Molecular signatures identified by integrating gene expression and methylation in non-seminoma and seminoma of testicular germ cell tumours

Comprehensive microRNA expression analysis of pediatric gonadal germ cell tumors: unveiling novel biomarkers and signatures

microRNAs (miRNAs) are small endogenous noncoding RNAs, and alterations in their expression may contribute to oncogenesis. Discovering a unique miRNA pattern holds the potential for early detection and novel treatment possibilities in cancer. This study aimed to evaluate miRNA expression in pediatric patients with gonadal germ cell tumors (GCTs), focusing on characterizing the miRNA profiles of each histological subtype and identifying a distinct histological miRNA signature for a total of 42 samples of pediatric gonadal GCTs. The analysis revealed distinct miRNA expression profiles for all histological types, regardless of the primary site. We identified specific miRNA expression signatures for each histological type, including 34 miRNAs for dysgerminomas, 13 for embryonal carcinomas, 25 for yolk sac tumors, and one for immature teratoma, compared to healthy controls. Furthermore, we identified 26 miRNAs that were commonly expressed in malignant tumors, with six miRNAs (miR-302a-3p, miR-302b-3p, miR-371a-5p, miR-372-3p, miR-373-3p, and miR-367-3p) showing significant overexpression. Notably, miR-302b-3p exhibited a significant association with all the evaluated clinical features. Our findings suggest that miRNAs have the potential to aid in the diagnosis, prognosis, and management of patients with malignant GCTs.

Recent Advancements in Research on DNA Methylation and Testicular Germ Cell Tumors: Unveiling the Intricate Relationship

Testicular germ cell tumors (TGCTs) are the most common type of testicular cancer, with a particularly high incidence in the 15-45-year age category. Although highly treatable, resistance to therapy sometimes occurs, with devastating consequences for the patients. Additionally, the young age at diagnosis and the treatment itself pose a great threat to patients' fertility. Despite extensive research concerning genetic and environmental risk factors, little is known about TGCT etiology. However, epigenetics has recently come into the spotlight as a major factor in TGCT initiation, progression, and even resistance to treatment. As such, recent studies have been focusing on epigenetic mechanisms, which have revealed their potential in the development of novel, non-invasive biomarkers. As the most studied epigenetic mechanism, DNA methylation was the first revelation in this particular field, and it continues to be a main target of investigations as research into its association with TGCT has contributed to a better understanding of this type of cancer and constantly reveals novel aspects that can be exploited through clinical applications. In addition to biomarker development, DNA methylation holds potential for developing novel treatments based on DNA methyltransferase inhibitors (DNMTis) and may even be of interest for fertility management in cancer survivors. This manuscript is structured as a literature review, which comprehensively explores the pivotal role of DNA methylation in the pathogenesis, progression, and treatment resistance of TGCTs.

Interaction and Collaboration of SP1, HIF-1, and MYC in Regulating the Expression of Cancer-Related Genes to Further Enhance Anticancer Drug Development

Specificity protein 1 (SP1), hypoxia-inducible factor 1 (HIF-1), and MYC are important transcription factors (TFs). SP1, a constitutively expressed housekeeping gene, regulates diverse yet distinct biological activities; MYC is a master regulator of all key cellular activities including cell metabolism and proliferation; and HIF-1, whose protein level is rapidly increased when the local tissue oxygen concentration decreases, functions as a mediator of hypoxic signals. Systems analyses of the regulatory networks in cancer have shown that SP1, HIF-1, and MYC belong to a group of TFs that function as master regulators of cancer. Therefore, the contributions of these TFs are crucial to the development of cancer. SP1, HIF-1, and MYC are often overexpressed in tumors, which indicates the importance of their roles in the development of cancer. Thus, proper manipulation of SP1, HIF-1, and MYC by appropriate agents could have a strong negative impact on cancer development. Under these circumstances, these TFs have naturally become major targets for anticancer drug development. Accordingly, there are currently many SP1 or HIF-1 inhibitors available; however, designing efficient MYC inhibitors has been extremely difficult. Studies have shown that SP1, HIF-1, and MYC modulate the expression of each other and collaborate to regulate the expression of numerous genes. In this review, we provide an overview of the interactions and collaborations of SP1, HIF1A, and MYC in the regulation of various cancer-related genes, and their potential implications in the development of anticancer therapy.

