Combinatorial Chemistry & High Throughput Screening - Volume 28, Issue 4, 2025

CFTR, which belongs to the ATP-binding cassette transporter family and whose members are always involved in cancer progression, is implicated in lung adenocarcinoma (LUAD) progression, but the underlying mechanism remains undefined. Therefore, this study intended to investigate how CFTR works exactly on LUAD progression.

Bioinformatics methods were utilized to analyze GATA6 and CFTR expression in LUAD and targeting relationship, followed by a pathway enrichment analysis of CFTR. GATA6 and CFTR expression levels were assessed by qRT-PCR. Cell viability and proliferation were detected through MTT and colony formation assays. An arachidonic acid (AA) assay kit was utilized to measure AA content. mRNA and protein expression levels of genes (cPLA2, COX-2, and CYP1A1) related to the AA metabolism pathway were detected by qRT-PCR and western blot, respectively. Moreover, the Dual-luciferase reporter gene assay and ChIP were used to verify the binding of GATA6 and CFTR promoters.

GATA6 and CFTR were lowly expressed in LUAD, and CFTR was enriched in the AA metabolism pathway. GATA6 activated CFTR transcription. Cellular and rescue experiments revealed that low or high CFTR expression could foster or hamper LUAD cell viability and proliferation, and concomitant treatment of indomethacin, an AA metabolism pathway inhibitor, mitigated stimulation on LUAD progression by low CFTR expression. Silencing of GATA6 reversed the suppressive impact of CFTR overexpression on LUAD progression via modulation of the AA metabolism pathway.

The activation of CFTR by GATA6 hampered LUAD progression by modulating the AA metabolism pathway, suggesting that GATA6/CFTR axis might be a therapeutic target for LUAD patients.

A link between cholesterol and endometrial cancer has been established, but current studies have been limited in their findings. We aimed to elucidate the causal relationship between cholesterol and endometrial cancer and to find prognostic genes for endometrial cancer.

We first explored the causal relationship between total cholesterol and endometrial cancer using two-sample Mendelian randomization and then obtained differential genes to screen for prognosis-related genes in endometrial cancer. Then, we utilized pan-cancer analysis based on RNA sequencing data to evaluate the expression pattern and immunological role of the Translocase of Outer Mitochondrial Membrane 40 (TOMM40). Through multiple transcriptome datasets and multi-omics in-depth analysis, we comprehensively explore the relationship of TOMM40 expression with clinicopathologic characteristics, clinical outcomes and mutations in endometrial cancer. Lastly, we systematically associated the TOMM40 with different cancers from immunological properties from numerous perspectives, such as immune cell infiltration, immune checkpoint inhibitors, immunotherapy, gene mutation load and microsatellite instability.

We found a negative association between cholesterol and endometrial cancer. A total of 78 genes were enriched by relevant single nucleotide polymorphisms (SNPs), of which 12 upregulated genes and 5 downregulated genes in endometrial cancer. TOMM40 was found to be a prognostic gene associated with endometrial cancer by prognostic analysis. TOMM40 was found to be positively correlated with the infiltration of most immune cells and immunization checkpoints in a subsequent study. Meanwhile, TOMM40 also was an oncogene in many cancer types. High TOMM40 was associated with lower genome stability.

The findings of our study indicate that the maintenance of normal total cholesterol metabolism is associated with a decreased risk of developing endometrial cancer. Moreover, TOMM40 may have potential as a prognostic indicator for endometrial cancer.

Vitamin A is essential not only for bone metabolism and development but also for the normal functioning of many physiological processes in the body. Despite vitamin A's important involvement in bone metabolism, its effect on orthodontic tooth movement is not entirely known.

Previous studies on animals have suggested that vitamin A may influence alveolar bone remodelling and tooth movement, but the effect of various doses of vitamin A on these processes remains poorly understood. This experiment was designed to examine the effect of vitamin A on the orthodontic tooth movement of male rats.

