BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

161 related articles for article (PubMed ID: 37722290)

  • 1. Machine learning developed a programmed cell death signature for predicting prognosis and immunotherapy benefits in lung adenocarcinoma.
    Ding D; Wang L; Zhang Y; Shi K; Shen Y
    Transl Oncol; 2023 Dec; 38():101784. PubMed ID: 37722290
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Pan-cancer analysis identifies proteasome 26S subunit, ATPase (PSMC) family genes, and related signatures associated with prognosis, immune profile, and therapeutic response in lung adenocarcinoma.
    Jia H; Tang WJ; Sun L; Wan C; Zhou Y; Shen WZ
    Front Genet; 2022; 13():1017866. PubMed ID: 36699466
    [No Abstract]   [Full Text] [Related]  

  • 3. A programmed cell death-related model based on machine learning for predicting prognosis and immunotherapy responses in patients with lung adenocarcinoma.
    Zhang Y; Wang Y; Chen J; Xia Y; Huang Y
    Front Immunol; 2023; 14():1183230. PubMed ID: 37671155
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Machine Learning-Based Integration Develops a Macrophage-Related Index for Predicting Prognosis and Immunotherapy Response in Lung Adenocarcinoma.
    Li Z; Guo M; Lin W; Huang P
    Arch Med Res; 2023 Nov; 54(7):102897. PubMed ID: 37865004
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Machine Learning Developed a Programmed Cell Death Signature for Predicting Prognosis, Ecosystem, and Drug Sensitivity in Ovarian Cancer.
    Wang L; Chen X; Song L; Zou H
    Anal Cell Pathol (Amst); 2023; 2023():7365503. PubMed ID: 37868825
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Molecular subtypes of lung adenocarcinoma patients for prognosis and therapeutic response prediction with machine learning on 13 programmed cell death patterns.
    Wei Q; Jiang X; Miao X; Zhang Y; Chen F; Zhang P
    J Cancer Res Clin Oncol; 2023 Oct; 149(13):11351-11368. PubMed ID: 37378675
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Machine learning-based cell death signature for predicting the prognosis and immunotherapy benefit in stomach adenocarcinoma.
    Li F; Feng Q; Tao R
    Medicine (Baltimore); 2024 Mar; 103(10):e37314. PubMed ID: 38457593
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Machine learning developed a CD8
    Chen R; Zheng Y; Fei C; Ye J; Fei H
    Sci Rep; 2024 Mar; 14(1):5794. PubMed ID: 38461331
    [TBL] [Abstract][Full Text] [Related]  

  • 9. A novel defined risk signature of cuproptosis-related long non-coding RNA for predicting prognosis, immune infiltration, and immunotherapy response in lung adenocarcinoma.
    Ma C; Li F; Gu Z; Yang Y; Qi Y
    Front Pharmacol; 2023; 14():1146840. PubMed ID: 37670938
    [No Abstract]   [Full Text] [Related]  

  • 10. Identification of immune activation-related gene signature for predicting prognosis and immunotherapy efficacy in lung adenocarcinoma.
    Zeng W; Wang J; Yang J; Chen Z; Cui Y; Li Q; Luo G; Ding H; Ju S; Li B; Chen J; Xie Y; Tong X; Liu M; Zhao J
    Front Immunol; 2023; 14():1217590. PubMed ID: 37492563
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Development and validation of a robust immune-related prognostic signature in early-stage lung adenocarcinoma.
    Wu P; Zheng Y; Wang Y; Wang Y; Liang N
    J Transl Med; 2020 Oct; 18(1):380. PubMed ID: 33028329
    [TBL] [Abstract][Full Text] [Related]  

  • 12. A novel pyroptosis-related prognostic signature for lung adenocarcinoma: Identification and multi-angle verification.
    Wang X; Zhou J; Li Z; Chen X; Wei Q; Chen K; Jiang R
    Front Genet; 2023; 14():1160915. PubMed ID: 37077542
    [No Abstract]   [Full Text] [Related]  

  • 13. Development of a copper metabolism-related gene signature in lung adenocarcinoma.
    Chang W; Li H; Zhong L; Zhu T; Chang Z; Ou W; Wang S
    Front Immunol; 2022; 13():1040668. PubMed ID: 36524120
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Computational identification and experimental verification of a novel signature based on SARS-CoV-2-related genes for predicting prognosis, immune microenvironment and therapeutic strategies in lung adenocarcinoma patients.
    Wang Y; Xu Y; Deng Y; Yang L; Wang D; Yang Z; Zhang Y
    Front Immunol; 2024; 15():1366928. PubMed ID: 38601163
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Machine learning developed an intratumor heterogeneity signature for predicting prognosis and immunotherapy benefits in skin cutaneous melanoma.
    Zhang W; Wang S
    Melanoma Res; 2024 Jun; 34(3):215-224. PubMed ID: 38364052
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Histone acetylation modification regulator-mediated tumor microenvironment infiltration characteristics and prognostic model of lung adenocarcinoma patients.
    Wang W; Shen Y; Zhang P; Liu L; Sha X; Li H; Wang S; Zhang H; Zhou Y; Shi J
    J Thorac Dis; 2022 Oct; 14(10):3886-3902. PubMed ID: 36389327
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Identification of N7-methylguanosine related signature for prognosis and immunotherapy efficacy prediction in lung adenocarcinoma.
    Li Z; Wang W; Wu J; Ye X
    Front Med (Lausanne); 2022; 9():962972. PubMed ID: 36091687
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Comprehensive Analysis of Immune Implication and Prognostic Value of
    Zeng Y; Zhang Z; Chen H; Fan J; Yuan W; Li J; Zhou S; Liu W
    Front Oncol; 2021; 11():798425. PubMed ID: 35047409
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Machine learning developed an intratumor heterogeneity signature for predicting prognosis and immunotherapy benefits in cholangiocarcinoma.
    Chen X; Sun B; Chen Y; Xiao Y; Song Y; Liu S; Peng C
    Transl Oncol; 2024 May; 43():101905. PubMed ID: 38387388
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Integrative learning in developing an immunologic lncRNA signature as a consensus risk-stratification tool for lung adenocarcinoma.
    Chen Z; Liu Y; Wan C; Huang W
    J Thorac Dis; 2023 Apr; 15(4):1823-1837. PubMed ID: 37197549
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 9.