EGR1 suppresses HCC growth and aerobic glycolysis by transcriptionally downregulating PFKL.

BACKGROUND: Hepatocellular Carcinoma (HCC) is a matter of great global public health importance; however, its current therapeutic effectiveness is deemed inadequate, and the range of therapeutic targets is limited. The aim of this study was to identify early growth response 1 (EGR1) as a transcription factor target in HCC and to explore its role and assess the potential of gene therapy utilizing EGR1 for the management of HCC. METHODS: In this study, both in vitro and in vivo assays were employed to examine the impact of EGR1 on the growth of HCC. The mouse HCC model and human organoid assay were utilized to assess the potential of EGR1 as a gene therapy for HCC. Additionally, the molecular mechanism underlying the regulation of gene expression and the suppression of HCC growth by EGR1 was investigated. RESULTS: The results of our investigation revealed a notable decrease in the expression of EGR1 in HCC. The decrease in EGR1 expression promoted the multiplication of HCC cells and the growth of xenografted tumors. On the other hand, the excessive expression of EGR1 hindered the proliferation of HCC cells and repressed the development of xenografted tumors. Furthermore, the efficacy of EGR1 gene therapy was validated using in vivo mouse HCC models and in vitro human hepatoma organoid models, thereby providing additional substantiation for the anti-cancer role of EGR1 in HCC. The mechanistic analysis demonstrated that EGR1 interacted with the promoter region of phosphofructokinase-1, liver type (PFKL), leading to the repression of PFKL gene expression and consequent inhibition of PFKL-mediated aerobic glycolysis. Moreover, the sensitivity of HCC cells and xenografted tumors to sorafenib was found to be increased by EGR1. CONCLUSION: Our findings suggest that EGR1 possesses therapeutic potential as a tumor suppressor gene in HCC, and that EGR1 gene therapy may offer benefits for HCC patients.

RAD51 Inhibition Shows Antitumor Activity in Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC), the major type of liver cancer, causes a high annual mortality worldwide. RAD51 is the critical recombinase responsible for homologous recombination (HR) repair in DNA damage. In this study, we identified that RAD51 was upregulated in HCC and that RAD51 silencing or inhibition reduced the proliferation, migration, and invasion of HCC cells and enhanced cell apoptosis and DNA damage. HCC cells with the combinatorial treatments of RAD51 siRNA or inhibitor and sorafenib demonstrated a synergistic effect in inhibiting HCC cell proliferation, migration, and invasion, as well as inducing cell apoptosis and DNA damage. Single RAD51 silencing or sorafenib reduced RAD51 protein expression and weakened HR efficiency, and their combination almost eliminated RAD51 protein expression and inhibited HR efficiency further. An in vivo tumor model confirmed the RAD51 inhibitor's antitumor activity and synergistic antitumor activity with sorafenib in HCC. RNA-Seq and gene set enrichment analysis (GSEA) in RAD51-inactivated Huh7 cells indicated that RAD51 knockdown upregulated cell apoptosis and G1/S DNA damage checkpoint pathways while downregulating mitotic spindle and homologous recombination pathways. Our findings suggest that RAD51 inhibition exhibits antitumor activities in HCC and synergizes with sorafenib. Targeting RAD51 may provide a novel therapeutic approach in HCC.

PLEKHG5 is stabilized by HDAC2-related deacetylation and confers sorafenib resistance in hepatocellular carcinoma.

Sorafenib is the first FDA-approved first-line targeted drug for advanced HCC. However, resistance to sorafenib is frequently observed in clinical practice, and the molecular mechanism remains largely unknown. Here, we found that PLEKHG5 (pleckstrin homology and RhoGEF domain containing G5), a RhoGEF, was highly upregulated in sorafenib-resistant cells. PLEKHG5 overexpression activated Rac1/AKT/NF-κB signaling and reduced sensitivity to sorafenib in HCC cells, while knockdown of PLEKHG5 increased sorafenib sensitivity. The increased PLEKHG5 was related to its acetylation level and protein stability. Histone deacetylase 2 (HDAC2) was found to directly interact with PLEKHG5 to deacetylate its lysine sites within the PH domain and consequently maintain its stability. Moreover, knockout of HDAC2 (HDAC2 KO) or selective HDAC2 inhibition reduced PLEKHG5 protein levels and thereby enhanced the sensitivity of HCC to sorafenib in vitro and in vivo, while overexpression of PLEKHG5 in HDAC2 KO cells reduced the sensitivity to sorafenib. Our work showed a novel mechanism: HDAC2-mediated PLEKHG5 posttranslational modification maintains sorafenib resistance. This is a proof-of-concept study on targeting HDAC2 and PLEKHG5 in sorafenib-treated HCC patients as a new pharmaceutical intervention for advanced HCC.

