Identification and validation of aging-related potential biomarkers for melasma via comprehensive transcriptome analysis

Identification and validation of aging-related potential biomarkers for melasma via comprehensive transcriptome analysis | 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 Article Identification and validation of aging-related potential biomarkers for melasma via comprehensive transcriptome analysis Xinxu Zhou, Shuchen Wang, Cangde Wang, Yueying Jin, Pei Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6873942/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Melasma is a pigmentary skin condition that affects appearance, while aging is an inevitable phenomenon in all higher organisms throughout their life cycle. However, the relationship of them remains unclear, further research is needed to identify potential aging-related genes (ARGs) associated with melasma. Methods: Melasma-related dataset was obtained from public database. Differential expression analysis, Maximum Clique Centrality (MCC) algorithm, and machine learning methods were utilized to select candidate biomarkers for melasma. Following these processes, receiver operating characteristic (ROC) and expression level analyses were integrated to identify biomarkers. Ultimately, the expression levels of biomarkers were validated through quantitative reverse-transcription polymerase chain reaction (qRT-PCR) and Western blotting analysis. Results: PTPN11, YWHAZ, FEN1, FOXO4, and TBP were identified as aging-related biomarkers in melasma. The area under ROC curve (AUC) value of these biomarkers was greater than 0.7, and except TBP had higher expression, remaining biomarkers had lower expression in melasma. Conclusively, experiments analysis demonstrated that FOXO4, PTPN11, and YWHAZ were down-regulated at mRNA and protein levels in the melasma group. Conclusion: In this study, 5 aging-related biomarkers-PTPN11, YWHAZ, FEN1, TBP and FOXO4-were identified in melasma, underscoring the potential of these biomarkers to provide theoretical insights for clinical applications for melasma patients. Biological sciences/Cell biology Biological sciences/Genetics Health sciences/Biomarkers Health sciences/Diseases Health sciences/Medical research Health sciences/Molecular medicine Aging Biomaker Melasma Nomgram Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1. Introduction Melasma is a chronic, acquired skin condition characterized by hyperpigmentation, mainly caused by increased activity of epidermal melanin units [1]. Its pathogenesis is usually accompanied by a complex pathogenesis triggered by a variety of factors such as genetic predisposition, sun exposure, and hormonal fluctuations [2,3]. It typically manifests as symmetrical, poorly defined brown patches on the face, particularly on the cheeks, nose, and forehead [4]. The condition manifests as symmetrical, poorly defined brown patches on the face [5], which predominantly affects women, with a prevalence of about 90%, and up to 30% in Asian women of reproductive age [6]. Although treatments like peeling, phototherapy, aesthetic procedures, and topical medications are available, achieving a complete cure remains difficult due to the limited effectiveness of these therapies and concerns over the safety of some medications [7,8]. Therefore, the development of new biomarkers for more effective diagnosis and treatment of melasma is crucial. Biological aging is a complex process, with an increase in the accumulation of senescent cells as individual age [9]. These cells not only impair tissue function, but also contribute to the onset of various diseases [10], such as Alzheimer's disease [11], osteosarcoma [12], and others. The skin, the largest and most intricate organ in the human body, consists of multiple layers with different cell types [13], making it a key indicator of aging. As aging advances, the skin undergoes noticeable changes at the appearance, structural, and cellular-molecular levels, especially in the manifestation of aging-related features [14]. One common skin condition that arises with aging is melasma, a pigmentation disorder often seen on the face [15]. Additionally, studies show a higher number of senescent fibroblasts in the skin of melasma patients compared to healthy individuals [16]. These fibroblasts are central to pigmentation processes and produce aging-related proteins, which contribute to the development of melasma [17]. Thus, identifying aging-related biomarkers in melasma patients holds significant promise for improving diagnosis and treatment of the condition. Therefore, aging-related biomarkers in melasma patients were identified through differential expression analysis and machine learning algorithms. These biomarkers were then used to construct a nomogram to predict the diagnostic probability of melasma. Subsequently, enrichment analysis, immune infiltration analysis, and drug prediction were conducted to explore the roles of these biomarkers in melasma. Finally, the expression differences of these biomarkers between the disease and control groups were validated through in vitro experiments. The goal of this research is to identify potential biomarkers and therapeutic targets for the early diagnosis and targeted treatment of melasma. 2. Materials and methods 2.1 Data source The Gene Expression Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo/) was utilized to download melasma’s transcriptomic dataset GSE72140 (GPL570), which comprised samples from skin epidermis with 22 patients and 22 controls. Additionally, a comprehensive list of 307 aging-related genes (ARGs) were collected from the Human Aging Genomic Resources database (https://genomics.senescence.info/) to facilitate the analysis[18]. 2.2 Differential expression analysis The limma package (version 3.60.4) [19] in R software was employed to extract DEGs between melasma and control groups within the GSE72140 dataset. The selection criteria for DEGs were |log 2 FC| > 0 and P < 0.05. To visualize gene expression differences, volcano plot and heatmap were generated using the ggplot2 (version 3.5.1) [20] and pheatmap (version 1.0.12) [21] packages, respectively. Additionally, an intersection of the identified DEGs with ARGs was taken to derive differentially expressed aging-related genes (DE-ARGs). 2.3 Functional enrichment analysis GO and KEGG enrichment analyses were conducted to predict potential biological functions of DE-ARGs using clusterProfiler package ( P < 0.05) (version 4.7.1.001) [22]. 2.4 Protein-protein interaction (PPI ) network construction and hub genes selection To investigate the interaction of DE-ARGs at protein levels, STRING database (http://string.embl.de/) was utilized to construct a PPI network, with an interaction score of 0.4. Subsequently, the CytoHubba application within Cytoscape software (version 3.10.0) [23] was employed to identify and rank the top 15 hub genes using the MCC algorithm. 2.5 Machine learning analysis Lasso regression analysis was performed on the DE-ARGs using the glmnet (version 4.1.4) [24] package, incorporating three-fold cross-validation to ensure the robustness of the model. Additionally, the e1071 (version 1.7-14) [25] package was utilized to implement the SVM-RFE method, which assessed the importance and ranking of each gene. During this process, error rates and accuracy metrics were recorded for each iteration, with the optimal gene combination being identified at the point of the lowest error rate. Furthermore, Boruta analysis was conducted using the Boruta package (version 8.0.0) [26] in R to enhance feature selection by determining significant predictors among DE-ARGs. Afterthat, an intersection of genes obtained from all three machine learning algorithms was generated using the ggvenn (version 0.1.9) [27] package in R. The intersected genes were designated as candidate biomarkers for further investigation. 2.6 Validation of biomarkers, localization and correlation analysis ROC curves for the candidate biomarkers were generated using the pROC package in R within the GSE72140 dataset. The expression differences of candidate biomarkers between melasma and control groups were visualized using boxplots, followed by wilcoxon test to assess the statistical significance ( P < 0.05). Based on ROC curve and differential expression analysis, we identified biomarkers for melasma. Then, pearson correlation analysis was conducted to examine the pairwise relationships among biomarkers. In addition, chromosomal localization analysis of the biomarkers was then performed using the RCircos package (version 1.2.0) [28] in R. The relevant gene files were downloaded from the NCBI database, and subcellular localization of the key genes was determined using the mRNALocater database. 2.7 Construction of the diagnostic nomogram Based on the expression levels of the selected biomarkers, the nomogram model was constructed using the rms package (version 6.8-1) [29] for estimating individual melasma risk probabilities. Each gene's expression level was assigned a corresponding score, and the total score was calculated by summing all individual scores to predict the probability of disease diagnosis. Higher scores were associated with an increased likelihood of disease. To assess the predictive accuracy of the model, calibration curves were generated and plotted. Additionally, ROC curves were constructed to evaluate the diagnostic performance of the model. Both the calibration and ROC curves provided a comprehensive assessment of the model's reliability and predictive power. 2.8 Immune infiltration analysis Immune cell infiltration in all samples was evaluated using the ssGSEA algorithm from the GSVA package (version 1.52.3) [30] in R to calculate the scores for 28 distinct immune cell types. The infiltration difference of immune cells between melasma and control samples was compared by wilcoxon test ( P < 0.05). Pearson correlation analysis was then performed to investigate the relationship between biomarkers and differential immune cells, which was visualized using ggcor package (version 0.9.8.1) [31]. 2.9 Gene functional similarity analysis, gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) Gene functional similarity analysis for the biomarkers was conducted using the GOSemSim (version 2.10) [32] package in R. This analysis incorporated functional annotation information from the GO database, which included Molecular Function (MF), Biological Process (BP), and Cellular Component (CC). Based on the median value of each biomarker expression, the samples in GSE72140 dataset were divided into high and low expression groups, followed by differential expression analysis, performed using the limma package (version 3.60.4) [33]. Sorting by log 2 FC, GSEA was subsequently conducted with KEGG background gene set (c2.cp.kegg.v7.0.symbols.gmt) using clusterProfiler package (version 4.2.2) [34]. The pathways were selected based on normalized enrichment score (|NES|) > 1 and adj. P 2 and P < 0.05. 2.10 Construction of regulatory networks for biomarkers The relationships between biomarkers and miRNAs were predicted using the miRNet database. Based on identified miRNAs, the interactions between miRNAs and long non-coding RNAs (lncRNAs) were subsequently predicted using the starBase database (https://rnasysu.com/encori/). A mRNA-miRNA-lncRNA network was then constructed by integrating the results from these two databases. Additionally, the ChEA3 database (https://maayanlab.cloud/chea3/) was employed to predict the transcription factor (TFs) associated with the biomarkers, and a biomarker-TF network was constructed. Above networks were both visualized by Cytoscape. Furthermore, the GeneMANIA database (http://genemania.org/) was utilized for selecting gene, which were functionally related with biomarkers, as well as establishing gene-gene interaction network. 