Novel a new prognostic model of Thyroid Carcinoma based on Macrophage and Cuproptosis-Related lncRNA

Novel a new prognostic model of Thyroid Carcinoma based on Macrophage and Cuproptosis-Related lncRNA | 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 Novel a new prognostic model of Thyroid Carcinoma based on Macrophage and Cuproptosis-Related lncRNA Jiahao Wu, Fan Yang, Yuting Xu, Guanqun Huang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6528117/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Thyroid carcinoma is a type of malignant tumor with a relatively good prognosis, but some subtypes have a poorer prognosis. Therefore, it is necessary to establish a new prognostic model to predict the survival outcomes and immune therapy responses of thyroid cancer patients. Methods Single-cell sequencing analysis was conducted to identifyt genes associated with cuproptosis and differentially expressed genes within macrophages. The Wilcoxon algorithm was employed to identify tumor-related genes. The overlapping genes were utilized to discover lncRNAs that are related to both macrophages and cuproptosis. Following this, Lasso analysis was applied to develop a prognostic model. We then stratified patients into two groups based on their median risk scores and assessed their survival outcomes and responses to immune therapy. Additionally, we examined the mutational profiles of patients in both the high-risk and low-risk groups. Results Our prognostic risk model has identified 11 lncRNAs: AC107214.1, AC135050.1, AC026355.3, FOXP1-AS1, AC123768.1, MIR3945HG, AC022819.1, AC009716.1, AC120498.9, AL133444.1, and AL132709.8. It has been confirmed that our prognostic model boasts an exceptionally high accuracy, with a precision rate exceeding 0.8 in forecasting the survival outcomes of patients in the TCGA-THCA cohort. In this study, we observed no significant disparity in gene mutation rates between the high-risk and low-risk groups. Patients categorized in the low-risk group exhibit heightened sensitivity to immunotherapy and demonstrate responsiveness to a variety of immunotherapeutic agents, such as dasatinib. Conclusion Our study highlights the potential of macrophage and cuproptosis-related lncRNAs as novel predictive biomarkers for thyroid carcinoma. Thyroid carcinoma scRNA Macrophage Cuproptosis lncRNA Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 1 Introduction Thyroid cancer is a significant global health issue, with an increasing number of diagnoses worldwide, particularly among women. The cancer is divided into several types, with differentiated thyroid cancer (DTC), including papillary and follicular types, making up the majority of cases. Papillary thyroid carcinoma (PTC) is the most common subtype, known for its positive prognosis and extremely high survival rate, especially in younger patients.[ 1 , 2 , 3 ] The global incidence of thyroid cancer has increased significantly, with estimates reaching up to 821,214 new cases in 2022.[ 4 ] Papillary thyroid carcinoma (PTC) remains the most commonly diagnosed type, comprising approximately 90% of cases. Recent studies suggest that the incidence rate for adults ranges from 4.5 to 14.4 per 100,000 individuals, with notable geographic variations.[ 5 , 6 ] Different types of thyroid cancer have varying prognoses; therefore, it is necessary to develop a new prognostic model. Macrophages are key components of the immune system, originating from monocytes and demonstrating versatile functions that are crucial in various biological processes, including tumor development. In thyroid cancer, tumor-associated macrophages (TAMs) significantly influence tumor behavior and prognosis. Although most thyroid cancers are well-differentiated and have favorable outcomes, the presence of TAMs has been associated with poorer prognoses, particularly in anaplastic and medullary types.[ 7 ] TAMs, particularly of the M2 phenotype, promote tumor growth and metastasis through the secretion of immunosuppressive factors and support angiogenesis.[ 8 ] Recent findings indicate that these macrophages contribute to creating a tumor microenvironment conducive to cancer cell survival, thereby impeding immunotherapeutic responses.[ 9 ] Notably, targeting macrophage polarization from M2 to M1 has emerged as a potential therapeutic strategy to enhance anti-tumor immunity.[ 10 ] Thus, further investigation into the mechanisms by which TAMs affect thyroid cancer is essential for developing effective prognostic models and targeted therapies that can improve patient outcomes.[ 11 , 12 , 13 ] Cuproptosis is a new concept in cancer biology, describing a novel form of regulated cell death induced by excess copper. Long non-coding RNAs (lncRNAs) associated with cuproptosis have been identified as key players in various types of cancer, including thyroid cancer. Recent studies suggest that these lncRNAs can influence tumor growth, metastasis, and response to therapy.[ 14 , 15 ] In thyroid cancer, specific cuproptosis-related lncRNAs have demonstrated prognostic potential. For instance, a study identified a signature of lncRNAs that could predict patient outcomes, correlating high expression levels with poorer survival rates.[ 16 ] Additionally, the role of TAMs (tumor-associated macrophages) in the tumor microenvironment is impacted by these lncRNAs, which can modulate macrophage polarization and enhance tumor-promoting activities.[ 17 , 18 ] Thus, cuproptosis lncRNAs hold promise as both prognostic markers and therapeutic targets in managing thyroid cancer, highlighting their relevance in understanding tumor pathogenesis and improving clinical strategies.[ 19 , 20 ] In order to investigate the effect of copper death genes on macrophages and thus understand their impact on the immune microenvironment, we designed this study. This study explores the impact of cuproptosis-associated lncRNAs on macrophages, the immune microenvironment of thyroid cancer, and predicts the prognosis and potential therapeutic targets for thyroid cancer patients based on a novel prognostic model. 2 Matherials and Methods 2.1 Single-cell data downloading and processing The single-cell RNA sequencing dataset GSE191288 was downloaded from the Gene Expression Omnibus database (GEO, https://www.ncbi.nlm.nih.gov/gds/ ). The dataset comprised 7 samples, including 6 tumor and 1 normal sample. We then used the "Seurat" package to analyze the single-cell sequencing data. Quality control was first performed on the data by retaining cells with less than 20% mitochondrial genes and 20% ribosomal genes, genes expressed in at least 3 cells, and the expression range between 200 to 10000. Subsequently, we set the number of highly variable genes at 2000 to identify these genes. "PCA" and "t-SNE" methods were used to reduce the dimensionality of the scRNA data. The "Harmony" package was used to eliminate the batch effect between the 7 samples. We then used the "FindNeighbors" and "FindClusters" functions to construct the cell clusters, which we subsequently visualized using the "t-SNE" method. Lastly, we used marker genes from previous studies to annotate the cell clusters (Supplementary Table S1 ). The "ssGSEA" method was used to calculate the activity of gene sets in each cell. The Wilcoxon test (p.adj < 0.05) was used to calculate the statistical significance of differentially expressed genes (DEGs), with all other parameters set to default values. We then used the "AUCell" package to calculate the "AUCell Score" in each cell to distinguish the cells into High and Low cuproptosis groups. Finally, the "DESeq2" package was used to calculate the statistical significance of differentially expressed genes (DEGs) between 8 cell types, with all parameters set to default values. 2.2 TCGA data collection and processing The Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov/ ) includes genomic sequencing data, proteomic data, methylation data, mRNA data, and miRNA data for 33 types of cancer. In this study, we downloaded transcriptome profiling (RNA-seq), single nucleotide data, and corresponding clinical information for TCGA-THCA, including 505 papillary thyroid cancer samples and 59 normal thyroid tissues. Patients with missing clinical data or 0 survival days were excluded. All data in this study were log2 transformed. 2.3 Identification of Tumor-related genes The "Limma" package was used to identify Differentially Expressed Genes (DEGs) between tumor and normal thyroid tissue. We set the threshold at |logFC| > 1 and p < 0.05 to identify the DEGs. 2.4 Identification of Macrophage-Related Cuproptosis lncRNAs We identified 100 Differentially Expressed Genes (DEGs) associated with Macrophage-Related Cuproptosis after intersecting the High_Cuproptosis_genes, TCGA-DEGs, and Macrophage genes. Subsequently, using the "limma" package, we performed co-expression analysis of Macrophage Cuproptosis-Related genes and lncRNAs, employing a standard of a "correlation coefficient" ≥ 0.3 and p < 0.05. Finally, we constructed a "sankey plot" and "co-expression network" to show the relationship between those genes and lncRNAs using the "ggplot2" and "igraph" packages. 2.5 Construction of a prognostic model The "Caret" package in R was used to randomly divide the samples in TCGA-THCA into training and validation cohorts in a 7:3 ratio. The patient data from the training set was used to identify prognostic lncRNAs and establish a prognostic model, while the patient data from the test set was used to verify the accuracy of the model. There were no significant differences in clinical characteristics such as age, gender, or TNM staging between the two groups of patients (P > 0.05) (Table 1). Univariate analysis was performed on the training set to identify the prognostic lncRNAs (Supplementary Figure S1 ). Subsequently, Least Absolute Shrinkage and Selection Operator (LASSO) was used to construct a prognostic model of the training cohort. Based on the median risk score, patients in the TCGA-THCA cohort were divided into high and low-risk groups. Then, we evaluated the model's precision using the internal validation set. 2.6 Assessment of the independence and validity of the prognostic model To investigate the impact of clinical characteristics on patient prognosis, we performed univariate and multivariate regression analyses on clinical features such as patient risk scores, gender, age, and pathological stage. By calculating the probabilities of Overall Survival (OS) at 1, 3, and 5 years, we developed a nomogram that integrates risk scores, age, gender, pathological stage, and other clinical parameters as independent prognostic factors. Simultaneously, to assess the accuracy of the nomogram, calibration and ROC curves were established. To evaluate the prognostic significance of the clinical characteristics of the risk score, stratified analysis was utilized (age, gender, grading, clinical stage, and pathological T, N, M stages). In order to investigate the relationship between risk scores and various clinical characteristics, we conducted a comprehensive analysis using the "survival" software package. Specifically, we examined the association between age, gender, clinical stage, and T (tumor), N (node), and M (metastasis) stages with risk scores. Through Kaplan-Meier analysis, we aimed to identify patterns and differences in survival outcomes for these variables and then visualized the results to enhance interpretability and promote a clearer understanding of the potential prognostic significance of these clinical factors. 