished G2a and G2b subtypes from each other, but also from histologic G1 and G3 tumors. In multivariate analyses, genetic grade was consistently found to be an independent prognostic indicator of disease recurrence comparable with that of lymph node status and tumor size. When incorporated into the Nottingham prognostic index, genetic grade enhanced detection of patients with less harmful tumors, likely to benefit little from adjuvant therapy. Our findings show that a genetic grade signature can improve prognosis and therapeutic planning for breast cancer patients, and support the view that low- and high-grade disease, as defined genetically, reflect independent pathobiological entities rather than a continuum of cancer progression.

Published
2006
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35. Genetic Reclassification of Histologic Grade Delineates New Clinical Subtypes of Breast Cancer

Author

Ivshina, Anna V., primary, George, Joshy, additional, Senko, Oleg, additional, Mow, Benjamin, additional, Putti, Thomas C., additional, Smeds, Johanna, additional, Lindahl, Thomas, additional, Pawitan, Yudi, additional, Hall, Per, additional, Nordgren, Hans, additional, Wong, John E.L., additional, Liu, Edison T., additional, Bergh, Jonas, additional, Kuznetsov, Vladimir A., additional, and Miller, Lance D., additional

Published
2006
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37. Pareto-Gamma Statistic Reveals Global Rescaling in Transcriptomes of Low and High Aggressive Breast Cancer Phenotypes.

Author

Rajapakse, Jagath C., Wong, Limsoon, Acharya, Raj, Chua, Alvin L. -S., Ivshina, Anna V., and Kuznetsov, Vladimir A.

Abstract

We propose a novel mixture probability model for the probability distribution function (PDF) of microarray signals, which comprises a noise and a signal component. The noise term, due to non-specific mRNA hybridization, is given by a lognormal distribution; and the true signal, from specific mRNA hybridization, is described by the generalized Pareto-gamma (GPG) function. The model, applied to expression data of 251 human breast cancer tumors on the Affymetrix microarray platform, yields accurate fits for all tumor samples. We observe that (i) high aggressive cancers have, in general, broader right tails in the GPG than low aggressive cancers; (ii) the exponent parameter value of the GPG distribution is not constant and correlates strongly with ~4000 expressed genes and several "gold standard" clinical risk factors. These results can not be obtained from so-called "scale-free network" models. We conclude that an accurate parameterization of scale-dependent GPG function could provide robust prognostic benefits for cancer patients. [ABSTRACT FROM AUTHOR]

Published
2006
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40. Data-Driven Approach to Predict Survival of Cancer Patients.

Author

Motakis, Efthimios, Ivshina, Anna V., and Kuznetsov, Vladimir A.

Subjects
CANCER patients, GENE expression, DRUG development, THERAPEUTICS
Abstract

The article presents a study which used the data-driven method to predict cancer patient survival. It says that the researchers applied this approach to evaluate Affymetrix U133 data sets of two large breast cancer cohorts and identified genes and gene pairs relating to breast cancer grade signatures. It adds that this approach led for the discovery of hundreds of novel smallest gene signatures that predict the survival of cancer patients, allowing for the development of anti-cancer drugs.

Published
2009
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41. Identification of two poorly prognosed ovarian carcinoma subtypes associated with CHEK2germ-line mutation and non-CHEK2somatic mutation gene signatures

Author

Ow, Ghim Siong, Ivshina, Anna V, Fuentes, Gloria, and Kuznetsov, Vladimir A

Abstract

High-grade serous ovarian cancer (HG-SOC), a major histologic type of epithelial ovarian cancer (EOC), is a poorly-characterized, heterogeneous and lethal disease where somatic mutations of TP53are common and inherited loss-of-function mutations in BRCA1/2predispose to cancer in 9.5–13% of EOC patients. However, the overall burden of disease due to either inherited or sporadic mutations is not known.  We performed bioinformatics analyses of mutational and clinical data of 334 HG-SOC tumor samples from The Cancer Genome Atlas to identify novel tumor-driving mutations, survival-significant patient subgroups and tumor subtypes potentially driven by either hereditary or sporadic factors.We identified a sub-cluster of high-frequency mutations in 22 patients and 58 genes associated with DNA damage repair, apoptosis and cell cycle. Mutations of CHEK2, observed with the highest intensity, were associated with poor therapy response and overall survival (OS) of these patients (P= 8.00e-05), possibly due to detrimental effect of mutations at the nuclear localization signal. A 21-gene mutational prognostic signature significantly stratifies patients into relatively low or high-risk subgroups with 5-y OS of 37% or 6%, respectively (P= 7.31e-08). Further analysis of these genes and high-risk subgroup revealed 2 distinct classes of tumors characterized by either germline mutations of genes such as CHEK2, RPS6KA2and MLL4, or somatic mutations of other genes in the signature.Our results could provide improvement in prediction and clinical management of HG-SOC, facilitate our understanding of this complex disease, guide the design of targeted therapeutics and improve screening efforts to identify women at high-risk of hereditary ovarian cancers distinct from those associated with BRCA1/2mutations.

Published
2014
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46. [Possibilities of adoptive immunochemotherapy in the treatment of non-small-cell lung cancer].

Author

Davydov MI, Normantovich VA, Polotskiĭ BE, Ivshina AV, and Mkheidze DM

Subjects
Adult, Carcinoma, Non-Small-Cell Lung diagnostic imaging, Carcinoma, Non-Small-Cell Lung drug therapy, Cyclophosphamide therapeutic use, Dactinomycin therapeutic use, Doxorubicin therapeutic use, Evaluation Studies as Topic, Fluorouracil therapeutic use, Humans, Lung Neoplasms diagnostic imaging, Lung Neoplasms drug therapy, Male, Methotrexate therapeutic use, Middle Aged, Mitomycin therapeutic use, Radiography, Thoracic, Vincristine therapeutic use, Antineoplastic Combined Chemotherapy Protocols therapeutic use, Carcinoma, Non-Small-Cell Lung therapy, Immunotherapy, Adoptive, Lung Neoplasms therapy
Published
1996

47. [Effect of the spread of the process and treatment on the phenotype of peripheral blood lymphocytes in patients with prostatic cancer].

