BACKGROUND:Hepatocellular carcinoma (HCC) remains a leading cause of cancer-related mortality worldwide, with a 5-year survival rate below 20 % for advanced-stage patients. Fuzheng Xiaozheng Prescription (FZXZP), a traditional Chinese medicine compound, has demonstrated promising anti-HCC activity in preliminary studies, but its key bioactive components and molecular targets remain undefined. PURPOSE:To systematically elucidate the active constituents and mechanistic targets of FZXZP against HCC using an integrated bulk RNA, single cell RNA sequencing (scRNA-seq), network pharmacology and experimental validation approach. METHODS:The anti-tumor efficacy of FZXZP was evaluated in a murine HCC model and in human HCC cell lines. Untargeted metabolomics (LC-MS/MS) was performed to identify bioactive components in FZXZP aqueous extract and drug-containing serum. Network pharmacology analyses were then conducted to predict potential molecular targets associated with these bioactive constituents. Transcriptomic analyses integrated bulk RNA-seq data from TCGA-LIHC and GEO (GSE87630, GSE149614) with scRNA-seq data (GSE149614). Differential expression analysis, weighted gene co-expression network analysis, univariate Cox regression and network pharmacology were integrated to identify candidate genes. Three machine-learning algorithms (XGBoost, maximal clique centrality, and Eccentricity) were used to refine key genes. Key findings were rigorously validated in independent cohorts at the single-cell level, and confirmed through in vitro and in vivo experiments, including flow cytometry, Western blot, and quantitative real-time PCR (qRT-PCR). Loss- and gain-of-function experiments were performed via cell transfection to validate key targets and mechanisms. Finally, molecular docking, molecular dynamics (MD) simulations and surface plasmon resonance (SPR) assay assessed compound-target binding. RESULTS:FZXZP treatment markedly inhibited HCC progression in murine models, significantly improving liver histopathology and serum ALT/AST levels. In vitro, FZXZP effectively suppressed HCC cell proliferation, migration, and invasion. A total of 134 bioactive components were identified in FZXZP aqueous extract and drug-containing serum, corresponding to 1181 predicted targets. Integration of 1208 differentially expressed genes, 297 hub genes, 9999 risk-associated genes, and 1181 drug targets yielded 17 candidate genes, significantly enriched in cell cycle regulation signaling pathways. Machine-learning algorithms consistently selected CCNB1 as the core regulatory gene. scRNA-seq analysis demonstrated that fibrotic cells exhibited the most pronounced intercellular communication with other cell types, and CCNB1 expression was significantly elevated within the fibrotic cell population in HCC. CCNB1 showed robust diagnostic performance (AUC of 0.971 in TCGA, 0.784 in GSE87630). Mechanistically, FZXZP induced G2/M phase arrest and downregulated CCNB1 and CDK1 expression at both the mRNA and protein levels. In cell models involving gene knockdown and overexpression, CCNB1 has been validated as an oncogene and identified as a key target for FZXZP against HCC. Molecular docking, MD simulations and SPR assay further confirmed stable binding between 7-Methoxyflavone, a principal active component, and CCNB1(binding energy:-83.218 kJ/mol; KD=1.72 µM). 7-Methoxyflavone alone recapitulated the anti-HCC effects, supporting its role as a representative bioactive compound of FZXZP. CONCLUSION:FZXZP exerts its anti-HCC effects primarily through its representative component, 7-Methoxyflavone, by suppressing CCNB1/CDK1 and consequently triggering G2/M cell cycle arrest. These findings establish CCNB1 as a therapeutic target and provide a mechanistic rationale for FZXZP in HCC treatment.

