Identification of Prognostic Biomarkers KLK13 and SLC5A8 in Canine Melanoma via Transcriptomics and WGCNA Analysis

Identification of Prognostic Biomarkers KLK13 and SLC5A8 in Canine Melanoma via Transcriptomics and WGCNA 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 Research Article Identification of Prognostic Biomarkers KLK13 and SLC5A8 in Canine Melanoma via Transcriptomics and WGCNA Analysis Heyi Zhao, Zixiang Lin, Hua Yao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8203845/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract This study performed a bioinformatics analysis of transcriptome and chip datasets from canine melanoma to identify potential therapeutic targets and diagnostic biomarkers. The results showed significant enrichment in immune-related signaling pathways, suggesting that inflammation and immune regulation were crucial to tumor progression. Through weighted gene co-expression network analysis (WGCNA), gene modules related to prognosis were identified, and two core genes, KLK13 and SLC5A8, were discovered. We validated the potential of KLK13 and SLC5A8 as new biomarkers by integrating our findings with public data on human melanoma and conducting survival curve analysis. This study offers theoretical support for treating and diagnosing canine melanoma while providing valuable insights for human melanoma research. Canine melanoma Immune-related Weighted gene co-expression network analysis (WGCNA) KLK13 SLC5A8 Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1 Introduction Canine melanoma is one of the most common malignant tumors in canines, Commonly found in the oral cavity and on the skin. According to a report by MDPI, research from the University of Perugia in Italy from 2005 to 2024 confirms that this trend is particularly significant in the oral and skin areas[1]. Oral melanoma is the most common malignant tumor in canines, accounting for approximately 30% to 40% of all oral tumors. skin melanoma is also relatively common but generally less aggressive than oral melanoma[2]. . The incidence of canine melanoma is related to their age[3], typically beginning around 10 years old, and the Level of onse increases significantly as the subjects age[4]. The clinical presentation and prognosis of melanoma differ among canine breeds. Oral melanoma is more common in large canines and usually has a worse prognosis, while melanoma in small canines is often less aggressive and has a better prognosis[5]. Breeds like Flat-Coated Retrievers and Bulldogs are at a higher risk of developing melanoma. In contrast, smaller breeds, such as Yorkshire Terriers and Chihuahuas, are less likely to develop these tumors[6]. While the biological characteristics of melanoma, such as aggressiveness and metastatic potential, significantly impact the prognosis of canines.Similarly, melanoma also pose a serious threat to humans, such as multiple metastases and a sharp decline in the five-year survival rate.. Melanoma is a highly heterogeneous malignant tumor that is particularly dangerous to humans globally[7], characterized by its aggressive behavior, propensity for early metastasis, and high mortality rate. Canines and humans share the same environment, making them susceptible to the same carcinogens[6]. Unlike mice, the canine genome and immune system are highly similar to humans[7] Canines are biologically closer to humans in their tendency to develop spontaneous epithelial cancers[6] .Canine tumors also show significant similarities to human cancers[8], including genetic mutations, pathway changes, and tumor mutational burden (TMB); genetic, environmental, and biological factors influence on canine. Canine mucosal melanoma can metastasize early, that may be leveraged to better understand the subtype in humans[9]. Canine melanoma shares similarities with human melanoma in its occurrence, especially in mucosal areas like the oral cavity, where it is more frequent. This similarity makes it a valuable model for researching corresponding conditions in humans[10]. Enhancing our understanding of canine melanoma as a predictive model for human melanoma can foster mutual benefits between these interdependent species. Canine carcinomas exhibit histological features similar to human cancers. They also show a variety of genomic abnormalities that precisely correspond to the fundamental molecular characteristics of human cancers. While basic carcinomas in canines share histological characteristics with human cancers and display extensive genomic abnormalities[11], these abnormalities successfully replicate the critical molecular features of human cancers[12]. Research on canine melanoma is limited by the few known molecular connections to human melanoma, which often leads studies to focus primarily on human tumors. The lifespan of canines is relatively short, and the progression of tumors is relatively fast[13]. This allows researchers to observe tumor occurrence, development, and metastasis over a shorter timeframe, thereby accelerating research progress[14]; clinical trials conducted in canines can yield results quickly, providing preliminary efficacy and safety assessments for human clinical trials. Cross-species comparisons identify universal and species-specific cancer mechanisms, leading to more precise treatment strategies[15]. This study uses bioinformatics to analyze transcriptome and chip datasets of canine melanoma in order to explore potential therapeutic targets and diagnostic biomarkers. It analyzes immune infiltration and signaling pathways to develop new targeted therapy and immunotherapy strategies in canine melanoma. This will provide necessary theoretical support for tumor treatment in canines and may offer insights for tumor research in other animals and humans[16]. 2 Materials and methods 2.1Transcriptome Data Collection and Differential Analysis We procured transcriptome data from the SRA database regarding mucosal and cutaneous melanoma and normal canine mucosal and dermal tissues (accession number PRJNA1106424) [14]. The genomic data is extensive and continuously updated, with clear and well-defined groupings, which is why this genome was selected for analysis. In the GSE266234 dataset, samples are categorized into four distinct groups: normal mucosa (CMN) with nine samples, mucosal melanoma (CMM) with 21 samples, normal skin (CCN) with nine samples, and cutaneous melanoma (CCM) with 14 samples. Mucosal melanoma presents a high risk of malignancy; despite having a relatively favorable prognosis, oral melanoma constitutes approximately 30–40% of all oral tumors, with around 85% classified as malignant. These tumors are most frequently located in the gums (60%), lips (22%), and tongue (18%), and they exhibit a metastasis rate as high as 80%. If left untreated, the median survival time for patients is typically less than 6 months. Our differential analysis focused exclusively on cutaneous melanoma (CCM) and mucosal melanoma (CMM). To ensure the acquisition of high-quality clean reads, the fastp (v0.20.0) software was employed to trim adapters and eliminate low-quality reads. The high-quality clean reads were subsequently aligned to the Canis lupus familiaris reference genome (CanFam3.1) utilizing hisat2 (v2.2.1). Raw read counts for mRNA genes were extracted through featurecount (v 2.0.6), serving as mRNA expression metrics. Normalization was executed using DESeq2 (v 1.46.0), focusing on retaining only uniquely mapped reads for HTseq counting. Mucosal and cutaneous melanoma data were selected to uncover differentially expressed genes (DEGs) via the R package DESeq2, applying a false discovery rate (FDR) 1, in conjunction with the GTF annotation database (ENSEMBL V104) for mRNA annotation. Gene ontology enrichment analysis for the differentially expressed mRNAs was performed utilizing the ClusterProfiler R package (v4.14.4). 2.2 Chip Data Collection, Differential Analysis, and GSEA Analysis Simultaneously, the canine melanoma chip dataset (PRJNA532635)[13].The dataset comprises eight metastatic samples and 10 non-metastatic samples, allowing for a comparative analysis between the two groups. In our previous attempts, we utilized the GSE88724 and GSE131923 datasets; however, the GSE129750 dataset stands out due to its larger size and well-defined classification criteria. Notably, the dataset (PRJNA532635) reveals that while patients experienced metastatic death, they exhibited favorable clinical outcomes following surgical intervention. This dataset was concurrently retrieved from the publicly accessible GEO database ( https://www.ncbi.nlm.nih.gov/geo/ ). We employed the "Limma" package to analyze differential gene expression, contrasting metastatic samples with their non-metastatic counterparts. Cutoff criteria were established with a false discovery rate (FDR) of less than 0.05 and a log2 |fold change| exceeding 1. To delve deeper into the functionalities of the significantly differentially expressed genes, the annotation, visualization, and integrated discovery (DAVID) database ( https://david.ncifcrf.gov/ ) was utilized to enrich biological themes within the realms of GO terms and KEGG pathways. GO functional enrichment analyses were conducted for biological processes (BP), cellular components (CC), and molecular functions (MF), ensuring no redundancy. Pathway analysis was executed within the framework of KEGG pathways. Gene set enrichment analysis was performed using GSEA software version 3.0 ( https://www.gsea-msigdb.org/gsea/index.jsp ) and the Molecular Signature Database (MSigDB) gene set version 6.2. A phenotype permutation analysis was performed on the entire gene expression dataset with 1000 permutations. Pathways associated with genes enriched at either the upper or lower extremes of the gene set were identified via a false discovery rate (FDR) threshold (P < 0.05). They were organized based on the normalized enrichment score. Pathways pertinent to the gene sets of interest were designated as Hallmark pathways. 2.3 Analysis of Immune Cell Infiltration in Canine Melanoma We applied the Cibersort algorithm to identify immune cell subpopulations in canine tissues from transcriptomic and microarray datasets. Compared to traditional deconvolution methods, the Cibersort algorithm estimates immunity in a contrary manner, as it can analyze unspecified data and noise[17], making it an excellent tool for calculating the abundance of specific cells in a mixed matrix [11] Subsequently, we took the intersection and selected the commonly different immune cells. 2.4 Establishing a Co-expression Network using Weighted Gene Co-expression Network Analysis and Identifying Hub Modules Related to Immune Cells The experiment used the WGCNA R package to build co-expression networks for skin melanoma transcriptome and chip datasets[18], excluding genes with FPKM < 1. Samples were clustered hierarchically based on Euclidean distance, considering patient skin and mucosa typing or metastasis and no-metastasis to remove outliers. A soft thresholding method (β = 6) preserved scale-free topology, and the dynamic tree cutting algorithm identified X non-gray modules with at least 30 genes. Characteristic genes were extracted, and their Pearson correlation with clinical phenotypes was analyzed (|r|>0.4, p < 0.05) to find modules linked to tumor progression[19]. The top 10% of hub genes were selected. Functional enrichment analysis was conducted on the intersection of co-expression modules and differentially expressed genes, identifying core genes related to melanoma prognosis[19]. 2.5 Differential Gene Drivers for Survival Prognosis Modeling in Human Skin Melanoma The experiment modeled survival prognosis using co-expressed differential genes from the TCGA-SKCM dataset of 472 melanoma samples, integrating expression data with clinical prognosis via TCGA-biolinks. The maxstat algorithm determined optimal gene expression cutoffs, categorizing samples into high and low expression groups. Kaplan-Meier curves were generated, and log-rank tests assessed group differences at p < 0.05. Significant genes underwent multi-factor Cox regression analysis, and the survminer package visualized survival curves with a two-color scheme and a risk table showing surviving samples over time, investigating the impact of differential genes on survival. 2.6 Receiver Operating Characteristic (ROC) The PROC software package is used for ROC analysis of differential genes with the TCGA-SKCM dataset, and GGPLOT2 is used to visualize the results. The area under the curve (AUC) is commonly used to evaluate diagnostic tests, and the value of AUC should range from 0.5 to 1. The closer the AUC is to 1, the better the significant effect of the variable in predicting the outcome. Identifying the optimal classification threshold is essential to enhance the accuracy and practicality of diagnostic or predictive models. Key statistical details, including sample size, variance, and confidence intervals, play a vital role in improving the reliability and robustness of predicted values. One effective method for determining the best cutoff value for classifying cases based on gene expression levels is the Youden Index, calculated as Youden Index = Sensitivity + Specificity − 1. This index provides a balanced approach by considering both the actual positive rate (TPR) and the false positive rate (FPR), facilitating a more informed decision-making process in classifying cases. 