Bin Zeng

Prof. Dr. Bin Zeng


Research Focus & Metrics

Academic Career Length

5Years (2022-Present)

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

Representative Publications

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13 January 2023 in Pharmacological Research
Verified
Journal Article
Open access
BZBin Zeng
Bin Zeng
YCYuting Chen
Yuting Chen
HCHao Chen
Hao Chen
QZQiting Zhao
Qiting Zhao
ZSZhiwei Sun
Zhiwei Sun
HXH.Rosie Xing
H.Rosie Xing
Despite the unprecedented advancement of cancer treatment, the prognosis for patients with metastatic stage of cancer remains poor. The challenge that underlines this clinical dilemma is the complexity of metastasis. The conventional experiment-driven discovery approaches (the "wet lab") yield overly simplified one-to-one mechanistic relationships that are inept of elucidating the complexity of metastasis. Metastasis research also suffers from the knowledge and skill deficiency of the individual investigators. The importance of the present study is the demonstration that the "dry-lab-driven discovery and wet-lab validation" approach can improve the efficiency of studying complex biological behaviors, and can yield more reliable, objective and comprehensive mechanistic findings that are have clinical significance. Specifically, we applied this approach to study the mechanisms that underline the involvement of exosomal miRNAs in transferring the metastatic capability between heterogenous melanoma cancer cells. We show that the highly metastatic melanoma tumor cells (POL) can transfer their metastatic competency to the low-metastatic melanoma tumor cells (OL) by exosomal miR-211-5p. The oncogenic activity of miR-211-5p is mediated by the target gene guanine nucleotide-binding protein subunit alpha-15 (GNA15) through modifying the immune function of the tumor microenvironment extrinsically; as well as through inhibiting pyroptosis and augmenting glycolysis within OL cells intrinsically. In addition, we show that exosomal sorting of miR-211-5p is like selective and is subjected to regulation by a transcriptional feedback loop between miR-211-5p and zinc finger FYVE-type containing 26 (ZFYVE26). Furthermore, the "8-genes pyroptosis Risk model" derived from LASSO regression analysis was verified as an independent prognostic factor for melanoma.

1 October 2022 in Biological Research
Verified
Journal Article
Open access
PDF file
XLXiaoshuang Li
Xiaoshuang Li
DLDoudou Liu
Doudou Liu
HCHao Chen
Hao Chen
BZBin Zeng
Bin Zeng
QZQiting Zhao
Qiting Zhao
HXH.Rosie Xing
H.Rosie Xing
Background: Metastatic melanoma has a high mortality rate and poor survival. This is associated with efficient metastatic colonization, but the underlying mechanisms remain elusive. Communication between cancer stem cells (CSCs) and cancer cells plays an important role in metastatic dissemination. Whether cancer stem cells can alter the metastatic properties of non-CSC cells; and whether exosomal crosstalk can mediate such interaction, have not been demonstrated in melanoma prior to this report. Results: The results revealed that exosomes secreted by highly metastatic melanoma CSCs (OL-SCs) promoted the invasiveness of the low metastatic melanoma cells (OL) and accelerated metastatic progression. miR-1268a was up-regulated in cells and exosomes of OL-SCs. Moreover, OL-SCs-derived exosomal miR-1268a, upon taking up by OL cells, promoted the metastatic colonization ability of OL cells in vitro and in vivo. In addition, the pro-metastatic activity of exosomal miR-1268a is achieved through inhibition of autophagy. Conclusion: Our study demonstrates that OL cells can acquire the “metastatic ability” from OL-SCs cells. OL-SCs cells achieves this goal by utilizing its exosomes to deliver functional miRNAs, such as miR-1268a, to the targeted OL cells which in turn augments metastatic colonization by inactivating the autophagy pathway in OL cells.

2 July 2025 in Scientific Reports
Verified
Journal Article
Open access
PDF file
BZBin Zeng
Bin Zeng
XLXiaoqin Liu
Xiaoqin Liu
JLJun Liu
Jun Liu
HQHongbo Qiu
Hongbo Qiu
TZTianwei Zhang
Tianwei Zhang
LWLin Wang
Lin Wang
Esophageal cancer is a highly lethal malignancy with high incidence and mortality rates, which continue to pose a significant threat to public health worldwide. Despite the adoption of multidisciplinary treatments, improving long-term survival rates remains a major challenge. Therefore, conducting in-depth research into the molecular mechanisms of esophageal cancer, identifying predictive biomarkers, and developing personalized treatment and monitoring strategies are crucial to enhancing clinical outcomes and patient prognosis. This study utilized advanced bioinformatics techniques to construct a gene co-expression network for esophageal cancer and compared it with sequencing data from real-world esophageal cancer samples. This approach successfully identified four key genes closely associated with the progression of esophageal cancer: MARCKSL1, MCM6, RFC4, and PLAU. A risk assessment model based on these genes demonstrated high predictive accuracy, particularly in assessing the risk of early-stage esophageal cancer, highlighting its significant potential for clinical application. Functional validation experiments and clinical data analysis further revealed that these genes play critical roles in esophageal cancer cell metastasis, glycolysis, and response to radiation therapy. These findings provide a new molecular basis for radiotherapy prognosis, helping to formulate more precise radiotherapy protocols and optimize treatment outcomes. Additionally, the regulatory networks of these genes revealed a complex molecular regulatory mechanism, offering new perspectives for precision treatment in esophageal cancer. In summary, these genes not only offer new insights into the early diagnosis and personalized treatment of esophageal cancer but also improve patient prognosis by correlating with radiotherapy response, thus laying a solid scientific foundation for future advancements.

14 March 2024 in Frontiers in Oncology
Verified
Journal Article
Open access
PDF file
TZTianwei Zhang
Tianwei Zhang
BZBin Zeng
Bin Zeng
KLKe Liu
Ke Liu
QZQin Zeng
Qin Zeng
NWNa Wang
Na Wang
LWLin Wang
Lin Wang
Tendon Sheath Giant Cell Tumor (TGCT) is a benign tumor that primarily grows within joints and bursae. However, it has a high postoperative recurrence rate, ranging from 15% to 45%. Although radiotherapy may reduce this recurrence rate, its applicability as a standard treatment is still controversial. Furthermore, the pathogenic mechanisms of TGCT are not clear, which limits the development of effective treatment methods. The unpredictable growth and high recurrence rate of TGCT adds to the challenges of disease management. Currently, our understanding of TGCT mainly depends on pathological slice analysis due to a lack of stable cell models. In this study, we first reviewed the medical records of two female TGCT patients who had undergone radiotherapy. Then, by combining bioinformatics and machine learning, we interpreted the pathogenesis of TGCT and its associations with other diseases from multiple perspectives. Based on a deep analysis of the case data, we provided empirical support for postoperative radiotherapy in TGCT patients. Additionally, our further analysis revealed the signaling pathways of differentially expressed genes in TGCT, as well as its potential associations with osteoarthritis and synovial sarcomas.