2.7 Mutation landscape and survival analysis The "maftools" package was used to generate the genetic mutation features of THCA patients downloaded from the TCGA database. Subsequently, we combined the genetic mutation features files with risk scores. Then, we used a waterfall plot to visualize the top 30 genes with the highest mutation frequency. To explore the impact of mutation frequency on the survival outcome of high- and low-risk THCA patients, we used the "survival" package to conduct a Kaplan-Meier analysis. 2.8 Differential gene expression analysis between two risk groups The "limma" package was used to explore the differentially expressed genes (DEGs) between high- and low-risk patients, aiming to investigate new therapeutic targets. Eventually, we visualized the DEGs and their expression levels between the two risk groups using the "ggplot2" and "pheatmap" packages. 2.9 KEGG and GO analysis In order to explore the potential biological pathways between two risk groups, we performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) Enrichment analysis. Based on the "org.Hs.eg.db" package online, we identified the Molecular Function (MF), Cellular Component (CC), and Biological Process between the two groups. 2.10 Gene set enrichment analysis and tumor immune microenvironment Gene Set Enrichment Analysis (GSEA) was utilized to perform GSEA. The "c2.cp.kegg.Hs.symbols.gmt" gene set from MsigDB was used to identify the potential pathways and molecular mechanisms between two risk groups. Subsequently, we performed "single-sample Gene Enrichment Analysis" on the high- and low-risk groups to identify the immune cells and immune functions in the tumor microenvironment based on the "GSVA" package and "immune.gmt" gene set (Supplementary Table S7-S8) from other articles. To further discover potential immune therapy targets, we conducted an immune checkpoint analysis on the high-risk and low-risk groups using known immune checkpoint genes (Supplementary Table S9) through the "limma" package. Next, the "estimate" package was used to assess the infiltration levels of immune cells, the extent of stromal cell infiltration, and the purity of the tumor in the two risk groups. We conducted a "Kaplan-Meier" analysis on the results from ESTIMATE and visualized the results. Finally, we utilized multiple immune infiltration algorithms and immune infiltration files from previous studies to evaluate the correlation between immune cells and risk scores. 2.12 Tumor immunotherapy response and drug sensitivity analysis Based on the risk file of the TCGA cohort, the online website TIDE ( http://tide.dfci.harvard.edu/ ) was used to predict the risk of tumor immune evasion for the high- and low-risk groups. Meanwhile, the "oncoPredict" package was utilized to detect the IC50 of chemotherapy agents in patients from different risk groups in order to evaluate their sensitivity to various chemotherapy drugs. 3 Results The flowchart of this study was illustrated in Fig. 1 3.1 Single-cell sequencing analysis. 7 Thyriod Cancer (THCA) samples were included in our study. We removed cells with mitochondrial genes and ribosomal genes exceeding 20%, and recalculated the correlation between these two types of genes and total RNA (Fig. 2 A-F). Then, we preserved cells with high-variable genes ranging from 200 to 7000. Subsequently, we performed dimensionality reduction on the data after quality control (Fig. 2 G). Harmony was used to eliminate batch effects between the 7 samples (Fig. 2 H). We set the resolution to 1 and divided all the cells into 29 subclusters (Fig. 2 I). Based on the marker genes, all cells were annotated into 8 cell types, including Thyroid follicular cells, T cells, Epithelial cells, Fibroblasts, Endothelial cells, Plasma cells, Macrophages, and B cells (Fig. 2 J-K). As shown in Fig. 2 L-M, we calculated the Cuproptosis gene(Table S2 ) score in different cell types. By setting the standard at a p-value 0.25, we identified 3584 High_Cuproptosis_Score genes(Table S3). Finally, 4789 Macrophage-related genes were identified with the criterion |log2FC| > 0.25 and min.pct > 0.25 (Fig. 2 N,Table S5). 3.2 Construction of the prognostic model associated with Macrophage and Curproptosis-related lncRNA. Wilcoxon analysis was performed to identify differentially expressed genes (DEGs). We identified 1255 DEGs by setting the standard |log2FC| > 1.5, p-value < 0.05 (Fig. 3 A-B,Table S4). Macrophage and cuproptosis-related genes were confirmed by intersecting DEGs, High_Cuproptosis_Score genes, and Macrophage DEGs (Fig. 3 C). Through co-expression analysis, 381 lncRNAs co-expressed with Macrophage and cuproptosis-related genes were obtained (Fig. 3 D-E). The TCGA-THCA cohort was divided into a training set and a validation set in a ratio of 7:3. Subsequently, we performed univariate Cox analysis on these lncRNAs in the TCGA training set and ultimately identified 11 lncRNAs associated with prognosis (Figure S1 ). To further filter the lncRNAs related to prognosis, Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis was performed. Finally, we identified 11 lncRNAs to construct a prognostic model (Fig. 3 F-G). The prognostic model can be represented by the formula below: Risk score = ∑(Coefi*Expi)= (-0.3970)* AC107214.1+ (6.1614)* AC135050.1 + (2.9477)* AC026355.3 + (1.9100)* FOXP1-AS1 + (-0.5844)* AC123768.1 + (2.5894)* MIR3945HG + (5.1806)* AC022819.1 + (1.5998)* AC009716.1 + (-7.7286)* AC120498.9 + (1.0665)* AL133444.1 + (1.6494)* AL132709.8 3.3 Validation of the Prognostic Risk Model. Based on the median risk score of the prognostic model, the TCGA training set, TCGA validation set, and the TCGA total set were divided into high- and low-risk groups. We performed Kaplan-Meier, ROC, and risk analyses on these groups. Through survival analysis of each set, we found that the high-risk group had a poorer prognosis than the low-risk group (Fig. 4 A-C). By conducting ROC curve analysis on each set, we were surprised to find that the AUC values for 1-, 3-, and 5-years in the TCGA training set were all close to 1, and the AUC values for 1-, 3-, and 5-years in both the TCGA validation and TCGA total sets were greater than 0.75, showing that our model has good accuracy (Fig. 4 D-F). Risk curve analysis was performed on the training set, validation set, and total sample set. The results showed that the number of patients who died increased significantly with the increase in risk scores. Additionally, the heatmap revealed that AC026355.3, AC022819.1, AL133444.1, MIR3945HG, AL132709.8, FOXP1-AS1, AC135050.1, and AC009716.1 are high-risk lncRNAs, while AC120498.9, AC107214.1, and AC123768.1 are low-risk lncRNAs (Fig. 4 G-O). Subsequently, the accuracy of the model's predictions was validated through Kaplan-Meier analysis of various clinical characteristic subgroups, including AJCC T/N/M, Age (≥ 65 years, < 65 years), Sex (Female, Male), and AJCC Stage. We found that low-risk patients had better survival outcomes across all subtypes of clinical characteristics (Fig. 5 A-F). 3.4 Construction of the Prognosis-related Nomogram. Based on the risk score and clinical characteristics of the TCGA total sample, we conducted univariate and multivariate analyses and found that age, Metastasis Stage (M), and risk score are independent risk factors for THCA patients (Fig. 6 A-B). Through the Nomogram, we found that risk scores, age, gender, clinical staging, T staging, N staging, and M staging can serve as independent factors affecting patient prognosis, and all these factors are risk factors for the prognosis of thyroid carcinoma patients. The survival rates of patients, as obtained from the aforementioned line charts for 1, 3, and 5 years, are all 0.99 (Fig. 6 C). Finally, through the calibration curve, it can be observed that the predicted values of this model are close to the actual values, indicating the model's accuracy (Fig. 6 D). We conducted ROC curve analysis on the Nomogram risk scores, model risk scores, and patient clinical characteristics and found that in all three groups of patients, the AUC values for both the Nomogram scores and age were over 0.9, suggesting that the Nomogram risk scores and patient age have good accuracy (Fig. 6 E-G). 3.5 Tumor Mutational Burden Analysis. Based on the risk score, we conducted a gene mutation analysis for the high and low-risk groups and found that the top 5 genes with higher mutation rates in both groups were BRAF, NRAS, TG, HRAS, and TTN (Fig. 7 A-B). There was no statistically significant difference in the mutation rates of these genes between the two groups, and there was no obvious correlation between gene mutations and high or low risk (p > 0.05, Figs. 7 C-D). Subsequently, we performed Kaplan-Meier analysis on the high and low mutation groups and found that the survival outcome of the high gene mutation group was poorer than that of the low mutation group (p < 0.001, Fig. 7 E). In the low-risk group, there was no significant difference in survival outcomes between patients with high and low mutation rates. In the high-risk group, a high mutation rate implied a poorer survival outcome (p < 0.001, Fig. 7 F). 3.6 Diferential analyis between two risk groups. Furthermore, differential analysis was performed on the two risk groups. We identified 7 down-regulated and 237 up-regulated genes in the high-risk group when we set the standard |log2FC| > 1 and p-value < 0.05 (Figure S2 A-B). Through GO and KEGG enrichment analysis of the high-risk and low-risk groups, we found that the 244 DEGs were mainly enriched in some potential biological pathways, including extracellular matrix organization, extracellular structure organization, external encapsulating structure organization, Cytoskeleton in muscle cells, Cytokine-cytokine receptor interaction, and the PI3K-Akt signaling pathway (p < 0.05, Figure S3A-F). 