200 related articles for article (PubMed ID: 37925432)
1. Integrated machine learning identifies epithelial cell marker genes for improving outcomes and immunotherapy in prostate cancer.
Zhu W; Zeng H; Huang J; Wu J; Wang Y; Wang Z; Wang H; Luo Y; Lai W
J Transl Med; 2023 Nov; 21(1):782. PubMed ID: 37925432
[TBL] [Abstract][Full Text] [Related]
2. Integrative multi-omics analysis unveils stemness-associated molecular subtypes in prostate cancer and pan-cancer: prognostic and therapeutic significance.
Zheng K; Hai Y; Xi Y; Zhang Y; Liu Z; Chen W; Hu X; Zou X; Hao J
J Transl Med; 2023 Nov; 21(1):789. PubMed ID: 37936202
[TBL] [Abstract][Full Text] [Related]
3. A novel ferroptosis-related gene prognostic index for prognosis and response to immunotherapy in patients with prostate cancer.
Wang Y; Fan J; Chen T; Xu L; Liu P; Xiao L; Wu T; Zhou Q; Zheng Q; Liu C; Chan FL; Wu D
Front Endocrinol (Lausanne); 2022; 13():975623. PubMed ID: 36034466
[TBL] [Abstract][Full Text] [Related]
4. A prognostic signature consisting of metabolism-related genes and SLC17A4 serves as a potential biomarker of immunotherapeutic prediction in prostate cancer.
Li H; Gu J; Tian Y; Li S; Zhang H; Dai Z; Wang Z; Zhang N; Peng R
Front Immunol; 2022; 13():982628. PubMed ID: 36325340
[TBL] [Abstract][Full Text] [Related]
5. A machine learning framework develops a DNA replication stress model for predicting clinical outcomes and therapeutic vulnerability in primary prostate cancer.
Huang RH; Hong YK; Du H; Ke WQ; Lin BB; Li YL
J Transl Med; 2023 Jan; 21(1):20. PubMed ID: 36635710
[TBL] [Abstract][Full Text] [Related]
6. Integrated multi-omics analysis and machine learning identify hub genes and potential mechanisms of resistance to immunotherapy in gastric cancer.
Wang J; Feng J; Chen X; Weng Y; Wang T; Wei J; Zhan Y; Peng M
Aging (Albany NY); 2024 Apr; 16(8):7331-7356. PubMed ID: 38656888
[TBL] [Abstract][Full Text] [Related]
7. Multi-omics analysis reveals a macrophage-related marker gene signature for prognostic prediction, immune landscape, genomic heterogeneity, and drug choices in prostate cancer.
Zhu W; Wu J; Huang J; Xiao D; Li F; Wu C; Li X; Zeng H; Zheng J; Lai W; Wen X
Front Immunol; 2023; 14():1122670. PubMed ID: 37122696
[TBL] [Abstract][Full Text] [Related]
8. Single-cell and bulk RNA sequencing reveal cancer-associated fibroblast heterogeneity and a prognostic signature in prostate cancer.
Liu W; Wang M; Wang M; Liu M
Medicine (Baltimore); 2023 Aug; 102(32):e34611. PubMed ID: 37565899
[TBL] [Abstract][Full Text] [Related]
9. Multi-omics identification of an immunogenic cell death-related signature for clear cell renal cell carcinoma in the context of 3P medicine and based on a 101-combination machine learning computational framework.
Liu J; Shi Y; Zhang Y
EPMA J; 2023 Jun; 14(2):275-305. PubMed ID: 37275552
[TBL] [Abstract][Full Text] [Related]
10. Plasma cell subtypes analyzed using artificial intelligence algorithm for predicting biochemical recurrence, immune escape potential, and immunotherapy response of prostate cancer.
Xie X; Dou CX; Luo MR; Zhang K; Liu Y; Zhou JW; Huang ZP; Xue KY; Liang HY; Ouyang AR; Ma SX; Yang JK; Zhou QZ; Guo WB; Liu CD; Zhao SC; Chen MK
Front Immunol; 2022; 13():946209. PubMed ID: 36569837
[TBL] [Abstract][Full Text] [Related]
11. The established chemokine-related prognostic gene signature in prostate cancer: Implications for anti-androgen and immunotherapies.