Eighty male rats weighing 200-250 grams were divided into eight equal parallel groups. An initial orthodontic force was applied to all groups with a specific appliance, and six different doses of vitamin A were administered (250-2500 IU/Kg intraperitoneally). Two control groups were also considered. Orthodontic tooth movement was measured at the beginning and end of the study period (day 14), and serum alkaline phosphatase (ALP) level was evaluated. The maxillary sections were also evaluated by histological examination.

Although there was a dose-dependent increase in tooth movement observed with vitamin A administration, the differences were not statistically significant. There was no significant difference in the number of osteoclasts or the presence of lacunae on the root surface between the study groups. Root resorption was observed in different areas of the root and was not related to different doses of vitamin A. The serum ALP level did not show any significant difference between the groups treated with different doses of vitamin A.

Despite the known effects of vitamin A on bone metabolism, the results of this study suggest that vitamin A did not increase alveolar bone remodeling and orthodontic tooth movement in male rats.

Vitiligo is an autoimmune skin disorder primarily characterized by the absence of melanocytes, leading to the development of white patches on the patient's skin. Narrowband Ultraviolet B (NB-UVB) therapy is among the most effective approaches for stimulating the reformation of hyperpigmentation. This treatment utilizes a narrow spectrum of NB-UVB wavelengths ranging from 311 to 313 nm to irradiate the affected area, thereby preventing the destruction of migrating and proliferating melanocytes. Nevertheless, the molecular alterations occurring in both the hair follicle and the interfollicular epidermis during NB-UVB treatment remain unknown.

In this study, we conducted a comprehensive analysis of the consistency of differentially expressed genes (DEGs) within the enrichment pathways both before and after NB-UVB treatment, utilizing a bioinformatics approach. Furthermore, we employed CYTOHUBBA and Random Forest algorithms to identify and sequence hub genes from the pool of DEGs. Following validation of these hub genes through ROC curve analysis, we proceeded to construct an interaction network between these hub genes, miRNA, and drugs. Real-Time Quantitative Polymerase Chain Reaction (RT-qPCR) was used to further verify the difference in the expression of hub genes between the disease group and the control group.

Gene Set Enrichment Analysis of DEGs indicated strong associations with vitiligo in most pathways. Subsequently, we conducted Gene Ontology and Metascape enrichment analyses on the overlapping genes from DEGs. We identified key genes (COL11A1, IGFBP7, LOX, NTRK2, SDC2, SEMA4D, and VEGFA) within the Protein-Protein Interaction (PPI) network. We further explored potential drugs that could be used for the clinical treatment of vitiligo through the drug-hub gene interaction network. Finally, the results of RT-qPCR experiments demonstrated that the expression levels of the identified hub genes in both groups were consistent with the bioinformatics analysis results.

The hub genes obtained in this study may be a biomarker related to the development of vitiligo pigmentation. Our research not only contributes to a better understanding of the treatment mechanisms of vitiligo but also provides valuable insights for future personalized medical approaches and targeted therapies for vitiligo.

Employing the technique of liquid chromatography-mass spectrometry (LC-MS) in conjunction with artificial intelligence (AI) technology to predict and screen for anti-rheumatoid arthritis (RA) active compounds in Xanthocerais lignum.

Natural products have become an important source of new drug discovery. RA is a chronic autoimmune disease characterized by joint inflammation and systemic inflammation. Although there are many drugs available for the treatment of RA, they still have many side effects and limitations. Therefore, finding more effective and safer natural products for the treatment of RA has become an important issue.

In this study, a collection of inhibitors targeting RA-related specific targets was gathered. Machine learning models and deep learning models were constructed using these inhibitors. The performance of the models was evaluated using a test set and ten-fold cross-validation, and the most optimal model was selected for integration. A total of five commonly used machine learning algorithms (logistic regression, k-nearest neighbors, support vector machines, random forest, XGBoost) and one deep learning algorithm (GCN) were employed in this research. Subsequently, a Xanthocerais lignum compound library was established through HPLC-Q-Exactive-MS analysis and relevant literature. The integrated model was utilized to predict and screen for anti-RA active compounds in Xanthocerais lignum.