3.7 The landscape of tumor microenvironment We performed Gene Set Enrichment Analysis (GSEA) on the high- and low-risk groups to identify potential pathways. In both risk groups, GSEA was analyzed using several GSEA diagrams based on the following screening criteria: FDR > 0 and P < 0.05 (Fig. 8 A-B). The five most significant functions associated with the high-risk group include "KEGG_CHEMOKINE_SIGNALING_PATHWAY", "KEGG_CYTOKINE_CYTOKINE_RECEPTOR_INTERACTION", "KEGG_ECM_RECEPTOR_INTERACTION", "KEGG_FOCAL_ADHESION", and "KEGG_HEMATOPOIETIC_CELL_LINEAGE". In addition, the five most significant functions associated with the low-risk group include "KEGG_HUNTINGTONS_DISEASE","KEGG_OXIDATIVE_PHOSPHORYLATION", "KEGG_PARKINSONS_DISEASE", "KEGG_RIBOSOME", and "KEGG_TYPE_II_DIABETES_MELLITUS". Thereafter, we applied the ssGSEA method to explore the infiltration level of 24 kinds of immune cells and retrieved scores of 13 immune functions to assess the relationship between the risk model and immune infiltration. The high-risk group had increased Macrophages and enhanced all the immunological functions (p < 0.05, Fig. 8 C-D). Subsequently, we applied immune checkpoint analysis based on the 47 immune genes. We found that the high-risk group had a higher expression level in those genes than the low-risk group (p < 0.05, Fig. 8 E). The results of Tumor Microenvironment (TME) analysis showed that the high-risk group had higher stromal scores, immune scores, and estimation scores, which means that the TME of the high-risk group was more complex and possibly associated with invasiveness, resistance to treatment, and a poorer prognosis (P < 0.05, Fig. 8 F). We also found that higher TME scores were associated with worse survival outcomes (P < 0.001, Figs. 8 G-I). Based on the discovery of TME analysis, we further investigated the tumor immune microenvironment. Relying on seven types of software, we found that various immune cells had a positive effect on tumor development, among which Macrophage M0, Macrophage M1, and Macrophage M2 cells were associated with higher risk scores (p < 0.05, Figs. 8 J-M). 3.8 The landscape of tumor microenvironment and drug sensitive To further explore the differences in the immune microenvironment between high- and low-risk groups, we analyzed a variety of common immune markers. Figures 9 A-B show that the high-risk group has high TIDE and CAF scores (p < 0.001), suggesting that the high-risk group may exhibit enhanced immune escape mechanisms and reduced efficacy of immunotherapy. However, there were no significance difference in CD8 and CD274 scores between the high- and low-risk groups (Fig. 9 C-D, p > 0.05). Concurrently, the high-risk group exhibited higher Dysfunction, Exclusion IFNG, Merck18, and TAM.M2 scores (Figs. 9 E-G, 9 I, 9 K, p 0.05), suggesting potential immune evasion and reduced effectiveness of immunotherapy. IC50 is an indicator that illustrates the tolerance of tumor cells to drugs. We conducted a differential sensitivity analysis of 198 drugs between high and low-risk groups. The results showed that Low-risk group THCA patients are highly sensitive to targeted drugs such as Afuresertib, Elephantin, Leflunomide, Linsitinib, Nilotinib, and Sabutoclax; whereas High-risk group THCA patients are highly sensitive to targeted drugs such as Cediranib, Dasatinib, Entospletinib, Olaparib, Osimertinib, and Saptinib (Fig. 10 A-L). Discussion Thyroid cancer (THCA) is a common malignancy typically associated with a favorable prognosis; however, a subset of patients experience aggressive disease with poor clinical outcomes [ 21 ]. Recent advancements in single-cell sequencing have revolutionized our understanding of tumor heterogeneity and the dynamics of the tumor microenvironment (TME) in various cancers, including THCA [ 22 , 23 ]. In this study, we employed comprehensive single-cell sequencing to elucidate the cell composition of THCA, with a particular focus on macrophage and cuproptosis-associated long non-coding RNAs (lncRNAs), and developed a novel prognostic model to predict patient outcomes. Single-cell sequencing analysis identified eight major cell types within THCA samples, including thyroid follicular cells, T cells, epithelial cells, fibroblasts, endothelial cells, plasma cells, macrophages, and B cells. Macrophages, in particular, have been widely implicated in cancer biology due to their dual roles in tumor progression and inhibition, depending on their phenotypic polarization (M1 vs. M2 macrophages) [ 24 , 25 ]. The interactions between macrophages and tumor cells within the TME are crucial for tumor growth, metastasis, and response to therapy [ 26 , 27 ]. Our analysis not only confirmed the presence of macrophages but also highlighted a significant correlation between macrophage gene expression profiles and patient prognosis, identifying 4,789 macrophage-related genes. Cuproptosis, a recently identified form of cell death related to copper-induced cytotoxicity, is emerging as a critical factor in cancer biology [ 28 ]. In this study, we calculated the cuproptosis score across the identified cell types and found it to be intricately associated with certain lncRNAs. Through rigorous statistical analyses, including Wilcoxon and LASSO regression, we identified 11 lncRNAs co-expressed with macrophage and cuproptosis-related genes, leading to the construction of a robust prognostic risk model. This model accurately stratified patients into high- and low-risk groups, as demonstrated by Kaplan-Meier survival analysis and ROC curve evaluations, with AUC values near 1 in the training set and over 0.75 in validation cohorts—indicators of excellent prognostic discriminatory power [ 29 , 30 ].The Kaplan-Meier survival analysis results of the prognostic model in different risk groups showed that in all subgroups (age, gender, stage staging, T staging, N staging, M staging), patients in the high-risk group had shorter overall survival (OS) compared to those in the low-risk group.To further illustrate the clinical utility of our risk model, we developed a prognostic nomogram, incorporating clinical features such as age, sex, and anatomical staging alongside the risk score. Univariate and multivariate analyses reinforced risk scores, age, and metastasis stage as independent predictors of patient outcomes, underscoring their significance in personalized patient management. The calibration curve of the nomogram closely mirrored actual patient survival, supporting the model's accuracy [ 31 , 32 ]. Tumor mutational burden (TMB) analysis did not reveal significant mutations correlated with risk stratification, although genes such as BRAF and NRAS, well-documented in thyroid cancer pathogenesis, were identified [ 33 , 34 ]. These results suggest that while TMB may not directly impact risk classification per se, it drives tumor aggressiveness, as evidenced by poorer survival in patients with high TMB.Differential expression analyses revealed upregulation of 237 genes in high-risk groups, implicating pathways related to extracellular matrix organization and cytokine-cytokine receptor interactions, involved in tumor invasiveness and aggression. These findings resonate with existing literature underscoring the roles of these pathways in numerous cancers [ 35 – 37 ]. Gene Set Enrichment Analysis (GSEA) validated these observations, pinpointing significant pathways—such as chemokine signaling and ECM-receptor interaction—that reflect more pronounced oncogenic signaling in high-risk groups [ 38 , 39 ].The TME landscape further emphasized that high-risk patients had elevated stromal and immune scores, suggesting a more complex and potentially treatment-resistant tumor milieu. Increased expression of immune checkpoint-related genes and enhanced macrophage infiltration in these patients support hypotheses of immune evasion and necessitate novel therapeutic strategies, such as macrophage-targeted therapies or immune checkpoint inhibitors, as potential interventions [ 40 – 42 ].The landscape of the tumor microenvironment in THCA, as revealed by GSEA and ssGSEA, highlights the enrichment of immune-related pathways and the infiltration level of immune cells in high-risk groups. The association between high-risk scores and increased immune cell infiltration, particularly macrophages, suggests a role for these cells in THCA progression and immune evasion. Macrophages in the TME were not only abundant in high-risk patients but also displayed enhanced immunological functions, as shown by our single-sample gene set enrichment analysis (ssGSEA). This is in line with existing literature suggesting that tumor-associated macrophages (TAMs) are critical for tumor immune evasion and progression. Recent studies have shown that targeting TAMs or modulating their phenotype could significantly impact tumor progression and patient outcomes [ 43 , 44 ]. The differential sensitivity of THCA to various therapeutics, as revealed by IC50 analyses, offers valuable insights for personalized medicine. High-risk groups showed sensitivity to drugs like Afuresertib and Nilotinib, aligning with markers of therapeutic susceptibility observed in other cancers [ 45 , 46 ]. Conversely, low-risk groups exhibited responsiveness to agents such as Osimertinib and Olaparib, known to have efficacy against genomic-driven cancers [ 47 , 48 ]. The delineation of chemotherapy sensitivity profiles could greatly enhance treatment strategies, tailoring regimens based on individual genetic and molecular landscapes. Despite these promising findings, our study has limitations. The sample size is limited, necessitating further validation in larger, more diverse cohorts. Additionally, functional studies are required to elucidate the precise roles of identified lncRNAs in THCA pathogenesis. Experimental validation of therapeutic targets suggested by our analyses is crucial for transitioning from theoretical models to clinical applications. Conclusion In conclusion, our study provides a comprehensive analysis of THCA at the single-cell level, revealing novel insights into cellular heterogeneity, prognostic markers, and therapeutic targets. The integration of these findings with clinical data offers a robust foundation for the development of personalized treatment strategies and improved patient outcomes. Abbreviations THCA Thyroid Carcinoma TCGA The Cancer Genome Atlas Program GEO Gene Expression Omnibus scRNA Single cell sequencing LASSO Least Absolute Shrinkage and Selection Operator C-index Concordance index OS Overall survival GSEA Gene set enrichment analysis ssGSEA Single-sample Gene set enrichment analysis KEGG Kyoto Encyclopedia of Genes and Genomes KM Kaplan Meier ROC Receiver Operating Characteristic LncRNA Long Non-coding RNA DEGs Differential Expressed Genes Declarations Ethics approval and consent to participate Not available. Consent for publication All authors agree to publish Data availability Data are available from the corresponding author upon reasonable request. Competing Interests The authors declare that they have no competing interests for this work. Funding No funding for this study Authors' contributions JW and FY designed and conducted this study, performed the data processing and wrote the initial manuscript. YX downloaded the raw data online and preprocessed it. GH performed data supervision and writing-review. All of the authors have read and approved the final manuscript. Acknowledgements We are grateful to the authors for their contributions to this study References Moleti M, Aversa T, Crisafulli S, Trifirò G, Corica D, Pepe G, Cannavò L, Di Mauro M, Paola G, Fontana A, Calapai F, Cannavò S, Wasniewska M. Global incidence and prevalence of differentiated thyroid cancer in childhood: systematic review and meta-analysis. Front Endocrinol (Lausanne). 