A 7-gene signature predicts the prognosis of patients with bladder cancer

TOMM40 Correlates with Cholesterol and is Predictive of a Favorable Prognosis in Endometrial Carcinoma

BACKGROUND

A link between cholesterol and endometrial cancer has been established, but current studies have been limited in their findings. We aimed to elucidate the causal relationship between cholesterol and endometrial cancer and to find prognostic genes for endometrial cancer.

METHODS

We first explored the causal relationship between total cholesterol and endometrial cancer using two-sample Mendelian randomization and then obtained differential genes to screen for prognosis-related genes in endometrial cancer. Then, we utilized pan-cancer analysis based on RNA sequencing data to evaluate the expression pattern and immunological role of the Translocase of Outer Mitochondrial Membrane 40 (TOMM40). Through multiple transcriptome datasets and multi-omics in-depth analysis, we comprehensively explore the relationship of TOMM40 expression with clinicopathologic characteristics, clinical outcomes and mutations in endometrial cancer. Lastly, we systematically associated the TOMM40 with different cancers from immunological properties from numerous perspectives, such as immune cell infiltration, immune checkpoint inhibitors, immunotherapy, gene mutation load and microsatellite instability.

RESULTS

We found a negative association between cholesterol and endometrial cancer. A total of 78 genes were enriched by relevant single nucleotide polymorphisms (SNPs), of which 12 upregulated genes and 5 downregulated genes in endometrial cancer. TOMM40 was found to be a prognostic gene associated with endometrial cancer by prognostic analysis. TOMM40 was found to be positively correlated with the infiltration of most immune cells and immunization checkpoints in a subsequent study. Meanwhile, TOMM40 also was an oncogene in many cancer types. High TOMM40 was associated with lower genome stability.

CONCLUSION

The findings of our study indicate that the maintenance of normal total cholesterol metabolism is associated with a decreased risk of developing endometrial cancer. Moreover, TOMM40 may have potential as a prognostic indicator for endometrial cancer.

Knockdown of integrin β1 inhibits proliferation and promotes apoptosis in bladder cancer cells

Bladder cancer (BC) is the most common urinary tract malignancy. Identifying biomarkers that predict prognosis and immune function in patients with BC can enhance our understanding of its pathogenesis and provide valuable guidance for diagnosis and treatment. Our findings indicate that increased ITGB1 expression is associated with higher clinical grade and stage, establishing ITGB1 as an independent prognostic risk factor for BC. Enrichment analysis revealed that the function of ITGB1 in BC was linked to the extracellular matrix. The experimental results showed that ITGB1 knockdown in the BC cell lines 5637 and RT112 reduced their proliferation, migration, and invasion. Furthermore, ITGB1 suppression promotes apoptosis in BC cells by inhibiting the PI3K-AKT pathway. A prognostic risk model incorporating CES1, NTNG1, SETBP1, and AIFM3 was developed based on ITGB1, this model can accurately predict patient prognosis based on immunological status. In conclusion, this study shows that knockdown of ITGB1 can restrain the migratory and invasive capabilities of BC cells and accelerate apoptosis, and this role might be associated with PI3K-AKT, highlighting its potential as a diagnostic marker and therapeutic target for BC.

Loss of NUMB drives aggressive bladder cancer via a RHOA/ROCK/YAP signaling axis

Advances in bladder cancer (BCa) treatment have been hampered by the lack of predictive biomarkers and targeted therapies. Here, we demonstrate that loss of the tumor suppressor NUMB promotes aggressive bladder tumorigenesis and worsens disease outcomes. Retrospective cohort studies show that NUMB-loss correlates with poor prognosis in post-cystectomy muscle-invasive BCa patients and increased risk of muscle invasion progression in non-muscle invasive BCa patients. In mouse models, targeted Numb ablation induces spontaneous tumorigenesis and sensitizes the urothelium to carcinogenic insults, accelerating tumor onset and progression. Integrative transcriptomic and functional analyses in mouse and human BCa models reveal that upregulation of YAP transcriptional activity via a RHOA/ROCK-dependent pathway is a hallmark of NUMB-deficient BCa. Pharmacological or genetic inhibition of this molecular pathway selectively inhibits proliferation and invasion of NUMB-deficient BCa cells in 3D-Matrigel organoids. Thus, NUMB-loss could serve as a biomarker for identifying high-risk patients who may benefit from targeted anti-RHOA/ROCK/YAP therapies.

