The construction and validation of an RNA binding protein-related prognostic model for bladder cancer

Single-cell RNA-binding protein pattern-mediated molecular subtypes depict the hallmarks of the tumor microenvironment in bladder urothelial carcinoma

Objectives

Bladder carcinoma (BC) is a common malignancy of the urinary tract. As a new hallmark of cancer for drug therapy, RNA-binding proteins (RBPs) are key regulatory factors in alternative splicing events. This work is to uncover the relationship between BC and RBP in order to find drug targets in BC.

Methods

In this work, data from single-cell RNA-seq GSE1355337, PRJNA662018, and the TCGA-Bladder urothelial carcinoma (BLCA) cohorts are integrated to identify their relationships. A scoring system is constructed according to RBPs gene expression and patients’ survival. A network is constructed to analyze the alternative splicing events and RBP genes.

Results

A scoring system identified 321 RBPs significantly associated with the prognosis of patients. Subsequent typing of these RBP genes in two single-cell datasets demonstrated that most of the RBP genes had variable copy numbers. Three RBP clusters were identified. Using RBP genes as a signature in BC epithelial cells allows for differentiation between different grades of BC samples. The novel RBP genes-based subtype system reflects BC clinical staging. Notably, CellChat analysis revealed that the RBP genes-associated cell subtypes of T cells had extensive interactions with epithelial cells. Further analysis showed that the ligand-receptor pair MIF-CXCR4 mediated the communication between RBP-associated subtypes of BC epithelial cells and T cells.

Conclusions

Taken together, RBP genes are associated with BC progress and offer new indicators for precision medicine in BC.

Investigation toward the economic feasibility of personalized medicine for healthcare service providers: the case of bladder cancer

In today's complex healthcare landscape, the pursuit of delivering optimal patient care while navigating intricate economic dynamics poses a significant challenge for healthcare service providers (HSPs). In this already complex dynamic, the emergence of clinically promising personalized medicine-based treatment aims to revolutionize medicine. While personalized medicine holds tremendous potential for enhancing therapeutic outcomes, its integration within resource-constrained HSPs presents formidable challenges. In this study, we investigate the economic feasibility of implementing personalized medicine. The central objective is to strike a balance between catering to individual patient needs and making economically viable decisions. Unlike conventional binary approaches to personalized treatment, we propose a more nuanced perspective by treating personalization as a spectrum. This approach allows for greater flexibility in decision-making and resource allocation. To this end, we propose a mathematical framework to investigate our proposal, focusing on Bladder Cancer (BC) as a case study. Our results show that while it is feasible to introduce personalized medicine, a highly efficient but highly expensive one would be short-lived relative to its less effective but cheaper alternative as the latter can be provided to a larger cohort of patients, optimizing the HSP's objective better.

Functional enrichment analysis reveals the involvement of DARS2 in multiple biological pathways and its potential as a therapeutic target in esophageal carcinoma

OBJECTIVE

The enzyme Aspartyl tRNA synthetase 2 (DARS2) is a crucial enzyme in the mitochondrial tRNA synthesis pathway, playing a critical role in maintaining normal mitochondrial function and protein synthesis. However, the role of DARS2 in ESCA is unclear.

MATERIALS AND METHODS

Transcriptional data of pan-cancer and ESCA were downloaded from UCSC XENA, TCGA, and GEO databases to analyze the differential expression of DARS2 between tumor samples and normal samples, and its correlation with clinicopathological features of ESCA patients. R was used for GO, KEGG, and GSEA functional enrichment analysis of DARS2 co-expression and to analyze the connection of DARS2 with glycolysis and m6A-related genes. In vitro experiments were performed to assess the effects of interfering with DARS2 expression on ESCA cells. TarBase v.8, mirDIP, miRTarBase, ENCORI, and miRNet databases were used to analyze and construct a ceRNA network containing DARS2.

