Abstract
Renal cell carcinoma (RCC) is one of the most common malignancies of the urinary system, accounting for 3% of adult malignancies. Long non-coding RNA (lncRNA) is abnormally regulated in many cancers and can be used as a molecular marker for early diagnosis and prognosis of RCC. Here, original lncRNA datas were retrieved from TCGA, differential co-expression analysis was performed to classify immune-related lncRNA (irlncRNA) with differential expression, and the improved 0 or 1 matrix cyclic single pairing method was used to verify lncRNA pairs. Then, we performed a univariate analysis in combination with an improved Lasso penalty regression that included cross-validation, multiple repetitions, and random stimulus procedures to determine different expression irlncRNA (DEirlncRNA) pairs. AUC values under Receiver Operating Characteristic curve (ROC) were calculated to obtain the optimal model, and AIC values of each point on AUC were calculated to obtain the optimal cut-off point to distinguish the high and low risk groups of Clear-cell renal cell carcinoma (ccRCC) patients. Finally, we evaluated the new model in a variety of clinical settings including survival, clinicopathological features, tumor-infiltrating immune cells, chemotherapy, and checkpoint related biomarkers, all showing promising clinical application.
Histologic Growth Patterns in Clear Cell Renal Cell Carcinoma Stratify Patients into Survival Risk Groups