2023 Sep 19;14:1270518. doi: 10.3389/fendo.2023.1270518. PMID: 37795368; PMCID: PMC10546309. Rybakov, S. (2023). Medullary thyroid cancer: epidemiology. INTERNATIONAL JOURNAL OF ENDOCRINOLOGY (Ukraine) , 19 (4), 306–311. https://doi.org/10.22141/2224-0721.19.4.2023.1291 Yu J. 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Supplementary Files SupplementaryTable.xlsx Supplementary Information Supplementary Table: Table S1: The marker genes of annotation, Table S2:Cuproptosis genes, Table S3:High_Cuproptosis_Score genes, Table S4:Differential expressed genes, Table S5:Differential expressed genes of Macrophage, Table S6: Macrophage and Cuproptosis-related lncRNA, Table S7-S8: The immune.gmt dataset, Table S9:Immune checkpoint gene SupplementaryFigures.docx Supplementary Figure: Figure S1:The result of Unicox regression analysis,Figure S2: The result of Diferential Analysis. Figure S3: GO and KEGG enrichment analysis Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6528117","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":456885399,"identity":"4243809c-ea7b-44c0-adaf-0824262a72f5","order_by":0,"name":"Jiahao Wu","email":"","orcid":"","institution":"Guangzhou Twelfth People’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jiahao","middleName":"","lastName":"Wu","suffix":""},{"id":456885401,"identity":"456950d4-cd91-4ab5-afee-ffc9b64d97d7","order_by":1,"name":"Fan Yang","email":"","orcid":"","institution":"Hui Ya Hospital of The First Affiliated Hospital, Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Fan","middleName":"","lastName":"Yang","suffix":""},{"id":456885404,"identity":"7d695eb0-bf20-47c9-ae39-624958828205","order_by":2,"name":"Yuting Xu","email":"","orcid":"","institution":"The Fifth Affiliated Hospital of Guangzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yuting","middleName":"","lastName":"Xu","suffix":""},{"id":456885405,"identity":"da558071-43a5-46df-a750-fa91ba2a2a0a","order_by":3,"name":"Guanqun Huang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA20lEQVRIiWNgGAWjYBACAwYGxgcfDGx4GNsbGx9+IFILs+GMgjQZ5p7DzcYSRGphE+b5cNiGfUZ6mwAPMVrM+RdvY+YxYObhnfmwjUGCwU5Ot4GAFssZz8oezjFg45Gcndj2oIAh2djsACGH3ThjbvDGgIfHcHZiu4EEw4HEbURoMZPgMZDgsb95sE2Chygt53vMJHkMDHgYZzASq+UGW7HhDIMEHsaeRGAgGxDjl/OHNz748Oe/PWP78YcPP1TYyRHUwiCRYIBsAiHlIMB/gChlo2AUjIJRMJIBACeuRZV2nLZ4AAAAAElFTkSuQmCC","orcid":"","institution":"Guangzhou Twelfth People’s Hospital","correspondingAuthor":true,"prefix":"","firstName":"Guanqun","middleName":"","lastName":"Huang","suffix":""}],"badges":[],"createdAt":"2025-04-25 10:38:38","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6528117/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6528117/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83040463,"identity":"3c7527c2-5731-40dc-aa98-e27e901793ef","added_by":"auto","created_at":"2025-05-19 10:39:16","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":49363,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe flowchart of this study.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6528117/v1/84e0ad1a9dcdceb59e90a8d2.png"},{"id":83040469,"identity":"3c4ee43f-ab0b-4f73-9d59-fc9b71b12a5d","added_by":"auto","created_at":"2025-05-19 10:39:16","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":682661,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003esingle-cell RNA sequencing. \u003c/strong\u003e(A-C) single-cell sequencing data quality control. (D-F) The percentage of nCount RNA, mitochondria RNA and ribosome RNA after quality control.(G) Dimensionality reduction of scRNA data.(H) Dimensionality of the batch effects by Harmony.(I) The result of cell clustering.(J-K) The result of cell annotation. (L-M) The result of Cuproptosis score between different celltypes. (N) The differential expressed genes(DEGs) between celltypes.\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6528117/v1/abe92da288762ad989c8b6b2.png"},{"id":83041497,"identity":"d34601db-7d49-442f-b753-6ed1c927fb64","added_by":"auto","created_at":"2025-05-19 10:47:16","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":575054,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConstructing a prognostic model. \u003c/strong\u003e(A) Show the volcano plot of DEGs. (B) Show the heatmap of the top 50 DEGs. (C) Intersecting the DEGs, High_Cuproptosis_Score gene and Macrophage genes. (D-E) The co-expression network of Macrophage and cuproptosis-related genes with lncRNAs. (F-G) Lasso regression was performed to construct a prognostic model.\u003c/p\u003e","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6528117/v1/6799a9acb263032d30af017e.png"},{"id":83040465,"identity":"f513b6d8-f2f5-4e61-ab7a-f568a48ec6a3","added_by":"auto","created_at":"2025-05-19 10:39:16","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":220367,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eValidation of the Risk Model.\u003c/strong\u003e(A-C) Kaplan-Meier Curve of the TCGA set. (D-F) ROC Curve of the TCGA set. (G-I) Risk curve of the TCGA set. (J-L) Survival Status Curve of the TCGA set. (M-O) Risk Score Heatmap of the TCGA set.\u003c/p\u003e","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6528117/v1/2d36c7628c75bf8b936326db.png"},{"id":83040475,"identity":"44d8e859-3961-4e60-9ade-c44e331461ed","added_by":"auto","created_at":"2025-05-19 10:39:16","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":123839,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eK-M curve of diferent clinical characteristic in TCGA-THCA.\u003c/strong\u003e(A) Kaplan-Meier Curve of T(1-2) and T(3-4). (B) Kaplan-Meier Curve of N0 and N1. (C) Kaplan-Meier Curve of M0 and M1. (D) Kaplan-Meier Curve of Age(\u0026gt;=65) and Age(\u0026lt;65). (E) Kaplan-Meier Curve of Male and Female. (F) Kaplan-Meier Curve of Stage(I-II) and Stage(III-IV).\u003c/p\u003e","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-6528117/v1/7fe6005d1057aee6d98891be.png"},{"id":83040491,"identity":"fac8aaad-7d9e-4532-a536-a7447b98a247","added_by":"auto","created_at":"2025-05-19 10:39:16","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":135353,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConstructing a Nomogram.\u003c/strong\u003e(A-B) Univariate and multivariate analysis based on the clinical characterstic. (C) The Nomogram. (D) Calibration curve of the Nomogram. (E-G) The ROC curve of 3 groups based on the clinical characteristic.\u003c/p\u003e","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-6528117/v1/7d370ed1bd50da78fc23bf66.png"},{"id":83041501,"identity":"849e6a75-acfc-44ec-b705-77e82e4d00a5","added_by":"auto","created_at":"2025-05-19 10:47:16","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":231406,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTumor Burden Mutational Analysis. \u003c/strong\u003e(A) The mutant landscape of the low-risk group. (B) The mutant landscape of the high-risk group. (C) A violin plot showing the difference in mutant rate between high- and low-risk group. (D) A corrlation map show the relationship between riskScore and TMB. (E) Kaplan-Meier curve shows the diference OS between the high- and low-TMB groups. (E) Kaplan-Meier curve shows the diference OS after combining the TMB with risk score.\u003c/p\u003e","description":"","filename":"Onlinefloatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-6528117/v1/96c7a97c6064da1e8739ce91.png"},{"id":83040509,"identity":"ef1c2ee8-d1e7-44dc-a702-d786932b9b41","added_by":"auto","created_at":"2025-05-19 10:39:17","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":335263,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe landscape of tumor. \u003c/strong\u003e(A) (B) The GSEA result of high- and low-risk groups. (C) The result of ssGSEA. (D) The result of ssGSEA immune function. (E) Immune checkpoint analysis. (F) The result of Tumor Microenvironment. (G)-(I) The result of Kaplan-Meier analysis based on the result of TME. (J) Immune infiltration analysis based on multiple algorithms. (K)-(M) Correlation between Macrophages and risk score.\u003c/p\u003e","description":"","filename":"Onlinefloatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-6528117/v1/f58b453029103741d6029f70.png"},{"id":83040519,"identity":"11ad036a-c7c8-46d2-99cd-c1b135911d79","added_by":"auto","created_at":"2025-05-19 10:39:17","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":189768,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTumor Burden Mutational Analysis. \u003c/strong\u003e(A) TIDE (B) CAF (C) CD8 (D) CD274 (E) Dysfunction (F) Exclusion (G) IFNG (H) MDSC (I)Merck18 (J) MSI (K)TAM.M2\u003c/p\u003e","description":"","filename":"Onlinefloatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-6528117/v1/7e7b402559cd3e4a2a681204.png"},{"id":83040467,"identity":"2986e03a-e08b-4ec7-94f3-744cc316251a","added_by":"auto","created_at":"2025-05-19 10:39:16","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":97785,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe chemotherapeutic response of two risk groups. \u003c/strong\u003e（A-L）Showed the immunotherapy sensitive drugs between high- and low-risk groups.