RESULTS

DARS2 was overexpressed in various types of tumors. In vitro experiments confirmed that interfering with DARS2 expression significantly affected the proliferation, migration, apoptosis, cell cycle, and glycolysis of ESCA cells. DARS2 may be involved in multiple biological pathways related to tumor development. Furthermore, correlation and differential analysis revealed that DARS2 may regulate ESCA m6A modification through its interaction with METTL3 and YTHDF1. A ceRNA network containing DARS2, DLEU2/has-miR-30a-5p/DARS2, was successfully predicted and constructed.

CONCLUSIONS

Our findings reveal the upregulation of DARS2 in ESCA and its association with clinical features, glycolysis pathway, m6A modification, and ceRNA network. These discoveries provide valuable insights into the molecular mechanisms underlying ESCA.

MicroRNA (let-7b-5p)-targeted DARS2 regulates lung adenocarcinoma growth by PI3K/AKT signaling pathway

Background

The aberrant intracellular expression of a mitochondrial aspartyl-tRNA synthetase 2 (DARS2) has been reported in human cancers. Nevertheless, its critical role and detailed mechanism in lung adenocarcinoma (LUAD) remain unexplored.

Methods

Initially, The Cancer Genome Atlas (TCGA)-based Gene Expression Profiling Interactive Analysis (GEPIA) database (http://gepia.cancer-pku.cn/) was used to analyze the prognostic relevance of DARS2 expression in LUAD. Further, cell counting kit (CCK)-8, immunostaining, and transwell invasion assays in LUAD cell lines in vitro, as well as DARS2 silence on LUAD by tumorigenicity experiments in vivo in nude mice, were performed. Besides, we analyzed the expression levels of p-PI3K (phosphorylated-Phosphotylinosital3 kinase), PI3K, AKT (Protein Kinase B), p-AKT (phosphorylated-Protein Kinase B), PCNA (proliferating cell nuclear antigen), cleaved-caspase 3, E-cadherin, and N-cadherin proteins using the Western blot analysis.

Results

LUAD tissues showed higher DARS2 expression compared to normal tissues. Upregulation of DARS2 could be related to Tumor-Node-Metastasis (TNM) stage, high lymph node metastasis, and inferior prognosis. DARS2 silence decreased the proliferation, migration, and invasion abilities of LUAD cells. In addition, the DARS2 downregulation decreased the PCNA and N-cadherin expression and increased cleaved-caspase 3 and E-cadherin expressions in LUAD cells, coupled with the inactivation of the PI3K/AKT signaling pathway. Moreover, DARS2 silence impaired the tumorigenicity of LUAD in vivo. Interestingly, let-7b-5p could recognize DARS2 through a complementary sequence. Mechanistically, the increased let-7b-5p expression attenuated the promo-oncogenic action of DARS2 during LUAD progression, which were inversely correlated to each other in the LUAD tissues.

Conclusion

In summary, let-7b-5p downregulated DARS2 expression, regulating the progression of LUAD cells by the PI3K/AKT signaling pathway.

Comprehensive analysis of N1-methylandenosine regulators and m1A-related mRNAs and lncRNAs as prognostic factors in bladder cancer

Bladder cancer (BC) is a lethal malignancy and recurs frequently. m1A plays a vital role in maintaining the biological functions of non-coding RNAs. The Cancer Genome Atlas (TCGA) is a free website from where transcriptome data of BC were obtained. We chose m1A methylation regulators for this study. Six m1A methylation regulator genes have a higher expression in BC tissue compared to normal tissue. The aberrant expression of those m1A regulator genes was remarkably related to BC prognosis and clinicopathological features. First, m1A-related mRNAs and long noncoding RNAs (lncRNAs) were identified. Next, univariate Cox regression, least absolute shrinkage and selection operator (LASSO) Cox regression and multivariate Cox regression were performed to get the optimum RNAs for the development of prognostic signatures. Also, a nomogram with T status, lncRNA risk scores and mRNA risk scores was constructed. It revealed an adequate capacity to predict the overall survival of BC cases in the training set as well as in the testing set and in the total TCGA cohort. In conclusion, m1A methylation regulator genes played an important role in predicting the overall survival of BC patients. In addition, m1A-related lncRNAs and mRNAs illustrated underlying mechanisms of tumorigenesis and development of BC.

