Exploring Medicinal Mechanism of Baicalin in Tumor Microenvironment of Melanoma via Bioinformatic and In Vitro Study

Exploring Medicinal Mechanism of Baicalin in Tumor Microenvironment of Melanoma via Bioinformatic and In Vitro Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Exploring Medicinal Mechanism of Baicalin in Tumor Microenvironment of Melanoma via Bioinformatic and In Vitro Study Zuohao Liu, Bingrong Dang, Xingsheng Wang, Yuhan Liu, Chunming Wang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7498099/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 26 Dec, 2025 Read the published version in Medical Oncology → Version 1 posted 9 You are reading this latest preprint version Abstract The occurrence and advancement of skin cutaneous melanoma (SKCM) is closely associated with tumor microenvironment (TME). Evaluating the condition of immune cell infiltration is pivotal to the comprehension regarding the features of TME in SKCM. Baicalin has been demonstrated to have anti-tumor effects by regulating the TME in tumors. However, its pharmacological potential in melanoma still needs to be elucidated. In this study, through unsupervised clustering analysis and network pharmacology, 32 potential baicalin targets have been identified. The prognostic model can effectively group patients and a more effective clinical individual prediction model can be constructed based on this model. Single-cell analysis demonstrated the expression of prognostic targets was associated with TME and mainly accumulated in mono/macro subset. Finally, in vitro experiments demonstrated that baicalin significantly reduced the viability, proliferation, and migration capabilities of melanoma cells. Additionally, baicalin promoted pro-inflammatory polarization of macrophages under co-culture with melanoma cells and baicalin exerted relatively high biological safety. In conclusion, this study demonstrates that baicalin inhibits melanoma by modulating the TME and establishes a prognostic model with predictive potential. These findings expand the therapeutic potential of baicalin and provide novel insights for melanoma treatment strategies. Melanoma baicalin tumor microenvironment immune infiltration prognostic model Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction The tumor microenvironment (TME) refers to the internal environment surrounding the tumor, encompassing the acellular components such as extracellular matrix, vascular system, and the cellular constituents like neoplastic cells, immune cells and fibroblasts [ 1 ]. While the proliferation of tumor cells initiates the creation of the tumor niche, non-transformed cell types within the milieu co-evolve with the tumor cells, thereby both of them participate in the process of tumorigenesis invasion, and response to therapies [ 2 ]. In the TME, tumor-infiltrating immune cells exhibit heterogeneity, demonstrating both functional and phenotypic flexibility, and they may manifest both pro-tumorigenic and anti-tumorigenic impacts [ 3 ]. The distribution of immune cell subgroups and their specific spatial positioning relative to cancer have been suggested as crucial factors in the growth and advancement of tumors, patient prognosis, and reaction to immunotherapy [ 4 ]. Skin cutaneous melanoma (SKCM), arising from the uncontrolled proliferation of melanocytes, is the most aggressive and lethal form of skin cancer [ 5 ]. Despite its historical classification as an infrequent malignancy, its frequency has increased steadily in the last decades [ 6 ]. The transformation of melanocytes into melanoma is hindered by numerous barriers, which are sequentially disrupted by genetic mutations. When initial genetic alterations drive cell proliferation, precursor lesions emerge [ 7 ]. Mutations in the B-Raf proto-oncogene (BRAF), neurofibromin 1 (NF1), and Neuroblastoma RAS viral oncogene homolog (NRAS) constitute the primary genetic determinants, and melanomas associated with skin that has undergone chronic sun exposure typically exhibit a high mutational load attributable to UV exposure [ 8 ]. Due to its remarkably elevated genomic mutational burden, melanoma represents one of the most immunogenic tumors, possessing the potential to provoke specific adaptive antitumor immune responses [ 9 ]. Various therapeutic approaches aimed at suppressing melanoma progression have particularly targeted the stimulation of the anti-tumor functions of TME subsets [ 10 ]. A recent study suggested that promoting interferon-γ-producing CD8 T cells could bolster immune checkpoint inhibitor (ICI) treatment in melanoma [ 11 ]. In tumor immunotherapy, immune checkpoint blockade (ICB) possesses remarkable ability to combat melanoma [ 12 ]. The quantity, distribution, and characteristics of tumor-infiltrating lymphocytes (TILs) serve as predictive markers for immunotherapy outcomes and act as critical regulators of tumor progression [ 12 ]. Moreover, melanoma cells with mesenchymal-like (MES) status were found to be significantly enriched in early biopsies from non-responders to ICB [ 13 ]. Natural products and their extracted compounds have long been regarded as reliable and potent sources for discovering anticancer drugs. Scutellaria baicalensis Georgi has been widely used as traditional Chinese medicines with the beneficial effects on various disorders [ 14 , 15 ]. Scutellaria baicalensis Georgi has also been incorporated into pharmacopoeias due to its effectiveness in managing skin conditions. The quality control standard for Scutellaria baicalensis Georgi is baicalin, with its content required to be no less than 85% in dried products [ 16 ]. As a key bioactive constituent of Scutellaria baicalensis Georgi, baicalin has been identified in modern research as a compound with diverse pharmacological properties, including but not limited to antibacterial, antitumor, anti-inflammatory, and antioxidant activities [ 17 – 20 ]. For instance, wen et al. found that baicalin may be a promising candidate for osteosarcomas treatment because it can induce ferroptosis in osteosarcomas through Nrf2/xCT/GPX4 regulatory axis [ 21 ]. Song et al. showed that baicalin induces apoptosis, suppresses migration, and promotes anti-tumor immunity in colorectal cancer through the TLR4/NF-κB signaling pathway [ 22 ]. In this study, we obtained bulk RNA data of patients from The Cancer Genome Atlas (TCGA) SKCM cohort. Then, we determined the core targets of baicalin influencing the immune cell infiltration of SKCM. These targets were subjected to construct a prognostic model, which was verified using an independent GEO dataset. In addition, we evaluated the predictive power of the prognostic model, the binding potential between baicalin and targets, and the distribution of the prognostic genes in cell subsets. Finally, we validated the potential therapeutic effects of baicalin on melanoma through in vitro experiments. 2. Materials and methods 2.1 Data sources RNA sequencing data (TPM) for SKCM and the clinical profiles were collected from TCGA database ( https://portal.gdc.cancer.gov/ ), including 469 tumor samples after removing the duplicates (accessed on 18 February 2024). The clinical features were shown in Table S1 . The testing set GSE53118 (79 tumor samples) was downloaded from Gene Expression Omnibus (GEO) database ( https://www.ncbi.nlm.nih.gov/geo/ ) (accessed on 27 February 2024) [ 23 ]. 2.2 Unsupervised clustering of tumor-infiltrating immune cells The classification of tumor-infiltrating immune cells in SKCM cases was conducted using the "CIBERSORT" package [ 24 ]. Unsupervised cluster analysis was performed to identify various patterns of tumor-infiltrating immune cells in SKCM, leading to the classification of samples into distinct clusters. The "ConsensuClusterPlus" package was applied to complete the unsupervised cluster analysis [ 25 ]. The "Limma" package was employed to identify differentially expressed genes (DEGs) between two clusters with a screening threshold of |log 2 FC| >1 and p < 0.05 [ 26 ]. 2.3 Gene set variation analysis (GSVA) GSVA can offer enhanced sensitivity for evaluating the enrichment alterations in biological pathway activity across samples [ 27 ]. It can transform alterations at the gene level into pathway level by assessing the gene set of interest. The "GSVA" package was utilized for conducting GSVA enrichment analysis to investigate variations in biological pathways across the different clusters. 2.4 Prediction of target genes of melanoma and baicalin The identification of target genes associated with melanoma was sourced from three databases (accessed on 22 February 2024): Disease Gene Interaction (DisGeNet, https://www.disgenet.org/ ) [ 28 ], and GeneCards ( http://www.genecards.org/ ) [ 29 ]. The identification of target genes associated with baicalin was sourced from six databases (accessed on 23 February 2024): The Swiss Target Prediction database ( http://www.swisstargetprediction.ch/ ) [ 30 ], SuperPred database ( https://prediction.charite.de/index.php/ )[ 31 ], GeneCards ( http://www.genecards.org/ ) [ 29 ], The Comparative Toxicogenomics Database (CTD, https://ctdbase.org/ ) [ 32 ], Encyclopedia of Traditional Chinese Medicine (ETCM, http://www.tcmip.cn/ETCM2/front/#/ ) [ 33 ] and ChEMBL Database ( https://www.ebi.ac.uk/chembl/ ) [ 34 ]. The selection of target genes was based on Homo sapiens and deduplicated, and the final list was presented in Supplementary Table 2. 2.5 Functional enrichment analysis Gene Ontology (GO) is a bioinformatics tool to describe the functions of genes and proteins, including molecular function (MF), cellular component (CC) and biological process (BP) [ 35 ]. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis is mainly focused on metabolic pathways [ 36 ]. The Mel_bcl_ICI was obtained by crossing the genes for melanoma, baicalin and immune infiltration DEGs. The Protein-Protein Interaction (PPI) analysis was performed through the STRING database ( https://string-db.org/ ) [ 37 ] and drawn in the cytoscape software [ 38 ]. Functional annotation of the Mel_bcl_ICI was conducted using the Metascape database ( https://www.metascape.org/ ) with a significance threshold of Min overlap ≥ 3 & p ≤ 0.01 [ 39 ]. 2.6 Construction of the prognostic model The univariate Cox regression model was used to identify genes in Mel_bcl_ICI significantly associated with survival outcome. The least absolute shrinkage and selection operator (LASSO) regression analysis was conducted via "glmnet" package [ 40 ]. The multivariate Cox regression analysis was used to construct a prognostic model. The risk score was calculated as follows: $$\:\text{r}\text{i}\text{s}\text{k}\:\text{s}\text{c}\text{o}\text{r}\text{e}={\varSigma\:}_{i=1}^{n}\text{e}\text{x}\text{p}\left(\text{i}\right)\times\:{\beta\:}\text{i}$$ where \(\:\text{e}\text{x}\text{p}\left(\text{i}\right)\) represents the expression of each gene and \(\:{\beta\:}\text{i}\) represents coefficient. Based on the median risk score, samples were categorized into high- and low-risk group. The survival difference between two groups was compared using "survival" package. The prediction accuracy was evaluated using Receiver Operating Characteristic (ROC) curve via "survivalROC" package. 2.7 The individualized prediction model (nomogram) The individualized prediction model was constructed based on risk score, age, race, node via the "rms" package, which can predict the 1-, 3-, 5-, 10- and 20-years survival of SKCM patients. A calibration curve was used to assess the consistency between predicted survival and actual survival. 2.8 Tumor mutation burden (TMB) and the sensitivity of chemotherapy The Mutation Annotation Format (MAF) data retrieved from TCGA database was processed using the "maftools" package [ 41 ]. Genomic changes were assessed by determining TMB score, transition and transversion condition, and the site/type of amino acid mutations of specific genes. The chemotherapy sensitivity was evaluated by the half-maximal inhibitory concentration (IC50) of ten common chemotherapeutic agents through "pRRophetic" package [ 42 ]. 2.9 Molecular docking AutoDockTools-1.5.7 was used to perform molecular docking [ 43 ]. The structure of CXCL12 (PBD: 2KEC), PLAU (PBD: 1C5Y), PIM1 (PBD: 5O11) PTK2B (PBD: 3CC6), and CCL8 (PBD: 7S5A) was obtained from Protein Data Bank (PDB, https://www.rcsb.org/ ) [ 44 ] and the structure of baicalin (Compound CID: 64982) was obtained from PubChem ( https://pubchem.ncbi.nlm.nih.gov/ ) [ 45 ]. The structure of LAP3 (UniProt: P28838) predicted by AlphaFold [ 46 ] was found through the UniProt ( https://www.uniprot.org/ ) [ 47 ]. The process of molecular docking is briefly as follows: First, dehydration, hydrogenation and detection of ligand rotatable bonds were performed. Then adjusting the scope of the Grid Box so that it can fully cover the structure of the protein. Molecular docking results were obtained by processing AutoGrid and AutoDock. The lowest bind energy results of molecular docking were exported and converted to pdb format by software OpenBabel 3.1.1 and plotted on software PyMOL 2.2.0. 2.10 Single cell analysis The scRNA-seq data of SKCM (GSE123139) was enrolled from GEO database [ 48 ], which was deposited in the Tumor Immune Single Cell Center 2 (TISCH2) ( http://tisch.comp-genomics.org ) [ 49 ]. This dataset contains 35494 cells from 25 SKCM patients and was uniformly processed to perform quality control, clustering and cell-type annotation. The immune-related mechanism of the prognostic signature in the TME of SKCM was studied at the single-cell level. 2.11 Chemicals and reagents Human skin melanoma cell line was purchased from IMMOCELL (Xiamen, China); THP-1 cells was purchased from Sunncell Biotechnology Co., Ltd (Wuhan, China); T25 cell culture flask (NEST Biotechnology, China); Fetal Bovine Serum, FBS (BDBIO HangZhou China); DMEM (VivaCell, Shanghai, China); Penicillin-Streptomycin (Shanghai Chuanqiu Biotechnology Co., Ltd, China); Cell Counting Kit 8 (Wuhan Fine Biotech Co., Ltd, China); AG RNAex Pro reagent (ACCURATE BIOTECHNOLOGY(HUNAN) Co., Ltd, China); cDNA Synthesis Kit, Primers (Beyotime Biotechnology); SYBR Green qPCR Master Mix and PMA (TargetMol, USA); RNase free microcentrifuge tubes (Guangzhou Jet Bio-Filtration Co., Ltd); RNase free tips (Nantong Feiyu Biological Technology Co., Ltd); RNase free strip tubes (Magic-bio); DEPC (Keygen BioTECH, China); Murine APC conjugated with anti-CD163 antibody (Proteintech Group, Inc.); Murine PE conjugated with anti-CD86 antibody (Sino Biological Inc.); Murine anti-CD16 antibody (Elabscience Biotechnology Co., Ltd). 