Overexpression of EPS8L3 promotes cell proliferation by inhibiting the transactivity of FOXO1 in HCC

Background: Hepatocellular carcinoma (HCC) is a malignant tumor with a high rate of recurrence and m metastasis that does not respond well to current therapies and has a very poor prognosis. Disulfidptosis is a novel mode of cell death that has been analyzed as a novel therapeutic target for HCC cells.Methods: This study integrated bulk ribonucleic acid (RNA) sequencing datasets, spatial transcriptomics (ST), and single-cell RNA sequencing to explore the landscape of disulfidptosis and the immune microenvironment of HCC cells.Results: We developed a novel model to predict the prognosis of patients with HCC based on disulfidptosis. The model has good stability, applicability, and prognostic and immune response prediction abilities. N-myc downregulated gene1 (NDRG1) may contribute to poor prognosis by affecting macrophage differentiation, thus allowing HCC cells to evade the immune system.Conclusion: Our study explores the disulfidptosis of HCC cells through multi-omics and establishes a new putative model that explores possible targets for HCC treatment.

Background: Liver hepatocellular carcinoma (LIHC) is a highly malignant tumor with high metastasis and recurrence rates. Due to the relation between lipid metabolism and the tumor immune microenvironment is constantly being elucidated, this work is carried out to produce a new prognostic gene signature that incorporates immune profiles and lipid metabolism of LIHC patients.Methods: We used the “DEseq2” R package and the “Venn” R package to identify differentially expressed genes related to lipid metabolism (LRDGs) in LIHC. Additionally, we performed unsupervised clustering of LIHC patients based on LRDGs to identify their subgroups and immuno-infiltration and Gene Ontology (GO) enrichment analysis on the subgroups. Next, we employed multivariate, LASSO and univariate Cox regression analyses to determine variables and to create a prognostic profile on the basis of immune- and lipid metabolism-related differential genes (IRDGs and LRDGs). We separated patients into low- and high-risk groups in accordance with the best cut-off value of risk score. We conducted Decision Curve Analysis (DCA), Receiver Operating Characteristic curve analysis as a function of time as well as Survival Analysis to evaluate this signature’s prognostic value. We incorporated the clinical characteristics of patients into the risk model to obtain a nomogram prognostic model. GEO14520 and ICGC-LIRI JP datasets were employed to externally confirm the accuracy and robustness of signature. The gene set variation analysis (GSVA) and gene set enrichment analysis (GSEA) were applied for investigating the underlying mechanisms. Immune infiltration analysis was implemented to examine the differences in immune between both risk groups. Single-cell RNA sequencing (scRNA-SEQ) was utilized to characterize the genes that were involved in the distribution of signature and expression characteristics of different LIHC cell types. The patients’ sensitivity in both risk groups to commonly used chemotherapeutic agents and semi-inhibitory concentrations (IC50) of the drugs was assessed using the GDSC database. On the basis of the differentially expressed genes (DEGs) in the two groups, the CMAP database was adopted for the prediction of potential small-molecule compounds. Small-molecule compounds were molecularly docked with prognostic markers. Lastly, we investigated the prognostic gene expression levels in normal and LIHC tissues with immunohistochemistry (IHC) and quantitative reverse transcription polymerase chain reaction(qRT-PCR).Results: We built and verified a prognostic signature with seven genes that incorporated immune profiles and lipid metabolism. Patients were classified as low- and high-risk groups depending on their prognostic profiles. The overall survival (OS) was markedly lower in the high-risk group as compared to low-risk group. Time-dependent ROC curves more precisely predicted patients' survival at 1, 3 and 5 years; the area under the ROC curve was 0.81 (1 year), 0.75 (3 years) and 0.77 (5 years). The DCA curves showed the value of the prognostic genes in this signature for clinical applications. We included the patients' clinical characteristics in the risk model for both multivariate and univariate Cox regression analyses, and the findings revealed that the risk model represents an independent factor that influences OS in LIHC patients. With immune analysis, GSVA and GSEA, we identified that there are remarkable differences between the two risk groups in immune pathways, lipid metabolism, tumor development, immune cell infiltration and immune microenvironment, response to immunotherapy, and sensitivity to chemotherapy. Moreover, those with higher risk scores presented greater sensitivity to the chemotherapeutic agents. Experiments in vitro further elucidated the roles of SPP1 and FLT3 in the LIHC immune microenvironment. Furthermore, four small-molecule drugs that could target LIHC were screened. In vitro qRT-PCR , IHC revealed that the SPP1,KIF18A expressions were raised in LIHC in tumor samples, whereas FLT3,SOCS2 showed the opposite trend.Conclusions: We developed and verified a new signature comprising immune- and lipid metabolism-associated markers and to assess the prognosis and the immune status of LIHC patients. This signature can be applied to survival prediction, individualized chemotherapy, and immunotherapeutic guidance for patients with liver cancer. This study also provides potential targeted therapeutics and novel ideas for the immune evasion and progression of LIHC.

Objective: Colorectal cancer is severely challenging because of the insufficient understanding of the mechanism underlying its resistance to clinical chemotherapy. The purpose of our study is to investigate the role of the LIM protein Ajuba (JUB) in the chemoresistance of colon cancer and its potential effect on clinical treatment. Material and Methods: The protein levels of JUB in colon cancer tissues were evaluated using Western blot analysis and immunohistochemistry assays. The correlation between JUB and the prognosis of patients with colorectal cancer was determined using Kaplan–Meier plot analysis. 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide assays were employed to determine the 50% inhibitory concentration of 5-fluorouracil (5-FU) and thus assess the effect of JUB on the effectiveness of 5-FU. In addition, the rate of cellular apoptosis was measured using fluorescence-activated cell sorting assays. Side population and sphere formation analyses were conducted to determine the role of JUB in promoting the stem cell-like traits of colon cancer cells. In vivo assays were performed and detect whether the downregulation of JUB induces 5-FU sensitivity. Moreover, luciferase and Western blot assays were employed to uncover the mechanism through which JUB promotes chemoresistance in colon cancer. Results: JUB expression was upregulated in chemoresistant colon cancer (P < 0.001) and correlated with relapse-free survival (P = 0.000002). Functionally, the upregulation of JUB conferred 5-FU resistance to colon cancer cells in vitro, whereas the downregulation of JUB induced 5-FU sensitivity in colon cancer cells in vivo. The high expression of JUB promoted the tumorigenic capability of colon cancer cells. Furthermore, the increased expression of JUB activated multiple downstream genes of the Notch signaling pathway with increased expression in JUB-overexpressing cells but reduced expression in JUB-silenced cells. Importantly, the inhibition of Notch signaling using a small-molecule inhibitor significantly suppressed JUB-induced chemoresistance. Conclusion: Results suggest that JUB plays an important role and may serve as a biomarker for the clinical treatment of patients with 5-FU-resistant colon cancer.