2.7 Statistical Analysis The gene expression profiles were log2 transformed for data normalization. Subsequently, receiver operating characteristic (ROC) curves were plotted based on gene expression levels and sample types to compare the predictive accuracy of different biomarkers. In addition, we calculated the P values for the log-rank test, using a P value of less than 0.05 as the criterion for determining statistical significance. All statistical analyses were performed using GraphPad Prism 7.0 or SPSS 19.0 software, with a two-sided P value of less than 0.05 considered statistically significant. 3 Results 3.1 Identification of Differentially Expressed Genes in Canine Melanoma The transcriptome data from GSE266234 (accession number PRJNA1106424) concerning canine melanoma in skin and normal mucosal and skin tissues unveiled differentially expressed genes (DEGs) distinguishing mucosal and cutaneous melanoma types. A total of 548 differential genes were identified, with 206 exhibiting significant upregulation, while 342 displayed significant downregulation (Fig. 1 A). The canine melanoma microarray dataset GSE129750 (PRJNA532635) examined DEGs between metastatic and non-metastatic tissue types. In this analysis, 369 differential genes were identified, of which 202 demonstrated significant increases, and 194 showed substantial decreases (Fig. 1 B). 3.2 Gene Set Enrichment Analysis of Canine Melanoma Metastatic Type The results of the GSEA enrichment analysis (Fig. 2 ) show high enrichment in immune-related pathways (immune response; acute inflammatory response; positive regulation of defense response) and signaling-related pathways (receptor ligand activity; signaling receptor regulator activity; signaling receptor activator activity), as well as high enrichment in cytokine activity and molecular function regulation pathways. The results of the GSEA GO enrichment analysis reveal that several critical biological pathways are significantly enriched in the samples, particularly those related to immune response, signal transduction, and the regulation of cytokine activity, all of which may be closely linked to tumor metastasis. The enrichment of immune-related pathways, such as the positive regulation of viral processes (GO_0044794), Toll-like receptor binding (GO_0035325), and the insertion of proteins into the mitochondrial outer membrane (GO_0045040), suggests a widespread activation of the immune system within the samples. However, this immune activation could be manipulated by tumor cells to facilitate their evasion of immune detection. For instance, tumor cells might disrupt the immune system's normal recognition processes by mimicking the signaling pathways associated with viral infections, altering Toll-like receptor signaling to dampen immune responses, and inhibiting apoptosis by regulating mitochondrial outer membrane protein insertion, thereby enhancing their survival. Additionally, the significant enrichment of pathways related to signal transduction, such as the regulation of plasminogen activation (GO_0010755), protein nitrosylation (GO_0017014), and peptide chain cysteine S-nitrosylation (GO_0018119), indicates potential activation or modulation of critical signaling pathways in the samples. These pathways may influence intercellular communication and signal transduction, ultimately affecting the recruitment and function of immune cells. Such alterations could promote the invasion and migration of tumor cells, thereby accelerating the process of tumor metastasis. The significant enrichment of pathways related to cytokine activity and molecular function regulation, such as the regulation of the prostaglandin biosynthetic process (GO_0031392), positive regulation of the phospholipid biosynthetic process (GO_0071073), and the extracellular matrix (EMC) complex (GO_0072546), indicates notable alterations in cytokine synthesis, secretion, and receptor function within the samples. These alterations can further influence on the activity and function of immune cells, potentially impacting the inflammatory response in the tumor microenvironment, tumor progression and metastasis. This observation highlights the extensive activation of the immune system and underscores the critical roles of signal transduction and cytokine activity regulation in tumor metastasis and immune evasion. Such findings provide valuable insights into the molecular mechanisms underlying tumor progression and establish a foundation for future research into the regulatory mechanisms of tumors. The GSEA KEGG enrichment analysis results also reveal a strong enrichment of immune-related pathways, including lggA generation, immune deficiency, and the IL-17 signaling pathway. 3.3 Immune Cell Infiltration in Canine Melanoma We used the CIBERSORT algorithm to analyze 22 immune cell types in transcriptome and chip datasets (Fig. 3 ). The Resucts revealed significant differences between metastatic and non-metastatic groups, especially in activated mast cells and M2 macrophages. Despite low macrophage expression, differences were notable; activated mast cells indicate an inflammatory response in metastatic melanoma, while M2 macrophages may aid tumor immune evasion. The transcriptome dataset showed significant variations in resting and activated mast cells, highlighting their immunosuppressive role. The main differences in immune cell infiltration between datasets were linked to activated mast cells, suggesting their key role in tumor immunosuppression of melanoma. 3.4 Using weighted gene co-expression network analysis to construct co-expression networks and identify hub modules associated with different prognosis groups. Using WGCNA, core genes linked to prognosis were identified. In the chip dataset, the green-yellow and magenta modules had correlations of 0.32 and 0.30, respectively; the magenta module had the highest difference in the non-transgenic group (R2 = 0.30, P = 0.1), yielding 142 genes. In the transcriptome dataset, the green module correlated with prognosis at 0.76 and showed significant differences in favorable skin types (R2 = 0.76, P = 1e-06), extracting 67 genes. Common differential genes KLK13 and SLC5A8 were found in both datasets. We performed a weighted gene co-expression network analysis (WGCNA) on the GSE129750 and GSE266234 datasets, identifying several gene modules. In the GSE129750 dataset, the green-yellow module showed a correlation of R² = 0.32 and a P-value of 0.09, but we could not identify any significantly expressed genes in our analysis. This indicates that, despite the correlation of the green-yellow module, its biological significance in our study is limited. Furthermore, although neither the green-yellow nor magenta module met our strict significance criteria (correlation coefficient > |0.5| and p-value < 0.05), both may still have potential prognostic links. Notably, the magenta module demonstrated a more pronounced difference in the non-translocation group, with a correlation of R² = 0.30 and a P-value of 0.1. Within this module, we identified 142 differentially expressed genes that exhibited significant expression differences in the non-translocation group, meeting our criteria of |correlation coefficient| > 0.5 and p-value < 0.05. A further analysis of the transcriptome data from the Green module of GSE266234 and the Magenta module of GSE129750 identified KLK13 and SLC5A8 as two commonly expressed differentially expressed genes, as determined by Venn diagram analysis. Both genes exhibited significant differences in expression across the modules and have been supported by other studies as closely linked to disease occurrence and progression. KLK13 is believed to promote tumor cells' invasion and migration capabilities by influencing the degradation and remodeling of the extracellular matrix, thus playing a crucial role in tumor metastasis. Abnormal expression of KLK13 in the tumor microenvironment is associated with invasive tumor behavior, indicating its potential as a target for early diagnosis and intervention in tumor metastasis. Meanwhile, the role of SLC5A8 in tumor metastasis is increasingly understood. The monocarboxylate transporter encoded by this gene is integral to cellular energy metabolism, and its abnormal expression may disrupt the energy balance and metabolic reprogramming of tumor cells, ultimately affecting their proliferation, migration, and invasion abilities. Furthermore, SLC5A8 likely influences tumor metastasis by regulating the acid-base balance in cells, impacting tumor cells' adaptability to their microenvironment. The significant differences observed in the Magenta module of the GSE129750 dataset for the non-translocation group and the presence of target genes indicate that this module may be crucial in specific stages or subgroups of disease progression. In summary, a gene co-expression network was successfully constructed through WGCNA( Figu 4 ), and the Magenta module and Green module significantly associated with specific prognostic differential populations were identified; co-expressed hub genes KLK13 and SLC5A8 were screened out. (B) The transcriptome data GSE266234 identifies and establishes gene co-expression modules displayed in different colors in the hierarchical clustering. (C) The relationship between consensus module feature genes from the transcriptome data GSE266234 and whether metastasis occurs. The rows in the figure correspond to consensus modules, and the columns correspond to cell subtypes. The numbers in each module represent the correlation coefficients to show the association between the respective module and metastasis, along with the p-values shown in parentheses below. (D)Is the chip dataset GSE129750 formed based on hierarchical clustering using soft threshold power (β). The scale independence index for soft threshold powers (β = 8) is estimated from 1 to 20. The average connectivity for soft threshold powers from 1 to 20 is determined. A transcriptome data GSE266234 is formed based on hierarchical clustering using soft threshold power (β). The scale independence index for soft threshold powers (β = 8) is estimated from 1 to 20. The average connectivity for soft threshold powers from 1 to 20 is determined. (E) The chip dataset GSE129750 identifies and establishes gene co-expression modules displayed in different colors in the hierarchical clustering. (F) The relationship between consensus module feature genes from the chip dataset GSE129750 and whether metastasis occurs. The rows in the figure correspond to consensus modules, and the columns correspond to cell subtypes. The numbers in each module represent the correlation coefficients to show the association between the respective module and metastasis, along with the p-values shown in parentheses below. 3.5 Differential Gene Drivers of Survival Prognosis in Human Skin Melanoma The transcriptome dataset GSE266234 analyzed canine mucosal melanoma (CMM). CMM is the most common malignant tumor of the oral cavity in canines, and it is more aggressive than cutaneous melanoma (CCM). Still, the specific mechanisms underlying this difference are not yet precise. More aggressive CMM is often associated with a poorer prognosis. Although there are limitations in survival records within the canine melanoma dataset, this dataset not only identifies an immune-suppressive stromal signature, providing potential biomarkers for predicting human clinical survival. This finding suggests that cross-species dataset analysis can offer new insights into human diseases; thus, exploring the common differentially expressed genes in transcriptome and chip datasets through human cases may facilitate a deeper understanding of the biological mechanisms of melanoma. The results indicate that the survival rate of the high expression group of SLC5A8 and KLK13 is significantly lower than that of the low expression group, with a significantly increased survival risk; P values are all < 0.05, associated with poorer survival prognosis (Fig. 5 ). Limited canine survival records led to human case exploration, revealing that high SLC5A8 and KLK13 expression groups had lower survival rates and increased mortality risk ( P < 0.05). AUC values of the ROC curve indicated clinical significance, suggesting SLC5A8 and KLK13 as accurate biomarkers for melanoma metastasis. KLK13's actual positive rate (TPR) is calculated at 0.6793, while the false positive rate (FPR) stands at 0.38832. The corresponding Youden index, which measures the effectiveness of a diagnostic test, is 0.29098, achieved at an optimal threshold of 0.4809889. This indicates that the model's classification ability at this threshold is significantly better than random guessing. In contrast, for SLC5A8, the TPR is 0.53451 and the FPR is 0.36548, leading to a Youden index of 0.16403, calculated at an optimal threshold of 0.01963076. Although this Youden index is relatively low, it remains above zero, indicating that the model's classification ability surpasses random guessing at this threshold. In summary, both biomarkers have Youden indices greater than 0, which is encouraging. This indicates their classification abilities are practical and surpass random guessing at their optimal thresholds. 