Runzhi Huang1,2, Zhiwei Zeng1, Penghui Yan1, Huabin Yin3, Xiaolong Zhu1, Peng Hu1, Juanwei Zhuang1, Jiaju Li1, Siqi Li4, Dianwen Song5, Tong Meng6,7, Zongqiang Huang8. 1. Department of Orthopedics, The First Affiliated Hospital of Zhengzhou University, 1 East Jianshe Road, Zhengzhou, 450052, China. 2. Division of Spine, Department of Orthopedics, Tongji Hospital Affiliated to Tongji University School of Medicine, 389 Xincun Road, Shanghai, China. 3. Department of Orthopedics, Shanghai General Hospital, School of Medicine, Shanghai Jiaotong University, 100 Haining Road, Shanghai, China. 4. Tongji University School of Medicine, 1239 Siping Road, Shanghai, 200092, China. 5. Department of Orthopedics, Shanghai General Hospital, School of Medicine, Shanghai Jiaotong University, 100 Haining Road, Shanghai, China. osongdianwen@126.com. 6. Division of Spine, Department of Orthopedics, Tongji Hospital Affiliated to Tongji University School of Medicine, 389 Xincun Road, Shanghai, China. mengtong@medmail.com.cn. 7. Department of Orthopedics, Shanghai General Hospital, School of Medicine, Shanghai Jiaotong University, 100 Haining Road, Shanghai, China. mengtong@medmail.com.cn. 8. Department of Orthopedics, The First Affiliated Hospital of Zhengzhou University, 1 East Jianshe Road, Zhengzhou, 450052, China. gzhuangzq@163.com.
Abstract
BACKGROUND: Soft tissue sarcomas (STS) has a high rate of early metastasis. In this study, we aimed to uncover the potential metastasis mechanisms and related signaling pathways in STS with differentially expressed genes and tumor-infiltrating cells. METHODS: RNA-sequencing (RNA-seq) of 261 STS samples downloaded from the Cancer Genome Atlas (TCGA) database were used to identify metastasis-related differentially expressed immune genes and transcription factors (TFs), whose relationship was constructed by Pearson correlation analysis. Metastasis-related prediction model was established based on the most significant immune genes. CIBERSORT algorithm was performed to identify significant immune cells co-expressed with key immune genes. The GSVA and GSEA were performed to identify prognosis-related KEGG pathways. Ultimately, we used the Pearson correlation analysis to explore the relationship among immune genes, immune cells, and KEGG pathways. Additionally, key genes and regulatory mechanisms were validated by single-cell RNA sequencing and ChIP sequencing data. RESULTS: A total of 204 immune genes and 12 TFs, were identified. The prediction model achieved a satisfactory effectiveness in distant metastasis with the Area Under Curve (AUC) of 0.808. LTB was significantly correlated with PAX5 (P < 0.001, R = 0.829) and hematopoietic cell lineage pathway (P < 0.001, R = 0.375). The transcriptional regulatory pattern between PAX5 and LTB was validated by ChIP sequencing data. CONCLUSIONS: We hypothesized that down-regulated LTB (immune gene) modulated by PAX5 (TF) in STSs may have the capability of inducing cancer cell metastasis in patients with STS.
BACKGROUND: Soft tissue sarcomas (STS) has a high rate of early metastasis. In this study, we aimed to uncover the potential metastasis mechanisms and related signaling pathways in STS with differentially expressed genes and tumor-infiltrating cells. METHODS: RNA-sequencing (RNA-seq) of 261 STS samples downloaded from the Cancer Genome Atlas (TCGA) database were used to identify metastasis-related differentially expressed immune genes and transcription factors (TFs), whose relationship was constructed by Pearson correlation analysis. Metastasis-related prediction model was established based on the most significant immune genes. CIBERSORT algorithm was performed to identify significant immune cells co-expressed with key immune genes. The GSVA and GSEA were performed to identify prognosis-related KEGG pathways. Ultimately, we used the Pearson correlation analysis to explore the relationship among immune genes, immune cells, and KEGG pathways. Additionally, key genes and regulatory mechanisms were validated by single-cell RNA sequencing and ChIP sequencing data. RESULTS: A total of 204 immune genes and 12 TFs, were identified. The prediction model achieved a satisfactory effectiveness in distant metastasis with the Area Under Curve (AUC) of 0.808. LTB was significantly correlated with PAX5 (P < 0.001, R = 0.829) and hematopoietic cell lineage pathway (P < 0.001, R = 0.375). The transcriptional regulatory pattern between PAX5 and LTB was validated by ChIP sequencing data. CONCLUSIONS: We hypothesized that down-regulated LTB (immune gene) modulated by PAX5 (TF) in STSs may have the capability of inducing cancer cell metastasis in patients with STS.
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