Prognostic implications of necroptosis-related long noncoding RNA signatures in muscle-invasive bladder cancer

Abstract

Background: Bladder cancer (BLCA) is the sixth most common cancer in men, with an increasing incidence of morbidity and mortality. Necroptosis is a type of programmed cell death and plays a critical role in the biological processes of bladder cancer (BLCA). However, current studies focusing on long noncoding RNA (lncRNA) and necroptosis in cancer are limited, and there is no research about necroptosis-related lncRNAs (NRLs) in BLCA. Methods: We obtained the RNA-seq data and corresponding clinical information of BLCA from The Cancer Genome Atlas (TCGA) database. The seven determined prognostic NLRs were analyzed by several methods and verified by RT-qPCR. Then, a risk signature was established based on the aforementioned prognostic NLRs. To identify it, we evaluated its prognostic value by Kaplan-Meier (K-M) survival curve and receiver operating characteristics (ROC) curve analysis. Moreover, the relationships between risk signature and clinical features, functional enrichment, immune landscape, and drug resistance were explored as well. Results: We constructed a signature based on seven defined NLRs (HMGA2-AS1, LINC02489, ETV7-AS1, EMSLR, AC005954.1, STAG3L5P-PVRIG2P-PILRB, and LINC02178). Patients in the low-risk cohort had longer survival times than those in the high-risk cohort, and the area under the ROC curve (AUC) value of risk signature was higher than other clinical variables. Functional analyses, the infiltrating level of immune cells and functions, ESTIMATE score, and immune checkpoint analysis all indicated that the high-risk group was in a relatively immune-activated state. In terms of treatments, patients in the high-risk group were more sensitive to immunotherapy, especially anti-PD1/PD-L1 immunotherapy and conventional chemotherapy. Conclusion: The novel NLR signature acts as an invaluable tool for predicting prognosis, immune microenvironment, and drug resistance in muscle-invasive bladder cancer (MIBC) patients.

Keywords: drug resistance; immune microenvironment; lncRNAs; muscle-invasive bladder cancer; necroptosis; prognosis.

FIGURE 1

Flow chart.

FIGURE 2

Identification of prognostic necroptosis-related lncRNAs (NLRs) in muscle-invasive bladder cancer (MIBC). (A) The network figure of necroptosis-related genes (NRGs) and NLRs (correlation coefficient > 0.4 and p < 0.001). (B) The heat map of different NLRs between tumor and normal samples. (C) The volcano plot of 689 differentially expressed NLRs. (D) The cross-validation for variable selection in the least absolute shrinkage and selection operator (LASSO) model. (E) The LASSO coefficient profile of 12 NLRs. (F) The Sankey diagram of the relationship between necroptosis-related genes (NRGs) and NLRs. (G,H) The forest plot and heat map of determined prognostic NLRs that executed by multivariate Cox proportional hazard regression analysis. (I) The qRT-PCR results of seven prognostic NLRs in bladder cancer cells. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001. ns, not significant.

FIGURE 3

Prognostic value of the necroptosis-related risk model. (A–C) Kaplan–Meier survival curves of overall survival (OS) between high- and low-risk groups in the entire, test, and train cohort. (D–F) The distribution of risk score among muscle-invasive bladder cancer (MIBC) patients in the entire, test, and train cohort. (G–I) Exhibition of survival time and status between high- and low-risk groups in the entire, test, and train cohort. (J–L) The heat map of seven prognostic necroptosis-related lncRNAs (NLRs) expression in the entire, test, and train cohort.

FIGURE 4

Further confirmation of prognostic value of the risk model combined with clinical features. Kaplan–Meier survival curves of high- and low-risk groups among muscle-invasive bladder cancer (MIBC) patients sorted based on different clinical features, including (A) age, (B) gender, (C) stage, (D) T stage, and (E) N stage. (F,G) Risk model was an independent factor of prognosis by using univariate Cox and multivariate Cox proportional hazard regression analyses. (H) The 1-, 2-, and 3-year receiver operating characteristics (ROC) curves of all MIBC patients. (I) The 1-year ROC curves of risk score and other clinical features. (J) The heat map of distinctions in clinical features between high- and low-risk groups. (K,L) The histogram showing the difference of risk scores in MIBC patients stratified by grade and stage. *p < 0.05, **p < 0.01.

FIGURE 5

Construction and calibration of the nomogram, followed by functional and tumor burden analyses. (A) Nomogram integrated age, gender, grade, stage, T stage, N stage, M stage, and risk score. (B) Calibration curves test consistency of the nomogram at 1, 3, and 5 years. (C) Activated pathways analyzed by gene set enrichment analyses (GSEA) in the high-risk group. (D) Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of differential genes between high- and low-risk groups. (E,F) The gene mutations between high- and low-risk groups.

FIGURE 6

Immune infiltration of muscle-invasive bladder cancer (MIBC) patients between high- and low-risk groups. (A) The bubble chart presented a correlation between risk score and infiltrating level of immune cells. (B,C) The distinction of immune infiltrating cells and immune-related functions between low- and high-risk groups. (D) The stromal score, immune score, and ESTIMATE score of the two groups.

FIGURE 7

Checkpoint and drug response between high- and low-risk groups. (A) The significant distinction of checkpoint between high- and low-risk groups. (B–H) IC50 of cisplatin, methotrexate, vinblastine, doxorubicin, docetaxel, gemcitabine, and paclitaxel between high- and low-risk groups.