2.12 Cell Culture and cell viability (CCK-8 Assay) Melanoma cells were cultured in DMEM containing 10% FBS, penicillin (100 U/mL) and streptomycin (100 µg/mL). THP-1 cells were cultures in 1640 medium, 10% FBS, 0.05 mM mercaptoethanol, penicillin (100 U/mL) and streptomycin (100 µg/mL). The culture environment was 37 ℃ with 5% CO 2 . The cells were passaged when they reached about 80% confluence. The cells were seeded into 96-well plates (5 × 10 3 cells/well) for 12 h. Then treated with baicalin and incubated for 24 h. 10 µL of CCK8 was added and incubated for 1 h. The absorbance was measured at 450 nm with a microplate reader (Bio-rad imark) and calculated cell viability. 2.13 PI/Hoechst33342 The cells were seeded into 12-well plates (5 × 10 4 cells/well) for 12 h. Then treated with baicalin and incubated for 24 h. Then stained with 1 µg/mL Hoechst33342 for 5 min and 1 µg/mL PI for 3 min. Observing under a fluorescence microscope (Olympus IX71). The images captured by fluorescence microscopy were processed using ImageJ software. PI positive cells were calculated by PI positive cells/total cells of the region of interest. 2.14 Wound healing The cells were seeded into 12-well plates (10 × 10 4 cells/well) for 12 h. Then treated with baicalin and incubated for 24 h. A scratch (cruciform) was made in each well with a 200 µL tip and the new culture medium with low serum was re-added after rinsing twice with PBS. Recorded the scratch moment as 0 h, the scratch sites observed at 0 h were marked and observed the same scratch area at 24 h and 48 h. The quantitative analysis of the area at the scratch site was processed using ImageJ. 2.15 Enzyme-linked immunosorbent assay (ELISA) The cells were seeded into 12-well plates (5 × 10 4 cells/well) for 12 h. Then treated with baicalin and incubated for 24 h. After cell pretreatment, removed the original medium, centrifuged (1000 rpm, 4 ℃, 5 min) and the supernatant was taken for subsequent detection. The samples to be tested were added to wells with precoated capture antibodies. Then proceeded with the sequential addition of biotinylated antibodies, enzyme-combination solution and the substrate TMB, ensuring to wash the plate (Add the washing solution and discard it 1 min later, then pat the residual washing solution away on an absorbent paper) before each addition. Finally, the stop buffer was added and the absorbance was measured at 450 nm. 2.17 Cell co-culture THP-1 cells were seeded into 12-well plates (1×10⁵ cells/well) and were induced polarization by adding PMA at a final concentration of 100 ng/mL for 48 hours. Replaced the culture medium with a complete medium without PMA when the induction was completed. SK-MEL-2 cells were seeded into 0.4 µm transwell chamber (1×10⁵ cells/chamber) and placed the chamber into 12-well plate containing M0 macrophages. After 24 hours of co-culture, assess macrophage polarization via flow cytometry. 2.18 Macrophage polarization assay Collected co-cultured macrophages by centrifugation (1000 rpm, 3 min) and discarded the supernatant. Resuspended cells in staining buffer (PBS containing 1% BSA) and centrifuged (1000 rpm, 3 min), removed the supernatant and resuspended in staining buffer. Blocked cells by adding anti-CD16 antibody (final concentration: 2 µg/mL) and incubated at room temperature for 10 min. Added fluorochrome-conjugated antibodies and incubated at 4°C in the dark for 30 min. Centrifuged (1000 rpm, 3 min), discarded supernatant, resuspended in staining buffer and analyzed using a flow cytometer. 2.19 Quantitative real-time PCR The cells were seeded into 6-well plates (2 × 10 5 cells/well) for 12 h. Then treated with baicalin and incubated for 24 h. Total RNA was isolated from cells using 1 mL RNAex reagent on ice for 10min. 200 µL chloroform was added and the mixture was centrifuged (4 ℃, 12000 rpm, 15 min), the RNA in the supernatant was transferred into tubes containing 500 µL isopropanol and mixed in RT for 10 min. Then centrifuged at 4 ℃, 12000 rpm, 10 min, discarded the isopropanol and added 1 mL 70% ethanol to dissolve the RNA. Centrifuged (4 ℃, 7500 rpm, 5 min), discarded the ethanol, dried at room temperature and dissolved in DEPC-treated water. Finally, we used cDNA Synthesis Kit to perform inverse transcription and SYBR Green qPCR Master Mix for qRT-PCR. 2.20 Statistical analysis The bioinformatic analyses were performed in R software 4.3.2. The Wilcoxon test was used to determine the significance of the difference between two groups. The log-rank test was used to determine the significance of the prognostic value in Kaplan-Meier analysis. Data processing for wet experiments was performed as previously described [ 15 ]. If not mentioned, an adjusted p-value < 0.05 was considered statistically significant. *p < 0.05; **p < 0.01; ***p < 0.001. 3. Results 3.1 The landscape of immune-cell infiltration in the TME of SKCM To explore the distinct characteristics of TME in SKCM, we classified the SKCM samples into clusters and assessed differences in immune cell infiltration patterns. This study identified two immune-cell infiltration patterns through unsupervised clustering: cluster_1 (250 samples, 6 samples lacking survival data), and cluster_2 (219 samples, 5 samples lacking survival data) (Fig. 1 A, Fig. S1 A-K). PCA results showed cluster_1 and cluster_2 could be well separated from each other, indicating two distinct subtypes of SKCM. (Fig. 1 C). Then, we conducted survival analysis on these two subtypes and found that cluster_2 had a longer overall survival (OS) than cluster_1 (Fig. 1 D). TME cell infiltration analyses showed that cluster_2 exhibited higher levels of immune cell infiltration, including CD8 T cells, naïve B cells and regulatory T cells (Fig. 1 E). Both in cluster_1 and cluster_2, the proportion of CD8 T cells, macrophage M0, and macrophage M2 were relatively higher than other type immune cells (Fig. 1 F, Fig. S1 L). The correlation heatmap showed the relationship between the immune cells in TME (Fig. 1 G). We can observe that the three largest cell populations mentioned earlier, CD8 T cells, and macrophages M0 and M2, exhibit a negative correlation. Additionally, CD8 T cells show a positive correlation with CD4 memory cells and macrophages M1. Intriguingly, we found that cluster_2 in Fig. 1 E, the abundance of CD8 T cells (immunopermissive subset) and regulatory T cells (immunosuppressive subset) is notably higher compared to cluster_1. Furthermore, through the correlation heatmap depicted in Fig. 1 G, a subtle positive correlation is observed between these two immune cell types, which are theoretically expected to be mutually exclusive. According to previous studies, it’s possible that persistent inflammation such as the overactivated immune cells and prolonged infiltration of immune cells, could stimulate a counteracting compensatory immunosuppression intended to protect tissue homeostasis [ 50 – 52 ]. As for biological pathways, B cell receptor signaling pathway and T cell receptor signaling pathway were significantly upregulated in cluster_2 (Fig. 1 H). To sum up, we categorized the SKCM samples into two clusters according to the immune cell infiltration level. Notably, the cluster with higher immune infiltration has better prognosis. 3.2 Enrichment analysis of baicalin targets acting on the TME of SKCM The differential gene set ICI_gene (1091 targets, Table S3) was obtained by comparing cluster_1 and cluster_2 (Fig. 2 A). We identified the 430 baicalin-related targets from the combination of six databases and 5200 melanoma-related targets from the combination of three databases. Finally, a total of 32 genes associated with the tumor microenvironment of SKCM, baicalin targets and melanoma were identified by Venn diagram (Fig. 2 B and Table S3). And we called this gene set Mel_bcl_ICI. The PPI network was performed to determine the interaction in Mel_bcl_ICI and among the three targets with the highest degree of connectivity were TNF, IFNG, and TLR4 (Fig. 2 C). In the GO analysis, positive regulation of cell locomotion, motility and migration were enriched in biological process, plasma membrane and perinuclear region in cellular component, and protein kinase activity, phosphotransferase activity and cytokine receptor binding in molecular function (Fig. 2 D). In the KEGG analysis, NF-kappa B signaling pathway and chemokine signaling pathway were significantly enriched (Fig. 2 E). Enrichment results were provided in Table S4. Additionally, we constructed a network map based on the enrichment results of GO and KEGG analyses to further capture the relationships between the terms (Fig. 2 F). We can observe that nodes named positive regulation of cell migration, positive regulation of response to external stimulus, and immune response-activating signaling pathway are connected with the highest number of edges, indicating that these three pathways are the primary ones through which the Mel_bcl_ICI target exerts its effects. 3.3 Construction of the prognostic risk model We conducted univariate Cox regression to identify 32 targets in Mel_bcl_ICI significantly linked to prognosis (Table S5). Then we performed LASSO regression analysis using the 25 targets that significantly linked to prognosis and CXCL12, PLAU, LAP3, PIM1, PTK2B and CCL8 were identified based on the minimum lambda value (Fig. 3 A-B). The prognostic model formula was defined by multivariate Cox regression (Fig. 3 C): Risk score = Exp (CXCL12) × (0.01963) + Exp (PLAU) × (0.07366) + Exp (LAP3) × (-0.12037) + Exp (PIM1) × (0.16793) + Exp (PTK2B) × (-0.21407) + Exp (CCL8) × (-0.21715). The SKCM patients in training set were classified into high- and low-risk groups according to the median risk score. Survival analysis showed low-risk patients had a significantly longer overall survival (OS). The ROC curve determined a certain sensitivity and specificity of prognostic model. The 5-year and 20-year AUCs are 701 and 755, respectively (Fig. 3 D). In the testing set, the OS in the low-risk group was also longer and the AUC value (5 years) was 0.622 (Fig. 3 E). Additionally, we performed an enrichment pathway analysis of prognostic genes (Fig. 3 F). The enrichment results show that the most highly enriched pathways for PTK2B, CCL8, and CXCL12 are the most relevant. The top three most significantly enriched pathways are the chemokine-mediated signaling pathway, response to chemokine, and cellular response to chemokine. From this, we can see that the pathways that are most enriched for prognostic targets are chemokine-related pathways, which play important roles in immune responses and immune cell infiltration in the occurrence and progression of tumors. 3.4 Tumor mutation burden, immunotherapy and chemotherapy of prognostic signature An overview of mutations across all samples was provided in Fig. S2 A. We noticed a higher TMB level in low-risk group (Fig. 4 A-C) while no significant difference was observed (Fig. 4 D). The single gene mutation with the largest difference between the high- and low-risk groups was shown in Fig. S2 B-D. Samples were categorized into high- and low-TMB groups based on median TMB and paired with risk groups for survival analysis. Results suggested high TMB positively influences patient prognosis (Fig. 4 E). Generally, a higher expression of immune checkpoints often implies a better response to immunotherapy for patients, which theoretically suggests that in this study the low-risk patient group may achieve better outcomes with immune checkpoint inhibition compared to the high-risk group. The transition and transversion in high- and low-risk groups were shown in Fig. S2 E and F. Theoretically, the higher the tumor mutation, the higher potential load of tumor antigens, rendering it more susceptible to immunotherapy. Henceforth, we assessed the immune checkpoints expression and found a higher expression in low-risk group in 10 common immune checkpoints (Fig. 4 F). Then we evaluated the sensitivity of ten common chemotherapeutic agents and observed a lower estimated half-maximal inhibitory concentration (IC50) in low-risk group (Fig. 4 G), indicating a lower resistance to chemotherapy. The data of immunotherapy and chemotherapy were provided in Table S6-7. (A) Gene mutation frequency in high- and low-risk groups. (B-C) Scatter plot of high- (B) and low-risk (C) groups according to TMB from low to high. (D) Comparison of TMB between high- and low-risk groups. (E) KM curve for the mixed group of high- and low-risk with high- and low-TMB. (F) Distribution of immune checkpoint (CD274, CTLA4, LAG3, PDCD1, HAVCR2, PDCD1LG2, TIGIT, SIGLEC7, VSIR, BTLA) expression in high- and low-risk groups. (G) The estimated IC50 for ten chemotherapeutic drugs (ABT.888, ATRA, Etoposide, AZD8055, Cisplatin, Bosutinib, Gefitinib, GDC0941, Lenalidomide, Methotrexate) in high- and low-risk groups. 3.5 The individualized prediction model for overall survival in SKCM To evaluate the predictive effect of prognostic model, univariate and multivariate Cox regression analysis of risk-score and clinical features were performed. The results demonstrated risk-score, age, race, and TNM classification were all independent prognostic factors (Fig. 5 A-B). Furthermore, an individualized prognostic prediction model for OS was constructed based on the independent prognostic factors, which could estimate the (1-, 3-, 5-, 10- and 20-year) OS (Fig. 5 C). The predictive and actual survival in the calibration curve showed a satisfactory overlap, which indicated a relatively desirable predictive accuracy (Fig. 5 D). The C-index of nomogram model was 0.728, better than other independent prognostic factors (Fig. 5 E). The combined use of risk scores and clinical characteristics can enhance the prediction accuracy and facilitate the clinical assessment of patients' prognosis. (A-B) Forest plot of Univariate (A) and Multivariate (B) Cox regression analysis of risk-score and clinical features. (C) The 1-, 3-, 5-, 10- and 20-year survival rate of patients in the nomogram. (D) The calibration plots of nomogram. (E) The predictive effect of the nomogram model, risk-score, and clinical prognostic features on survival was evaluated by C-index. 