DARS2 overexpression is associated with PET/CT metabolic parameters and affects glycolytic activity in lung adenocarcinoma

BACKGROUND

This study investigated the correlation between the expression of DARS2 and metabolic parameters of 18F-FDG PET/CT, and explored the potential mechanisms of DARS2 affecting the proliferation and glycolysis of lung adenocarcinoma (LUAD) cells.

METHODS

This study used genomics and proteomics to analyze the difference in DARS2 expression between LUAD samples and control samples. An analysis of 62 patients with LUAD who underwent 18F-FDG PET/CT examinations before surgery was conducted retrospectively. The correlation between DARS2 expression and PET/CT metabolic parameters, including SUVmax, SUVmean, MTV, and TLG, was examined by Spearman correlation analysis. In addition, the molecular mechanism of interfering with DARS2 expression in inhibiting LUAD cell proliferation and glycolysis was analyzed through in vitro cell experiments.

RESULTS

DARS2 expression was significantly higher in LUAD samples than in control samples (p < 0.001). DARS2 has high specificity (98.4%) and sensitivity (95.2%) in the diagnosis of LUAD. DARS2 expression was positively correlated with SUVmax, SUVmean, and TLG (p < 0.001). At the same time, the sensitivity and specificity of SUVmax in predicting DARS2 overexpression in LUAD were 88.9% and 65.9%, respectively. In vitro cell experiments have shown that interfering with DARS2 expression can inhibit the proliferation and migration of LUAD cells, promote cell apoptosis, and inhibit the glycolytic activity of tumor cells by inhibiting the expression of glycolytic related genes SLC2A1, GPI, ALDOA, and PGAM1.

CONCLUSIONS

Overexpression of DARS2 is associated with metabolic parameters on 18F-FDG PET/CT, which can improve LUAD diagnosis accuracy. DARS2 may be a useful biomarker to diagnose, prognosis, and target treatment of LUAD patients.

The RNA-Binding Proteins OAS1, ZFP36L2, and DHX58 Are Involved in the Regulation of CD44 mRNA Splicing in Colorectal Cancer Cells

Regulation of alternative splicing is carried out by RNA-binding proteins. Each alternative splicing event is controlled by several RNA-binding proteins, which in combination create the distribution of alternative splicing products in a given cell type. Transmembrane protein CD44 plays an important role at various stages of the metastatic cascade and is considered as a promising molecule for the therapy of tumor diseases and the construction of prognostic classifiers. However, the functions of specific isoforms of this protein may differ significantly. In this work, we performed a bioinformatic search of RNA-binding proteins that can determine the expression of clinically significant isoforms 3 and 4 of CD44 protein. The analysis revealed five RNA-binding proteins, three of which (OAS1, ZFP36L2, and DHX58) are shown for the first time as potential regulators of the studied process.

The emerging roles and mechanism of N6-methyladenosine (m6A) modifications in urologic tumours progression

Prostate cancer (PCa), bladder cancer (BC), and renal cell cancer (RCC) are the most common urologic tumours in males. N6-methyladenosine (m⁶A), adenosine N6 methylation, is the most prevalent RNA modification in mammals. Increasing evidence suggests that m⁶A plays a crucial role in cancer development. In this review, we comprehensively analyzed the influence of m⁶A methylation on Prostate cancer, bladder cancer, and renal cell cancer and the relationship between the expression of relevant regulatory factors and their development and occurrence, which provides new insights and approaches for the early clinical diagnosis and targeted therapy of urologic malignancies.

