New insight into the mechanisms of Ginkgo biloba leaves in the treatment of cancer.

BACKGROUND: Ginkgo biloba leaves (GBLs), as an herbal dietary supplement and a traditional Chinese medicine, have been used in treating diseases for hundred years. Recently, increasing evidence reveals that the extracts and active ingredients of GBLs have anti-cancer (chemo-preventive) properties. However, the molecular mechanism of GBLs in anti-cancer has not been comprehensively summarized. PURPOSE: To systematically summarize the literatures for identifying the molecular mechanism of GBLs in cellular, animal models and clinical trials of cancers, as well as for critically evaluating the current evidence of efficacy and safety of GBLs for cancers. METHODS: Employing the search terms "Ginkgo biloba" and "cancer" till July 25, 2023, a comprehensive search was carried out in four electronic databases including Scopus, PubMed, Google Scholar and Web of Science. The articles not contained in the databases are performed by manual searches and all the literatures on anti-cancer research and mechanism of action of GBLs was extracted and summarized. The quality of methodology was assessed independently through PRISMA 2020. RESULTS: Among 84 records found in the database, 28 were systematic reviews related to GBLs, while the remaining 56 records were related to the anticancer effects of GBLs, which include studies on the anticancer activities and mechanisms of extracts or its components in GBLs at cellular, animal, and clinical levels. During these studies, the top six cancer types associated with GBLs are lung cancer, hepatocellular carcinoma, gastric cancer, breast cancer, colorectal cancer, and cervical cancer. Further analysis reveals that GBLs primarily exert their anticancer effects by stimulating cancer cell apoptosis, inhibiting cell proliferation, invasion and migration of cancers, exhibiting anti-inflammatory and antioxidant properties, and modulating signaling pathways. Besides, the pharmacology, toxicology, and clinical research on the anti-tumor activity of GBLs have also been discussed. CONCLUSIONS: This is the first paper to thoroughly investigate the pharmacology effect, toxicology, and the mechanisms of action of GBLs for anti-cancer properties. All the findings will reinforce the need to explore the new usage of GBLs in cancers and offer comprehensive reference data and recommendations for future research on this herbal medicine.

Association between systemic immune-inflammation index and diabetes: a population-based study from the NHANES.

Background: Systemic Immune-Inflammation Index (SII) has been reported to be associated with diabetes. We aimed to assess possible links between SII and diabetes. Methods: Data were obtained from the 2017-2020 National Health and Nutrition Examination Survey (NHANES) database. After removing missing data for SII and diabetes, we examined patients older than 20 years. Simultaneously, the relationship between SII and diabetes was examined using weighted multivariate regression analysis, subgroup analysis, and smooth curve fitting. Results: There were 7877 subjects in this study, the average SII was 524.91 ± 358.90, and the prevalence of diabetes was 16.07%. Weighted multivariate regression analysis found that SII was positively associated with diabetes, and in model 3, this positive association remained stable (OR = 1.04; 95% CI: 1.02-1.06; p = 0.0006), indicating that each additional unit of SII, the possibility of having diabetes increased by 4%. Gender, age, BMI, regular exercise, high blood pressure, and smoking did not significantly affect this positive link, according to the interaction test (p for trend>0.05). Discussion: Additional prospective studies are required to examine the precise connection between higher SII levels and diabetes, which may be associated with higher SII levels.

Identification of Breast Cancer Subtypes by Integrating Genomic Analysis with the Immune Microenvironment.

Objectives: We aim to identify the breast cancer (BC) subtype clusters and the crucial gene classifier prognostic signatures by integrating genomic analysis with the tumor immune microenvironment (TME). Methods: Data sets of BC were derived from the Cancer Genome Atlas (TCGA), METABRIC, and Gene Expression Omnibus (GEO) databases. Unsupervised consensus clustering was carried out to obtain the subtype clusters of BC patients. Weighted gene coexpression network analysis (WGCNA), least absolute shrinkage and selection operator (LASSO), and univariate and multivariate regression analysis were employed to obtain the gene classifier signatures and their biological functions, which were validated by the BC dataset from the METABRIC database. Additionally, to evaluate the overall survival rates of BC patients, Kaplan-Meier survival analysis was carried out. Moreover, to assess how BC subtype clusters are related to the TME, single-cell analysis was performed. Finally, the drug sensitivity and the immune cell infiltration for different phenotypes of BC patients were also calculated by the CIBERSORT and ESTIMATE algorithms. Results : TCGA-BC samples were divided into three subtype clusters, S1, S2, and S3, among which the prognosis of S2 was poor and that of S1 and S3 were better. Three key pathways and 10 crucial prognostic-related gene signatures are screened. Finally, single-cell analysis suggests that S1 samples have the most types of immune cells, S2 with more sensitivity to tumor treatment drugs are enriched with more neutrophils, and more multilymphoid progenitor cells are involved in subtype cluster S3. Conclusions: Our novelty was to identify the BC subtype clusters and the gene classifier signatures employing a large-amount dataset combined with multiple bioinformatics methods. All of the results provide a basis for clinical precision treatment of BC.

