Yuling Sun

Dr. Yuling Sun

orcid

Research Focus & Metrics

Academic Career Length

17Years (2010-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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10 September 2019 in Journal of Cellular and Molecular Medicine
Verified
Journal Article
Open access
PDF file
TBTao Bai
Tao Bai
PLPengxu Lei
Pengxu Lei
HZHao Zhou
Hao Zhou
RLRuopeng Liang
Ruopeng Liang
RZRongtao Zhu
Rongtao Zhu
YSYuling Sun
Yuling Sun
Sigma‐1 receptor (S1R) regulates reactive oxygen species (ROS) accumulation via nuclear factor erythroid 2‐related factor 2 (NRF2), which plays a vital role in ferroptosis. Sorafenib is a strong inducer of ferroptosis but not of apoptosis. However, the mechanism of sorafenib‐induced ferroptosis in hepatocellular carcinoma (HCC) remains unclear. In this study, we found for the first time that sorafenib induced most of S1Rs away from nucleus compared to control groups in Huh‐7 cells, and ferrostatin‐1 completely blocked the translocation. S1R protein expression, but not mRNA expression, in HCC cells was significantly up‐regulated by sorafenib. Knockdown of NRF2, but not of p53 or hypoxia‐inducible factor 1‐alpha (HIF1α), markedly induced S1R mRNA expression in HCC cells. Inhibition of S1R (by RNAi or antagonists) increased sorafenib‐induced HCC cell death in vitro and in vivo. Knockdown of S1R blocked the expression of glutathione peroxidase 4 (GPX4), one of the core targets of ferroptosis, in vitro and in vivo. Iron metabolism and lipid peroxidation increased in the S1R knockdown groups treated with sorafenib compared to the control counterpart. Ferritin heavy chain 1 (FTH1) and transferrin receotor protein 1 (TFR1), both of which are critical for iron metabolism, were markedly up‐regulated in HCC cells treated with erastin and sorafenib, whereas knockdown of S1R inhibited these increases. In conclusion, we demonstrate that S1R protects HCC cells against sorafenib and subsequent ferroptosis. A better understanding of the role of S1R in ferroptosis may provide novel insight into this biological process.

12 December 2017 in BioMed Research International
Verified
Journal Article
Open access
PDF file
SWShuai Wang
Shuai Wang
SHShuai Huang
Shuai Huang
YSYuling Sun
Yuling Sun
Pancreatic ductal adenocarcinoma (PDAC) is one of the most aggressive solid malignancies and is characterized by its insensitivity to current therapy. The invasion and metastasis of solid tumors such as PDAC are complex processes involving many factors. Recent insights into the role of cancer stem cells (CSCs) and the epithelial-mesenchymal transition (EMT) in tumorigenesis have increased the knowledge base and highlighted new therapeutic targets of this disease. The process of EMT is regulated by a complex network of cytokines, transcription factors, growth factors, signaling pathways, and the tumor microenvironment, exhibiting CSC-like properties. The transition of solid cancer cells from an epithelial to a mesenchymal phenotype increases their migratory and invasive properties, thus promoting metastasis. In PDAC, the exact influence of EMT on the biological behaviors of cancer cells and its impact on clinical therapy remain controversial, but the therapeutic strategy of combining EMT inhibition with chemotherapy deserves attention. Alternatively, anti-inflammatory therapy that targets the interaction between inflammation and EMT is a valid strategy for treating the premalignant stage of tumor progression. In this review, we summarize the latest research on EMT and the potential relationship between EMT and PDAC.

25 October 2022 in eLife
Verified
Journal Article
Open access
LWLibo Wang
Libo Wang
ZLZaoqu Liu
Zaoqu Liu
RLRuopeng Liang
Ruopeng Liang
WWWeijie Wang
Weijie Wang
RZRongtao Zhu
Rongtao Zhu
YSYuling Sun
Yuling Sun
As the most aggressive tumor, the outcome of pancreatic cancer (PACA) has not improved observably over the last decade. Anatomy-based TNM staging does not exactly identify treatment-sensitive patients, and an ideal biomarker is urgently needed for precision medicine. Based on expression files of 1280 patients from 10 multicenter cohorts, we screened 32 consensus prognostic genes. Ten machine-learning algorithms were transformed into 76 combinations, of which we selected the optimal algorithm to construct an artificial intelligence-derived prognostic signature (AIDPS) according to the average C-index in the nine testing cohorts. The results of the training cohort, nine testing cohorts, Meta-Cohort, and three external validation cohorts (290 patients) consistently indicated that AIDPS could accurately predict the prognosis of PACA. After incorporating several vital clinicopathological features and 86 published signatures, AIDPS exhibited robust and dramatically superior predictive capability. Moreover, in other prevalent digestive system tumors, the nine-gene AIDPS could still accurately stratify the prognosis. Of note, our AIDPS had important clinical implications for PACA, and patients with low AIDPS owned a dismal prognosis, higher genomic alterations, and denser immune cell infiltrates as well as were more sensitive to immunotherapy. Meanwhile, the high AIDPS group possessed observably prolonged survival, and panobinostat may be a potential agent for patients with high AIDPS. Overall, our study provides an attractive tool to further guide the clinical management and individualized treatment of PACA.

21 January 2020 in Journal of Cellular Physiology
Verified
Journal Article
Closed access
PDF file
TBTao Bai
Tao Bai
RLRuopeng Liang
Ruopeng Liang
RZRongtao Zhu
Rongtao Zhu
WWWeijie Wang
Weijie Wang
LZLin Zhou
Lin Zhou
YSYuling Sun
Yuling Sun

1 September 2017 in Biochemical and Biophysical Research Communications
Verified
Journal Article
Closed access
TBTAO BAI
TAO BAI
SWShuai Wang
Shuai Wang
YZYipu Zhao
Yipu Zhao
RZRongtao Zhu
Rongtao Zhu
WWWeijie Wang
Weijie Wang
YSYuling Sun
Yuling Sun

22 July 2015 in Alimentary Pharmacology & Therapeutics
Verified
Journal Article
Closed access
PDF file
YZY.‐P. Zhang
Y.‐P. Zhang
WLW.‐Q. Li
W.‐Q. Li
YSYuling Sun
Yuling Sun
RZR.‐T. Zhu
R.‐T. Zhu
WWW.‐J. Wang
W.‐J. Wang