Research Paper Volume 11, Issue 22 pp 10116—10143

The construction and analysis of tumor-infiltrating immune cell and ceRNA networks in recurrent soft tissue sarcoma

Runzhi Huang 1, 2, *, , Tong Meng 2, 3, *, , Rui Chen 4, *, , Penghui Yan 1, , Jie Zhang 5, , Peng Hu 1, , Xiaolong Zhu 1, , Huabin Yin 3, , Dianwen Song 3, , Zongqiang Huang 1, ,

  • 1 Department of Orthopaedics, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
  • 2 Division of Spine, Department of Orthopedics, Tongji Hospital Affiliated to Tongji University School of Medicine, Shanghai 200065, China
  • 3 Department of Orthopedics, Shanghai General Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai 200080, China
  • 4 Department of Urology, Shanghai Changhai Hospital, Second Military Medical University, Shanghai 200433, China
  • 5 Shanghai East Hospital, Key Laboratory of Arrhythmias, Ministry of Education, Tongji University School of Medicine, Shanghai 200120, China
* Equal contribution and co-first author

received: August 3, 2019 ; accepted: October 28, 2019 ; published: November 18, 2019 ;

https://doi.org/10.18632/aging.102424
How to Cite

Copyright © 2019 Huang et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

Soft tissue sarcoma (STS) is one of the most challenging tumors for medical oncologists, with a high rate of recurrence after initial resection. In this study, a recurrent STS-specific competitive endogenous RNA (ceRNA) network including seven recurrence and overall survival (OS)-associated genes (LPP-AS2, MUC1, GAB2, hsa-let-7i-5p, hsa-let-7f-5p, hsa-miR-101-3p and hsa-miR-1226-3p) was established based on the gene expression profiling of 259 primary sarcomas and 3 local recurrence samples from the TCGA database. The algorithm “cell type identification by estimating relative subsets of RNA transcripts (CIBERSORT)” was applied to estimate the fraction of immune cells in sarcomas. Based on 5 recurrence and OS-associated immune cells (NK cells activated, dendritic cells resting, mast cells resting, mast cells activated and macrophages M1), we constructed a recurrent STS-specific immune cells network. Both nomograms were identified to have good reliabilities (Area Under Curve (AUC) of 5-year survival is 0.724 and 0.773, respectively). Then the co-expression analysis was performed to identify the potential regulation network among recurrent STS-specific immune cells and ceRNAs. Hsa-miR-1226-3p and MUC1 were significantly correlated and dendritic cells resting was related to hsa-miR-1226-3p. Additionally, the expression of MUC1 and dendritic cell marker CD11c were also verified by immunohistochemistry (IHC) assay and multidimensional databases. In conclusion, this study illustrated the potential mechanism of hsa-miR-1226-3p regulating MUC1 and dendritic cells resting might play an important role in STS recurrence. These findings might provide potential prognostic biomarkers and therapeutic targets for recurrent STS.

Abbreviations

STS: Soft tissue sarcoma; AUC: Area under curve; ceRNA: competitive endogenous RNA; mRNA: messenger RNA; lncRNA: long non-coding RNA; miRNA: microRNA; CIBERSORT: Cell type identification by estimating relative subsets of RNA transcripts; IHC: Immunohistochemistry; MUC1: Mucin 1; LR: Local recurrence; TCGA: The Cancer Genome Atlas; FPKM: Fragments per kilobase of exon per million reads mapped; FDR: False discovery rate; SD: Standard deviation; ROC: Receiver operating characteristic curves; DAB: 3: 3-diaminobenzidine tetrahydrochloride; DCs: Dendritic cells; APC: Antigen-presenting cells; pDCs: plasmacytoid DCs; 3’ UTR: 3’ untranslated region; CTGF: Connective tissue growth factor; VEGF-A: Vascular endothelial growth factor-A; PDGF-A: Platelet-derived growth factor A; EMT: Epithelial to mesenchymal transition.