Systematic investigation of the mechanism of herbal medicines for the treatment of prostate cancer.

Due to various unpleasant side effects and general ineffectiveness of current treatments for prostate cancer (PCa), more and more people with PCa try to look for complementary and alternative medicine such as herbal medicine. However, since herbal medicine has multi-components, multi-targets and multi-pathways features, its underlying molecular mechanism of action is not yet known and still needs to be systematically explored. Presently, a comprehensive approach consisting of bibliometric analysis, pharmacokinetic assessment, target prediction and network construction is firstly performed to obtain PCa-related herbal medicines and their corresponding candidate compounds and potential targets. Subsequently, a total of 20 overlapping genes between DEGs in PCa patients and the target genes of the PCa-related herbs, as well as five hub genes, i.e., CCNA2, CDK2, CTH, DPP4 and SRC were determined employing bioinformatics analysis. Further, the roles of these hub genes in PCa were also investigated through survival analysis and tumour immunity analysis. Moreover, to validate the reliability of the C-T interactions and to further explore the binding modes between ingredients and their targets, the molecular dynamics (MD) simulations were carried out. Finally, based on the modularization of the biological network, four signaling pathways, i.e., PI3K-Akt, MAPK, p53 and cell cycle were integrated to further analyze the therapeutic mechanism of PCa-related herbal medicine. All the results show the mechanism of action of herbal medicines on treating PCa from the molecular to systematic levels, providing a reference for the treatment of complex diseases using TCM.

Systematic investigation of the clinical significance and prognostic value of the CBXs in esophageal cancer.

Esophageal cancer (ESCA), one of the most aggressive malignant tumors, has been announced to be the ninth most common cancer and the sixth leading cause of cancer-related death in the world. Chromobox family members (CBXs) are important epigenetic regulators which are related with the transcription of target genes. The role of CBXs in carcinomas has been reported in many studies. However, the function and prognostic value of different CBXs in ESCA are still largely unknown. In this article, we first performed differential expression analysis through several methods including Oncomine and Gene Expression Profiling Interactive Analysis. The results led us to determine the differential expression of CBXs in pan-cancer, especially ESCA. Then we evaluated the prognostic value of different CBX messenger RNA (mRNA) expression in patients with ESCA through the Kaplan-Meier plotter and the Human Protein Atlas database. In addition, we used cBioPortal to explore all genetic alterations and mutations in the CBXs in ESCA. Simultaneously, the correlation between its expression and the level of immune infiltration of ESCA was visualized by TIMER. Finally, the biological function of CBXs in ESCA is obtained through Biological Enrichment Analysis including gene ontology and Kyoto Encyclopedia of Genes and Genomes. The expression levels of CBX3/4/5 and CBX8 in ESCA tissues increased significantly and the expression level of CBX7 decreased through differential expression analysis. Additionally, CBX1 is significantly related to the clinical cancer stage and disease-free survival of ESCA patients. The high mRNA expression of CBX4 is related to the short overall survival of patients with esophageal squamous cell carcinoma, and the high mRNA expression of CBX3/7/8 is related to the short overall survival of patients with esophageal adenocarcinoma, indicating that CBX1/3/4/7/8 may be a potential prognostic biomarker for the survival of ESCA patients. Besides, the expression of CBXs is significantly related to the infiltration of a variety of immune cells, including six types of CD4-positive T-lymphocytes, macrophages, neutrophils, bursindependentlymphocyte, CD8-positive T-lymphocytes cells and dendritic cells in ESCA. Moreover, we found that CBXs are mainly associated with the inhibition of cell cycle and apoptosis pathway. Further, enrichment analysis indicated that CBXs and correlated genes were enriched in mismatch repair, DNA replication, cancer pathways, and spliceosomes. Our research may provide new insights into the choice of prognosis biomarkers of the CBXs in ESCA.

A classification method of gastric cancer subtype based on residual graph convolution network.

