DataSheet_1_Gene Expression Classifier Reveals Prognostic Osteosarcoma Microenvironment Molecular Subtypes.pdf (72.76 kB)
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DataSheet_1_Gene Expression Classifier Reveals Prognostic Osteosarcoma Microenvironment Molecular Subtypes.pdf

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posted on 20.04.2021, 05:11 authored by Yi-Jiang Song, Yanyang Xu, Chuangzhong Deng, Xiaojun Zhu, Jianchang Fu, Hongmin Chen, Jinchang Lu, Huaiyuan Xu, Guohui Song, Qinglian Tang, Jin Wang

Osteosarcoma (OSA) is the most common bone malignancy and displays high heterogeneity of molecular phenotypes. This study aimed to characterize the molecular features of OSA by developing a classification system based on the gene expression profile of the tumor microenvironment. Integrative analysis was performed using specimens and clinical information for OSA patients from the TARGET program. Using a matrix factorization method, we identified two molecular subtypes significantly associated with prognosis, S1 (infiltration type) and S2 (escape type). Both subtypes displayed unique features of functional significance features and cellular infiltration characteristics. We determined that immune and stromal infiltrates were abundant in subtype S1 compare to that in subtype S2. Furthermore, higher expression of immune checkpoint PDCD1LG2 and HAVCR2 was associated with improved prognosis, while a preferable chemotherapeutic response was associated with FAP-positive fibroblasts in subtype S1. Alternatively, subtype S2 is characterized by a lack of effective cytotoxic responses and loss of major histocompatibility complex class I molecule expression. A gene classifier was ultimately generated to enable OSA classification and the results were confirmed using the GSE21257 validation set. Correlations between the percentage of fibroblasts and/or fibrosis and CD8+ cells, and their clinical responses to chemotherapy were assessed and verified based on 47 OSA primary tumors. This study established a new OSA classification system for stratifying OSA patient risk, thereby further defining the genetic diversity of OSA and allowing for improved efficiency of personalized therapy.

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