3.6 Molecular docking of baicalin and prognostic targets The molecular docking was conducted to evaluate the potential binding capacity of baicalin with CXCL12, PLAU, LAP3, PIM1, PTK2B, CCL8. Among them, the binding energy of baicalin to CXCL12 is -6.34 kcal/mol, forming hydrogen bonds with 12Arg and 13Phe respectively (Fig. S3A). The binding energy of baicalin to PLAU is -10.03 kcal/mol, forming hydrogen bond with 190Ser (Fig. S3B). The binding energy of baicalin to LAP3 is -7.04 kcal/mol, forming hydrogen bond with 175Ala (Fig. S3C). The binding energy of baicalin to PIM1 is -8.34 kcal/mol, forming hydrogen bond with 121Glu (Fig. S3D). The binding energy of baicalin to PTK2B is -10.05 kcal/mol, forming hydrogen bonds with 478Met, 482Asp, 483His, 486Ile and 487Val respectively (Fig. S3E). The binding energy of baicalin to CCL8 is -5.71 kcal/mol, forming hydrogen bond interactions with 10Thr (Fig. S3F). 3.7 Expression of prognostic genes in TME and the effect of baicalin on macrophages We investigated the distribution of prognostic genes by single cell analysis through the TISCH2. The TME cells were clustered into 21 clusters (Fig. 6 A), which were then categorized into eight cell types through cell annotation (Fig. 6 B). The marker genes and proportion of each cell subset were presented in Fig. S4A-B. Moreover, the cell interactions indicated that the intercellular interactions among monocytes/macrophages were the most frequent (Fig. S4C). The cell-cell interaction analysis showed that mono/macro-C5 mainly interacted with mono/macrophages, Tprolif cells and dendritic cells (DCs) (Fig. S4D). As shown in Fig. 6 C-H, CXCL12 and PLAU were mostly enriched in mono/macrophages. LAP3, PIM1 and PTK2B were mainly expressed in CD4/CD8 T cells and mono/macrophages. CCL8 was mostly enriched in mono/macrophages with a slight expression in CD4 T cells. It can be seen that prognosis genes are mainly distributed in monocytes/macrophages. The distribution details of prognostic gene in TME cell subsets were provided in Table S8. Therefore, we next detected the effects of different concentrations of baicalin (the concentration selection criteria are based on Fig. 7 ) on macrophages based on the co-culture of melanoma cells and human macrophages. The transcription levels of prognostic target genes in macrophages were detected. In the high and low concentration drug groups, the transcription levels of CCL8, CXCL12, PIM1 and PTK2B all increased, while the transcription levels of PLAU and LAP3 decreased (Fig. 6 L). Among them, CCL8 and CXCL12 are chemokines, PIM1 is related to cell proliferation and survival, and PTK2B is related to cell migration and adhesion. The transcriptional upregulation of these genes may represent the activation of macrophages. In addition, through flow cytometry, it was found that with the increase of drug concentration in the co-culture condition, the proportion of M1 type of macrophages also increased to a certain extent (Fig. 6 M). (A) The 21 cell clusters in the GSE123139 dataset. (B) The 8 cell types were identified. (C-H) The expression of prognostic genes in cell subsets, with the red box highlighting cells exhibiting perceptible expression of these genes. (I) The transcriptional levels of prognostic genes in macrophages. (J) Representative flow cytometric analysis of CD86 + and CD163 + macrophages 3.8 Safety assessment of baicalin. Finally, in this study, the toxicity of the small molecule drug baicalin on melanoma cells and normal tissue cells was evaluated to assess its safety for use. Initially, we conducted a CCK-8 assay to determine the cytotoxicity of baicalin on melanoma cells. By co-culturing the cells with a concentration gradient ranging from 5 µM to 500 µM for 24 h, among which 50 µM of baicalin began to significantly affect cell activity and 250 µM of baicalin reduced the viability of melanoma cells to approximately 60% (Fig. 7 A). For subsequent experiments, we selected 50 µM and 250 µM as the low and high concentrations of baicalin, respectively. In the cell morphological observation, we found that as the concentration of baicalin increased, the number of dead melanoma cells increased accordingly (Fig. 7 B). In the PI/Hoechst 33342 staining, we observed an increase in PI staining intensity with rising baicalin concentrations, while the overall cell count (Hoechst 33342-stained cells) decreased, indicating that baicalin induces melanoma cell death (Fig. 7 D). Furthermore, in the wound-healing assay, the cell migration ability decreased with increasing baicalin concentrations, consistent with the previous findings (Fig. 7 E). We also measured the level of IL-1β released by the melanoma cells using an ELISA assay, and the results showed that the level of IL-1β in the extracellular space of melanoma cells increased significantly after baicalin treatment, indicating that the inflammatory level of melanoma cells was upregulated. (Fig. 7 F). Then, we examined the effects of baicalin treatment on the activity of human monocyte THP-1 cells and macrophages, and found that the concentrations of baicalin that produced significant cytotoxicity for these two types of cells were all above 100 µM (Fig. 7 G-H). Furthermore, our prior study indicated that baicalin began to exert toxicity on HaCaT cells at concentrations of 250 µM and above[ 15 ]. And literature review revealed that baicalin showed no significant toxicity to fibroblasts within 100 µM, which falls within the generally accepted non-toxic range for drug administration [ 53 – 55 ]. In summary, whether based on the data from this study or the data from literature investigations, the toxicity concentrations of baicalin for monocytes/macrophages, keratinocytes, and skin fibroblasts are higher than the concentrations that cause significant cytotoxicity in human skin melanoma cells, indicating that melanoma cells in the skin are more sensitive to baicalin. (A) Cell viability of melanoma cells treated with baicalin for 24 hours. (B) Morphological observation. (C) Quantification of PI-positive cells. (D) PI/Hoechst 33342 dual staining to detect cell death. (E) Wound-healing assay to assess cell migration ability. Scale bar = 50 µm. (F) The IL-1β level released by melanoma cells. (G-H) Cell viability of THP-1 cells (G) and macrophages (H) treated with different concentrations of baicalin for 24 hours. 4. Discussion In order to explore the targets and mechanisms of baicalin against TME in SKCM, we constructed a prognostic model including 6 genes. In addition, a comparative analysis regarding the abnormal expression of 6 prognostic genes was performed with the literature (Table S9). The results showed the upregulation of CXCL12 and PIM1, the downregulation of PLAU in SKCM were confirmed in previous experimental research, while the LAP3, PTK2B and CCL8 had different results in different literatures, which were bidirectional. We speculate that the differences in research outcomes may be attributed to variations in the genetic backgrounds of the study subjects and substantial sample heterogeneity, arising from the use of different cell lines, mouse strains, and sample sources by various research groups. Additionally, while some studies focus solely on melanoma cells, it's important to note that the TME comprises not only tumor cells but others, which may contribute to discrepancies in their findings. Niknafs et al. found through pan-cancer analysis and clinical data on immunotherapy that tumor mutation burden was associated with the efficacy of immune checkpoint blockade [ 56 ]. They pointed out that tumors with a high tumor mutation burden background had a more inflammatory tumor microenvironment [ 56 ]. Newell et al. also showed that tumors with high tumor mutation burden exhibited the best response to immunotherapy in advanced cutaneous melanoma [ 57 ]. In this study, we found the TMB value of high-risk group was lower than low-risk group. The survival analysis indicated high TMB is favorable for patient prognosis. The analysis of immunotherapy and chemotherapy showed a better response in low-risk group. A molecular docking verification was performed on six prognostic targets and baicalin. The lower the binding energy score, the greater the binding force between small molecules and proteins. The minimum binding energy of baicalin and prognostic targets was all lower than − 5.0 kcal/mol, indicating that baicalin has a certain binding potential with these targets [ 58 ]. The results of molecular docking have suggested that baicalin may be directly involved in the regulation of prognostic target signaling pathways. However, whether the specific components can spontaneously combine and regulate the activity still needs further experiments to verify. Additionally, we explored the distribution of prognostic targets in the TME and found prognostic genes were mainly concentrated in CD4/CD8 T cells and mono/macrophages. Xin et al. illustrated the inhibition of PIM-1 disrupted the immunosuppressive TME and restored CD8 T cell-mediated antitumor immunity [ 59 ]. Babazadeh et al. determined the role of mesenchymal stem cells-derived CXCL12 in macrophage phenotypic switching to M2, which promoted their function in tumorigenesis [ 60 ]. Moreover, CXCL12-mediated monocyte recruitment was found to be important in neuroinflammation [ 61 ]. In addition, PTK2B could regulate STING-TBK1 activation in macrophages and increased innate immune response [ 62 ]. We believe that analyzing the distribution of prognostic genes in TME will enhance our understanding of their biological processes and mechanisms, thereby advancing research in prognostic studies. Regarding how baicalin affects the progression of melanoma by regulating the TME, based on existing literature, we speculate that its functions include but are not limited to inhibiting the growth of tumor cells, activating immune cells, promoting the transition of tumor-associated macrophages (TAMs) from the M2 subtype to the M1 subtype, and inhibiting transformation from inflammation to cancer. For example, it was reported that baicalin-loaded poly(lactic-co-glycolic acid) nanoparticles possess the ability to activate dendritic cells (DCs) and can elicit apoptosis in melanoma (B16) cells by inducing cell-cycle arrest at the G2/M phase [ 63 ]. Furthermore, by encapsulating tumor-specific antigenic peptide and an immune stimulant (CpG) in conjunction with baicalin within biomimetic nanoparticles, these particles exhibit potent targeting ability towards TAMs, reversing their M2 phenotype to the M1 phenotype, thereby inducing promising antitumor therapeutic effects [ 64 ]. Moreover, baicalin facilitated the repolarization of TAMs towards an M1-like phenotype without exerting selective toxicity towards either macrophage phenotype. When hepatocellular carcinoma (HCC) cells were cocultured with TAMs that had been treated with baicalin, a decrease in both proliferation and motility of the HCC cells was observed [ 65 ]. Furthermore, baicalin could enhance antitumor immune responses by blocking the PD-L1/PD-1 pathway via inhibiting the expression of PD-L1 in HCC cells [ 66 ]. In addition, chronic inflammation predisposes to the tumor progression, activates tumorigenesis and promotes development [ 20 ]. Baicalin can also mitigate the detrimental effects of inflammatory diseases, such as ulcerative colitis [ 67 ], inflammatory bowel diseases [ 68 ], allergic asthma [ 69 ], and allergic rhinitis [ 70 ] by suppressing inflammatory levels, thereby inhibiting the potential for such syndromes to induce tumorigenesis. In vitro experiments demonstrated that baicalin inhibits the viability and migration of melanoma cells in a concentration-dependent manner, consistent with previous research [ 71 ]. Furthermore, baicalin significantly increase the death of melanoma cells and upregulate cellular inflammatory levels. Based on single-cell analysis, we found that monocytes/macrophages play an important role in the immune microenvironment of melanoma, with prognostic genes highly expressed in this cell population. To further investigate the effects of baicalin on TME in melanoma, we established a co-culture system of melanoma cells and macrophages and examined the expression of prognostic genes in macrophages under baicalin treatment. The results revealed that baicalin upregulated the transcriptional levels of CCL8, CXCL12, PIM1 and PTK2B in macrophages, while downregulating the expression of LAP3 and PLAU. CCL8 and CXCL12 are chemokines for monocytes/macrophages and neutrophils, respectively. Their upregulation suggests enhanced innate immune cell recruitment, potentially indicating an immune-enhancing effect. PIM1, as an oncogene, promotes the cancer cell survival by inhibiting apoptotic pathways[ 72 , 73 ]. We hypothesize that PIM1 upregulation in macrophages may support their survival and functional activity. PTK2B is a non-receptor type tyrosine kinase belonging to the focal adhesion kinase family. It has been reported that its activation promotes the migration/adhesion of tumor cells, contributing to therapy resistance[ 74 , 75 ]. We speculate that the upregulation of PTK2B in macrophages may facilitate the recruitment and migration of macrophages to the target tissue. PLAU (also known as uPA, urokinase plasminogen activator), is highly expressed in most cancer tissues and is associated with drug resistance[ 76 , 77 ]. LAP3 (Leucine Aminopeptidase 3), has been reported to promote tumor proliferation, migration and invasion[ 78 ]. The down-regulation of the transcriptional levels of PLAU and LAP3 following baicalin treatment may contribute to the suppression of tumor progression. Additionally, macrophages can be classified into two distinct phenotypes: M1 pro-inflammatory, anti-tumor type and M2 anti-inflammatory, pro-tumor type. We observed that baicalin treatment promoted the polarization of macrophages toward the M1 phenotype under co-culture conditions with melanoma cells, suggesting a tendency to establish an inflammatory microenvironment and a potential anti-tumor effect. In conclusion, baicalin exerts its anti-tumor effect through both direct actions on melanoma cells and indirect modulation of the TME by promoting M1-type macrophage polarization. However, it is important to note that the TME in vivo is far more complex than the simplified co-culture system used in this study. Therefore, the anti-melanoma effects of baicalin require further investigation. Furthermore, we discovered that melanoma cells exhibit greater sensitivity to baicalin compared to normal skin tissue cells. This differential sensitivity is advantageous for minimizing off-target effects and reducing damage to normal tissues during drug administration. We hypothesize that this phenomenon may be attributed to the elevated metabolic activity and oxidative stress levels in tumor cells, rendering them more susceptible to the antioxidant properties of baicalin. Nevertheless, this hypothesis warrants further experimental validation. Collectively, these findings suggest that baicalin possesses favorable biological safety profiles. However, this study still has several limitations. For instance, the predictive ability of prognostic model still needs improvement and only three of the six prognostic genes are identified as independent prognostic factors. In addition, more wet-lab experiments such as selecting more cell lines and introducing mice tumor-bearing experiments, etc., are needed to verify the potential therapeutic efficacy of baicalin in melanoma. 