DNA methylation of tumor associated calcium signal transducer 2 (TACSTD2) loci shows association with clinically aggressive renal cell cancers

Clustering single-cell RNA sequencing data via iterative smoothing and self-supervised discriminative embedding

Single-cell transcriptome sequencing (scRNA-seq) is a high-throughput technique used to study gene expression at the single-cell level. Clustering analysis is a commonly used method in scRNA-seq data analysis, helping researchers identify cell types and uncover interactions between cells. However, the choice of a robust similarity metric in the clustering procedure is still an open challenge due to the complex underlying structures of the data and the inherent noise in data acquisition. Here, we propose a deep clustering method for scRNA-seq data called scRISE (scRNA-seq Iterative Smoothing and self-supervised discriminative Embedding model) to resolve this challenge. The model consists of two main modules: an iterative smoothing module based on graph autoencoders designed to denoise the data and refine the pairwise similarity in turn to gradually incorporate cell structural features and enrich the data information; and a self-supervised discriminative embedding module with adaptive similarity threshold for partitioning samples into correct clusters. Our approach has shown improved quality of data representation and clustering on seventeen scRNA-seq datasets against a number of state-of-the-art deep learning clustering methods. Furthermore, utilizing the scRISE method in biological analysis against the HNSCC dataset has unveiled 62 informative genes, highlighting their potential roles as therapeutic targets and biomarkers.

Insights into the molecular mechanisms and signalling pathways of epithelial to mesenchymal transition (EMT) in colorectal cancer: A systematic review and bioinformatic analysis of gene expression

Colorectal cancer (CRC) is ranked as the second leading cause of mortality worldwide, mainly due to metastasis. Epithelial to mesenchymal transition (EMT) is a complex cellular process that drives CRC metastasis, regulated by changes in EMT-associated gene expression. However, while numerous genes have been identified as EMT regulators through various in vivo and in vitro studies, little is known about the genes that are differentially expressed in CRC tumour tissue and their signalling pathway in regulating EMT. Using an integration of systematic search and bioinformatic analysis, gene expression profiles of CRC tumour tissues were compared to non-tumour adjacent tissues to identify differentially expressed genes (DEGs), followed by performing systematic review on common identified DEGs. Fifty-eight common DEGs were identified from the analysis of 82 tumour tissue samples obtained from four gene expression datasets (NCBI GEO). These DEGS were then systematically searched for their roles in modulating EMT in CRC based on previously published studies. Following this, 10 common DEGs (CXCL1, CXCL8, MMP1, MMP3, MMP7, TACSTD2, VIP, HPGD, ABCG2, CLCA4) were included in this study and subsequently subjected to further bioinformatic analysis. Their roles and functions in modulating EMT in CRC were discussed in this review. This study enhances our understanding of the molecular mechanisms underlying EMT and uncovers potential candidate genes and pathways that could be targeted in CRC.

Identification of TACSTD2 as novel therapeutic targets for cisplatin-induced acute kidney injury by multi-omics data integration

Cisplatin-induced acute kidney injury (CP-AKI) is a common complication in cancer patients. Although ferroptosis is believed to contribute to the progression of CP-AKI, its mechanisms remain incompletely understood. In this study, after initially processed individual omics datasets, we integrated multi-omics data to construct a ferroptosis network in the kidney, resulting in the identification of the key driver TACSTD2. In vitro and in vivo results showed that TACSTD2 was notably upregulated in cisplatin-treated kidneys and BUMPT cells. Overexpression of TACSTD2 accelerated ferroptosis, while its gene disruption decelerated ferroptosis, likely mediated by its potential downstream targets HMGB1, IRF6, and LCN2. Drug prediction and molecular docking were further used to propose that drugs targeting TACSTD2 may have therapeutic potential in CP-AKI, such as parthenolide, progesterone, premarin, estradiol and rosiglitazone. Our findings suggest a significant association between ferroptosis and the development of CP-AKI, with TACSTD2 playing a crucial role in modulating ferroptosis, which provides novel perspectives on the pathogenesis and treatment of CP-AKI.

DNA demethylation and tri-methylation of H3K4 at the TACSTD2 promoter are complementary players for TROP2 regulation in colorectal cancer cells

TROP2 is a powerful cancer driver in colorectal cancer cells. Divergent epigenetic regulation mechanisms for the corresponding TACSTD2 gene exist such as miRNAs or DNA methylation. However, the role of TACSTD2 promoter hypermethylation in colorectal cancer has not been investigated yet. In this study, TROP2 expression strongly correlated with promoter methylation in different colorectal tumor cell lines. Treatment with 5-Azacytidine, a DNMT1 inhibitor, led to demethylation of the TACSTD2 promoter accompanied by an increase in TROP2 protein expression. TROP2 expression correlated with promoter methylation in vivo in human colon tumor tissue, thereby verifying promoter methylation as an important factor in the regulation of TROP2 expression in colorectal cancer. When performing a ChIP-Seq analysis in HCT116 and HT29 cells, we found that TACSTD2 promoter demethylation was accompanied by tri-methylation of H3K4. In silico analysis of GSE156613 data set confirmed that a higher binding of histone mark H3K4me3 around the TACSTD2 promoter was found in TACSTD2 high expressing tumors of colon cancer patients compared to the corresponding adjacent tumor tissue. Moreover, the link between TROP2 and the H3K4me3 code was even evident in tumors showing high intratumoral heterogeneity for TROP2 staining. Our data provide novel evidence for promoter demethylation and simultaneous gains of the active histone mark H3K4me3 across CpG-rich sequences, both being complementary mechanisms in the transcriptional regulation of TACSTD2 in colon cancer. The functional consequences of TROP2 loss in colorectal cancer needs to be further investigated.