Epigenetic Regulation of Driver Genes in Testicular Tumorigenesis

In testicular germ cell tumor type II (TGCT), a seminoma subtype expresses an induced pluripotent stem cell (iPSC) panel with four upregulated genes, OCT4/POU5F1, SOX17, KLF4, and MYC, and embryonal carcinoma (EC) has four upregulated genes, OCT4/POU5F1, SOX2, LIN28, and NANOG. The EC panel can reprogram cells into iPSC, and both iPSC and EC can differentiate into teratoma. This review summarizes the literature on epigenetic regulation of the genes. Epigenetic mechanisms, such as methylations of cytosines on the DNA string and methylations and acetylations of histone 3 lysines, regulate expression of these driver genes between the TGCT subtypes. In TGCT, the driver genes contribute to well-known clinical characteristics and the driver genes are also important for aggressive subtypes of many other malignancies. In conclusion, epigenetic regulation of the driver genes are important for TGCT and for oncology in general.

Comprehensive characteristics of pathological subtypes in testicular germ cell tumor: Gene expression, mutation and alternative splicing

Background

Testicular germ cell tumor (TGCT) is the most common tumor in young men, but molecular signatures, especially the alternative splicing (AS) between its subtypes have not yet been explored.

Methods

To investigate the differences between TGCT subtypes, we comprehensively analyzed the data of gene expression, alternative splicing (AS), and somatic mutation in TGCT patients from the TCGA database. The gene ontology (GO) enrichment analyses were used to explore the function of differentially expressed genes and spliced genes respectively, and Spearman correlation analysis was performed to explore the correlation between differential genes and AS events. In addition, the possible patterns in which AS regulates gene expression were elaborated by the ensemble database transcript atlas. And, we identified important transcription factors that regulate gene expression and AS and functionally validated them in TGCT cell lines.

Results

We found significant differences between expression and AS in embryonal carcinoma and seminoma, while mixed cell tumors were in between. GO enrichment analyses revealed that both differentially expressed and spliced genes were enriched in transcriptional regulatory pathways, and obvious correlation between expression and AS events was determined. By analyzing the transcript map and the sites where splicing occurs, we have demonstrated that AS regulates gene expression in a variety of ways. We further identified two pivot AS-related molecules (SOX2 and HDAC9) involved in AS regulation, which were validated in embryonal carcinoma and seminoma cell lines. Differences in somatic mutations between subtypes are also of concern, with our results suggesting that mutations in some genes (B3GNT8, CAPN7, FAT4, GRK1, TACC2, and TRAM1L1) occur only in embryonal carcinoma, while mutations in KIT, KARS, and NRAS are observed only in seminoma.

Conclusions

In conclusion, our analysis revealed the differences in gene expression, AS and somatic mutation among TGCT subtypes, providing a molecular basis for clinical diagnosis and precise therapy of TGCT patients.

Synergy between the Levels of Methylation of microRNA Gene Sets in Primary Tumors and Metastases of Ovarian Cancer Patients

We studied the correlations between the levels of methylation of a group of 21 microRNA genes in 99 primary tumors and 29 macroscopic peritoneal metastases of ovarian cancer. Analysis of the level of methylation by quantitative methylation-specific PCR showed that co-methylation was detected for 13 pairs of microRNA genes in primary tumors and for 22 pairs in metastases. Pairs of microRNA genes that have shown significant co-methylation can be involved in common processes and pathways of gene regulation and interaction and can have common target genes. The results are highly significant and pairs of microRNA genes can be proposed as new potential markers for the diagnosis and prognosis of ovarian cancer metastasis.

