Metabolic disorder is an essential characteristic of tumor development. Ketogenesis is a heterogeneous factor in multiple cancers, but the effect of ketogenesis on hepatocellular carcinoma (HCC) is elusive.We aimed to explain the role of ketogenesis-related hydroxy-methyl-glutaryl-CoA lyase (HMGCL) on HCC suppression. Expression pattern of HMGCL in HCC specimens was evaluated by immunohistochemistry (IHC). HMGCL was depleted or overexpressed in HCC cells to investigate the functions of HMGCL in vitro and in vivo. The anti-tumor function of HMGCL was studied in subcutaneous xenograft and Trp53Δhep/Δhep; c-Myc-driven HCC mouse models. The mechanism of HMGCL-mediated tumor suppression was studied by IHC, western blot (WB) and Cut & Tag.HMGCL depletion promoted HCC proliferation and metastasis, whereas its overexpression reversed this trend. As HMGCL catalyzes β-hydroxy-butyric acid (β-OHB) production, we discovered that HMGCL increased acetylation at histone H3K9, which further promoted the transcription of dipeptidyl peptidase 4 (DPP4), a key protein maintains intracellular lipid peroxidation and iron accumulation, leading to HCC cells vulnerability to erastin- and sorafenib-induced ferroptosis.Our study identified a critical role of HMGCL on HCC suppression, of which HMGCL regulated H3K9 acetylation through β-OHB and modulating the expression of DPP4 in a dose-dependent manner, which led to ferroptosis in HCC cells.
Abstract Background: Metabolic disorder is an essential characteristic of tumor development. Ketogenesis as a heterogeneous factor in multiple cancer, but the effect of ketogenesis on hepatocellular carcinoma (HCC) is elusive. Methods: We aimed to explain a role of ketogenesis related hydroxymethylglutaryl-CoA lyase (HMGCL) on HCC suppression. Expression pattern of HMGCL in HCC specimens was evaluated by immunohistochemistry (IHC). HMGCL was depleted or overexpressed in HCC cells to investigate the functions of HMGCL in vitro and in vivo . The antitumor function of HMGCL was studied in subcutaneous xenograft and Trp53Δhep/Δhep; c-Myc -driven HCC mouse models. The mechanism of HMGCL mediated tumor suppression was studied by IHC, western blot (WB) and Cut & Tag. Results: HMGCL depletion promoted HCC proliferation and metastasis, whereas its overexpression reversed this trend. As HMGCL catalyzes β-hydroxybutyric acid (β-OHB) production, we discovered that HMGCL increased acetylation at histone H3K9, which further promoted the transcription of Dipeptidyl peptidase-4 (DPP4), a key protein maintains intracellular lipid peroxidation and iron accumulation, leading to HCC cells vulnerability to erastin- and sorafenib-induced ferroptosis. Conclusions: Our study identified a critical role of HMGCL on HCC suppression, of which HMGCL regulated H3K9 acetylation through β-OHB and modulating the expression of DPP4 in a dose-dependent manner, which led to ferroptosis in HCC cells.
Background: Tumor microenvironment (TME) refers to the cellular environment where tumors exist, including immune cells, fibroblasts, stromal cells, chemokines, etc. TME is closely related to the prognosis of various tumors; nevertheless, limited studies have established predictive prognosis models based on TME. This work aims to construct a survival prediction model for melanoma patients based on TME. Methods: Data of 482 melanoma patients were extracted from The Cancer Genome Atlas (TCGA) database. Based on the infiltration of immune cells (Immune score), stromal cells (Stromal score), and tumor purity (Estimate score), the “Estimate” algorithm was used to construct 3 scores for each patient. To identify the differentially expressed genes (DEGs), Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted using DAVID database and visualized using the R software. The STRING database was used to construct the protein-protein interaction (PPI) network and functional modules. FGD2 expression was confirmed via Western Blotting and quantitative reverse transcription PCR (RT-qPCR) analyses. Results: Patients with higher immune scores estimate scores showed better OS than those with lower scores. All three scores were related to age and primary tumor stage. Further, DEGs between patients with high immune/stromal scores and low immune/stromal scores were screened. Eventually, 10 down-regulated DEGs and 201 up-regulated DEGs were identified as TME associated genes. Out of these, the FGD2 gene demonstrated close association with survival and was confirmed in the included melanoma patients. Conclusion: In summary, TME is closely associated with the prognosis of melanoma patients. Besides, genes including FGD2 promote the TME-mediated regulation of melanoma. Keywords: tumor microenvironment, the cancer genome atlas, melanoma, FGD2
Background . To form a radiomic model on the basis of noncontrast computed tomography (CT) to distinguish hepatic hemangioma (HH) and hepatocellular carcinoma (HCC). Methods . In this retrospective study, a total of 110 patients were reviewed, including 72 HCC and 38 HH. We accomplished feature selection with the least absolute shrinkage and operator (LASSO) and built a radiomics signature. Another improved model (radiomics index) was established using forward conditional multivariate logistic regression. Both models were tested in an internal validation group (38 HCC and 21 HH). Results . The radiomic signature we built including 5 radiomic features demonstrated significant differences between the hepatic HH and HCC groups ( P < 0.05). The improved model demonstrated a higher net benefit based on only 2 radiomic features. In the validation group, radiomics signature and radiomics index achieved great diagnostic performance with AUC values of 0.716 (95% confidence interval (CI): 0.581, 0.850) and 0.870 (95% CI: 0.782, 0.957), respectively. Conclusions . Our developed radiomics‐based model can successfully distinguish HH and HCC patients, which can help clinical decision‐making with lower cost.