5. Conclusion To summary, the pivotal targets and mechanisms of baicalin against SKCM were explored and a prognostic model related to the effect of baicalin on the TME in SKCM was constructed. This model can be used to create an individualized prediction model, reflect genomic mutation and predict the effect of immunotherapy or chemotherapy. In addition, molecular docking and single-cell analysis were performed to study their binding potential and possible mechanisms. Finally, in vitro experiments demonstrated the potential of baicalin as a therapeutic agent against melanoma. Overall, our study suggests that baicalin may exert therapeutic effects on melanoma by modulating immune cell infiltration and the prognostic model with a certain predictive ability may provide clinical utility. Declarations Declaration of Competing Interest The authors declare no conflict of interest. Funding resource This work was supported by the LZU-IMPCAS cooperation ((20)0920). 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Leukemia. 2022;36(10):2418-29.https://doi.org/10.1038/s41375-022-01687-x Al-Juboori SI, Vadakekolathu J, Idri S, Wagner S, Zafeiris D, Pearson JR, et al. PYK2 promotes HER2-positive breast cancer invasion. J Exp Clin Cancer Res. 2019;38(1):210.https://doi.org/10.1186/s13046-019-1221-0 Shi K, Zhou J, Li M, Yan W, Zhang J, Zhang X, et al. Pan-cancer analysis of PLAU indicates its potential prognostic value and correlation with neutrophil infiltration in BLCA. Biochim Biophys Acta Mol Basis Dis. 2024;1870(2):166965.https://doi.org/10.1016/j.bbadis.2023.166965 Zheng Y, Zhang L, Zhang K, Wu S, Wang C, Huang R, et al. PLAU promotes growth and attenuates cisplatin chemosensitivity in ARID1A-depleted non-small cell lung cancer through interaction with TM4SF1. Biol Direct. 2024;19(1):7.https://doi.org/10.1186/s13062-024-00452-7 He X, Huang Q, Qiu X, Liu X, Sun G, Guo J, et al. LAP3 promotes glioma progression by regulating proliferation, migration and invasion of glioma cells. Int J Biol Macromol. 2015;72:1081-9.https://doi.org/10.1016/j.ijbiomac.2014.10.021 Additional Declarations No competing interests reported. Supplementary Files AppendixA.SupplementaryFigure.docx AppendixB.SupplementaryTable.xlsx Cite Share Download PDF Status: Published Journal Publication published 26 Dec, 2025 Read the published version in Medical Oncology → Version 1 posted Editorial decision: Revision requested 02 Dec, 2025 Reviews received at journal 29 Nov, 2025 Reviewers agreed at journal 20 Nov, 2025 Reviews received at journal 24 Oct, 2025 Reviewers agreed at journal 21 Oct, 2025 Reviewers invited by journal 20 Oct, 2025 Editor assigned by journal 01 Sep, 2025 Submission checks completed at journal 01 Sep, 2025 First submitted to journal 30 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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1","display":"","copyAsset":false,"role":"figure","size":1471681,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe landscape of immune-cell infiltration in the TME of SKCM.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003e Unsupervised consensus clustering when k = 2. \u003cstrong\u003e(B)\u003c/strong\u003e Consensus CDF curve (k ranges from 2 to 9).\u003cstrong\u003e (C)\u003c/strong\u003e PCA analysis. \u003cstrong\u003e(D)\u003c/strong\u003e Kaplan-Meier curve of SKCM patients. \u003cstrong\u003e(E)\u003c/strong\u003e Immune infiltration of 22 TME-associated cells in two immune cell infiltration -modified clusters. \u003cstrong\u003e(F) \u003c/strong\u003eThe heatmap of immune cell infiltration in two clusters. \u003cstrong\u003e(G)\u003c/strong\u003e The heatmap of correlation among the 22 immune cells. \u003cstrong\u003e(H)\u003c/strong\u003e The heatmap of biological pathways in two 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Mel_bcl_ICI. \u003cstrong\u003e(F)\u003c/strong\u003e Network map of GO analysis and KEGG analysis (each node represents an enriched term and is colored first by its cluster ID).\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7498099/v1/710f2b20f9a000e05cb73daa.png"},{"id":94856339,"identity":"3c7b882f-2163-4194-bd41-ea10125b4467","added_by":"auto","created_at":"2025-10-31 12:14:35","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1016741,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConstruction of the prognostic risk model.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A-B)\u003c/strong\u003e The targets in Mel_bcl_ICI associated with prognosis were selected using the LASSO method, and the lambda value with minimum partial likelihood deviation was selected. \u003cstrong\u003e(C)\u003c/strong\u003eMultivariate Cox regression analysis of prognostic genes. \u003cstrong\u003e(D) \u003c/strong\u003eSurvival curve and ROC curve of the prognostic model for the high-risk group and low-risk group in the training set.\u003cstrong\u003e (E) \u003c/strong\u003eSurvival curve and ROC curve of the prognostic model for the high-risk group and low-risk group in the testing set. \u003cstrong\u003e(F)\u003c/strong\u003e Enrichment pathway analysis of prognostic genes.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7498099/v1/8204699fae62dc29851be987.png"},{"id":94984854,"identity":"44d9a5ec-2f5d-4aa0-8b17-3e11bef59a21","added_by":"auto","created_at":"2025-11-03 06:56:39","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":6006358,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTumor mutation burden, immunotherapy and chemotherapy of prognostic signature.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003e Gene mutation frequency in high- and low-risk groups. \u003cstrong\u003e(B-C)\u003c/strong\u003e Scatter plot of high- \u003cstrong\u003e(B)\u003c/strong\u003e and low-risk \u003cstrong\u003e(C)\u003c/strong\u003e groups according to TMB from low to high. \u003cstrong\u003e(D)\u003c/strong\u003e Comparison of TMB between high- and low-risk groups. \u003cstrong\u003e(E)\u003c/strong\u003e KM curve for the mixed group of high- and low-risk with high- and low-TMB. \u003cstrong\u003e(F) \u003c/strong\u003eDistribution of immune checkpoint (CD274, CTLA4, LAG3, PDCD1, HAVCR2, PDCD1LG2, TIGIT, SIGLEC7, VSIR, BTLA) expression in high- and low-risk groups. \u003cstrong\u003e(G)\u003c/strong\u003e The estimated IC50 for ten chemotherapeutic drugs (ABT.888, ATRA, Etoposide, AZD8055, Cisplatin, Bosutinib, Gefitinib, GDC0941, Lenalidomide, Methotrexate) in high- and low-risk groups.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7498099/v1/05a0495fd2bd607bb56fced8.png"},{"id":94985718,"identity":"b1fb74f5-a810-4c2d-8142-a1c0d7ffd272","added_by":"auto","created_at":"2025-11-03 06:58:45","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1055491,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe individualized prediction model for overall survival in SKCM.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A-B)\u003c/strong\u003e Forest plot of Univariate \u003cstrong\u003e(A)\u003c/strong\u003e and Multivariate \u003cstrong\u003e(B)\u003c/strong\u003e Cox regression analysis of risk-score and clinical features. \u003cstrong\u003e(C) \u003c/strong\u003eThe 1-, 3-, 5-, 10- and 20-year survival rate of patients in the nomogram. \u003cstrong\u003e(D)\u003c/strong\u003e The calibration plots of nomogram. \u003cstrong\u003e(E)\u003c/strong\u003e The predictive effect of the nomogram model, risk-score, and clinical prognostic features on survival was evaluated by C-index.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7498099/v1/7ed77af466ccd713a48c2b53.png"},{"id":94856349,"identity":"2a76b585-dec8-4b8c-a051-3e0096e63017","added_by":"auto","created_at":"2025-10-31 12:14:35","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":329710,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe distribution of 6 prognostic genes in different TME cell subsets.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003e The 21 cell clusters in the GSE123139 dataset. \u003cstrong\u003e(B)\u003c/strong\u003e The 8 cell types were identified. \u003cstrong\u003e(C-H)\u003c/strong\u003e The expression of prognostic genes in cell subsets, with the red box highlighting cells exhibiting perceptible expression of these genes.\u003cstrong\u003e (I) \u003c/strong\u003eThe transcriptional levels of prognostic genes in macrophages.\u003cstrong\u003e (J)\u003c/strong\u003e Representative flow cytometric analysis of CD86\u003csup\u003e+\u003c/sup\u003e and CD163\u003csup\u003e+\u003c/sup\u003e macrophages\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7498099/v1/989d318ee7ed663b5d5d14e1.png"},{"id":94856357,"identity":"418109f9-5e42-4abd-b4dd-3c77d3ace122","added_by":"auto","created_at":"2025-10-31 12:14:35","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":858499,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe toxicity of baicalin on melanoma cells and monocytes/macrophages.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A) \u003c/strong\u003eCell viability of melanoma cells treated with baicalin for 24 hours.\u003cstrong\u003e (B) \u003c/strong\u003eMorphological observation.\u003cstrong\u003e (C) \u003c/strong\u003eQuantification of PI-positive cells.\u003cstrong\u003e (D) \u003c/strong\u003ePI/Hoechst 33342 dual staining to detect cell death.\u003cstrong\u003e (E) \u003c/strong\u003eWound-healing assay to assess cell migration ability.\u003cstrong\u003e \u003c/strong\u003eScale bar = 50 μm.\u003cstrong\u003e (F) \u003c/strong\u003eThe IL-1β level released by melanoma cells.\u003cstrong\u003e (G-H) \u003c/strong\u003eCell viability of THP-1 cells \u003cstrong\u003e(G)\u003c/strong\u003e and macrophages \u003cstrong\u003e(H) \u003c/strong\u003etreated with different concentrations of baicalin for 24 hours.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-7498099/v1/8af9b114a24d46a5b4c44a12.png"},{"id":99172342,"identity":"de226651-ae40-4e19-a005-be414ee00ed1","added_by":"auto","created_at":"2025-12-29 16:08:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":13263264,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7498099/v1/84ff4f91-3052-466e-b845-08cc82af70e1.pdf"},{"id":94985998,"identity":"adc4008f-b8ff-4a6b-8b60-08b5ca2c6668","added_by":"auto","created_at":"2025-11-03 06:59:31","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":7345839,"visible":true,"origin":"","legend":"","description":"","filename":"AppendixA.SupplementaryFigure.docx","url":"https://assets-eu.researchsquare.com/files/rs-7498099/v1/51c936cf8f68e5314cd6a29a.docx"},{"id":94856343,"identity":"cb70507c-8e11-43aa-a40f-371f93f4fb25","added_by":"auto","created_at":"2025-10-31 12:14:35","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":409202,"visible":true,"origin":"","legend":"","description":"","filename":"AppendixB.SupplementaryTable.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7498099/v1/0364407f7c94f0358f0a0c40.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Exploring Medicinal Mechanism of Baicalin in Tumor Microenvironment of Melanoma via Bioinformatic and In Vitro Study","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe tumor microenvironment (TME) refers to the internal environment surrounding the tumor, encompassing the acellular components such as extracellular matrix, vascular system, and the cellular constituents like neoplastic cells, immune cells and fibroblasts [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. While the proliferation of tumor cells initiates the creation of the tumor niche, non-transformed cell types within the milieu co-evolve with the tumor cells, thereby both of them participate in the process of tumorigenesis invasion, and response to therapies [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In the TME, tumor-infiltrating immune cells exhibit heterogeneity, demonstrating both functional and phenotypic flexibility, and they may manifest both pro-tumorigenic and anti-tumorigenic impacts [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The distribution of immune cell subgroups and their specific spatial positioning relative to cancer have been suggested as crucial factors in the growth and advancement of tumors, patient prognosis, and reaction to immunotherapy [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSkin cutaneous melanoma (SKCM), arising from the uncontrolled proliferation of melanocytes, is the most aggressive and lethal form of skin cancer [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Despite its historical classification as an infrequent malignancy, its frequency has increased steadily in the last decades [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The transformation of melanocytes into melanoma is hindered by numerous barriers, which are sequentially disrupted by genetic mutations. When initial genetic alterations drive cell proliferation, precursor lesions emerge [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Mutations in the B-Raf proto-oncogene (BRAF), neurofibromin 1 (NF1), and Neuroblastoma RAS viral oncogene homolog (NRAS) constitute the primary genetic determinants, and melanomas associated with skin that has undergone chronic sun exposure typically exhibit a high