Corrigendum to: "Identification of Potential Molecular Targets of Doxorubicin for Application in Skin Cancer: A Network Pharmacology and Molecular Docking Perspective".

The primary objective was to find the pharmacological targets of doxorubicin and their mechanisms of action, with a dual focus on their therapeutic relevance in skin cancer treatment and their potential involvement in resistance to doxorubicin in cancer cells. The targets of skin cancer and potential targets of doxorubicin were searched from multiple databases. Common targets were chosen using the GeneVenn tool and then imported into the STRING database to construct a protein-protein interaction network. Topological factors were evaluated with Cytoscape to identify core targets. FunRich was used to identify the signaling pathways, molecular functions, cellular components, and biological processes involving the top targets. Molecular docking was conducted using the Molecular Operating Environment software. The top five target genes identified as therapeutic targets of doxorubicin for treatment of skin cancer are poly(ADP-ribose) polymerase, epidermal growth factor receptors, heat shock protein 90 alpha family class A member 1, Harvey rat sarcoma viral oncogene homolog, and mammalian target of rapamycin. In addition, doxorubicin-induced resistance mechanisms were also predicted. Further research on innovative methods of delivering doxorubicin to maximize its effectiveness in treating skin cancer and to prevent the development of resistance to the drug is necessary.

Network Pharmacology in Traditional Chinese Medicine

Head and neck squamous cell carcinoma (HNSCC) represents one of the most malignant and heterogeneous tumors, and the patients have low 5-year survival. Traditional Chinese medicine (TCM) has been demonstrated as an effective complementary and/or alternative therapy for advanced malignancies including HNSCC. It has been noted that several herbs that are used for preparing Yinchen Wuling San (YWLS) have anti-tumor activities, whereas their mechanisms of action remain elusive. In this study, network pharmacology and molecular docking studies were employed to explore the underlying mechanisms of action of YWLS against HNSCC. The 58 active ingredients from six herbs used for YWLS and their 506 potential targets were screened from the traditional Chinese medicine systems pharmacology database and analysis platform (TCMSP) and SwissTargetPrediction database. A total of 2,173 targets associated with HNSCC were mainly identified from the DisGeNET and GeneCards databases. An active components-targets-disease network was constructed in the Cytoscape. Top 20 hub targets, such as AKT1, EGFR, TNF, ESR1, SRC, HSP90AA1, MAPK3, ERBB2, and CCND1, were identified by a degree in the protein–protein interaction (PPI) network. Gene functional enrichment analysis showed that PI3K-AKT, MAPK, Ras, TNF, and EGFR were the main signaling pathways of YWLS in treating HNSCC. There were 48 intersected targets such as EGFR, AKT1, and TNF that were associated with patients’ outcomes by the univariate Cox analysis, and most of them had increased expression in the tumor as compared to normal tissues. The area under curves of receiver operating characteristic indicated their diagnostic potential. Inhibition of these survival-related targets and/or combination with EGFR or AKT inhibitors were promising therapeutic options in HNSCC. The partial active components of YWLS exhibited good binding with the hub targets, and ADME analysis further evaluated the drug-likeness of the active components. These compounds and targets identified in this study might provide novel treatment strategies for HNSCC patients, and the subsequent work is essential to verify the underlying mechanisms of YWLS against HNSCC.