2.11 Drug prediction and molecular docking The potential drugs and molecular compounds that could interact with the identified biomarkers were predicted using the DSigDB database. A drug-biomarker interaction network was subsequently constructed and visualized using Cytoscape software. Corresponding protein structures of the biomarkers were obtained from the AlphaFold Protein Structure Database (https://alphafold.ebi.ac.uk/), and the 3D molecular structures of associated compounds were retrieved from the PubChem database (https://pubchem.ncbi.nlm.nih.gov/). Molecular docking simulations were performed using AutoDock Vina to assess the binding affinity between the biomarker protein structures and their corresponding compounds, and the results were visualized using PyMOL software. 2.12 Cell culture The SK-MEL-28 human melanoma cells were purchased from IMMOCELL Corporation. After thawing, the cells were cultured in a humidified incubator at 37°C with 5% CO₂. When the cells reached 80-90% confluence, they were passaged. For experimental treatments, the SK-MEL-28 cells were seeded in 6-well plates at a density of 1×10⁵ cells per well. After 24 hours of incubation, the medium was replaced with serum-free medium for 12 hours to synchronize the cells. Subsequently, the cells were divided into two groups using the SK-MEL-28 cell-specific culture medium: the control group was cultured for 24 hours, and the experimental group was treated with 300 nmol of melanocyte-stimulating hormone and cultured for 24 hours. After the predetermined culture period, the cells were collected for qRT-PCR analysis. Throughout the cell culture process, the morphology of the cells was regularly monitored under an inverted microscope to ensure healthy growth and the absence of contamination. 2.12 Quantitative reverse-transcription polymerase chain reaction (qRT-PCR) Using above cells, qRT-PCR experiments were conducted. Initially, total RNA was extracted from these cells, followed with reverse transcription utilizing the ABScript III RT Master Mix for qRT-PCR with gDNA Remover kit. The temperature program for the reverse transcription process was set as follows: incubation at 37℃ for 2 minutes, followed by 55℃ for 15 minutes, and concluding with a final step at 85℃ for 5 minutes. To ensure the accuracy and reproducibility of the experimental results, all experiments were performed in triplicate. The sequences of the primers used were meticulously documented in Table 1 . During the data analysis phase, relative quantification was conducted using the 2 -ΔΔ Ct method. 2.13 Western blotting analysis After collecting cells, total protein solution was extracted by RIPA lysis buffer with protease inhibitor (PSMF). The protein concentration was measured using a BCA protein assay kit. The samples were then denatured in a metal bath at 95℃ for 10 minutes before proceeding to SDS-PAGE electrophoresis. Upon completion of electrophoresis, the transferred membrane was placed in an incubation box containing TBST for washing. A blocking step was performed using 5% non-fat dry milk for 30 minutes, followed by three washes with TBST. The membrane was then incubated overnight at 4℃ with the primary antibody. The following day, after washing twice with PBS, the PVDF membrane was incubated with the secondary antibody at room temperature for 30 minutes. Finally, protein analysis was conducted using a fluorescence imaging analyzer. This experiment was repeated three times, and data analysis was performed using ImageJ software. 2.14 Statistical analysis All analyses were performed using R version 4.1.2. The significance was evaluated using the wilcoxon test. In graphical representations, a P value of less than 0.05 was indicated as statistically significant. 3. Results 3.1 Identification and enrichment analysis of DE-ARGs Through differential expression analysis on GSE72140 dataset, 1,172 DEGs were identified between melasma and control samples, comprising 530 upregulated genes and 642 downregulated genes ( Figure 1A, B ). Subsequently, an intersection was made between 1,172 DEGs and 307 ARGs, resulting in the identification of 22 DE-ARGs ( Figure 1C ). Furthermore, enrichment analysis was performed to explore the biological pathways associated with the 22 DE-ARGs. GO enrichment analysis revealed 518 terms, including response to oxidative stress, fibroblast proliferation, cellular response to external stimulus, regulation of fibroblast proliferation, and melanosome ( Figure 1D ). Additionally, KEGG analysis enriched 29 pathways, including pathways related to melanoma, Ras signaling, PI3K-Akt signaling, FoxO signaling, and phospholipase D signaling ( Figure 1E ). This comprehensive analysis not only highlighted key DE-ARGs, but also elucidated their potential involvement in relevant biological processes and pathways linked to both melasma and aging. 3.2 Acquisition of 6 candidate biomarkers The interactions of DE-ARGs was explored, and a PPI network was constructed with 20 nodes and 26 interaction relationships. Among these relation pairs, PTPN11 interacted with multiple other genes, such as IL-7R, PDGFRA, and FGF23 ( Figure 2A ). Additionally, MCC algorithm in CytoHubba identified the top 15 hub genes, which included PTPN11, YWHAZ, PRDX1, FEN1, MAP3K5, SOD2, HELLS, E2F1, TBP, NCOR1, WRN, FGF23, PDGFRA, FOXO4, and AGTR1. Further refinement through Lasso regression analysis led to the selection of 13 relevant genes at lambda.min = 0.0495 ( Figure 2B ). Results from Boruta algorithm analysis indicated that PTPN11, YWHAZ, FEN1, TBP, FGF23, FOXO4, and AGTR1 as significant genes ( Figure 2C ). When SVM-RFE model set gene count at 14, the error rate achieved minimum of 0.117 ( Figure 2D ). Ultimately, the gene intersection of these three machine learning methods resulted in the identification of 6 candidate biomarkers: PTPN11, YWHAZ, FEN1, TBP, FGF23, and FOXO4 ( Figure 2E ). This study highlighted candidate biomarkers for melasma that may play an important role in aging-related processes. 3.3 Acquisition of 5 biomarkers with diagnostic value for melasma To further refine the selection of biomarkers, ROC curves were plotted for the 6 candidate biomarkers using the GSE72140 dataset. The results indicated that candidate biomarkers exhibited AUC values greater than 0.7 except FGF23, demonstrating decent diagnostic performance for melasma ( Figure 3A ). Subsequently, expression differences of candidate genes between disease and control groups were analyzed, and significant difference was found in PTPN11, YWHAZ, FEN1, FOXO4, and TBP. Except TBP was up-regulated in melasma group, remaining genes were down-regulaed ( Figure 3B ). Therefore, PTPN11, YWHAZ, FEN1, TBP, and FOXO4 were retained as biomarkers for subsequent analysis. Correlation analysis among these biomarkers was conducted to elucidate their interrelations, a strong positive correlation was then observed between PTPN11 and YWHAZ (cor = 0.413, P < 0.01) ( Figure 3C ). Furthermore, chromosomal localization analyses revealed that PTPN11, YWHAZ, FEN1, FOXO4, and TBP were located on chromosomes 12, 8, 11, X and 6, respectively ( Figure 3D ). The biological function of proteins can only be stabilized in specific suborganelles, because different subcellular localisation determines different protein functions. It was observed that FEN1, YWHAZ, PTPN11, and TBP were predominantly expressed in the nucleus, whereas FOXO4 was primarily localized in the cytoplasm ( Figure 3E ). These findings underscore the potential roles of these genes as biomarkers, highlighting their interrelations that may contribute to further research into ARGs and their interactions with melasma. 3.4 Establishment of a diagnostic nomogram based on biomarkers Utilizing the expression levels of 5 identified biomarkers, the diagnostic nomogram was developed to assess individual risk of melasma ( Figure 4A ). To further validate the performance of constructed nomogram model in terms of accuracy, calibration curves were plotted, and the slope of this curve approached 1 ( Figure 4B ), indicating a high level of accuracy for the model. Additionally, the predictive capability of nomogram was confirmed through ROC curve, which demonstrated an AUC value exceeding 0.7 ( Figure 4C ), thereby reflected its robust predictive performance. These findings suggested that the nomogram model serves as a valuable tool for assessing disease risk based on biomarker expression levels, providing insights into its potential application in clinical settings for personalized medicine. 3.5 Relevance between biomarkers and the infiltration of immune cells To further investigate the differences in immune cell profiles between melasma and control groups, scores for 28 types of immune cells were calculated, and their infiltration abundances were presented as proportions within the two sample groups ( Figure 5A ). The box plot revealed that only mast cells and activated B cells exhibited significant differences in immune infiltration between these two groups ( P < 0.05) ( Figure 5B ). Subsequently, the correlation between biomarkers and differential immune cells was examined. The results indicated that only PTPN11 demonstrated a significant positive correlation with mast cells (cor = 0.303, P < 0.05) ( Figure 5C ). These findings underscore the specific immunological alterations associated with the disease state and highlight PTPN11’s potential role in modulating mast cell activity within this context. 3.6 Investigation of functional similarity and enrichment pathways for biomarkers A functional similarity analysis was conducted on biomarkers to identify those with the strongest interactions with other genes. The results indicated significant functional connections among the five biomarkers, with FOXO4 occupying a central position, followed by YWHAZ and PTPN11 ( Figure 6A ). The biological functions and pathway associations of the identified biomarkers were investigated through GSEA. The results revealed that PTPN11 was significantly enriched in 17 pathways, while both FEN1 and TBP exhibited enrichment in 9 pathways each. YWHAZ was associated with 3 pathways, and the FOXO4 only enriched peroxisome ( Figure 6B ). Notably, the oxidative phosphorylation was activated in both PTPN11 and YWHAZ, whereas the cytokine-cytokine receptor interaction showed suppression in these two genes but activation in FEN1. Additionally, the valine, leucine, and isoleucine degradation pathway was activated across PTPN11, FEN1, and TBP. The peroxisome pathway demonstrated activation in YWHAZ and TBP but suppression in FOXO4. Further analysis using GSVA elucidated additional details regarding specific pathways that were either inhibited or activated within individual gene ( Figure 6C ). In PTPN11, suppression was observed in the toll-like receptor signaling pathway, T cell receptor signaling pathway, TGF-β signaling pathway, as well as complement and coagulation cascades. Conversely, activation occurred in pathways related to steroid hormone biosynthesis, oxidative phosphorylation, glutathione metabolism, and alpha-linolenic acid metabolism. For FOXO4, pathways associated with tyrosine metabolism, glycolysis/gluconeogenesis, lysosomal function, and insulin signaling were found to be suppressed. Moreover, FEN1 exhibited activation of several key pathways including VEGF signaling pathway, apoptosis pathway, toll-like receptor signaling pathway, and mTOR signaling pathway. Activation of the pantothenate and CoA biosynthesis pathway was also noted for TBP. Lastly, YWHAZ displayed suppression of pathways involved in sphingolipid metabolism as well as RIG-I-like receptor signaling pathways; additionally, it impacted circadian rhythm regulation in mammals and lysosomal function. These findings provide insights into the complex regulatory networks involving these biomarkers and their potential roles in various biological processes relevant to aging and melasma. 