The integrated model exhibited an AUC greater than 0.94 for all target datasets, demonstrating improved stability and accuracy compared to individual models. This enhancement enables better activity prediction for unknown compounds. By employing the integrated model, the activity of 69 identified compounds in Xanthocerais lignum was predicted. The results indicated that isorhamnetin-3-O-glucoside, myricetin, rutinum, cinnamtannin B1, and dihydromyricetin exhibited inhibitory effects on multiple targets. Furthermore, myricetin and dihydromyricetin were found to have relatively higher relative abundances in Xanthocerais lignum, suggesting that they may serve as the primary active components contributing to its anti-RA effects.

In this study, we utilized AI technology to learn from a large number of compounds and predict the activity of natural products from Xanthocerais lignum on specific targets. By combining AI technology and the LC-MS approach, rapid screening and prediction of the activity of natural products based on specific targets can be achieved, significantly enhancing the efficiency of discovering new bioactive molecules from medicinal plants.

This study aimed to explore new therapeutic drugs for multiple myeloma (MM). MM is a common plasma cell malignant proliferative disease, accounting for 15% of hematological malignancies. The role of daptomycin (DAP), a potential anti-tumor drug, remains unclear in MM. In the present research, we investigated the anticancer effect of DAP in MM cell line RPMI 8226.

RPMI 8226 cells were treated with DAP (20 μM, 40 μM, and 80 μM) with 20 nM bortezomib (BZ) as a positive control. Cell function was detected using CCK8, flow cytometry, and transwell assay.

In MM cells, DAP inhibited proliferation and induced apoptosis. The cell cycle was arrested at the G1 phase after the treatment of DAP. The migration and invasion abilities were also inhibited by DAP treatment in RPMI 8226 cells. Importantly, the mRNA and protein levels of RPS19 were downregulated in DAP-treated RPMI 8226 cells.

DAP inhibited the proliferation, migration, and invasion and promoted the apoptosis of MM cells. Mechanistically, the RPS19 expression was significantly decreased in DAP-treated cells. This research provides a potential therapeutic drug for MM therapy.

Previous studies have found that matrine (MAT) effectively treated Ulcerative Colitis (UC). The purpose of this study is to explore its mechanism based on the HMGB1/NLRP3/Caspase-1 signaling pathway.

MAT was administered intragastrically to DSS-induced UC mice for 14 days. The Disease Activity Index (DAI) and histological staining were measured to detect histopathological changes in colon. The levels of IL-1β, IL-6, and TNF-α in serum were measured by ELISA. The protein and mRNA expression of HMGB1/NLRP3/Caspase-1 in the colon were detected by immunohistochemistry, western Blotting or qRT-PCR.

MAT improved the histological pathological changes of UC mice, as assessed by DAI, colonic length, and colonic mucosal injury. MAT also reduced colonic inflammatory damage by reducing the serum IL-1β, IL-6, and TNF-α content and decreasing the expression of HMGB1, NLRP3, Caspase-1, and IL-1β and proteins and mRNA in the colon.

MAT could significantly alleviate DSS-induced UC symptoms by reducing the expressions of pro-inflammatory cytokines, such as IL-1β, TNF-α, and IL-6, the mechanism of which is related to the inhibition of HMGB1/NLRP3/Caspase-1 signaling pathway.

Dysmenorrhea is one of the most common ailments affecting young and middle-aged women, significantly impacting their quality of life. Traditional Chinese Medicine (TCM) offers unique advantages in treating dysmenorrhea. However, an accurate diagnosis is essential to ensure correct treatment. This research integrates the age-old wisdom of TCM with modern Machine Learning (ML) techniques to enhance the precision and efficiency of dysmenorrhea syndrome differentiation, a pivotal process in TCM diagnostics and treatment planning.

