Long noncoding RNAs to predict postoperative recurrence in bladder cancer and to develop a new molecular classification system.

BACKGROUND

Reliable molecular markers are much needed for early prediction of recurrence in muscle-invasive bladder cancer (MIBC) patients. We aimed to build a long-noncoding RNA (lncRNA) signature to improve recurrence prediction and lncRNA-based molecular classification of MIBC.

METHODS

LncRNAs of 320 MIBC patients from the Cancer Genome Atlas (TCGA) database were analyzed, and a nomogram was established. A molecular classification system was created, and immunotherapy and chemotherapy response predictions, immune score analysis, immune infiltration analysis, and mutational data analysis were conducted. Survival analysis validation was also performed.

RESULTS

An eight-lncRNA signature classifed the patients into high- and low-risk subgroups, and these groups had significantly different (disease-free survival) DFS. The ability of the eight-lncRNA signature to make an accurate prognosis was tested using a validation dataset from our samples. The nomogram achieved a C-index of 0.719 (95% CI, 0.674-0.764). Time-dependent receiver operating characteristic curve (ROC) analysis indicated the superior prognostic accuracy of nomograms for DFS prediction (0.76, 95% CI, 0.697-0.807). Further, the four clusters (median DFS = 11.8, 15.3, 17.9, and 18.9 months, respectively) showed a high frequency of TTN (cluster 1), fibroblast growth factor receptor-3 (cluster 2), TP53 (cluster 3), and TP53 mutations (cluster 4), respectively. They were enriched with M2 macrophages (cluster 1), CD8 T cells (cluster 2), M0 macrophages (cluster 3), and M0 macrophages (cluster 4), respectively. Clusters 2 and 3 demonstrated potential sensitivity to immunotherapy and insensitivity to chemotherapy, whereas cluster 4 showed potential insensitivity to immunotherapy and sensitivity to chemotherapy.

CONCLUSIONS

The eight-lncRNA signature risk model may be a reliable prognostic signature for MIBC, which provides new insights into prediction of recurrence of MIBC. The model may help clinical decision and eventually benefit patients.