Development and Verification of a Hypoxic Gene Signature for Predicting Prognosis, Immune Microenvironment, and Chemosensitivity for Osteosarcoma

Abstract

Objective: Hypoxic tumors contribute to local failure and distant metastases. Nevertheless, the molecular hallmarks of hypoxia remain ill-defined in osteosarcoma. Here, we developed a hypoxic gene signature in osteosarcoma prognoses.Methods: With the random survival forest algorithm, a prognostic hypoxia-related gene signature was constructed for osteosarcoma in the TARGET cohort. Overall survival (OS) analysis, receiver operating characteristic (ROC) curve, multivariate cox regression analysis, and subgroup analysis were utilized for assessing the predictive efficacy of this signature. Also, external validation was presented in the GSE21257 cohort. GSEA was applied for signaling pathways involved in the high- and low-risk samples. Correlation analyses between risk score and immune cells, stromal/immune score, immune checkpoints, and sensitivity of chemotherapy drugs were performed in osteosarcoma. Then, a nomogram was built by integrating risk score, age, and gender.Results: A five-hypoxic gene signature was developed for predicting survival outcomes of osteosarcoma patients. ROC curves confirmed that this signature possessed the well predictive performance on osteosarcoma prognosis. Furthermore, it could be independently predictive of prognosis. Metabolism of xenobiotics by cytochrome P450 and nitrogen metabolism were activated in the high-risk samples while cell adhesion molecules cams and intestinal immune network for IgA production were enriched in the low-risk samples. The low-risk samples were characterized by elevated immune cell infiltrations, stromal/immune scores, TNFRSF4 expression, and sensitivity to cisplatin. The nomogram accurately predicted 1-, 3-, and 5-years survival duration.Conclusion: These findings might offer an insight into the optimization of prognosis risk stratification and individualized therapy for osteosarcoma patients.

A glycometabolic gene signature associating with immune infiltration and chemosensitivity and predicting the prognosis of patients with osteosarcoma

“…Recently, several gene signatures have been established from different perspectives which could serve as diagnostic biomarkers and predict the prognosis of OS. These prognostic signatures can be divided into several categories, including immune related gene signatures ( 36 , 37 ), hypoxic gene signatures ( 38 , 39 ), cell death related gene signature ( 40 – 43 ), noncoding RNA related prognostic signatures ( 44 , 45 ), epigenetics related gene signatures ( 46 , 47 ), and metastasis associated gene signatures ( 48 , 49 ). In addition, some metabolism related prognostic signatures have also been developed ( 50 , 51 ).…”

Unfolded Protein Response–Related Signature Associates With the Immune Microenvironment and Prognostic Prediction in Osteosarcoma

“…Wen et al developed a 3-gene nomogram model with 3-year and 5-year AUC values of 0.853 and 0.818, respectively ( Wen et al, 2020 ). Wu et al established a hypoxic nomogram model with an AUC value of only 0.73 ( Wu et al, 2021 ). In our study, the constructed nomogram model had 3-year and 5-year AUC values of 0.93 and 0.90, respectively, which were consistently superior to other models.…”

Hypoxia-immune-related microenvironment prognostic signature for osteosarcoma

“…Time-dependent receiver-operating characteristic (ROC) curves were used to verify the performance of the risk signature by comparing the prediction efficiency with the clinical features. With the help of area under the curve (AUC) values and Cox regression analysis, we compared our prognostic signature with other published osteosarcoma models generated by Fu Y et al ( Fu et al, 2021 ) and Wu F et al ( Wu et al, 2021 ). A webserver, GEOexplorer ( Hunt et al, 2022 ), was applied to integrate the TARGET and GSE21257 datasets, and the integrated dataset was used as an additional validation dataset.…”