Osteosarcoma, originating from mesenchymal tissue, is the most frequent primary malignant bone tumor and accounts for around 15% of all bone cancers
Abstract
The purpose of this study was to develop a new prognostic model for osteosarcoma based on cuproptosis-mitochondrion genes.
Introduction
Osteosarcoma, originating from mesenchymal tissue, is the most frequent primary malignant bone tumor and accounts for around 15% of all bone cancers [1, 2]. The annual incidence of osteosarcoma is around 4.0 to 5.0 occurrences per million, with majority of cases occurring in children and adolescents [3–5]. The lung is the most common site of recurrence and distant metastasis, and nearly 20% of patients presented lung metastasis when diagnosed with osteosarcoma [6, 7]. Although neoadjuvant chemotherapy has greatly improved the survival of osteosarcoma, patients with lung metastasis or recurrent disease presented a dismal survival outcome [8, 9]. Therefore, developing an accurate prognostic predictive model and discovering new therapeutic targets for patients with osteosarcoma are critical.
Materials and methods
We used TARGET database (https://ocg.cancer.gov/programs/target) to obtain mRNA expression as well as corresponding clinical data of 88 osteosarcoma patients. To further corroborate the discoveries in the TARGET construction cohort, we downloaded data from the GEO database (https://www.ncbi.nlm.nih.gov/geo/) as validation cohort (GSE21257). Patients without complete clinical data (follow-up time, age, gender, metastasis and survival status) would be excluded. Finally, a total of 84 patients with osteosarcoma from TARGET database and 53 cases from GSE21257 were included. Ethical approval was not required for this study because there was no unethical behavior and no human clinical trials or animal experiments were involved in this study.
Results
The results revealed that risk score and metastasis were two independent prognostic factors for osteosarcoma (Fig 3A and 3B). Furthermore, the predictive performance of risk score was clarified in subgroup analysis (S4 Fig). Overall, the risk score showed satisfactory performance of prediction in clinical subgroup analysis.
Discussion
Our study found that FDX1 was associated with poor prognosis, suggesting FDX1 might participate in tumorigenesis and development of osteosarcoma. According to some research, FDX1 was strongly expressed in osteosarcoma tissue, and patients who had high levels of FDX1 expression had a bad prognosis [41, 42]. Furthermore, there are no literature findings on FDX1 migration and proliferation in osteosarcoma. We conducted some functional experiments and found that FDX1 mainly involves the migration of osteosarcoma and didn’t affect the proliferation of osteosarcoma. Therefore, we speculate that the high expression of FDX1 may aggravate the malignancy of osteosarcoma by promoting the migration of osteosarcoma cells. These results indicate that FDX1 may play a certain role in osteosarcoma progress.
Conclusion
Our study developed a novel risk score with six cuproptosis-mitochondrion genes to predict the survival outcome of osteosarcoma. A nomogram model to predict prognosis was then constructed with good discrimination and calibration. Besides, the close relationship between cuproptosis-mitochondrion genes and immune landscape was revealed. These findings offer helpful insights on survival prediction and individual treatment of osteosarcoma in clinical practice.
Citation: Feng J, Wang J, Xu Y, Lu F, Zhang J, Han X, et al. (2023) Construction and validation of a novel cuproptosis-mitochondrion prognostic model related with tumor immunity in osteosarcoma. PLoS ONE 18(7): e0288180. https://doi.org/10.1371/journal.pone.0288180
Editor: Suyan Tian, The First Hospital of Jilin University, CHINA
Received: March 17, 2023; Accepted: June 20, 2023; Published: July 5, 2023
Copyright: © 2023 Feng et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: The raw data of this study were derived from the TARGET database (https://ocg.cancer.gov/programs/target) and the GEO database
(https://www.ncbi.nlm. nih.gov/geo), which are publicly available databases. The minimum dataset could be found in the Supplementary Material file.
Funding: This work was supported by the National Natural Science Foundation of China [grant nos. 81872184 and 81773031]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0288180#abstract0