mutational load attributable to UV exposure [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Due to its remarkably elevated genomic mutational burden, melanoma represents one of the most immunogenic tumors, possessing the potential to provoke specific adaptive antitumor immune responses [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Various therapeutic approaches aimed at suppressing melanoma progression have particularly targeted the stimulation of the anti-tumor functions of TME subsets [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. A recent study suggested that promoting interferon-γ-producing CD8 T cells could bolster immune checkpoint inhibitor (ICI) treatment in melanoma [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In tumor immunotherapy, immune checkpoint blockade (ICB) possesses remarkable ability to combat melanoma [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The quantity, distribution, and characteristics of tumor-infiltrating lymphocytes (TILs) serve as predictive markers for immunotherapy outcomes and act as critical regulators of tumor progression [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Moreover, melanoma cells with mesenchymal-like (MES) status were found to be significantly enriched in early biopsies from non-responders to ICB [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eNatural products and their extracted compounds have long been regarded as reliable and potent sources for discovering anticancer drugs. \u003cem\u003eScutellaria baicalensis\u003c/em\u003e Georgi has been widely used as traditional Chinese medicines with the beneficial effects on various disorders [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. \u003cem\u003eScutellaria baicalensis\u003c/em\u003e Georgi has also been incorporated into pharmacopoeias due to its effectiveness in managing skin conditions. The quality control standard for \u003cem\u003eScutellaria baicalensis\u003c/em\u003e Georgi is baicalin, with its content required to be no less than 85% in dried products [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. As a key bioactive constituent of Scutellaria baicalensis Georgi, baicalin has been identified in modern research as a compound with diverse pharmacological properties, including but not limited to antibacterial, antitumor, anti-inflammatory, and antioxidant activities [\u003cspan additionalcitationids=\"CR18 CR19\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. For instance, wen et al. found that baicalin may be a promising candidate for osteosarcomas treatment because it can induce ferroptosis in osteosarcomas through Nrf2/xCT/GPX4 regulatory axis [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Song et al. showed that baicalin induces apoptosis, suppresses migration, and promotes anti-tumor immunity in colorectal cancer through the TLR4/NF-κB signaling pathway [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn this study, we obtained bulk RNA data of patients from The Cancer Genome Atlas (TCGA) SKCM cohort. Then, we determined the core targets of baicalin influencing the immune cell infiltration of SKCM. These targets were subjected to construct a prognostic model, which was verified using an independent GEO dataset. In addition, we evaluated the predictive power of the prognostic model, the binding potential between baicalin and targets, and the distribution of the prognostic genes in cell subsets. Finally, we validated the potential therapeutic effects of baicalin on melanoma through \u003cem\u003ein vitro\u003c/em\u003e experiments.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Data sources\u003c/h2\u003e\u003cp\u003eRNA sequencing data (TPM) for SKCM and the clinical profiles were collected from TCGA database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.gdc.cancer.gov/\u003c/span\u003e\u003cspan address=\"https://portal.gdc.cancer.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), including 469 tumor samples after removing the duplicates (accessed on 18 February 2024). The clinical features were shown in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. The testing set GSE53118 (79 tumor samples) was downloaded from Gene Expression Omnibus (GEO) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (accessed on 27 February 2024) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Unsupervised clustering of tumor-infiltrating immune cells\u003c/h2\u003e\u003cp\u003eThe classification of tumor-infiltrating immune cells in SKCM cases was conducted using the \"CIBERSORT\" package [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Unsupervised cluster analysis was performed to identify various patterns of tumor-infiltrating immune cells in SKCM, leading to the classification of samples into distinct clusters. The \"ConsensuClusterPlus\" package was applied to complete the unsupervised cluster analysis [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The \"Limma\" package was employed to identify differentially expressed genes (DEGs) between two clusters with a screening threshold of |log\u003csub\u003e2\u003c/sub\u003eFC| \u0026gt;1 and p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Gene set variation analysis (GSVA)\u003c/h2\u003e\u003cp\u003eGSVA can offer enhanced sensitivity for evaluating the enrichment alterations in biological pathway activity across samples [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. It can transform alterations at the gene level into pathway level by assessing the gene set of interest. The \"GSVA\" package was utilized for conducting GSVA enrichment analysis to investigate variations in biological pathways across the different clusters.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Prediction of target genes of melanoma and baicalin\u003c/h2\u003e\u003cp\u003eThe identification of target genes associated with melanoma was sourced from three databases (accessed on 22 February 2024): Disease Gene Interaction (DisGeNet, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.disgenet.org/\u003c/span\u003e\u003cspan address=\"https://www.disgenet.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], and GeneCards (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.genecards.org/\u003c/span\u003e\u003cspan address=\"http://www.genecards.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The identification of target genes associated with baicalin was sourced from six databases (accessed on 23 February 2024): The Swiss Target Prediction database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.swisstargetprediction.ch/\u003c/span\u003e\u003cspan address=\"http://www.swisstargetprediction.ch/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], SuperPred database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://prediction.charite.de/index.php/\u003c/span\u003e\u003cspan address=\"https://prediction.charite.de/index.php/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], GeneCards (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.genecards.org/\u003c/span\u003e\u003cspan address=\"http://www.genecards.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], The Comparative Toxicogenomics Database (CTD, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ctdbase.org/\u003c/span\u003e\u003cspan address=\"https://ctdbase.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], Encyclopedia of Traditional Chinese Medicine (ETCM, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.tcmip.cn/ETCM2/front/#/\u003c/span\u003e\u003cspan address=\"http://www.tcmip.cn/ETCM2/front/#/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] and ChEMBL Database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ebi.ac.uk/chembl/\u003c/span\u003e\u003cspan address=\"https://www.ebi.ac.uk/chembl/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. The selection of target genes was based on Homo sapiens and deduplicated, and the final list was presented in Supplementary Table\u0026nbsp;2.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Functional enrichment analysis\u003c/h2\u003e\u003cp\u003eGene Ontology (GO) is a bioinformatics tool to describe the functions of genes and proteins, including molecular function (MF), cellular component (CC) and biological process (BP) [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis is mainly focused on metabolic pathways [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. The Mel_bcl_ICI was obtained by crossing the genes for melanoma, baicalin and immune infiltration DEGs. The Protein-Protein Interaction (PPI) analysis was performed through the STRING database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://string-db.org/\u003c/span\u003e\u003cspan address=\"https://string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] and drawn in the cytoscape software [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Functional annotation of the Mel_bcl_ICI was conducted using the Metascape database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.metascape.org/\u003c/span\u003e\u003cspan address=\"https://www.metascape.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) with a significance threshold of Min overlap\u0026thinsp;\u0026ge;\u0026thinsp;3 \u0026amp; p\u0026thinsp;\u0026le;\u0026thinsp;0.01 [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Construction of the prognostic model\u003c/h2\u003e\u003cp\u003eThe univariate Cox regression model was used to identify genes in Mel_bcl_ICI significantly associated with survival outcome. The least absolute shrinkage and selection operator (LASSO) regression analysis was conducted via \"glmnet\" package [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. The multivariate Cox regression analysis was used to construct a prognostic model. The risk score was calculated as follows:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\text{r}\\text{i}\\text{s}\\text{k}\\:\\text{s}\\text{c}\\text{o}\\text{r}\\text{e}={\\varSigma\\:}_{i=1}^{n}\\text{e}\\text{x}\\text{p}\\left(\\text{i}\\right)\\times\\:{\\beta\\:}\\text{i}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{e}\\text{x}\\text{p}\\left(\\text{i}\\right)\\)\u003c/span\u003e\u003c/span\u003e represents the expression of each gene and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}\\text{i}\\)\u003c/span\u003e\u003c/span\u003e represents coefficient. Based on the median risk score, samples were categorized into high- and low-risk group. The survival difference between two groups was compared using \"survival\" package. The prediction accuracy was evaluated using Receiver Operating Characteristic (ROC) curve via \"survivalROC\" package.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.7 The individualized prediction model (nomogram)\u003c/h2\u003e\u003cp\u003eThe individualized prediction model was constructed based on risk score, age, race, node via the \"rms\" package, which can predict the 1-, 3-, 5-, 10- and 20-years survival of SKCM patients. A calibration curve was used to assess the consistency between predicted survival and actual survival.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e2.8 Tumor mutation burden (TMB) and the sensitivity of chemotherapy\u003c/h2\u003e\u003cp\u003eThe Mutation Annotation Format (MAF) data retrieved from TCGA database was processed using the \"maftools\" package [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Genomic changes were assessed by determining TMB score, transition and transversion condition, and the site/type of amino acid mutations of specific genes. The chemotherapy sensitivity was evaluated by the half-maximal inhibitory concentration (IC50) of ten common chemotherapeutic agents through \"pRRophetic\" package [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e2.9 Molecular docking\u003c/h2\u003e\u003cp\u003eAutoDockTools-1.5.7 was used to perform molecular docking [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. The structure of CXCL12 (PBD: 2KEC), PLAU (PBD: 1C5Y), PIM1 (PBD: 5O11) PTK2B (PBD: 3CC6), and CCL8 (PBD: 7S5A) was obtained from Protein Data Bank (PDB, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.rcsb.org/\u003c/span\u003e\u003cspan address=\"https://www.rcsb.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e] and the structure of baicalin (Compound CID: 64982) was obtained from PubChem (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubchem.ncbi.nlm.nih.gov/\u003c/span\u003e\u003cspan address=\"https://pubchem.ncbi.nlm.nih.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. The structure of LAP3 (UniProt: P28838) predicted by AlphaFold [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e] was found through the UniProt (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.uniprot.org/\u003c/span\u003e\u003cspan address=\"https://www.uniprot.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. The process of molecular docking is briefly as follows: First, dehydration, hydrogenation and detection of ligand rotatable bonds were performed. Then adjusting the scope of the Grid Box so that it can fully cover the structure of the protein. Molecular docking results were obtained by processing AutoGrid and AutoDock. The lowest bind energy results of molecular docking were exported and converted to pdb format by software OpenBabel 3.1.1 and plotted on software PyMOL 2.2.0.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e2.10 Single cell analysis\u003c/h2\u003e\u003cp\u003eThe scRNA-seq data of SKCM (GSE123139) was enrolled from GEO database [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e], which was deposited in the Tumor Immune Single Cell Center 2 (TISCH2) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://tisch.comp-genomics.org\u003c/span\u003e\u003cspan address=\"http://tisch.comp-genomics.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. This dataset contains 35494 cells from 25 SKCM patients and was uniformly processed to perform quality control, clustering and cell-type annotation. The immune-related mechanism of the prognostic signature in the TME of SKCM was studied at the single-cell level.