4 Discussion Studying human melanoma offers essential insights into canine melanoma because both share many similarities in their histology, biology, molecular mechanisms, and treatment responses. For example, canine oral melanoma is similar to human mucosal melanoma in cell structure, invasiveness, metastatic behavior, and the dysregulation of signaling pathways; additionally, their responses to specific treatments are also quite alike. However, there are essential differences between the two. Human melanoma is mainly caused by ultraviolet (UV) exposure, while genetic factors and local environmental conditions largely influence canine melanoma.Furthermore, differences in genetic mutations and the immune environment set canine melanoma apart from human melanoma.Based on the attributes of canine metastatic melanoma, we discovered that signaling pathways associated with inflammatory responses and immune regulation are notably prevalent in this condition. Chronic inflammation is regarded as a critical factor that fosters the emergence and progression of various tumors, and there exists a relative scarcity of research about canine melanoma, underscoring the need for an in-depth investigation into the extent of immune cell infiltration and the underlying mechanisms. Prolonged states of chronic inflammation can induce immune cell dysfunction[20, 21], consequently facilitating tumor initiation and advancement, which is closely linked to the clinical manifestations of canine melanoma[22]. The genomic and epigenetic characteristics of canines and their naturally occurring disease patterns are similar to those of humans and differ from genetically modified or xenotransplanted rodent models[23]. Canine models are advantageous for studying complexity, heterogeneity, and host-tumor interactions, leading to a deeper understanding of human cancer[24]. Therefore, canines serve as an effective model for investigating human-like diseases, particularly cancer[25]. Additionally, the costs of canine clinical trials are relatively low, and canine tumor samples encompass various breeds, ages, and sexes, providing a rich resource for studying tumor heterogeneity and individual differences[26]. Tumor occurrence, development, and metastasis can be observed in a shorter time frame, thus accelerating research progress[25, 27]. Consequently, gene mutations and pathway alterations identified in canine tumor research can inform human cancer studies, accelerating the development of new therapies and clinical applications[28]. Gene set enrichment analysis (GSEA) indicated a significant enrichment of pathways related to immune response and signaling, implying that these pathways may play crucial roles in tumor advancement and immune evasion[29].. Tumor cells evade host immune system attacks by upregulating immunosuppressive factors and modifying the microenvironment[28]. Investigations into immune evasion mechanisms have revealed that tumors exploit immune regulation to foster growth and metastasis. This suggests that future therapeutic approaches should enhance immune responses to counteract metastatic melanoma[16]. Existing literature has demonstrated that inflammatory responses within the tumor microenvironment are intricately linked to tumor development[9]; inflammatory mediators stimulate cell proliferation and modify metastatic potential by altering signaling pathways [30]. Tumors increase their survival and spread by regulating the expression of specific genes and refining the microenvironment. The results of the enrichment analysis concerning signaling receptor activity regulation revealed a p-value < 0.01, further substantiating that tumors modulate the microenvironment and facilitate growth and metastasis through the regulation of signaling receptor activity[9]. Prior studies have highlighted that the regulation of signaling receptors is pivotal in tumor cell growth, migration, and treatment responses[31]. The correlation between immune cell infiltration status in canine melanoma and chronic inflammation emphasizes the significance of the immune microenvironment in the progression of melanoma[3]. M2 macrophages and mast cells are integral to canine metastatic melanoma, particularly M2 macrophages[32], which may enhance tumor growth and metastasis; mast cells are implicated in angiogenesis and metastasis, with their presence positively correlating with tumor cell proliferation indices, potentially influencing prognosis by promoting angiogenesis and immune evasion [33] However, research on immune cell infiltration in canine metastatic melanoma is limited, and an immunosuppressive environment may prevail [5] indicated by significant alterations in the ratio of CD8 + T cells to regulatory T cells (Treg)[2], suggesting that canine melanoma may be affected by such an immunosuppressive milieu.The activity levels of these signaling pathways are closely related to the immune evasion mechanisms of tumor cells; developing new treatment strategies targeting these pathways may improve the prognosis of canine melanoma patients[10]. Comparative analysis with other cancer types also reveals that the immune regulatory mechanisms of canine melanoma have particular specificity[34], providing new ideas for clinical treatment[35]. In the analysis of inflammatory factors, the significant increase of IL-6 and TNF-α suggests the presence of an inflammatory state in the microenvironment of canine melanoma, which may promote tumor occurrence and development[36]. The elevation of these cytokines is closely related to the interaction with immune cells, and future treatments may provide new targets for these cytokines[37] Intervention strategies targeting these factors in clinical treatment are expected to improve the immune response in canine melanoma patients, thereby enhancing treatment efficacy. Immunosuppression plays a crucial role in cancer development[38], Identify new biomarkers of poor prognosis affecting canine melanoma. To this end, over 400 core genes related to WGCNA infiltration modules were selected, ultimately focusing on two genes: KLK13 and SLC5A8. KLK13 (Kallikrein-related peptidase 13) is a peptidase expressed in various tissues and is linked to tumor processes like proliferation and apoptosis[39]. Its expression correlates with cancer prognosis, particularly in esophageal and bladder cancers [40], where high levels indicate poor outcomes. Environmental and genetic factors can influence KLK13 expression[41, 42], with certain carcinogens potentially promoting melanoma[43], Sarwar. High KLK13 levels in melanoma patients are associated with worse survival[44], suggesting it could be a prognostic marker and therapeutic target [45]. The abnormal expression of members of the KLK family in melanoma is significantly linked to the tumor's aggressiveness and metastatic potential, correlating with a poor prognosis[46]. Notably, high levels of KLK6 in mucosal melanoma are associated with improved recurrence-free survival, likely due to KLK6's complex role within the tumor microenvironment[47]. Similarly, the expression of KLK8 in melanoma is tied to the tumor's malignant behavior[48], as it can cleave Activin-A through unconventional pathways, influencing tumor growth[49]. The KLK family members contribute to the malignant progression of melanoma through various mechanisms[50], including the regulation of extracellular matrix degradation and remodeling, which facilitates the invasion and metastasis of tumor cells. KLK6, for instance, modifies the composition and structure of the extracellular matrix, creating a favorable environment for tumor cell migration. In contrast[51], KLK8 activates downstream signaling pathways by cleaving Activin-A[48], promoting tumor cell growth. Furthermore, KLK family members play a role in tumor immune evasion by interacting with immune cells in the tumor microenvironment [52]; KLK6 can enhance immune evasion by affecting the polarization of tumor-associated macrophages[52]. Consequently, inhibitors targeting KLK6 may impede tumor invasion and metastasis by disrupting its function in the tumor microenvironment[8], offering new therapeutic strategies for melanoma treatment. Therefore, KLK13 not only appears as a potential adverse prognostic biomarker for melanoma but also as a potential target[53]. The SLC5A8 gene encodes a transporter protein essential for moving small molecules like lactate and pyruvate within cells. This gene is crucial for metabolic processes and has implications in various diseases, particularly cancer[54], where it exhibits significant changes in expression levels in melanoma. Factors in the microenvironment, such as macrophage infiltration and inflammatory mediators[55], can affect SLC5A8 expression. This, in turn, may influence melanoma progression and suggest an inhibitory effect on tumor development. Monitoring the levels of SLC5A8 could provide valuable insights for clinicians in assessing patient prognosis. Although the precise mechanisms by which the SLC5 family[56], including SLC5A8, affects melanoma are still under investigation, evidence suggests a correlation with patient prognosis. For instance, the SLC5A1 gene has been linked to immune response characteristics in tumors[42], and it is recognized as one of the differentially expressed genes compared to matched normal tissues. Nevertheless, there is a notable gap in research on the overall impact of the SLC5 family on melanoma [57], highlighting the need for further investigation. The future can further confirm the marker role of SLC5A8 in the poor prognosis of melanoma. In clinical analyses of humans, high expression of KLK13 and SLC5A8 is associated with poor prognosis[58], but there is no significant correlation with tumor immune suppression[59]. These two core genes demonstrated an essential adverse prognostic effect in our study.KLK13 and SLC5A8 as novel biomarkers for canine melanoma, aiding in the understanding of tumor progression and immune infiltration[41]. Despite its limitations, it provides a critical foundation for personalized treatment and prognostic assessment[60]. Future efforts are needed to validate its clinical application potential, advance diagnostic and therapeutic developments, support the development of new therapies, and promote the health of both animals and humans. Inevitably, there are some limitations in our experiments[35]..This study has limitations due to the scarcity of genomic data on canine melanoma, particularly concerning prognosis[52]. While research findings on human melanoma offer valuable insights for treating canine melanoma, effective treatment still requires targeted research tailored to the unique characteristics of canine melanoma.Additionally, when selecting the GSE266234 transcriptome public dataset, we ensured timely updates to mitigate the effects of irregular changes that could alter analysis results. However, these dynamic updates may introduce heterogeneity, particularly due to technical and biological differences across experimental batches, potentially affecting data consistency. Sample representativeness is crucial, as the dataset may not adequately reflect the diversity of the affected animals, leading to selection bias and limiting the generalizability of the results. Even with data preprocessing and normalization, batch effects may still impact result interpretation. Therefore, it is essential to consider these sources of variation during analysis carefully. Although we applied statistical models or integrated analysis methods and made efforts to increase sample diversity or control for confounding variables to reduce the impact of sample selection bias, we can more accurately derive insights from public transcriptome datasets. In the future, we can further improve this by combining similar transcriptome datasets of canine oral melanoma, such as the GSE228574 dataset. However; The expression of KLK13 and SLC5A8 is related to the metabolic state of tumor cells, and their combined effect is essential for assessing the prognosis of melanoma. This could provide new insights into understanding its complex biology and clinical treatment. 