LAUSR

Background We sought to screen and verify the long non-coding ribonucleic acids (lncRNAs) related to immune infiltration in metastatic osteosarcoma (OS). Methods We downloaded the RNA-sequencing expression data from The Cancer Genome Atlas (TCGA) database as the training data set. We downloaded the GSE39055 data set from the National Center for Biotechnology Information, Gene Expression Omnibus as the validation data set. The least absolute shrinkage and selection operator (LASSO) regression algorithm was used to screen the optimized lncRNA combinations. Kaplan-Meier curves were used to evaluate the associations between the lncRNAs and actual prognosis. The independent survival prognosis clinical factors were obtained by univariate and multivariate Cox analyses. A nomogram was established to explore the correlation between survival rate and risk information. The Tumor IMmune Estimation Resource was applied to estimate the composition of 6 subtypes of immune infiltration cells. Results In total, 1,398 lncRNAs and 14,631 messenger RNAs were screened from TCGA data set, and divided into the low and high immunity groups. The Estimation of STromal and Immune cells in MAlignant Tumour tissues using Expression data (ESTIMATE) scores differed significantly between the samples in the two groups. Additionally, 5 optimized lncRNA combinations were obtained using the LASSO algorithm. Risk factors including age, metastatic tumor, and risk-score (RS) were related to the prognosis of OS patients. The survival rates predicted by the nomogram model were consistent with the actual survival rates of OS patients. Finally, we found that RS was negatively correlated with the proportion of immune cells. Conclusions In total, 5 feature lncRNAs were identified as novel biomarkers for OS. Next, a RS nomogram model was constructed based on the 5 identified lncRNAs. This model predicted the survival rates and prognoses of OS patients well.