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已发表论文
巨噬细胞来源转录特征预测甲状腺癌预后和药物敏感性：SMYD3 的整合分析和实验验证
Authors Xu S, Zhang X, Sun J, Qin M, Du H, Luo B
Received 13 September 2025
Accepted for publication 9 December 2025
Published 9 January 2026 Volume 2026:15 565624
DOI https://doi.org/10.2147/ITT.S565624
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Professor Michael Shurin
Sufang Xu, Xin Zhang, Jingru Sun, Man Qin, Huarong Du, Bing Luo
Department of Medical Laboratory Center, Anhui No.2 Provincial People’s Hospital, Hefei, Anhui, People’s Republic of China
Correspondence: Bing Luo, Department of Medical Laboratory Center, Anhui No.2 Provincial People’s Hospital, 1868 Dangshan Road, North Second Ring, Yaohai District, Hefei, Anhui, People’s Republic of China, Email luob2008lb@126.com
Background: Thyroid cancer is the most common malignancy of the endocrine system. Tumor-associated macrophages (TAMs) play a pivotal role in modulating the tumor microenvironment and promoting tumor progression. However, the prognostic implications of macrophage heterogeneity in thyroid cancer remain unclear.
Methods: Single-cell RNA-seq analysis was conducted to identify tumor-enriched macrophage subpopulations and hdWGCNA was used to define related gene modules. A prognostic model was built using 117 machine learning algorithm combinations and validated by Kaplan–Meier analysis. A nomogram combining clinical features and risk scores was established. Genomic alterations, immune profiles, and treatment responses were compared between risk groups using TCGA and GDSC2 datasets. In vitro experiments were performed to validate the role of SMYD3 in tumor progression and drug sensitivity.
Results: Single-cell analysis identified a tumor-enriched macrophage subset with distinct functional states. hdWGCNA revealed macrophage-related gene modules linked to poor prognosis, and a machine learning–based model effectively stratified patient risk. High-risk patients had worse survival, older age, advanced stage, and lower BRAF mutation frequency but more diverse oncogenic alterations. They also showed enhanced T-cell exclusion and altered immune infiltration. Drug prediction analysis indicated greater sensitivity to Rapamycin, BDP-00009066, AZD5363_1916, and Cediranib_1922 in the high-risk group. Functional assays confirmed that SMYD3 knockdown suppressed proliferation and migration, and reduced sensitivity to AZD5363_1916, highlighting its role in modulating therapeutic response.
Conclusion: This study identifies a tumor-enriched macrophage subpopulation with prognostic relevance and develops a robust machine learning–based model for risk stratification in thyroid cancer. SMYD3 is implicated in both tumor progression and drug sensitivity, offering a potential biomarker for precision treatment strategies.
Plain Language Summary: Thyroid cancer is the most common type of endocrine system cancer. Although many patients respond well to treatment, some experience poor outcomes, and the reasons for this are not fully understood. In this study, researchers aimed to improve how we predict the behavior of thyroid cancer and identify treatments that might work best for individual patients. They focused on a type of immune cell called macrophages, which are often found in tumors and can either help fight or promote cancer. Using advanced genetic analysis, the researchers discovered a special group of macrophages that are enriched in thyroid tumors and may contribute to cancer progression. They then built a risk prediction model using gene patterns from these macrophages. One gene, SMYD3, stood out as especially important. In lab experiments, reducing SMYD3 in thyroid cancer cells led to slower growth and less movement, suggesting that this gene helps cancer cells spread. The researchers also found that SMYD3 may influence how well cancer cells respond to certain drugs—particularly AZD5363, a drug that targets cancer cell growth pathways. Patients with high SMYD3 levels may be more sensitive to this treatment. These findings provide new insights into how the immune system and specific genes influence thyroid cancer and suggest that SMYD3 could be a useful marker for guiding treatment in the future.
Keywords: thyroid cancer, tumor-associated macrophages, single-cell RNA sequencing, machine learning, drug sensitivity, SMYD3
Department of Medical Laboratory Center, Anhui No.2 Provincial People’s Hospital, Hefei, Anhui, People’s Republic of China
Correspondence: Bing Luo, Department of Medical Laboratory Center, Anhui No.2 Provincial People’s Hospital, 1868 Dangshan Road, North Second Ring, Yaohai District, Hefei, Anhui, People’s Republic of China, Email luob2008lb@126.com
Background: Thyroid cancer is the most common malignancy of the endocrine system. Tumor-associated macrophages (TAMs) play a pivotal role in modulating the tumor microenvironment and promoting tumor progression. However, the prognostic implications of macrophage heterogeneity in thyroid cancer remain unclear.
Methods: Single-cell RNA-seq analysis was conducted to identify tumor-enriched macrophage subpopulations and hdWGCNA was used to define related gene modules. A prognostic model was built using 117 machine learning algorithm combinations and validated by Kaplan–Meier analysis. A nomogram combining clinical features and risk scores was established. Genomic alterations, immune profiles, and treatment responses were compared between risk groups using TCGA and GDSC2 datasets. In vitro experiments were performed to validate the role of SMYD3 in tumor progression and drug sensitivity.
Results: Single-cell analysis identified a tumor-enriched macrophage subset with distinct functional states. hdWGCNA revealed macrophage-related gene modules linked to poor prognosis, and a machine learning–based model effectively stratified patient risk. High-risk patients had worse survival, older age, advanced stage, and lower BRAF mutation frequency but more diverse oncogenic alterations. They also showed enhanced T-cell exclusion and altered immune infiltration. Drug prediction analysis indicated greater sensitivity to Rapamycin, BDP-00009066, AZD5363_1916, and Cediranib_1922 in the high-risk group. Functional assays confirmed that SMYD3 knockdown suppressed proliferation and migration, and reduced sensitivity to AZD5363_1916, highlighting its role in modulating therapeutic response.
Conclusion: This study identifies a tumor-enriched macrophage subpopulation with prognostic relevance and develops a robust machine learning–based model for risk stratification in thyroid cancer. SMYD3 is implicated in both tumor progression and drug sensitivity, offering a potential biomarker for precision treatment strategies.
Plain Language Summary: Thyroid cancer is the most common type of endocrine system cancer. Although many patients respond well to treatment, some experience poor outcomes, and the reasons for this are not fully understood. In this study, researchers aimed to improve how we predict the behavior of thyroid cancer and identify treatments that might work best for individual patients. They focused on a type of immune cell called macrophages, which are often found in tumors and can either help fight or promote cancer. Using advanced genetic analysis, the researchers discovered a special group of macrophages that are enriched in thyroid tumors and may contribute to cancer progression. They then built a risk prediction model using gene patterns from these macrophages. One gene, SMYD3, stood out as especially important. In lab experiments, reducing SMYD3 in thyroid cancer cells led to slower growth and less movement, suggesting that this gene helps cancer cells spread. The researchers also found that SMYD3 may influence how well cancer cells respond to certain drugs—particularly AZD5363, a drug that targets cancer cell growth pathways. Patients with high SMYD3 levels may be more sensitive to this treatment. These findings provide new insights into how the immune system and specific genes influence thyroid cancer and suggest that SMYD3 could be a useful marker for guiding treatment in the future.
Keywords: thyroid cancer, tumor-associated macrophages, single-cell RNA sequencing, machine learning, drug sensitivity, SMYD3