Identification and validation of the mitochondrial function related hub genes by unsupervised machine learning and multi-omics analyses in lung adenocarcinoma

Background

The mitochondrion and its associated genes were heavily implicated in developing and therapy tumors as the primary cellular organelle in charge of metabolic reprogramming and ferroptosis. Our work focuses on discovering new potential targets while analyzing the multi-omics data of mitochondria-related genes in lung adenocarcinoma (LUAD).

Methods

The Cancer Genome Atlas (TCGA) database provided multi-omics data for LUAD patients. Based on the expression profile of the genes associated with mitochondria, the patients were grouped by the unsupervised clustering method. R was used to explore the differential expressed protein-code gene, miRNA, and lncRNA, as well as their enriched functions and ceRNA networks. Additionally, the discrepancy between immune infiltration and genetic variation was comprehensively characterized. Our clinical samples and in vitro experiments investigated the hub gene determined by LASSO and batch analysis.

Results

Two clusters are distinguished using unsupervised consensus clustering based on mitochondrial heterogeneity. The integrated analysis emphasized that patients in cluster B had a worse prognosis, higher mutation frequencies, and less immune cell infiltration. The hub genes DARS2 and COX5B are identified by further analysis using LASSO penalization. In vitro experiments indicated that DARS2 and COX5B knockdown inhibited tumor cell proliferation. The specimen of our hospital cohort conducted the immunohistochemistry analysis and validated that DARS2 and COX5B's expression was significantly higher in the tumor than in adjacent normal tissue and correlated to LUAD patients' prognosis.

Conclusion

Our observations implied that LUAD patients' tumors had distinct mitochondrial function heterogeneity with different clinical and molecular characteristics. DARS2 and COX5B might be critical genes involved in mitochondrial alterations and potential therapeutic targets.

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.

Establishment and Validation of a Tumor Microenvironment Prognostic Model for Predicting Bladder Cancer Survival Status Based on Integrated Bioinformatics Analyses

This study was designed to analyze the characteristics of bladder cancer-related genes and establish a prognostic model of bladder cancer. The model passed an independent external validation set test. Differentially expressed genes (DEGs) related to bladder cancer were obtained from the Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA), and Genotype-Tissue Expression (GTEx) databases. WGCNA was used to fit the GSE188715, TCGA, and GTEx RNA-Seq data. Fusing the module genes with the high significance in tumor development extracted from WGCNA and DEGs screened from multiple databases. 709 common prognostic-related genes were obtained. The 709 genes were enriched in the Gene Ontology database. Univariate Cox and LASSO regression analyses were used to screen out 21 prognostic-related genes and further multivariate Cox regression established a bladder cancer prognostic model consisting of 8 genes. After the eight-gene prognostic model was established, the Human Protein Atlas (HPA) database, GEPIA 2, and quantitative real-time PCR (qRT-PCR) verified the differential expression of these genes. Gene Set Enrichment Analysis and immune infiltration analysis found biologically enrichment pathways and cellular immune infiltration related to this bladder cancer prognostic model. Then, we selected bladder cancer patients in the TCGA database to evaluate the predictive ability of the model on the training set and validation set. The overall survival status of the two TCGA patient groups in the training and the test sets was obtained by Kaplan–Meier survival analysis. Three-year survival rates in the training and test sets were 37.163% and 25.009% for the low-risk groups and 70.000% and 62.235% for the high-risk groups, respectively. Receiver operating characteristic curve (ROC) analysis showed that the areas under the curve (AUCs) for the training and test sets were above 0.7. In an external independent validation database GSE13507, Kaplan–Meier survival analysis showed that the three-year survival rates of the high-risk and the low-risk groups in this database were 56.719% and 76.734%, respectively. The AUCs of the ROC drawn in the external validation set were both above 0.65. Here, we constructed a prognostic model of bladder cancer based on data from the GEO, TCGA, and GTEx databases. This model has potential prognostic and clinical auxiliary diagnostic value.

