Combinatorial Chemistry & High Throughput Screening - Volume 28, Issue 19, 2025

RA is a recurrent autoimmune disease that has significant adverse effects on the physical and mental health of patients. Traditional Chinese medicine has shown significant advantages in the prevention and treatment of RA. Numerous clinical and experimental studies have confirmed that traditional Chinese medicine components have clear therapeutic effects and minimal adverse reactions in treating RA. The research on traditional Chinese medicine for the prevention and treatment of RA has become a hot topic in the field of autoimmune diseases.

The related references about the mechanisms and Q-markers of anti-RA of traditional Chinese medicine in this review were collected from Willy, SpringLink, Web of Science, Elsevier, PubMed, SciFinder, Scopus, ACS publications, Baidu Scholar, Google Scholar, and CNKI.

The traditional Chinese medicine components such as terpenoids, flavonoids, and alkaloids have significant anti-RA effects, and their mechanisms are mainly to inhibit NF-κB signaling pathway, inhibit the proliferation of RA fibroblasts like synovial cells, and regulate Th1/Th2 cell balance, and so on. Predicting and studying the Q-markers of traditional Chinese medicine anti-RA by plant phylogeny and chemical componentss, traditional medicinal properties, pharmacokinetics, component measurability, correlation between composition and efficacy, and gut microbiota will provide scientific foundations for the research and further development of anti-RA traditional Chinese medicine.

The active components of traditional Chinese medicine exhibited the characteristic of multiple mechanisms in the treatment of RA, such as terpenoids had anti-angiogenesis effects, flavonoids had anti-inflammatory and cartilage protective effects, and alkaloids had anti-inflammatory and analgesic effects. The proposal of Q-markers for anti-RA provided new research ideas for promoting the development of new drugs for anti-RA and ensuring the safety and effectiveness of clinical medications.

The prevalence of depression in COVID-19 patients is notably high, disrupting daily life routines and compounding the burden of other chronic health conditions. In addition, to elucidate the connection between COVID-19 and depression, we conducted an analysis of commonly differentially expressed genes (co-DEGs), uncovering potential biomarkers and therapeutic avenues specific to COVID-19-related depression.

We obtained gene expression profiles from the Gene Expression Omnibus (GEO) database with strategic keyword searches (“COVID-19”, “depression”, and “SARS”). We used functional enrichment analysis of the co-DEGs to decipher their likely biological roles. Then, we utilized protein-protein interaction (PPI) network analysis to identify hub genes among the co-DEGs. These findings were validated via an independent third-party dataset.

Our analysis of blood samples from COVID-19 patients revealed 10,716 upregulated genes and 10,319 downregulated genes. In addition, by applying the same approach to depression samples, we identified 571 upregulated and 847 downregulated genes. Furthermore, by intersecting these datasets, we extracted 121 upregulated and 175 downregulated co-DEGs. Through PPI network construction and hub gene selection, we identified MPO, ARG1, CD163, FCGR1A, ELANE, LCN2, and CR1 as co-upregulated hub genes and MRPL13, RPS23, and MRPL1 as co-downregulated hub genes. The incorporation of third-party datasets revealed that these hub genes are specific targets of SARS-CoV-2, not generic viral response mechanisms.

The identification of potential biomarkers represents a groundbreaking strategy for assessing and treating depression in the context of COVID-19, with the potential to reduce its prevalence among these patients. However, to fully harness this potential, additional clinical research is paramount.

Colorectal cancer is one of the common malignant tumors in clinical practice, and traditional Chinese medicine, as an important adjuvant treatment method, plays important roles in the treatment of malignant tumors.

This study aims to explore the mechanism of action of the Qizhu anti-cancer recipe on colorectal cancer through transcriptome sequencing.

The control group and Qizhu anti-cancer recipe group were established separately, and sequencing of the cells of the two groups was performed using the Illumina sequencing platform. Two sets of Differentially Expressed Genes (DEGs) were screened using the DESeq2 algorithm, and Principal Component Analysis (PCA), Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Reactome, Disease Ontology (DO), and Protein-Protein Interaction (PPI) were used to comprehensively analyze the molecular functions and signaling pathways enriched by DEGs.

A total of 122 DEGs were identified through differential analysis, including 24 upregulated genes and 98 downregulated genes. GO analysis showed that DEGs were mainly enriched in functions such as alkaline phase activity, ion transport, cell differentiation, etc.; KEGG analysis showed that DEGs were mainly enriched in pathways such as Thiamine metabolism, apoptosis, signaling pathways regulating pluripotency of stem cells, cellular senescence and so on. Reactom analysis showed that DEGs were mainly enriched in response pathways such as EGR1,2,3 bind to the NAB2 promoter, EGR binds ARC gene, EGR-dependent NAB2 gene expression, etc.; DO analysis showed that differentially expressed genes were mainly enriched in diseases such as disease of cellular proliferation, disease of anatomical entity, organ system cancer, etc.; PPI analysis identified key differentially expressed genes, including DDIT3, CHAC1, TRIB3, and ASNS.

