Machine learning and experiments identifies SPINK1 as a candidate diagnostic and prognostic biomarker for hepatocellular carcinoma.

Machine learning techniques have been widely used in predicting disease prognosis, including cancer prognosis. One of the major challenges in cancer prognosis is to accurately classify cancer types and stages to optimize early screening and detection, and machine learning techniques have proven to be very useful in this regard. In this study, we aimed at identifying critical genes for diagnosis and outcomes of hepatocellular carcinoma (HCC) patients using machine learning. The HCC expression dataset was downloaded from GSE65372 datasets and TCGA datasets. Differentially expressed genes (DEGs) were identified between 39 HCC and 15 normal samples. For the purpose of locating potential biomarkers, the LASSO and the SVM-RFE assays were performed. The ssGSEA method was used to analyze the TCGA to determine whether there was an association between SPINK1 and tumor immune infiltrates. RT-PCR was applied to examine the expression of SPINK1 in HCC specimens and cells. A series of functional assays were applied to examine the function of SPINK1 knockdown on the proliferation of HCC cells. In this study, 103 DEGs were obtained. Based on LASSO and SVM-RFE analysis, we identified nine critical diagnostic genes, including C10orf113, SPINK1, CNTLN, NRG3, HIST1H2AI, GPRIN3, SCTR, C2orf40 and PITX1. Importantly, we confirmed SPINK1 as a prognostic gene in HCC. Multivariate analysis confirmed that SPINK1 was an independent prognostic factor for overall survivals of HCC patients. We also found that SPINK1 level was positively associated with Macrophages, B cells, TFH, T cells, Th2 cells, iDC, NK CD56bright cells, Th1 cells, aDC, while negatively associated with Tcm and Eosinophils. Finally, we demonstrated that SPINK1 expression was distinctly increased in HCC specimens and cells. Functionally, silence of SPINK1 distinctly suppressed the proliferation of HCC cells via regulating Wnt/ß-catenin pathway. The evidence provided suggested that SPINK1 may possess oncogenic properties by inducing dysregulated immune infiltration in HCC. Additionally, SPINK1 was identified as a novel biomarker and therapeutic target for HCC.

Gentiopicroside ameliorates CCl4-induced liver injury in mice by regulating the PPAR-γ/Nrf2 and NF-κB/IκB signaling pathways.

OBJECTIVE: This study explored the mechanisms by which gentiopicroside protects against carbon tetrachloride (CCl4)-induced liver injury. METHODS: Male mice were randomly assigned to the control; CCl4; bifendate 100 mg/kg; or gentiopicroside 25, 50, or 100 mg/kg groups. Both vehicle and drugs were administered intragastrically for 7 days. Mice were administered CCl4 intraperitoneally 1 hour after the last drug dose. After 24 hours, we collected blood and liver samples for testing. RESULTS: Gentiopicroside significantly reduced serum alanine aminotransferase, aspartate aminotransferase, and lactate dehydrogenase activities with corresponding reductions in hepatocyte denaturation and necrosis. Gentiopicroside enhanced superoxide dismutase (SOD) and glutathione peroxidase (GSH-Px) activities and glutathione levels and reduced heme oxygenase 1 (HO-1) activity and malondialdehyde levels in the liver, and these effects were attributed to peroxisome proliferator-activated receptor (PPAR)-γ/nuclear factor erythroid 2-related factor 2 (Nrf2) activation. Meanwhile, gentiopicroside significantly downregulated HO-1 and upregulated SOD and GSH-Px at the mRNA level in the liver. Furthermore, gentiopicroside significantly suppressed serum tumor necrosis factor-α and interleukin-1ß secretion, which was associated with the inhibition of nuclear factor-kappa B (NF-κB)/inhibitor of NF-κB (IκB). CONCLUSIONS: Gentiopicroside ameliorated CCl4-induced liver injury in mice via the PPAR-γ/Nrf2 and NF-κB/IκB pathways.

Construction of a Novel Diagnostic Model Based on Ferroptosis-Related Genes for Hepatocellular Carcinoma Using Machine and Deep Learning Methods.

Hepatocellular carcinoma (HCC) is one of the most general malignant tumors. Ferroptosis, a type of necrotic cell death that is oxidative and iron-dependent, has a strong correlation with the development of tumors and the progression of cancer. The present study was designed to identify potential diagnostic Ferroptosis-related genes (FRGs) using machine learning. From GEO datasets, two publicly available gene expression profiles (GSE65372 and GSE84402) from HCC and nontumor tissues were retrieved. The GSE65372 database was used to screen for FRGs with differential expression between HCC cases and nontumor specimens. Following this, a pathway enrichment analysis of FRGs was carried out. In order to locate potential biomarkers, an analysis using the support vector machine recursive feature elimination (SVM-RFE) model and the LASSO regression model were carried out. The levels of the novel biomarkers were validated further using data from the GSE84402 dataset and the TCGA datasets. In this study, 40 of 237 FRGs exhibited a dysregulated level between HCC specimens and nontumor specimens from GSE65372, including 27 increased and 13 decreased genes. The results of KEGG assays indicated that the 40 differential expressed FRGs were mainly enriched in the longevity regulating pathway, AMPK signaling pathway, the mTOR signaling pathway, and hepatocellular carcinoma. Subsequently, HSPB1, CDKN2A, LPIN1, MTDH, DCAF7, TRIM26, PIR, BCAT2, EZH2, and ADAMTS13 were identified as potential diagnostic biomarkers. ROC assays confirmed the diagnostic value of the new model. The expression of some FRGs among 11 FRGs was further confirmed by the GSE84402 dataset and TCGA datasets. Overall, our findings provided a novel diagnostic model using FRGs. Prior to its application in a clinical context, there is a need for additional research to evaluate the diagnostic value for HCC.