250 related articles for article (PubMed ID: 37575252)
1. Unveiling efferocytosis-related signatures through the integration of single-cell analysis and machine learning: a predictive framework for prognosis and immunotherapy response in hepatocellular carcinoma.
Liu T; Li C; Zhang J; Hu H; Li C
Front Immunol; 2023; 14():1237350. PubMed ID: 37575252
[TBL] [Abstract][Full Text] [Related]
2. Efferocytosis signatures as prognostic markers for revealing immune landscape and predicting immunotherapy response in hepatocellular carcinoma.
Xu K; Liu Y; Luo H; Wang T
Front Pharmacol; 2023; 14():1218244. PubMed ID: 37383726
[No Abstract] [Full Text] [Related]
3. Prognostic signatures of sphingolipids: Understanding the immune landscape and predictive role in immunotherapy response and outcomes of hepatocellular carcinoma.
Zhang X; Zhuge J; Liu J; Xia Z; Wang H; Gao Q; Jiang H; Qu Y; Fan L; Ma J; Tan C; Luo W; Luo Y
Front Immunol; 2023; 14():1153423. PubMed ID: 37006285
[TBL] [Abstract][Full Text] [Related]
4. Prognostic model for hepatocellular carcinoma based on anoikis-related genes: immune landscape analysis and prediction of drug sensitivity.
Zhang D; Liu S; Wu Q; Ma Y; Zhou S; Liu Z; Sun W; Lu Z
Front Med (Lausanne); 2023; 10():1232814. PubMed ID: 37502362
[TBL] [Abstract][Full Text] [Related]
5. The cell death-related genes machine learning model for precise therapy and clinical drug selection in hepatocellular carcinoma.
Du M; Qu Y; Qin L; Zheng J; Sun W
J Cell Mol Med; 2024 Apr; 28(7):e18168. PubMed ID: 38494848
[TBL] [Abstract][Full Text] [Related]
6. T-cell exhaustion signatures characterize the immune landscape and predict HCC prognosis
Chi H; Zhao S; Yang J; Gao X; Peng G; Zhang J; Xie X; Song G; Xu K; Xia Z; Chen S; Zhao J
Front Immunol; 2023; 14():1137025. PubMed ID: 37006257
[TBL] [Abstract][Full Text] [Related]
7. A novel prognostic model based on immunogenic cell death-related genes for improved risk stratification in hepatocellular carcinoma patients.
Liu T; Chen X; Peng B; Liang C; Zhang H; Wang S
J Cancer Res Clin Oncol; 2023 Sep; 149(12):10255-10267. PubMed ID: 37269346
[TBL] [Abstract][Full Text] [Related]
8. Comprehensive analysis and immune landscape of chemokines- and chemokine receptors-based signature in hepatocellular carcinoma.
Zhang Z; Mao M; Wang F; Zhang Y; Shi J; Chang L; Wu X; Zhang Z; Xu P; Lu S
Front Immunol; 2023; 14():1164669. PubMed ID: 37545521
[TBL] [Abstract][Full Text] [Related]
9. Identifying the programmed cell death index of hepatocellular carcinoma for prognosis and therapy response improvement by machine learning: a bioinformatics analysis and experimental validation.
Shi Y; Feng Y; Qiu P; Zhao K; Li X; Deng Z; Wang J
Front Immunol; 2023; 14():1298290. PubMed ID: 38170006
[TBL] [Abstract][Full Text] [Related]
10. Integrated Machine Learning and Bioinformatic Analyses Constructed a Novel Stemness-Related Classifier to Predict Prognosis and Immunotherapy Responses for Hepatocellular Carcinoma Patients.
Chen D; Liu J; Zang L; Xiao T; Zhang X; Li Z; Zhu H; Gao W; Yu X
Int J Biol Sci; 2022; 18(1):360-373. PubMed ID: 34975338
[TBL] [Abstract][Full Text] [Related]
11. Characterization of cancer-related fibroblasts (CAF) in hepatocellular carcinoma and construction of CAF-based risk signature based on single-cell RNA-seq and bulk RNA-seq data.
Yu L; Shen N; Shi Y; Shi X; Fu X; Li S; Zhu B; Yu W; Zhang Y
Front Immunol; 2022; 13():1009789. PubMed ID: 36211448
[TBL] [Abstract][Full Text] [Related]
12. A novel prognostic model for predicting patient survival and immunotherapy responsiveness in hepatocellular carcinoma: insights into the involvement of T-cell proliferation.
Tang S; Sun R; Tang K; Wei X; Liu M; Zhang H
Clin Transl Oncol; 2024 Jun; 26(6):1368-1383. PubMed ID: 38123874
[TBL] [Abstract][Full Text] [Related]
13. Identification of the cuproptosis-related molecular subtypes and an immunotherapy prognostic model in hepatocellular carcinoma.
Zhang L; Xu J; Chu X; Zhang H; Yao X; Zhang J; Guo Y
BMC Bioinformatics; 2022 Nov; 23(1):485. PubMed ID: 36384423
[TBL] [Abstract][Full Text] [Related]
14. A cellular senescence-related classifier based on a tumorigenesis- and immune infiltration-guided strategy can predict prognosis, immunotherapy response, and candidate drugs in hepatocellular carcinoma.
Luo Y; Liu H; Fu H; Ding GS; Teng F
Front Immunol; 2022; 13():974377. PubMed ID: 36458010
[TBL] [Abstract][Full Text] [Related]
15. A T-cell-related signature for prognostic stratification and immunotherapy response in hepatocellular carcinoma based on transcriptomics and single-cell sequencing.
Chen X; Peng C; Chen Y; Ding B; Liu S; Song Y; Li Y; Sun B; Yang R
BMC Bioinformatics; 2023 May; 24(1):216. PubMed ID: 37231356
[TBL] [Abstract][Full Text] [Related]
16. A Differentiation-Related Gene Prognostic Index Contributes to Prognosis and Immunotherapy Evaluation in Patients with Hepatocellular Carcinoma.
Xiao J; Liu T; Liu Z; Xiao C; Du J; Zuo S; Li H; Gu H
Cells; 2022 Jul; 11(15):. PubMed ID: 35892599
[TBL] [Abstract][Full Text] [Related]
17. Multi-omic analysis of glycolytic signatures: exploring the predictive significance of heterogeneity and stemness in immunotherapy response and outcomes in hepatocellular carcinoma.
Zhang S; Pei Y; Zhu F
Front Mol Biosci; 2023; 10():1210111. PubMed ID: 37351550
[No Abstract] [Full Text] [Related]
18. Machine learning-based prognostic modeling of lysosome-related genes for predicting prognosis and immune status of patients with hepatocellular carcinoma.
Li W; Wang Q; Lu J; Zhao B; Geng Y; Wu X; Chen X
Front Immunol; 2023; 14():1169256. PubMed ID: 37275878
[TBL] [Abstract][Full Text] [Related]
19. Potential biomarkers for the prognosis and treatment of HCC immunotherapy.
Li H; He Q; Zhou GM; Wang WJ; Shi PP; Wang ZH
Eur Rev Med Pharmacol Sci; 2023 Mar; 27(5):2027-2046. PubMed ID: 36930502
[TBL] [Abstract][Full Text] [Related]
20. Machine learning-based integration develops a neutrophil-derived signature for improving outcomes in hepatocellular carcinoma.
Gong Q; Chen X; Liu F; Cao Y
Front Immunol; 2023; 14():1216585. PubMed ID: 37575244
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
