A Competing Endogenous RNA Network and an 8-lncRNA Prognostic Signature Identify MYO16-AS1 as an Oncogenic lncRNA in Bladder Cancer

Identification of necroptosis-related long non-coding RNAs prognostic signature and the crucial lncRNA in bladder cancer

BACKGROUND

Research on the relationships between long non-coding RNAs (lncRNAs) and cancer is attractive and has progressed very rapidly. Necroptosis-related biomarkers can potentially be used for predicting the prognosis of cancer patients. This study aimed to establish a necroptosis-related lncRNA (NPlncRNA) signature to predict the prognosis of patients with bladder cancer (BCa).

METHODS

First, NPlncRNAs were identified using Pearson correlation analysis and machine learning algorithms, including SVM-RFE, least absolute shrinkage and selection operator (LASSO) regression, and random forest. The prognostic NPlncRNA signature was constructed using univariate and multivariate Cox regression analyses and the diagnostic efficacy and clinically predictive efficiency were evaluated and validated. The biological functions of the signature were analysed using gene set enrichment analysis (GSEA) and functional enrichment analysis. We further integrated the RNA-seq dataset (GSE133624) with our outcomes to reveal the crucial NPlncRNA that was functionally verified by assessing cell viability, proliferation, and apoptosis in BCa cells.

RESULTS

The prognostic NPlncRNAs signature was composed of PTOV1-AS2, AC083862.2, MAFG-DT, AC074117.1, AL049840.3, and AC078778.1, and a risk score based on this signature was proven to be an independent prognostic factor for the BCa patients, indicated by poor overall survival (OS) of patients in the high-risk group. Additionally, the NPlncRNAs signature had a higher diagnostic validity than that of other clinicopathological variables, with a greater area under the receptor operating characteristic and concordance index curves. A nomogram established by integrating clinical variables and risk score confirmed that the signature can accurately predict the OS of patients and has high clinical practicability. Functional enrichment analysis and GSEA revealed that some cancer-related and necroptosis-related pathways were enriched in high-risk groups. The crucial NPlncRNA MAFG-DT was associated with poor prognosis and was highly expressed in BCa cells. MAFG-DT silencing notably inhibited proliferation and enhanced apoptosis of BCa cells.

CONCLUSIONS

A novel prognostic NPlncRNAs signature was identified in BCa in this study, which provides potential therapeutic targets among which MAFG-DT plays critical roles in the tumorigenesis of BCa.

Accurate Diagnosis and Survival Prediction of Bladder Cancer Using Deep Learning on Histological Slides

(1) Background: Early diagnosis and treatment are essential to reduce the mortality rate of bladder cancer (BLCA). We aimed to develop deep learning (DL)-based weakly supervised models for the diagnosis of BLCA and prediction of overall survival (OS) in muscle-invasive bladder cancer (MIBC) patients using whole slide digitized histological images (WSIs). (2) Methods: Diagnostic and prognostic models were developed using 926 WSIs of 412 BLCA patients from The Cancer Genome Atlas cohort. We collected 250 WSIs of 150 BLCA patients from the Renmin Hospital of Wuhan University cohort for external validation of the models. Two DL models were developed: a BLCA diagnostic model (named BlcaMIL) and an MIBC prognostic model (named MibcMLP). (3) Results: The BlcaMIL model identified BLCA with accuracy 0.987 in the external validation set, comparable to that of expert uropathologists and outperforming a junior pathologist. The C-index values for the MibcMLP model on the internal and external validation sets were 0.631 and 0.622, respectively. The risk score predicted by MibcMLP was a strong predictor independent of existing clinical or histopathologic indicators, as demonstrated by univariate Cox (HR = 2.390, p < 0.0001) and multivariate Cox (HR = 2.414, p < 0.0001) analyses. The interpretability of DL models can help in the analysis of critical regions associated with tumors to enrich the information obtained from WSIs. Furthermore, the expression of six genes (ANAPC7, MAPKAPK5, COX19, LINC01106, AL161431.1 and MYO16-AS1) was significantly associated with MibcMLP-predicted risk scores, revealing possible potential biological correlations. (4) Conclusions: Our study developed DL models for accurately diagnosing BLCA and predicting OS in MIBC patients, which will help promote the precise pathological diagnosis of BLCA and risk stratification of MIBC to improve clinical treatment decisions.

