Dissecting the effect of sphingolipid metabolism gene in progression and microenvironment of osteosarcoma to develop a prognostic signature

Abstract

Sphingolipid metabolism (SM) fuels tumorigenesis and the malignant progression of osteosarcoma (OS), which leads to an unfavorable prognosis. Elucidating the molecular mechanisms underlying SM in osteosarcoma and developing a SM-based prognostic signature could be beneficial in the clinical setting. This study included 88 frozen OS samples to recognize the vital SM-relevant genes in the development of OS utilizing univariate Cox regression. The Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis was conducted on the SM- relevant genes to minimize the risk of overfitting. The prognostic signature was generate utilizing the multivariable Cox regression analysis and was verified in the validation cohort. Moreover, cellular and molecular mechanisms associated with SM have an unfavorable prognosis for OS patients and have been widely studied. Resultantly, an SM-based prognostic risk model was established according to critical prognostic genes (CBS, GLB1, and HACD1), which had an excellent ability to predict the prognosis of OS patients (AUC for the train cohort was 0.887 and AUC for validation cohort was 0.737). The high-risk OS patients identified based on this prognostic signature had significantly poor immune microenvironment, indicated by significantly low immune score (mean=216.290 ± 662.463), reduced infiltrations of 25 immune cells, including NK cells (LogFC= -0.3597), CD8+T cells ((LogFC=-0.2346), Cytolytic activity ((LogFC=-0.1998), etc. The immunosuppressive microenvironment could be due to dysregulated SM of glycolipids. Further, a nomogram was constructed by integrating the SM-based prognostic signature and clinical paraments to facilitate clinical application. The nomogram could accurately predict the prognosis of OS invalids. Collectively, this study clarified the function of SM in the development of OS and helped develop a tool for risk stratification based on SM-related genes with application in clinical settings. The results of our study will aid in identifying high-risk patients and provide individualized treatments.

Identification prognostic features related to sphingolipid metabolism and experimental validation of TRIM47 in hepatocellular carcinoma

“…G6PD can mediate crosstalk between cancer cells and M2 macrophages through the CCL2/TGF-β1/IL-10 signaling axis to promote tumor progression [32]. Sphingolipid metabolism is involved in the malignant progression of tumors and the regulation of immunosuppressive microenvironment [33]. Patients in the High-Sphingolipid score group had higher immune sores.…”

Prognostic signatures of sphingolipids: Understanding the immune landscape and predictive role in immunotherapy response and outcomes of hepatocellular carcinoma

“…Sphingolipids are known to play a crucial role in tumor growth and its interactions with various pathways related to cancer (60-65). However, many genes associated with sphingolipids remain poorly understood and have not been extensively researched as potential therapeutic targets in clinical settings (66,67). We identified six genes that form a strong risk score signature.…”

Integrated single-cell sequencing, spatial transcriptome sequencing and bulk RNA sequencing highlights the molecular characteristics of parthanatos in gastric cancer

“…E) The “survival” and “survminer” packages were used to evaluate the modeling effect of the prognostic model by comparing the survival differences between different risk groups of GC patients [ 40 ]. F) The “survivalROC” package was utilized to plot the ROC curve and calculate the AUC value to evaluate the predictive accuracy of the model [ 41 ]. Subsequently, in the internal validation set 1, internal validation set 2, and external validation set, the same model genes selected from the training cohort were subjected to multivariate Cox regression.…”