3.7 Construction of the regulatory network for biomakers TFs influence cellular functions by binding to DNA and regulating gene transcription levels. This interaction provides a comprehensive understanding of the structure and dynamics of gene regulatory networks, revealing the interactions among genes. In this study, 86 TFs were predicted corresponding to biomarkers, and biomarker-TF network comprised 91 nodes and 143 relationships. Notably, the GABPA was simultaneously regulated FEN1, TBP, PTPN11, and YWHAZ ( Figure 7A ). Furthermore, potential regulatory relationships among biomarkers, miRNAs, and lncRNAs were also predicted, followed with construction of network, including 65 nodes and 141 relationships. Among these, hsa-let-7b-5p was found to regulate all biomarkers concurrently ( Figure 7B ). These findings elucidate the intricate regulatory mechanisms involving transcription factors and non-coding RNAs that may play crucial roles in modulating biomarker expression and thereby influencing disease processes. To further investigate gene interactions and shared functionalities among the biomarkers, a GeneMANIA network was constructed. This network included the five biomarkers along with 20 genes exhibiting strong interaction profiles ( Figure 7C ). The associated functions encompassed general transcription initiation factor activity, regulation of protein insertion into mitochondrial membranes involved in apoptotic signaling pathways, and core promoter sequence-specific DNA binding. These findings provided insights into the complex interplay between these biomarkers and their potential roles in various cellular processes, thereby highlighting their significance in understanding disease mechanisms and identifying therapeutic targets. 3.8 Potential compounds and molecular docking for melasma To predict potential therapeutic compounds, an analysis was conducted focusing on five biomarkers. This analysis identified 127 potential therapeutic compounds and revealed 170 interaction relationships, among which doxycycline and oxytetracycline were found to be associated with FEN1 ( Figure 8A ). Furthermore, molecular docking studies were performed to assess the interactions between these potential compounds and the biomarkers ( Table 2 ). The binding energy of FEN1 with 6-Amino-1-naphthol-3-sulfonic Acid Hydrate was measured at -5.831 kcal/mol, involving three residues TYR-173, ARG-19, ARG-211 ( Figure 8B ). The binding energy of FOXO4 with Tamibarotene was determined to be -6.400 kcal/mol, with residue LYS-151 ( Figure 8C ). PTPN11 exhibited a binding energy of -7.035 kcal/mol when associated with 6-Amino-1-naphthol-3-sulfonic Acid Hydrate, through residues ARG-111, THR-218, THR-219, and GLY-246 ( Figure 8D ). TBP demonstrated a binding energy of -6.164 kcal/mol in its interaction with Chlortetracycline, involving residues ARG-205, GLU-206, and LYS-236 ( Figure 8E ). YWHAZ had a binding energy of -6.164 kcal/mol when interacting with Ipratropium, which involved two residues ASN-42 and ARG-41 ( Figure 8F ). These findings provide a potential basis for further development of therapeutic strategies targeting related diseases. 3.9 Validation of biomarkers expression To further validate the changes in biomarker expression, RT-qPCR was conducted. The results indicated that the expression of PTPN11, YWHAZ, FOXO4, and TBP were significantly reduced compared to the control group ( Figure 9A ), while the expression level of FEN1 was found to be elevated. Notably, the observed expression changes of PTPN11, YWHAZ, and FOXO4 were consistent with the findings from bioinformatics analyses. Consequently, Western blotting experiments were performed on these 3 biomarkers to verify their expression at the protein level. The experimental results corroborated the RT-qPCR findings, demonstrating that the protein expressions of FOXO4, PTPN11, and YWHAZ were all significantly downregulated in melasma group, which was in strong agreement with the RT-qPCR results ( Figure 9B ). This discovery not only confirmed the transcriptional level changes but also provided further validation at the protein level, thereby offering comprehensive evidence supporting the role of these relevant biomarkers in disease progression and development. 4. Discussion Aging is a gradual physiological process, with its speed and extent varying across different organs [35]. As senescent cells accumulate in the skin over time, this often leads to signs of aging such as hyperpigmentation and reduced elasticity, resulting in pathological conditions like melasma [36]. Researchers, including Cho Y, have observed that as aging progresses, melanocytes accumulate in the skin, leading to a mottled hyperpigmentation effect [37]. Furthermore, prolonged sun exposure to aging skin triggers irregular pigmentation, contributing to melasma formation [38]. To better understand the molecular mechanisms behind melasma, this study screened five aging-related biomarkers-PTPN11, YWHAZ, FEN1, TBP, and FOXO4-through bioinformatics analysis. All of these biomarkers demonstrated good diagnostic value for melasma, with their ROC curve AUC values exceeding 0.7. Additionally, bio-expression analysis revealed that PTPN11, YWHAZ, and FOXO4 were consistently and significantly under-expressed in patients with melasma, suggesting these biomarkers may play a crucial role in its development. PTPN11 (Protein Tyrosine Phosphatase, Non-Receptor Type 11) encodes the SH2 domain of the tyrosine phosphatase SHP2, which is expressed in various tissues and cells [39,40]. Mutations in PTPN11 are linked to several disorders, including LEOPARD syndrome, which causes freckles to appear on the skin, such as on the face and back [41,42]. Additionally, aging has been associated with disrupted lipid metabolism, and the PTPN11 gene has been identified as a potential regulator of adipose tissue aging [43]. This suggests that PTPN11 may also influence melasma in patients through aging-related mechanisms. Furthermore, YWHAZ (Tyrosine 3-monooxygenase), which encodes the 14-3-3ζ protein, is a highly conserved transmembrane protein that plays crucial roles in numerous biological processes. Recent findings suggest that YWHAZ is closely associated with telomere shortening, a key marker of cellular aging [44]. This indicates that YWHAZ may not only regulate cellular function but also contribute to the aging process. FOXO4 (Forkhead Box O4), an important transcription factor in the FOX family, plays a role in cellular senescence, oxidative stress, cell proliferation, and metabolism. Research by Li Y et al. demonstrated that FOXO4 is highly expressed in senescent cells, where it interacts with p53 to prevent apoptosis and maintain cell survival in these cells [45]. Furthermore, Meng J et al. found that FOXO4-p53 interfering peptides, such as FOXO4-DRI, specifically induced apoptosis in senescent-like cancer-associated fibroblasts and enhanced radiosensitivity in cancer cells during radiotherapy [46]. These studies suggest that FOXO4 has a significant role in aging and may hold potential for clinical applications in cancer treatment. FEN1 (Flap Endonuclease 1), a structure-specific nuclease, is involved in DNA metabolic pathways [47]. Its role in cancer is well-documented, with studies showing that FEN1 is essential in cellular senescence in various cancers, including breast and pancreatic cancer [48,49]. TBP (TATA Box-Binding Protein) is a transcription factor that plays a role in cellular processes, particularly in oncogene-induced transformation [50]. TBP’s promoter can stably express proteins and regulate transcriptional activity throughout the cell lifecycle, potentially influencing cancer progression [51]. These genes are involved in key biological processes such as aging and cancer, and may also be implicated in regulating skin pigmentation. However, their exact roles in melasma remain underexplored. Further investigation into these genes’ contributions to melasma could offer valuable insights into its pathogenesis and the development of novel therapeutic strategies. Immune cells are key regulators of cellular senescence, influencing the development and progression of various diseases [52]. In our analysis of immune infiltration between melasma patients and controls, we found notable differences in the extent of mast cell and activated B cell infiltration. Mast cells, originating from bone marrow mononuclear cells, release various biologically active substances like cytokines and growth factors that are involved in skin aging processes [53]. In particular, when exposed to sunlight, mast cells play a crucial role in regulating elastic tissue, basement membranes, and vasodilation [54,55]. Moreover, our functional enrichment analysis identified a significant association between oxidative phosphorylation processes and melasma biomarkers. As we age, oxidative metabolism levels decrease, leading to pathological changes that contribute to various diseases [56]. Research by Wu H et al. indicates that mitochondrial oxidative phosphorylation significantly affects oxidative stress in aging dermal fibroblasts, regulating their function and impacting epidermal cells, thus promoting skin aging [57]. In light of these findings, we propose that the biomarkers we identified are closely linked to immune cell infiltration and changes in oxidative phosphorylation pathways, playing a key role in the pathogenesis of melasma. Further research is needed to explore the specific functions of these immune cells and metabolic pathways in skin aging and related conditions, paving the way for new diagnostic and therapeutic approaches. This study identified potential biomarkers linked to aging in melasma, offering a new theoretical framework for its early diagnosis and treatment. However, there are still some limitations. First, in the in vitro experiments, the expression trends of two genes, FEN1 and TBP, were not fully aligned with the bioinformatics analysis results. This indicates that while bioinformatics analysis provides an initial foundation for biomarker screening, actual experimental outcomes may be influenced by various factors. Therefore, additional experimental studies are necessary to better understand the role of these genes in melasma development. Second, the biological sample size in this study was relatively small, which could impact the generalizability and accuracy of the results. To improve the reliability of these findings, future research should expand the sample size and include more comprehensive validation to ensure these biomarkers yield consistent results across diverse populations, thereby providing a more robust foundation for clinical melasma applications. 5. Conclusion This study performed extensive bioinformatics analyses and identified five aging-related biomarkers-PTPN11, YWHAZ, FEN1, TBP, and FOXO4-in melasma, offering new targets for its diagnosis and treatment. A nomogram based on these biomarkers was developed and demonstrated strong predictive performance. Additionally, immune infiltration analysis revealed significant differences in mast cells and activated B cells between control and melasma samples, suggesting their potential role in the disease's development and progression. Lastly, in vitro experiments showed that the expression levels of PTPN11, YWHAZ, and FOXO4 were stably reduced in melasma samples. Declarations Acknowledgements Not applicable Author contributions Xinxu Zhou and Pei Liu wrote the main manuscript text and Shuchen Wang prepared figures 1–5. All authors reviewed the manuscript. Competing interests The author(s) declare no competing interests. Data availability The GSE72140 dataset analyzed in this study was collected from Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/) database. Ethics declarations Not applicable. Reference Espósito, A. C. C., Brianezi, G., de Souza, N. P., Miot, L. D. B. & Miot, H. A. 