Results 161 to 170 of about 4,605,994 (345)
Expression and DNA methylation of 20S proteasome subunits as prognostic and resistance markers in cancer
Molecular Oncology, EarlyView.Comprehensive analysis of genomic mutations, gene expression, DNA methylation, and pathway analysis of TCGA data was carried out to define cancer types in which proteasome subunits expression is associated with worse survival. Albeit the effect of specific proteasome subunits on cellular function, the main role of the proteasome is better evaluated ...Ruba Al‐Abdulla, Simone Venz, Ruslan Al‐Ali, Martin Wendlandt, Mandy Radefeldt, Elke Krüger +5 morewiley +1 more sourceDetecting homologous recombination deficiency for breast cancer through integrative analysis of genomic data
Molecular Oncology, EarlyView.This study develops a semi‐supervised classifier integrating multi‐genomic data (1404 training/5893 validation samples) to improve homologous recombination deficiency (HRD) detection in breast cancer. Our method demonstrates prognostic value and predicts chemotherapy/PARP inhibitor sensitivity in HRD+ tumours.Rong Zhu, Katherine Eason, Suet‐Feung Chin, Paul A. W. Edwards, Raquel Manzano Garcia, Richard Moulange, Jia Wern Pan, Soo Hwang Teo, Sach Mukherjee, Maurizio Callari, Carlos Caldas, Stephen‐John Sammut, Oscar M. Rueda +12 morewiley +1 more sourceData‐driven discovery of gene expression markers distinguishing pediatric acute lymphoblastic leukemia subtypes
Molecular Oncology, EarlyView.This study investigates gene expression differences between two major pediatric acute lymphoblastic leukemia (ALL) subtypes, B‐cell precursor ALL, and T‐cell ALL, using a data‐driven approach consisting of biostatistics and machine learning methods. Following analysis of a discovery dataset, we find a set of 14 expression markers differentiating the ...Mona Nourbakhsh, Nikola Tom, Anna Schrøder Lassen, Helene Brasch Lind Petersen, Ulrik Kristoffer Stoltze, Karin Wadt, Kjeld Schmiegelow, Matteo Tiberti, Elena Papaleo +8 morewiley +1 more sourceAn Enigmatic PeVatron in an Area around H ii Region G35.6−0.5
The Astrophysical JournalIdentifying Galactic PeVatrons (PeV particle accelerators) from ultrahigh-energy (UHE, >100 TeV) γ -ray sources plays a crucial role in revealing the origin of Galactic cosmic rays.Zhen Cao, F. Aharonian, Axikegu, Y. X. Bai, Y. W. Bao, D. Bastieri, X. J. Bi, Y. J. Bi, W. Bian, A. V. Bukevich, Q. Cao, W. Y. Cao, Zhe Cao, J. Chang, J. F. Chang, A. M. Chen, B. Q. Chen, E. S. Chen, H. X. Chen, Liang Chen, Lin Chen, Long Chen, M. J. Chen, M. L. Chen, Q. H. Chen, S. Chen, S. H. Chen, S. Z. Chen, T. L. Chen, Y. Chen, N. Cheng, Y. D. Cheng, M. C. Chu, M. Y. Cui, S. W. Cui, X. H. Cui, Y. D. Cui, B. Z. Dai, H. L. Dai, Z. G. Dai, Danzengluobu, X. Q. Dong, K. K. Duan, J. H. Fan, Y. Z. Fan, J. Fang, J. H. Fang, K. Fang, C. F. Feng, H. Feng, L. Feng, S. H. Feng, X. T. Feng, Y. Feng, Y. L. Feng, S. Gabici, B. Gao, C. D. Gao, Q. Gao, W. Gao, W. K. Gao, M. M. Ge, T. T. Ge, L. S. Geng, G. Giacinti, G. H. Gong, Q. B. Gou, M. H. Gu, F. L. Guo, J. Guo, X. L. Guo, Y. Q. Guo, Y. Y. Guo, Y. A. Han, O. A. Hannuksela, M. Hasan, H. H. He, H. N. He, J. Y. He, Y. He, Y. K. Hor, B. W. Hou, C. Hou, X. Hou, H. B. Hu, Q. Hu, S. C. Hu, C. Huang, D. H. Huang, T. Q. Huang, W. J. Huang, X. T. Huang, X. Y. Huang, Y. Huang, Y. Y. Huang, X. L. Ji, H. Y. Jia, K. Jia, H. B. Jiang, K. Jiang, X. W. Jiang, Z. J. Jiang, M. Jin, M. M. Kang, I. Karpikov, D. Khangulyan, D. Kuleshov, K. Kurinov, B. B. Li, C. M. Li, Cheng