5 Conclusion In summary, metastatic and canine mucosal melanoma both exhibit an enrichment of inflammation and immunity; there are differences in the expression of mast cells under immunosuppressive conditions. We identified two core genes, KLK13 and SLC5A8, in our analysis following mast cell infiltration, and combined with public data on human melanoma and survival curve analysis, revealed the potential of KLK13 and SLC5A8 as new markers. The role of KLK13 in melanoma metastasis has been validated, while SLC5A8 can serve as a basis for further research; the interaction mechanism between KLK13 and SLC5A8 will become an important research direction for melanoma diagnosis and treatment. Abbreviations Abbreviations full form WGCNA Weighted Gene Co-expression Network Analysis KLK13 Kallikrein-related peptidase 13 SLC5A8 Solute carrier family 5 member 8 MDPI Multidisciplinary Digital Publishing Institute TMB Tumor mutational burden SRA Sequence Read Archive CMN Canine normal mucosa CCN Canine normal skin CCM Canine cutaneous melanoma fastp Fast quality control tool for FASTQ files CanFam3.1 Canis lupus familiaris genome assembly 3.1 hisat2 Hierarchical Indexing for Spliced Alignment of Transcripts 2 featureCount Subread package read summarization tool DESeq2 Differential expression analysis based on negative binomial distribution FDR False Discovery Rate GTF Gene Transfer Format ENSEMBL V104 Ensembl database version 104 GO Gene Ontology ClusterProfiler R package for functional enrichment GEO Gene Expression Omnibus Limma Linear Models for Microarray Data DAVID Database for Annotation, Visualization and Integrated Discovery KEGG Kyoto Encyclopedia of Genes and Genomes BP Biological Process CC Cellular Component MF Molecular Function GSEA Gene Set Enrichment Analysis MSigDB Molecular Signatures Database CIBERSORT Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts FPKM Fragments Per Kilobase of transcript per Million mapped reads TCGA-SKCM The Cancer Genome Atlas – Skin Cutaneous Melanoma TCGA-biolinks R package for TCGA data retrieval Kaplan-Meier Kaplan-Meier survival estimator log-rank Log-rank test Cox regression Cox proportional hazards regression survminer R package for survival analysis visualization ROC Receiver Operating Characteristic AUC Area Under the Curve TPR True Positive Rate FPR False Positive Rate Youden Index Youden's J statistic GraphPad Prism 7.0 GraphPad Prism software version 7.0 SPSS 19.0 Statistical Package for the Social Sciences v19.0 IL-17 Interleukin-17 IgA Immunoglobulin A IL-6 Interleukin-6 TNF-α Tumor Necrosis Factor-alpha CD8+ Cluster of Differentiation 8 positive Treg Regulatory T cells KLK6 Kallikrein-related peptidase 6 KLK8 Kallikrein-related peptidase 8 SLC5A1 Solute carrier family 5 member 1 UV Ultraviolet Declarations Ethics approval and consent to participate This study did not involve any animal experiments or human participant research, and therefore did not require approval from an ethics committee. The [RNA-Seq/microarray] data reanalyzed in this study were derived from a public dataset available in the [GEO (Gene Expression Omnibus)] repository under accession numberPRJNA532635 and PRJNA1106424. Consent for publication All authors have read and approved the final manuscript and consent to its publication. Availability of data and materials The original contributions presented in the study are included in the article or supplementary material. The datasets used and/or analysed during the cur rent study are available from the corresponding author on reasonable request. Competing Interests We are submitting this manuscript only to this Journal, and all au- thors have approved of its submission. The authors declare no conflict of interest. Funding The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. 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19:51:12","extension":"xml","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":118041,"visible":true,"origin":"","legend":"","description":"","filename":"855397184083464cbfdc308e09b462451structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8203845/v1/8760cac4becd9c4e4a96b60b.xml"},{"id":98775775,"identity":"576f392d-c276-406e-9fcc-86184f311693","added_by":"auto","created_at":"2025-12-22 12:21:02","extension":"html","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":128454,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8203845/v1/deefcaeeac6f5a0d20b8ab9e.html"},{"id":98775622,"identity":"3ad16068-d082-41ea-81da-92eab5ec0a93","added_by":"auto","created_at":"2025-12-22 12:20:36","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":248113,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA identifies differentially expressed genes in canine melanoma types; cCAS represents cutaneous melanoma, while mCAS represents mucosal melanoma, with canine mucosal melanoma being the most aggressive type. B shows the chip dataset results, where \"not-met\" indicates the absence of metastasis, and \"met\" indicates the presence of metastasis.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8203845/v1/cd580850c41d124691df79b6.jpg"},{"id":98644419,"identity":"37d265bf-4baf-47e1-b9aa-1741244105f5","added_by":"auto","created_at":"2025-12-19 19:51:12","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":161836,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA shows the top 20 significant pathways enriched in GO, which are related to immune response regulation pathways (Toll-like receptor signaling), cell signaling pathways (such as nitrosylation modification and plasminogen activation), and cytokine activity regulation pathways; B shows the immune-related pathways significantly enriched in GSEA.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8203845/v1/78ba617e9e7cd68e73dd4ccb.jpg"},{"id":98644420,"identity":"5ee3f109-943a-4ecf-b913-4655358c8e96","added_by":"auto","created_at":"2025-12-19 19:51:12","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":115070,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe infiltration of various types of immune cells in different organizations, * \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e \u0026lt;0.05; **\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003e p\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e \u0026lt;0.01; when\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003e p \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e\u0026lt;0.05 and at least one * is present, there is a significant difference between groups; when ** is present, i.e., \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003ep \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e\u0026lt;0.01, the difference is hugely important. A is the chip dataset no-met representing good prognosis without metastasis; met represents metastasis. B is the differentially expressed genes from the transcriptome data, cCAS represents cutaneous melanoma, mCAS represents mucosal melanoma; C is the intersection of the differential immune cell infiltration from the two datasets.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8203845/v1/1fd36ab93fb66483ad5e5a1b.jpg"},{"id":98775663,"identity":"4cf43fa0-19d4-40a2-b09c-07b484d1e319","added_by":"auto","created_at":"2025-12-22 12:20:43","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":261055,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(A) transcriptome data GSE266234 is formed based on hierarchical clustering using soft threshold power (β). The scale independence index for soft threshold powers (β=8) is estimated from 1 to 20. The average connectivity for soft threshold powers from 1 to 20 is determined.\u003cbr\u003e\n(B) The transcriptome data GSE266234 identifies and establishes gene co-expression modules displayed in different colors in the hierarchical clustering.\u003cbr\u003e\n(C) The relationship between consensus module feature genes from the transcriptome data GSE266234 and whether metastasis occurs. The rows in the figure correspond to consensus modules, and the columns correspond to cell subtypes. The numbers in each module represent the correlation coefficients to show the association between the respective module and metastasis, along with the p-values shown in parentheses below.\u003cbr\u003e\n(D)Is the chip dataset GSE129750 formed based on hierarchical clustering using soft threshold power (β). The scale independence index for soft threshold powers (β=8) is estimated from 1 to 20. The average connectivity for soft threshold powers from 1 to 20 is determined. A transcriptome data GSE266234 is formed based on hierarchical clustering using soft threshold power (β). The scale independence index for soft threshold powers (β=8) is estimated from 1 to 20. The average connectivity for soft threshold powers from 1 to 20 is determined.\u003cbr\u003e\n(E) The chip dataset GSE129750 identifies and establishes gene co-expression modules displayed in different colors in the hierarchical clustering.\u003cbr\u003e\n(F) The relationship between consensus module feature genes from the chip dataset GSE129750 and whether metastasis occurs. The rows in the figure correspond to consensus modules, and the columns correspond to cell subtypes. The numbers in each module represent the correlation coefficients to show the association between the respective module and metastasis, along with the p-values shown in parentheses below.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8203845/v1/44aa06efad369272d70b54b7.jpg"},{"id":98774964,"identity":"8e1417be-fc7c-4884-ba25-4438759f7a2d","added_by":"auto","created_at":"2025-12-22 12:17:35","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":195623,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA represents the effect of KLK13 expression levels on the survival time of skin melanoma patients regarding their survival prognosis; B represents the effect of SLC5A8 expression levels on the survival time of skin melanoma patients regarding their survival prognosis; C represents the ROC curve of KLK13 for tumor survival time; D represents the ROC curve of SLC5A8 for tumor survival time.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8203845/v1/bc1ffc5923cb1e213a2fe56a.jpg"},{"id":100406356,"identity":"233a0f01-60d8-4c68-857e-ad0dabe78e5b","added_by":"auto","created_at":"2026-01-16 13:00:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6871253,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8203845/v1/5655c386-5584-42c1-8f57-05675e3f23d3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identification of Prognostic Biomarkers KLK13 and SLC5A8 in Canine Melanoma via Transcriptomics and WGCNA Analysis","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eCanine melanoma is one of the most common malignant tumors in canines, Commonly found in the oral cavity and on the skin. According to a report by MDPI, research from the University of Perugia in Italy from 2005 to 2024 confirms that this trend is particularly significant in the oral and skin areas[1]. Oral melanoma is the most common malignant tumor in canines, accounting for approximately 30% to 40% of all oral tumors. skin melanoma is also relatively common but generally less aggressive than oral melanoma[2].\u003c/p\u003e \u003cp\u003e.\u003c/p\u003e \u003cp\u003eThe incidence of canine melanoma is related to their age[3], typically beginning around 10 years old, and the Level of onse increases significantly as the subjects age[4]. The clinical presentation and prognosis of melanoma differ among canine breeds. Oral melanoma is more common in large canines and usually has a worse prognosis, while melanoma in small canines is often less aggressive and has a better prognosis[5]. Breeds like Flat-Coated Retrievers and Bulldogs are at a higher risk of developing melanoma. In contrast, smaller breeds, such as Yorkshire Terriers and Chihuahuas, are less likely to develop these tumors[6]. While the biological characteristics of melanoma, such as aggressiveness and metastatic potential, significantly impact the prognosis of canines.Similarly, melanoma also pose a serious threat to humans, such as multiple metastases and a sharp decline in the five-year survival rate.. Melanoma is a highly heterogeneous malignant tumor that is particularly dangerous to humans globally[7], characterized by its aggressive behavior, propensity for early metastasis, and high mortality rate.\u003c/p\u003e \u003cp\u003eCanines and humans share the same environment, making them susceptible to the same carcinogens[6]. Unlike mice, the canine genome and immune system are highly similar to humans[7] Canines are biologically closer to humans in their tendency to develop spontaneous epithelial cancers[6] .Canine tumors also show significant similarities to human cancers[8], including genetic mutations, pathway changes, and tumor mutational burden (TMB); genetic, environmental, and biological factors influence on canine. Canine mucosal melanoma can metastasize early, that may be leveraged to better understand the subtype in humans[9]. Canine melanoma shares similarities with human melanoma in its occurrence, especially in mucosal areas like the oral cavity, where it is more frequent. This similarity makes it a valuable model for researching corresponding conditions in humans[10].