Background
Osteosarcoma is the most common bone tumor, especially in children and adolescents [
1]. It was reported that approximately 60% of patients are between 10 and 20 years old and osteosarcoma is considered as the second leading cause of death in this age group [
2]. Currently, surgery and chemotherapy are still major treatments for osteosarcoma patients, and these therapies are constantly improving in recent years. However, due to the susceptibility of local aggressiveness and lung metastasis in osteosarcoma patients, the prognosis of osteosarcoma remains unfavorable [
3]. Previous studies indicated that the 5-years survival rates were 27.4 and 70% in metastatic and non-metastatic patients, respectively [
4]. Therefore, it is necessary to investigate the mechanism of pathogenesis and progression of osteosarcoma and accurately classify the risk of patients.
Recently, an increasing number of diagnostic and prognostic biomarkers of osteosarcoma patients have been identified. For example, Chen et al. [
5] reported that tumor suppressor p27 is a novel biomarker for the metastasis and survival status in osteosarcoma patients. Moreover, Huang et al. [
6] discovered that dysregulated circRNAs serve as prognostic and diagnostic biomarkers in osteosarcoma patients, and the relative potential mechanism mainly attributes to the regulation of downstream signaling pathways by sponging microRNA. In addition, lncRNA [
7], microRNA [
8], and many clinical data [
9] were also identified as prognostic biomarkers for osteosarcoma patients. However, osteosarcoma is one of the malignant cancers entities characterized by the high level of heterogeneity in humans. Therefore, it is necessary to find accurate biomarkers for osteosarcoma.
In recent years, researchers have paid more and more attention to the role of the tumor microenvironment (TME) in malignant tumors. The function of TME in the tumorigenesis, progression, and therapy of tumors have been initially understood [
10,
11]. More importantly, Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE), an algorithm to quantify the score of immune cells and stromal cells by analyzing the gene expression data, was developed in 2013 [
12]. Based on the algorithm, the prognostic value of immune and stromal cells in bladder cancer, acute myeloid leukemia, gastric cancer, cervical squamous cell carcinoma, adrenocortical carcinoma, clear cell renal cell carcinoma, hepatocellular carcinoma, thyroid cancer, and cutaneous melanoma have been reported [
13‐
23]. Generally, the above research indicated that TME can serve as the prognostic biomarker in tumors, and many TME-related genes were determined as the prognostic genes. However, the role of TME and TME-related genes in osteosarcoma patients remains unclear.
In the present study, gene expression data and corresponding clinicopathologic data were obtained from The Therapeutically Applicable Research to Generate Effective Treatments (TARGET) dataset. Then, the ESTIMATE algorithm was performed to quantify the immune score of osteosarcoma and the TME-related genes were identified by the differential expression analysis. Subsequently, the prognostic value of TME and TME-related genes were determined by a series of bioinformatics methods.
Discussion
The relationship between TME and tumor have been widely studied in recent years. In the present study, ESTIMATE algorithm was utilized to quantify the immune score based on gene expression profiles in 85 osteosarcoma patients from TARGET database. We confirmed that the TME is significantly associated with the prognosis of osteosarcoma patients, including OS and DFS. In addition, functional enrichment analyses of TME-related genes indicated that immune-related processes known to contribute to tumor progression. More importantly, DEGs based on the TME were identified as important prognostic biomarkers for osteosarcoma patients, and two nomograms were developed for predicting the OS and DFS of osteosarcoma patients, respectively.
In recent years, an increasing number of studies focused on the carcinogenesis and progression of tumors based on the TME, and the ESTIMATE algorithm is one of the most important quantitative tools for this research field. Based on the ESTIMATE algorithm, the association between the prognosis and TME has been initially elucidated in some tumors, such as cervical squamous cell carcinoma, gastric cancer, cutaneous melanoma, acute myeloid leukemia, bladder cancer, and clear cell renal carcinoma [
13,
16,
17,
19‐
23]. However, previous studies indicated that TME scores serve as a different role in different tumors. For example, for hepatocellular carcinoma, gastric cancer, acute myeloid leukemia, bladder cancer, and clear cell renal carcinoma, patients with high immune score have a worse prognosis [
14,
16,
17,
20‐
23]. However, for cervical squamous cell carcinoma, adrenocortical carcinoma, and cutaneous melanoma, patients with high immune score have a favorable prognosis [
13,
18,
19]. Therefore, we can find great heterogeneity among different tumors from the perspective of TME. For osteosarcoma patients, the present study indicated that patients with higher immune score had a better OS and DFS. Hence, the present study indicated that immune cells infiltrating tumor tissue may play an important role in suppressing tumor progression.
