SLCO4C1 promoter methylation is a potential biomarker for prognosis associated with biochemical recurrence-free survival after radical prostatectomy

Involvement of circRNA Regulators MBNL1 and QKI in the Progression of Esophageal Squamous Cell Carcinoma

OBJECTIVES

To investigate the role of circRNA regulators MBNL1 and QKI in the progression of esophageal squamous cell carcinoma.

BACKGROUND

MBNL1 and QKI are pivotal regulators of pre-mRNA alternative splicing, crucial for controlling circRNA production - an emerging biomarker and functional regulator of tumor progression. Despite their recognized roles, their involvement in ESCC progression remains unexplored.

METHODS

The expression levels of MBNL1 and QKI were examined in 28 tissue pairs from ESCC and adjacent normal tissues using data from the GEO database. Additionally, a total of 151 ESCC tissue samples, from stage T1 to T4, consisting of 13, 43, 87, and 8 cases per stage, respectively, were utilized for immunohistochemical (IHC) analysis. RNA sequencing was utilized to examine the expression profiles of circRNAs, lncRNAs, and mRNAs across 3 normal tissues, 3 ESCC tissues, and 3 pairs of KYSE150 cells in both wildtype (WT) and those with MBNL1 or QKI knockouts. Transwell, colony formation, and subcutaneous tumorigenesis assays assessed the impact of MBNL1 or QKI knockout on ESCC cell migration, invasion, and proliferation.

RESULTS

ESCC onset significantly altered MBNL1 and QKI expression levels, influencing diverse RNA species. Elevated MBNL1 or QKI expression correlated with patient age or tumor invasion depth, respectively. MBNL1 or QKI knockout markedly enhanced cancer cell migration, invasion, proliferation, and tumor growth. Moreover, the absence of either MBNL1 or QKI modulated the expression profiles of multiple circRNAs, causing extensive downstream alterations in the expression of numerous lncRNAs and mRNAs. While the functions of circRNA and lncRNA among the top 20 differentially expressed genes remain unclear, mRNAs like SLCO4C1, TMPRSS15, and MAGEB2 have reported associations with tumor progression.

CONCLUSIONS

This study underscores the tumor-suppressive roles of MBNL1 and QKI in ESCC, proposing them as potential biomarkers and therapeutic targets for ESCC diagnosis and treatment.

Periprostatic adipose tissue thromboinflammation triggers prostatic neoplasia in early metabolic impairment: Interruption by rivaroxaban

AIMS

Prostate cancer is among the highest incidence malignancies in men with a prevalence rate increasing in parallel to the rising global trends in metabolic disorders. Whereas a sizeable body of evidence links metabolic impairment to negative prognosis of prostate cancer, the molecular mechanism underlying this connection has not been thoroughly examined. Our previous work showed that localized adipose tissue inflammation occurring in select adipose depots in early metabolic derangement instigated significant molecular, structural, and functional alterations in neighboring tissues underlying the complications observed at this stage. In this context, the periprostatic adipose tissue (PPAT) constitutes an understudied microenvironment with potential influence on the prostatic milieu.

MAIN METHODS AND RESULTS

We show that PPAT inflammation occurs in early prediabetes with signs of increased thrombogenic activity including enhanced expression and function of Factor X. This was mirrored by early neoplastic alterations in the prostate with fibrosis, increased epithelial thickness with marked luminal cellular proliferation and enhanced formation of intraepithelial neoplasia. Significantly, interruption of the procoagulant state in PPAT by a 10-day anticoagulant rivaroxaban treatment not only mitigated PPAT inflammation, but also reduced signs of prostatic neoplastic changes. Moreover, rivaroxaban decreased the murine PLum-AD epithelial prostatic cell viability, proliferation, migration, and colony forming capacity, while increasing oxidative stress. A protease-activated receptor-2 agonist reversed some of these effects.

SIGNIFICANCE

We provide some evidence of a molecular framework for the crosstalk between PPAT and prostatic tissue leading to early neoplastic changes in metabolic impairment mediated by upregulation of PPAT thromboinflammation.

Immune infiltration landscape and immune-marker molecular typing of pulmonary fibrosis with pulmonary hypertension

BACKGROUND

Pulmonary arterial hypertension (PH) secondary to pulmonary fibrosis (PF) is one of the most common complications in PF patients, it causes severe disease and usually have a poor prognosis. Whether the combination of PH and PF is a unique disease phenotype is unclear. We aimed to screen the key modules associated with PH-PF immune infiltration based on WGCNA and identify the hub genes for molecular typing.

METHOD

Using the gene expression profile GSE24988 of PF patients with or without PH from the Gene Expression Omnibus (GEO) database, we evaluated immune cell infiltration using Cibersortx and immune cell gene signature files. Different immune cell types were screened using the Wilcoxon test; differentially expressed genes were screened using samr. The molecular pathways implicated in these differential responses were identified using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes functional enrichment analyses. A weighted co-expression network of the differential genes was constructed, relevant co-expression modules were identified, and relationships between modules and differential immune cell infiltration were calculated. The modules most relevant to this disease were identified using weighted correlation network analysis. From these, we constructed a co-expression network; using the STRING database, we integrated the values into the human protein-protein interaction network before constructing a co-expression interaction subnet, screening genes associated with immunity and unsupervised molecular typing, and analyzing the immune cell infiltration and expression of key genes in each disease type.

RESULTS

Of the 22 immune cell types from the PF GEO data, 20 different immune cell types were identified. There were 1622 differentially expressed genes (295 upregulated and 1327 downregulated). The resulting weighted co-expression network identified six co-expression modules. These were screened to identify the modules most relevant to the disease phenotype (the green module). By calculating the correlations between modules and the differentially infiltrated immune cells, extracting the green module co-expression network (46 genes), extracting 25 key genes using gene significance and module-membership thresholds, and combining these with the 10 key genes in the human protein-protein interaction network, we identified five immune cell-related marker genes that might be applied as biomarkers. Using these marker genes, we evaluated these disease samples using unsupervised clustering molecular typing.