Improve clinical feature-based bladder cancer survival prediction models through integration with gene expression profiles and machine learning techniques

Background

Bladder cancer (BCa), one of the most common cancers worldwide, is characterized by high rates of recurrence, progression, and mortality. Machine learning algorithms offer promising advancements in enhancing predictive models. This study aims to develop robust machine learning models for predicting BCa survival using clinical and gene expression data.

Methods

Clinical data from BCa patients were obtained from the Surveillance, Epidemiology, and End Results database. Cox proportional hazards regression models assessed the association between clinical variables and overall survival. Machine learning algorithms, including logistic regression, random forest, XGBoost, decision tree, and LightGBM, were employed to predict survival at 1, 3, and 5 years. The TAGO database, combined with the data from The Cancer Genome Atlas and four databases from the Gene Expression Omnibus, which have available genomic data and clinical data, were selected. Gene expression data were transformed into gene sets data, and the performance of models based on clinical data and gene sets data and their combination were compared. Furthermore, the impact of model-derived scores on overall survival was evaluated.

Results

Among 138,741 BCa patients with available clinical data, key independent predictors of survival included age, race, marital status, surgery, chemotherapy, radiation, and TNM stages. Clinical data machine learning (CML) models used these clinical predictors to achieve AUC values of 0.860, 0.821, and 0.804 in the testing sets for predicting survival at 1, 3, and 5 years, respectively. In the TAGO database, which has 863 patients with clinical and genomic data, the integrated clinical and gene expression machine learning model (IML) outperformed the CML and gene expression machine learning (GML) models in survival prediction. Patients with higher IML and GML model scores exhibited poorer survival outcomes.

Conclusions

This study successfully identifies key clinical and genomic predictors, a significant step forward in BCa research. The development of predictive models for BCa survival underscores the potential of integrated data approaches in improving BCa management and treatment strategies.

Exploration of potential biomarkers for early bladder cancer based on urine proteomics

Background

Bladder cancer is a common malignant tumor of the urinary system. The progression of the condition is associated with a poor prognosis, so it is necessary to identify new biomarkers to improve the diagnostic rate of bladder cancer.

Methods

In this study, 338 urine samples (144 bladder cancer, 123 healthy control, 32 cystitis, and 39 upper urinary tract cancer samples) were collected, among which 238 samples (discovery group) were analyzed by LC-MS. The urinary proteome characteristics of each group were compared with those of bladder cancer, and the differential proteins were defined by bioinformatics analysis. The pathways and functional enrichments were annotated. The selected proteins with the highest AUC score were used to construct a diagnostic panel. One hundred samples (validation group) were used to test the effect of the panel by ELISA.

Results

Compared with the healthy control, cystitis and upper urinary tract cancer samples, the number of differential proteins in the bladder cancer samples was 325, 158 and 473, respectively. The differentially expressed proteins were mainly related to lipid metabolism and iron metabolism and were involved in the proliferation, metabolism and necrosis of bladder cancer cells. The AUC of the panel of APOL1 and ITIH3 was 0.96 in the discovery group. ELISA detection showed an AUC of 0.92 in the validation group.

Conclusion

This study showed that urinary proteins can reflect the pathophysiological changes in bladder cancer and that important molecules can be used as biomarkers for bladder cancer screening. These findings will benefit the application of the urine proteome in clinical research.

