更新于：2024-11-01

PDCD2

更新于：2024-11-01

基本信息

别名

PDCD2、programmed cell death 2、Programmed cell death protein 2

+ [4]

简介

May be a DNA-binding protein with a regulatory function. May play an important role in cell death and/or in regulation of cell proliferation.

关联

项与 PDCD2 相关的药物

XJZ-25

靶点

PDCD2

作用机制

PDCD2 inhibitors

在研机构-

原研机构-

在研适应症-

非在研适应症-

最高研发阶段-

首次获批国家/地区-

首次获批日期1800-01-20

100 项与 PDCD2 相关的临床结果

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100 项与 PDCD2 相关的转化医学

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0 项与 PDCD2 相关的专利（医药）

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项与 PDCD2 相关的文献（医药）

2024-09-01·Genes & Diseases

PDCD2 as a prognostic biomarker in glioma correlates with malignant phenotype

Article

作者: Cheng, Xing ; Zhou, Xiangyi ; Zhou, Li ; Hao, Jiaqi ; Zhao, Yi ; Dai, Fengsheng ; Tian, Rui ; Xiang, Tingxiu ; Yuan, Yixiao

Programmed cell death 2 (PDCD2) is related to cancer progression and chemotherapy sensitivity. The role of PDCD2 in solid cancers (excluding hematopoietic malignancies) and their diagnosis and prognosis remains unclear. The TCGA, CGGA, GEPIA, cBioPortal, and GTEx databases were analyzed for expression, prognostic value, and genetic modifications of PDCD2 in cancer patients. Functional enrichment analysis, CCK8, colony formation assay, transwell assay, and xenograft tumor model were undertaken to study the PDCD2's biological function in glioma (GBMLGG). The PDCD2 gene was associated with solid cancer progression. In the functional enrichment analysis results, PDCD2 was shown to participate in several important GBMLGG biological processes. GBMLGG cells may be inhibited in their proliferation, migration, invasion, and xenograft tumor growth by knocking down PDCD2. Our research can provide new insights into solid cancer prognostic biomarkers of PDCD2.

2023-10-23·Angewandte Chemie International Edition

Proteomics‐Based Discovery of First‐in‐Class Chemical Probes for Programmed Cell Death Protein 2 (PDCD2)

Article

作者: Che, Jianwei ; Fischer, Eric S. ; Gray, Nathanael S. ; Ji, Wenzhi ; Zhu, Xijun ; Yuan, Linjie ; Byun, Woong Sub ; Zhang, Tinghu ; Abulaiti, Xianmixinuer ; Donovan, Katherine A. ; Corsello, Steven M. ; Dwyer, Brendan G. ; Lu, Wenchao

AbstractChemical probes are essential tools for understanding biological systems and for credentialing potential biomedical targets. Programmed cell death 2 (PDCD2) is a member of the B‐cell lymphoma 2 (Bcl‐2) family of proteins, which are critical regulators of apoptosis. Here we report the discovery and characterization of 10 e, a first‐in‐class small molecule degrader of PDCD2. We discovered this PDCD2 degrader by serendipity using a chemical proteomics approach, in contrast to the conventional approach for making bivalent degraders starting from a known binding ligand targeting the protein of interest. Using 10 e as a pharmacological probe, we demonstrate that PDCD2 functions as a critical regulator of cell growth by modulating the progression of the cell cycle in T lymphoblasts. Our work provides a useful pharmacological probe for investigating PDCD2 function and highlights the use of chemical proteomics to discover selective small molecule degraders of unanticipated targets.

2023-06-19·Aging

Significance of liquid-liquid phase separation (LLPS)-related genes in breast cancer: a multi-omics analysis.

Article

作者: Xie, Jiaheng ; Wang, Ming ; Ou, Mengmeng ; Wu, Dan ; Pei, Shengbin ; Zhu, Zhechen ; Chen, Liang ; Shi, Jingping ; Ruan, Shujie ; Tang, Qikai ; Wang, Yue ; Liu, Shengxuan

Currently, the role of liquid-liquid phase separation (LLPS) in cancer has been preliminarily explained. However, the significance of LLPS in breast cancer is unclear. In this study, single cell sequencing datasets GSE188600 and GSE198745 for breast cancer were downloaded from the GEO database. Transcriptome sequencing data for breast cancer were downloaded from UCSC database. We divided breast cancer cells into high-LLPS group and low-LLPS group by down dimension clustering analysis of single-cell sequencing data set, and obtained differentially expressed genes between the two groups. Subsequently, weighted co-expression network analysis (WGCNA) was performed on transcriptome sequencing data, and the module genes most associated with LLPS were obtained. COX regression and Lasso regression were performed and the prognostic model was constructed. Subsequently, survival analysis, principal component analysis, clinical correlation analysis, and nomogram construction were used to evaluate the significance of the prognostic model. Finally, cell experiments were used to verify the function of the model's key gene, PGAM1. We constructed a LLPS-related prognosis model consisting of nine genes: POLR3GL, PLAT, NDRG1, HMGB3, HSPH1, PSMD7, PDCD2, NONO and PGAM1. By calculating LLPS-related risk scores, breast cancer patients could be divided into high-risk and low-risk groups, with the high-risk group having a significantly worse prognosis. Cell experiments showed that the activity, proliferation, invasion and healing ability of breast cancer cell lines were significantly decreased after knockdown of the key gene PGAM1 in the model. Our study provides a new idea for prognostic stratification of breast cancer and provides a novel marker: PGAM1.