Cyclophosphamide (CTX) is one of the most widely used drugs in the clinical treatment of tumors and autoimmune diseases. The correlation between CYP, GST, and ABC gene polymorphisms and CTX activity and its induced toxicity has been extensively studied, but with inconsistent conclusions. In this study, a meta-analysis protocol was employed to comprehensively evaluate the relationship between the gene polymorphisms, including CYP2C9, CYP2C19, CYP2B6, CYP3A5, GSTA1, GSTM1, GSTT1, GSTP1, ABCB1, ABCC4, and ABCG2, and the safety and efficacy of CTX. Forty-five eligible literatures were retrieved from PubMed, Web of Science, Embase, and China National Knowledge Infrastructure (CNKI) databases. The results showed that CYP, GST, and ABC gene polymorphisms analyzed in the study were not associated with the efficacy but related to the safety of CTX. CYP2C19*2 polymorphism showed low risk with CTX-induced gastrointestinal toxicity (RR, 3.70; 95% CI, 1.60-8.55; p = 0.002). The GSTT1-present genotype showed low risk with hematological (RR, 0.63; 95% CI, 0.42-0.96; p = 0.03), gastrointestinal toxicity (RR, 0.62; 95% CI, 0.41-0.94; p = 0.02) and other toxicities (RR, 0.60; 95% CI, 0.38-0.97; p = 0.04). The GSTP1 (rs1695) wild-type showed low risk with gastrointestinal toxicity (RR, 0.69; 95% CI, 0.52-0.92; p = 0.01). Additionally, the ABCC4 (rs9561778) wild-type also showed low risk with gastrointestinal toxicity (RR, 0.50; 95% CI, 0.28-0.88; p = 0.02). Our findings confirm that the polymorphisms of CYP2C19*2, GSTT1, GSTP1 (rs1695), and ABCC4 (rs9561778) play an important role in predicting the risk of hematological, gastrointestinal, and other toxicities in patients undergoing CTX treatment.

This editorial focuses on the relationship between nonalcoholic fatty pancreas disease (NAFPD) and the development and remission of type 2 diabetes (T2D). NAFPD is characterized by intrapancreatic fatty deposition associated with obesity and not associated with alcohol abuse, viral infections, and other factors. Ectopic fat deposition in the pancreas is associated with the development of T2D, and the underlying mechanism is lipotoxic β-cell dysfunction. However, the results on the relationship between intrapancreatic fat deposition (IPFD) and β-cell function are conflicting. Regardless of the therapeutic approach, weight loss improves IPFD, glycemia, and β-cell function. Pancreatic imaging is valuable for clinically monitoring and evaluating the management of T2D.

Over the last decade, immuno-oncologic drugs especially CD3-engaging bispecific antibodies (biAbs) are experiencing fast-paced evolution, but big challenges still exist in the clinical development of biAbs in solid tumors, especially non-small cell lung cancer (NSCLC). In this study, we choose a ROR1 × CD3 biAb in scFv-Fc format, named R11 × v9 biAb, to investigate its tumor-inhibiting role in NSCLC. Notably, the ROR1-engaging arm binds both human and mouse ROR1. We found that R11 × v9 biAb specifically binds T cells and tumor cells simultaneously, and dose-dependent cytotoxicity was detected for various ROR1+ NSCLC cell lines. Further, R11 × v9 biAb mediated T-cell derived proinflammatory cytokine secretion, boosted granzyme B and perforin production from CD8+ T cells, and recruited more CD4+ T cells and CD8+ T cells into the tumor tissues. The antitumor activity of R11 × v9 biAb was confirmed in two xenograft mouse models of ROR1+ NSCLC. Importantly, no harmful side effects were observed in these in vivo studies, warranting further preclinical and clinical studies of R11 × v9 biAb in NSCLC.

Medicinal plants are the primary sources for the discovery of novel medicines and the basis of ethnopharmacological research. While existing studies mainly focus on the chemical compounds, there is little research about the functions of other contents in medicinal plants. Extracellular vesicles (EVs) are functionally active, nanoscale, membrane-bound vesicles secreted by almost all eukaryotic cells. Intriguingly, plant-derived extracellular vesicles (PDEVs) also have been implicated to play an important role in therapeutic application. PDEVs were reported to have physical and chemical properties similar to mammalian EVs, which are rich in lipids, proteins, nucleic acids, and pharmacologically active compounds. Besides these properties, PDEVs also exhibit unique advantages, especially intrinsic bioactivity, high stability, and easy absorption. PDEVs were found to be transferred into recipient cells and significantly affect their biological process involved in many diseases, such as inflammation and tumors. PDEVs also could offer unique morphological and compositional characteristics as natural nanocarriers by innately shuttling bioactive lipids, RNA, proteins, and other pharmacologically active substances. In addition, PDEVs could effectively encapsulate hydrophobic and hydrophilic chemicals, remain stable, and cross stringent biological barriers. Thus, this study focuses on the pharmacological action and mechanisms of PDEVs in therapeutic applications. We also systemically deal with facets of PDEVs, ranging from their isolation to composition, biological functions, and biotherapeutic roles. Efforts are also made to elucidate recent advances in re-engineering PDEVs applied as stable, effective, and non-immunogenic therapeutic applications to meet the ever-stringent demands. Considering its unique advantages, these studies not only provide relevant scientific evidence on therapeutic applications but could also replenish and inherit precious cultural heritage.