\u003c/p\u003e","description":"","filename":"Onlinefloatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-6528117/v1/7847abe1d3210524b849dc21.png"},{"id":86701260,"identity":"3119fd4c-1941-4c16-b4ae-049b06e9c71d","added_by":"auto","created_at":"2025-07-14 16:20:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4674459,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6528117/v1/e480b71a-5aee-494c-96ce-2babb97d8ab0.pdf"},{"id":83042437,"identity":"c1a4d532-8248-4461-9587-dcb7ddc48b0a","added_by":"auto","created_at":"2025-05-19 10:55:16","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":162650,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSupplementary Table: Table S1: The marker genes of annotation, Table S2:Cuproptosis genes, Table S3:High_Cuproptosis_Score genes, Table S4:Differential expressed genes, Table S5:Differential expressed genes of Macrophage, Table S6: Macrophage and Cuproptosis-related lncRNA, Table S7-S8: The immune.gmt dataset, Table S9:Immune checkpoint gene\u003c/p\u003e","description":"","filename":"SupplementaryTable.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6528117/v1/f54a95927249cbca6c96708b.xlsx"},{"id":83040473,"identity":"4eeba06a-41df-490e-84ff-d66caaab8a2e","added_by":"auto","created_at":"2025-05-19 10:39:16","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1359722,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Figure: Figure S1:The result of Unicox regression analysis,Figure S2: The result of Diferential Analysis. Figure S3: GO and KEGG enrichment analysis\u003c/p\u003e","description":"","filename":"SupplementaryFigures.docx","url":"https://assets-eu.researchsquare.com/files/rs-6528117/v1/b7bb10d2a1c76d8e7c9a15d9.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Novel a new prognostic model of Thyroid Carcinoma based on Macrophage and Cuproptosis-Related lncRNA","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eThyroid cancer is a significant global health issue, with an increasing number of diagnoses worldwide, particularly among women. The cancer is divided into several types, with differentiated thyroid cancer (DTC), including papillary and follicular types, making up the majority of cases. Papillary thyroid carcinoma (PTC) is the most common subtype, known for its positive prognosis and extremely high survival rate, especially in younger patients.[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] The global incidence of thyroid cancer has increased significantly, with estimates reaching up to 821,214 new cases in 2022.[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] Papillary thyroid carcinoma (PTC) remains the most commonly diagnosed type, comprising approximately 90% of cases. Recent studies suggest that the incidence rate for adults ranges from 4.5 to 14.4 per 100,000 individuals, with notable geographic variations.[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] Different types of thyroid cancer have varying prognoses; therefore, it is necessary to develop a new prognostic model.\u003c/p\u003e \u003cp\u003eMacrophages are key components of the immune system, originating from monocytes and demonstrating versatile functions that are crucial in various biological processes, including tumor development. In thyroid cancer, tumor-associated macrophages (TAMs) significantly influence tumor behavior and prognosis. Although most thyroid cancers are well-differentiated and have favorable outcomes, the presence of TAMs has been associated with poorer prognoses, particularly in anaplastic and medullary types.[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] TAMs, particularly of the M2 phenotype, promote tumor growth and metastasis through the secretion of immunosuppressive factors and support angiogenesis.[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] Recent findings indicate that these macrophages contribute to creating a tumor microenvironment conducive to cancer cell survival, thereby impeding immunotherapeutic responses.[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] Notably, targeting macrophage polarization from M2 to M1 has emerged as a potential therapeutic strategy to enhance anti-tumor immunity.[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] Thus, further investigation into the mechanisms by which TAMs affect thyroid cancer is essential for developing effective prognostic models and targeted therapies that can improve patient outcomes.[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eCuproptosis is a new concept in cancer biology, describing a novel form of regulated cell death induced by excess copper. Long non-coding RNAs (lncRNAs) associated with cuproptosis have been identified as key players in various types of cancer, including thyroid cancer. Recent studies suggest that these lncRNAs can influence tumor growth, metastasis, and response to therapy.[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] In thyroid cancer, specific cuproptosis-related lncRNAs have demonstrated prognostic potential. For instance, a study identified a signature of lncRNAs that could predict patient outcomes, correlating high expression levels with poorer survival rates.[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] Additionally, the role of TAMs (tumor-associated macrophages) in the tumor microenvironment is impacted by these lncRNAs, which can modulate macrophage polarization and enhance tumor-promoting activities.[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] Thus, cuproptosis lncRNAs hold promise as both prognostic markers and therapeutic targets in managing thyroid cancer, highlighting their relevance in understanding tumor pathogenesis and improving clinical strategies.[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eIn order to investigate the effect of copper death genes on macrophages and thus understand their impact on the immune microenvironment, we designed this study. This study explores the impact of cuproptosis-associated lncRNAs on macrophages, the immune microenvironment of thyroid cancer, and predicts the prognosis and potential therapeutic targets for thyroid cancer patients based on a novel prognostic model.\u003c/p\u003e"},{"header":"2 Matherials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Single-cell data downloading and processing\u003c/h2\u003e \u003cp\u003eThe single-cell RNA sequencing dataset GSE191288 was downloaded from the Gene Expression Omnibus database (GEO, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/gds/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/gds/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e The dataset comprised 7 samples, including 6 tumor and 1 normal sample. We then used the \"Seurat\" package to analyze the single-cell sequencing data. Quality control was first performed on the data by retaining cells with less than 20% mitochondrial genes and 20% ribosomal genes, genes expressed in at least 3 cells, and the expression range between 200 to 10000. Subsequently, we set the number of highly variable genes at 2000 to identify these genes. \"PCA\" and \"t-SNE\" methods were used to reduce the dimensionality of the scRNA data. The \"Harmony\" package was used to eliminate the batch effect between the 7 samples. We then used the \"FindNeighbors\" and \"FindClusters\" functions to construct the cell clusters, which we subsequently visualized using the \"t-SNE\" method. Lastly, we used marker genes from previous studies to annotate the cell clusters (Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The \"ssGSEA\" method was used to calculate the activity of gene sets in each cell. The Wilcoxon test (p.adj\u0026thinsp;\u0026lt;\u0026thinsp;0.05) was used to calculate the statistical significance of differentially expressed genes (DEGs), with all other parameters set to default values. We then used the \"AUCell\" package to calculate the \"AUCell Score\" in each cell to distinguish the cells into High and Low cuproptosis groups. Finally, the \"DESeq2\" package was used to calculate the statistical significance of differentially expressed genes (DEGs) between 8 cell types, with all parameters set to default values.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 TCGA data collection and processing\u003c/h2\u003e \u003cp\u003eThe Cancer Genome Atlas (TCGA, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.gdc.cancer.gov/\u003c/span\u003e\u003cspan address=\"https://portal.gdc.cancer.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e includes genomic sequencing data, proteomic data, methylation data, mRNA data, and miRNA data for 33 types of cancer. In this study, we downloaded transcriptome profiling (RNA-seq), single nucleotide data, and corresponding clinical information for TCGA-THCA, including 505 papillary thyroid cancer samples and 59 normal thyroid tissues. Patients with missing clinical data or 0 survival days were excluded. All data in this study were log2 transformed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Identification of Tumor-related genes\u003c/h2\u003e \u003cp\u003eThe \"Limma\" package was used to identify Differentially Expressed Genes (DEGs) between tumor and normal thyroid tissue. We set the threshold at |logFC| \u0026gt; 1 and p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 to identify the DEGs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Identification of Macrophage-Related Cuproptosis lncRNAs\u003c/h2\u003e \u003cp\u003eWe identified 100 Differentially Expressed Genes (DEGs) associated with Macrophage-Related Cuproptosis after intersecting the High_Cuproptosis_genes, TCGA-DEGs, and Macrophage genes. Subsequently, using the \"limma\" package, we performed co-expression analysis of Macrophage Cuproptosis-Related genes and lncRNAs, employing a standard of a \"correlation coefficient\" \u0026ge; 0.3 and p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Finally, we constructed a \"sankey plot\" and \"co-expression network\" to show the relationship between those genes and lncRNAs using the \"ggplot2\" and \"igraph\" packages.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Construction of a prognostic model\u003c/h2\u003e \u003cp\u003eThe \"Caret\" package in R was used to randomly divide the samples in TCGA-THCA into training and validation cohorts in a 7:3 ratio. The patient data from the training set was used to identify prognostic lncRNAs and establish a prognostic model, while the patient data from the test set was used to verify the accuracy of the model. There were no significant differences in clinical characteristics such as age, gender, or TNM staging between the two groups of patients (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Table\u0026nbsp;1). Univariate analysis was performed on the training set to identify the prognostic lncRNAs (Supplementary Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Subsequently, Least Absolute Shrinkage and Selection Operator (LASSO) was used to construct a prognostic model of the training cohort. Based on the median risk score, patients in the TCGA-THCA cohort were divided into high and low-risk groups. Then, we evaluated the model's precision using the internal validation set.