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e2.11 Chemicals and reagents\u003c/h2\u003e\u003cp\u003eHuman skin melanoma cell line was purchased from IMMOCELL (Xiamen, China); THP-1 cells was purchased from Sunncell Biotechnology Co., Ltd (Wuhan, China); T25 cell culture flask (NEST Biotechnology, China); Fetal Bovine Serum, FBS (BDBIO HangZhou China); DMEM (VivaCell, Shanghai, China); Penicillin-Streptomycin (Shanghai Chuanqiu Biotechnology Co., Ltd, China); Cell Counting Kit 8 (Wuhan Fine Biotech Co., Ltd, China); AG RNAex Pro reagent (ACCURATE BIOTECHNOLOGY(HUNAN) Co., Ltd, China); cDNA Synthesis Kit, Primers (Beyotime Biotechnology); SYBR Green qPCR Master Mix and PMA (TargetMol, USA); RNase free microcentrifuge tubes (Guangzhou Jet Bio-Filtration Co., Ltd); RNase free tips (Nantong Feiyu Biological Technology Co., Ltd); RNase free strip tubes (Magic-bio); DEPC (Keygen BioTECH, China); Murine APC conjugated with anti-CD163 antibody (Proteintech Group, Inc.); Murine PE conjugated with anti-CD86 antibody (Sino Biological Inc.); Murine anti-CD16 antibody (Elabscience Biotechnology Co., Ltd).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e2.12 Cell Culture and cell viability (CCK-8 Assay)\u003c/h2\u003e\u003cp\u003eMelanoma cells were cultured in DMEM containing 10% FBS, penicillin (100 U/mL) and streptomycin (100 \u0026micro;g/mL). THP-1 cells were cultures in 1640 medium, 10% FBS, 0.05 mM mercaptoethanol, penicillin (100 U/mL) and streptomycin (100 \u0026micro;g/mL). The culture environment was 37 ℃ with 5% CO\u003csub\u003e2\u003c/sub\u003e. The cells were passaged when they reached about 80% confluence. The cells were seeded into 96-well plates (5 \u0026times; 10\u003csup\u003e3\u003c/sup\u003e cells/well) for 12 h. Then treated with baicalin and incubated for 24 h. 10 \u0026micro;L of CCK8 was added and incubated for 1 h. The absorbance was measured at 450 nm with a microplate reader (Bio-rad imark) and calculated cell viability.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e2.13 PI/Hoechst33342\u003c/h2\u003e\u003cp\u003eThe cells were seeded into 12-well plates (5 \u0026times; 10\u003csup\u003e4\u003c/sup\u003e cells/well) for 12 h. Then treated with baicalin and incubated for 24 h. Then stained with 1 \u0026micro;g/mL Hoechst33342 for 5 min and 1 \u0026micro;g/mL PI for 3 min. Observing under a fluorescence microscope (Olympus IX71). The images captured by fluorescence microscopy were processed using ImageJ software. PI positive cells were calculated by PI positive cells/total cells of the region of interest.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e2.14 Wound healing\u003c/h2\u003e\u003cp\u003eThe cells were seeded into 12-well plates (10 \u0026times; 10\u003csup\u003e4\u003c/sup\u003e cells/well) for 12 h. Then treated with baicalin and incubated for 24 h. A scratch (cruciform) was made in each well with a 200 \u0026micro;L tip and the new culture medium with low serum was re-added after rinsing twice with PBS. Recorded the scratch moment as 0 h, the scratch sites observed at 0 h were marked and observed the same scratch area at 24 h and 48 h. The quantitative analysis of the area at the scratch site was processed using ImageJ.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e2.15 Enzyme-linked immunosorbent assay (ELISA)\u003c/h2\u003e\u003cp\u003eThe cells were seeded into 12-well plates (5 \u0026times; 10\u003csup\u003e4\u003c/sup\u003e cells/well) for 12 h. Then treated with baicalin and incubated for 24 h. After cell pretreatment, removed the original medium, centrifuged (1000 rpm, 4 ℃, 5 min) and the supernatant was taken for subsequent detection. The samples to be tested were added to wells with precoated capture antibodies. Then proceeded with the sequential addition of biotinylated antibodies, enzyme-combination solution and the substrate TMB, ensuring to wash the plate (Add the washing solution and discard it 1 min later, then pat the residual washing solution away on an absorbent paper) before each addition. Finally, the stop buffer was added and the absorbance was measured at 450 nm.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e2.17 Cell co-culture\u003c/h2\u003e\u003cp\u003eTHP-1 cells were seeded into 12-well plates (1\u0026times;10⁵ cells/well) and were induced polarization by adding PMA at a final concentration of 100 ng/mL for 48 hours. Replaced the culture medium with a complete medium without PMA when the induction was completed. SK-MEL-2 cells were seeded into 0.4 \u0026micro;m transwell chamber (1\u0026times;10⁵ cells/chamber) and placed the chamber into 12-well plate containing M0 macrophages. After 24 hours of co-culture, assess macrophage polarization via flow cytometry.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e2.18 Macrophage polarization assay\u003c/h2\u003e\u003cp\u003eCollected co-cultured macrophages by centrifugation (1000 rpm, 3 min) and discarded the supernatant. Resuspended cells in staining buffer (PBS containing 1% BSA) and centrifuged (1000 rpm, 3 min), removed the supernatant and resuspended in staining buffer. Blocked cells by adding anti-CD16 antibody (final concentration: 2 \u0026micro;g/mL) and incubated at room temperature for 10 min. Added fluorochrome-conjugated antibodies and incubated at 4\u0026deg;C in the dark for 30 min. Centrifuged (1000 rpm, 3 min), discarded supernatant, resuspended in staining buffer and analyzed using a flow cytometer.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e2.19 Quantitative real-time PCR\u003c/h2\u003e\u003cp\u003eThe cells were seeded into 6-well plates (2 \u0026times; 10\u003csup\u003e5\u003c/sup\u003e cells/well) for 12 h. Then treated with baicalin and incubated for 24 h. Total RNA was isolated from cells using 1 mL RNAex reagent on ice for 10min. 200 \u0026micro;L chloroform was added and the mixture was centrifuged (4 ℃, 12000 rpm, 15 min), the RNA in the supernatant was transferred into tubes containing 500 \u0026micro;L isopropanol and mixed in RT for 10 min. Then centrifuged at 4 ℃, 12000 rpm, 10 min, discarded the isopropanol and added 1 mL 70% ethanol to dissolve the RNA. Centrifuged (4 ℃, 7500 rpm, 5 min), discarded the ethanol, dried at room temperature and dissolved in DEPC-treated water. Finally, we used cDNA Synthesis Kit to perform inverse transcription and SYBR Green qPCR Master Mix for qRT-PCR.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e2.20 Statistical analysis\u003c/h2\u003e\u003cp\u003eThe bioinformatic analyses were performed in R software 4.3.2. The Wilcoxon test was used to determine the significance of the difference between two groups. The log-rank test was used to determine the significance of the prognostic value in Kaplan-Meier analysis. Data processing for wet experiments was performed as previously described [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. If not mentioned, an adjusted p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e\u003ch2\u003e3.1 The landscape of immune-cell infiltration in the TME of SKCM\u003c/h2\u003e\u003cp\u003eTo explore the distinct characteristics of TME in SKCM, we classified the SKCM samples into clusters and assessed differences in immune cell infiltration patterns. This study identified two immune-cell infiltration patterns through unsupervised clustering: cluster_1 (250 samples, 6 samples lacking survival data), and cluster_2 (219 samples, 5 samples lacking survival data) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA-K). PCA results showed cluster_1 and cluster_2 could be well separated from each other, indicating two distinct subtypes of SKCM. (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Then, we conducted survival analysis on these two subtypes and found that cluster_2 had a longer overall survival (OS) than cluster_1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). TME cell infiltration analyses showed that cluster_2 exhibited higher levels of immune cell infiltration, including CD8 T cells, na\u0026iuml;ve B cells and regulatory T cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). Both in cluster_1 and cluster_2, the proportion of CD8 T cells, macrophage M0, and macrophage M2 were relatively higher than other type immune cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF, Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eL). The correlation heatmap showed the relationship between the immune cells in TME (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eG). We can observe that the three largest cell populations mentioned earlier, CD8 T cells, and macrophages M0 and M2, exhibit a negative correlation. Additionally, CD8 T cells show a positive correlation with CD4 memory cells and macrophages M1. Intriguingly, we found that cluster_2 in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE, the abundance of CD8 T cells (immunopermissive subset) and regulatory T cells (immunosuppressive subset) is notably higher compared to cluster_1. Furthermore, through the correlation heatmap depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eG, a subtle positive correlation is observed between these two immune cell types, which are theoretically expected to be mutually exclusive. According to previous studies, it\u0026rsquo;s possible that persistent inflammation such as the overactivated immune cells and prolonged infiltration of immune cells, could stimulate a counteracting compensatory immunosuppression intended to protect tissue homeostasis [\u003cspan additionalcitationids=\"CR51\" citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. As for biological pathways, B cell receptor signaling pathway and T cell receptor signaling pathway were significantly upregulated in cluster_2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eH). To sum up, we categorized the SKCM samples into two clusters according to the immune cell infiltration level. Notably, the cluster with higher immune infiltration has better prognosis.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Enrichment analysis of baicalin targets acting on the TME of SKCM\u003c/h2\u003e\u003cp\u003eThe differential gene set ICI_gene (1091 targets, Table S3) was obtained by comparing cluster_1 and cluster_2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). We identified the 430 baicalin-related targets from the combination of six databases and 5200 melanoma-related targets from the combination of three databases. Finally, a total of 32 genes associated with the tumor microenvironment of SKCM, baicalin targets and melanoma were identified by Venn diagram (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB and Table S3). And we called this gene set Mel_bcl_ICI. The PPI network was performed to determine the interaction in Mel_bcl_ICI and among the three targets with the highest degree of connectivity were TNF, IFNG, and TLR4 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). In the GO analysis, positive regulation of cell locomotion, motility and migration were enriched in biological process, plasma membrane and perinuclear region in cellular component, and protein kinase activity, phosphotransferase activity and cytokine receptor binding in molecular function (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). In the KEGG analysis, NF-kappa B signaling pathway and chemokine signaling pathway were significantly enriched (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). Enrichment results were provided in Table S4. Additionally, we constructed a network map based on the enrichment results of GO and KEGG analyses to further capture the relationships between the terms (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF). We can observe that nodes named positive regulation of cell migration, positive regulation of response to external stimulus, and immune response-activating signaling pathway are connected with the highest number of edges, indicating that these three pathways are the primary ones through which the Mel_bcl_ICI target exerts its effects.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec25\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Construction of the prognostic risk model\u003c/h2\u003e\u003cp\u003eWe conducted univariate Cox regression to identify 32 targets in Mel_bcl_ICI significantly linked to prognosis (Table S5). Then we performed LASSO regression analysis using the 25 targets that significantly linked to prognosis and CXCL12, PLAU, LAP3, PIM1, PTK2B and CCL8 were identified based on the minimum lambda value (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA-B). The prognostic model formula was defined by multivariate Cox regression (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC): Risk score\u0026thinsp;=\u0026thinsp;Exp (CXCL12) \u0026times; (0.01963)\u0026thinsp;+\u0026thinsp;Exp (PLAU) \u0026times; (0.07366)\u0026thinsp;+\u0026thinsp;Exp (LAP3) \u0026times; (-0.12037)\u0026thinsp;+\u0026thinsp;Exp (PIM1) \u0026times; (0.16793)\u0026thinsp;+\u0026thinsp;Exp (PTK2B) \u0026times; (-0.21407)\u0026thinsp;+\u0026thinsp;Exp (CCL8) \u0026times; (-0.21715). The SKCM patients in training set were classified into high- and low-risk groups according to the median risk score. Survival analysis showed low-risk patients had a significantly longer overall survival (OS). The ROC curve determined a certain sensitivity and specificity of prognostic model. The 5-year and 20-year AUCs are 701 and 755, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). In the testing set, the OS in the low-risk group was also longer and the AUC value (5 years) was 0.622 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE). Additionally, we performed an enrichment pathway analysis of prognostic genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF). The enrichment results show that the most highly enriched pathways for PTK2B, CCL8, and CXCL12 are the most relevant. The top three most significantly enriched pathways are the chemokine-mediated signaling pathway, response to chemokine, and cellular response to chemokine. From this, we can see that the pathways that are most enriched for prognostic targets are chemokine-related pathways, which play important roles in immune responses and immune cell infiltration in the occurrence and progression of tumors.