Identification and validation of prognostic signature genes of bladder cancer by integrating methylation and transcriptomic analysis

Being a frequent malignant tumor of the genitourinary system, Bladder Urothelial Carcinoma (BLCA) has a poor prognosis. This study focused on identifying and validating prognostic biomarkers utilizing methylation, transcriptomics, and clinical data from The Cancer Genome Atlas Bladder Urothelial Carcinoma (TCGA BLCA) cohort. The impact of altered differentially methylated hallmark pathway genes was subjected to clustering analysis to observe changes in the transcriptional landscape on BLCA patients and identify two subtypes of patients from the TCGA BLCA population where Subtype 2 was associated with the worst prognosis with a p-value of 0.00032. Differential expression and enrichment analysis showed that subtype 2 was enriched in immune-responsive and cancer-progressive pathways, whereas subtype 1 was enriched in biosynthetic pathways. Following, regression and network analyses revealed Epidermal Growth Factor Receptor (EGFR), Fos-related antigen 1 (FOSL1), Nuclear Factor Erythroid 2 (NFE2), ADP-ribosylation factor-like protein 4D (ARL4D), SH3 domain containing ring finger 2 (SH3RF2), and Cadherin 3 (CDH3) genes to be the most significant prognostic gene markers. These genes were used to construct a risk model that separated the BLCA patients into high and low-risk groups. The risk model was also validated in an external dataset by performing survival analysis between high and low-risk groups with a p-value < 0.001 and the result showed the high group was significantly associated with poor prognosis compared to the low group. Single-cell analyses revealed the elevated level of these genes in the tumor microenvironment and associated with immune response. High-grade patients also tend to have a high expression of these genes compared to low-grade patients. In conclusion, this research developed a six-gene signature that is pertinent to the prediction of overall survival (OS) and might contribute to the advancement of precision medicine in the management of bladder cancer.

Silencing LINC00663 inhibits inflammation and angiogenesis through downregulation of NR2F1 via EBF1 in bladder cancer

This study is to elucidate the effect of the LINC00663/EBF1/NR2F1 axis on inflammation and angiogenesis in bladder cancer (BC) and related molecular mechanisms. After transfection, functional experiments were conducted to test cell proliferation and invasion, tube formation ability, and content of inflammatory factors, Snail, E-cadherin, and VEGFA. Meanwhile, the relationships among LINC00663, EBF1, and NR2F1 were predicted and verified. In addition, xenograft experiments in nude mice were performed to observe the oncogenicity of 5637 BC cells in vivo. In BC tissues and cells, LINC00663 and NR2F1 were upregulated. Silencing NR2F1 or LINC00663 repressed cell proliferation and invasion, weakened vascular mimicry in vitro, decreased inflammatory factor, Snail, and VEGFA levels, and increased expression of E-cadherin. LINC00663 positively regulated NR2F1 expression through EBF1. Additionally, in vivo experiments showed that NR2F1 upregulation reversed the suppression effects of LINC00663 silencing on tumour growth, inflammation, and angiogenesis. Silencing LINC00663 decreased NR2F1 expression by mediating EBF1, thereby inhibiting BC inflammation and angiogenesis.

KLRB1 is a novel prognostic biomarker in endometrial cancer and is associated with immune infiltration

Background

Endometrial cancer (EC) has the characteristics of high mortality and poor prognosis in the advanced stage, which seriously threatens women's health. Killer cell lectin-like receptor B1 (KLRB1) is a promising immune checkpoint of which the expression level can regulate the killing effect on tumor cells of the immune system, thereby affecting the survival and prognosis of tumor patients. However, it is still unclear whether KLRB1 is associated with survival and prognosis in patients with EC. Therefore, our study focused on the relationship between KLRB1 and immune cells to explore the role of KLRB1 on the immune microenvironment, and to further explore its feasibility as a prognostic marker in EC.

