Hualin Chen
Soochow University (Suzhou)
18 Papers
7 Citations
Hualin Chen is an academic researcher from Soochow University (Suzhou). The author has contributed to research in topics: Medicine & Biology. The author has an hindex of 3, co-authored 5 publications.
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Papers
Crosstalk between Mesenchymal Stem Cells and Cancer Stem Cells Reveals a Novel Stemness-Related Signature to Predict Prognosis and Immunotherapy Responses for Bladder Cancer Patients
TL;DR: In this article , a stemness-related signature (Stem. Sig) derived from mesenchymal stem cells and cancer stem cells (CSCs) was developed by analyzing the communication network and gene regulatory network.
PLAGL2 promotes bladder cancer progression via RACGAP1/RhoA GTPase/YAP1 signaling
TL;DR: Findings suggest that PLAGL2 promotes BCa progression via RACGAP1/RhoA GTPase/YAP1 signaling Hence, the core nodes of signaling may be promising therapeutic targets for BCa.
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Machine learning-based identification of tumor-infiltrating immune cell-associated model with appealing implications in improving prognosis and immunotherapy response in bladder cancer patients
TL;DR: In this paper , a 10-fold cross-validation framework with 101 combinations of 10 machine-learning algorithms was employed to develop a consensus immune cell infiltration-related signature (IRS).
The Value of Serum Exosomal miR-184 in the Diagnosis of NSCLC
Shujun Li,Yanming Lin,Yanxia Wu,Hualin Chen,Zhong Huang,M. Lin,Jiali Dong,Yongcun Wang,Zhixiong Yang +8 more
TL;DR: The expression level of miR-184 in serum exosomes of NSCLC patients is significantly increased, which has a certain value for the differential diagnosis of the nature of benign and malignant lung diseases and is closely related to the prognosis of patients.
Machine learning to improve prognosis prediction of metastatic clear-cell renal cell carcinoma treated with cytoreductive nephrectomy and systemic therapy
Wenjie Yang,Lin Ma,Jie Dong,Mengchao Wei,Ruoyu Ji,Hualin Chen,Xiaoqiang Xue,Yingjie Li,Zhaoheng Jin,Weifeng Xu,Zhigang Ji +10 more
TL;DR: Wang et al. as mentioned in this paper used four ML models, i.e., a gradient boosting machine (GBM), support vector machine (SVM), random forest (RF), and logistic regression (LR), to predict the cancer-specific survival rate (CSS) at 1, 3, and 5 years.
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