Background: Clinical diagnosis and treatment of tumors are greatly complicated by their heterogeneity, and the subtype classification of cancer frequently plays a significant role in the subsequent treatment of tumors. Presently, the majority of studies rely far too heavily on gene expression data, omitting the enormous power of multi-omics fusion data and the potential for patient similarities. Method: In this study, we created a gastric cancer subtype classification model called RRGCN based on residual graph convolutional network (GCN) using multi-omics fusion data and patient similarity network. Given the multi-omics data's high dimensionality, we built an artificial neural network Autoencoder (AE) to reduce the dimensionality of the data and extract hidden layer features. The model is then built using the feature data. In addition, we computed the correlation between patients using the Pearson correlation coefficient, and this relationship between patients forms the edge of the graph structure. Four graph convolutional network layers and two residual networks with skip connections make up RRGCN, which reduces the amount of information lost during transmission between layers and prevents model degradation. Results: The results show that RRGCN significantly outperforms other classification methods with an accuracy as high as 0.87 when compared to four other traditional machine learning methods and deep learning models. Conclusion: In terms of subtype classification, RRGCN excels in all areas and has the potential to offer fresh perspectives on disease mechanisms and disease progression. It has the potential to be used for a broader range of disorders and to aid in clinical diagnosis.

The Prognostic Value of Deficient Mismatch Repair in Stage II-IVa Nasopharyngeal Carcinoma in the Era of IMRT.

In the era of intensity-modulated radiotherapy (IMRT), it is important to analyse the prognostic value of deficient mismatch repair (dMMR) in nasopharyngeal carcinoma (NPC). In this study, in pretreatment biopsies of 69 patients with stage II-IVa NPC, the expression levels of MMR proteins, including MLH1, MSH2, MSH6 and PMS2, were assessed by immunohistochemistry (IHC). The median follow-up time was 37.5 months (3.1-87.4 months). 50.7% of cases (35/69) showed preserved expression of all 4 MMR proteins, which was interpreted as proficient mismatch repair (pMMR). Only 1.5% of cases (1/69) lost expression of all 4 MMR proteins, 26.1% of cases (18/69) have PMS2 loss alone and 21.7% of cases (15/69) lost expression of both PMS2 and MLH1. Thus, 49.3% of cases (34/69) lost expression of one or more MMR proteins, which was interpreted as dMMR. There was no significant difference (P > 0.05) in terms of sex, age, clinical stage, T category, N category or therapy regimens between the dMMR and pMMR groups. The multivariate Cox regression analysis revealed that dMMR was an independent significant prognostic factor for distant metastasis-free survival (DMFS) (dMMR vs pMMR: P = 0.01, HR = 0.25, 95% CI: 0.09~0.75). Therefore, NPC patients with dMMR had significantly superior DMFS compared with patients with pMMR. It can be expected that dMMR will become a new independent prognostic factor for NPC.

Comprehensive Analysis of CDCAs Methylation and Immune Infiltrates in Hepatocellular Carcinoma.

BACKGROUND: As essential components of cycle growth, the cell division cycle-associated family genes (CDCAs) have crucial roles in tumor development and progression, especially in hepatocellular carcinoma (HCC). However, due to the tumor heterogeneity of HCC, little is known about the methylation variability of CDCAs in mediating phenotypic changes (e.g., immune infiltrates) in HCC. Presently, we aim to comprehensively explore the expression and prognosis of CDCAs methylation with regard to immune infiltrates of HCC. METHODS: We first identified the correlating differentially expressed genes (co-DEGs) among 19 different types of cancer cohorts (a total of 7,783 patients) and then constructed the weighted gene co-expressed and co-methylated networks. Applying the clustering analysis, significant modules of DEGs including CDCAs were selected and their functional bioinformatics analyses were performed. Besides, using DiseaseMeth and TIMER, the correlation between the methylation levels of CDCAs and tumor immune infiltrates was also analyzed. In final, to assess the influence of CDCAs methylation on clinical prognosis, Kaplan-Meier and Cox regression analysis were carried out. RESULT: A total of 473 co-DEGs are successfully identified, while seven genes of CDCAs (CDCA1-3 and CDCA5-8) have significant over-expression in HCC. Co-expressed and co-methylated networks reveal the strong positive correlations in mRNA expression and methylation levels of CDCAs. Besides, the biological enrichment analysis of CDCAs demonstrates that they are significantly related to the immune function regulation of infiltrating immune cells in HCC. Also, the methylation analysis of CDCAs depicts the strong association with the tumor immunogenicity, i.e., low-methylation of CDCA1, CDCA2, and CDCA8 dramatically reduced the immune infiltrate levels of T cells and cytotoxic lymphocytes. Additionally, CDCA1-6 and CDCA8 with low-methylation levels significantly deteriorate the overall survival of patients in HCC. CONCLUSIONS: The co-expressed and co-methylated gene networks of CDCAs show a powerful association with immune function regulation. And the methylation levels of CDCAs suggesting the prognostic value and infiltrating immune differences could be a novel and predictive biomarker for the response of immunotherapy.