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Y., Choi, H., Ham, M. & Kim, K. H. Melasolv™: a potential preventive and depigmenting agent for the senescence of melanocytes. Front Mol Biosci 10 , 1228640 (2023). Kang, H. Y., Lee, J. W., Papaccio, F., Bellei, B. & Picardo, M. Alterations of the pigmentation system in the aging process. Pigment Cell Melanoma Res 34 , 800-813 (2021). Yi, J. S., Perla, S., Enyenihi, L. & Bennett, A. M. Tyrosyl phosphorylation of PZR promotes hypertrophic cardiomyopathy in PTPN11-associated Noonan syndrome with multiple lentigines. JCI Insight 5 (2020). Li, R. et al. Generation of an induced pluripotent stem cell line (TRNDi003-A) from a Noonan syndrome with multiple lentigines (NSML) patient carrying a p.Q510P mutation in the PTPN11 gene. Stem Cell Res 34 , 101374 (2019). Polubothu, S. et al. PTPN11 Mosaicism Causes a Spectrum of Pigmentary and Vascular Neurocutaneous Disorders and Predisposes to Melanoma. J Invest Dermatol 143 , 1042-1051.e1043 (2023). Guo, W. & Xu, Q. 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The effect of aging on mast cell density in human skin: a comparative analysis of photoexposed and photoprotected regions. Folia Morphol (Warsz) (2024). Kwon, S. H., Hwang, Y. J., Lee, S. K. & Park, K. C. Heterogeneous Pathology of Melasma and Its Clinical Implications. Int J Mol Sci 17 (2016). Phansuk, K., Vachiramon, V., Jurairattanaporn, N., Chanprapaph, K. & Rattananukrom, T. Dermal Pathology in Melasma: An Update Review. Clin Cosmet Investig Dermatol 15 , 11-19 (2022). Elmansi, A. M. & Miller, R. A. Oxidative phosphorylation and fatty acid oxidation in slow-aging mice. Free Radic Biol Med 224 , 246-255 (2024). Wu, H. et al. Improving dermal fibroblast-to-epidermis communications and aging wound repair through extracellular vesicle-mediated delivery of Gstm2 mRNA. J Nanobiotechnology 22 , 307 (2024). Tables Table 1 Primers used in this study Primers for RT-qPCR H-GAPDH F:5‘-GGAGTCCACTGGCGTCTTCA -3’ R:5‘-GTCATGAGTCCTTCCACGATACC -3’ PTPN11 F:5'-TCTCCATGCCTCTAACACTCG-3' R:5'-CCAGGTCAACAGACGTGTCAG-3' YWHAZ F:5'-GGTGGCTCTGGATACAAAGGG-3' R:5'-ACTTCTCCATGTAAGCGTTGTC-3' FEN1 F:5'-CACCTGATGGGCATGTTCTAC-3' R:5'-CTCGCCTGACTTGAGCTGT-3' FOXO4 F:5'-GGCTGCCGCGATCATAGAC-3' R:5'-GGCTGGTTAGCGATCTCTGG-3' TBP F:5'-CCACTCACAGACTCTCACAAC-3' R:5'-CTGCGGTACAATCCCAGAACT-3' Table 2 Molecular docking binding energy of biomarkers Symbol PDB/Uniprot Molecule Name Pub Chem CID Affinity (kcal/mol) hydrogen bonds FEN1 P39748 6-AMino-1-naphthol-3-sulfonic Acid Hydrate 45051793 -5.831 4 FOXO4 P98177 Tamibarotene 108143 -6.400 1 PTPN11 Q06124 6-AMino-1-naphthol-3-sulfonic Acid Hydrate 45051793 -7.035 6 TBP P20226 Chlortetracycline 54675777 -6.164 5 YWHAZ P63104 Ipratropium 657309 -6.640 2 Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6873942","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":480871093,"identity":"4f7817fb-3e44-4d34-a058-240567c0ebf9","order_by":0,"name":"Xinxu Zhou","email":"","orcid":"","institution":"Shanghai qiji outpatient Department","correspondingAuthor":false,"prefix":"","firstName":"Xinxu","middleName":"","lastName":"Zhou","suffix":""},{"id":480871094,"identity":"ae11f13a-5d05-477d-a7e6-ba4eea5fb3cd","order_by":1,"name":"Shuchen Wang","email":"","orcid":"","institution":"Chenxi Medical Aesthetics Group","correspondingAuthor":false,"prefix":"","firstName":"Shuchen","middleName":"","lastName":"Wang","suffix":""},{"id":480871095,"identity":"48387321-84e1-4235-8eb2-952195af9481","order_by":2,"name":"Cangde Wang","email":"","orcid":"","institution":"Zhuojue Medical Aesthetics Group","correspondingAuthor":false,"prefix":"","firstName":"Cangde","middleName":"","lastName":"Wang","suffix":""},{"id":480871096,"identity":"7e92815e-0f1f-46d0-b3ff-92bc2038fc43","order_by":3,"name":"Yueying Jin","email":"","orcid":"","institution":"Chenxiaoxi Medical Aesthetics Group","correspondingAuthor":false,"prefix":"","firstName":"Yueying","middleName":"","lastName":"Jin","suffix":""},{"id":480871097,"identity":"d131b0cc-7944-42a5-9c8e-89b90be937a2","order_by":4,"name":"Pei Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvElEQVRIiWNgGAWjYBACPgglUd/PzHz4AVFa2CCUBePMdrY0A1K0VDBuOM+jIEGcFukes8e8OySYjQ/zMBgw1NhEE9Yic8bcmPeMBJvZYd4DDxiOpeU2ENQikWMmzdsmwWN2mC/BgLHhMPFaJIybeQwkSNJiYMBMtBaZY+WGc89IJEgcBgZyAjF+4Zdu3vbg7Y66BP7+w4cffKixIayFQYLDjIkXpiyBoHKwFvZnjD8JmzwKRsEoGAUjGQAAGqs3OesT/6MAAAAASUVORK5CYII=","orcid":"","institution":"Zhongkeyanxi Dermatological Medicine Research Institute","correspondingAuthor":true,"prefix":"","firstName":"Pei","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2025-06-11 17:08:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6873942/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6873942/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86218272,"identity":"56712aab-592b-481a-be48-7ea87d1f8630","added_by":"auto","created_at":"2025-07-08 06:24:48","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":12857010,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification and enrichment analysis of differentially expressed aging-related genes (DE-ARGs).\u003c/strong\u003e (A) The volcano map of differentially expressed genes (DEGs) in GSE72140. (B) Heat map of differentially expressed genes (DEGs) in GSE72140. (C) Venn graph of DE-ARG. (D) Gene Ontology (GO) analysis of DE-ARG. (E) Kyoto Encyclopedia of Genes and Genomes (KEGG) of DE-ARG.\u003c/p\u003e","description":"","filename":"F1.png","url":"https://assets-eu.researchsquare.com/files/rs-6873942/v1/74e6a3cf0c73cca2e3ee0590.png"},{"id":86218266,"identity":"623807dd-7eec-4ed9-877b-1098da55b092","added_by":"auto","created_at":"2025-07-08 06:24:48","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":4933187,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification of candidate biomarkers through machine learning. \u003c/strong\u003e(A) Protein-protein interaction (PPI) network construction of DE-ARGs. (B) The candidate genes screened by Least absolute shrinkage and selection operator (Lasso) regression. (C)The candidate genes screened by Boruta regression. (D)The candidate genes screened by the SVM-RFE machine learning algorithm. (E) Venn graph of the genes obtained by three machine learning algorithms.\u003c/p\u003e","description":"","filename":"F2.png","url":"https://assets-eu.researchsquare.com/files/rs-6873942/v1/7652800f75f34f7c2600d542.png"},{"id":86218411,"identity":"b97ede73-1f9d-46b3-b27a-043671cbde49","added_by":"auto","created_at":"2025-07-08 06:32:48","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":6704149,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eVerification of the identified biomarkers. \u003c/strong\u003e(A) Receiver operating characteristic\u003cstrong\u003e (\u003c/strong\u003eROC) curve for candidate biomarkers in GSE72140 dataset. (B) The boxplot of the expression levels of candidate biomarkers in GSE72140 dataset. (C) Correlation analysis between all biomarkers. (D) The chromosomal positions of the biomarkers. (E) Subcellular localisation of biomakers.\u003c/p\u003e","description":"","filename":"F3.png","url":"https://assets-eu.researchsquare.com/files/rs-6873942/v1/5c80843a45ac809a1b386424.png"},{"id":86218265,"identity":"3b0f4579-b30d-4508-a71d-e2a84b697dcb","added_by":"auto","created_at":"2025-07-08 06:24:48","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2104078,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEfficacy assessment of biomarkers.\u003c/strong\u003e (A) Construction of the nomogram based on biomarkers. (B) Calibration curves verify the effectiveness of the nomogram. (C) ROC curves of the nomogram\u003c/p\u003e","description":"","filename":"F4.png","url":"https://assets-eu.researchsquare.com/files/rs-6873942/v1/8eb65ddd88b1702a8c08a809.png"},{"id":86218410,"identity":"03bf9ba3-2ee8-4790-9436-bfc3ef9d4388","added_by":"auto","created_at":"2025-07-08 06:32:48","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":6666524,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImmune infiltration analysis.\u003c/strong\u003e (A) A stacked bar chart illustrated the infiltration abundance of 28 types of immune cells in melasma and control groups. (B) Comparison of the infiltration abundances of 22 immune cell types between melasma and control groups. (C) Spearman correlation analysis between differentially abundant immune cells and biomarkers. *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001, ****\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.0001, ns: \u003cem\u003eP\u003c/em\u003e \u0026gt; 0.05.\u003c/p\u003e","description":"","filename":"F5.png","url":"https://assets-eu.researchsquare.com/files/rs-6873942/v1/8f2f90d92611dafc110e2a85.png"},{"id":86218274,"identity":"d597bcc8-8c66-4f82-a907-7dfdc180db46","added_by":"auto","created_at":"2025-07-08 06:24:48","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":34519714,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunctional similarity and enrichment pathways for biomarkers. \u003c/strong\u003e(A) Boxplot showing the correlations of expressions among biomarkers.\u003cstrong\u003e \u003c/strong\u003e(B) Gene set enrichment analysis (GSEA) results of biomakers. (C) Gene set variation analysis (GSVA) analysis of biomakers.\u003c/p\u003e","description":"","filename":"F6.png","url":"https://assets-eu.researchsquare.com/files/rs-6873942/v1/49982b1bb295f657e5b52814.png"},{"id":86218270,"identity":"99663148-6289-445b-9f9a-6b007d2e1cc4","added_by":"auto","created_at":"2025-07-08 06:24:48","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":14041865,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRegulatory networks of biomarkers.\u003c/strong\u003e (A) Transcription factor (TF)-mRNA regulatory network. (B) Construction of mRNA-miRNA-lncRNA regulatory network. (C)The gene-gene interaction network for biomakers.\u003c/p\u003e","description":"","filename":"F7.png","url":"https://assets-eu.researchsquare.com/files/rs-6873942/v1/80481440eeed99adc9392d04.png"},{"id":86218269,"identity":"3c3d4bc3-2092-46f6-b97f-993da2e636fb","added_by":"auto","created_at":"2025-07-08 06:24:48","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":8735738,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePredicting the drugs targeting the diagnostic biomarkers. \u003c/strong\u003e(A) Drug-gene network. (B) The molecular docking results between 6-AMino-1-naphthol-3-sulfonic Acid Hydrate and FEN1. (C) The molecular docking results between Tamibarotene and FOXO4. (D) The molecular docking results between 6-AMino-1-naphthol-3-sulfonic Acid Hydrate and PTPN11. (E) The molecular docking results between Chlortetracycline and TBP. (F) The molecular docking results between Ipratropium and YWHAZ.\u003c/p\u003e","description":"","filename":"F8.png","url":"https://assets-eu.researchsquare.com/files/rs-6873942/v1/eeb3ec3a0051a5d42844d70e.png"},{"id":86218412,"identity":"70227ce1-c7f9-4dbf-aa18-74401a92e377","added_by":"auto","created_at":"2025-07-08 06:32:48","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":10523896,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe expressions of 5 biomarkers were validated by quantitative real-time PCR (qRT-PCR) and Western blotting.\u003c/strong\u003e (A) Expression of biomarkers at the mRNA level by qRT-PCR. (B) Expression of biomarkers at the protein level by Western blotting. *P \u0026lt; 0.05; **P \u0026lt; 0.01; ***P \u0026lt; 0.001. ns, no significance.\u003c/p\u003e","description":"","filename":"F9.png","url":"https://assets-eu.researchsquare.com/files/rs-6873942/v1/ef20a100124f7b588821bf39.