Role of RBMS3 Novel Potential Regulator of the EMT Phenomenon in Physiological and Pathological Processes

RNA-binding protein 3 (RBMS3) plays a significant role in embryonic development and the pathogenesis of many diseases, especially cancer initiation and progression. The multiple roles of RBMS3 are conditioned by its numerous alternative expression products. It has been proven that the main form of RBMS3 influences the regulation of microRNA expression or stabilization. The absence of RBMS3 activates the Wnt/β-catenin pathway. The expression of c-Myc, another target of the Wnt/β-catenin pathway, is correlated with the RBMS3 expression. Numerous studies have focused solely on the interaction of RBMS3 with the epithelial-mesenchymal transition (EMT) protein machinery. EMT plays a vital role in cancer progression, in which RBMS3 is a new potential regulator. It is also significant that RBMS3 may act as a prognostic factor of overall survival (OS) in different types of cancer. This review presents the current state of knowledge about the role of RBMS3 in physiological and pathological processes, with particular emphasis on carcinogenesis. The molecular mechanisms underlying the role of RBMS3 are not fully understood; hence, a broader explanation and understanding is still needed.

High expression of DARS2 indicates poor prognosis in lung adenocarcinoma

Background

DARS2 was overexpressed in multiple tumor types, but the biological role of DARS2 in lung adenocarcinoma (LUAD) have not been elucidated.

Methods

Firstly, the DARS2 expression in LUAD was explored using The Cancer Genome Atlas (TCGA). Then, qRT‐PCR and Western blot were performed to confirm DARS2 expression in LUAD. Next, Cox regression and Kaplan–Meier methods were utilized to evaluate whether DARS2 expression can affect the overall survival. The relationships between DARS2 expression and clinicopathological characteristics were investigated by TCGA database. Moreover, we utilized Gene Set Enrichment Analysis (GSEA) to detect DARS2‐related signaling pathways in LUAD. Finally, the special function of DARS2 in cell proliferation, invasion and apoptosis was assessed in vitro.

Results

The higher expression of DARS2 was found in LUAD compared to para‐carcinoma tissues and significantly related to tumor stage, T stage, and M stage. The survival analysis indicated that DARS2 overexpression was related to poor prognosis in LUAD. Multivariate analysis suggested that DARS2 expression was a prognostic indicator. GSEA revealed that DARS2 was primarily involved in cell cycle‐related pathways. In addition, upregulation of DARS2 facilitated LUAD cell proliferation, migration, invasion and inhabited apoptosis, DARS2 knockdown showed an opposite result.

Conclusion

DARS2 modulates the proliferation, invasion and apoptosis of LUAD cells, and sever as a promising therapeutic target for LUAD.

An Integrated Bioinformatics Analysis towards the Identification of Diagnostic, Prognostic, and Predictive Key Biomarkers for Urinary Bladder Cancer

Simple Summary

Bladder cancer is evidently a challenge as far as its prognosis and treatment are concerned. The investigation of potential biomarkers and therapeutic targets is indispensable and still in progress. Most studies attempt to identify differential signatures between distinct molecular tumor subtypes. Therefore, keeping in mind the heterogeneity of urinary bladder tumors, we attempted to identify a consensus gene-related signature between the common expression profile of bladder cancer and control samples. In the quest for substantive features, we were able to identify key hub genes, whose signatures could hold diagnostic, prognostic, or therapeutic significance, but, primarily, could contribute to a better understanding of urinary bladder cancer biology.