Analysis of Methylome, Transcriptome, and Lipid Metabolites to Understand the Molecular Abnormalities in Polycystic Ovary Syndrome

Purpose

This study aimed to identify differentially methylated genes (DMGs) and differentially expressed genes (DEGs) to investigate new biomarkers for the diagnosis and treatment of polycystic ovary syndrome (PCOS).

Methods

To explore the potential biomarkers of PCOS diagnosis and treatment, we performed methyl-binding domain sequencing (MBD-seq) and RNA sequencing (RNA-seq) on ovarian granulosa cells (GCs) from PCOS patients and healthy controls. MBD-seq was also performed on the ovarian tissue of constructed prenatally androgenized (PNA) mice. Differential methylation and expression analysis were implemented to identify DMGs and DEGs, respectively. The identified gene was further verified by real-time quantitative PCR (RT-qPCR) and methylation-specific PCR (MSP) in clinical samples. Furthermore, ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) was carried out on PCOS patients and healthy controls to identify differential lipid metabolites.

Results

Compared to the control group, 13,526 DMGs related to the promoter region and 2429 DEGs were found. The function analysis of DMGs and DEGs showed that they were mainly enriched in glycerophospholipid, ovarian steroidogenesis, and other lipid metabolic pathways. Moreover, 5753 genes in DMGs related to the promoter region were screened in the constructed PNA mice. Integrating the DMGs data from PCOS patients and PNA mice, we identified the following 8 genes: CDC42EP4, ERMN, EZR, PIK3R1, ARHGEF18, NECTIN2, TSC2, and TACSTD2. RT-qPCR and MSP verification results showed that the methylation and expression of TACSTD2 were consistent with sequencing data. Additionally, 15 differential lipid metabolites were shown in the serum of PCOS patients. The differential lipids were involved in glycerophospholipid and glycerolipid metabolism.

Conclusion

Using integration of methylome and lipid metabolites profiling we identified 8 potential epigenetic markers and 15 potential lipid metabolite markers for PCOS. Our results suggest that aberrant DNA methylation and lipid metabolite disorders may provide novel insights into the diagnosis and etiology of PCOS.

Identification of a 5-Hydroxymethylation Signature in Circulating Cell-Free DNA for the Noninvasive Detection of Colorectal Cancer

Background

As a crucial epigenetic modification, DNA 5-hydroxymethylcytosine (5-hmC) plays a key role during colorectal cancer (CRC) carcinogenesis. Nevertheless, the levels of 5-hmC-related genes in the circulating DNA of CRC remain largely unknown.

Methods and Results

The GSE81314 dataset from the Gene Expression Omnibus (GEO), which was generated by chemical marking-based low-input shotgun sequencing to detect 5-hmC in circulating cell-free DNA (cfDNA) was used in the present study. The GSE81314 dataset includes data for 8 plasma samples from healthy individuals and 4 plasma samples from CRC patients. The difference in the 5-hmC levels in cfDNA between the CRC group and healthy individuals was analyzed by the differentially expressed genes (DEG) package. Weighted gene coexpression network analysis (WGCNA) was conducted to analyze gene coexpression modules associated with sample characteristics. DEG analysis identified 19 upregulated and 9 downregulated 5-hmC-related genes. WGCNA showed that the pink, purple, and brown modules, which contain 531 genes in total, were significantly correlated with CRC (0.66, 0.61, and -0.59, respectively). We used gene set enrichment analysis (GSEA) software to compare 5-hmC-related genes and pathways between CRC patients and healthy controls. We further performed a protein–protein interaction (PPI) analysis and identified 4 nodes (LCN2, LRG1, S100P, and TACSTD2) that played key roles in the network, and we analyzed the expression of these nodes S100P in the GEPIA database. Consistent with the 5-hmC levels in CRC patient plasma, our external validation results from the GEPIA and UALCAN databases showed that LCN2, LRG1, S100P, and TACSTD2 were highly expressed in CRC tissue compared with controls. The DNA promoter methylation levels of LCN2, LRG1, and S100P were lower in CRC tissue than in normal control tissue.

Conclusion

The present findings suggest that abnormality in cell-free DNA hydroxylation in plasma may be associated with CRC. In addition, the 5-hmC levels of LCN2, LRG1, S100P, and TACSTD2 in circulating cfDNA may be used as potential noninvasive markers for CRC.

