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Yuyong Jiang

Capital Medical University

ORCID: 0000-0002-6082-1180

Publishes on Liver Disease Diagnosis and Treatment, Hepatitis B Virus Studies, Hepatocellular Carcinoma Treatment and Prognosis. 76 papers and 2k citations.

76Publications
2kTotal Citations

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Top publicationsby citations

T-cell exhaustion in the tumor microenvironment
Yuyong Jiang, Yongsheng Li, Bo Zhu|Cell Death and Disease|2015
Cited by 1.1kOpen Access

T-cell exhaustion was originally identified during chronic infection in mice, and was subsequently observed in humans with cancer. The exhausted T cells in the tumor microenvironment show overexpressed inhibitory receptors, decreased effector cytokine production and cytolytic activity, leading to the failure of cancer elimination. Restoring exhausted T cells represents an inspiring strategy for cancer treatment, which has yielded promising results and become a significant breakthrough in the cancer immunotherapy. In this review, we overview the updated understanding on the exhausted T cells in cancer and their potential regulatory mechanisms and discuss current therapeutic interventions targeting exhausted T cells in clinical trials.

A prognostic nomogram based on LASSO Cox regression in patients with alpha-fetoprotein-negative hepatocellular carcinoma following non-surgical therapy
Dongdong Zhou, Xiaoli Liu, Xinhui Wang et al.|BMC Cancer|2021
Cited by 58Open Access

BACKGROUND: Alpha-fetoprotein-negative hepatocellular carcinoma (AFP-NHCC) (< 8.78 ng/mL) have special clinicopathologic characteristics and prognosis. The aim of this study was to apply a new method to establish and validate a new model for predicting the prognosis of patients with AFP-NHCC. METHODS: A total of 410 AFP-negative patients with clinical diagnosed with HCC following non-surgical therapy as a primary cohort; 148 patients with AFP-NHCC following non-surgical therapy as an independent validation cohort. In primary cohort, independent factors for overall survival (OS) by LASSO Cox regression were all contained into the nomogram1; by Forward Stepwise Cox regression were all contained into the nomogram2. Nomograms performance and discriminative power were assessed with concordance index (C-index) values, area under curve (AUC), Calibration curve and decision curve analyses (DCA). The results were validated in the validation cohort. RESULTS: The C-index of nomogram1was 0.708 (95%CI: 0.673-0.743), which was superior to nomogram2 (0.706) and traditional modes (0.606-0.629). The AUC of nomogram1 was 0.736 (95%CI: 0.690-0.778). In the validation cohort, the nomogram1 still gave good discrimination (C-index: 0.752, 95%CI: 0.691-0.813; AUC: 0.784, 95%CI: 0.709-0.847). The calibration curve for probability of OS showed good homogeneity between prediction by nomogram1 and actual observation. DCA demonstrated that nomogram1 was clinically useful. Moreover, patients were divided into three distinct risk groups for OS by the nomogram1: low-risk group, middle-risk group and high-risk group, respectively. CONCLUSIONS: Novel nomogram based on LASSO Cox regression presents more accurate and useful prognostic prediction for patients with AFP-NHCC following non-surgical therapy. This model could help patients with AFP-NHCC following non-surgical therapy facilitate a personalized prognostic evaluation.