Methods

In this study, The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were used to analyze the messenger RNA (mRNA) expression level of KLRB1 in normal endometrial and EC tissues. The University of Alabama at Birmingham Cancer data analysis Portal (UALCAN) database was used to determine the correlation between KLRB1 mRNA expression and clinical features among the EC patients. KLRB1 expression levels were investigated in the Tumor IMmune Estimation Resource (TIMER) database to reveal its relationship with immune cell infiltration of EC. Finally, using the R package clusterProfiler, enrichment analysis was performed on KLRB1 to study its potential function.

Results

The results suggested that KLRB1 expression varied in different tumor tissues, and the EC group had lower mRNA expression levels than did the control group. It was also found that patients with high expression of KLRB1 had a better prognosis. According to further enrichment and immune infiltration analyses, KLRB1 expression had a closed relationship with the level of infiltration of some immune cell types, such as B cells memory, eosinophils, and Tregs, among others.

Conclusions

KLRB1 expression is associated with the infiltration of immune cells and can be used as a prognostic biomarker in EC.

The impact of SETBP1 mutations in neurological diseases and cancer

SE translocation (SET) is a cancer-promoting factor whose expression is upregulated in many cancers. High SET expression positively correlates with a poor cancer prognosis. SETBP1 (SET-binding protein 1/SEB/MRD29), identified as SET-binding protein, is the causative gene of Schinzel-Giedion syndrome, which is characterized by severe intellectual disability and a distorted facial appearance. Mutations in these genetic regions are also observed in some blood cancers, such as myelodysplastic syndromes, and are associated with a poor prognosis. However, the physiological role of SETBP1 and the molecular mechanisms by which the mutations lead to disease progression have not yet been fully elucidated. In this review, we will describe the current epidemiological data on SETBP1 mutations and shed light on the current knowledge about the SET-dependent and -independent functions of SETBP1.

Construction and experimental validation of a B cell-related gene signature to predict the prognosis and immunotherapeutic sensitivity in bladder cancer

BACKGROUND

B cells are essential components of tumor microenvironment and exert important functions in anti-tumor immune response. However, the prognosis value of B cell-related genes in bladder cancer (BLCA) remains obscure.

MATERIALS AND METHODS

The infiltrating levels of B cells were measured via the CD20 staining in the local samples and the computational biology analyses in the TCGA-BLCA cohort. The single-cell RNA sequencing analysis, gene-pair strategy, LASSO regression, random forest, and Cox regression were used for B cell-related signature construction. TCGA-BLCA cohort was chosen as the training cohort, and three independent cohorts from GEO and the local cohort were used for external validation. 326 B cells were adopted to explore the association between the model and B cells' biological processes. TIDE algorithm and two BLCA cohorts receiving anti-PD1/PDL1 treatment were utilized to detect its predictive ability to the immunotherapeutic response.

RESULTS

High infiltration levels of B cells heralded favorable prognosis, both in the TCGA-BLCA cohort and the local cohort (all P < 0.05). A 5-gene-pair model was established and served as a significant prognosis predictor across multiple cohorts (pooled hazard ratio = 2.79, 95% confidence interval = 2.22-3.49). The model could evaluate the prognosis effectively in 21 of 33 cancer types (P < 0.05). The signature was negatively associated with B cells' activation, proliferation, and infiltrating levels, and could serve as a potential predictor of immunotherapeutic outcomes.

CONCLUSIONS

A B cell-related gene signature was constructed to predict the prognosis and immunotherapeutic sensitivity in BLCA, helping to guide the personalized treatment.

