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Development of a novel vasculogenic mimicry-associated gene signature for the prognostic assessment of osteosarcoma patients

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Abstract

Background

Osteosarcoma (OS) is a form of primary bone malignancy associated with poor prognostic outcomes. Recent work has highlighted vasculogenic mimicry (VM) as a key mechanism that supports aggressive tumor growth. The patterns of VM-associated gene expression in OS and the relationship between these genes and patient outcomes, however, have yet to be defined.

Methods

Here, 48 VM-related genes were systematically assessed to examine correlations between the expression of these genes and OS patient prognosis in the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) cohort. Patients were classified into three OS subtypes. Differentially expressed genes for these three OS subtypes were then compared with hub genes detected in a weighted gene co-expression network analysis, leading to the identification of 163 overlapping genes that were subject to further biological activity analyses. A three-gene signature (CGREF1, CORT, and GALNT14) was ultimately constructed through a least absolute shrinkage and selection operator Cox regression analysis, and this signature was used to separate patients into low- and high-risk groups. The K–M survival analysis, receiver operating characteristic analysis, and decision curve analysis were adopted to evaluate the prognostic prediction performance of the signature. Furthermore, the expression patterns of three genes derived from the prognostic model were validated by quantitative real-time polymerase chain reaction (RT-qPCR).

Results

VM-associated gene expression patterns were successfully established, and three VM subtypes of OS that were associated with patient prognosis and copy number variants were defined. The developed three-gene signature was constructed, which served as independent prognostic markers and prediction factors for the clinicopathological features of OS. Finally, lastly, the signature may also have a guiding effect on the sensitivity of different chemotherapeutic drugs.

Conclusion

Overall, these analyses facilitated the development of a prognostic VM-associated gene signature capable of predicting OS patient outcomes. This signature may be of value for both studies of the mechanistic basis for VM and clinical decision-making in the context of OS patient management.

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Data availability

The datasets analyzed for this study can be found in the public database. All data generated or analyzed during this study are included as supplementary information file.

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Acknowledgements

The authors gratefully acknowledge contributions from the GTEx, TARGET, and STRING databases for free use and the reviewers for their helpful comments on this study.

Funding

Sponsorship for this study was funded by grants from the National Natural Science Foundation of China (No. 81802204), China Postdoctoral Science Foundation (2020M671453).

Author information

Author notes

  1. Lei Yan and Ruoqi Li have contributed equally to this work.

Authors and Affiliations

  1. Department of Orthopaedic Surgery, The First Affliated Hospital, Zhejiang University School of Medicine, Hangzhou, China

    Lei Yan & Bin Wang

  2. Second Clinical Medical College, Shanxi Medical University, 382 Wuyi Road, Taiyuan, Shanxi, China

    Lei Yan, Dijun Li & Zhi Lv

  3. Department of Orthopedics, Shanxi Key Laboratory of Bone and Soft Tissue Injury Repair, The Second Hospital of Shanxi Medical University, 382 Wuyi Road, Taiyuan, Shanxi, China

    Lei Yan, Dijun Li & Zhi Lv

  4. General Surgery Department, Third Hospital of Shanxi Medical University, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Taiyuan, Shanxi, China

    Ruoqi Li

  5. State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China

    Yuanyuan Zhu

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  1. Lei Yan
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Contributions

Conceptualization, L.Y., R.L, and B.W.; methodology, L.Y., D.L., and R.L; software, R.L; validation, Z.L., Y.Z., and B.W.; formal analysis, L.Y. and R.L.; data curation, R.L. and D.L.; writing—original draft preparation, L.Y. and R.L.; writing—review and editing, Y.Z. and B.W.; visualization, L.Y. and R.L.; supervision, Z.L. and B.W.; project administration, B.W.; funding acquisition, B.W.

Corresponding authors

Correspondence to Yuanyuan Zhu, Zhi Lv or Bin Wang.

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All named authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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We performed this research though publicly available datasets. There was no ethical approval required.

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Yan, L., Li, R., Li, D. et al. Development of a novel vasculogenic mimicry-associated gene signature for the prognostic assessment of osteosarcoma patients. Clin Transl Oncol 25, 3501–3518 (2023). https://doi.org/10.1007/s12094-023-03218-1

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