Construction of a hepatocytes-related and protein kinase–related gene signature in HCC based on ScRNA-Seq analysis and machine learning algorithm

With recent advancements in single-cell sequencing and machine learning methods, new insights into hepatocellular carcinoma (HCC) progression have been provided. Protein kinase–related genes (PKRGs) affect cell growth, differentiation, apoptosis, and signaling during HCC progression, making the predictive relevance of PKRGs in HCC highly necessary for personalized medicine. In this study, we analyzed single-cell data of HCC and used the machine learning method of LASSO regression to construct PKRG prediction models in six major cell types. CDK4 and AURKB were found to be the best PKRG prognostic signature for predicting the overall survival of HCC patients (including TCGA, ICGC, and GEO datasets) in hepatocytes. Independent clinical factors were further screened out using the COX regression method, and a nomogram combining PKRGs and cancer status was created. Treatment with Palbociclib (CDK4 Inhibitor) and Barasertib (AURKB Inhibitor) inhibited HCC cell migration. Patients classified as PKRG high- or low-risk groups showed different tumor mutation burdens, immune infiltrations, and gene enrichment. The PKRG high-risk group showed higher tumor mutation burdens and gene set enrichment analysis indicated that cell cycle, base excision repair, and RNA degradation pathways were more enriched in these patients. Additionally, the PKRG high-risk group demonstrated higher infiltration levels of Naïve CD8+ T cells, Endothelial cells, M2 macrophage, and Tregs than the low-risk group. In summary, this study established the hepatocytes-related PKRG signature for prognostic stratification at the single-cell level by using machine learning algorithms in HCC and identified potential HCC treatment targets based on the PKRG signature. (AU)

Construction of a hepatocytes-related and protein kinase-related gene signature in HCC based on ScRNA-Seq analysis and machine learning algorithm.

With recent advancements in single-cell sequencing and machine learning methods, new insights into hepatocellular carcinoma (HCC) progression have been provided. Protein kinase-related genes (PKRGs) affect cell growth, differentiation, apoptosis, and signaling during HCC progression, making the predictive relevance of PKRGs in HCC highly necessary for personalized medicine. In this study, we analyzed single-cell data of HCC and used the machine learning method of LASSO regression to construct PKRG prediction models in six major cell types. CDK4 and AURKB were found to be the best PKRG prognostic signature for predicting the overall survival of HCC patients (including TCGA, ICGC, and GEO datasets) in hepatocytes. Independent clinical factors were further screened out using the COX regression method, and a nomogram combining PKRGs and cancer status was created. Treatment with Palbociclib (CDK4 Inhibitor) and Barasertib (AURKB Inhibitor) inhibited HCC cell migration. Patients classified as PKRG high- or low-risk groups showed different tumor mutation burdens, immune infiltrations, and gene enrichment. The PKRG high-risk group showed higher tumor mutation burdens and gene set enrichment analysis indicated that cell cycle, base excision repair, and RNA degradation pathways were more enriched in these patients. Additionally, the PKRG high-risk group demonstrated higher infiltration levels of Naïve CD8+ T cells, Endothelial cells, M2 macrophage, and Tregs than the low-risk group. In summary, this study established the hepatocytes-related PKRG signature for prognostic stratification at the single-cell level by using machine learning algorithms in HCC and identified potential HCC treatment targets based on the PKRG signature.

Activity of Sanguinarine against Candida albicans Biofilms.

Candida albicans biofilms show resistance to many clinical antifungal agents and play a considerable contributing role in the process of C. albicans infections. New antifungal agents against C. albicans biofilms are sorely needed. The aim of this study was to evaluate sanguinarine (SAN) for its activity against Candida albicans biofilms and explore the underlying mechanism. The MIC50 of SAN was 3.2 µg/ml, while ≥0.8 µg/ml of SAN could suppress C. albicans biofilms. Further study revealed that ≥0.8 µg/ml of SAN could decrease cellular surface hydrophobicity (CSH) and inhibited hypha formation. Real-time reverse transcription-PCR (RT-PCR) results indicated that the exposure of C. albicans to SAN suppressed the expression of some adhesion- and hypha-specific/essential genes related to the cyclic AMP (cAMP) pathway, including ALS3, HWP1, ECE1, HGC1, and CYR1 Consistently, the endogenous cAMP level of C. albicans was downregulated after SAN treatment, and the addition of cAMP rescued the SAN-induced filamentation defect. In addition, SAN showed relatively low toxicity to human umbilical vein endothelial cells, the 50% inhibitory concentration (IC50) being 7.8 µg/ml. Collectively, the results show that SAN exhibits strong activity against C. albicans biofilms, and the activity was associated with its inhibitory effect on adhesion and hypha formation due to cAMP pathway suppression.