Identification of five long noncoding RNAs signature and risk score for prognosis of bladder urothelial carcinoma

Abstract

Nowadays, an increasing number of studies illustrated that bladder urothelial cancer (BLCA) may act as the most common subtype of urological malignancies with a high rate of recurrence and metastasis. In this study, we attempted to establish a prognostic model and identify the possible pathway crosstalk. Long noncoding RNAs (lncRNAs) and mRNA expression and corresponding clinical information of patients with BLCA were downloaded from The Cancer Genome Atlas (TCGA). The differentially expressed genes analysis, univariate Cox analysis, the least absolute shrinkage, and selection operator Cox (LASSO Cox) regression model were then applied to identify five crucial lncRNAs (AC092725.1, AC104071.1, AL023584.1, AL132642.1, and AL137804.1). The multivariate cox analysis was utilized to calculate the regression coefficients (βi). The risk‐score model was subsequently constructed as follows: (0.13541AC092725.1) + (0.20968AC104071.1) + (0.1525AL023584.1) − (0.14768AL132642.1) + (0.14387AL137804.1). Nomogram and assessment of overall survival (OS) prediction were verificated by the receiver operating characteristic curve in the testing group. As to 3‐, 5‐year OS prediction, the area under curve (AUC) for the nomogram of training data set was 0.83 and 0.86. Besides, the AUC (0.883 and 0.879) presented excellent predictive power in the testing group. In addition, the calibration plots validated the predictive performance of the nomogram. Weighted correlation network analysis (WGCNA) coupled with functional enrichment analysis contributed to explore the potential pathways, including PI3K‐Akt, HIF‐1, and Jak‐STAT signaling pathways. Construction of the risk‐score model and data analysis were both derived from multiple packages on the basis of the R platform chiefly.

Identification and Validation of an Individualized Prognostic Signature of Bladder Cancer Based on Seven Immune Related Genes

“…Till date, no report on IRG-based prognostic signatures exists, although considerable efforts have been made to develop prognostic signatures based on differentially expressed genes (Gao et al, 2019;Yan et al, 2019;Zhang et al, 2019). We identified seven prognostic IRGs based on a comprehensive analysis which could serve as valuable biomarkers in the clinical setting.…”

<p>LncRNA RNF144A-AS1 Promotes Bladder Cancer Progression via RNF144A-AS1/miR-455-5p/SOX11 Axis</p>

“…23,24 Some lncRNAs have been identified as molecular biomarkers for BC patients; however, novel correlated lncRNAs are urgently needed. [25][26][27] RNF144A-AS1 has been identified as an optimal diagnostic and prognostic biomarker for several cancer types, 10−12−28 , and has also been shown to promote cell migration and invasion in BC. 13 However, the mechanism behind the regulation remains largely unclear.…”

A nomogram combining long non-coding RNA expression profiles and clinical factors predicts survival in patients with bladder cancer

“…LncRNAs thus comprise an enormous reservoir of potential cancer treatment targets, and have been found to mark specific states of tumor progression and even predict outcomes [20][21][22][23][24][25]. Although some molecular biomarkers have been identified and tested among BCa patients [26][27][28], most studies have had small sample sizes, employed different platforms or failed to combine diverse prognostic variables. For these reasons, the identification of robust prognostic biomarkers remains an urgent clinical challenge.…”