A total of 853 effective cases of dysmenorrhea were retrieved from the CNKI database, including patients’ syndrome types, symptoms, and features, to establish the TCM information database of dysmenorrhea. Subsequently, 42 critical features were isolated from a potential set of 86 using a selection procedure augmented by Python's Scikit-Learn Library. Various machine learning models were employed, including Logistic Regression, Random Forest Classifier, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Artificial Neural Networks (ANN), each chosen for their potential to unearth complex patterns within the data.

Based on accuracy, precision, recall, and F1-score metrics, SVM emerged as the most effective model, showcasing an impressive precision of 98.29% and an accuracy of 98.24%. This model's analytical prowess not only highlighted the critical features pivotal to the syndrome differentiation process but also stands to significantly aid clinicians in formulating personalized treatment strategies by pinpointing nuanced symptoms with high precision.

The study paves the way for a synergistic approach in TCM diagnostics, merging ancient wisdom with computational acuity, potentially innovating the diagnosis and treatment mode of TCM. Despite the promising outcomes, further research is needed to validate these models in real-world settings and extend this approach to other diseases addressed by TCM.

Zuogui pill (ZGP) is the traditional Chinese medicine for tonifying kidney yin. Clinical and animal studies have shown that ZGP effectively enhances neurologic impairment after ischemic stroke, which may be related to promoting neurite outgrowth. This investigation aimed to prove the pro-neurite outgrowth impact of ZGP and define the underlying molecular pathway in vitro.

The major biochemical components in the ZGP were investigated using UPLC-QTOF-MS. All-trans retinoic acid (ATRA) was employed to stimulate SH-SY5Y cells to develop into mature neurons, followed by oxygen-glucose deprivation and reoxygenation damage (OGD/R). Then the cells were supplemented with different concentrations of ZGP, and cell viability was identified by CCK-8. The neurites' outgrowth abilities were detected by wound healing test, while immunofluorescence staining of β-III-tubulin was used to label neurites and measure their length. Western blot was employed to discover the changes in protein levels.

ZGP improved the cell viability of differentiated SH-SY5Y cells following OGD/R damage, according to the CCK-8 assay. Concurrently, ZGP promoted neurite outgrowth and improved neurite crossing and migration ability. Protein expression analysis showed that ZGP upregulated the expression of GAP43, OPN, p-IGF-1R, mTOR, and p-S6 proteins but downregulated the expression of PTEN protein. Blocking assay with IGF-1R specific inhibitor Linstinib suggested IGF-1R mediated mTOR signaling pathway was involved in the pro-neurite outgrowth effect of ZGP.

This work illustrated the molecular mechanism underpinning ZGP's action and offered more proof of its ability to promote neurite outgrowth and regeneration following ischemic stroke.

The efficacy of chemotherapy in treating Kidney Renal Clear Cell Carcinoma (KIRC) is limited, whereas immunotherapy has shown some promising clinical outcomes. In this context, KIF4A is considered a potential therapeutic target for various cancers. Therefore, identifying the mechanism of KIF4A that can predict the prognosis and immunotherapy response of KIRC would be of significant importance.

Based on the TCGA Pan-Cancer dataset, the prognostic significance of the KIF4A expression across 33 cancer types was analyzed by univariate Cox algorithm. Furthermore, overlapping differentially expressed genes (DEGs1) between the KIF4A high- and low-expression groups and DEGs2 between the KIRC and normal groups were also analyzed. Machine learning and Cox regression algorithms were performed to obtain biomarkers and construct a prognostic model. Finally, the role of KIF4A in KIRC was analyzed using quantitative real-time PCR, transwell assay, and EdU experiment.

Our analysis revealed that KIF4A was significant for the prognosis of 13 cancer types. The highest correlation with KIF4A was found for KICH among the tumour mutation burden (TMB) indicators. Subsequently, a prognostic model developed with UBE2C, OTX1, PPP2R2C, and RFLNA was obtained and verified with the Renal Cell Cancer-EU/FR dataset. There was a positive correlation between risk score and immunotherapy. Furthermore, the experiment results indicated that KIF4A expression was considerably increased in the KIRC group. Besides, the proliferation, migration, and invasion abilities of KIRC tumor cells were significantly weakened after KIF4A was knocked out.