Unsupervised Feature Selection Using an Integrated Strategy of Hierarchical Clustering With Singular Value Decomposition: An Integrative Biomarker Discovery Method With Application to Acute Myeloid Leukemia

In this article, we propose a novel unsupervised feature selection method by combining hierarchical feature clustering with singular value decomposition (SVD). The proposed algorithm first generates several feature clusters by adopting the hierarchical clustering on the feature space and then applies SVD to each of these feature clusters to find out the feature that contributes most to the SVD-entropy. The proposed feature selection method selects an optimal feature subset that not only minimizes the mutual dependency among the selected features but also maximizes the mutual dependency of the selected features against their nearest neighbor non-selected features to some extent. Each of the selected features also contributes the maximum SVD-entropy among all features of the same feature cluster. The experimental results demonstrate that the proposed algorithm performs well against several state-of-the-art methods of feature selection in terms of various evaluation criteria such as classification accuracy, redundancy rate, and representation entropy. The superiority of the proposed algorithm is demonstrated through analysis of Acute Myeloid Leukemia (AML) multi-omics data that consist of five datasets: gene expression, exon expression, methylation, microRNA, and pathway activity dataset (paradigm IPLs) from The Cancer Genome Atlas (TCGA). Our analysis pinpoints a candidate gene-marker, EREG for AML with an integrative omics evidence. EREG is targeted by two top ranked microRNAs, hsa-miR-1286 and hsa-miR-1976, here in the datasets. The method and results will be useful for biomarker discovery in the era of in precision medicine.

A Deep Learning-Based Framework for Supporting Clinical Diagnosis of Glioblastoma Subtypes

Understanding molecular features that facilitate aggressive phenotypes in glioblastoma multiforme (GBM) remains a major clinical challenge. Accurate diagnosis of GBM subtypes, namely classical, proneural, and mesenchymal, and identification of specific molecular features are crucial for clinicians for systematic treatment. We develop a biologically interpretable and highly efficient deep learning framework based on a convolutional neural network for subtype identification. The classifiers were generated from high-throughput data of different molecular levels, i.e., transcriptome and methylome. Furthermore, an integrated subsystem of transcriptome and methylome data was also used to build the biologically relevant model. Our results show that deep learning model outperforms the traditional machine learning algorithms. Furthermore, to evaluate the biological and clinical applicability of the classification, we performed weighted gene correlation network analysis, gene set enrichment, and survival analysis of the feature genes. We identified the genotype-phenotype relationship of GBM subtypes and the subtype-specific predictive biomarkers for potential diagnosis and treatment.

Secondhand smoke affects reproductive functions by altering the mouse testis transcriptome, and leads to select intron retention in Pde1a

BACKGROUND

Human exposure to secondhand smoke (SHS) is known to result in adverse effects in multiple organ systems. However, the impact of SHS on the male reproductive system, particularly on the regulation of genes and molecular pathways that govern sperm production, maturation, and functions remains largely understudied.

OBJECTIVE

We investigated the effects of SHS on the testis transcriptome in a validated mouse model.

METHODS

Adult male mice were exposed to SHS (5 h/day, 5 days/week for 4 months) as compared to controls (clean air-exposed). RNA-seq analysis was performed on the testis of SHS-exposed mice and controls. Variant discovery and plink association analyses were also conducted to detect exposure-related transcript variants in SHS-treated mice.

RESULTS

Exposure of mice to SHS resulted in the aberrant expression of 131 testicular genes. Whilst approximately two thirds of the differentially expressed genes were protein-coding, the remaining (30.5%) comprised noncoding elements, mostly lncRNAs (19.1%). Variant discovery analysis identified a homozygous frameshift variant that is statistically significantly associated with SHS exposure (P = 7.744e-06) and is generated by retention of a short intron within Pde1a, a key regulator of spermatogenesis. Notably, this SHS-associated intron variant harbors an evolutionarily conserved, premature termination codon (PTC) that disrupts the open reading frame of Pde1a, presumably leading to its degradation via nonsense-mediated decay.