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identification and validation of aging-related potential biomarkers for melasma via comprehensive transcriptome analysis","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eMelasma is a chronic, acquired skin condition characterized by hyperpigmentation, mainly caused by increased activity of epidermal melanin units [1]. Its pathogenesis is usually accompanied by a complex pathogenesis triggered by a variety of factors such as genetic predisposition, sun exposure, and hormonal fluctuations [2,3]. It typically manifests as symmetrical, poorly defined brown patches on the face, particularly on the cheeks, nose, and forehead [4]. The condition manifests as symmetrical, poorly defined brown patches on the face [5], which predominantly affects women, with a prevalence of about 90%, and up to 30% in Asian women of reproductive age [6]. Although treatments like peeling, phototherapy, aesthetic procedures, and topical medications are available, achieving a complete cure remains difficult due to the limited effectiveness of these therapies and concerns over the safety of some medications [7,8]. Therefore, the development of new biomarkers for more effective diagnosis and treatment of melasma is crucial.\u003c/p\u003e\n\u003cp\u003eBiological aging is a complex process, with an increase in the accumulation of senescent cells as individual age [9]. These cells not only impair tissue function, but also contribute to the onset of various diseases [10], such as Alzheimer's disease [11], osteosarcoma [12], and others. The skin, the largest and most intricate organ in the human body, consists of multiple layers with different cell types [13], making it a key indicator of aging. As aging advances, the skin undergoes noticeable changes at the appearance, structural, and cellular-molecular levels, especially in the manifestation of aging-related features [14]. One common skin condition that arises with aging is melasma, a pigmentation disorder often seen on the face [15]. Additionally, studies show a higher number of senescent fibroblasts in the skin of melasma patients compared to healthy individuals [16]. These fibroblasts are central to pigmentation processes and produce aging-related proteins, which contribute to the development of melasma [17]. Thus, identifying aging-related biomarkers in melasma patients holds significant promise for improving diagnosis and treatment of the condition.\u003c/p\u003e\n\u003cp\u003eTherefore, aging-related biomarkers in melasma patients were identified through differential expression analysis and machine learning algorithms. These biomarkers were then used to construct a nomogram to predict the diagnostic probability of melasma. Subsequently, enrichment analysis, immune infiltration analysis, and drug prediction were conducted to explore the roles of these biomarkers in melasma. Finally, the expression differences of these biomarkers between the disease and control groups were validated through \u003cem\u003ein vitro\u003c/em\u003e experiments. The goal of this research is to identify potential biomarkers and therapeutic targets for the early diagnosis and targeted treatment of melasma.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cp\u003e\u003cstrong\u003e2.1 Data source\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Gene Expression Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo/) was utilized to download melasma\u0026rsquo;s transcriptomic dataset GSE72140 (GPL570), which comprised samples from skin epidermis with 22 patients and 22 controls. Additionally, a comprehensive list of 307 aging-related genes (ARGs) were collected from the Human Aging Genomic Resources database (https://genomics.senescence.info/) to facilitate the analysis[18].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Differential expression analysis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe limma package (version 3.60.4) [19] in R software was employed to extract DEGs between melasma and control groups within the GSE72140 dataset. The selection criteria for DEGs were |log\u003csub\u003e2\u003c/sub\u003eFC| \u0026gt; 0 and \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05. To visualize gene expression differences, volcano plot and heatmap were generated using the ggplot2 (version 3.5.1) [20] and pheatmap (version 1.0.12) [21] packages, respectively. Additionally, an intersection of the identified DEGs with ARGs was taken to derive differentially expressed aging-related genes (DE-ARGs).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Functional enrichment analysis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGO and KEGG enrichment analyses were conducted to predict potential biological functions of DE-ARGs using clusterProfiler package (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05) (version 4.7.1.001) [22].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 Protein-protein interaction (PPI\u003c/strong\u003e)\u003cstrong\u003e\u0026nbsp;network construction and hub genes selection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo investigate the interaction of DE-ARGs at protein levels, STRING database (http://string.embl.de/) was utilized to construct a PPI network, with an interaction score of 0.4. Subsequently, the CytoHubba application within Cytoscape software\u0026nbsp;(version 3.10.0) [23]\u0026nbsp;was employed to identify and rank the top 15 hub genes using the MCC algorithm.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5 Machine learning analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLasso regression analysis was performed on the DE-ARGs using the glmnet\u0026nbsp;(version\u0026nbsp;4.1.4) [24] package, incorporating three-fold cross-validation to ensure the robustness of the model. Additionally, the e1071 (version 1.7-14)\u0026nbsp;[25]\u0026nbsp;package was utilized to implement the SVM-RFE method, which assessed the importance and ranking of each gene. During this process, error rates and accuracy metrics were recorded for each iteration, with the optimal gene combination being identified at the point of the lowest error rate. Furthermore, Boruta analysis was conducted using the Boruta package\u0026nbsp;(version 8.0.0)\u0026nbsp;[26]\u0026nbsp;in R to enhance feature selection by determining significant predictors among DE-ARGs. Afterthat, an intersection of genes obtained from all three machine learning algorithms was generated using the ggvenn\u0026nbsp;(version 0.1.9)\u0026nbsp;[27]\u0026nbsp;package in R. The intersected genes were designated as candidate biomarkers for further investigation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.6 Validation of biomarkers, localization and correlation analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eROC curves for the candidate biomarkers were generated using the pROC package in R within the GSE72140 dataset. The expression differences of candidate biomarkers between melasma and control groups were visualized using boxplots, followed by wilcoxon test to assess the statistical significance (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05).\u0026nbsp;Based on ROC curve and differential expression analysis, we identified biomarkers for\u0026nbsp;melasma.\u0026nbsp;Then, pearson correlation analysis was conducted to examine the pairwise relationships among biomarkers. In addition, chromosomal localization analysis of the biomarkers was then performed using the RCircos package\u0026nbsp;(version 1.2.0) [28]\u0026nbsp;in R. The relevant gene files were downloaded from the NCBI database, and subcellular localization of the key genes was determined using the mRNALocater database.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.7 Construction of the diagnostic nomogram\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on the expression levels of the selected biomarkers, the nomogram model was constructed using the rms package (version\u0026nbsp;6.8-1) [29] for estimating individual melasma risk probabilities.\u0026nbsp;Each gene\u0026apos;s expression level was assigned a corresponding score, and the total score was calculated by summing all individual scores to predict the probability of disease diagnosis. Higher scores were associated with an increased likelihood of disease. To assess the predictive accuracy of the model, calibration curves were generated and plotted. Additionally, ROC curves were constructed to evaluate the diagnostic performance of the model. Both the calibration and ROC curves provided a comprehensive assessment of the model\u0026apos;s reliability and predictive power.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.8 Immune infiltration analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eImmune cell infiltration in all samples was evaluated using the ssGSEA algorithm from the GSVA package (version\u0026nbsp;1.52.3) [30] in R to calculate the scores for 28 distinct immune cell types. The infiltration difference of immune cells between melasma and control samples was compared by wilcoxon test (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). Pearson correlation analysis was then performed to investigate the relationship between biomarkers and differential immune cells, which was visualized using ggcor package (version\u0026nbsp;0.9.8.1)\u0026nbsp;[31].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.9 Gene functional similarity analysis, gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGene functional similarity analysis for the biomarkers was conducted using the GOSemSim (version\u0026nbsp;2.10) [32] package in R. This analysis incorporated functional annotation information from the GO database, which included Molecular Function (MF), Biological Process (BP), and Cellular Component (CC). Based on the median value of each biomarker expression, the samples in GSE72140 dataset were divided into high and low expression groups, followed by differential expression analysis, performed using the limma package (version\u0026nbsp;3.60.4)\u0026nbsp;[33]. Sorting by log\u003csub\u003e2\u003c/sub\u003eFC, GSEA was subsequently conducted with KEGG background gene set (c2.cp.kegg.v7.0.symbols.gmt) using clusterProfiler package (version\u0026nbsp;4.2.2)\u0026nbsp;[34]. The pathways were selected based on normalized enrichment score (|NES|) \u0026gt; 1 and adj.\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05. Additionally, GSVA package was employed to enriched pathways, and \u0026ldquo;limma\u0026rdquo; package was utilized to perform differential expression analysis on pathways with thresholds set at |t| \u0026gt; 2 and \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.10 Construction of regulatory networks for biomarkers\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe relationships between biomarkers and miRNAs were predicted using the miRNet database. Based on identified miRNAs, the interactions between miRNAs and long non-coding RNAs (lncRNAs) were subsequently predicted using the starBase database (https://rnasysu.com/encori/). A mRNA-miRNA-lncRNA network was then constructed by integrating the results from these two databases. Additionally, the ChEA3 database (https://maayanlab.cloud/chea3/) was employed to predict the transcription factor (TFs) associated with the biomarkers, and a biomarker-TF network was constructed. Above networks were both visualized by Cytoscape.\u0026nbsp;Furthermore, the GeneMANIA database (http://genemania.org/) was utilized for selecting gene, which were functionally related with biomarkers, as well as establishing gene-gene interaction network.