Abstract

Bladder cancer (BCa) is one of the most prevalent cancers worldwide and accounts for high morbidity and mortality. This study intended to elucidate potential key biomarkers related to the occurrence, development, and prognosis of BCa through an integrated bioinformatics analysis. In this context, a systematic meta-analysis, integrating 18 microarray gene expression datasets from the GEO repository into a merged meta-dataset, identified 815 robust differentially expressed genes (DEGs). The key hub genes resulted from DEG-based protein–protein interaction and weighted gene co-expression network analyses were screened for their differential expression in urine and blood plasma samples of BCa patients. Subsequently, they were tested for their prognostic value, and a three-gene signature model, including COL3A1, FOXM1, and PLK4, was built. In addition, they were tested for their predictive value regarding muscle-invasive BCa patients’ response to neoadjuvant chemotherapy. A six-gene signature model, including ANXA5, CD44, NCAM1, SPP1, CDCA8, and KIF14, was developed. In conclusion, this study identified nine key biomarker genes, namely ANXA5, CDT1, COL3A1, SPP1, VEGFA, CDCA8, HJURP, TOP2A, and COL6A1, which were differentially expressed in urine or blood of BCa patients, held a prognostic or predictive value, and were immunohistochemically validated. These biomarkers may be of significance as prognostic and therapeutic targets for BCa.

Roles and mechanisms of the m6A reader YTHDC1 in biological processes and diseases

N6-methyladenosine (m⁶A) is a key area in Epigenetics and has been increasingly focused these years. In the m⁶A process, readers recognize the m⁶A modification on mRNAs or noncoding RNAs and mediate different downstream events. Emerging studies have shown that YTHDC1, an important m⁶A reader, plays a key role in many biological functions and disease progression, especially cancers. Here we summarized the current mechanisms of YTHDC1 in biological functions and diseases and offered guidance for future researches to provide potential strategy for clinical diagnose and therapy.

Identification of a Novel Tumor Microenvironment Prognostic Signature for Bladder Urothelial Carcinoma

Background

The tumor microenvironment (TME) regulates the proliferation and metastasis of solid tumors and the effectiveness of immunotherapy against them. We investigated the prognostic role of TME-related genes based on transcriptomic data of bladder urothelial carcinoma (BLCA) and formulated a prediction model of TME-related signatures.

Methods

Molecular subtypes were identified using the non-negative matrix factorization (NMF) algorithm based on TME-related genes from the TCGA database. TME-related genes with prognostic significance were screened with univariate Cox regression analysis and lasso regression. Nomogram was developed based on risk genes. Receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were used for inner and outer validation of the model. Risk scores (RS) of patients were calculated and divided into high-risk group (HRG) and low-risk group (LRG) to compare the differences in clinical characteristics and PD-L1 treatment responsiveness between HRG and LRG.

Results

We identified two molecular subtypes (C1 and C2) according to the NMF algorithm. There were significant differences in overall survival (OS) (p<0.05), progression-free survival (PFS) (p<0.05), and immune cell infiltration between the two subtypes. A total of eight TME-associated genes (CABP4, ZNF432, BLOC1S3, CXCL11, ANO9, OAS1, FBN2, CEMIP) with independent prognostic significance were screened to build prognostic risk models. Age (p<0.001), grade (p<0.001), and RS (p<0.001) were independent predictors of survival in BLCA patients. The developed RS nomogram was able to predict the prognosis of BLCA patients at 1, 3, and 5 years more potentially than the models of other investigators according to ROC and DCA. RS showed significantly higher values (p = 0.047) in patients with stable disease (SD)/progressive disease (PD) compared to patients with complete response (CR)/partial response (PR).

Conclusions

We successfully clustered and constructed predictive models for TME-associated genes and helped guide immunotherapy strategies.

Construction of prognosis model of bladder cancer based on transcriptome

OBJECTIVE

To screen for prognosis related genes in bladder cancer, and to establish prognosis model of bladder cancer.

METHODS

The clinical information and bladder tissue RNA sequencing data of 406 bladder cancer patients, and the bladder tissue RNA sequencing data of 28 healthy individuals were downloaded from The Cancer Genome Atlas (TCGA) database, Genotype-Tissue Expression (GTEx) database through the UCSC Xena platform. The weighted gene co-expression network analysis (WGCNA), univariate Cox regression, LASSO regression analysis and multivariate Cox regression analysis were used to screen the prognosis-related genes of bladder cancer and the prognostic model was established. The prognostic model was evaluated with receiver operator characteristic curve (ROC curve).