Li, Cong Li, D. Li, F. Li, H. B. Li, H. C. Li, Jian Li, Jie Li, K. Li, S. D. Li, W. L. Li, W. L. Li, X. R. Li, Xin Li, Y. Z. Li, Zhe Li, Zhuo Li, E. W. Liang, Y. F. Liang, S. J. Lin, B. Liu, C. Liu, D. Liu, D. B. Liu, H. Liu, H. D. Liu, J. Liu, J. L. Liu, M. Y. Liu, R. Y. Liu, S.M. Liu, W. Liu, Y. Liu, Y. N. Liu, Q. Luo, Y. Luo, H. K. Lv, B. Q. Ma, L. L. Ma, X. H. Ma, J. R. Mao, Z. Min, W. Mitthumsiri, H. J. Mu, Y. C. Nan, A. Neronov, K. C. Y. Ng, L. J. Ou, P. Pattarakijwanich, Z. Y. Pei, J. C. Qi, M. Y. Qi, B. Q. Qiao, J. J. Qin, A. Raza, D. Ruffolo, A. Sáiz, M. Saeed, D. Semikoz, L. Shao, O. Shchegolev, X. D. Sheng, F. W. Shu, H. C. Song, Yu. V. Stenkin, V. Stepanov, Y. Su, D. X. Sun, Q. N. Sun, X. N. Sun, Z. B. Sun, J. Takata, P. H. T. Tam, Q. W. Tang, R. Tang, Z. B. Tang, W. W. Tian, L. H. Wan, C. Wang, C. B. Wang, G. W. Wang, H. G. Wang, H. H. Wang, J. C. Wang, Kai Wang, Kai Wang, L. P. Wang, L. Y. Wang, P. H. Wang, R. Wang, W. Wang, X. G. Wang, X. Y. Wang, Y. Wang, Y. D. Wang, Y. J. Wang, Z. H. Wang, Z. X. Wang, Zhen Wang, Zheng Wang, D. M. Wei, J. J. Wei, Y. J. Wei, T. Wen, C. Y. Wu, H. R. Wu, Q. W. Wu, S. Wu, X. F. Wu, Y. S. Wu, S. Q. Xi, J. Xia, G. M. Xiang, D. X. Xiao, G. Xiao, Y. L. Xin, Y. Xing, D. R. Xiong, Z. Xiong, D. L. Xu, R. F. Xu, R. X. Xu, W. L. Xu, L. Xue, D. H. Yan, J. Z. Yan, T. Yan, C. W. Yang, C. Y. Yang, F. Yang, F. F. Yang, L. L. Yang, M. J. Yang, R. Z. Yang, W. X. Yang, Y. H. Yao, Z. G. Yao, L. Q. Yin, N. Yin, X. H. You, Z. Y. You, Y. H. Yu, Q. Yuan, H. Yue, H. D. Zeng, T. X. Zeng, W. Zeng, M. Zha, B. B. Zhang, F. Zhang, H. Zhang, H. M. Zhang, H. Y. Zhang, J. L. Zhang, Li Zhang, P. F. Zhang, P. P. Zhang, R. Zhang, S. B. Zhang, S. R. Zhang, S. S. Zhang, X. Zhang, X. P. Zhang, Y. F. Zhang, Yi Zhang, Yong Zhang, B. Zhao, J. Zhao, L. Zhao, L. Z. Zhao, S. P. Zhao, X. H. Zhao, F. Zheng, W. J. Zhong, B. Zhou, H. Zhou, J. N. Zhou, M. Zhou, P. Zhou, R. Zhou, X. X. Zhou, X. X. Zhou, B. Y. Zhu, C. G. Zhu, F. R. Zhu, H. Zhu, K. J. Zhu, Y. C. Zou, X. Zuo, (The LHAASO Collaboration) +299 moredoaj +1 more sourceComprehensive omics‐based classification system in adult patients with B‐cell acute lymphoblastic leukemia
Molecular Oncology, EarlyView.The COMBAT classification system, developed through multi‐omics integration, stratifies adult patients with B‐cell acute lymphoblastic leukemia(B‐ALL) into three molecular subtypes with distinct surface antigen patterns, immune landscape, methylation patterns, biological pathways and prognosis.Yang Song, Ting Liu, Qishan Hao, Qiuyun Fang, Xiaoyuan Gong, Yan Li, Zheng Tian, Hui Wei, Min Wang, Jianxiang Wang, Tao Cheng, Yingchang Mi +11 morewiley +1 more sourceUnraveling LINE‐1 retrotransposition in head and neck squamous cell carcinoma
Molecular Oncology, EarlyView.The novel RetroTest method allows the detection of L1 activation in clinical samples with low DNA input, providing global L1 activity and the identification of the L1 source element. We applied RetroTest to a real‐world cohort of HNSCC patients where we reported an early L1 activation, with more than 60% of T1 patients showing L1 activity.Jenifer Brea‐Iglesias, Ana Oitabén, Sonia Zumalave, Bernardo Rodriguez‐Martin, María Gallardo‐Gómez, Martín Santamarina, Ana Pequeño‐Valtierra, Laura Juaneda‐Magdalena, Ramón García‐Escudero, José Luis López‐Cedrún, Máximo Fraga, José M. C. Tubio, Mónica Martínez‐Fernández +12 morewiley +1 more source