Construction of Molecular Subtype and Prognosis Prediction Model of Osteosarcoma Based on Aging-Related Genes

Abstract

Background: Osteosarcoma (OS) is a rare form of malignant bone cancer that is usually detected in young adults and adolescents. This disease shows a poor prognosis owing to its metastatic status and resistance to chemotherapy. Hence, it is necessary to design a risk model that can successfully forecast the OS prognosis in patients.

Methods: The researchers retrieved the RNA sequencing data and follow-up clinical data related to OS patients from the TARGET and GEO databases, respectively. The coxph function in R software was used for carrying out the Univariate Cox regression analysis for deriving the aging-based genes related sto the OS prognosis. The researchers conducted consistency clustering using the ConcensusClusterPlus R package. The R software package ESTIMATE, MCPcounter, and GSVA packages were used for assessing the immune scores of various subtypes using the ssGSEA technique, respectively. The Univariate Cox and Lasso regression analyses were used for screening and developing a risk model. The ROC curves were constructed, using the pROC package. The performance of their developed risk model and designed survival curve was conducted, with the help of the Survminer package.

Results: The OS patients were classified into 2 categories, as per the aging-related genes. The results revealed that the Cluster 1 patients showed a better prognosis than the Cluster 2 patients. Both clusters showed different immune microenvironments. Additional screening of the prognosis-associated genes revealed the presence of 5 genes, i.e., ERCC4, GPX4, EPS8, TERT, and STAT5A, and these data were used for developing the risk model. This risk model categorized the training set samples into the high- and low-risk groups. The patients classified into the high-risk group showed a poor OS prognosis compared to the low-risk patients. The researchers verified the reliability and robustness of the designed 5-gene signature using the internal and external datasets. This risk model was able to effectively predict the prognosis even in the samples having differing clinical features. Compared with other models, the 5- gene model performs better in predicting the risk of osteosarcoma.

Conclusion: The 5-gene signature developed by the researchers in this study could be effectively used for forecasting the OS prognosis in patients.

Figure 1

Analysis of the flow chart.

Figure 2

(a) Consistency matrix heat map if k = 2. (b) Cumulative distribution of the Cluster consistency. (c) Cluster heat map of the prognosis-linked genes. (d) KM plots for the OS of the subgroup patients retrieved from target.

Figure 3

(a) Comparison of ssGSEA immune scores between different molecular subtypes. (b) Comparison of the calculated immune scores between different molecular subtypes. (c) Comparison of the MCPcounter immune scores noted between various molecular subtypes. (d) Heat map comparison of the immune scores using 3 immune software packages between different molecular subtypes.

Figure 4

(a) Change track of every independent variable, where the X-axis denotes the log value of an independent variable (lambda), while the Y-axis denotes the coefficient of an independent variable. (b) The confidence interval included under every lambda.

Figure 5

KM curves of 5 genes derived from the TARGET training set.

Figure 6

Expression levels of the above-mentioned 5 genes that were categorized into the high- and low-risk groups.

Figure 7

(a) RiskScore, survival status, survival time, and the expression of 5 genes retrieved from the TARGET training set. (b) ROC curve and the AUC of the novel 5-gene signature. (c) Distribution of the KM survival curves for the 5-gene signature included in the TARGET training set.

Figure 8

(a) RiskScore, survival status, survival time, and the expression of 5 genes retrieved from the TARGET test set. (b) ROC curve and AUC of the novel 5-gene signature. (c) Distribution of KM survival curves for the 5-gene signature included in the TARGET test set.

Figure 9

(a) RiskScore, survival status, survival time, and expression of 5 genes retrieved from all TARGET datasets. (b) ROC curve and AUC of the novel 5-gene signature. (c) Distribution of KM survival curves for the 5-gene signature included in the TARGET datasets.

Figure 10

(a) RiskScore, survival status, survival time, and expression of 5 genes retrieved from the independent validation data set GSE21257. (b) ROC curve and the novel AUC of the 5-gene signature. (c) Distribution of the KM survival curves for the 5-gene signature included in the independent validation data set GSE21257.

Figure 11

(a) KM curves for the high- and the low-risk groups that included patients more than 15 years of age. (b) KM curves for the high-risk and low-risk groups that included patients less than 15 years of age. (c) KM curves for the high- and low-risk groups that included female patients. (d) KM curves for the high- and low-risk groups that included male patients.

Figure 12

(a) Clustering of the correlation coefficients noted between the RiskScore and the KEGG pathway, where the RiskScore correlation was >0.35. (b) Variations in the ssGSEA score of the KEGG pathway having a correlation <0.35 in every sample with increasing RiskScore. The X-axis denotes the sample, where the RiskScore value increased from left to right.

Figure 13

(a) Univariate analysis based on the newly constructed 5-gene signature, age, and gender of the patients. (b) Multivariate analysis based on the novel 5-gene signature, age, and gender of the patients included in the study.

Figure 14

(a) RiskScore, survival status, survival time, and expression of 3 genes retrieved from all the TARGET datasets. (b) ROC curve and AUC of the 3-gene signature. (c) Distribution of KM survival curves for the 3-gene signature included in all the TARGET datasets.

Figure 15

(a) RiskScore, survival status, survival time, and expression of 7 genes retrieved from all the TARGET datasets. (b) ROC curve and AUC of the 7-gene signature. (c) Distribution of KM survival curves for the 7-gene signature included in all the TARGET datasets.