\u003c/p\u003e \u003cp\u003eEnhancing our understanding of canine melanoma as a predictive model for human melanoma can foster mutual benefits between these interdependent species. Canine carcinomas exhibit histological features similar to human cancers. They also show a variety of genomic abnormalities that precisely correspond to the fundamental molecular characteristics of human cancers. While basic carcinomas in canines share histological characteristics with human cancers and display extensive genomic abnormalities[11], these abnormalities successfully replicate the critical molecular features of human cancers[12]. Research on canine melanoma is limited by the few known molecular connections to human melanoma, which often leads studies to focus primarily on human tumors. The lifespan of canines is relatively short, and the progression of tumors is relatively fast[13]. This allows researchers to observe tumor occurrence, development, and metastasis over a shorter timeframe, thereby accelerating research progress[14]; clinical trials conducted in canines can yield results quickly, providing preliminary efficacy and safety assessments for human clinical trials. Cross-species comparisons identify universal and species-specific cancer mechanisms, leading to more precise treatment strategies[15].\u003c/p\u003e \u003cp\u003eThis study uses bioinformatics to analyze transcriptome and chip datasets of canine melanoma in order to explore potential therapeutic targets and diagnostic biomarkers. It analyzes immune infiltration and signaling pathways to develop new targeted therapy and immunotherapy strategies in canine melanoma. This will provide necessary theoretical support for tumor treatment in canines and may offer insights for tumor research in other animals and humans[16].\u003c/p\u003e"},{"header":"2 Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1Transcriptome Data Collection and Differential Analysis\u003c/h2\u003e \u003cp\u003eWe procured transcriptome data from the SRA database regarding mucosal and cutaneous melanoma and normal canine mucosal and dermal tissues (accession number PRJNA1106424) [14]. The genomic data is extensive and continuously updated, with clear and well-defined groupings, which is why this genome was selected for analysis. In the GSE266234 dataset, samples are categorized into four distinct groups: normal mucosa (CMN) with nine samples, mucosal melanoma (CMM) with 21 samples, normal skin (CCN) with nine samples, and cutaneous melanoma (CCM) with 14 samples. Mucosal melanoma presents a high risk of malignancy; despite having a relatively favorable prognosis, oral melanoma constitutes approximately 30\u0026ndash;40% of all oral tumors, with around 85% classified as malignant. These tumors are most frequently located in the gums (60%), lips (22%), and tongue (18%), and they exhibit a metastasis rate as high as 80%. If left untreated, the median survival time for patients is typically less than 6 months. Our differential analysis focused exclusively on cutaneous melanoma (CCM) and mucosal melanoma (CMM). To ensure the acquisition of high-quality clean reads, the fastp (v0.20.0) software was employed to trim adapters and eliminate low-quality reads. The high-quality clean reads were subsequently aligned to the Canis lupus familiaris reference genome (CanFam3.1) utilizing hisat2 (v2.2.1). Raw read counts for mRNA genes were extracted through featurecount (v 2.0.6), serving as mRNA expression metrics. Normalization was executed using DESeq2 (v 1.46.0), focusing on retaining only uniquely mapped reads for HTseq counting.\u003c/p\u003e \u003cp\u003eMucosal and cutaneous melanoma data were selected to uncover differentially expressed genes (DEGs) via the R package DESeq2, applying a false discovery rate (FDR)\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and absolute log2\u0026thinsp;\u0026gt;\u0026thinsp;1, in conjunction with the GTF annotation database (ENSEMBL V104) for mRNA annotation. Gene ontology enrichment analysis for the differentially expressed mRNAs was performed utilizing the ClusterProfiler R package (v4.14.4).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Chip Data Collection, Differential Analysis, and GSEA Analysis\u003c/h2\u003e \u003cp\u003eSimultaneously, the canine melanoma chip dataset (PRJNA532635)[13].The dataset comprises eight metastatic samples and 10 non-metastatic samples, allowing for a comparative analysis between the two groups. In our previous attempts, we utilized the GSE88724 and GSE131923 datasets; however, the GSE129750 dataset stands out due to its larger size and well-defined classification criteria. Notably, the dataset (PRJNA532635) reveals that while patients experienced metastatic death, they exhibited favorable clinical outcomes following surgical intervention. This dataset was concurrently retrieved from the publicly accessible GEO database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). We employed the \"Limma\" package to analyze differential gene expression, contrasting metastatic samples with their non-metastatic counterparts. Cutoff criteria were established with a false discovery rate (FDR) of less than 0.05 and a log2 |fold change| exceeding 1. To delve deeper into the functionalities of the significantly differentially expressed genes, the annotation, visualization, and integrated discovery (DAVID) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://david.ncifcrf.gov/\u003c/span\u003e\u003cspan address=\"https://david.ncifcrf.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was utilized to enrich biological themes within the realms of GO terms and KEGG pathways. GO functional enrichment analyses were conducted for biological processes (BP), cellular components (CC), and molecular functions (MF), ensuring no redundancy. Pathway analysis was executed within the framework of KEGG pathways.\u003c/p\u003e \u003cp\u003eGene set enrichment analysis was performed using GSEA software version 3.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gsea-msigdb.org/gsea/index.jsp\u003c/span\u003e\u003cspan address=\"https://www.gsea-msigdb.org/gsea/index.jsp\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and the Molecular Signature Database (MSigDB) gene set version 6.2. A phenotype permutation analysis was performed on the entire gene expression dataset with 1000 permutations. Pathways associated with genes enriched at either the upper or lower extremes of the gene set were identified via a false discovery rate (FDR) threshold (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). They were organized based on the normalized enrichment score. Pathways pertinent to the gene sets of interest were designated as Hallmark pathways.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Analysis of Immune Cell Infiltration in Canine Melanoma\u003c/h2\u003e \u003cp\u003eWe applied the Cibersort algorithm to identify immune cell subpopulations in canine tissues from transcriptomic and microarray datasets. Compared to traditional deconvolution methods, the Cibersort algorithm estimates immunity in a contrary manner, as it can analyze unspecified data and noise[17], making it an excellent tool for calculating the abundance of specific cells in a mixed matrix [11] Subsequently, we took the intersection and selected the commonly different immune cells.\u003c/p\u003e \u003cp\u003e \u003cb\u003e2.4 Establishing a Co-expression Network using Weighted Gene Co-expression Network Analysis and Identifying Hub Modules Related to Immune Cells\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe experiment used the WGCNA R package to build co-expression networks for skin melanoma transcriptome and chip datasets[18], excluding genes with FPKM\u0026thinsp;\u0026lt;\u0026thinsp;1. Samples were clustered hierarchically based on Euclidean distance, considering patient skin and mucosa typing or metastasis and no-metastasis to remove outliers. A soft thresholding method (β\u0026thinsp;=\u0026thinsp;6) preserved scale-free topology, and the dynamic tree cutting algorithm identified X non-gray modules with at least 30 genes. Characteristic genes were extracted, and their Pearson correlation with clinical phenotypes was analyzed (|r|\u0026gt;0.4, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) to find modules linked to tumor progression[19]. The top 10% of hub genes were selected. Functional enrichment analysis was conducted on the intersection of co-expression modules and differentially expressed genes, identifying core genes related to melanoma prognosis[19].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Differential Gene Drivers for Survival Prognosis Modeling in Human Skin Melanoma\u003c/h2\u003e \u003cp\u003eThe experiment modeled survival prognosis using co-expressed differential genes from the TCGA-SKCM dataset of 472 melanoma samples, integrating expression data with clinical prognosis via TCGA-biolinks. The maxstat algorithm determined optimal gene expression cutoffs, categorizing samples into high and low expression groups. Kaplan-Meier curves were generated, and log-rank tests assessed group differences at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Significant genes underwent multi-factor Cox regression analysis, and the survminer package visualized survival curves with a two-color scheme and a risk table showing surviving samples over time, investigating the impact of differential genes on survival.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Receiver Operating Characteristic (ROC)\u003c/h2\u003e \u003cp\u003eThe PROC software package is used for ROC analysis of differential genes with the TCGA-SKCM dataset, and GGPLOT2 is used to visualize the results. The area under the curve (AUC) is commonly used to evaluate diagnostic tests, and the value of AUC should range from 0.5 to 1. The closer the AUC is to 1, the better the significant effect of the variable in predicting the outcome.\u003c/p\u003e \u003cp\u003eIdentifying the optimal classification threshold is essential to enhance the accuracy and practicality of diagnostic or predictive models. Key statistical details, including sample size, variance, and confidence intervals, play a vital role in improving the reliability and robustness of predicted values. One effective method for determining the best cutoff value for classifying cases based on gene expression levels is the Youden Index, calculated as Youden Index\u0026thinsp;=\u0026thinsp;Sensitivity\u0026thinsp;+\u0026thinsp;Specificity \u0026minus;\u0026thinsp;1. This index provides a balanced approach by considering both the actual positive rate (TPR) and the false positive rate (FPR), facilitating a more informed decision-making process in classifying cases.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Statistical Analysis\u003c/h2\u003e \u003cp\u003eThe gene expression profiles were log2 transformed for data normalization. Subsequently, receiver operating characteristic (ROC) curves were plotted based on gene expression levels and sample types to compare the predictive accuracy of different biomarkers. In addition, we calculated the P values for the log-rank test, using a P value of less than 0.05 as the criterion for determining statistical significance. All statistical analyses were performed using GraphPad Prism 7.0 or SPSS 19.0 software, with a two-sided P value of less than 0.05 considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Identification of Differentially Expressed Genes in Canine Melanoma\u003c/h2\u003e \u003cp\u003eThe transcriptome data from GSE266234 (accession number PRJNA1106424) concerning canine melanoma in skin and normal mucosal and skin tissues unveiled differentially expressed genes (DEGs) distinguishing mucosal and cutaneous melanoma types. A total of 548 differential genes were identified, with 206 exhibiting significant upregulation, while 342 displayed significant downregulation (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003eThe canine melanoma microarray dataset GSE129750 (PRJNA532635) examined DEGs between metastatic and non-metastatic tissue types. In this analysis, 369 differential genes were identified, of which 202 demonstrated significant increases, and 194 showed substantial decreases (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Gene Set Enrichment Analysis of Canine Melanoma Metastatic Type\u003c/h2\u003e \u003cp\u003eThe results of the GSEA enrichment analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) show high enrichment in immune-related pathways (immune response; acute inflammatory response; positive regulation of defense response) and signaling-related pathways (receptor ligand activity; signaling receptor regulator activity; signaling receptor activator activity), as well as high enrichment in cytokine activity and molecular function regulation pathways.