Construction of a novel mRNA-signature prediction model for prognosis of bladder cancer based on a statistical analysis

Abstract

Background Bladder cancer (BC) is a common malignancy neoplasm diagnosed in advanced stages in most cases. It is crucial to screen ideal biomarkers and construct a more accurate prognostic model than conventional clinical parameters. The aim of this research was to develop and validate an mRNA-based signature for predicting the prognosis of patients with bladder cancer. Methods The RNA-seq data was downloaded from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) were screened in three datasets, and prognostic genes were identified from the training set of TCGA dataset. The common genes between DEGs and prognostic genes were narrowed down to six genes via Least Absolute Shrinkage and Selection Operator (LASSO) regression, and stepwise multivariate Cox regression. Then the gene-based risk score was calculated via Cox coefficient. Time-dependent receiver operating characteristic (ROC) and Kaplan-Meier (KM) survival analysis were used to assess the prognostic power of risk score. Multivariate Cox regression analysis was applied to construct a nomogram. Decision curve analysis (DCA), calibration curves, and time-dependent ROC were performed to assess the nomogram. Finally, functional enrichment of candidate genes was conducted to explore the potential biological pathways of candidate genes. Results SORBS2, GPC2, SETBP1, FGF11, APOL1, and H1–2 were screened to be correlated with the prognosis of BC patients. A nomogram was constructed based on the risk score, pathological stage, and age. Then, the calibration plots for the 1-, 3-, 5-year OS were predicted well in entire TCGA-BLCA patients. Decision curve analysis (DCA) indicated that the clinical value of the nomogram was higher than the stage model and TNM model in predicting overall survival analysis. The time-dependent ROC curves indicated that the nomogram had higher predictive accuracy than the stage model and risk score model. The AUC of nomogram time-dependent ROC was 0.763, 0.805, and 0.806 for 1-year, 3-year, and 5-year, respectively. Functional enrichment analysis of candidate genes suggested several pathways and mechanisms related to cancer. Conclusions In this research, we developed an mRNA-based signature that incorporated clinical prognostic parameters to predict BC patient prognosis well, which may provide a novel prognosis assessment tool for clinical practice and explore several potential novel biomarkers related to the prognosis of patients with BC.

An Immunosenescence-Related Gene Signature to Evaluate the Prognosis, Immunotherapeutic Response, and Cisplatin Sensitivity of Bladder Cancer

“…The more accurate and reliable prediction for the prognosis and treatment response is always one of the hottest issues in BLCA. Many predictive gene models have been proposed, providing useful clinical tools for clinicians and BLCA patients [29,30]. However, the number of the risk model used for the evaluation of prognosis, immunotherapeutic response, and chemosensitivity at the same time is limited to date.…”

“…248 148 65 30 Figure 5: Pan-cancer analysis indicated that IRS is capable of predicting the OS of adrenocortical carcinoma (a), breast invasive carcinoma (b), cervical squamous cell carcinoma and endocervical adenocarcinoma (c), head and neck squamous cell carcinoma (d), chromophobe renal cell carcinoma (e), papillary renal cell carcinoma (f), hepatocellular carcinoma (g), lung adenocarcinoma (h), mesothelioma (i), ovarian serous cystadenocarcinoma (j), sarcoma (k), skin cutaneous melanoma (l), stomach adenocarcinoma (m), thyroid carcinoma (n), and endometrial carcinoma (o). IRS: immunosenescence-related score; OS: overall survival.…”

An HDAC9-associated immune-related signature predicts bladder cancer prognosis

“…The performance of prognosis prediction was also assessed through a nomogram and calibrations via the rms R package. To compare the predictive ability of our risk signature to other published signatures, we included risk signatures based only on prognostic-related genes [ 20 – 23 ] and those based on other risk factors [ 24 – 28 ] in the comparison of the AUCs of the ROC curves via the timeROC R package.…”

The impact of SETBP1 mutations in neurological diseases and cancer

“…These include acute myeloid leukemia (AML), chronic myelomonocytic leukemia (CMML), myelodysplastic syndrome (MDS), and solid tumors, such as bladder cancer. Its expression is also associated with poor prognosis (Cristóbal et al, 2010; Eisfeld et al, 2020; Elena et al, 2016; Hwang et al, 2019; Jiang et al, 2020; Li et al, 2021; Lucas et al, 2018; Martín et al, 2020; Robinson et al, 2020; Tang et al, 2022). The major cause of elevated SETBP1 expression is mutations in and around the SKI region; however, there are also known cases that occur in the absence of SETBP1 mutations (discussed later).…”