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec26\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Tumor mutation burden, immunotherapy and chemotherapy of prognostic signature\u003c/h2\u003e\u003cp\u003eAn overview of mutations across all samples was provided in Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eA. We noticed a higher TMB level in low-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA-C) while no significant difference was observed (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). The single gene mutation with the largest difference between the high- and low-risk groups was shown in Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eB-D. Samples were categorized into high- and low-TMB groups based on median TMB and paired with risk groups for survival analysis. Results suggested high TMB positively influences patient prognosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE). Generally, a higher expression of immune checkpoints often implies a better response to immunotherapy for patients, which theoretically suggests that in this study the low-risk patient group may achieve better outcomes with immune checkpoint inhibition compared to the high-risk group. The transition and transversion in high- and low-risk groups were shown in Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eE and F. Theoretically, the higher the tumor mutation, the higher potential load of tumor antigens, rendering it more susceptible to immunotherapy. Henceforth, we assessed the immune checkpoints expression and found a higher expression in low-risk group in 10 common immune checkpoints (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF). Then we evaluated the sensitivity of ten common chemotherapeutic agents and observed a lower estimated half-maximal inhibitory concentration (IC50) in low-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eG), indicating a lower resistance to chemotherapy. The data of immunotherapy and chemotherapy were provided in Table S6-7.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e(A)\u003c/b\u003e Gene mutation frequency in high- and low-risk groups. \u003cb\u003e(B-C)\u003c/b\u003e Scatter plot of high- \u003cb\u003e(B)\u003c/b\u003e and low-risk \u003cb\u003e(C)\u003c/b\u003e groups according to TMB from low to high. \u003cb\u003e(D)\u003c/b\u003e Comparison of TMB between high- and low-risk groups. \u003cb\u003e(E)\u003c/b\u003e KM curve for the mixed group of high- and low-risk with high- and low-TMB. \u003cb\u003e(F)\u003c/b\u003e Distribution of immune checkpoint (CD274, CTLA4, LAG3, PDCD1, HAVCR2, PDCD1LG2, TIGIT, SIGLEC7, VSIR, BTLA) expression in high- and low-risk groups. \u003cb\u003e(G)\u003c/b\u003e The estimated IC50 for ten chemotherapeutic drugs (ABT.888, ATRA, Etoposide, AZD8055, Cisplatin, Bosutinib, Gefitinib, GDC0941, Lenalidomide, Methotrexate) in high- and low-risk groups.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec27\" class=\"Section2\"\u003e\u003ch2\u003e3.5 The individualized prediction model for overall survival in SKCM\u003c/h2\u003e\u003cp\u003eTo evaluate the predictive effect of prognostic model, univariate and multivariate Cox regression analysis of risk-score and clinical features were performed. The results demonstrated risk-score, age, race, and TNM classification were all independent prognostic factors (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA-B). Furthermore, an individualized prognostic prediction model for OS was constructed based on the independent prognostic factors, which could estimate the (1-, 3-, 5-, 10- and 20-year) OS (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). The predictive and actual survival in the calibration curve showed a satisfactory overlap, which indicated a relatively desirable predictive accuracy (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). The C-index of nomogram model was 0.728, better than other independent prognostic factors (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE). The combined use of risk scores and clinical characteristics can enhance the prediction accuracy and facilitate the clinical assessment of patients' prognosis.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e(A-B)\u003c/b\u003e Forest plot of Univariate \u003cb\u003e(A)\u003c/b\u003e and Multivariate \u003cb\u003e(B)\u003c/b\u003e Cox regression analysis of risk-score and clinical features. \u003cb\u003e(C)\u003c/b\u003e The 1-, 3-, 5-, 10- and 20-year survival rate of patients in the nomogram. \u003cb\u003e(D)\u003c/b\u003e The calibration plots of nomogram. \u003cb\u003e(E)\u003c/b\u003e The predictive effect of the nomogram model, risk-score, and clinical prognostic features on survival was evaluated by C-index.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec28\" class=\"Section2\"\u003e\u003ch2\u003e3.6 Molecular docking of baicalin and prognostic targets\u003c/h2\u003e\u003cp\u003eThe molecular docking was conducted to evaluate the potential binding capacity of baicalin with CXCL12, PLAU, LAP3, PIM1, PTK2B, CCL8. Among them, the binding energy of baicalin to CXCL12 is -6.34 kcal/mol, forming hydrogen bonds with 12Arg and 13Phe respectively (Fig. S3A). The binding energy of baicalin to PLAU is -10.03 kcal/mol, forming hydrogen bond with 190Ser (Fig. S3B). The binding energy of baicalin to LAP3 is -7.04 kcal/mol, forming hydrogen bond with 175Ala (Fig. S3C). The binding energy of baicalin to PIM1 is -8.34 kcal/mol, forming hydrogen bond with 121Glu (Fig. S3D). The binding energy of baicalin to PTK2B is -10.05 kcal/mol, forming hydrogen bonds with 478Met, 482Asp, 483His, 486Ile and 487Val respectively (Fig. S3E). The binding energy of baicalin to CCL8 is -5.71 kcal/mol, forming hydrogen bond interactions with 10Thr (Fig. S3F).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec29\" class=\"Section2\"\u003e\u003ch2\u003e3.7 Expression of prognostic genes in TME and the effect of baicalin on macrophages\u003c/h2\u003e\u003cp\u003eWe investigated the distribution of prognostic genes by single cell analysis through the TISCH2. The TME cells were clustered into 21 clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA), which were then categorized into eight cell types through cell annotation (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). The marker genes and proportion of each cell subset were presented in Fig. S4A-B. Moreover, the cell interactions indicated that the intercellular interactions among monocytes/macrophages were the most frequent (Fig. S4C). The cell-cell interaction analysis showed that mono/macro-C5 mainly interacted with mono/macrophages, Tprolif cells and dendritic cells (DCs) (Fig. S4D). As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC-H, CXCL12 and PLAU were mostly enriched in mono/macrophages. LAP3, PIM1 and PTK2B were mainly expressed in CD4/CD8 T cells and mono/macrophages. CCL8 was mostly enriched in mono/macrophages with a slight expression in CD4 T cells. It can be seen that prognosis genes are mainly distributed in monocytes/macrophages. The distribution details of prognostic gene in TME cell subsets were provided in Table S8.\u003c/p\u003e\u003cp\u003eTherefore, we next detected the effects of different concentrations of baicalin (the concentration selection criteria are based on Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e) on macrophages based on the co-culture of melanoma cells and human macrophages. The transcription levels of prognostic target genes in macrophages were detected. In the high and low concentration drug groups, the transcription levels of CCL8, CXCL12, PIM1 and PTK2B all increased, while the transcription levels of PLAU and LAP3 decreased (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eL). Among them, CCL8 and CXCL12 are chemokines, PIM1 is related to cell proliferation and survival, and PTK2B is related to cell migration and adhesion. The transcriptional upregulation of these genes may represent the activation of macrophages. In addition, through flow cytometry, it was found that with the increase of drug concentration in the co-culture condition, the proportion of M1 type of macrophages also increased to a certain extent (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eM).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e(A)\u003c/b\u003e The 21 cell clusters in the GSE123139 dataset. \u003cb\u003e(B)\u003c/b\u003e The 8 cell types were identified. \u003cb\u003e(C-H)\u003c/b\u003e The expression of prognostic genes in cell subsets, with the red box highlighting cells exhibiting perceptible expression of these genes. \u003cb\u003e(I)\u003c/b\u003e The transcriptional levels of prognostic genes in macrophages. \u003cb\u003e(J)\u003c/b\u003e Representative flow cytometric analysis of CD86\u003csup\u003e+\u003c/sup\u003e and CD163\u003csup\u003e+\u003c/sup\u003e macrophages\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec30\" class=\"Section2\"\u003e\u003ch2\u003e3.8 Safety assessment of baicalin.\u003c/h2\u003e\u003cp\u003eFinally, in this study, the toxicity of the small molecule drug baicalin on melanoma cells and normal tissue cells was evaluated to assess its safety for use. Initially, we conducted a CCK-8 assay to determine the cytotoxicity of baicalin on melanoma cells. By co-culturing the cells with a concentration gradient ranging from 5 \u0026micro;M to 500 \u0026micro;M for 24 h, among which 50 \u0026micro;M of baicalin began to significantly affect cell activity and 250 \u0026micro;M of baicalin reduced the viability of melanoma cells to approximately 60% (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). For subsequent experiments, we selected 50 \u0026micro;M and 250 \u0026micro;M as the low and high concentrations of baicalin, respectively. In the cell morphological observation, we found that as the concentration of baicalin increased, the number of dead melanoma cells increased accordingly (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). In the PI/Hoechst 33342 staining, we observed an increase in PI staining intensity with rising baicalin concentrations, while the overall cell count (Hoechst 33342-stained cells) decreased, indicating that baicalin induces melanoma cell death (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD). Furthermore, in the wound-healing assay, the cell migration ability decreased with increasing baicalin concentrations, consistent with the previous findings (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eE). We also measured the level of IL-1β released by the melanoma cells using an ELISA assay, and the results showed that the level of IL-1β in the extracellular space of melanoma cells increased significantly after baicalin treatment, indicating that the inflammatory level of melanoma cells was upregulated. (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eF).\u003c/p\u003e\u003cp\u003eThen, we examined the effects of baicalin treatment on the activity of human monocyte THP-1 cells and macrophages, and found that the concentrations of baicalin that produced significant cytotoxicity for these two types of cells were all above 100 \u0026micro;M (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eG-H). Furthermore, our prior study indicated that baicalin began to exert toxicity on HaCaT cells at concentrations of 250 \u0026micro;M and above[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. And literature review revealed that baicalin showed no significant toxicity to fibroblasts within 100 \u0026micro;M, which falls within the generally accepted non-toxic range for drug administration [\u003cspan additionalcitationids=\"CR54\" citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn summary, whether based on the data from this study or the data from literature investigations, the toxicity concentrations of baicalin for monocytes/macrophages, keratinocytes, and skin fibroblasts are higher than the concentrations that cause significant cytotoxicity in human skin melanoma cells, indicating that melanoma cells in the skin are more sensitive to baicalin.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e(A)\u003c/b\u003e Cell viability of melanoma cells treated with baicalin for 24 hours. \u003cb\u003e(B)\u003c/b\u003e Morphological observation. \u003cb\u003e(C)\u003c/b\u003e Quantification of PI-positive cells. \u003cb\u003e(D)\u003c/b\u003e PI/Hoechst 33342 dual staining to detect cell death. \u003cb\u003e(E)\u003c/b\u003e Wound-healing assay to assess cell migration ability. Scale bar =\u0026thinsp;50 \u0026micro;m. \u003cb\u003e(F)\u003c/b\u003e The IL-1β level released by melanoma cells. \u003cb\u003e(G-H)\u003c/b\u003e Cell viability of THP-1 cells \u003cb\u003e(G)\u003c/b\u003e and macrophages \u003cb\u003e(H)\u003c/b\u003e treated with different concentrations of baicalin for 24 hours.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn order to explore the targets and mechanisms of baicalin against TME in SKCM, we constructed a prognostic model including 6 genes. In addition, a comparative analysis regarding the abnormal expression of 6 prognostic genes was performed with the literature (Table S9). The results showed the upregulation of CXCL12 and PIM1, the downregulation of PLAU in SKCM were confirmed in previous experimental research, while the LAP3, PTK2B and CCL8 had different results in different literatures, which were bidirectional. We speculate that the differences in research outcomes may be attributed to variations in the genetic backgrounds of the study subjects and substantial sample heterogeneity, arising from the use of different cell lines, mouse strains, and sample sources by various research groups. Additionally, while some studies focus solely on melanoma cells, it's important to note that the TME comprises not only tumor cells but others, which may contribute to discrepancies in their findings.\u003c/p\u003e\u003cp\u003eNiknafs et al. found through pan-cancer analysis and clinical data on immunotherapy that tumor mutation burden was associated with the efficacy of immune checkpoint blockade [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. They pointed out that tumors with a high tumor mutation burden background had a more inflammatory tumor microenvironment [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Newell et al. also showed that tumors with high tumor mutation burden exhibited the best response to immunotherapy in advanced cutaneous melanoma [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. In this study, we found the TMB value of high-risk group was lower than low-risk group. The survival analysis indicated high TMB is favorable for patient prognosis. The analysis of immunotherapy and chemotherapy showed a better response in low-risk group.