Author

Ivshina AV, Zhumagazin ZhD, Zabotina TN, Matveev BP, and Kadagidze ZG

Subjects
Combined Modality Therapy, Humans, Lymphocyte Count, Lymphocytes classification, Male, Prostatic Neoplasms pathology, Prostatic Neoplasms therapy, Immunophenotyping methods, Lymphocytes immunology, Prostatic Neoplasms immunology
Abstract

A wide panel of monoclonal antibodies to differentiation antigens has been originally used to determine phenotype of peripheral blood lymphocytes in patients with prostatic cancer (PC). Patients with local PC exhibited marked lymphocytopenia existent in vertually all populations and subpopulations. In generalized PC the above changes are more pronounced. Treatment of PC adds to immunodepression observed in this disease. Thus, immunotherapy as an adjuvant modality of PC treatment is quite appropriate.

Published
1995

48. [Role of immune disorders in the pathogenesis of suppurative inflammatory diseases of the adnexa uteri].

Author

Strizhakov AN, Podzolkova NM, and Ivshina AV

Subjects
Adolescent, Adult, Antibodies, Monoclonal, Antigen-Antibody Complex analysis, B-Lymphocytes immunology, Complement System Proteins analysis, Fallopian Tube Diseases immunology, Female, HLA-DR Antigens analysis, Humans, Immune System Diseases diagnosis, Immune System Diseases immunology, Immunoglobulin G analysis, Immunoglobulin M analysis, Immunohistochemistry, Inflammation, Suppuration, T-Lymphocytes immunology, Fallopian Tube Diseases etiology, Immune System Diseases complications
Abstract

Clinical and immunological studies were carried out in 60 patients with tubal pyoinflammatory diseases and in 20 healthy controls of a reproductive age. The immunity status was studied using IKO monoclonal antibodies to differentiation antigens of peripheral blood mononuclears. An acute purulent process was found to be characterized by an adaptive increase in the count of mononuclears carrying the markers of B lymphocytes (CD22, IgM mu-chain), of activated cells (CD38, HLA-D(r)), and of the total number of positive correlations between immunocompetent cells. A persistent course of tubal inflammation is connected with hyperproduction of antibodies and immune complexes, increased expression of HLA-Dr antigens in combination with congenital or acquired insufficiency of T-helper/inductors and suppressors/cytotoxic T-cells. An important factor in the development of a chronic suppurative process is triggering of immunocomplex mechanisms associated with fixation of IgG, IgM, and the complement in the walls of small vessels in the uterine tubes, with a high level of circulating immune complexes, and appearance of plasma cells containing IgG and IgM in the tissues of uterine tubes.

Published
1994

49. [The effect of indomethacin and leukinferon on the level of blood plasma thromboxane B2, platelet aggregation and the immune system of patients with endometrial cancer in the perioperative period].

Author

Mistakopulo NF, Shoshiashvili VD, Saltanov AI, Kushlinskiĭ NE, Ivshina AV, Madzhuga AI, Liakina LT, Ukrainets AM, Zabotina TN, and Kadagidze ZG

Subjects
Drug Combinations, Female, Humans, Middle Aged, Platelet Aggregation Inhibitors pharmacology, Cytokines pharmacology, Endometrial Neoplasms surgery, Immune System drug effects, Indomethacin pharmacology, Interferon Type I pharmacology, Platelet Aggregation drug effects, Thromboxane B2 blood
Abstract

The perioperative influence of leukinferon, indomethacin and their combination on the blood plasma level of thromboxane B2 (TxB2), platelet aggregation ability and humoral and cellular immunity has been assessed in 40 endometrial cancer patients. It has been found that the perioperative use of indomethacin diminishes the blood plasma level of TxB2 and platelet aggregation ability. Leukinferon did not affect substantially the parameters. However, the combination of leukinferon with indomethacin causes a more stable reduction in platelet aggregation and TxB2 level than the use of indomethacin alone. The use of these drugs and their combination prevented postoperative immune suppression in the endometrial cancer patients. However, leukinferon alone or its combination with indomethacin were more effective than the use of indomethacin alone. Possible mechanisms of indomethacin and leukinferon effect on tumor cell metabolism of arachidonic acid and possible role of eicosanoids in the pathogenetic mechanisms of tumor growth and metastasis dissemination are discussed.

Published
1992

50. [The immune status of patients with lymphoproliferative diseases of the skin and Kaposi's sarcoma].

Author

Faradzhev ZG, Derevnina NA, and Ivshina AV

Subjects
Adolescent, Adult, Aged, Antibody Formation immunology, Chronic Disease, Female, Humans, Immunity, Cellular immunology, Lymphatic Diseases immunology, Male, Middle Aged, Mycosis Fungoides immunology, Lymphoproliferative Disorders immunology, Sarcoma, Kaposi immunology, Skin Diseases immunology, Skin Neoplasms immunology
Abstract

Manifest disorders of cellular and humoral immunity are detectable with the use of immunologic tests in patients with mycosis fungoides, skin reticulosis, and Kaposi's sarcoma. The pattern of changes in Kaposi's sarcoma differs from that in malignant lymphomas of the skin. The detected immune status disorders necessitate the use of immunomodulators in combined therapy of such patients.

Published
1990

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