These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

208 related articles for article (PubMed ID: 35281472)

  • 1. Glycosylation-Related Genes Predict the Prognosis and Immune Fraction of Ovarian Cancer Patients Based on Weighted Gene Coexpression Network Analysis (WGCNA) and Machine Learning.
    Zhao C; Xiong K; Zhao F; Adam A; Li X
    Oxid Med Cell Longev; 2022; 2022():3665617. PubMed ID: 35281472
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Development and Validation of an Immune-Related Prognostic Signature for Ovarian Cancer Based on Weighted Gene Coexpression Network Analysis.
    An Y; Yang Q
    Biomed Res Int; 2020; 2020():7594098. PubMed ID: 33381581
    [TBL] [Abstract][Full Text] [Related]  

  • 3. A signature based on glycosyltransferase genes provides a promising tool for the prediction of prognosis and immunotherapy responsiveness in ovarian cancer.
    Xu X; Wu Y; Jia G; Zhu Q; Li D; Xie K
    J Ovarian Res; 2023 Jan; 16(1):5. PubMed ID: 36611197
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Metabolism-related gene vaccines and immune infiltration in ovarian cancer: A novel risk score model of machine learning.
    Fu Y; Huang Z; Huang J; Xiong J; Liu H; Wan X
    J Gene Med; 2024 Jan; 26(1):e3568. PubMed ID: 37455244
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Glycosylation-related genes mediated prognostic signature contribute to prognostic prediction and treatment options in ovarian cancer: based on bulk and single‑cell RNA sequencing data.
    You Y; Yang Q
    BMC Cancer; 2024 Feb; 24(1):207. PubMed ID: 38355446
    [TBL] [Abstract][Full Text] [Related]  

  • 6. A novel risk score system for assessment of ovarian cancer based on co-expression network analysis and expression level of five lncRNAs.
    Zhao Q; Fan C
    BMC Med Genet; 2019 Jun; 20(1):103. PubMed ID: 31182053
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Development of a multi-gene-based immune prognostic signature in ovarian Cancer.
    Cao T; Shen H
    J Ovarian Res; 2021 Jan; 14(1):20. PubMed ID: 33509250
    [TBL] [Abstract][Full Text] [Related]  

  • 8. A signature based on anoikis-related genes for the evaluation of prognosis, immunoinfiltration, mutation, and therapeutic response in ovarian cancer.
    Duan Y; Xu X
    Front Endocrinol (Lausanne); 2023; 14():1193622. PubMed ID: 37383389
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Identification of STEAP3-based molecular subtype and risk model in ovarian cancer.
    Zhao Z; Sun C; Hou J; Yu P; Wei Y; Bai R; Yang P
    J Ovarian Res; 2023 Jun; 16(1):126. PubMed ID: 37386521
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Peripheral and tumor-infiltrating immune cells are correlated with patient outcomes in ovarian cancer.
    Zhang W; Ling Y; Li Z; Peng X; Ren Y
    Cancer Med; 2023 Apr; 12(8):10045-10061. PubMed ID: 36645174
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Construction and validation of a transcription factors-based prognostic signature for ovarian cancer.
    Cheng Q; Li L; Yu M
    J Ovarian Res; 2022 Feb; 15(1):29. PubMed ID: 35227285
    [TBL] [Abstract][Full Text] [Related]  

  • 12. A 19‑miRNA Support Vector Machine classifier and a 6‑miRNA risk score system designed for ovarian cancer patients.
    Dong J; Xu M
    Oncol Rep; 2019 Jun; 41(6):3233-3243. PubMed ID: 31002358
    [TBL] [Abstract][Full Text] [Related]  

  • 13. A random forest classifier predicts recurrence risk in patients with ovarian cancer.
    Cheng L; Li L; Wang L; Li X; Xing H; Zhou J
    Mol Med Rep; 2018 Sep; 18(3):3289-3297. PubMed ID: 30066910
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Integrated analysis of tumor-associated macrophage infiltration and prognosis in ovarian cancer.
    Tan Q; Liu H; Xu J; Mo Y; Dai F
    Aging (Albany NY); 2021 Oct; 13(19):23210-23232. PubMed ID: 34633990
    [TBL] [Abstract][Full Text] [Related]  

  • 15. A novel DNA methylation-related gene signature for the prediction of overall survival and immune characteristics of ovarian cancer patients.
    Wang S; Fu J; Fang X
    J Ovarian Res; 2023 Mar; 16(1):62. PubMed ID: 36978087
    [TBL] [Abstract][Full Text] [Related]  

  • 16. A combined hypoxia and immune gene signature for predicting survival and risk stratification in triple-negative breast cancer.
    Yang X; Weng X; Yang Y; Zhang M; Xiu Y; Peng W; Liao X; Xu M; Sun Y; Liu X
    Aging (Albany NY); 2021 Aug; 13(15):19486-19509. PubMed ID: 34341184
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Identification of immune microenvironment subtypes that predicted the prognosis of patients with ovarian cancer.
    Wang X; Li X; Wang X
    J Cell Mol Med; 2021 Apr; 25(8):4053-4061. PubMed ID: 33675171
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Identification and verification of a ten-gene signature predicting overall survival for ovarian cancer.
    Liu J; Xu F; Cheng W; Gao L
    Exp Cell Res; 2020 Oct; 395(2):112235. PubMed ID: 32805252
    [TBL] [Abstract][Full Text] [Related]  

  • 19. A large cohort study identifying a novel prognosis prediction model for lung adenocarcinoma through machine learning strategies.
    Li Y; Ge D; Gu J; Xu F; Zhu Q; Lu C
    BMC Cancer; 2019 Sep; 19(1):886. PubMed ID: 31488089
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Ovarian cancer classification and prognosis assessment model based on prognostic target genes in key microRNA-target gene networks.
    Chen X; Li Y; He J
    J Gene Med; 2024 Jan; 26(1):e3575. PubMed ID: 37548130
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 11.