\u003c/p\u003e\u003cp\u003eA molecular docking verification was performed on six prognostic targets and baicalin. The lower the binding energy score, the greater the binding force between small molecules and proteins. The minimum binding energy of baicalin and prognostic targets was all lower than \u0026minus;\u0026thinsp;5.0 kcal/mol, indicating that baicalin has a certain binding potential with these targets [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. The results of molecular docking have suggested that baicalin may be directly involved in the regulation of prognostic target signaling pathways. However, whether the specific components can spontaneously combine and regulate the activity still needs further experiments to verify.\u003c/p\u003e\u003cp\u003eAdditionally, we explored the distribution of prognostic targets in the TME and found prognostic genes were mainly concentrated in CD4/CD8 T cells and mono/macrophages. Xin et al. illustrated the inhibition of PIM-1 disrupted the immunosuppressive TME and restored CD8 T cell-mediated antitumor immunity [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. Babazadeh et al. determined the role of mesenchymal stem cells-derived CXCL12 in macrophage phenotypic switching to M2, which promoted their function in tumorigenesis [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. Moreover, CXCL12-mediated monocyte recruitment was found to be important in neuroinflammation [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. In addition, PTK2B could regulate STING-TBK1 activation in macrophages and increased innate immune response [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. We believe that analyzing the distribution of prognostic genes in TME will enhance our understanding of their biological processes and mechanisms, thereby advancing research in prognostic studies.\u003c/p\u003e\u003cp\u003eRegarding how baicalin affects the progression of melanoma by regulating the TME, based on existing literature, we speculate that its functions include but are not limited to inhibiting the growth of tumor cells, activating immune cells, promoting the transition of tumor-associated macrophages (TAMs) from the M2 subtype to the M1 subtype, and inhibiting transformation from inflammation to cancer. For example, it was reported that baicalin-loaded poly(lactic-co-glycolic acid) nanoparticles possess the ability to activate dendritic cells (DCs) and can elicit apoptosis in melanoma (B16) cells by inducing cell-cycle arrest at the G2/M phase [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. Furthermore, by encapsulating tumor-specific antigenic peptide and an immune stimulant (CpG) in conjunction with baicalin within biomimetic nanoparticles, these particles exhibit potent targeting ability towards TAMs, reversing their M2 phenotype to the M1 phenotype, thereby inducing promising antitumor therapeutic effects [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. Moreover, baicalin facilitated the repolarization of TAMs towards an M1-like phenotype without exerting selective toxicity towards either macrophage phenotype. When hepatocellular carcinoma (HCC) cells were cocultured with TAMs that had been treated with baicalin, a decrease in both proliferation and motility of the HCC cells was observed [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. Furthermore, baicalin could enhance antitumor immune responses by blocking the PD-L1/PD-1 pathway via inhibiting the expression of PD-L1 in HCC cells [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. In addition, chronic inflammation predisposes to the tumor progression, activates tumorigenesis and promotes development [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Baicalin can also mitigate the detrimental effects of inflammatory diseases, such as ulcerative colitis [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e], inflammatory bowel diseases [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e], allergic asthma [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e], and allergic rhinitis [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e] by suppressing inflammatory levels, thereby inhibiting the potential for such syndromes to induce tumorigenesis.\u003c/p\u003e\u003cp\u003e\u003cem\u003eIn vitro\u003c/em\u003e experiments demonstrated that baicalin inhibits the viability and migration of melanoma cells in a concentration-dependent manner, consistent with previous research [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]. Furthermore, baicalin significantly increase the death of melanoma cells and upregulate cellular inflammatory levels. Based on single-cell analysis, we found that monocytes/macrophages play an important role in the immune microenvironment of melanoma, with prognostic genes highly expressed in this cell population. To further investigate the effects of baicalin on TME in melanoma, we established a co-culture system of melanoma cells and macrophages and examined the expression of prognostic genes in macrophages under baicalin treatment. The results revealed that baicalin upregulated the transcriptional levels of CCL8, CXCL12, PIM1 and PTK2B in macrophages, while downregulating the expression of LAP3 and PLAU. CCL8 and CXCL12 are chemokines for monocytes/macrophages and neutrophils, respectively. Their upregulation suggests enhanced innate immune cell recruitment, potentially indicating an immune-enhancing effect. PIM1, as an oncogene, promotes the cancer cell survival by inhibiting apoptotic pathways[\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e]. We hypothesize that PIM1 upregulation in macrophages may support their survival and functional activity. PTK2B is a non-receptor type tyrosine kinase belonging to the focal adhesion kinase family. It has been reported that its activation promotes the migration/adhesion of tumor cells, contributing to therapy resistance[\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e]. We speculate that the upregulation of PTK2B in macrophages may facilitate the recruitment and migration of macrophages to the target tissue. PLAU (also known as uPA, urokinase plasminogen activator), is highly expressed in most cancer tissues and is associated with drug resistance[\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e]. LAP3 (Leucine Aminopeptidase 3), has been reported to promote tumor proliferation, migration and invasion[\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e]. The down-regulation of the transcriptional levels of PLAU and LAP3 following baicalin treatment may contribute to the suppression of tumor progression.\u003c/p\u003e\u003cp\u003eAdditionally, macrophages can be classified into two distinct phenotypes: M1 pro-inflammatory, anti-tumor type and M2 anti-inflammatory, pro-tumor type. We observed that baicalin treatment promoted the polarization of macrophages toward the M1 phenotype under co-culture conditions with melanoma cells, suggesting a tendency to establish an inflammatory microenvironment and a potential anti-tumor effect. In conclusion, baicalin exerts its anti-tumor effect through both direct actions on melanoma cells and indirect modulation of the TME by promoting M1-type macrophage polarization. However, it is important to note that the TME \u003cem\u003ein vivo\u003c/em\u003e is far more complex than the simplified co-culture system used in this study. Therefore, the anti-melanoma effects of baicalin require further investigation. Furthermore, we discovered that melanoma cells exhibit greater sensitivity to baicalin compared to normal skin tissue cells. This differential sensitivity is advantageous for minimizing off-target effects and reducing damage to normal tissues during drug administration. We hypothesize that this phenomenon may be attributed to the elevated metabolic activity and oxidative stress levels in tumor cells, rendering them more susceptible to the antioxidant properties of baicalin. Nevertheless, this hypothesis warrants further experimental validation. Collectively, these findings suggest that baicalin possesses favorable biological safety profiles.\u003c/p\u003e\u003cp\u003eHowever, this study still has several limitations. For instance, the predictive ability of prognostic model still needs improvement and only three of the six prognostic genes are identified as independent prognostic factors. In addition, more wet-lab experiments such as selecting more cell lines and introducing mice tumor-bearing experiments, etc., are needed to verify the potential therapeutic efficacy of baicalin in melanoma.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eTo summary, the pivotal targets and mechanisms of baicalin against SKCM were explored and a prognostic model related to the effect of baicalin on the TME in SKCM was constructed. This model can be used to create an individualized prediction model, reflect genomic mutation and predict the effect of immunotherapy or chemotherapy. In addition, molecular docking and single-cell analysis were performed to study their binding potential and possible mechanisms. Finally, \u003cem\u003ein vitro\u003c/em\u003e experiments demonstrated the potential of baicalin as a therapeutic agent against melanoma. Overall, our study suggests that baicalin may exert therapeutic effects on melanoma by modulating immune cell infiltration and the prognostic model with a certain predictive ability may provide clinical utility.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDeclaration of Competing Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding resource\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the LZU-IMPCAS cooperation ((20)0920).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData supporting the results of this study can be obtained from the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors are grateful to the Core Facility of School of Life Sciences, Lanzhou University.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eZH.L. and CJ.L. designed the experiments and wrote the main manuscript text. 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Int J Biol Macromol. 2015;72:1081-9.https://doi.org/10.1016/j.ijbiomac.2014.10.021\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"medical-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"medo","sideBox":"Learn more about [Medical Oncology](https://www.springer.com/journal/12032)","snPcode":"12032","submissionUrl":"https://submission.nature.com/new-submission/12032/3","title":"Medical Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Melanoma, baicalin, tumor microenvironment, immune infiltration, prognostic model","lastPublishedDoi":"10.21203/rs.3.rs-7498099/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7498099/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe occurrence and advancement of skin cutaneous melanoma (SKCM) is closely associated with tumor microenvironment (TME). Evaluating the condition of immune cell infiltration is pivotal to the comprehension regarding the features of TME in SKCM. Baicalin has been demonstrated to have anti-tumor effects by regulating the TME in tumors. However, its pharmacological potential in melanoma still needs to be elucidated. In this study, through unsupervised clustering analysis and network pharmacology, 32 potential baicalin targets have been identified. The prognostic model can effectively group patients and a more effective clinical individual prediction model can be constructed based on this model. Single-cell analysis demonstrated the expression of prognostic targets was associated with TME and mainly accumulated in mono/macro subset. Finally, \u003cem\u003ein vitro\u003c/em\u003e experiments demonstrated that baicalin significantly reduced the viability, proliferation, and migration capabilities of melanoma cells. Additionally, baicalin promoted pro-inflammatory polarization of macrophages under co-culture with melanoma cells and baicalin exerted relatively high biological safety. In conclusion, this study demonstrates that baicalin inhibits melanoma by modulating the TME and establishes a prognostic model with predictive potential. These findings expand the therapeutic potential of baicalin and provide novel insights for melanoma treatment strategies.\u003c/p\u003e","manuscriptTitle":"Exploring Medicinal Mechanism of Baicalin in Tumor Microenvironment of Melanoma via Bioinformatic and In Vitro Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-31 12:14:30","doi":"10.21203/rs.3.rs-7498099/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-03T02:44:39+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-29T08:22:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"87145716924542376109768650201404883127","date":"2025-11-21T00:42:10+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-24T08:52:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"291335877240379872626254493461782474038","date":"2025-10-21T04:48:36+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-21T03:31:26+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-01T06:02:23+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-01T06:00:11+00:00","index":"","fulltext":""},{"type":"submitted","content":"Medical Oncology","date":"2025-08-31T03:02:35+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"medical-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"medo","sideBox":"Learn more about [Medical Oncology](https://www.springer.com/journal/12032)","snPcode":"12032","submissionUrl":"https://submission.nature.com/new-submission/12032/3","title":"Medical Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"27e396be-be79-4505-8078-c50d765eb2b3","owner":[],"postedDate":"October 31st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-29T16:02:25+00:00","versionOfRecord":{"articleIdentity":"rs-7498099","link":"https://doi.org/10.1007/s12032-025-03205-2","journal":{"identity":"medical-oncology","isVorOnly":false,"title":"Medical Oncology"},"publishedOn":"2025-12-26 15:58:11","publishedOnDateReadable":"December 26th, 2025"},"versionCreatedAt":"2025-10-31 12:14:30","video":"","vorDoi":"10.1007/s12032-025-03205-2","vorDoiUrl":"https://doi.org/10.1007/s12032-025-03205-2","workflowStages":[]},"version":"v1","identity":"rs-7498099","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7498099","identity":"rs-7498099","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}