Identification of prognostic biomarkers associated with metastasis and immune infiltration in osteosarcoma

Abstract

Osteosarcoma is the most common primary malignancy of the bones, and is associated with a high rate of metastasis and a poor prognosis. A tight association between the tumor microenvironment (TME) and osteosarcoma metastasis has been established. In the present study, the Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) algorithm was applied to calculate the immune and stromal scores of patients with osteosarcoma based on data from The Cancer Genome Atlas database. A metagene approach and deconvolution method were used to reveal distinct TME landscapes in patients with osteosarcoma. Bioinformatics analysis was used to identify differentially expressed genes (DEGs) associated with metastasis and immune infiltration in osteosarcoma, and a risk model was constructed using the DEGs with potential prognostic significance. Subsequently, gene set enrichment and Spearman's correlation analyses were used to delineate the biological processes associated with these prognostic biomarkers. Finally, immunohistochemical (IHC) analysis was performed to evaluate the expression levels of immune infiltrates and prognostic biomarkers in clinical osteosarcoma tissues. The results of the ESTIMATE demonstrated that patients with non-metastatic osteosarcoma presented with higher immune/stromal scores and a more favorable prognosis compared with those with metastatic osteosarcoma. The TME landscapes in patients with osteosarcoma suggested that high levels of tumor-infiltrating immune cells (TIICs) may suppress metastasis. Increased numbers of CD56 bright natural killer cells, immature B cells, M1 macrophages and neutrophils, and lower levels of M2 macrophages were observed in the non-metastatic tissues compared with those in the metastatic tissues. A total of 69 DEGs were identified to be associated with metastasis and immune infiltration in osteosarcoma. Of these, GATA3, LPAR5, EVI2B, RIAM and CFH exhibited prognostic potential and were highly expressed in non-metastatic osteosarcoma tissues based on the IHC analysis results. These biomarkers were involved in various immune-related biological processes and were positively associated with multiple TIICs and immune signatures. The risk model constructed using these prognostic biomarkers demonstrated high predictive accuracy for the prognosis of osteosarcoma. In conclusion, the present study proposed a five-biomarker prognostic signature for the prediction of metastasis and immune infiltration in patients with osteosarcoma.

The Value of Immune-Related Genes Signature in Osteosarcoma Based on Weighted Gene Co-expression Network Analysis

“…In the same study, EVI2B was found to correlate with the prognosis of patients significantly negatively with colon cancer (p < 0:05), and EVI2B may be involved in the prognosis of colon cancer patients [37,63]. EVI2B can similarly be used as a prognosis for osteosarcoma compared to the study by Yang et al [64].…”

Prognostic Signature of Osteosarcoma Based on 14 Autophagy-Related Genes

“…For example, Qu et al constructed a 5-gene-signature for the prognosis prediction of osteosarcoma based on the super-enhancer-associated genes [84] The model basing on the biomarkers including RBC, PNI, CRE, Ca 2 ⁺and LSR in blood presented good performance in predicting the overall survival of osteosarcoma patients [85]. Lin et al established a predictive model with five differentially expressed genes related to metastasis between the metastatic and non-metastatic samples for the prognosis of osteosarcoma patients [86]. To our knowledge, we are the first to construct a prognostic model of osteosarcoma with autophagy related genes via integrated methods of bioinformatics and machine learning.…”

Predicting the Survival and Immune Landscape of Colorectal Cancer Patients Using an Immune-Related lncRNA Pair Model

“…Tumor-infiltrating immune cells is crucial in tumor progression and treatment. The types and densities of TIICs not only have connection with prognosis of patients, but also influence responses to treatment ( Zhu et al, 2020 ; Li et al, 2021 ; Xu et al, 2021 ; Yang B. et al, 2021 ; Yang Y. et al, 2021 ). Hence, understanding the role of TIICs may provide fresh insights into searching for novel clinical biomarkers for malignancies and facilitating cancer patient treatment by means of immunotherapy.…”