Artificial intelligence with a deep learning network for the quantification and distinction of functional adrenal tumors based on contrast-enhanced CT images.

Background: Functional adrenal tumors (FATs) are mainly diagnosed by biochemical analysis. Traditional imaging tests have limitations and cannot be used alone to diagnose FATs. In this study, we aimed to establish an artificially intelligent diagnostic model based on computed tomography (CT) images to distinguish different types of FATs. Methods: A cohort study of 375 patients diagnosed with hyperaldosteronism (HA), Cushing's syndrome (CS), and pheochromocytoma in our center between March 2015 and June 2020 was conducted. Retrospectively, patients were randomly divided into three data sets: the training set (270 cases), the testing set (60 cases), and the retrospective trial set (45 cases). An artificially intelligent diagnostic model based on CT images was established by transferring data from the training set into the deep learning network. The testing set was then used to evaluate the accuracy of the model compared to that of physicians' judgments. The retrospective trial set was used to evaluate the quantification and distinction performance. Results: The deep learning model achieved an average area under the receiver operating characteristic (ROC) curve (AUC) of 0.915, and the AUCs in all three FAT types were greater than 0.882. The AUC of the model tested on the retrospective dataset reached above 0.849. In the quantitative evaluation of tumor lesion area recognition, the diagnostic model also obtained a segmentation Dice coefficient of 0.69. With the help of the proposed model, clinicians reached 92.5% accuracy in distinguishing FATs, compared to 80.6% accuracy when using only their judgment (P<0.05). Conclusions: The result of our study shows that the diagnostic model based on a deep learning network can distinguish and quantify three common FAT types based on texture features of contrast-enhanced CT images. The model can quantify and distinguish functional tumors without any endocrine tests and can assist clinicians in the diagnostic procedure.

Traditional Chinese Medicine in Cancer Care: An Overview of 5834 Randomized Controlled Trials Published in Chinese.

BACKGROUND: Traditional Chinese medicine (TCM) is widely integrated into cancer care in China. An overview in 2011 identified 2384 randomized and non-randomized controlled trials (RCTs, non-RCTs) on TCM for cancer published in the Chinese literature. This article summarizes updated evidence of RCTs on TCM for cancer care. METHODS: We searched 4 main Chinese databases: China National Knowledge Infrastructure, Chinese Scientific Journal Database, SinoMed, and Wanfang. RCTs on TCM used in cancer care were analyzed in this bibliometric study. RESULTS: Of 5834 RCTs (477 157 cancer patients), only 62 RCTs were indexed in MEDLINE. The top 3 cancers treated were lung, stomach, and breast cancer. About 4752 RCTs (81.45%) tested TCM combined with conventional treatment, and 1082 RCTs (18.55%) used TCM alone for treating symptoms and side-effects. Herbal medicine was the most frequently used TCM modality (5087 RCTs; 87.20%). The most frequently reported outcome was symptom improvement (3712 RCTs; 63.63%) followed by quality of life (2725 RCTs; 46.71%), and biomarkers (2384 RCTs; 40.86%). The majority of RCTs (4051; 69.44%) concluded there were beneficial effects using either TCM alone or TCM plus conventional treatment compared with conventional treatment. CONCLUSION: Substantial randomized trials demonstrated different types/stages of cancer were treated by various TCM modalities, alone or in combination with conventional medicine. Further evaluation on the effects and safety of TCM modalities focusing on outcomes such as quality of life is required.