Chen L; Zheng Y; Jiang C; Yang C; Zhang L; Liang C
Front Immunol; 2022; 13():1009634. PubMed ID: 36275733
[TBL] [Abstract][Full Text] [Related]
12. A TMEFF2-regulated cell cycle derived gene signature is prognostic of recurrence risk in prostate cancer.
Georgescu C; Corbin JM; Thibivilliers S; Webb ZD; Zhao YD; Koster J; Fung KM; Asch AS; Wren JD; Ruiz-Echevarría MJ
BMC Cancer; 2019 May; 19(1):423. PubMed ID: 31060542
[TBL] [Abstract][Full Text] [Related]
13. Integrated analysis of single-cell and bulk RNA sequencing identifies a signature based on macrophage marker genes involved in prostate cancer prognosis and treatment responsiveness.
Li X; Zheng C; Xue X; Wu J; Li F; Song D; Li X
Funct Integr Genomics; 2023 Apr; 23(2):115. PubMed ID: 37010617
[TBL] [Abstract][Full Text] [Related]
14. Identification and validation of cancer-associated fibroblast-related subtypes and the prognosis model of biochemical recurrence in prostate cancer based on single-cell and bulk RNA sequencing.
Li T; Zhou Z; Xie Z; Fan X; Zhang Y; Zhang Y; Song X; Ruan Y
J Cancer Res Clin Oncol; 2023 Oct; 149(13):11379-11395. PubMed ID: 37369799
[TBL] [Abstract][Full Text] [Related]
15. Machine learning-derived identification of prognostic signature for improving prognosis and drug response in patients with ovarian cancer.
Huan Q; Cheng S; Ma HF; Zhao M; Chen Y; Yuan X
J Cell Mol Med; 2024 Jan; 28(1):e18021. PubMed ID: 37994489
[TBL] [Abstract][Full Text] [Related]
16. Use of machine learning-based integration to develop an immune-related signature for improving prognosis in patients with gastric cancer.
Ning J; Sun K; Fan X; Jia K; Meng L; Wang X; Li H; Ma R; Liu S; Li F; Wang X
Sci Rep; 2023 Apr; 13(1):7019. PubMed ID: 37120631
[TBL] [Abstract][Full Text] [Related]
17. MT1G, an emerging ferroptosis-related gene: A novel prognostic biomarker and indicator of immunotherapy sensitivity in prostate cancer.
Cheng B; Lai Y; Huang H; Peng S; Tang C; Chen J; Luo T; Wu J; He H; Wang Q; Huang H
Environ Toxicol; 2024 Feb; 39(2):927-941. PubMed ID: 37972062
[TBL] [Abstract][Full Text] [Related]
18. Immune Microenvironment and Response in Prostate Cancer Using Large Population Cohorts.
Ren X; Chen X; Zhang X; Jiang S; Zhang T; Li G; Lu Z; Zhang D; Wang S; Qin C
Front Immunol; 2021; 12():686809. PubMed ID: 34777331
[TBL] [Abstract][Full Text] [Related]
19. Classification of pyroptosis patterns and construction of a novel prognostic model for prostate cancer based on bulk and single-cell RNA sequencing.
Fu J; Li G; Luo R; Lu Z; Wang Y
Front Endocrinol (Lausanne); 2022; 13():1003594. PubMed ID: 36105400
[TBL] [Abstract][Full Text] [Related]
20. Crafting a Personalized Prognostic Model for Malignant Prostate Cancer Patients Using Risk Gene Signatures Discovered through TCGA-PRAD Mining, Machine Learning, and Single-Cell RNA-Sequencing.
Lyu F; Gao X; Ma M; Xie M; Shang S; Ren X; Liu M; Chen J
Diagnostics (Basel); 2023 Jun; 13(12):. PubMed ID: 37370891
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]