Investigations into the therapeutic potential of Astragalus Mongholicus (AM, huáng qí) and Largehead Atractylodes (LA, bái zhú) reveal significant efficacy in mitigating the onset and progression of knee osteoarthritis (KOA), albeit with an elusive mechanistic understanding. This study delineates the primary bioactive constituents and their molecular targets within the AM-LA synergy by harnessing the comprehensive Traditional Chinese Medicine (TCM) network databases, including TCMSP, TCMID, and ETCM. Furthermore, an analysis of 3 gene expression datasets, sourced from the gene expression omnibus database, facilitated the identification of differential genes associated with KOA. Integrating these findings with data from 5 predominant databases yielded a refined list of KOA-associated targets, which were subsequently aligned with the gene signatures corresponding to AM and LA treatment. Through this alignment, specific molecular targets pertinent to the AM-LA therapeutic axis were elucidated. The construction of a protein-protein interaction network, leveraging the shared genetic markers between KOA pathology and AM-LA intervention, enabled the identification of pivotal molecular targets via the topological analysis facilitated by CytoNCA plugins. Subsequent GO and KEGG enrichment analyses fostered the development of a holistic herbal-ingredient-target network and a core target-signal pathway network. Molecular docking techniques were employed to validate the interaction between 5 central molecular targets and their corresponding active compounds within the AM-LA complex. Our findings suggest that the AM-LA combination modulates key biological processes, including cellular activity, reactive oxygen species modification, metabolic regulation, and the activation of systemic immunity. By either augmenting or attenuating crucial signaling pathways, such as MAPK, calcium, and PI3K/AKT pathways, the AM-LA dyad orchestrates a comprehensive regulatory effect on immune-inflammatory responses, cellular proliferation, differentiation, apoptosis, and antioxidant defenses, offering a novel therapeutic avenue for KOA management. This study, underpinned by gene expression omnibus gene chip analyses and network pharmacology, advances our understanding of the molecular underpinnings governing the inhibitory effects of AM and LA on KOA progression, laying the groundwork for future explorations into the active components and mechanistic pathways of TCM in KOA treatment.

Objective Mingmu Dihuang Pill (MMDHP) is a traditional Chinese formula that has shown remarkable improvements of dry eyes, tearing, and blurry vision; however, the mechanisms underlying MMDHP treatment for diabetic retinopathy have not been fully understood. This study is aimed at identifying the molecular targets and active ingredients of MMDHP for the treatment of diabetic retinopathy based on network pharmacology. Methods All active ingredients of MMDHP were retrieved from TCMSP and BATMAN-TCM databases, and the targets of active ingredients of MMDHP were predicted on the SwissTargetPrediction website. Diabetic retinopathy-related target sets were retrieved from GeneCards and OMIM databases, and the intersecting targets between targets of active ingredients of MMDHP and potential therapeutic targets of diabetic retinopathy were collected to generate the traditional Chinese medicine-ingredient-target-diabetic retinopathy network and to create the protein-protein interaction network. In addition, GO terms and KEGG pathway enrichment analyses were performed to identify the potential pathways, and molecular docking was employed to verify the binding of active ingredients of MMDHP to key targets of diabetic retinopathy. Results Network pharmacology predicted 183 active ingredients and 904 targets from MMDHP, and 203 targets were intersected with the therapeutic targets of diabetic retinopathy. The top 10 hub targets included PIK3RA, TP53, SRC, JUN, HRAS, AKT1, VEGFA, EGFR, ESR1, and PI3KCA. GO terms and KEGG pathway enrichment analyses identified AGE-RAGE, PI3K-AKT, and Rap1 signaling pathways as major pathways involved in MMDHP treatment for diabetic retinopathy. Molecular docking confirmed a good binding affinity of active ingredients of MMDHP, including luteolin, acacetin, naringenin, and alisol B, with AKT1, SRC, and VEGFA as the three key targets of diabetic retinopathy. Conclusion MMDHP may be effective for the treatment of diabetic retinopathy through active ingredients luteolin, acacetin, naringenin, and alisol B via AKT1, SRC, and VEGFA in AGE-RAGE, PI3K-AKT, and Rap1 signaling pathways.