CONCLUSION

Our results demonstrated that all PF combined with PH samples belonged to four categories. Studies on the five key genes are required to validate their diagnostic and prognostic value.

Signature Panel of 11 Methylated mRNAs and 3 Methylated lncRNAs for Prediction of Recurrence-Free Survival in Prostate Cancer Patients

Background

Radical prostatectomy is the main treatment for prostate cancer (PCa), a common cancer type among men. Recurrence frequently occurs in a proportion of patients. Therefore, there is a great need to early screen those patients to specifically schedule adjuvant therapy to improve the recurrence-free survival (RFS) rate. This study aims to develop a biomarker to predict RFS for patients with PCa based on the data of methylation, an important heritable contributor to carcinogenesis.

Methods

Methylation expression data of PCa patients were downloaded from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus database (GSE26126), and the European Bioinformatics Institute (E-MTAB-6131). The stable co-methylation modules were identified by weighted gene co-expression network analysis. The genes in modules were overlapped with differentially methylated RNAs (DMRs) screened by MetaDE package in three datasets, which were used to screen the prognostic genes using least absolute shrinkage and selection operator analyses. The prognostic performance of the prognostic signature was assessed by survival curve analysis.

Results

Five co-methylation modules were considered preserved in three datasets. A total of 192 genes in these 5 modules were overlapped with 985 DMRs, from which a signature panel of 11 methylated messenger RNAs and 3 methylated long non-coding RNAs was identified. This signature panel could independently predict the 5-year RFS of PCa patients, with an area under the receiver operating characteristic curve (AUC) of 0.969 for the training TCGA dataset and 0.811 for the testing E-MTAB-6131 dataset, both of which were higher than the predictive accuracy of Gleason score (AUC = 0.689). Also, the patients with the same Gleason score (6–7 or 8–10) could be further divided into the high-risk group and the low-risk group.

Conclusion

These results suggest that our prognostic model may be a promising biomarker for clinical prediction of RFS in PCa patients.

Development and validation of a risk prediction model and nomogram for colon adenocarcinoma based on methylation-driven genes

Evidence suggests that abnormal DNA methylation patterns play a crucial role in the etiology and pathogenesis of colon adenocarcinoma (COAD). In this study, we identified a total of 97 methylation-driven genes (MDGs) through a comprehensive analysis of the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Univariate Cox regression analysis identified four MDGs (CBLN2, RBM47, SLCO4C1, and TMEM220) associated with overall survival (OS) in COAD patients. A risk prediction model was then developed based on these four MDGs to predict the prognosis of COAD patients. We also created a nomogram that incorporated risk scores, age, and TNM stage to promote a personalized prediction of OS in COAD patients. Compared with the traditional TNM staging system, our new nomogram was better at predicting the OS of COAD patients. In cell experiments, we confirmed that the mRNA expression levels of CLBN2 and TMEM220 were regulated by the methylation of their promoter regions. Moreover, immunohistochemistry showed that CBLN2 and TMEM220 were potential prognostic biomarkers for COAD patients. In summary, we have established a risk prediction model and nomogram that might be effectively utilized to promote the prediction of OS in COAD patients.

Analysis of DNA methylation-driven genes for predicting the prognosis of patients with colorectal cancer

Aberrant promoter methylation and ensuing abnormal gene expression are important epigenetic mechanisms that contribute to colorectal oncogenesis. Yet, the prognostic significance of such methylation-driven genes in colorectal cancer (CRC) remains obscure. Herein, a total of 181 genes were identified as the methylation-driven molecular features of CRC by integrated analysis of the expression profiles and the matched DNA methylation data from The Cancer Genome Atlas (TCGA) database. Among them, a five-gene signature (POU4F1, NOVA1, MAGEA1, SLCO4C1, and IZUMO2) was developed as a risk assessment model for predicting the clinical outcomes in CRC. The Kaplan-Meier analysis and Harrell's C index demonstrated that the risk assessment model significantly distinguished the patients in high or low-risk groups (p-value < 0.0001 log-rank test, HR: 2.034, 95% CI: 1.419-2.916, C index: 0.655). The sensitivity and specificity were validated by the receiver operating characteristic (ROC) analysis. Furthermore, different pharmaceutical treatment responses were observed between the high-risk and low-risk groups. Indeed, the methylation-driven gene signature could act as an independent prognostic evaluation biomarker for assessing the OS of CRC patients and guiding the pharmaceutical treatment. Compared with known biomarkers, the methylation-driven gene signature could reveal cross-omics molecular features for improving clinical stratification and prognosis.

Advances in Prognostic Methylation Biomarkers for Prostate Cancer

There is a major clinical need for accurate biomarkers for prostate cancer prognosis, to better inform treatment strategies and disease monitoring. Current clinically recognised prognostic factors, including prostate-specific antigen (PSA) levels, lack sensitivity and specificity in distinguishing aggressive from indolent disease, particularly in patients with localised intermediate grade prostate cancer. There has therefore been a major focus on identifying molecular biomarkers that can add prognostic value to existing markers, including investigation of DNA methylation, which has a known role in tumorigenesis. In this review, we will provide a comprehensive overview of the current state of DNA methylation biomarker studies in prostate cancer prognosis, and highlight the advances that have been made in this field. We cover the numerous studies into well-established candidate genes, and explore the technological transition that has enabled hypothesis-free genome-wide studies and the subsequent discovery of novel prognostic genes.