Bioinformatics analysis of lncRNAs in the occurrence and development of osteosarcoma

Hua Liu; Chenyu Zong; Jiacheng Sun; Haiyang Li; Guangzhen Qin; Xiaojian Wang; Jianwei Zhu; Yang Yang; Qiang Xue; Xianchen Liu

doi:10.21037/tp-22-253

Bioinformatics analysis of lncRNAs in the occurrence and development of osteosarcoma

Transl Pediatr. 2022 Jul;11(7):1182-1198. doi: 10.21037/tp-22-253.

Authors

Hua Liu^#^{1

2}, Chenyu Zong^#³, Jiacheng Sun^#⁴, Haiyang Li⁵, Guangzhen Qin², Xiaojian Wang², Jianwei Zhu³, Yang Yang⁶, Qiang Xue¹, Xianchen Liu¹

Affiliations

¹ Department of Radiation Oncology, Affiliated Hospital of Nantong University, Nantong, China.
² Department of Orthopedics, Haian Hospital of Traditional Chinese Medicine, Haian, China.
³ Department of Orthopedics, Affiliated Hospital of Nantong University, Nantong, China.
⁴ Xinglin College, Nantong University, Nantong, China.
⁵ Department of Oncology, Binhai County People's Hospital, Yancheng, China.
⁶ Department of Trauma Center, Affiliated Hospital of Nantong University, Nantong, China.

^# Contributed equally.

Abstract

Background: Osteosarcoma (OS) is a disease with high mortality in children and adolescents, and metastasis is one of its important clinical features. However, the molecular mechanism of OS occurrence is not completely clear. Thus, we screened potential biomarkers of OS and analyze their prognostic value.

Methods: The Cancer Genome Atlas (TCGA) datasets were used to analyze the differential lncRNAs in patients with OS of different immune score and the lncRNAs expressed by immune cells. Cox regression was used to develop the prognosis prediction model and specify the prognosis outcomes. Risk-proportional regression model was constructed, and the samples were divided into high and low groups based on the risk scores for the survival analysis. The areas under the receiver operating characteristic (ROC) curve were calculated and the risk-score model was verified. Finally, using 4 gene sets (comprising chemokines, immune checkpoint blockades, immune activity-related genes, and immune cells), and 4 analysis tools (CIBERSORT, TIMER, XCELL and MCP) to evaluated tumor immune infiltration.

Results: Twenty-nine long non-coding ribonucleic acids (lncRNAs) were obtained from the intersection of the screened lncRNAs. Caspase recruitment domain-containing protein 8-antisense RNA 1 (CARD8-AS1), lncRNA five prime to Xist (FTX), KAT8 regulatory NSL complex unit 1-antisense RNA 1 (KANSL1-AS1), Neuroplastin Intronic Transcript 1 (NPTN-IT1), oligodendrocyte maturation-associated long intervening non-coding RNA (OLMALINC) and RPARP Antisense RNA 1 (RPARP-AS1) were found to be correlated with survival. Univariate and multivariate regression analysis showed risk score [HR (hazard ratio) 3.5, P value 0.0043; HR 3.7, P value 0.0033] and metastasis (HR 4.7, P value 6.60E-05; HR 4.8, P value 8.36E-05) were the key factors of patients with OS. The areas under curves (AUCs) of the 1-, 3-, and 5-year ROC curves of the prognostic model were 0.715, 0.729, and 0.771. The low-risk patients tended to have a high abundance of immune cells.

Conclusions: This study showed that a risk score based on 6 lncRNAs has potential value in the prognosis of OS, and patients with low-risk scores have high immune cell infiltration and good prognosis. This study may enrich understandings of underlying mechanisms related to the occurrence and development of OS.

Keywords: Osteosarcoma (OS); biomarker; lncRNA; prognosis.