PLAGL2-EGFR-HIF-1/2α Signaling Loop Promotes HCC Progression and Erlotinib Insensitivity.

BACKGROUND AND AIMS: Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related deaths worldwide, hence a major public health threat. Pleomorphic adenoma gene like-2 (PLAGL2) has been reported to play a role in tumorigenesis. However, its precise function in HCC remains poorly understood. APPROACH AND RESULTS: In this study, we demonstrated that PLAGL2 was up-regulated in HCC compared with that of adjacent nontumorous tissues and also correlated with overall survival times. We further showed that PLAGL2 promoted HCC cell proliferation, migration, and invasion both in vitro and in vivo. PLAGL2 expression was positively correlated with epidermal growth factor receptor (EGFR) expression. Mechanistically, this study demonstrated that PLAGL2 functions as a transcriptional regulator of EGFR and promotes HCC cell proliferation, migration, and invasion through the EGFR-AKT pathway. Moreover, hypoxia was found to significantly induce high expression of PLAGL2, which promoted hypoxia inducible factor 1/2 alpha subunit (HIF1/2A) expression through EGFR. Therefore, this study demonstrated that a PLAGL2-EGFR-HIF1/2A signaling loop promotes HCC progression. More importantly, PLAGL2 expression reduced hepatoma cells' response to the anti-EGFR drug erlotinib. PLAGL2 knockdown enhanced the response to erlotinib. CONCLUSIONS: This study reveals the pivotal role of PLAGL2 in HCC cell proliferation, metastasis, and erlotinib insensitivity. This suggests that PLAGL2 can be a potential therapeutic target of HCC.

EFNB1 Acts as a Novel Prognosis Marker in Glioblastoma through Bioinformatics Methods and Experimental Validation.

PURPOSE: Ephrin B1 (EFNB1), the Eph-associated receptor tyrosine kinase ligand, is suggested to have an important function in neurodevelopment. However, its contribution to glioblastoma multiforme (GBM) remains uncertain. This study aimed to determine the prognostic power and immune implication of EFNB1 in GBM. METHODS: We first identified differentially coexpressed genes within GBM relative to noncarcinoma samples from GEO and TCGA databases by WGCNA. The STRING online database and the maximum cluster centrality (MCC) algorithm in Cytoscape software were used to design for predicting protein-protein interactions (PPI) and calculating pivot nodes, respectively. The expression of hub genes in cancer and noncancer tissues was verified by an online tool gene expression profile interactive analysis (GEPIA). Thereafter, the TISIDB online tool with Cox correlation regression method was employed to screen for immunomodulators associated with EFNB1 and to model the risk associated with immunomodulators. RESULTS: Altogether 201 differentially expressed genes (DEGs) were discovered. After that, 10 hub genes (CALB2, EFNB1, ENO2, EPHB4, NES, OBSCN, RAB9B, RPL23A, STMN2, and THY1) were incorporated to construct the PPI network. As revealed by survival analysis, EFNB1 upregulation predicted poor overall survival (OS) for GBM cases. Furthermore, we developed a prognostic risk signature according to the EFNB1-associated immunomodulators. Kaplan-Meier survival analysis and receiver operating characteristic method were adopted for analysis, which revealed that our signature showed favorable accuracy of prognosis prediction. Finally, EFNB1 inhibition was found to block cell proliferation and migration in GBM cells. CONCLUSION: The above results indicate that EFNB1 participates in cancer immunity and progression, which is the candidate biomarker for GBM.