\u003c/p\u003e \u003cp\u003eThe results of the GSEA GO enrichment analysis reveal that several critical biological pathways are significantly enriched in the samples, particularly those related to immune response, signal transduction, and the regulation of cytokine activity, all of which may be closely linked to tumor metastasis. The enrichment of immune-related pathways, such as the positive regulation of viral processes (GO_0044794), Toll-like receptor binding (GO_0035325), and the insertion of proteins into the mitochondrial outer membrane (GO_0045040), suggests a widespread activation of the immune system within the samples. However, this immune activation could be manipulated by tumor cells to facilitate their evasion of immune detection. For instance, tumor cells might disrupt the immune system's normal recognition processes by mimicking the signaling pathways associated with viral infections, altering Toll-like receptor signaling to dampen immune responses, and inhibiting apoptosis by regulating mitochondrial outer membrane protein insertion, thereby enhancing their survival. Additionally, the significant enrichment of pathways related to signal transduction, such as the regulation of plasminogen activation (GO_0010755), protein nitrosylation (GO_0017014), and peptide chain cysteine S-nitrosylation (GO_0018119), indicates potential activation or modulation of critical signaling pathways in the samples. These pathways may influence intercellular communication and signal transduction, ultimately affecting the recruitment and function of immune cells. Such alterations could promote the invasion and migration of tumor cells, thereby accelerating the process of tumor metastasis.\u003c/p\u003e \u003cp\u003eThe significant enrichment of pathways related to cytokine activity and molecular function regulation, such as the regulation of the prostaglandin biosynthetic process (GO_0031392), positive regulation of the phospholipid biosynthetic process (GO_0071073), and the extracellular matrix (EMC) complex (GO_0072546), indicates notable alterations in cytokine synthesis, secretion, and receptor function within the samples. These alterations can further influence on the activity and function of immune cells, potentially impacting the inflammatory response in the tumor microenvironment, tumor progression and metastasis. This observation highlights the extensive activation of the immune system and underscores the critical roles of signal transduction and cytokine activity regulation in tumor metastasis and immune evasion. Such findings provide valuable insights into the molecular mechanisms underlying tumor progression and establish a foundation for future research into the regulatory mechanisms of tumors. The GSEA KEGG enrichment analysis results also reveal a strong enrichment of immune-related pathways, including lggA generation, immune deficiency, and the IL-17 signaling pathway.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Immune Cell Infiltration in Canine Melanoma\u003c/h2\u003e \u003cp\u003eWe used the CIBERSORT algorithm to analyze 22 immune cell types in transcriptome and chip datasets (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The Resucts revealed significant differences between metastatic and non-metastatic groups, especially in activated mast cells and M2 macrophages. Despite low macrophage expression, differences were notable; activated mast cells indicate an inflammatory response in metastatic melanoma, while M2 macrophages may aid tumor immune evasion. The transcriptome dataset showed significant variations in resting and activated mast cells, highlighting their immunosuppressive role. The main differences in immune cell infiltration between datasets were linked to activated mast cells, suggesting their key role in tumor immunosuppression of melanoma.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e3.4 Using weighted gene co-expression network analysis to construct co-expression networks and identify hub modules associated with different prognosis groups.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eUsing WGCNA, core genes linked to prognosis were identified. In the chip dataset, the green-yellow and magenta modules had correlations of 0.32 and 0.30, respectively; the magenta module had the highest difference in the non-transgenic group (R2\u0026thinsp;=\u0026thinsp;0.30, P\u0026thinsp;=\u0026thinsp;0.1), yielding 142 genes. In the transcriptome dataset, the green module correlated with prognosis at 0.76 and showed significant differences in favorable skin types (R2\u0026thinsp;=\u0026thinsp;0.76, P\u0026thinsp;=\u0026thinsp;1e-06), extracting 67 genes. Common differential genes KLK13 and SLC5A8 were found in both datasets.\u003c/p\u003e \u003cp\u003eWe performed a weighted gene co-expression network analysis (WGCNA) on the GSE129750 and GSE266234 datasets, identifying several gene modules. In the GSE129750 dataset, the green-yellow module showed a correlation of R\u0026sup2; = 0.32 and a P-value of 0.09, but we could not identify any significantly expressed genes in our analysis. This indicates that, despite the correlation of the green-yellow module, its biological significance in our study is limited. Furthermore, although neither the green-yellow nor magenta module met our strict significance criteria (correlation coefficient \u0026gt; |0.5| and p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05), both may still have potential prognostic links. Notably, the magenta module demonstrated a more pronounced difference in the non-translocation group, with a correlation of R\u0026sup2; = 0.30 and a P-value of 0.1. Within this module, we identified 142 differentially expressed genes that exhibited significant expression differences in the non-translocation group, meeting our criteria of |correlation coefficient| \u0026gt; 0.5 and p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05. A further analysis of the transcriptome data from the Green module of GSE266234 and the Magenta module of GSE129750 identified KLK13 and SLC5A8 as two commonly expressed differentially expressed genes, as determined by Venn diagram analysis.\u003c/p\u003e \u003cp\u003eBoth genes exhibited significant differences in expression across the modules and have been supported by other studies as closely linked to disease occurrence and progression. KLK13 is believed to promote tumor cells' invasion and migration capabilities by influencing the degradation and remodeling of the extracellular matrix, thus playing a crucial role in tumor metastasis. Abnormal expression of KLK13 in the tumor microenvironment is associated with invasive tumor behavior, indicating its potential as a target for early diagnosis and intervention in tumor metastasis. Meanwhile, the role of SLC5A8 in tumor metastasis is increasingly understood. The monocarboxylate transporter encoded by this gene is integral to cellular energy metabolism, and its abnormal expression may disrupt the energy balance and metabolic reprogramming of tumor cells, ultimately affecting their proliferation, migration, and invasion abilities.\u003c/p\u003e \u003cp\u003eFurthermore, SLC5A8 likely influences tumor metastasis by regulating the acid-base balance in cells, impacting tumor cells' adaptability to their microenvironment. The significant differences observed in the Magenta module of the GSE129750 dataset for the non-translocation group and the presence of target genes indicate that this module may be crucial in specific stages or subgroups of disease progression. In summary, a gene co-expression network was successfully constructed through WGCNA(\u003cb\u003eFigu 4\u003c/b\u003e), and the Magenta module and Green module significantly associated with specific prognostic differential populations were identified; co-expressed hub genes KLK13 and SLC5A8 were screened out.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e(B) The transcriptome data GSE266234 identifies and establishes gene co-expression modules displayed in different colors in the hierarchical clustering.\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e(C) The relationship between consensus module feature genes from the transcriptome data GSE266234 and whether metastasis occurs. The rows in the figure correspond to consensus modules, and the columns correspond to cell subtypes. The numbers in each module represent the correlation coefficients to show the association between the respective module and metastasis, along with the p-values shown in parentheses below.\u003c/b\u003e \u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e(D)Is the chip dataset GSE129750 formed based on hierarchical clustering using soft threshold power (β). The scale independence index for soft threshold powers (β\u0026thinsp;=\u0026thinsp;8) is estimated from 1 to 20. The average connectivity for soft threshold powers from 1 to 20 is determined. A transcriptome data GSE266234 is formed based on hierarchical clustering using soft threshold power (β). The scale independence index for soft threshold powers (β\u0026thinsp;=\u0026thinsp;8) is estimated from 1 to 20. The average connectivity for soft threshold powers from 1 to 20 is determined.\u003c/b\u003e \u003c/p\u003e\u003cp\u003e \u003cb\u003e(E) The chip dataset GSE129750 identifies and establishes gene co-expression modules displayed in different colors in the hierarchical clustering.\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e(F) The relationship between consensus module feature genes from the chip dataset GSE129750 and whether metastasis occurs. The rows in the figure correspond to consensus modules, and the columns correspond to cell subtypes. The numbers in each module represent the correlation coefficients to show the association between the respective module and metastasis, along with the p-values shown in parentheses below.\u003c/b\u003e \u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Differential Gene Drivers of Survival Prognosis in Human Skin Melanoma\u003c/h2\u003e \u003cp\u003eThe transcriptome dataset GSE266234 analyzed canine mucosal melanoma (CMM). CMM is the most common malignant tumor of the oral cavity in canines, and it is more aggressive than cutaneous melanoma (CCM). Still, the specific mechanisms underlying this difference are not yet precise. More aggressive CMM is often associated with a poorer prognosis. Although there are limitations in survival records within the canine melanoma dataset, this dataset not only identifies an immune-suppressive stromal signature, providing potential biomarkers for predicting human clinical survival. This finding suggests that cross-species dataset analysis can offer new insights into human diseases; thus, exploring the common differentially expressed genes in transcriptome and chip datasets through human cases may facilitate a deeper understanding of the biological mechanisms of melanoma. The results indicate that the survival rate of the high expression group of SLC5A8 and KLK13 is significantly lower than that of the low expression group, with a significantly increased survival risk; P values are all \u0026lt;\u0026thinsp;0.05, associated with poorer survival prognosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Limited canine survival records led to human case exploration, revealing that high SLC5A8 and KLK13 expression groups had lower survival rates and increased mortality risk (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). AUC values of the ROC curve indicated clinical significance, suggesting SLC5A8 and KLK13 as accurate biomarkers for melanoma metastasis.\u003c/p\u003e \u003cp\u003eKLK13's actual positive rate (TPR) is calculated at 0.6793, while the false positive rate (FPR) stands at 0.38832. The corresponding Youden index, which measures the effectiveness of a diagnostic test, is 0.29098, achieved at an optimal threshold of 0.4809889. This indicates that the model's classification ability at this threshold is significantly better than random guessing. In contrast, for SLC5A8, the TPR is 0.53451 and the FPR is 0.36548, leading to a Youden index of 0.16403, calculated at an optimal threshold of 0.01963076. Although this Youden index is relatively low, it remains above zero, indicating that the model's classification ability surpasses random guessing at this threshold. In summary, both biomarkers have Youden indices greater than 0, which is encouraging. This indicates their classification abilities are practical and surpass random guessing at their optimal thresholds.