RESULTS

A total of 2308 differentially expressed genes related to bladder cancer were obtained from the analysis. Six gene modules were obtained by WGCNA, and 829 genes with significant effect on bladder cancer prognosis were screened out. Univariate Cox regression and LASSO regression analysis showed that 24 genes were related to the prognosis of bladder cancer patients. Multivariate Cox regression analysis revealed 9 genes as independent predictors in training set, namely ADCY9, MAFG_DT, EMP1, CAST, PCOLCE2, LTBP1, CSPG4, NXPH4, SLC1A6, which were used to establish the prognosis model of bladder cancer patients. The 3-year survival rates of the high-risk group and the low-risk group in the training set were 31.814% and 59.821%, respectively. The 3-year survival rates of the high-risk group and the low-risk group in the test set were 32.745% and 68.932%, respectively. The areas under the ROC curve of the model for predicting the prognosis of bladder cancer patients in both the training set and the test set were above 0.7.

CONCLUSION

The established model in this study has good predictive ability for the survival of bladder cancer patients.

A novel risk signature with 6 RNA binding proteins for prognosis prediction in patients with glioblastoma

Recent studies suggested that RNA binding proteins (RBPs) were related to the tumorigenesis and progression of glioma. This study was conducted to identify prognostic RBPs of glioblastoma (GBM) and construct an RBP signature to predict the prognosis of GBM.Univariate Cox regression analysis was carried out to identify the RBPs associated with overall survival of GBM in the The Cancer Genome Atlas (TCGA), GSE16011, and Repository for Molecular Brain Neoplasia data (Rembrandt) datasets, respectively. Overlapping RBPs from the TCGA, GSE16011, and Rembrandt datasets were selected. The biological role of prognostic RBPs was assessed by Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and protein-protein interaction analyses. Least absolute shrinkage and selection operator regression analysis and multivariate Cox regression analysis were used to construct an RBP-related risk signature. The prognostic value of RBP signature was measured by Kaplan-Meier method and time-dependent receiver operating characteristic curve. A nomogram based on independent prognostic factors was established to predict survival for GBM. The CGGA cohort was used as the validation cohort for external validation.This study identified 27 RBPs associated with the prognosis of GBM and constructed a 6-RPBs signature. Kaplan-Meier curves suggested that high-risk score was associated with a poor prognosis. Area under the curve of 1-, 3-, and 5-year overall survival was 0.618, 0.728, and 0.833 for TCGA cohort, 0.655, 0.909, and 0.911 for GSE16011 cohort, and 0.665, 0.792, and 0.781 for Rembrandt cohort, respectively. A nomogram with 4 parameters (age, chemotherapy, O6-methylguanine-DNA methyltransferase promoter status, and risk score) was constructed. The calibration curve showed that the nomogram prediction was in good agreement with the actual observation.The 6-RBPs signature could effectively predict the prognosis of GBM, and our findings supplemented the prognostic index of GBM to a certain extent.

Identification of Signature Genes Associated With Invasiveness and the Construction of a Prognostic Model That Predicts the Overall Survival of Bladder Cancer