DISCUSSION

SHS alters the expression of genes involved in molecular pathways that are crucial for normal testis development and function. Preferential targeting of lncRNAs in the testis of SHS-exposed mice is especially significant considering their crucial role in the spatial and temporal modulation of spermatogenesis. Equally important is our discovery of a novel homozygous frameshift variant that is exclusively and significantly associated with SHS-exposure and is likely to represent a safeguard mechanism to regulate transcription of Pde1a and preserve normal testis function during harmful exposure to environmental agents.

circKCNN2 suppresses the recurrence of hepatocellular carcinoma at least partially via regulating miR-520c-3p/methyl-DNA-binding domain protein 2 axis

BACKGROUND

Recurrence is the major cause of hepatocellular carcinoma (HCC) death. We aimed to identify circular RNA (circRNA) with predictive and therapeutic value for recurrent HCC.

METHODS

Tissue samples from recurrent and non-recurrent HCC patients were subjected to circRNA sequencing and transcriptome sequencing. circKCNN2 was identified through multi-omics analyses. The effects of circKCNN2 on HCC were evaluated in cells, animals, database of The Cancer Genome Atlas, and a cohort with 130 HCC patients. circRNA precipitation, chromatin immunoprecipitation assay, RNA pull-down, luciferase assay, and cell experiments were applied to evaluate the interaction of circKCNN2 with miRNAs and proteins. The association between circKCNN2 and the therapeutic effect of lenvatinib was investigated in HCC cell lines and HCC tissue-derived organoids.

RESULTS

The expression of circKCNN2 was downregulated in HCC tissues and predicted a favorable overall survival and recurrence-free survival. The expression of circKCNN2 was positively correlated with the parental gene, potassium calcium-activated channel subfamily N member (KCNN2). Nuclear transcription factor Y subunit alpha (NFYA) was proven to inhibit the promoter activity of KCNN2, downregulate the expression of KCNN2 and circKCNN2, and predict an unfavorable recurrence-free survival. Ectopic expression of circKCNN2 inhibited HCC cell proliferation, colony formation, migration, and tumor formation in a mouse model. miR-520c-3p sponged by circKCNN2 could reverse the inhibitory effect of circKCNN2 on HCC cells and down-regulate the expression of methyl-DNA-binding domain protein 2 (MBD2). The intratumoral expression of MBD2 predicted a favorable recurrence-free survival. circKCNN2 down-regulated the expression of fibroblast growth factor receptor 4 (FGFR4), which can be reversed by miR-520c-3p and knockdown of MBD2. Lenvatinib inhibited the expression of FGFR4 and upregulated the expression of circKCNN2 and MBD2. Ectopic expression of circKCNN2 in HCC cells enhanced the therapeutic effect of lenvatinib. However, the high inherent level of circKCNN2 in HCC cells was associated with lenvatinib resistance.

CONCLUSIONS

circKCNN2, transcriptionally repressed by NFYA, suppresses HCC recurrence via the miR-520c-3p/MBD2 axis. Inherent level of circKCNN2 in HCC cells predisposes anti-tumor effect of lenvatinib possibly because both circKCNN2 and lenvatinib repress the expression of FGFR4. circKCNN2 may be a promising predictive biomarker and therapeutic agent for HCC recurrence.

MiR-7 in Cancer Development

MicroRNAs (miRNAs) are short non-coding RNA involved in the regulation of specific mRNA translation. They participate in cellular signaling circuits and can act as oncogenes in tumor development, so-called oncomirs, as well as tumor suppressors. miR-7 is an ancient miRNA involved in the fine-tuning of several signaling pathways, acting mainly as tumor suppressor. Through downregulation of PI3K and MAPK pathways, its dominant role is the suppression of proliferation and survival, stimulation of apoptosis and inhibition of migration. Besides these functions, it has numerous additional roles in the differentiation process of different cell types, protection from stress and chromatin remodulation. One of the most investigated tissues is the brain, where its downregulation is linked with glioblastoma cell proliferation. Its deregulation is found also in other tumor types, such as in liver, lung and pancreas. In some types of lung and oral carcinoma, it can act as oncomir. miR-7 roles in cell fate determination and maintenance of cell homeostasis are still to be discovered, as well as the possibilities of its use as a specific biotherapeutic.