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Assessment of the independence and validity of the prognostic model\u003c/h2\u003e \u003cp\u003eTo investigate the impact of clinical characteristics on patient prognosis, we performed univariate and multivariate regression analyses on clinical features such as patient risk scores, gender, age, and pathological stage. By calculating the probabilities of Overall Survival (OS) at 1, 3, and 5 years, we developed a nomogram that integrates risk scores, age, gender, pathological stage, and other clinical parameters as independent prognostic factors. Simultaneously, to assess the accuracy of the nomogram, calibration and ROC curves were established. To evaluate the prognostic significance of the clinical characteristics of the risk score, stratified analysis was utilized (age, gender, grading, clinical stage, and pathological T, N, M stages). In order to investigate the relationship between risk scores and various clinical characteristics, we conducted a comprehensive analysis using the \"survival\" software package. Specifically, we examined the association between age, gender, clinical stage, and T (tumor), N (node), and M (metastasis) stages with risk scores. Through Kaplan-Meier analysis, we aimed to identify patterns and differences in survival outcomes for these variables and then visualized the results to enhance interpretability and promote a clearer understanding of the potential prognostic significance of these clinical factors.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Mutation landscape and survival analysis\u003c/h2\u003e \u003cp\u003eThe \"maftools\" package was used to generate the genetic mutation features of THCA patients downloaded from the TCGA database. Subsequently, we combined the genetic mutation features files with risk scores. Then, we used a waterfall plot to visualize the top 30 genes with the highest mutation frequency. To explore the impact of mutation frequency on the survival outcome of high- and low-risk THCA patients, we used the \"survival\" package to conduct a Kaplan-Meier analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Differential gene expression analysis between two risk groups\u003c/h2\u003e \u003cp\u003eThe \"limma\" package was used to explore the differentially expressed genes (DEGs) between high- and low-risk patients, aiming to investigate new therapeutic targets. Eventually, we visualized the DEGs and their expression levels between the two risk groups using the \"ggplot2\" and \"pheatmap\" packages.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9 KEGG and GO analysis\u003c/h2\u003e \u003cp\u003eIn order to explore the potential biological pathways between two risk groups, we performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) Enrichment analysis. Based on the \"org.Hs.eg.db\" package online, we identified the Molecular Function (MF), Cellular Component (CC), and Biological Process between the two groups.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.10 Gene set enrichment analysis and tumor immune microenvironment\u003c/h2\u003e \u003cp\u003eGene Set Enrichment Analysis (GSEA) was utilized to perform GSEA. The \"c2.cp.kegg.Hs.symbols.gmt\" gene set from MsigDB was used to identify the potential pathways and molecular mechanisms between two risk groups. Subsequently, we performed \"single-sample Gene Enrichment Analysis\" on the high- and low-risk groups to identify the immune cells and immune functions in the tumor microenvironment based on the \"GSVA\" package and \"immune.gmt\" gene set (Supplementary Table S7-S8) from other articles. To further discover potential immune therapy targets, we conducted an immune checkpoint analysis on the high-risk and low-risk groups using known immune checkpoint genes (Supplementary Table S9) through the \"limma\" package. Next, the \"estimate\" package was used to assess the infiltration levels of immune cells, the extent of stromal cell infiltration, and the purity of the tumor in the two risk groups. We conducted a \"Kaplan-Meier\" analysis on the results from ESTIMATE and visualized the results. Finally, we utilized multiple immune infiltration algorithms and immune infiltration files from previous studies to evaluate the correlation between immune cells and risk scores.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.12 Tumor immunotherapy response and drug sensitivity analysis\u003c/h2\u003e \u003cp\u003eBased on the risk file of the TCGA cohort, the online website TIDE (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://tide.dfci.harvard.edu/\u003c/span\u003e\u003cspan address=\"http://tide.dfci.harvard.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e was used to predict the risk of tumor immune evasion for the high- and low-risk groups. Meanwhile, the \"oncoPredict\" package was utilized to detect the IC50 of chemotherapy agents in patients from different risk groups in order to evaluate their sensitivity to various chemotherapy drugs.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cp\u003eThe flowchart of this study was illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Single-cell sequencing analysis.\u003c/h2\u003e \u003cp\u003e7 Thyriod Cancer (THCA) samples were included in our study. We removed cells with mitochondrial genes and ribosomal genes exceeding 20%, and recalculated the correlation between these two types of genes and total RNA (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-F). Then, we preserved cells with high-variable genes ranging from 200 to 7000. Subsequently, we performed dimensionality reduction on the data after quality control (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG). Harmony was used to eliminate batch effects between the 7 samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eH). We set the resolution to 1 and divided all the cells into 29 subclusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eI). Based on the marker genes, all cells were annotated into 8 cell types, including Thyroid follicular cells, T cells, Epithelial cells, Fibroblasts, Endothelial cells, Plasma cells, Macrophages, and B cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eJ-K). As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eL-M, we calculated the Cuproptosis gene(Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e) score in different cell types. By setting the standard at a p-value \u0026lt; 0.05 and min.pct \u0026gt; 0.25, we identified 3584 High_Cuproptosis_Score genes(Table S3). Finally, 4789 Macrophage-related genes were identified with the criterion |log2FC| \u0026gt; 0.25 and min.pct \u0026gt; 0.25 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eN,Table S5).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Construction of the prognostic model associated with Macrophage and Curproptosis-related lncRNA.\u003c/h2\u003e \u003cp\u003eWilcoxon analysis was performed to identify differentially expressed genes (DEGs). We identified 1255 DEGs by setting the standard |log2FC| \u0026gt; 1.5, p-value \u0026lt; 0.05 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA-B,Table S4). Macrophage and cuproptosis-related genes were confirmed by intersecting DEGs, High_Cuproptosis_Score genes, and Macrophage DEGs (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). Through co-expression analysis, 381 lncRNAs co-expressed with Macrophage and cuproptosis-related genes were obtained (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD-E). The TCGA-THCA cohort was divided into a training set and a validation set in a ratio of 7:3. Subsequently, we performed univariate Cox analysis on these lncRNAs in the TCGA training set and ultimately identified 11 lncRNAs associated with prognosis (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). To further filter the lncRNAs related to prognosis, Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis was performed. Finally, we identified 11 lncRNAs to construct a prognostic model (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF-G). The prognostic model can be represented by the formula below:\u003c/p\u003e \u003cp\u003eRisk score = ∑(Coefi*Expi)= (-0.3970)* AC107214.1+ (6.1614)* AC135050.1 + (2.9477)* AC026355.3 + (1.9100)* FOXP1-AS1 + (-0.5844)* AC123768.1 + (2.5894)* MIR3945HG + (5.1806)* AC022819.1 + (1.5998)* AC009716.1 + (-7.7286)* AC120498.9 + (1.0665)* AL133444.1 + (1.6494)* AL132709.8\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Validation of the Prognostic Risk Model.\u003c/h2\u003e \u003cp\u003eBased on the median risk score of the prognostic model, the TCGA training set, TCGA validation set, and the TCGA total set were divided into high- and low-risk groups. We performed Kaplan-Meier, ROC, and risk analyses on these groups. Through survival analysis of each set, we found that the high-risk group had a poorer prognosis than the low-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA-C). By conducting ROC curve analysis on each set, we were surprised to find that the AUC values for 1-, 3-, and 5-years in the TCGA training set were all close to 1, and the AUC values for 1-, 3-, and 5-years in both the TCGA validation and TCGA total sets were greater than 0.75, showing that our model has good accuracy (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD-F). Risk curve analysis was performed on the training set, validation set, and total sample set. The results showed that the number of patients who died increased significantly with the increase in risk scores. Additionally, the heatmap revealed that AC026355.3, AC022819.1, AL133444.1, MIR3945HG, AL132709.8, FOXP1-AS1, AC135050.1, and AC009716.1 are high-risk lncRNAs, while AC120498.9, AC107214.1, and AC123768.1 are low-risk lncRNAs (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eG-O).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSubsequently, the accuracy of the model's predictions was validated through Kaplan-Meier analysis of various clinical characteristic subgroups, including AJCC T/N/M, Age (≥ 65 years, \u0026lt; 65 years), Sex (Female, Male), and AJCC Stage. We found that low-risk patients had better survival outcomes across all subtypes of clinical characteristics (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA-F).