Novel Prognosis and Therapeutic Response Model of Immune-Related lncRNA Pairs in Clear Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (ccRCC) is the most common type of renal carcinoma. It is particularly important to accurately judge the prognosis of patients. Since most tumor prediction models depend on the specific expression level of related genes, a better model therefore needs to be constructed. To provide an immune-related lncRNA (irlncRNAs) tumor prognosis model that is independent of the specific gene expression levels, we first downloaded and sorted out the data on ccRCC in the TCGA database and screened irlncRNAs using co-expression analysis and then obtained the differently expressed irlncRNA (DEirlncRNA) pairs by means of univariate analysis. In addition, we modified LASSO penalized regression. Subsequently, the ROC curve was drawn, and we compared the area under the curve, calculated the Akaike information standard value of the 5-year receiver operating characteristic curve, and determined the cut-off point to establish the best model to distinguish the high- or low-disease-risk group of ccRCC. Subsequently, we reassessed the model from the perspectives of survival, clinic-pathological characteristics, tumor-infiltrating immune cells, chemotherapeutics efficacy, and immunosuppressed biomarkers. A total of 17 DEirlncRNAs pairs (AL031710.1 vertical bar AC104984.5, AC020907.4 vertical bar AC127-24.4, AC091185.1 vertical bar AC005104.1, AL513218.1 vertical bar AC079015.1, AC104564.3 vertical bar HOXB-AS3, AC003070.1 vertical bar LINC01355, SEMA6A-AS1 vertical bar CR936218.1, AL513327.1 vertical bar AS005785.1, AC084876.1 vertical bar AC009704.2, IGFL2-AS1 vertical bar PRDM16-DT, AC011462.4 vertical bar MMP25-AS1, AL662844.3I vertical bar TGB2-AS1, ARHGAP27P1 vertical bar AC116914.2, AC093788.1 vertical bar AC007098.1, MCF2L-AS1 vertical bar AC093001.1, SMIM25 vertical bar AC008870.2, and AC027796.4 vertical bar LINC00893) were identified, all of which were included in the Cox regression model. Using the cut-off point, we can better distinguish patients according to different factors, such as survival status, invasive clinic-pathological features, tumor immune infiltration, whether they are sensitive to chemotherapy or not, and expression of immunosuppressive biomarkers. We constructed the irlncRNA model by means of pairing, which can better eliminate the dependence on the expression level of the target genes. In other words, the signature established by pairing irlncRNA regardless of expression levels showed promising clinical prediction value.