The cell division cycle associated 8 (CDCA8) is a crucial component of the chromosome passenger complex (CPC). It has been implicated in the regulation of cell dynamic localization during mitosis. However, its role in hepatocellular carcinoma (HCC) is not clearly known. In this study, data of 374 patients with HCC were retrieved from the Cancer Genome Atlas (TCGA) database. Pan analysis of Gene Expression Profiling Interactive Analysis (GEPIA) database was performed to profile the mRNA expression of CDCA8 in HCC. Then, the Kaplan-Meier plotter database was analysed to determine the prognostic value of CDCA8 in HCC. In addition, samples of tumour and adjacent normal tissues were collected from 88 HCC patients to perform immunohistochemistry (IHC), reverse transcription-quantitative polymerase chain reaction (qRT-PCR) and Western blotting. The results obtained from bioinformatic analyses were validated through CCK-8 assay, EdU assay, colony formation assay, cell cycle assays and Western blotting experiments. Analysis of the Kaplan-Meier plotter database showed that high expression of CDCA8 may lead to poor overall survival (OS, p = 4.06e-05) in patients with HCC. For the 88 patients with HCC, we found that stages and grades appeared to be strongly linked with CDCA8 expression. Furthermore, the high expression of CDCA8 was found to be correlated with poor OS (p = 0.0054) and progression-free survival (PFS, p = 0.0009). In vitro experiments revealed that inhibition of CDCA8 slowed cell proliferation and blocked the cell cycle at the G0/G1 phase. In vivo experiments demonstrated that inhibition of CDCA8 inhibited tumour growth. Finally, blockade of CDCA8 reduced the expression levels of cyclin A2, cyclin D1, CDK4, CDK6, Ki67 and PCNA. And, there is an interaction between CDCA8 and E2F1. In conclusion, this research demonstrates that CDCA8 may serve as a biomarker for early diagnosis and prognosis prediction of HCC patients. In addition, CDCA8 could be an effective therapeutic target in HCC.
Abstract Background: Long non-coding RNAs (lncRNAs) have diverse roles in modulating gene expression on both transcriptional and translational aspects, whereas its role in the metastasis of osteosarcoma (OS) is unclear. Method: Expression and clinical data were downloaded from TARGET datasets. The OS metastasis model was established by seven lncRNAs screened by univariate cox regression, lasso regression and multivariate cox regression analysis. The area under receiver operating characteristic curve (AUC) values were used to evaluate the models. Results: The predictive ability of this model is extraordinary (1 year: AUC = 0.92, 95% Cl = 0.83–1.01; 3 years: AUC = 0.87, 95% Cl = 0.79–0.96; 5 years: AUC = 0.86, 95% Cl = 0.76–0.96). Patients in high group had poor survival compared to low group (p < 0.0001). “NOTCH_SIGNALING”, and “WNT_BETA_CATENIN_SIGNALING” were enriched via the GSEA analysis and dendritic cells resting were associated with the AL512422.1, AL357507.1 and AC006033.2 (p < 0.05). Conclusion: We constructed a novel model with high reliability and accuracy to predict the metastasis of OS patients based on seven prognosis-related lncRNAs.
Long non-coding RNAs (lncRNAs) have diverse roles in modulating gene expression on both transcriptional and translational levels, but their involvement in osteosarcoma (OS) metastasis remains unknown.Transcriptional and clinical data were downloaded from TARGET datasets. A total of seven lncRNAs screened by univariate cox regression, lasso regression, and multivariate cox regression analysis were used to establish the OS metastasis model. The area under the receiver operating characteristic curve (AUC) was used to evaluate the model.The established model showed exceptional predictive performance (1 year: AUC = 0.92, 95% Cl = 0.83-0.99; 3 years: AUC = 0.87, 95% Cl = 0.79-0.96; 5 years: AUC = 0.86, 95% Cl = 0.76-0.96). Patients in the high group had a poor survival outcome than those in the low group (p < 0.0001). GSEA analysis revealed that "NOTCH_SIGNALING" and "WNT_BETA_CATENIN_SIGNALING" were significantly enriched and that resting dendritic cells were associated with AL512422.1, AL357507.1, and AC006033.2 (p < 0.05).Based on seven prognosis-related lncRNAs, we constructed a novel model with high reliability and accuracy for predicting metastasis in OS patients.
Severe acute respiratory syndrome-coronavirus 2 (COVID-19) vaccines may incur changes in thyroid functions followed by mood changes, and patients with Hashimoto thyroiditis (HT) were suggested to bear a higher risk.We primarily aim to find whether COVID-19 vaccination could induce potential subsequent thyroid function and mood changes. The secondary aim was to find inflammatory biomarkers associated with risk.The retrospective, multi-center study recruited patients with HT receiving COVID-19-inactivated vaccines. C-reactive proteins (CRPs), thyroid-stimulating hormones (TSHs), and mood changes were studied before and after vaccination during a follow-up of a 6-month period. Independent association was investigated between incidence of mood state, thyroid functions, and inflammatory markers. Propensity score-matched comparisons between the vaccine and control groups were carried out to investigate the difference.Final analysis included 2,765 patients with HT in the vaccine group and 1,288 patients in the control group. In the matched analysis, TSH increase and mood change incidence were both significantly higher in the vaccine group (11.9% versus 6.1% for TSH increase and 12.7% versus 8.4% for mood change incidence). An increase in CRP was associated with mood change (p< 0.01 by the Kaplan-Meier method) and severity (r = 0.75) after vaccination. Baseline CRP, TSH, and antibodies of thyroid peroxidase (anti-TPO) were found to predict incidence of mood changes.COVID-19 vaccination seemed to induce increased levels and incidence of TSH surge followed by mood changes in patients with HT. Higher levels of pre-vaccine serum TSH, CRP, and anti-TPO values were associated with higher incidence in the early post-vaccine phase.
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