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Construction of the Prognosis-related Nomogram.\u003c/h2\u003e \u003cp\u003eBased on the risk score and clinical characteristics of the TCGA total sample, we conducted univariate and multivariate analyses and found that age, Metastasis Stage (M), and risk score are independent risk factors for THCA patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA-B). Through the Nomogram, we found that risk scores, age, gender, clinical staging, T staging, N staging, and M staging can serve as independent factors affecting patient prognosis, and all these factors are risk factors for the prognosis of thyroid carcinoma patients. The survival rates of patients, as obtained from the aforementioned line charts for 1, 3, and 5 years, are all 0.99 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). Finally, through the calibration curve, it can be observed that the predicted values of this model are close to the actual values, indicating the model's accuracy (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). We conducted ROC curve analysis on the Nomogram risk scores, model risk scores, and patient clinical characteristics and found that in all three groups of patients, the AUC values for both the Nomogram scores and age were over 0.9, suggesting that the Nomogram risk scores and patient age have good accuracy (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE-G).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Tumor Mutational Burden Analysis.\u003c/h2\u003e \u003cp\u003eBased on the risk score, we conducted a gene mutation analysis for the high and low-risk groups and found that the top 5 genes with higher mutation rates in both groups were BRAF, NRAS, TG, HRAS, and TTN (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA-B). There was no statistically significant difference in the mutation rates of these genes between the two groups, and there was no obvious correlation between gene mutations and high or low risk (p \u0026gt; 0.05, Figs.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC-D). Subsequently, we performed Kaplan-Meier analysis on the high and low mutation groups and found that the survival outcome of the high gene mutation group was poorer than that of the low mutation group (p \u0026lt; 0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eE). In the low-risk group, there was no significant difference in survival outcomes between patients with high and low mutation rates. In the high-risk group, a high mutation rate implied a poorer survival outcome (p \u0026lt; 0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eF).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Diferential analyis between two risk groups.\u003c/h2\u003e \u003cp\u003eFurthermore, differential analysis was performed on the two risk groups. We identified 7 down-regulated and 237 up-regulated genes in the high-risk group when we set the standard |log2FC| \u0026gt; 1 and p-value \u0026lt; 0.05 (Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eA-B). Through GO and KEGG enrichment analysis of the high-risk and low-risk groups, we found that the 244 DEGs were mainly enriched in some potential biological pathways, including extracellular matrix organization, extracellular structure organization, external encapsulating structure organization, Cytoskeleton in muscle cells, Cytokine-cytokine receptor interaction, and the PI3K-Akt signaling pathway (p \u0026lt; 0.05, Figure S3A-F).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.7 The landscape of tumor microenvironment\u003c/h2\u003e \u003cp\u003eWe performed Gene Set Enrichment Analysis (GSEA) on the high- and low-risk groups to identify potential pathways. In both risk groups, GSEA was analyzed using several GSEA diagrams based on the following screening criteria: FDR \u0026gt; 0 and P \u0026lt; 0.05 (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA-B). The five most significant functions associated with the high-risk group include \"KEGG_CHEMOKINE_SIGNALING_PATHWAY\", \"KEGG_CYTOKINE_CYTOKINE_RECEPTOR_INTERACTION\", \"KEGG_ECM_RECEPTOR_INTERACTION\", \"KEGG_FOCAL_ADHESION\", and \"KEGG_HEMATOPOIETIC_CELL_LINEAGE\". In addition, the five most significant functions associated with the low-risk group include \"KEGG_HUNTINGTONS_DISEASE\",\"KEGG_OXIDATIVE_PHOSPHORYLATION\", \"KEGG_PARKINSONS_DISEASE\", \"KEGG_RIBOSOME\", and \"KEGG_TYPE_II_DIABETES_MELLITUS\".\u003c/p\u003e \u003cp\u003eThereafter, we applied the ssGSEA method to explore the infiltration level of 24 kinds of immune cells and retrieved scores of 13 immune functions to assess the relationship between the risk model and immune infiltration. The high-risk group had increased Macrophages and enhanced all the immunological functions (p \u0026lt; 0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eC-D). Subsequently, we applied immune checkpoint analysis based on the 47 immune genes. We found that the high-risk group had a higher expression level in those genes than the low-risk group (p \u0026lt; 0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003eThe results of Tumor Microenvironment (TME) analysis showed that the high-risk group had higher stromal scores, immune scores, and estimation scores, which means that the TME of the high-risk group was more complex and possibly associated with invasiveness, resistance to treatment, and a poorer prognosis (P \u0026lt; 0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eF). We also found that higher TME scores were associated with worse survival outcomes (P \u0026lt; 0.001, Figs.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eG-I).\u003c/p\u003e \u003cp\u003eBased on the discovery of TME analysis, we further investigated the tumor immune microenvironment. Relying on seven types of software, we found that various immune cells had a positive effect on tumor development, among which Macrophage M0, Macrophage M1, and Macrophage M2 cells were associated with higher risk scores (p \u0026lt; 0.05, Figs.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eJ-M).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e3.8 The landscape of tumor microenvironment and drug sensitive\u003c/h2\u003e \u003cp\u003eTo further explore the differences in the immune microenvironment between high- and low-risk groups, we analyzed a variety of common immune markers. Figures\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA-B show that the high-risk group has high TIDE and CAF scores (p \u0026lt; 0.001), suggesting that the high-risk group may exhibit enhanced immune escape mechanisms and reduced efficacy of immunotherapy. However, there were no significance difference in CD8 and CD274 scores between the high- and low-risk groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eC-D, p \u0026gt; 0.05). Concurrently, the high-risk group exhibited higher Dysfunction, Exclusion IFNG, Merck18, and TAM.M2 scores (Figs.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eE-G, \u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eI, \u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eK, p \u0026lt; 0.05), while the MDSC scores showed no significant difference (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eH, p \u0026gt; 0.05), suggesting potential immune evasion and reduced effectiveness of immunotherapy.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIC50 is an indicator that illustrates the tolerance of tumor cells to drugs. We conducted a differential sensitivity analysis of 198 drugs between high and low-risk groups. The results showed that Low-risk group THCA patients are highly sensitive to targeted drugs such as Afuresertib, Elephantin, Leflunomide, Linsitinib, Nilotinib, and Sabutoclax; whereas High-risk group THCA patients are highly sensitive to targeted drugs such as Cediranib, Dasatinib, Entospletinib, Olaparib, Osimertinib, and Saptinib (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eA-L).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThyroid cancer (THCA) is a common malignancy typically associated with a favorable prognosis; however, a subset of patients experience aggressive disease with poor clinical outcomes [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Recent advancements in single-cell sequencing have revolutionized our understanding of tumor heterogeneity and the dynamics of the tumor microenvironment (TME) in various cancers, including THCA [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In this study, we employed comprehensive single-cell sequencing to elucidate the cell composition of THCA, with a particular focus on macrophage and cuproptosis-associated long non-coding RNAs (lncRNAs), and developed a novel prognostic model to predict patient outcomes.\u003c/p\u003e\u003cp\u003eSingle-cell sequencing analysis identified eight major cell types within THCA samples, including thyroid follicular cells, T cells, epithelial cells, fibroblasts, endothelial cells, plasma cells, macrophages, and B cells. Macrophages, in particular, have been widely implicated in cancer biology due to their dual roles in tumor progression and inhibition, depending on their phenotypic polarization (M1 vs. M2 macrophages) [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The interactions between macrophages and tumor cells within the TME are crucial for tumor growth, metastasis, and response to therapy [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Our analysis not only confirmed the presence of macrophages but also highlighted a significant correlation between macrophage gene expression profiles and patient prognosis, identifying 4,789 macrophage-related genes.