In silico ranking of phenolics for therapeutic effectiveness on cancer stem cells

BACKGROUND

Cancer stem cells (CSCs) have features such as the ability to self-renew, differentiate into defined progenies and initiate the tumor growth. Treatments of cancer include drugs, chemotherapy and radiotherapy or a combination. However, treatment of cancer by various therapeutic strategies often fail. One possible reason is that the nature of CSCs, which has stem-like properties, make it more dynamic and complex and may cause the therapeutic resistance. Another limitation is the side effects associated with the treatment of chemotherapy or radiotherapy. To explore better or alternative treatment options the current study aims to investigate the natural drug-like molecules that can be used as CSC-targeted therapy. Among various natural products, anticancer potential of phenolics is well established. We collected the 21 phytochemicals from phenolic group and their interacting CSC genes from the publicly available databases. Then a bipartite graph is constructed from the collected CSC genes along with their interacting phytochemicals from phenolic group as other. The bipartite graph is then transformed into weighted bipartite graph by considering the interaction strength between the phenolics and the CSC genes. The CSC genes are also weighted by two scores, namely, DSI (Disease Specificity Index) and DPI (Disease Pleiotropy Index). For each gene, its DSI score reflects the specific relationship with the disease and DPI score reflects the association with multiple diseases. Finally, a ranking technique is developed based on PageRank (PR) algorithm for ranking the phenolics.

RESULTS

We collected 21 phytochemicals from phenolic group and 1118 CSC genes. The top ranked phenolics were evaluated by their molecular and pharmacokinetics properties and disease association networks. We selected top five ranked phenolics (Resveratrol, Curcumin, Quercetin, Epigallocatechin Gallate, and Genistein) for further examination of their oral bioavailability through molecular properties, drug likeness through pharmacokinetic properties, and associated network with CSC genes.

CONCLUSION

Our PR ranking based approach is useful to rank the phenolics that are associated with CSC genes. Our results suggested some phenolics are potential molecules for CSC-related cancer treatment.

A Linear Regression and Deep Learning Approach for Detecting Reliable Genetic Alterations in Cancer Using DNA Methylation and Gene Expression Data

DNA methylation change has been useful for cancer biomarker discovery, classification, and potential treatment development. So far, existing methods use either differentially methylated CpG sites or combined CpG sites, namely differentially methylated regions, that can be mapped to genes. However, such methylation signal mapping has limitations. To address these limitations, in this study, we introduced a combinatorial framework using linear regression, differential expression, deep learning method for accurate biological interpretation of DNA methylation through integrating DNA methylation data and corresponding TCGA gene expression data. We demonstrated it for uterine cervical cancer. First, we pre-filtered outliers from the data set and then determined the predicted gene expression value from the pre-filtered methylation data through linear regression. We identified differentially expressed genes (DEGs) by Empirical Bayes test using Limma. Then we applied a deep learning method, "nnet" to classify the cervical cancer label of those DEGs to determine all classification metrics including accuracy and area under curve (AUC) through 10-fold cross validation. We applied our approach to uterine cervical cancer DNA methylation dataset (NCBI accession ID: GSE30760, 27,578 features covering 63 tumor and 152 matched normal samples). After linear regression and differential expression analysis, we obtained 6287 DEGs with false discovery rate (FDR) <0.001. After performing deep learning analysis, we obtained average classification accuracy 90.69% (±1.97%) of the uterine cervical cancerous labels. This performance is better than that of other peer methods. We performed in-degree and out-degree hub gene network analysis using Cytoscape. We reported five top in-degree genes (PAIP2, GRWD1, VPS4B, CRADD and LLPH) and five top out-degree genes (MRPL35, FAM177A1, STAT4, ASPSCR1 and FABP7). After that, we performed KEGG pathway and Gene Ontology enrichment analysis of DEGs using tool WebGestalt(WEB-based Gene SeT AnaLysis Toolkit). In summary, our proposed framework that integrated linear regression, differential expression, deep learning provides a robust approach to better interpret DNA methylation analysis and gene expression data in disease study.