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eStudying human melanoma offers essential insights into canine melanoma because both share many similarities in their histology, biology, molecular mechanisms, and treatment responses. For example, canine oral melanoma is similar to human mucosal melanoma in cell structure, invasiveness, metastatic behavior, and the dysregulation of signaling pathways; additionally, their responses to specific treatments are also quite alike. However, there are essential differences between the two. Human melanoma is mainly caused by ultraviolet (UV) exposure, while genetic factors and local environmental conditions largely influence canine melanoma.Furthermore, differences in genetic mutations and the immune environment set canine melanoma apart from human melanoma.Based on the attributes of canine metastatic melanoma, we discovered that signaling pathways associated with inflammatory responses and immune regulation are notably prevalent in this condition. Chronic inflammation is regarded as a critical factor that fosters the emergence and progression of various tumors, and there exists a relative scarcity of research about canine melanoma, underscoring the need for an in-depth investigation into the extent of immune cell infiltration and the underlying mechanisms. Prolonged states of chronic inflammation can induce immune cell dysfunction[20, 21], consequently facilitating tumor initiation and advancement, which is closely linked to the clinical manifestations of canine melanoma[22].\u003c/p\u003e \u003cp\u003eThe genomic and epigenetic characteristics of canines and their naturally occurring disease patterns are similar to those of humans and differ from genetically modified or xenotransplanted rodent models[23]. Canine models are advantageous for studying complexity, heterogeneity, and host-tumor interactions, leading to a deeper understanding of human cancer[24]. Therefore, canines serve as an effective model for investigating human-like diseases, particularly cancer[25]. Additionally, the costs of canine clinical trials are relatively low, and canine tumor samples encompass various breeds, ages, and sexes, providing a rich resource for studying tumor heterogeneity and individual differences[26]. Tumor occurrence, development, and metastasis can be observed in a shorter time frame, thus accelerating research progress[25, 27]. Consequently, gene mutations and pathway alterations identified in canine tumor research can inform human cancer studies, accelerating the development of new therapies and clinical applications[28].\u003c/p\u003e \u003cp\u003eGene set enrichment analysis (GSEA) indicated a significant enrichment of pathways related to immune response and signaling, implying that these pathways may play crucial roles in tumor advancement and immune evasion[29].. Tumor cells evade host immune system attacks by upregulating immunosuppressive factors and modifying the microenvironment[28]. Investigations into immune evasion mechanisms have revealed that tumors exploit immune regulation to foster growth and metastasis. This suggests that future therapeutic approaches should enhance immune responses to counteract metastatic melanoma[16]. Existing literature has demonstrated that inflammatory responses within the tumor microenvironment are intricately linked to tumor development[9]; inflammatory mediators stimulate cell proliferation and modify metastatic potential by altering signaling pathways [30]. Tumors increase their survival and spread by regulating the expression of specific genes and refining the microenvironment. The results of the enrichment analysis concerning signaling receptor activity regulation revealed a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01, further substantiating that tumors modulate the microenvironment and facilitate growth and metastasis through the regulation of signaling receptor activity[9]. Prior studies have highlighted that the regulation of signaling receptors is pivotal in tumor cell growth, migration, and treatment responses[31].\u003c/p\u003e \u003cp\u003eThe correlation between immune cell infiltration status in canine melanoma and chronic inflammation emphasizes the significance of the immune microenvironment in the progression of melanoma[3]. M2 macrophages and mast cells are integral to canine metastatic melanoma, particularly M2 macrophages[32], which may enhance tumor growth and metastasis; mast cells are implicated in angiogenesis and metastasis, with their presence positively correlating with tumor cell proliferation indices, potentially influencing prognosis by promoting angiogenesis and immune evasion [33] However, research on immune cell infiltration in canine metastatic melanoma is limited, and an immunosuppressive environment may prevail [5] indicated by significant alterations in the ratio of CD8\u0026thinsp;+\u0026thinsp;T cells to regulatory T cells (Treg)[2], suggesting that canine melanoma may be affected by such an immunosuppressive milieu.The activity levels of these signaling pathways are closely related to the immune evasion mechanisms of tumor cells; developing new treatment strategies targeting these pathways may improve the prognosis of canine melanoma patients[10]. Comparative analysis with other cancer types also reveals that the immune regulatory mechanisms of canine melanoma have particular specificity[34], providing new ideas for clinical treatment[35]. In the analysis of inflammatory factors, the significant increase of IL-6 and TNF-α suggests the presence of an inflammatory state in the microenvironment of canine melanoma, which may promote tumor occurrence and development[36]. The elevation of these cytokines is closely related to the interaction with immune cells, and future treatments may provide new targets for these cytokines[37] Intervention strategies targeting these factors in clinical treatment are expected to improve the immune response in canine melanoma patients, thereby enhancing treatment efficacy.\u003c/p\u003e \u003cp\u003eImmunosuppression plays a crucial role in cancer development[38], Identify new biomarkers of poor prognosis affecting canine melanoma. To this end, over 400 core genes related to WGCNA infiltration modules were selected, ultimately focusing on two genes: KLK13 and SLC5A8.\u003c/p\u003e \u003cp\u003eKLK13 (Kallikrein-related peptidase 13) is a peptidase expressed in various tissues and is linked to tumor processes like proliferation and apoptosis[39]. Its expression correlates with cancer prognosis, particularly in esophageal and bladder cancers [40], where high levels indicate poor outcomes. Environmental and genetic factors can influence KLK13 expression[41, 42], with certain carcinogens potentially promoting melanoma[43], Sarwar. High KLK13 levels in melanoma patients are associated with worse survival[44], suggesting it could be a prognostic marker and therapeutic target [45]. The abnormal expression of members of the KLK family in melanoma is significantly linked to the tumor's aggressiveness and metastatic potential, correlating with a poor prognosis[46]. Notably, high levels of KLK6 in mucosal melanoma are associated with improved recurrence-free survival, likely due to KLK6's complex role within the tumor microenvironment[47]. Similarly, the expression of KLK8 in melanoma is tied to the tumor's malignant behavior[48], as it can cleave Activin-A through unconventional pathways, influencing tumor growth[49]. The KLK family members contribute to the malignant progression of melanoma through various mechanisms[50], including the regulation of extracellular matrix degradation and remodeling, which facilitates the invasion and metastasis of tumor cells. KLK6, for instance, modifies the composition and structure of the extracellular matrix, creating a favorable environment for tumor cell migration. In contrast[51], KLK8 activates downstream signaling pathways by cleaving Activin-A[48], promoting tumor cell growth. Furthermore, KLK family members play a role in tumor immune evasion by interacting with immune cells in the tumor microenvironment [52]; KLK6 can enhance immune evasion by affecting the polarization of tumor-associated macrophages[52]. Consequently, inhibitors targeting KLK6 may impede tumor invasion and metastasis by disrupting its function in the tumor microenvironment[8], offering new therapeutic strategies for melanoma treatment. Therefore, KLK13 not only appears as a potential adverse prognostic biomarker for melanoma but also as a potential target[53].\u003c/p\u003e \u003cp\u003eThe SLC5A8 gene encodes a transporter protein essential for moving small molecules like lactate and pyruvate within cells. This gene is crucial for metabolic processes and has implications in various diseases, particularly cancer[54], where it exhibits significant changes in expression levels in melanoma. Factors in the microenvironment, such as macrophage infiltration and inflammatory mediators[55], can affect SLC5A8 expression. This, in turn, may influence melanoma progression and suggest an inhibitory effect on tumor development. Monitoring the levels of SLC5A8 could provide valuable insights for clinicians in assessing patient prognosis. Although the precise mechanisms by which the SLC5 family[56], including SLC5A8, affects melanoma are still under investigation, evidence suggests a correlation with patient prognosis. For instance, the SLC5A1 gene has been linked to immune response characteristics in tumors[42], and it is recognized as one of the differentially expressed genes compared to matched normal tissues. Nevertheless, there is a notable gap in research on the overall impact of the SLC5 family on melanoma [57], highlighting the need for further investigation. The future can further confirm the marker role of SLC5A8 in the poor prognosis of melanoma.\u003c/p\u003e \u003cp\u003eIn clinical analyses of humans, high expression of KLK13 and SLC5A8 is associated with poor prognosis[58], but there is no significant correlation with tumor immune suppression[59]. These two core genes demonstrated an essential adverse prognostic effect in our study.KLK13 and SLC5A8 as novel biomarkers for canine melanoma, aiding in the understanding of tumor progression and immune infiltration[41]. Despite its limitations, it provides a critical foundation for personalized treatment and prognostic assessment[60]. Future efforts are needed to validate its clinical application potential, advance diagnostic and therapeutic developments, support the development of new therapies, and promote the health of both animals and humans.\u003c/p\u003e \u003cp\u003eInevitably, there are some limitations in our experiments[35]..This study has limitations due to the scarcity of genomic data on canine melanoma, particularly concerning prognosis[52]. While research findings on human melanoma offer valuable insights for treating canine melanoma, effective treatment still requires targeted research tailored to the unique characteristics of canine melanoma.Additionally, when selecting the GSE266234 transcriptome public dataset, we ensured timely updates to mitigate the effects of irregular changes that could alter analysis results. However, these dynamic updates may introduce heterogeneity, particularly due to technical and biological differences across experimental batches, potentially affecting data consistency. Sample representativeness is crucial, as the dataset may not adequately reflect the diversity of the affected animals, leading to selection bias and limiting the generalizability of the results. Even with data preprocessing and normalization, batch effects may still impact result interpretation. Therefore, it is essential to consider these sources of variation during analysis carefully. Although we applied statistical models or integrated analysis methods and made efforts to increase sample diversity or control for confounding variables to reduce the impact of sample selection bias, we can more accurately derive insights from public transcriptome datasets. In the future, we can further improve this by combining similar transcriptome datasets of canine oral melanoma, such as the GSE228574 dataset.\u003c/p\u003e \u003cp\u003eHowever; The expression of KLK13 and SLC5A8 is related to the metabolic state of tumor cells, and their combined effect is essential for assessing the prognosis of melanoma. This could provide new insights into understanding its complex biology and clinical treatment.\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eIn summary, metastatic and canine mucosal melanoma both exhibit an enrichment of inflammation and immunity; there are differences in the expression of mast cells under immunosuppressive conditions. We identified two core genes, KLK13 and SLC5A8, in our analysis following mast cell infiltration, and combined with public data on human melanoma and survival curve analysis, revealed the potential of KLK13 and SLC5A8 as new markers. The role of KLK13 in melanoma metastasis has been validated, while SLC5A8 can serve as a basis for further research; the interaction mechanism between KLK13 and SLC5A8 will become an important research direction for melanoma diagnosis and treatment.