Immunotherapy has shown excellent therapeutic effects on various malignant tumors; however, to date, immunotherapy for osteosarcoma is still suboptimal. In this study, we performed comprehensive bioinformatic analysis of immune-related genes (IRGs) and tumor-infiltrating immune cells (TIICs). Datasets of differentially expressed IRGs were extracted from the GEO database (GSE16088). The functions and prognostic values of these differentially expressed IRGs were systematically investigated using a series of bioinformatics methods. In addition, CCK8 and plate clone formation assays were used to explore the effect of PGF on osteosarcoma cells, and twenty-nine differentially expressed IRGs were identified, of which 95 were upregulated and 34 were downregulated. Next, PPI was established for Identifying Hub genes and biology networks by Cytoscape. Six IRGs (APLNR, TPM2, PGF, CD86, PROCR, and SEMA4D) were used to develop an overall survival (OS) prediction model, and two IRGs (HLA-B and PGF) were used to develop a relapse-free survival (RFS) prediction model. Compared with the low-risk patients in the training cohort (GSE39058) and TARGET validation cohorts, high-risk patients had poorer OS and RFS. Using these identified IRGs, we used OS and RFS prediction nomograms to generate a clinical utility model. The risk scores of the two prediction models were associated with the infiltration proportions of some TIICs, and the activation of memory CD4 T-cells was associated with OS and RFS. CD86 was associated with CTLA4 and CD28 and influenced the infiltration of different TIICs. In vitro experiments showed that the knockdown of PGF inhibited the proliferation and viability of osteosarcoma cells. In conclusion, these findings help us better understand the prognostic roles of IRGs and TIICs in osteosarcoma, and CD86 and PGF may serve as specific immune targets.
Previous studies have demonstrated the involvement of the solute carrier family 17 member 9 ( SLC17A9 ) in certain types of cancer; however, the precise role of SLC17A9 is not well defined. In the present study, a comprehensive analysis was performed to determine the involvement of SLC17A9 in a pan-cancer panel. First, data on SLC17A9 expression levels from publicly available databases were obtained to determine SLC17A9 expression profiles in various types of cancer. Next, the involvement of SLC17A9 in the prognosis of patients, stemness indices and the immune microenvironment was examined in 34 types of cancer. Furthermore, CCK-8 and colony-formation assays were performed to determine the effect of SLC17A9 on osteosarcoma (OSS) cells. In a pan-cancer panel, a difference in SLC17A9 expression levels was observed in the tumor tissues as compared with healthy tissues. Furthermore, survival analysis revealed a significant association between SLC17A9 expression levels and the prognosis of patients with various cancer types, including adrenocortical carcinoma, kidney renal clear cell carcinoma, glioblastoma, kidney renal papillary cell carcinoma, low grade glioma, liver hepatocellular carcinoma, mesothelioma, lung adenocarcinoma, skin cutaneous melanoma, uveal melanoma, stomach adenocarcinoma and OSS. The results of the present study revealed correlations between stemness indices, tumor immunity and SLC17A9 expression levels. Furthermore, univariate and multivariate Cox regression analyses indicated that SLC17A9 may be utilized as an independent risk factor for overall survival of patients with OSS. In vitro experiments demonstrated that SLC17A9 promotes the proliferation and viability of OSS cells. Taken together, the results of the present study suggest an association between SLC17A9 and the prognosis of patients as well as tumor immunity in various cancer types. SLC17A9 may serve as a novel prognostic biomarker and target for improving the prognosis of patients with OSS.
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