The recurrence or metastasis related gene predicts the prognosis of extremity and trunk soft tissue sarcoma

Abstract

Background: Relapsed soft tissue sarcomas (STS) have poor prognosis and limited treatment options. However, the molecular mechanism underlying recurrence and the prognostic predictor for STS are unclear.

Methods: We enrolled 35 extremity and trunk STS patients. Tumor specimens of 20 relapsed and 15 primary STS underwent sequencing to detect DNA mutation, RNA expression, and DNA methylation. Moreover, 206 STS cases from The Cancer Genome Atlas (TCGA) were utilized to construct the relapse-associated risk score model (RRSM), validated using three Gene Expression Omnibus datasets. Key model genes, COL6A3, FZD7, ITPKA, and PRKAG1, were validated in formalin-fixed paraffin-embedded tissue sections from primary and relapsed STS patients, confirming their potential involvement in STS recurrence.

Results: The primary STS exhibited an immune-enriched tumor microenvironment, whereas the tumor microenvironment of relapsed STS had features that promote tumor recurrence or metastasis. The RRSM could predict relapse-free survival in TCGA STS and performed well in the validation cohort. Multivariate analysis revealed that RRSM was an independent prognostic factor. Moreover, the nomogram developed had excellent predictive ability.

Conclusions: This study revealed different multi-omic profiles between relapsed and primary STS. RRSM is a potential prognostic predictor for STS and lays a foundation for early intervention of high-risk STS patients. The expression of genes FZD7, ITPKA, and PRKAG1 may guide STS treatment decisions.

Keywords

soft tissue sarcoma / recurrence / multi-omics / prognostic model / relapse-associated risk score model / therapeutic targets

Acknowledgments

This work was supported by the Clinical Research Project of The First Hospital of Jilin University in 2024 (grant No. JDYYLCYJ-20240004), the National Key Research and development Program of China (grant No. 2022YFC2405805), and the National Natural Science Foundation of China (grant No. U23A20490). We also thank the Department of Biobank, Division of Clinical Research, The First Hospital of Jilin University, for providing human tissue samples. In addition, we sincerely thank Dr. Jianming Zeng (University of Macau) and all members of his bioinformatics team, biotrainee, for generously sharing their experience and codes, which greatly contributed to our analyses.

Author contributions

Duo Wang (Writing—original draft), Dawei Sun (Investigation, Resources), Jihao Tu (Investigation, Resources), Xingyao Cui (Data curation, Formal analysis), Limei Qu (Investigation, Methodology), Lei Chen (Investigation, Resources), Zhixin Zhang (Investigation, Resources), Ziping Jiang (Investigation, Resources), Ruijun Li (Investigation, Resources), Zhaopeng Xuan (Investigation, Resources), Jianli Cui (Investigation, Resources), Xiguang Sun (Investigation, Resources), Xiaoyan Jia (Writing—review & editing), Pengcheng Liu (Writing—review & editing), Ying Xiong (Investigation, Resources), Jianing Wang (Investigation, Resources), Yanfang Jiang (Conceptualization, Supervision), and Bin Liu (Conceptualization, Funding acquisition, Supervision).

Conflicts of interest

D.S. is a shareholder in Beijing ChosenMed Clinical Laboratory Co., Ltd., which supplied some of the reagents used in this study but did not provide any financial support. All authors declare no conflicts of interest. In addition, as an Editorial Board Member of Precision Clinical Medicine, the corresponding author Y.J. was blinded from reviewing and making decisions on this manuscript.

Ethics statement

This study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki. Ethical approval for the research was obtained from the Ethics Committee of the First Hospital of Jilin University, under approval number 23K246-001. Written informed consent was obtained from all participants prior to their inclusion in the study.

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