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"633\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAbbreviations\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003efull form\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eWGCNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eWeighted Gene Co-expression Network Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eKLK13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eKallikrein-related peptidase 13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSLC5A8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSolute carrier family 5 member 8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMDPI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMultidisciplinary Digital Publishing Institute\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTMB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTumor mutational burden\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSRA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSequence Read Archive\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCanine normal mucosa\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCCN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCanine normal skin\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCCM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCanine cutaneous melanoma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003efastp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFast quality control tool for FASTQ files\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCanFam3.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCanis lupus familiaris genome assembly 3.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ehisat2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eHierarchical Indexing for Spliced Alignment of Transcripts 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003efeatureCount\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSubread package read summarization tool\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDESeq2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eDifferential expression analysis based on negative binomial distribution\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFDR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFalse Discovery Rate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGTF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGene Transfer Format\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eENSEMBL V104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eEnsembl database version 104\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGene Ontology\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eClusterProfiler\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eR package for functional enrichment\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGEO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGene Expression Omnibus\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLimma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eLinear Models for Microarray Data\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDAVID\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eDatabase for Annotation, Visualization and Integrated Discovery\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eKEGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eKyoto Encyclopedia of Genes and Genomes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eBiological Process\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCellular Component\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMolecular Function\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGSEA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGene Set Enrichment Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMSigDB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMolecular Signatures Database\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCIBERSORT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCell-type Identification By Estimating Relative Subsets Of RNA Transcripts\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFPKM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFragments Per Kilobase of transcript per Million mapped reads\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTCGA-SKCM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eThe Cancer Genome Atlas – Skin Cutaneous Melanoma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTCGA-biolinks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eR package for TCGA data retrieval\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eKaplan-Meier\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eKaplan-Meier survival estimator\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003elog-rank\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eLog-rank test\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCox regression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCox proportional hazards regression\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003esurvminer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eR package for survival analysis visualization\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eROC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eReceiver Operating Characteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eArea Under the Curve\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTPR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTrue Positive Rate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFPR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFalse Positive Rate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eYouden Index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eYouden's J statistic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGraphPad Prism 7.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGraphPad Prism software version 7.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSPSS 19.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eStatistical Package for the Social Sciences v19.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eIL-17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eInterleukin-17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eIgA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eImmunoglobulin A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eIL-6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eInterleukin-6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTNF-α\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTumor Necrosis Factor-alpha\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCD8+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCluster of Differentiation 8 positive\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTreg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRegulatory T cells\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eKLK6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eKallikrein-related peptidase 6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eKLK8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eKallikrein-related peptidase 8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSLC5A1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSolute carrier family 5 member 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eUV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eUltraviolet\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study did not involve any animal experiments or human participant research, and therefore did not require approval from an ethics committee. The [RNA-Seq/microarray] data reanalyzed in this study were derived from a public dataset available in the [GEO (Gene Expression Omnibus)] repository under accession numberPRJNA532635 and PRJNA1106424.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors have read and approved the final manuscript and consent to its publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe original contributions presented in the study are included in the article or supplementary material. The datasets used and/or analysed during the cur rent study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are submitting this manuscript only to this Journal, and all au- thors have approved of its submission. The authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH Z:\u0026nbsp;\u003c/strong\u003eWriting\u0026nbsp;–\u0026nbsp;review \u0026amp; editing, Investigation, original draft, Software, Methodology, Formal analysis, Data curation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eZ L:\u0026nbsp;\u003c/strong\u003eWriting–\u0026nbsp;review \u0026amp; editing, Funding acquisition, Conceptualization.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH Y(corresponding author):\u003c/strong\u003e Methodology, Funding acquisition, Conceptualization.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was received for conducting this study. This article has been polished by Kimi（https://www.newidea.ai/） and newidea（https://www.newidea.ai/） to enhance its accuracy and fluency.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSteven D: \u003cb\u003eA Role for Dogs in Advancing Cancer Immunotherapy Research\u003c/b\u003e. \u003cem\u003eFrontiers in immunology\u003c/em\u003e 2019, \u003cb\u003e10\u003c/b\u003e:2935.\u003c/li\u003e\n\u003cli\u003eGiudice AL, Porcellato I, Giglia G, Sforna M, Lepri E, Mandara MT, Leonardi L, Mechelli L, Brachelente C: \u003cb\u003eExploring the Epidemiology of Melanocytic Tumors in Canine and Feline Populations: A Comprehensive Analysis of Diagnostic Records from a Single Pathology Institution in Italy\u003c/b\u003e. \u003cem\u003eVeterinary sciences\u003c/em\u003e 2024, \u003cb\u003e11\u003c/b\u003e(9):435–435.\u003c/li\u003e\n\u003cli\u003eProuteau A, André C: \u003cb\u003eCanine Melanomas as Models for Human Melanomas: Clinical, Histological, and 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\u003cb\u003e15\u003c/b\u003e:1397656–1397656.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-veterinary-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [BMC Veterinary Research](http://bmcvetres.biomedcentral.com/)","snPcode":"12917","submissionUrl":"https://submission.nature.com/new-submission/12917/3?","title":"BMC Veterinary Research","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Canine melanoma, Immune-related, Weighted gene co-expression network analysis (WGCNA), KLK13, SLC5A8","lastPublishedDoi":"10.21203/rs.3.rs-8203845/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8203845/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study performed a bioinformatics analysis of transcriptome and chip datasets from canine melanoma to identify potential therapeutic targets and diagnostic biomarkers. The results showed significant enrichment in immune-related signaling pathways, suggesting that inflammation and immune regulation were crucial to tumor progression. Through weighted gene co-expression network analysis (WGCNA), gene modules related to prognosis were identified, and two core genes, KLK13 and SLC5A8, were discovered. We validated the potential of KLK13 and SLC5A8 as new biomarkers by integrating our findings with public data on human melanoma and conducting survival curve analysis. This study offers theoretical support for treating and diagnosing canine melanoma while providing valuable insights for human melanoma research.\u003c/p\u003e","manuscriptTitle":"Identification of Prognostic Biomarkers KLK13 and SLC5A8 in Canine Melanoma via Transcriptomics and WGCNA Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-19 19:51:07","doi":"10.21203/rs.3.rs-8203845/v1","editorialEvents":[{"type":"communityComments","content":2},{"type":"reviewerAgreed","content":"219636216337447525624805118044608604284","date":"2026-03-25T13:52:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"80964962533092033158842817664565959961","date":"2026-03-24T03:25:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"119509909325945976318126741332913348036","date":"2026-01-08T00:17:58+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-07T05:28:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"202220147752751313199334994718838971057","date":"2026-01-03T06:29:41+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-18T00:55:17+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-11T07:00:39+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-12-09T05:19:11+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-05T22:31:39+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Veterinary Research","date":"2025-11-30T15:24:46+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-veterinary-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [BMC Veterinary Research](http://bmcvetres.biomedcentral.com/)","snPcode":"12917","submissionUrl":"https://submission.nature.com/new-submission/12917/3?","title":"BMC Veterinary Research","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b30059d4-8a2d-403e-80e0-4c039411077e","owner":[],"postedDate":"December 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-12-19T19:51:07+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-19 19:51:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8203845","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8203845","identity":"rs-8203845","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}