In our research, 769 TME-related genes were identified by comparing the high-score and low-score osteosarcoma patients. The functional enrichment, including GO and KEGG analyses, showed that TME-related genes were mainly involved in the immune features, such as regulation of leukocyte activation, MHC protein complex, MHC protein, and complex binding. More importantly, the unsupervised cluster analysis based on DEGs was performed and all patients were divided into two clusters. Immune score and T cell CD4 memory activated fraction were significant difference between two clusters, which further elucidated the relationship between DEGs and immune features.
Due to the poor prognosis of osteosarcoma patients, identifying robust prognostic biomarker is very important. The tumor immune microenvironment is closely related to the prognosis of bone tumor patients. Emilie et.al [
24] performed the first genome-wide study to describe the role of immune cells in osteosarcoma and found that tumor-associated macrophages are associated with reduced metastasis and improved survival in high-grade osteosarcoma. Recently, the prognostic signature based on TME-related genes have been established for many tumors [
18,
20,
32], but only one study focused on osteosarcoma patients [
33]. Compared with the study performed by Zhang et al. [
33], we think that our research have some advantages. Firstly, our signatures were established based on several validated genes, and both two signatures were successfully validated in independent cohorts. Secondly, the outcome of DFS was not reported in the previous study. As reported in published studies, tumor recurrence is a terrible medical problem for osteosarcoma patients, and the 5-year survival rate for osteosarcoma patients with metastasis or relapse remains disappointing [
34,
35]. Hence, the DFS nomogram can improve the management of osteosarcoma patients. Finally, two nomograms incorporated TME-related signature and clinical variables were established in our research, which further facilitated the clinical application of our findings.
In our research, five genes were incorporated into the final prognostic signatures. FCGR2B, GFAP, and MPP7 were identified and validated as OS-related biomarkers, while CYP2S1 and ICAM3 were DFS-related biomarkers. The role of these genes in tumor prognosis had been widely reported in previous studies [
36‐
40]. FCGR2B has been confirmed as an immune-related gene previously [
41]. Although the relationship between FCGR2B and prognosis in sarcoma patients had not been reported, the prognostic value of FCGR2B had been widely confirmed in other cancers, such as hepatocellular carcinoma and glioblastoma [
36,
42]. In addition, New M et.al [
37] demonstrated that MPP7 is novel regulators of autophagy, which was thought to be responsible for the prognosis of pancreatic ductal adenocarcinoma. CYP2S1, described as Cytochrome P450 Family 2 Subfamily S Member 1, was reported significantly associated with colorectal cancer. In primary colorectal cancer, CYP2S1 was present at a significantly higher level of intensity compared with normal colon [
43]. More importantly, the presence of strong CYP2S1 immunoreactivity was associated with poor prognosis [
43]. The role of ICAM3 in cancer was also widely reported in published studies, and the Akt pathway plays an important role in the impact of ICAM3 on tumors. YG Kim et.al [
44] reported that ICAM3 can induce the proliferation of cancer cells through the PI3K/Akt pathway. Additionally, JK Park et.al showed that the ICAM3 can enhance the migratory and invasive potential of human non-small cell lung cancer cells by inducing MMP-2 and MMP-9 via Akt pathway [
45] showed that the ICAM3 can enhance the migratory and invasive potential of human non-small cell lung cancer cells by inducing MMP-2 and MMP-9 via Akt pathway.
Although the role of TME and TME-related genes in osteosarcoma patients have been initially studied by bioinformatic and statistical analyses in our research, some limitations should be elucidated. Firstly, the treatment information cannot be obtained from the TARGET database, which may influence the prognosis of osteosarcoma patients. Secondly, two nomograms were generated and showed good performance in our study. However, external validation by a large cohort is needed. Thirdly, many independent prognostic genes for osteosarcoma patients were identified in the present study, but the potential mechanism to influence osteosarcoma remains unclear. Finally, in the training cohort, 160 and 120 DEGs were identified as OS- and DFS-related DEGs, respectively. However, only five OS- and five DFS-related genes were identified in the validation cohort. The different age structures, smaller sample sizes and the platform covering only part of the genes may contribute to this result.
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