Yudantong decoction (YDTD) is a therapeutic prescription for cholestatic liver disease (CLD) and is clinically effective in our medical institution. However, the exact constituents and mechanisms of YDTD in treating CLD remain unknown. This project aimed to explore the primary constituents and mechanism of YDTD in the treatment of CLD through ultra-performance liquid chromatography-high resolution mass spectrometry (UPLC-HRMS), network pharmacology, molecular docking technology, and in vivo experiments. The chemical constituents of YDTD were identified via UPLC-HRMS, and Bioinformatics analysis tool for molecular mechANism of traditional Chinese medicine (BATMAN-TCM) was employed to screen target proteins. Cytoscape 3.7.1 was used to build the herbal component-target network. The CLD targets were identified by querying the OMIM and DisGeNET databases and determining the overlap between the targets of the YDTD chemical constituents and those of CLD. The STRING database was utilized to construct a protein-protein interaction (PPI) network for subsequent analysis. Gene ontology (GO) biological process enrichment analysis and Kyoto encyclopedia of genes and genomes (KEGG) signaling pathway enrichment analysis were carried out using the database for annotation, visualization and integrated discovery (DAVID) database. Molecular docking validation against core targets with PyMOL software was conducted for the active compounds. Finally, in vivo experiments were performed to investigate the therapeutic effect of YDTD in a murine model of α-naphthyl isothiocyanate (ANIT)-induced CLD. YDTD has 112 major components, among which 59 have 1,478 potential targets. We identified a total of 1,957 potential therapeutic targets for CLD and 269 overlapping targets between CLD-associated targets and YDTD active component targets to construct a PPI network. Through topology analysis, IL-6, INS, IL1B, AKT1, BCL2, NFKB1, PTGS2, and TP53 were identified as key targets along with their corresponding primary chemical components. KEGG analysis revealed significant enrichment of the phosphoinositide 3-Kinase (PI3K)-Akt and nuclear factor Kappa-B (NF-κB) signaling pathways. The molecular docking results indicated strong binding affinities between glycyrrhizin-AKT1, l-arginine-IL1B, p-coumaric acid-IL1B, 2,4-dihydroxybenzoic acid-IL1B, p-coumaric acid-TNF, 2-hydroxycinnamic acid-TNF, uridine-AKT1, p-coumaric acid-IL6, and trigonelline-INS. In vivo experiments demonstrated that YDTD downregulated the expression of p-PI3K, p-AKT, p-NF-κB, IL-6, TNF-α, and IL-1β, and reduced immune cell infiltration in the liver to ameliorate liver damage in CLD patients. The present study clarified the active components and potential anti-inflammatory mechanism of YDTD in treating CLD, providing a solid foundation for future research on its therapeutic mechanisms.

To investigate the molecular targets and underlying mechanisms of Fuzheng Shengbai Decoction (FZSBD) against colon cancer (CC). Multiple network pharmacology approaches were used to predict the molecular targets and underlying mechanisms of FZSBD against CC. The expression of potential molecular targets was determined. The effects of FZSBD on cell viability, proliferation, migration, invasion, and the cell cycle of CC cells were investigated. The therapeutic efficacy, hematological, immunological, and inflammatory data in patients with CC were evaluated after treatment with the XELOX regimen with and without FZSBD. A total of 912 potential targets in FZSBD and 2765 DEGs in CC specimens were screened. Five hub genes (TP53, MYC, VEGFA, CCND1, and IL1B) closely associated with immune-related signaling pathways and the cell cycle process were identified. The five hub genes were of prognostic value in CC. The gene and protein expression of the five hub genes was significantly higher in CC tumor tissue samples than that of normal tissue samples. Furthermore, with increasing doses, FZSBD increasingly inhibited growth, migration, and invasion, and suppressed the cell cycle process of CC cells. Supplementing of FZSBD to the XELOX regimen enhanced immune modulation and alleviated inflammatory responses. This study identified the molecular targets and underlying mechanisms of FZSBD treatment against CC and may provide clues for future research on the treatment of CC with FZSBD.

This study aims to identify potential target genes of luteolin (LUT) for treating hepatocellular carcinoma (HCC) through integrated in vitro experiments, network pharmacology, bioinformatics, and transcriptome sequencing (RNA-seq). Potential LUT-associated therapeutic targets for HCC were predicted using network pharmacology. The anti-HCC effects of LUT were evaluated in vitro by assessing its impact on SMMC-7721 and HepG2 cell viability, apoptosis, migration, and invasion. Transcriptome sequencing was performed to identify differentially expressed genes (DEGs) in LUT-treated HepG2 cells, followed by bioinformatics analyses to validate hub targets and their associated pathways. Network pharmacology predicted 100 potential protein targets of LUT for HCC treatment, implicating pathways related to inflammation, cell migration, cell cycle regulation, and apoptosis, including the HIF-1α signaling axis. In vitro experiments demonstrated that LUT (40, 60, and 90 µmol·L-¹) significantly inhibited proliferation, induced apoptosis, and suppressed migration and invasion in SMMC-7721 and HepG2 cells. Transcriptome analysis identified 975 DEGs in LUT-treated HepG2 cells, with MMP9 and SRC emerging as key targets. Bioinformatics validation further linked LUT's anti-HCC effects to cell cycle modulation, wound healing, enzyme inhibition, and the TNFα and HIF-1 signaling pathways. LLUT suppresses HCC progression by inhibiting proliferation, regulating cell cycle and apoptosis, and blocking invasion and migration. Its therapeutic mechanisms likely involve targeting MMP9 and modulating the HIF-1α signaling pathway.