Risk Classification of Bladder Cancer by Gene Expression and Molecular Subtype

This study evaluated a panel including the molecular taxonomy subtype and the expression of 27 genes as a diagnostic tool to stratify bladder cancer patients at risk of aggressive behavior, using a well-characterized series of non-muscle invasive bladder cancer (NMIBC) as well as muscle-invasive bladder cancer (MIBC). The study was conducted using the novel NanoString nCounter gene expression analysis. This technology allowed us to identify the molecular subtype and to analyze the gene expression of 27 bladder-cancer-related genes selected through a recent literature search. The differential gene expression was correlated with clinicopathological variables, such as the molecular subtypes (luminal, basal, null/double negative), histological subtype (conventional urothelial carcinoma, or carcinoma with variant histology), clinical subtype (NMIBC and MIBC), tumor stage category (Ta, T1, and T2–4), tumor grade, PD-L1 expression (high vs. low expression), and clinical risk categories (low, intermediate, high and very high). The multivariate analysis of the 19 genes significant for cancer-specific survival in our cohort study series identified TP53 (p = 0.0001), CCND1 (p = 0.0001), MKI67 (p < 0.0001), and molecular subtype (p = 0.005) as independent predictors. A scoring system based on the molecular subtype and the gene expression signature of TP53, CCND1, or MKI67 was used for risk assessment. A score ranging from 0 (best prognosis) to 7 (worst prognosis) was obtained and used to stratify our patients into two (low [score 0–2] vs. high [score 3–7], model A) or three (low [score 0–2] vs. intermediate [score 3–4] vs. high [score 5–7], model B) risk categories with different survival characteristics. Mean cancer-specific survival was longer (122 + 2.7 months) in low-risk than intermediate-risk (79.4 + 9.4 months) or high-risk (6.2 + 0.9 months) categories (p < 0.0001; model A); and was longer (122 + 2.7 months) in low-risk than high-risk (58 + 8.3 months) (p < 0.0001; model B). In conclusion, the molecular risk assessment model, as reported here, might be used better to select the appropriate management for patients with bladder cancer.

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.

Abnormal GRHL2 Methylation Confers Malignant Progression to Acute Leukemia

Purpose

Abnormal methylation of Grainyhead-like 2 (GRHL2) is associated with a substantial role in the malignant phenotype of tumor patients. Our present research is aimed at studying the abnormal expression of GRHL2 and the association of methylation in patients with acute leukemia and its relationship with prognosis.

Materials and Methods

We used quantitative real-time polymerase chain reaction (qRT-PCR) for detecting the aberrant expression level of GRHL2 in 60 patients with acute leukemia and 60 normal controls. We analyzed the significant correlation between the expression level of GRHL2 with clinicopathological features and patients' prognosis in acute leukemia using the corresponding statistical methods. Secondly, we employed qRT-PCR and Western blotting to detect the mRNA and protein levels of GRHL2 in leukemia cell lines. Next, we used methylation-specific polymerase chain reaction (MSP) technology for detecting the methylation of GRHL2 in clinical samples with acute leukemia and cell lines. Then we investigated the demethylating effect of arsenic trioxide and 5-azacitidine on the mRNA and protein expression levels of GRHL2 in cell lines of acute leukemia. Finally, we studied the effects of arsenide trioxide and 5-azacitidine on the proliferation of leukemia cells and the TGF-β signaling pathway.

Results

We found a lower level of GRHL2 expression not only in acute leukemia patients but also in cell lines when compared with normal controls. At the same time, the expression level of GRHL2 in patients with acute leukemia was significantly correlated with leukocyte count, platelet count, and cytogenetic risk grouping. In addition, the lower GRHL2 expression group showed a significantly lower overall survival rate in acute leukemia patients than that of patients with a higher GRHL2 expression group. Univariate and multivariate analyses revealed that the expression of GRHL2 is an independent risk factor in acute leukemia patients. The methylation level of the GRHL2 promoter region in acute leukemia patients and cell lines was significantly higher than the normal control group, and we found the elevated mRNA and protein levels of GRHL2 in acute leukemia cell lines after the use of the demethylation drug arsenic trioxide and 5-azacitidine. At the same time, arsenide trioxide and 5-azacitidine are associated with the inhibition of cellular proliferation of acute leukemia cells and also promote the elevated expression of TGF-β signaling pathway-linked proteins, including TGF-β, Smad2, Smad3, and Smad4.

Conclusion

Increased expression and methylation level of GRHL2 are closely associated with the prognosis and malignant phenotype of acute leukemia patients and play an irreplaceable role in the occurrence and development of patients with acute leukemia.