Background: Bladder cancer has become the tenth most diagnosed cancer worldwide. The prognosis has been shown to differ between non-muscle invasive bladder cancer (NMIBC) and muscle invasive bladder cancer (MIBC). We aimed to identify signature genes that are associated with the invasiveness and survival of bladder cancer and to identify potential treatments. Methods: We downloaded gene expression profiles of bladder cancer from the Gene Expression Omnibus database to identify differentially expressed genes and perform weighted gene co-expression network analysis. Functional enrichment was analyzed by GO and KEGG analyses. Hub genes were identified from the significant module. Another dataset was also acquired to verify the expression of hub genes. Univariate and multivariate Cox regression analyses were applied to the dataset downloaded from The Cancer Genome Atlas database. Risk scores were calculated and the effect was evaluated by Kaplan-Meier survival analysis. A nomogram was constructed and validated using training and testing samples, respectively. Analysis of the tumor immune microenvironment was conducted with the CIBERSORT algorithm. Results: In total, 1,245 differentially expressed genes (DEGs) were identified. A distinct module was identified that was significantly correlated to invasiveness. The genes within this module were found to be significantly associated with extracellular exosomes, GTPase activity, metabolic pathways, etc. Three hub genes (VSIG2, PPFIBP2, and DENND2D) were identified as biomarkers of invasiveness; two of these (PPFIBP2 and DENND2D) were closely associated with prognosis. The risk score was regarded as an independent prognostic factor. The nomogram was associated with acceptable accuracy for predicting 1- and 5-year overall survival. The infiltrating levels of resting NK cells, activated natural killer (NK) cells, CD8⁺ T cells, activated memory CD4⁺ T cells, and T follicular helper cells, were significantly higher in the group with lower risk scores. The group with higher risk scores showed predominant infiltration by regulatory T cells (Tregs). Conclusion: We successfully identified three signature genes related to invasiveness and constructed a nomogram of bladder cancer with acceptable performance. Differences suggested by risk scores between groups of patients showing diverse patterns of immune cell infiltration may be beneficial for selecting therapeutic approaches and predicting prognosis.

Over-activation of EFTUD2 correlates with tumor propagation and poor survival outcomes in hepatocellular carcinoma

PURPOSE

Elongation factor Tu GTP-binding domain containing 2 (EFTUD2) is an essential constituent of U5 small nuclear ribonucleoproteins (snRNPs) and plays a crucial role in spliceosome activation and cancer. The mechanism of EFTUD2 on carcinogenesis and development of liver cancer still need further study.

METHODS

Bioinformatic analysis was performed to find differential expressed genes and related pathways. Western blotting and quantitative PCR assays were used to verify the EFTUD2 expression in HCC cell lines and tumor tissues of liver cancer patients. Transfection of shRNAs in SKHEP1 and Huh7 cell lines was conducted to explore the mechanisms of EFTUD2 in HCC. CCK-8 method, colony formation, and cell cycle detection kit were used to detect the proliferation. A tumor model in nude mice was used to explore the role of EFTUD2 in liver cancer in vivo.

RESULTS

Based on the tumor tissues and para-tumor tissues in our HCC patients, we identified EFTUD2 as highly expressed in HCC tissues (P < 0.001). Bioinformatic analysis from the TCGA database also supported this biological phenomenon (P = 1.911e-17). Furtherly, the results of clinical specimens and TCGA data suggested that higher EFTUD2 expression levels correlated with high histologic grades, high pathological grades, and poor survival prognoses in HCC patients. And knockdown of EFTUD2 suppressed cell proliferation and colony formation in vitro. In vivo, knockdown of EFTUD2 constrained the tumor growing and expansion derived from SKHEP1 cells and induced a decrease in the tumor volume and tumor weight resected from nude mice. Furthermore, RNA sequencing based on EFTUD2 knockdown revealed that EFTUD2 affected target genes concerned with the cell cycle. Flow cytometric analyses in the SKHEP1 cell model revealed that knockdown significantly suppressed cell cycle course and caused cell cycle arrest in the G1 phase. CyclinD1 proteins were also inhibited by knocking down of EFTUD2.

CONCLUSION

EFTUD2 is markedly overexpressed in HCC tumor tissues. High EFTUD2 expression in HCC patients is associated with clinical features. Moreover, we confirmed that EFTUD2 shows a pivotal role in HCC cell proliferation and cell cycle course and could be a possible therapeutic avenue in HCC through disturbing EFTUD2.