The aim of this study was to investigate the potential mechanisms of Rhaponticin (Rha) in the treatment of periodontitis. Network pharmacology and molecular docking techniques were used to identify potential targets of Rha for the treatment of periodontitis and its ability to bind to the targets. Next, in vitro as well as in vivo experiments were conducted to validate Rha’s potential role in treating periodontitis. We found in network pharmacology and molecular docking that the HIF-hypoxia signaling pathway is involved in the potential mechanism of Rha treatment of periodontitis and that it can bind stably to HIF1A. In vitro experiments, based on the hypoxia-induced inhibition of proliferation, migration, and osteogenic differentiation of hPDLSCs, we found that Rha inhibited the expression of HIIF1A, promoted the expression of PCNA, CXCR4, and OCN, and enhanced their proliferation, migration, and osteogenic differentiation. In in vitro experiments, Rha promoted alveolar bone repair and inhibited gingival inflammation in periodontitis rats. Rha is a potential drug for the treatment of periodontitis. Therefore, this study provides new insights into the potential mechanisms of Rha in periodontitis treatment.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-025-26734-2.

Molecular targets in cancer diagnosis and therapy have come to the fore of the oncology field in the last decade. Their identification is rooted in basic science investigation and enhanced knowledge in the fields of genetics, biochemistry, molecular and tumor biology, and pathology among others. A medical literature search in English using MEDLINE/PUBMed was performed on the topics of molecular targets, targeted therapy, and biomarkers in the areas of bladder, prostate, and renal cancers. This information was analyzed and combined with the author's personal knowledge in the identification and development of molecular targets. Data is included from the author's laboratory regarding examples of target development and clinical translation. Molecular targets are often biomarkers; either prognostic ones that reflect the natural history of the cancer or predictive ones that reflect the impact of a therapy. Molecular targets in urologic cancer may arise from four sources: the host, the tumor, as a result of a treatment, or associated with a specific disease stage. Understanding the continuum of targets through the progression of a urologic cancer is central to the translational applications of diagnostics, individualized medicine and targeted therapeutics. Urologists are most familiar with targeted therapy in renal cancer with the introduction of tyrosine kinase inhibitors. Yet, herein are examples of biomarkers and targets across the spectrum of urologic tumors, stages and treatments. Identification of events, signals, and pathways in urologic cancer are opportunities to develop biomarkers and targets for diagnosis and treatment.

Despite the widespread use of selective serotonin reuptake inhibitors like sertraline, the intricate molecular mechanisms underlying major depression and the therapeutic efficacy of these treatments remain not fully elucidated. Building on our preliminary findings, this study investigates the antidepressant effects of fasudil, a Rho-associated protein kinase (ROCK) inhibitor typically utilized as a vasodilator and antispasmodic, and compares its effects with those of sertraline using a chronic restraint stress model in rats. Specifically, we examined the effects of chronic administration on dendritic spine density, key molecular survival pathways, and miRNA levels in the hippocampus. Adult male Sprague-Dawley rats were administered sertraline, fasudil (10 mg/kg/day), or saline over 14 days, with a subset experiencing daily restraint stress. Our findings demonstrate that both sertraline and fasudil effectively prevented stress-induced reductions in dendritic spine density and miR-138 levels in the rat hippocampus. Additionally, by employing a network pharmacology approach, we explored the converging molecular pathways influenced by both drugs, facilitating the identification of novel molecular targets and pathways implicated in the pathophysiology of depression and its treatment. Pharmacoinformatic analysis revealed common signaling cascades and critical proteins that may potentially underlie the observed pharmacological effects, contributing to a paradigm shift in understanding depression by integrating drug repurposing and network pharmacology, offering valuable insights into the underlying mechanisms of depression and the antidepressant effect from a new network-based paradigm rather than focusing solely on a single protein target.