\u003c/p\u003e\u003cp\u003eCuproptosis, a recently identified form of cell death related to copper-induced cytotoxicity, is emerging as a critical factor in cancer biology [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. In this study, we calculated the cuproptosis score across the identified cell types and found it to be intricately associated with certain lncRNAs. Through rigorous statistical analyses, including Wilcoxon and LASSO regression, we identified 11 lncRNAs co-expressed with macrophage and cuproptosis-related genes, leading to the construction of a robust prognostic risk model. This model accurately stratified patients into high- and low-risk groups, as demonstrated by Kaplan-Meier survival analysis and ROC curve evaluations, with AUC values near 1 in the training set and over 0.75 in validation cohorts—indicators of excellent prognostic discriminatory power [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].The Kaplan-Meier survival analysis results of the prognostic model in different risk groups showed that in all subgroups (age, gender, stage staging, T staging, N staging, M staging), patients in the high-risk group had shorter overall survival (OS) compared to those in the low-risk group.To further illustrate the clinical utility of our risk model, we developed a prognostic nomogram, incorporating clinical features such as age, sex, and anatomical staging alongside the risk score. Univariate and multivariate analyses reinforced risk scores, age, and metastasis stage as independent predictors of patient outcomes, underscoring their significance in personalized patient management. The calibration curve of the nomogram closely mirrored actual patient survival, supporting the model's accuracy [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTumor mutational burden (TMB) analysis did not reveal significant mutations correlated with risk stratification, although genes such as BRAF and NRAS, well-documented in thyroid cancer pathogenesis, were identified [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. These results suggest that while TMB may not directly impact risk classification per se, it drives tumor aggressiveness, as evidenced by poorer survival in patients with high TMB.Differential expression analyses revealed upregulation of 237 genes in high-risk groups, implicating pathways related to extracellular matrix organization and cytokine-cytokine receptor interactions, involved in tumor invasiveness and aggression. These findings resonate with existing literature underscoring the roles of these pathways in numerous cancers [\u003cspan additionalcitationids=\"CR36\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e–\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Gene Set Enrichment Analysis (GSEA) validated these observations, pinpointing significant pathways—such as chemokine signaling and ECM-receptor interaction—that reflect more pronounced oncogenic signaling in high-risk groups [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].The TME landscape further emphasized that high-risk patients had elevated stromal and immune scores, suggesting a more complex and potentially treatment-resistant tumor milieu. Increased expression of immune checkpoint-related genes and enhanced macrophage infiltration in these patients support hypotheses of immune evasion and necessitate novel therapeutic strategies, such as macrophage-targeted therapies or immune checkpoint inhibitors, as potential interventions [\u003cspan additionalcitationids=\"CR41\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e–\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e].The landscape of the tumor microenvironment in THCA, as revealed by GSEA and ssGSEA, highlights the enrichment of immune-related pathways and the infiltration level of immune cells in high-risk groups. The association between high-risk scores and increased immune cell infiltration, particularly macrophages, suggests a role for these cells in THCA progression and immune evasion. Macrophages in the TME were not only abundant in high-risk patients but also displayed enhanced immunological functions, as shown by our single-sample gene set enrichment analysis (ssGSEA). This is in line with existing literature suggesting that tumor-associated macrophages (TAMs) are critical for tumor immune evasion and progression. Recent studies have shown that targeting TAMs or modulating their phenotype could significantly impact tumor progression and patient outcomes [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe differential sensitivity of THCA to various therapeutics, as revealed by IC50 analyses, offers valuable insights for personalized medicine. High-risk groups showed sensitivity to drugs like Afuresertib and Nilotinib, aligning with markers of therapeutic susceptibility observed in other cancers [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Conversely, low-risk groups exhibited responsiveness to agents such as Osimertinib and Olaparib, known to have efficacy against genomic-driven cancers [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. The delineation of chemotherapy sensitivity profiles could greatly enhance treatment strategies, tailoring regimens based on individual genetic and molecular landscapes.\u003c/p\u003e\u003cp\u003eDespite these promising findings, our study has limitations. The sample size is limited, necessitating further validation in larger, more diverse cohorts. Additionally, functional studies are required to elucidate the precise roles of identified lncRNAs in THCA pathogenesis. Experimental validation of therapeutic targets suggested by our analyses is crucial for transitioning from theoretical models to clinical applications.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, our study provides a comprehensive analysis of THCA at the single-cell level, revealing novel insights into cellular heterogeneity, prognostic markers, and therapeutic targets. The integration of these findings with clinical data offers a robust foundation for the development of personalized treatment strategies and improved patient outcomes.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eTHCA \u0026nbsp;Thyroid Carcinoma\u003c/p\u003e\n\u003cp\u003eTCGA \u0026nbsp;The Cancer Genome Atlas Program\u003c/p\u003e\n\u003cp\u003eGEO \u0026nbsp;Gene Expression Omnibus\u003c/p\u003e\n\u003cp\u003escRNA \u0026nbsp;Single cell sequencing\u003c/p\u003e\n\u003cp\u003eLASSO \u0026nbsp;Least Absolute Shrinkage and Selection Operator\u003c/p\u003e\n\u003cp\u003eC-index \u0026nbsp; Concordance index\u003c/p\u003e\n\u003cp\u003eOS \u0026nbsp; \u0026nbsp;Overall survival\u003c/p\u003e\n\u003cp\u003eGSEA \u0026nbsp;Gene set enrichment analysis\u003c/p\u003e\n\u003cp\u003essGSEA \u0026nbsp;Single-sample Gene set enrichment analysis\u003c/p\u003e\n\u003cp\u003eKEGG \u0026nbsp;Kyoto Encyclopedia of Genes and Genomes\u003c/p\u003e\n\u003cp\u003eKM \u0026nbsp;Kaplan Meier\u003c/p\u003e\n\u003cp\u003eROC \u0026nbsp;Receiver Operating Characteristic\u003c/p\u003e\n\u003cp\u003eLncRNA \u0026nbsp;Long Non-coding RNA\u003c/p\u003e\n\u003cp\u003eDEGs \u0026nbsp; \u0026nbsp; Differential Expressed Genes\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot available.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors agree to publish\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests for this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding for this study\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJW and FY designed and conducted this study, performed the data processing and wrote the initial manuscript. YX downloaded the raw data online and preprocessed it. GH performed data supervision and writing-review. All of the authors have read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are grateful to the authors for their contributions to this study\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMoleti M, Aversa T, Crisafulli S, Trifir\u0026ograve; G, Corica D, Pepe G, Cannav\u0026ograve; L, Di Mauro M, Paola G, Fontana A, Calapai F, Cannav\u0026ograve; S, Wasniewska M. Global incidence and prevalence of differentiated thyroid cancer in childhood: systematic review and meta-analysis. Front Endocrinol (Lausanne). 2023 Sep 19;14:1270518. doi: 10.3389/fendo.2023.1270518. PMID: 37795368; PMCID: PMC10546309.\u003c/li\u003e\n\u003cli\u003eRybakov, S. (2023). 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PMID: 32693293.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Thyroid carcinoma, scRNA, Macrophage, Cuproptosis, lncRNA","lastPublishedDoi":"10.21203/rs.3.rs-6528117/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6528117/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThyroid carcinoma is a type of malignant tumor with a relatively good prognosis, but some subtypes have a poorer prognosis. Therefore, it is necessary to establish a new prognostic model to predict the survival outcomes and immune therapy responses of thyroid cancer patients.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eSingle-cell sequencing analysis was conducted to identifyt genes associated with cuproptosis and differentially expressed genes within macrophages. The Wilcoxon algorithm was employed to identify tumor-related genes. The overlapping genes were utilized to discover lncRNAs that are related to both macrophages and cuproptosis. Following this, Lasso analysis was applied to develop a prognostic model. We then stratified patients into two groups based on their median risk scores and assessed their survival outcomes and responses to immune therapy. Additionally, we examined the mutational profiles of patients in both the high-risk and low-risk groups.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOur prognostic risk model has identified 11 lncRNAs: AC107214.1, AC135050.1, AC026355.3, FOXP1-AS1, AC123768.1, MIR3945HG, AC022819.1, AC009716.1, AC120498.9, AL133444.1, and AL132709.8. It has been confirmed that our prognostic model boasts an exceptionally high accuracy, with a precision rate exceeding 0.8 in forecasting the survival outcomes of patients in the TCGA-THCA cohort. In this study, we observed no significant disparity in gene mutation rates between the high-risk and low-risk groups. Patients categorized in the low-risk group exhibit heightened sensitivity to immunotherapy and demonstrate responsiveness to a variety of immunotherapeutic agents, such as dasatinib.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eOur study highlights the potential of macrophage and cuproptosis-related lncRNAs as novel predictive biomarkers for thyroid carcinoma.\u003c/p\u003e","manuscriptTitle":"Novel a new prognostic model of Thyroid Carcinoma based on Macrophage and Cuproptosis-Related lncRNA","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-19 10:39:11","doi":"10.21203/rs.3.rs-6528117/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e5c456f4-6831-41e4-909d-f16c901e92cb","owner":[],"postedDate":"May 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-07-14T16:12:18+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-19 10:39:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6528117","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6528117","identity":"rs-6528117","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}