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.11 Drug prediction and molecular docking\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe potential drugs and molecular compounds that could interact with the identified biomarkers were predicted using the DSigDB database. A drug-biomarker interaction network was subsequently constructed and visualized using Cytoscape software. Corresponding protein structures of the biomarkers were obtained from the AlphaFold Protein Structure Database (https://alphafold.ebi.ac.uk/), and the 3D molecular structures of associated compounds were retrieved from the PubChem database (https://pubchem.ncbi.nlm.nih.gov/). Molecular docking simulations were performed using AutoDock Vina to assess the binding affinity between the biomarker protein structures and their corresponding compounds, and the results were visualized using PyMOL software.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.12 Cell culture\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe SK-MEL-28 human melanoma cells were purchased from IMMOCELL Corporation. After thawing, the cells were cultured in a humidified incubator at 37\u0026deg;C with 5% CO₂. When the cells reached 80-90% confluence, they were passaged. For experimental treatments, the SK-MEL-28 cells were seeded in 6-well plates at a density of 1\u0026times;10⁵ cells per well. After 24 hours of incubation, the medium was replaced with serum-free medium for 12 hours to synchronize the cells. Subsequently, the cells were divided into two groups using the SK-MEL-28 cell-specific culture medium: the control group was cultured for 24 hours, and the experimental group was treated with 300 nmol of melanocyte-stimulating hormone and cultured for 24 hours. After the predetermined culture period, the cells were collected for qRT-PCR analysis. Throughout the cell culture process, the morphology of the cells was regularly monitored under an inverted microscope to ensure healthy growth and the absence of contamination.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.12 Quantitative reverse-transcription polymerase chain reaction\u0026nbsp;(qRT-PCR)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUsing above cells, qRT-PCR experiments were conducted. Initially, total RNA was extracted from these cells, followed with reverse transcription utilizing the ABScript III RT Master Mix for qRT-PCR with gDNA Remover kit. The temperature program for the reverse transcription process was set as follows: incubation at 37℃ for 2 minutes, followed by 55℃ for 15 minutes, and concluding with a final step at 85℃ for 5 minutes. To ensure the accuracy and reproducibility of the experimental results, all experiments were performed in triplicate. The sequences of the primers used were meticulously documented in\u0026nbsp;\u003cstrong\u003eTable 1\u003c/strong\u003e. During the data analysis phase, relative quantification was conducted using the 2\u003csup\u003e-\u0026Delta;\u0026Delta;\u003c/sup\u003e\u003csup\u003eCt\u003c/sup\u003e method.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.13 Western blotting analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter collecting cells, total protein solution was extracted by RIPA lysis buffer with protease inhibitor (PSMF). The protein concentration was measured using a BCA protein assay kit. The samples were then denatured in a metal bath at 95℃ for 10 minutes before proceeding to SDS-PAGE electrophoresis. Upon completion of electrophoresis, the transferred membrane was placed in an incubation box containing TBST for washing. A blocking step was performed using 5% non-fat dry milk for 30 minutes, followed by three washes with TBST. The membrane was then incubated overnight at 4℃ with the primary antibody. The following day, after washing twice with PBS, the PVDF membrane was incubated with the secondary antibody at room temperature for 30 minutes. Finally, protein analysis was conducted using a fluorescence imaging analyzer. This experiment was repeated three times, and data analysis was performed using ImageJ software.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.14 Statistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll analyses were performed using R version 4.1.2. The significance was evaluated using the wilcoxon test. In graphical representations, a \u003cem\u003eP\u003c/em\u003e value of less than 0.05 was indicated as statistically significant.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e3.1 Identification and enrichment analysis of DE-ARGs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThrough differential expression analysis on GSE72140 dataset, 1,172 DEGs were identified between melasma and control samples, comprising 530 upregulated genes and 642 downregulated genes (\u003cstrong\u003eFigure 1A, B\u003c/strong\u003e). Subsequently, an intersection was made between 1,172 DEGs and 307 ARGs, resulting in the identification of 22 DE-ARGs (\u003cstrong\u003eFigure 1C\u003c/strong\u003e). Furthermore, enrichment analysis was performed to explore the biological pathways associated with the 22 DE-ARGs. GO enrichment analysis revealed 518 terms, including response to oxidative stress, fibroblast proliferation, cellular response to external stimulus, regulation of fibroblast proliferation, and melanosome (\u003cstrong\u003eFigure 1D\u003c/strong\u003e). Additionally, KEGG analysis enriched 29 pathways, including pathways related to melanoma, Ras signaling, PI3K-Akt signaling, FoxO signaling, and phospholipase D signaling (\u003cstrong\u003eFigure 1E\u003c/strong\u003e). This comprehensive analysis not only highlighted key DE-ARGs, but also elucidated their potential involvement in relevant biological processes and pathways linked to both melasma and aging.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Acquisition of 6 candidate biomarkers\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe interactions of DE-ARGs was explored, and a PPI network was constructed with 20 nodes and 26 interaction relationships. Among these relation pairs, PTPN11 interacted with multiple other genes, such as IL-7R, PDGFRA, and FGF23 (\u003cstrong\u003eFigure 2A\u003c/strong\u003e). Additionally, MCC algorithm in CytoHubba identified the top 15 hub genes, which included PTPN11, YWHAZ, PRDX1, FEN1, MAP3K5, SOD2, HELLS, E2F1, TBP, NCOR1, WRN, FGF23, PDGFRA, FOXO4, and AGTR1. Further refinement through Lasso regression analysis led to the selection of 13 relevant genes at lambda.min = 0.0495 (\u003cstrong\u003eFigure 2B\u003c/strong\u003e). Results from Boruta algorithm analysis indicated that PTPN11, YWHAZ, FEN1, TBP, FGF23, FOXO4, and AGTR1 as significant genes (\u003cstrong\u003eFigure 2C\u003c/strong\u003e). When SVM-RFE model set gene count at 14, the error rate achieved minimum of 0.117 (\u003cstrong\u003eFigure 2D\u003c/strong\u003e). Ultimately, the gene intersection of these three machine learning methods resulted in the identification of 6 candidate biomarkers: PTPN11, YWHAZ, FEN1, TBP, FGF23, and FOXO4 (\u003cstrong\u003eFigure 2E\u003c/strong\u003e). This study highlighted candidate biomarkers for melasma that may play an important role in aging-related processes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Acquisition of 5 biomarkers with diagnostic value for melasma\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo further refine the selection of biomarkers, ROC curves were plotted for the 6 candidate biomarkers using the GSE72140 dataset. The results indicated that candidate biomarkers exhibited AUC values greater than 0.7 except FGF23, demonstrating decent diagnostic performance for melasma (\u003cstrong\u003eFigure 3A\u003c/strong\u003e). Subsequently, expression differences of candidate genes between disease and control groups were analyzed, and significant difference was found in PTPN11, YWHAZ, FEN1, FOXO4, and TBP. Except TBP was up-regulated in melasma group, remaining genes were down-regulaed (\u003cstrong\u003eFigure 3B\u003c/strong\u003e). Therefore, PTPN11, YWHAZ, FEN1, TBP, and FOXO4 were retained as biomarkers for subsequent analysis. Correlation analysis among these biomarkers was conducted to elucidate their interrelations, a strong positive correlation was then observed between PTPN11 and YWHAZ (cor = 0.413, \u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.01) (\u003cstrong\u003eFigure 3C\u003c/strong\u003e). Furthermore, chromosomal localization analyses revealed that PTPN11, YWHAZ, FEN1, FOXO4, and TBP were located on chromosomes 12, 8, 11, X and 6, respectively (\u003cstrong\u003eFigure 3D\u003c/strong\u003e). The biological function of proteins can only be stabilized in specific suborganelles, because different subcellular localisation determines different protein functions. It was observed that FEN1, YWHAZ, PTPN11, and TBP were predominantly expressed in the nucleus, whereas FOXO4 was primarily localized in the cytoplasm (\u003cstrong\u003eFigure 3E\u003c/strong\u003e). These findings underscore the potential roles of these genes as biomarkers, highlighting their interrelations that may contribute to further research into ARGs and their interactions with melasma.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 Establishment of a diagnostic nomogram based on biomarkers\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUtilizing the expression levels of 5 identified biomarkers, the diagnostic nomogram was developed to assess individual risk of melasma (\u003cstrong\u003eFigure 4A\u003c/strong\u003e). To further validate the performance of constructed nomogram model in terms of accuracy, calibration curves were plotted, and the slope of this curve approached 1 (\u003cstrong\u003eFigure 4B\u003c/strong\u003e), indicating a high level of accuracy for the model. Additionally, the predictive capability of nomogram was confirmed through ROC curve, which demonstrated an AUC value exceeding 0.7\u0026nbsp;(\u003cstrong\u003eFigure 4C\u003c/strong\u003e), thereby reflected its robust predictive performance. These findings suggested that the nomogram model serves as a valuable tool for assessing disease risk based on biomarker expression levels, providing insights into its potential application in clinical settings for personalized medicine.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5 Relevance between biomarkers and the infiltration of immune cells\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo further investigate the differences in immune cell profiles between melasma and control groups, scores for 28 types of immune cells were calculated, and their infiltration abundances were presented as proportions within the two sample groups (\u003cstrong\u003eFigure 5A\u003c/strong\u003e). The box plot revealed that only mast cells and activated B cells exhibited significant differences in immune infiltration between these two groups (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05) (\u003cstrong\u003eFigure 5B\u003c/strong\u003e). Subsequently, the correlation between biomarkers and differential immune cells was examined. The results indicated that only PTPN11 demonstrated a significant positive correlation with mast cells (cor = 0.303, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05) (\u003cstrong\u003eFigure 5C\u003c/strong\u003e). These findings underscore the specific immunological alterations associated with the disease state and highlight PTPN11’s potential role in modulating mast cell activity within this context.