Hypermethylation of NF-κB-Activating Protein-Like (NKAPL) Promoter in Hepatocellular Carcinoma Suppresses Its Expression and Predicts a Poor Prognosis

Beyond histology: A tissue algorithm predictive of post-surgical recurrence in hepatocellular carcinomas, including TERT promoter mutation

Surgical resection for hepatocellular carcinoma (HCC) is burdened with a high recurrence rate and a lack of reliable prognostic factors. The aim of this study was to integrate the HCC pathological features with gene mutations to improve the prognostic role of pathological analysis. This is a monocentric prospective study, including 67 patients resected for HCC. All clinical data and histological features were collected, including tumor grade, architecture, margins, microvascular invasion, and microscopic portal vascular invasion (MPVI). Next-generation sequencing (NGS) was performed using a laboratory-developed multi-gene panel, allowing to amplify 330 amplicons (21.77 kb), covering the relevant targets for solid tumor analysis. The most represented mutations were TERT promoter (n = 41, 61.2%), TP53 (n = 18, 26.9%) and CTNNB1 (n = 17, 25.4%). At follow-up, 13 (19.4%) patients experienced HCC recurrence: at multivariate analysis, tumor dimensions (p = 0.040), MPVI (p = 0.010), and TERT mutation (p = 0.034) correlated with recurrence. Dimensions ≥ 4.5 cm (very close to AJCC stage pT3; 9 recurrences, p = 0.041, odd-ratio = 3.7), MPVI (9 recurrences, p = 0.062, OR = 3.3), and TERT (11 recurrences, p = 0.049, OR = 4.4) correlated with disease-free survival also at univariate analysis. The concomitant occurrence of these three variables was present in 7 cases, among which 5 recurred (p = 0.002, OR = 15.94). In conclusion, NGS analysis in resected HCC could not only be used for future therapies but should be integrated with histopathology to predict the risk of tumor recurrence after surgical resection: TERT mutation is among the strongest predictors of tumor recurrence, together with tumor stage (dimensions) and the occurrence of MPVI, which should always be reported separately from the classic MVI.

Unveiling epigenetic regulatory elements associated with breast cancer development

Breast cancer is the most common cancer in women and the 2nd most common cancer worldwide, yearly impacting over 2 million females and causing 650 thousand deaths. It has been widely studied, but its epigenetic variation is not entirely unveiled. We aimed to identify epigenetic mechanisms impacting the expression of breast cancer related genes to detect new potential biomarkers and therapeutic targets. We considered The Cancer Genome Atlas database with over 800 samples and several omics datasets such as mRNA, miRNA, DNA methylation, which we used to select 2701 features that were statistically significant to differ between cancer and control samples using the Monte Carlo Feature Selection and Interdependency Discovery algorithm, from an initial total of 417,486. Their biological impact on cancerogenesis was confirmed using: statistical analysis, natural language processing, linear and machine learning models as well as: transcription factors identification, drugs and 3D chromatin structure analyses. Classification of cancer vs control samples on the selected features returned high classification weighted Accuracy from 0.91 to 0.98 depending on feature-type: mRNA, miRNA, DNA methylation, and classification algorithm. In general, cancer samples showed lower expression of differentially expressed genes and increased β-values of differentially methylated sites. We identified mRNAs whose expression is well explained by miRNA expression and differentially methylated sites β-values. We recognized differentially methylated sites possibly affecting NRF1 and MXI1 transcription factors binding, causing a disturbance in NKAPL and PITX1 expression, respectively. Our 3D models showed more loosely packed chromatin in cancer. This study successfully points out numerous possible regulatory dependencies.

Unveiling the epigenomic mechanisms of acquired platinum-resistance in high-grade serous ovarian cancer

Resistance to platinum-based chemotherapy is the major cause of death from high-grade serous ovarian cancer (HGSOC). We hypothesise that detection of specific DNA methylation changes may predict platinum resistance in HGSOC. Using a publicly available "discovery" dataset we examined epigenomic and transcriptomic alterations between primary platinum-sensitive (n = 32) and recurrent acquired drug resistant HGSOC (n = 28) and identified several genes involved in immune and chemoresistance-related pathways. Validation via high-resolution melt analysis of these findings, in cell lines and HGSOC tumours, demonstrated the most consistent changes were observed in three of the genes: APOBEC3A, NKAPL and PDCD1. Plasma samples from an independent HGSOC cohort (n = 17) were analysed using droplet digital PCR. Hypermethylation of NKAPL was detected in 46% and hypomethylation of APOBEC3A in 69% of plasma samples taken from women with relapsed HGSOC (n = 13), with no alterations identified in disease-free patients (n = 4). Following these results, and using a CRISPR-Cas9 approach, we were also able to demonstrate that in vitro NKAPL promoter demethylation increased platinum sensitivity by 15%. Overall, this study demonstrates the importance of aberrant methylation, especially of the NKAPL gene, in acquired platinum resistance in HGSOC.