NXPH4 Used as a New Prognostic and Immunotherapeutic Marker for Muscle-Invasive Bladder Cancer

Background

One of the most common malignant tumors of the urinary system is muscle-invasive bladder cancer (MIBC). With the increased use of immunotherapy, its importance in the field of cancer is becoming abundantly evident. This study classifies MIBC according to GSVA score from the perspective of the GSEA immune gene set.

Methods

This study integrated the sequencing and clinical data of MIBC patients in TCGA and GEO databases, then scored the data using the GSVA algorithm, the CNMF algorithm was implemented to divide the subtypes of GEO and TCGA datasets, respectively, and finally screened and determined the key pathways in combination with clinical data. Simultaneously, LASSO Cox regression model was constructed based on key pathway genes to assess the model's predictive ability (ROC) and describe the immune landscape differences between high- and low-risk groups; key genes were further analyzed and verified in patient tissues.

Results

404 TCGA and 297 GEO datasets were divided into C1-3 groups (TCGA-C1:120/C2:152/C3:132; GEO- C1:112/C2:101/C3:84), of which TCGA-C2 (n = 152) subtype and GEO-C1 (n = 112) subtype had the worst prognosis. LASSO Cox regression model with ROC (train set = 0.718, test set = 0.667) could be constructed. When combined with the Cancer Immunome Atlas database, it was found that patients with high-risk scores were more sensitive to PD-1 inhibitor and PD-1 inhibitor combined with CTLA-4. NXPH4, as a key gene, plays a role in MIBC with tissue validation results show that nxph4 is highly expressed in tumor.

Conclusion

The immune gene score of MIBC data in TCGA and GEO databases was successfully evaluated using GSVA in this research. The lasso Cox expression model was successfully constructed by screening immune genes, the high-risk group had a worse prognosis and higher sensitivity to immunotherapy, PD-1 inhibitors or PD-1 combined with CTLA-4 inhibitors can be preferentially used in high-risk patients who are sensitive to immunotherapy, and NXPH4 may be a molecular target to adjust the effect of immunotherapy.

Prediction of bladder cancer outcome by identifying and validating a mutation-derived genomic instability-associated long noncoding RNA (lncRNA) signature

Bladder cancer is one of the most common malignant tumors worldwide. Accordingly, its incidence and mortality are high. One of the characteristics of cancer is genomic instability. New studies suggest that long non-coding RNAs (lncRNAs) play an important role in maintaining genomic instability. This study aimed to identify a genomic instability-associated lncRNA signature to predict the outcome of patients with bladder cancer. We downloaded data for bladder cancer patients from The Cancer Genome Atlas database to obtain lncRNA expression profiles as well as somatic mutation profiles. Using the lncRNA computational framework, a genomic instability-related lncRNA signature (GIlncSig) was established and the prognostic value of this signature was assessed and validated. A five-lncRNA signature based on genomic instability (CFAP58-DT, MIR100HG, LINC02446, AC078880.3, and LINC01833) was obtained from 58 differentially expressed lncRNAs. Patients were divided into high-risk and low-risk groups, with the high-risk group having a substantially worse prognosis than the low-risk group. Univariate and multivariate Cox analyses indicated that GIlncSig may be an independent prognostic factor; this finding was subsequently validated. In addition, enrichment analysis indicated that GIlncSig is associated with genomic instability in bladder cancer. GIlncSig has a predictive value for the prognosis of bladder cancer patients and provides guidance for the clinical treatment of these patients.