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.6 Investigation of functional similarity and enrichment pathways for biomarkers\u003c/strong\u003e\u003c/p\u003e\n\u003cp id=\"_Toc176339303\"\u003eA functional similarity analysis was conducted on biomarkers to identify those with the strongest interactions with other genes. The results indicated significant functional connections among the five biomarkers, with FOXO4 occupying a central position, followed by YWHAZ and PTPN11 (\u003cstrong\u003eFigure 6A\u003c/strong\u003e). The biological functions and pathway associations of the identified biomarkers were investigated through GSEA. The results revealed that PTPN11 was significantly enriched in 17 pathways, while both FEN1 and TBP exhibited enrichment in 9 pathways each. YWHAZ was associated with 3 pathways, and the FOXO4 only enriched peroxisome (\u003cstrong\u003eFigure 6B\u003c/strong\u003e). Notably, the oxidative phosphorylation was activated in both PTPN11 and YWHAZ, whereas the cytokine-cytokine receptor interaction showed suppression in these two genes but activation in FEN1. Additionally, the valine, leucine, and isoleucine degradation pathway was activated across PTPN11, FEN1, and TBP. The peroxisome pathway demonstrated activation in YWHAZ and TBP but suppression in FOXO4. Further analysis using GSVA elucidated additional details regarding specific pathways that were either inhibited or activated within individual gene (\u003cstrong\u003eFigure 6C\u003c/strong\u003e). In PTPN11, suppression was observed in the toll-like receptor signaling pathway, T cell receptor signaling pathway, TGF-β signaling pathway, as well as complement and coagulation cascades. Conversely, activation occurred in pathways related to steroid hormone biosynthesis, oxidative phosphorylation, glutathione metabolism, and alpha-linolenic acid metabolism. For FOXO4, pathways associated with tyrosine metabolism, glycolysis/gluconeogenesis, lysosomal function, and insulin signaling were found to be suppressed. Moreover, FEN1 exhibited activation of several key pathways including VEGF signaling pathway, apoptosis pathway, toll-like receptor signaling pathway, and mTOR signaling pathway. Activation of the pantothenate and CoA biosynthesis pathway was also noted for TBP. Lastly, YWHAZ displayed suppression of pathways involved in sphingolipid metabolism as well as RIG-I-like receptor signaling pathways; additionally, it impacted circadian rhythm regulation in mammals and lysosomal function. These findings provide insights into the complex regulatory networks involving these biomarkers and their potential roles in various biological processes relevant to aging and melasma.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.7\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eConstruction of the regulatory network for biomakers\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTFs influence cellular functions by binding to DNA and regulating gene transcription levels. This interaction provides a comprehensive understanding of the structure and dynamics of gene regulatory networks, revealing the interactions among genes. In this study, 86 TFs were predicted corresponding to biomarkers, and biomarker-TF network comprised 91 nodes and 143 relationships. Notably, the GABPA was simultaneously regulated FEN1, TBP, PTPN11, and YWHAZ (\u003cstrong\u003eFigure 7A\u003c/strong\u003e). Furthermore, potential regulatory relationships among biomarkers, miRNAs, and lncRNAs were also predicted, followed with construction of network, including 65 nodes and 141 relationships. Among these, hsa-let-7b-5p was found to regulate all biomarkers concurrently (\u003cstrong\u003eFigure 7B\u003c/strong\u003e). These findings elucidate the intricate regulatory mechanisms involving transcription factors and non-coding RNAs that may play crucial roles in modulating biomarker expression and thereby influencing disease processes. To further investigate gene interactions and shared functionalities among the biomarkers, a GeneMANIA network was constructed. This network included the five biomarkers along with 20 genes exhibiting strong interaction profiles (\u003cstrong\u003eFigure 7C\u003c/strong\u003e). The associated functions encompassed general transcription initiation factor activity, regulation of protein insertion into mitochondrial membranes involved in apoptotic signaling pathways, and core promoter sequence-specific DNA binding. These findings provided insights into the complex interplay between these biomarkers and their potential roles in various cellular processes, thereby highlighting their significance in understanding disease mechanisms and identifying therapeutic targets.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.8 Potential compounds and molecular docking for melasma\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo predict potential therapeutic compounds, an analysis was conducted focusing on five biomarkers. This analysis identified 127 potential therapeutic compounds and revealed 170 interaction relationships, among which doxycycline and oxytetracycline were found to be associated with FEN1 (\u003cstrong\u003eFigure 8A\u003c/strong\u003e). Furthermore, molecular docking studies were performed to assess the interactions between these potential compounds and the biomarkers (\u003cstrong\u003eTable 2\u003c/strong\u003e). The binding energy of FEN1 with 6-Amino-1-naphthol-3-sulfonic Acid Hydrate was measured at -5.831 kcal/mol, involving three residues TYR-173, ARG-19, ARG-211 (\u003cstrong\u003eFigure 8B\u003c/strong\u003e). The binding energy of FOXO4 with Tamibarotene was determined to be -6.400 kcal/mol, with residue LYS-151 (\u003cstrong\u003eFigure 8C\u003c/strong\u003e). PTPN11 exhibited a binding energy of -7.035 kcal/mol when associated with 6-Amino-1-naphthol-3-sulfonic Acid Hydrate, through residues ARG-111, THR-218, THR-219, and GLY-246 (\u003cstrong\u003eFigure 8D\u003c/strong\u003e). TBP demonstrated a binding energy of -6.164 kcal/mol in its interaction with Chlortetracycline, involving \u0026nbsp;residues ARG-205, GLU-206, and LYS-236 (\u003cstrong\u003eFigure 8E\u003c/strong\u003e). YWHAZ had a binding energy of -6.164 kcal/mol when interacting with Ipratropium, which involved two residues ASN-42 and ARG-41 (\u003cstrong\u003eFigure 8F\u003c/strong\u003e). These findings provide a potential basis for further development of therapeutic strategies targeting related diseases.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.9 Validation of biomarkers expression\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo further validate the changes in biomarker expression, RT-qPCR was conducted. The results indicated that the expression of PTPN11, YWHAZ, FOXO4, and TBP were significantly reduced compared to the control group (\u003cstrong\u003eFigure 9A\u003c/strong\u003e), while the expression level of FEN1 was found to be elevated. Notably, the observed expression changes of PTPN11, YWHAZ, and FOXO4 were consistent with the findings from bioinformatics analyses. Consequently, Western blotting experiments were performed on these 3 biomarkers to verify their expression at the protein level. The experimental results corroborated the RT-qPCR findings, demonstrating that the protein expressions of FOXO4, PTPN11, and YWHAZ were all significantly downregulated in melasma group, which was in strong agreement with the RT-qPCR results (\u003cstrong\u003eFigure 9B\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis discovery not only confirmed the transcriptional level changes but also provided further validation at the protein level, thereby offering comprehensive evidence supporting the role of these relevant biomarkers in disease progression and development.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eAging is a gradual physiological process, with its speed and extent varying across different organs [35]. As senescent cells accumulate in the skin over time, this often leads to signs of aging such as hyperpigmentation and reduced elasticity, resulting in pathological conditions like melasma [36]. Researchers, including Cho Y, have observed that as aging progresses, melanocytes accumulate in the skin, leading to a mottled hyperpigmentation effect [37]. Furthermore, prolonged sun exposure to aging skin triggers irregular pigmentation, contributing to melasma formation [38]. To better understand the molecular mechanisms behind melasma, this study screened five aging-related biomarkers-PTPN11, YWHAZ, FEN1, TBP, and FOXO4-through bioinformatics analysis. All of these biomarkers demonstrated good diagnostic value for melasma, with their ROC curve AUC values exceeding 0.7. Additionally, bio-expression analysis revealed that PTPN11, YWHAZ, and FOXO4 were consistently and significantly under-expressed in patients with melasma, suggesting these biomarkers may play a crucial role in its development.\u003c/p\u003e\n\u003cp\u003ePTPN11 (Protein Tyrosine Phosphatase, Non-Receptor Type 11) encodes the SH2 domain of the tyrosine phosphatase SHP2, which is expressed in various tissues and cells [39,40]. Mutations in PTPN11 are linked to several disorders, including LEOPARD syndrome, which causes freckles to appear on the skin, such as on the face and back [41,42]. Additionally, aging has been associated with disrupted lipid metabolism, and the PTPN11 gene has been identified as a potential regulator of adipose tissue aging [43]. This suggests that PTPN11 may also influence melasma in patients through aging-related mechanisms. Furthermore, YWHAZ (Tyrosine 3-monooxygenase), which encodes the 14-3-3\u0026zeta; protein, is a highly conserved transmembrane protein that plays crucial roles in numerous biological processes. Recent findings suggest that YWHAZ is closely associated with telomere shortening, a key marker of cellular aging [44]. This indicates that YWHAZ may not only regulate cellular function but also contribute to the aging process. FOXO4 (Forkhead Box O4), an important transcription factor in the FOX family, plays a role in cellular senescence, oxidative stress, cell proliferation, and metabolism. Research by Li Y \u003cem\u003eet al.\u003c/em\u003e demonstrated that FOXO4 is highly expressed in senescent cells, where it interacts with p53 to prevent apoptosis and maintain cell survival in these cells [45]. Furthermore, Meng J \u003cem\u003eet al.\u003c/em\u003e found that FOXO4-p53 interfering peptides, such as FOXO4-DRI, specifically induced apoptosis in senescent-like cancer-associated fibroblasts and enhanced radiosensitivity in cancer cells during radiotherapy [46]. These studies suggest that FOXO4 has a significant role in aging and may hold potential for clinical applications in cancer treatment. FEN1 (Flap Endonuclease 1), a structure-specific nuclease, is involved in DNA metabolic pathways [47]. Its role in cancer is well-documented, with studies showing that FEN1 is essential in cellular senescence in various cancers, including breast and pancreatic cancer [48,49]. TBP (TATA Box-Binding Protein) is a transcription factor that plays a role in cellular processes, particularly in oncogene-induced transformation [50]. TBP\u0026rsquo;s promoter can stably express proteins and regulate transcriptional activity throughout the cell lifecycle, potentially influencing cancer progression [51]. These genes are involved in key biological processes such as aging and cancer, and may also be implicated in regulating skin pigmentation. However, their exact roles in melasma remain underexplored. Further investigation into these genes\u0026rsquo; contributions to melasma could offer valuable insights into its pathogenesis and the development of novel therapeutic strategies.\u003c/p\u003e\n\u003cp\u003eImmune cells are key regulators of cellular senescence, influencing the development and progression of various diseases [52]. In our analysis of immune infiltration between melasma patients and controls, we found notable differences in the extent of mast cell and activated B cell infiltration. Mast cells, originating from bone marrow mononuclear cells, release various biologically active substances like cytokines and growth factors that are involved in skin aging processes [53]. In particular, when exposed to sunlight, mast cells play a crucial role in regulating elastic tissue, basement membranes, and vasodilation [54,55]. Moreover, our functional enrichment analysis identified a significant association between oxidative phosphorylation processes and melasma biomarkers. As we age, oxidative metabolism levels decrease, leading to pathological changes that contribute to various diseases [56]. Research by Wu H \u003cem\u003eet al.