We identified four KIF4A-related biomarkers that hold potential for prognostic assessment in KIRC. Specifically, early implementation of immunotherapy targeting these biomarkers may yield improved outcomes for patients with KIRC.

The Shoutai pill (STP) is a classic formulation in traditional Chinese medicine. Preliminary experimental observations from our study suggest that it is effective in enhancing endometrial receptivity. However, the underlying mechanisms by which STP influences endometrial receptivity remain to be elucidated.

The objective of this study is to investigate the effects and mechanisms of the STP formulation in enhancing endometrial receptivity in controlled ovarian hyperstimulation (COH) model mice.

The network pharmacology analysis identified target proteins associated with the reduction of endometrial receptivity by STP. The COH mouse model was established using the GnRHa+PMSG+HCG protocol. The levels of MHC-1 and MHC-2 in mouse serum were measured using the ELISA method, while the levels of IL-1β, IL-4, IL-10, IP-10, IL-1a, IL-2, IL-17, TNF-a, and IFN-y were measured using liquid chip technology.

STP exhibited a significant improvement in the immune environment of COH model mice. The major active components of STP were identified as beta-sitosterol and quercetin, among others. Furthermore, AKT1, VEGFA, and several immune factors, such as TNF, IFN, IL-1β, and IL-10, were identified as key targets for regulating endometrial receptivity. STP enhanced the expression of IL-10, IL-4, and IP-10 in the mice while reducing the expression levels of IL-2, IL-17, TNF-α, and IFN-γ in COH mice. These effects led to the modulation of early high expression of IL-1β and an improvement in endometrial receptivity.

This study demonstrates that STP can modulate in-vivo immune factors throughout the COH process, subsequently restoring the immune equilibrium within the endometrium, thereby enhancing the endometrial receptivity in the COH model mice.

Premature Ovarian Insufficiency (POI) is a disease suffered by women under the age of 40 when ovarian function has declined, seriously affecting both the physical and mental health of women. Guiluoshi Anzang decoction (GLSAZD) has been used for a long time and has a unique therapeutic effect on improving ovarian function. This study aims to investigate the mechanism of GLSAZD in treating POI through network pharmacology, molecular docking, and experimental verification.

In this study, the active ingredients of Guiluoshi Anzang Decoction and the targets of POI were obtained from TCMSP, BATMANN-TCM, Uniprot, GeneCards, and other databases, and network pharmacology analysis was performed. Molecular docking was conducted to validate the affinity of the main active ingredient of GLSAZD to key POI targets. A POI SD rat model was established, and HE staining, ELISA, Real-time PCR, and Western blot experiments were performed to verify the predicted core targets and the therapeutic effects.

10 core targets and the top 5 ingredients were screened out. Molecular docking showed core targets AKT1, CASP3, TNF, TP53, and IL6 had stable binding with the core 5 ingredients quercetin, kaempferol, beta-sitosterol, luteolin, and Stigmasterol. GO and KEGG enrichment analysis demonstrated the mechanism involved in the positive regulation of gene expression, PI3K-AKT signaling pathway, and apoptosis signaling pathways. Animal experiments indicated GLSAZD could up-regulate the protein expression of p-PI3K and p-AKT1 and the mRNA expression of STAT3 and VEGF, down-regulate TP53 and Cleaved Caspase-3 protein expression in rat`s ovarian tissues and serum TNF-α and IL-6 protein levels, activate PI3K-AKT signaling pathway and inhibit the apoptosis signaling pathway.

GLSAZD treats POI through multi-component, multi-target, and multi-pathway approaches. This study provided evidence for its clinical application in treating POI and shed light on the study of traditional medicine of the Guangxi Zhuang Autonomous Region in China.