Based on transcriptome sequencing and bioinformatics analysis, it was found that the Qizhu anti-cancer recipe may involve DEGs and signaling pathways in the treatment of colorectal cancer. Our study may provide potential drug targets for developing new treatment strategies for colorectal cancer.

As a classical formula to invigorate blood circulation, Huoxue Tongluo Qiwei Decoction (HTQD) can effectively treat hypertensive erectile dysfunction (ED), but its exact mechanism of action is not yet clear. The goal of this research was to explore the potential mechanism of HTQD in improving hypertensive erectile dysfunction in rats through transcriptomics, network pharmacology, and associated animal experimentations.

The HTQD chemical constituents were screened using high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS). Furthermore, transcriptomics analysis was performed via mRNA sequencing to identify significantly differentially expressed proteins. Moreover, the key target proteins of HTQD in the treatment of hypertensive ED were screened by network pharmacology and transcriptomics. In addition, the endothelial cells of the corpus cavernosum were assessed using hematoxylin-eosin staining. The transcript and protein expressions were evaluated via western blotting and Real-time reverse transcription-quantitative polymerase chain reaction (RT-qPCR).

The network pharmacology and transcriptome mRNA sequencing revealed that KCNE1 may be the target protein of HTQD in improving hypertensive ED. After HTQD treatment, the systolic and diastolic blood pressure (BP) of hypertensive rats decreased, the number of erections increased, and the pathological structure of the penis was improved. Moreover, HTQD downregulated the protein and mRNA expression of AngII, AT1R, DAG, and PKCε, whereas it up-regulated the transcript and protein expression of KCNE1.

HTQD may activate the PKCε pathway through AngII, inhibit the expression of KCNE1 protein, relax vascular smooth muscles, and improve erectile function.

In this study, we aimed to identify novel biomarkers related to Peripheral Neural Invasion (PNI) in head and neck squamous cell carcinoma (HNSCC).

The PNI-related differentially expressed mRNAs (DE-mRNAs) in HNSCC were identified to construct a PNI-related risk score model. The expression level and ROC curve for Tachykinin Precursor 1 (TAC1) were calculated. Additionally, two kinds of in vitro models of PNI were established for investigation, including the Matrigel-PNI model and the Transwell-PNI model. Furthermore, the transcription factor of the TAC1 was predicted and verified by qRT-PCR.

A total of 139 DE-mRNAs were identified in PNI positive and negative groups of HNSCC patients. The risk-score marker model incorporating 20 PNI-related DE-mRNAs was established. The TAC1 was identified as a potential highly expressed PNI marker, which exhibited good performance in predicting PNI events. Patients with higher TAC1 expressions demonstrated significantly shorter survival rates compared to those with lower TAC1 expressions in HNSCC. Besides, the knockdown of TAC1 significantly repressed neural invasion in HNSCC cells in vitro, according to the Matrigel-PNI model and Transwell-PNI model. Furthermore, KLF15 was predicted and verified as a transcription activator of TAC1 in HNSCC.

This study highlights that the activation of KLF15 transcription of TAC1 promotes PNI in HNSCC cells, which provides guidance regarding the molecular diagnosis of PNI in HNSCC cells.

Age-related Macular Degeneration (AMD) is a predominant cause of blindness in the elderly. The present study is the first to investigate the alteration of lncRNAs and mRNAs in neovascular AMD.

Nine patients with neovascular AMD were included in the study. The control group comprised seven patients with epiretinal membranes. RNA sequencing was performed to obtain the differentially expressed mRNAs (DEmRNAs) and lncRNAs (DElncRNAs). Then, the DElncRNA-DEmRNA co-expression network, ceRNA network, and immune-related ceRNA subnetwork were constructed. Functional annotation of DEmRNAs between the two groups and DEmRNAs in networks was conducted. The immune cell distribution in neovascular AMD was also evaluated. Real-time qPCR (RT-qPCR) was used to validate the expression levels of key markers.

A total of 342 DEmRNAs and 157 DElncRNAs were obtained in neovascular AMD. Functional annotation indicated that these DEmRNAs significantly enriched immune system-related processes, such as positive regulation of B cell activation, immunoglobulin receptor binding, complement activation, and classical pathway. The DElncRNA-DEmRNA co-expression network, including 185 DElncRNA-DEmRNA co-expression pairs, and the ceRNA (DElncRNA-miRNA-DEmRNA) network, containing 45 lncRNA-miRNA pairs and 73 miRNA-mRNA pairs, were constructed. The immune-related ceRNA subnetwork, including 2 lncRNAs, 5 miRNAs, and 3 mRNAs, was constructed. In addition, the distribution of immune cells was slightly different between the neovascular AMD group and the control group. RT-qPCR validation indicated the consistency between the RT-qPCR results and RNA sequencing results.