Identification of a Novel Renal Metastasis Associated CpG-Based DNA Methylation Signature (RMAMS)

Approximately 21% of patients with renal cell cancer (RCC) present with synchronous metastatic disease at the time of diagnosis, and metachronous metastatic disease occurs in 20–50% of cases within 5 years. Recent advances in adjuvant treatment of aggressive RCC following surgery suggest that biomarker-based prediction of risk for distant metastasis could improve patient selection. Biometrical analysis of TCGA-KIRC data identified candidate loci in the NK6 homeobox 2 gene (NKX6-2) that are hypermethylated in primary metastatic RCC. Analyses of NKX6-2 DNA methylation in three gene regions including a total of 16 CpG sites in 154 tumor-adjacent normal tissue, 189 RCC, and 194 metastatic tissue samples from 95 metastasized RCC patients revealed highly significant tumor-specific, primary metastatic-specific, and metastatic tissue-specific hypermethylation of NKX6-2. Combined CpG site methylation data for NKX6-2 and metastasis-associated genes (INA, NHLH2, and THBS4) demonstrated similarity between metastatic tissues and metastatic primary RCC tissues. The random forest method and evaluation of an unknown test cohort of tissues using receiver operator characteristic curve analysis revealed that metastatic tissues can be differentiated by a median area under the curve of 0.86 (p = 1.7 × 10−8–7.5 × 10−3) in 1000 random runs. Analysis of variable importance demonstrated an above median contribution for decision-making of at least one CpG site in each of the genes, suggesting superior informativity for sites annotated to NHLH2 and NKX6-2. Thus, DNA methylation of NKX6-2 is associated with the metastatic state of RCC tissues and contributes to a four-gene-based statistical predictor of tumoral and metastatic renal tissues.

Construction and Verification of a Combined Hypoxia and Immune Index for Clear Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (ccRCC) is one of the most aggressive malignancies in humans. Hypoxia-related genes are now recognized as a reflection of poor prognosis in cancer patients with cancer. Meanwhile, immune-related genes play an important role in the occurrence and progression of ccRCC. Nevertheless, reliable prognostic indicators based on hypoxia and immune status have not been well established in ccRCC. The aims of this study were to develop a new gene signature model using bioinformatics and open databases and to validate its prognostic value in ccRCC. The data used for the model structure can be accessed from The Cancer Genome Atlas database. Univariate, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses were used to identify the hypoxia- and immune-related genes associated with prognostic risk, which were used to develop a characteristic model of prognostic risk. Kaplan-Meier and receiver-operating characteristic curve analyses were performed as well as independent prognostic factor analyses and correlation analyses of clinical characteristics in both the training and validation cohorts. In addition, differences in tumor immune cell infiltrates were compared between the high and low risk groups. Overall, 30 hypoxia- and immune-related genes were identified, and five hypoxia- and immune-related genes (EPO, PLAUR, TEK, TGFA, TGFB1) were ultimately selected. Survival analysis showed that the high-risk score on the hypoxia- and immune-related gene signature was significantly associated with adverse survival outcomes. Furthermore, clinical ccRCC samples from our medical center were used to validate the differential expression of the five genes in tumor tissue compared to normal tissue through quantitative real-time polymerase chain reaction (qRT-PCR). However, more clinical trials are needed to confirm these results, and future experimental studies must verify the potential mechanism behind the predictive value of the hypoxia- and immune-related gene signature.

DNA Methylation in INA, NHLH2, and THBS4 Is Associated with Metastatic Disease in Renal Cell Carcinoma

The detection of DNA methylation in primary tumor tissues could be relevant for early stratification of aggressive renal cell carcinomas (RCCs) as a basis for future personalized adjuvant therapy. Methylated TCGA KIRC based candidate CpG loci in INA, NHLH2, and THBS4 that are possibly associated with RCC metastasis were evaluated by pyrosequencing in 154 paired normal adjacent and primary tumor tissues, as well as in 202 metastatic tissues. Statistical analysis was carried out by bivariate logistic regression for group comparisons, log rank survival analysis, and unsupervised and supervised analysis for the classification of tumors. Increased methylation of INA, NHLH2, and THBS4 loci were significantly associated with distant metastasis in primary tumors (p < 0.05), tissue-specific hypermethylation in metastatic (p = 7.88 × 10^-8, 5.57 × 10^-10, 2.06 × 10^-7) and tumor tissues (p = 3.72 × 10^-24, 3.17 × 10^-13, 1.58 × 10^-19), and shortened progression free survival in patients (p = 0.03). Combined use of CpG site-specific methylation permits the discrimination of tissues with metastatic disease and reveals a significant contribution of CpG sites in all genes to the statistical classification model. Thus, metastasis in RCC is significantly associated with methylation alterations in INA, NHLH2, and THBS4 loci, providing independent information for the potential early detection of aggressive renal cancers as a rationale for stratifying patients to adjuvant therapies.