A novel machine learning derived RNA-binding protein gene-based score system predicts prognosis of hepatocellular carcinoma patients

Metformin: A New Inhibitor of the Wnt Signaling Pathway in Cancer

The biguanide drug metformin is widely used in type 2 diabetes mellitus therapy, due to its ability to decrease serum glucose levels, mainly by reducing hepatic gluconeogenesis and glycogenolysis. A considerable number of studies have shown that metformin, besides its antidiabetic action, can improve other disease states, such as polycystic ovary disease, acute kidney injury, neurological disorders, cognitive impairment and renal damage. In addition, metformin is well known to suppress the growth and progression of different types of cancer cells both in vitro and in vivo. Accordingly, several epidemiological studies suggest that metformin is capable of lowering cancer risk and reducing the rate of cancer deaths among diabetic patients. The antitumoral effects of metformin have been proposed to be mainly mediated by the activation of the AMP-activated protein kinase (AMPK). However, a number of signaling pathways, both dependent and independent of AMPK activation, have been reported to be involved in metformin antitumoral action. Among these, the Wingless and Int signaling pathway have recently been included. Here, we will focus our attention on the main molecular mechanisms involved.

IHGA: An interactive web server for large-scale and comprehensive discovery of genes of interest in hepatocellular carcinoma

Mining gene expression data is valuable for discovering novel biomarkers and therapeutic targets in hepatocellular carcinoma (HCC). Although emerging data mining tools are available for pan-cancer-related gene data analysis, few tools are dedicated to HCC. Moreover, tools specifically designed for HCC have restrictions such as small data scale and limited functionality. Therefore, we developed IHGA, a new interactive web server for discovering genes of interest in HCC on a large-scale and comprehensive basis. Integrative HCC Gene Analysis (IHGA) contains over 100 independent HCC patient-derived datasets (with over 10,000 tissue samples) and more than 90 cell models. IHGA allows users to conduct a series of large-scale and comprehensive analyses and data visualizations based on gene mRNA levels, including expression comparison, correlation analysis, clinical characteristics analysis, survival analysis, immune system interaction analysis, and drug sensitivity analysis. This method notably enhanced the richness of clinical data in IHGA. Additionally, IHGA integrates artificial intelligence (AI)-assisted gene screening based on natural language models. IHGA is free, user-friendly, and can effectively reduce time spent during data collection, organization, and analysis. In conclusion, IHGA is competitive in terms of data scale, data diversity, and functionality. It effectively alleviates the obstacles caused by HCC heterogeneity to data mining work and helps advance research on the molecular mechanisms of HCC.

Hypoxia-responsive PPARGC1A/BAMBI/ACSL5 axis promotes progression and resistance to lenvatinib in hepatocellular carcinoma

Emerging evidence has indicated that peroxisome proliferator-activated receptor-gamma coactivator-1α (PPARGC1A) is involved in hepatocellular carcinoma (HCC). However, its detailed function and up- and downstream mechanisms are incompletely understood. In this study, we confirmed that PPAGC1A is lowly expressed in HCC and is associated with poor prognosis using large-scale public datasets and in-house cohorts. PPAGC1A was found to impair the progression and sensitivity of HCC to lenvatinib. Mechanistically, PPAGC1A repressed bone morphogenetic protein and activin membrane-bound inhibitor (BAMBI) by inhibiting WNT/β-catenin signaling. BAMBI mediated the function of PPARGC1A and regulated ACSL5 through TGF-β/SMAD signaling. PPARGC1A/BAMBI regulated ROS production and ferroptosis-related cell death by controlling ACSL5. PPARGC1A/BAMBI/ACSL5 axis was hypoxia-responsive. METTL3 and WTAP silenced PPARGC1A in an m6A-YTHDF2-dependent way under normoxia and hypoxia, respectively. Metformin restored PPARGC1A expression by reducing its m6A modification via inhibiting METTL3. In animal models and patient-derived organoids, consistent functional data of PPARGC1A/BAMBI/ACSL5 were observed. Conclusions: These findings provide new insights into the role of the aberrant PPARGC1A/BAMBI/ACSL5 axis in HCC. And the mechanism of PPARGC1A dysregulation was explained by m6A modification. Metformin may benefit HCC patients with PPARGC1A dysregulation.

Development and validation of a mitochondrial energy metabolism-related risk model in hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is one of the most prevalent cancers and ranks third inmortality. Mitochondria are the energy manufacturers of cells. Disruption of mitochondrial energy metabolism pathways is strongly correlated with the onset and progression of HCC. Aberrant genes in mitochondrial energy metabolism pathways may represent a unique diagnostic and therapeutic targets that act as indicators for HCC.

METHODS

Gene expression data from 374 HCC patients and 50 controls were acquired from TCGA database. A total of 188 mitochondrial energy metabolism-related genes (MMRGs) were obtained from KEGG PATHWAY database. A total of 368 patients with survival data were randomly split into training and validation groups in a 7: 3 ratio. Prognosis-related MMRGs were selected by univariate Cox and LASSO analyses. Kaplan-Meier and ROC curves were employed to analyze the model precision, whereas the validation set was used for model verification. Furthermore, clinical examinations, immune infiltration analysis, GSVA, and immunotherapy analysis were conducted in the high- and low-risk groups. Finally, the risk model was combined with the clinical variables of HCC patients to perform univariate and multivariate Cox regression analyses to obtain independent risk indicators and draw a nomogram. Therefore, we evaluated the accuracy of the predictions using calibration curves.

RESULTS

A total of 6032 differentially expressed genes (DEGs) were detected in the HCC and control samples. After overlapping DEGs with 188 MMRGs, 42 mitochondrial energy metabolism-related DEGs (DEMMRGs) were identified. A 17 specific genes-based risk score model of HCC was created, which revealed effectiveness in each TCGA training and validation dataset. Moreover, patients categorized by risk scores exhibited distinct immune infiltration status, immunotherapy responsiveness, and functional properties. Finally, univariate and multivariate Cox regression analyses revealed that risk score and stage T were independent predictive variables. Based on the T stage and risk score, a nomogram for estimating the survival of HCC patients was created. The calibration curves demonstrated that the prediction model had a high level of accuracy.

CONCLUSIONS

Our study constructed a mitochondrial energy metabolism-related risk model, that may be utilized to anticipate HCC prognosis and represent the immunological microenvironment of HCC.