\u003c/em\u003e indicates that mitochondrial oxidative phosphorylation significantly affects oxidative stress in aging dermal fibroblasts, regulating their function and impacting epidermal cells, thus promoting skin aging [57]. In light of these findings, we propose that the biomarkers we identified are closely linked to immune cell infiltration and changes in oxidative phosphorylation pathways, playing a key role in the pathogenesis of melasma. Further research is needed to explore the specific functions of these immune cells and metabolic pathways in skin aging and related conditions, paving the way for new diagnostic and therapeutic approaches.\u003c/p\u003e\n\u003cp\u003eThis study identified potential biomarkers linked to aging in melasma, offering a new theoretical framework for its early diagnosis and treatment. However, there are still some limitations. First, in the in vitro experiments, the expression trends of two genes, FEN1 and TBP, were not fully aligned with the bioinformatics analysis results. This indicates that while bioinformatics analysis provides an initial foundation for biomarker screening, actual experimental outcomes may be influenced by various factors. Therefore, additional experimental studies are necessary to better understand the role of these genes in melasma development. Second, the biological sample size in this study was relatively small, which could impact the generalizability and accuracy of the results. To improve the reliability of these findings, future research should expand the sample size and include more comprehensive validation to ensure these biomarkers yield consistent results across diverse populations, thereby providing a more robust foundation for clinical melasma applications.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study performed extensive bioinformatics analyses and identified five aging-related biomarkers-PTPN11, YWHAZ, FEN1, TBP, and FOXO4-in melasma, offering new targets for its diagnosis and treatment. A nomogram based on these biomarkers was developed and demonstrated strong predictive performance. Additionally, immune infiltration analysis revealed significant differences in mast cells and activated B cells between control and melasma samples, suggesting their potential role in the disease's development and progression. Lastly, in vitro experiments showed that the expression levels of PTPN11, YWHAZ, and FOXO4 were stably reduced in melasma samples.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXinxu Zhou and Pei Liu wrote the main manuscript text and Shuchen Wang prepared figures 1\u0026ndash;5. All authors reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author(s) declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe GSE72140 dataset analyzed in this study was collected from Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/) database.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"Reference","content":"\u003col\u003e\n \u003cli\u003eEsp\u0026oacute;sito, A. C. C., Brianezi, G., de Souza, N. P., Miot, L. D. B. \u0026amp; Miot, H. A. 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Heterogeneous Pathology of Melasma and Its Clinical Implications. \u003cem\u003eInt J Mol Sci\u003c/em\u003e\u003cstrong\u003e17\u003c/strong\u003e (2016).\u003c/li\u003e\n \u003cli\u003ePhansuk, K., Vachiramon, V., Jurairattanaporn, N., Chanprapaph, K. \u0026amp; Rattananukrom, T. Dermal Pathology in Melasma: An Update Review. \u003cem\u003eClin Cosmet Investig Dermatol\u003c/em\u003e\u003cstrong\u003e15\u003c/strong\u003e, 11-19 (2022).\u003c/li\u003e\n \u003cli\u003eElmansi, A. M. \u0026amp; Miller, R. A. Oxidative phosphorylation and fatty acid oxidation in slow-aging mice. \u003cem\u003eFree Radic Biol Med\u003c/em\u003e\u003cstrong\u003e224\u003c/strong\u003e, 246-255 (2024).\u003c/li\u003e\n \u003cli\u003eWu, H.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Improving dermal fibroblast-to-epidermis communications and aging wound repair through extracellular vesicle-mediated delivery of Gstm2 mRNA. \u003cem\u003eJ Nanobiotechnology\u003c/em\u003e\u003cstrong\u003e22\u003c/strong\u003e, 307 (2024).\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1 Primers used in this study\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"Left\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"454\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 454px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrimers for RT-qPCR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 89px;\"\u003e\n \u003cp\u003eH-GAPDH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 366px;\"\u003e\n \u003cp\u003eF:5\u0026lsquo;-GGAGTCCACTGGCGTCTTCA -3\u0026rsquo;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 366px;\"\u003e\n \u003cp\u003eR:5\u0026lsquo;-GTCATGAGTCCTTCCACGATACC -3\u0026rsquo;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 89px;\"\u003e\n \u003cp\u003ePTPN11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 366px;\"\u003e\n \u003cp\u003eF:5\u0026apos;-TCTCCATGCCTCTAACACTCG-3\u0026apos;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 366px;\"\u003e\n \u003cp\u003eR:5\u0026apos;-CCAGGTCAACAGACGTGTCAG-3\u0026apos;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 89px;\"\u003e\n \u003cp\u003eYWHAZ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 366px;\"\u003e\n \u003cp\u003eF:5\u0026apos;-GGTGGCTCTGGATACAAAGGG-3\u0026apos;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 366px;\"\u003e\n \u003cp\u003eR:5\u0026apos;-ACTTCTCCATGTAAGCGTTGTC-3\u0026apos;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 89px;\"\u003e\n \u003cp\u003eFEN1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 366px;\"\u003e\n \u003cp\u003eF:5\u0026apos;-CACCTGATGGGCATGTTCTAC-3\u0026apos;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 366px;\"\u003e\n \u003cp\u003eR:5\u0026apos;-CTCGCCTGACTTGAGCTGT-3\u0026apos;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 89px;\"\u003e\n \u003cp\u003eFOXO4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 366px;\"\u003e\n \u003cp\u003eF:5\u0026apos;-GGCTGCCGCGATCATAGAC-3\u0026apos;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 366px;\"\u003e\n \u003cp\u003eR:5\u0026apos;-GGCTGGTTAGCGATCTCTGG-3\u0026apos;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 89px;\"\u003e\n \u003cp\u003eTBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 366px;\"\u003e\n \u003cp\u003eF:5\u0026apos;-CCACTCACAGACTCTCACAAC-3\u0026apos;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 366px;\"\u003e\n \u003cp\u003eR:5\u0026apos;-CTGCGGTACAATCCCAGAACT-3\u0026apos;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2 Molecular docking binding energy of biomarkers\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"108%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eSymbol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003ePDB/Uniprot\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003eMolecule Name\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003ePub Chem CID\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eAffinity (kcal/mol)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003ehydrogen bonds\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eFEN1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eP39748\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003e6-AMino-1-naphthol-3-sulfonic Acid Hydrate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e45051793\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e-5.831\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eFOXO4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eP98177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003eTamibarotene\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e108143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e-6.400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003ePTPN11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eQ06124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003e6-AMino-1-naphthol-3-sulfonic Acid Hydrate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e45051793\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e-7.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eTBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eP20226\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003eChlortetracycline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e54675777\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e-6.164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eYWHAZ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eP63104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003eIpratropium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e657309\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e-6.640\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":false,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Aging, Biomaker, Melasma, Nomgram","lastPublishedDoi":"10.21203/rs.3.rs-6873942/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6873942/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eMelasma is a pigmentary skin condition that affects appearance, while aging is an inevitable phenomenon in all higher organisms throughout their life cycle. However, the relationship of them remains unclear, further research is needed to identify potential aging-related genes (ARGs) associated with melasma.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eMelasma-related dataset was obtained from public database. Differential expression analysis, Maximum Clique Centrality (MCC) algorithm, and machine learning methods were utilized to select candidate biomarkers for melasma. Following these processes, receiver operating characteristic (ROC) and expression level analyses were integrated to identify biomarkers. Ultimately, the expression levels of biomarkers were validated through quantitative reverse-transcription polymerase chain reaction (qRT-PCR) and Western blotting analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e PTPN11, YWHAZ, FEN1, FOXO4, and TBP were identified as aging-related biomarkers in melasma. The area under ROC curve (AUC) value of these biomarkers was greater than 0.7, and except TBP had higher expression, remaining biomarkers had lower expression in melasma. Conclusively, experiments analysis demonstrated that FOXO4, PTPN11, and YWHAZ were down-regulated at mRNA and protein levels in the melasma group.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eIn this study, 5 aging-related biomarkers-PTPN11, YWHAZ, FEN1, TBP and FOXO4-were identified in melasma, underscoring the potential of these biomarkers to provide theoretical insights for clinical applications for melasma patients.\u003c/p\u003e","manuscriptTitle":"Identification and validation of aging-related potential biomarkers for melasma via comprehensive transcriptome analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-08 06:24:43","doi":"10.21203/rs.3.rs-6873942/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b6ce144e-2440-4295-a698-11e8a53a0b36","owner":[],"postedDate":"July 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":51070845,"name":"Biological sciences/Cell biology"},{"id":51070846,"name":"Biological sciences/Genetics"},{"id":51070847,"name":"Health sciences/Biomarkers"},{"id":51070848,"name":"Health sciences/Diseases"},{"id":51070849,"name":"Health sciences/Medical research"},{"id":51070850,"name":"Health sciences/Molecular medicine"}],"tags":[],"updatedAt":"2025-10-28T19:38:20+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-08 06:24:43","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6873942","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6873942","identity":"rs-6873942","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}