Traditional Chinese medicine (TCM) is applied in the anticancer adjuvant therapy of various malignancies and pancreatic cancer included. Xiaoji recipe consists several TCM materials with anticancer activities. In our work, we intended to analyze the molecular targets as well as the underlying mechanisms of Xiaoji recipe against pancreatic cancer. A total of 32 active components and 522 potential targets of Xiaoji recipe were selected using the TCMSP and SwissTargetPrediction databases. The potential target gene prediction in pancreatic cancer was performed using OMIM, Disgenet, and Genecards databases, and totally, 998 target genes were obtained. The component-disease network was constructed using the Cytoscape software, and 116 shared targets of pancreatic cancer and Xiaoji recipe were screened out. As shown in the protein–protein interaction (PPI) network, the top 20 hub genes such as TP53, HRAS, AKT1, VEGFA, STAT3, EGFR, and SRC were further selected by degree. GO and KEGG functional enrichment analysis revealed that Xiaoji recipe may affect pancreatic cancer progression by targeting the PI3K/AKT and MAPK signaling pathways. Moreover, we performed in vitro assays to explore the effect of Xiaoji recipe on pancreatic cancer cells. The results revealed that Xiaoji recipe suppressed the viability and migration and promoted the apoptosis of pancreatic cancer cells via the inactivation of PI3K/AKT, MAPK, and STAT3 pathways. The findings of our study suggested the potential of Xiaoji recipe in the targeting therapy of pancreatic cancer.

Fibrosis, also known as scarring, sclerosis or cirrhosis, is characterised by excessive accumulation of extracellular matrix (ECM) proteins leading to tissue contraction, disruption of tissue architecture and eventually chronic organ failure (Wynn, 2007; Xu et al., 2007). Research and development of anti-fibrotic drugs are generally based on two distinct but interactive strategies, with one based on mechanism studies and another based on exploring efficacy. In principle, the mechanism-based strategy begins with identification of molecular targets through mechanistic studies, and then development of inhibitors or enhancers targeting the molecules. On the other hand, efficacy-based strategy starts with screening drug candidates in disease models to identify activities and efficacy, with less reliance on analysis of mechanisms of action. There are certain limitations in both the mechanism-based strategy and the efficacy-based strategy, which largely account for the lack of success in development of anti-fibrotic drugs. The former is often associated with identification of multiple molecular targets impeding development of a single drug that tackles multiple targets, while the latter is often hampered by establishment of apt models ideal for efficacy-driven drug screens. Efficacy-based strategy has been employed in development of both traditional and modern medicines. In the context of traditional medicine, the knowledge about efficacy of a given drug is largely derived from a trial-and-error process, namely by assessing patients’ response upon treatment with natural drug candidates. However, in modern medicine, it is impossible to directly test any new drugs in patients. Solid scientific evidence on efficacy and safety of a given drug in experimental models is required prior to clinical trials. Understandably, quality of these models would determine the specificity and efficiency of the tested drug.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder, characterized by the accumulation of amyloid-beta, tau hyperphosphorylation, neuroinflammation, and oxidative stress. With current pharmacological treatments providing symptomatic relief, the need for other therapeutic approaches becomes evident. Traditional Chinese Medicine, with its multi-component and multi-target approach, offers promising potential for the management of AD, but the complex formulations have proved challenging to discern precise mechanisms of therapy. Network pharmacology, a systems biology approach, has emerged as a powerful tool in understanding the mechanisms of action of TCM by mapping bioactive compounds to AD-related pathways. This method enables the identification of synergistic interactions and key molecular targets, facilitating drug discovery and optimization. Furthermore, AI, particularly machine learning and deep learning algorithms, has revolutionized TCM research by analyzing large datasets, predicting compound-target interactions, and enabling personalized treatment approaches. AI-driven virtual screening and computational modeling have rapidly accelerated the identification of potential neuroprotective compounds, such as curcumin, ginsenosides, and huperzine A, which modulate multiple AD-associated pathways. The integration of network pharmacology and AI offers a systematic framework for validating TCM formulations and optimizing their therapeutic potential. This review highlights recent advancements in AI-assisted TCM research, discusses key bioactive compounds, and explores their mechanisms in AD treatment. While standardization and regulatory approval continue to be challenging, the synthesis of ancient knowledge with contemporary computing technologies holds enormous promise for effective, multi-target interventions for AD, thereby ushering in a new wave of innovative therapeutic approaches.

Retracted: Identification of Molecular Targets and Underlying Mechanisms of Xiaoji Recipe against Pancreatic Cancer Based on Network Pharmacology