Background: PLS-DA of high-dimensional metabolomics data is frequently employed to capture the most pertinent features to sample classification. But the presence of numerous insignificant input features could distort the PLS-DA model, blow up and scramble the selected differential features. Usually, univariate filtration is subsequently complemented to refine the selected features, but often giving unstable results. Whereas by precluding insignificant features through univariate data prefiltration assessed by FDR adjusted p-value, PLS-DA can generate more stable and reliable differential features. We explored and compared these two data analysis procedures to gain insights into the underlying mechanisms responsible for the disparate results. Results: The effect of univariate data filtration preceding and succeeding PLS-DA analysis on the identified discriminative features/metabolites was investigated using LC-MS data acquired on the samples of human serum and C. elegans extracts, with and without metabolite standards spiked to simulate the treated and control groups of biological samples. It was shown that the univariate data prefiltration before PLS-DA usually gave less but more stable and likely more reliable and meaningful differential features, while PLS-DA applied directly to the original data could be affected by the presence of insignificant features and orthogonal noise. Large number of insignificant variables and orthogonal noise could distort the generated PLS-DA model and affect the p(corr) value, and artificially inflate the calculated VIP values of relevant features due to the increased total number of input features for model construction, thus leading to more false positives selected by the conventional VIP threshold of 1.0. Significance and novelty: Univariate data filtration preceding PLS-DA was important for the identification of reliable differential features if using a conventional threshold of VIP of 1.0. Presence of insignificant features could distort the PLS-DA model and inflate VIP values. Appropriate VIP threshold is associated with the numbers of input features and the model components. For PLS-DA without univariate prefiltration, threshold of VIP larger than 1.0 is recommended for the selection of discriminative features to reduce the false positives.

Qixue Shuangbu Prescription (QSP) is a famous traditional Chinese medicine (TCM) formula widely used for the treatment of chronic heart failure (CHF). Previous clinical studies have found that the efficacy of processed QSP has been significantly enhanced in the treatment of CHF. However, the synergistic mechanisms of processed QSP to enhance the treatment of CHF are still unclear. Generally, the changes in clinical effects mainly result from the variations of inside chemical basis caused by the TCM processing procedure. In this study, we developed a network pharmacology-integrated metabolomics strategy to clarify the difference of the effective compounds between crude and processed QSP, and further explain the mechanism of processed QSP to produce a synergistic effects. As a result, 69 different compounds were successfully screened, identified, quantified and verified as the most potential marker compounds. These different chemical components may play an anti-CHF and enhance the therapeutic effect through 52 action pathways such as estrogen signaling pathway, ubiquitin mediated proteolysis, protein processing in endoplasmic reticulum, etc. This study revealed that the proposed network pharmacology-integrated metabolomics strategy was a powerful tool for explaining the mechanism of synergistic action in the processing of QSP, further controlling the quality and understanding the processing mechanism of TCM formulae.

Background: Progranulin (PGRN), a novel pro-inflammatory adipokine, was reported to be related to the development and progression of diabetic retinopathy(DR). However, recently PGRN was established as a renal function-dependent protein, but no data regarding PGRN and DR excluded the influence of decreased renal elimination. This study aimed to investigate the correlation between serum PGRN and DR excluding the effect of deteriorating renal function.Methods: 338 subjects with estimated glomerular filtration rate (eGFR) ≥ 60 ml/min/1.73m2 were divided into four groups of normal controls (NC)(n=76), simple diabetes mellitus (SDM)(n=195) , nonproliferative diabetic retinopathy (NPDR)(n=41), and proliferative diabetic retinopathy (PDR)(n=26). Serum PGRN was quantified by enzyme-linked immunosorbent assay, and further analyses of serum PGRN in different groups were conducted. Results: There was no significant difference of serum PGRN between NC, SDM, NPDR, and PDR groups (P>0.05). Serum PGRN in all subjects negatively and significantly correlated with eGFR (r=-0.144, P<0.05), triglycerides and glucose (TyG) index (r=-0.127, P<0.05), and triglyceride (TG) (r=-0.132, P<0.05), while positively and significantly correlated with low-density lipoprotein cholesterol (LDL-C) (r=0.140, P<0.05). Multivariate stepwise regression analysis indicated only LDL-C (β=1.030, P<0.05) was independently associated with serum PGRN. Conclusions: We demonstrated that serum PGRN levels did not correlate with severity of DR in Chinese patients with type 2 diabetes and eGFR ≥ 60ml/min/1.73m2, but positively and independently correlated with LDL-C.