In conclusion, STC1, S100A1, MEG3, MEG3-hsa-miR-608-S100A1, and MEG3-hsa-miR-130b-3p/hsa-miR-149-3p-STC1 may be related to the occurrence and development of neovascular AMD.

Organic thiocyanates are important pharmaceutical intermediates. This study aimed to develop a selective and efficient approach for synthesizing organic thiocyanates.

Under mild reaction conditions, an array of alkenes, KSCN, and diaryliodonium salts are considered good substrates, providing various aryl-substituted alkylthiocyanates with modest to excellent yield. Radical trapping and nucleophilic trapping experiments were carried out to explore the mechanistic pathways. The experiments indicated the involvement of free-radical and carbenium ion intermediate processes. Diaryliodonium salts were used as the radical arylating reagent, and KSCN was the electrophilic cyanating reagent. Under irradiation, the excited photocatalyst reduced aryldiazonium salt to aryl radical. Then, the radical was added to olefin to generate a new radical. Finally, the generated radical was further oxidized and arrested by SCN anion.

This coupling reaction provides a straightforward and practical route to construct various aryl-substituted alkylthiocyanates.

HJ11 (HJ11 decoction), which is based on the traditional prescription Si-Miao-Yong-An decoction, has exerted a remarkable effect on atherosclerosis (AS). Nevertheless, the main components and underlying mechanisms of HJ11 for treating AS remain unclear.

This study was designed to elucidate the mechanism of HJ11 in the treatment of AS through network pharmacology and in vivo experimental validation.

Network pharmacology was employed to explore the primary bioactive components and targets of HJ11. AS-related genes were obtained from the GeneCards and DisGeNET databases and screened for intersections with HJ11. A herb-compound-target interaction network was constructed by Cytoscape 3.9.1, and molecular docking analyses were constructed on key targets. By using a mouse model, the mechanism of action of HJ11 was further confirmed.

A total of 231 active components of HJ11, 1681 AS-related genes, and 156 common targets were identified. Through the establishment of numerous networks, it was discovered that the main association of the mechanism of HJ11 in AS therapy pertained to anti-inflammation. Important substances included quercetin, kaempferol, and luteolin, while TNF-α, AKT1, IL-6, and VEGFA were the main targets. Molecular docking demonstrated that there were favorable binding interactions between active drugs (quercetin, kaempferol, and luteolin) and targets (TNF-α, AKT1, IL-6, and VEGFA). In the in vivo study, HJ11 reduced the expression of TNF-α, AKT1, IL-6, and VEGFA at both the mRNA and protein levels, inhibited atherosclerotic lesions in AS mouse models, and retarded the development of retroarterioid sclerosis.

HJ11 can inhibit inflammation and the progression of AS, and the mechanism might involve downregulating the expression of TNF-α, AKT1, IL-6, and VEGFA.

Quantitative Structure–Property Relationship (QSPR) models play a crucial role in predicting the chemical and physical characteristics of molecules.

This study introduces Zagreb rho indices derived from graph theory to assess the physico-chemical properties of benzenes. The rho degree of vertices in connected graphs was formulated and used to compute these indices.

Strong correlations (R> 0.94) were observed between Zagreb rho indices and various molecular properties such as boiling point, molecular weight, and electron energy.

The findings demonstrate that Zagreb rho indices can serve as reliable predictors within QSPR frameworks, offering structural sensitivity and outperforming traditional topological indices in several aspects.

Latent tuberculosis infection (LTBI) population is the principle source of active tuberculosis (ATB) patients. The identification of reliable diagnostic biomarkers is critical to the prevention and control of progression from LTBI to ATB. The aim of this study is to screen biomarkers that can distinguish LTBI from ATB patients by a comprehensive bioinformatic analysis strategy.

The transcriptomic datasets were obtained from GEO database. Hub genes and critical signal pathways for differentiating latent and active TB were identified by a comprehensive bioinformatic analysis strategy comprising weighted gene co-expression network analysis (WGCNA), differentially expressed gene (DEG), protein-protein interaction (PPI), Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, and hub genes were verified by RT-qPCR in this study.

The transcriptome profiles of GSE193777, GSE157657, GSE168519, GSE107991, and GSE107992 were extracted from the GEO database, in which a total of 18,397 protein-coding genes from 206 samples were included in the bioinformatics analysis. Combined with weighted gene co-expression network, differentially expressed gene, functional enrichment and protein-protein interaction analyses, six hub genes were identified. RT-qPCR confirmed significantly lower expression of HLA-DOA (p=0.002), ECH1 (p=7.19e-8), PARN (p=9.22e-9), and TRAPPC4 (p=6.81e-7) in LTBI versus ATB.

Our findings may provide crucial clues to potential biomarkers that can distinguish patients with LTBI from those with ATB, aiding the understanding of the mechanism of LTBI progression to ATB.