Curcumin alters distinct molecular pathways in breast cancer subtypes revealed by integrated miRNA/mRNA expression analysis

BACKGROUND

Curcumin is well known for its anticancer properties. Its cytotoxic activity has been documented in several cancer cell lines, including breast cancer. The pleiotropic activity of curcumin as an antioxidant, an antiangiogenic, antiproliferative, and pro-apoptotic, is due to its diverse targets, such as signaling pathways, protein/enzyme, or noncoding gene.

AIM

This study aimed to identify key miRNAs and mRNAs induced by curcumin in breast cancer cells MCF7, T47D (hormone positive), versus MDA-MB231 (hormone negative) using comparative analysis of global gene expression profiles.

METHODS

RNA was isolated and subjected to mRNA and miRNA library sequencing to study the global gene expression profile of curcumin-treated breast cancer cells. The differential expression of gene and miRNA was performed using the DESeq R package. The enriched pathways were studied using cluster profileR, and integrated miRNA-mRNA analysis was carried out using miRtarvis and miRmapper tools.

RESULTS

Curcumin treatment led to upregulation of 59% TSGs in MCF7, 21% in MDA-MB-231 cells, and 36% TSGs in T47D, and downregulation of 57% oncogenes in MCF7, 76% in MDA-MB-231, and 91% in T47D. Similarly, curcumin treatment led to upregulation of 32% TSmiRs in MCF7, 37.5% in MDA-MB231, and 62.5% in T47D, and downregulation of 77% oncomiRs in MCF7, 50% in MDA-MB231 and 28.6% in T47D. Integrated analysis of miRNA-mRNA led to the identification of a common NFKB pathway altered by curcumin in all three cell lines. Analysis of uniquely enriched pathway revealed non-integrin membrane-ECM interactions and laminin interactions in MCF7; extracellular matrix organization and degradation in MDA-MB-231 and cell cycle arrest and G2/M transition in T47D.

CONCLUSION

Curcumin regulates miRNA and mRNA in a cell type-specific manner. The integrative analysis led to the detection of miRNAs and mRNAs pairs, which can be used as biomarkers associated with carcinogenesis, diagnostic, and treatment response in breast cancer.

FAT10 promotes hepatocellular carcinoma (HCC) carcinogenesis by mediating P53 degradation and acts as a prognostic indicator of HCC

Background

With the advancement of hepatocellular carcinoma (HCC) treatment technology, the treatment options for HCC patients have increased. However, due to high heterogeneity, among other reasons, the five-year survival rate of patients is still very low. Currently, gene expression prognostic models can suggest more appropriate strategies for the treatment of HCC. This study investigates the role of FAT10 in hepatocarcinogenesis and its underlying mechanism.

Methods

The expression of FAT10 was detected by immunohistochemical method using tissue arrays containing 4 specimens of patients with digestive cancer. The expression of FAT10 was determined by a tissue microarray which included 286 pairs of HCC samples and corresponding normal mucosae and was further confirmed by real-time polymerase chain reaction (PCR) and western blot. The Kaplan-Meier survival curve was used to determine the correlation of FAT10 expression with patients' recurrence and overall survival (OS) rate. In vivo, liver fibrosis, cirrhosis, and HCC models were established to assess the FAT10 expression. Moreover, FAT10 over-expressing cell lines were used to determine the molecular mechanism underlying the FAT10-induced cell proliferation and hepatocarcinogenesis by reporter gene measure, real-time PCR, and western blot. Based on TCGA database, signal pathways associated with FAT10 and HCC invasion and metastasis were analyzed by KEGG enrichment analyze.

Results

Overexpression of FAT10 in HCC was observed in this study compared with its expression in other digestive tumors. Clinicopathological analysis revealed that FAT10 expression levels were closely associated with tumor diameters and poor prognosis of HCC. This study also confirmed through in vivo experiments that the expression of FAT10 in liver fibrosis, cirrhosis, and HCC gradually increases. Further study revealed that forced FAT10 expression enhanced the growth ability of HCC cells and mediated the degradation of the critical anti-cancer protein p53, which led to carcinogenesis. Finally, 9 signal pathways related to HCC metastasis were obtained through bioinformatics analysis.

Conclusions

FAT10 may act as a proto-oncogene that facilitates HCC carcinogenesis by mediating p53 degradation, and the expression of FAT10 is negatively correlated with the prognosis of HCC patients. FAT10 is expected to become a potential combined target and prognostic warning marker for HCC treatment.

A dynamic recursive feature elimination framework (dRFE) to further refine a set of OMIC biomarkers

MOTIVATION

A feature selection algorithm may select the subset of features with the best associations with the class labels. The recursive feature elimination (RFE) is a heuristic feature screening framework and has been widely used to select the biological OMIC biomarkers. This study proposed a dynamic recursive feature elimination (dRFE) framework with more flexible feature elimination operations. The proposed dRFE was comprehensively compared with 11 existing feature selection algorithms and five classifiers on the eight difficult transcriptome datasets from a previous study, the ten newly collected transcriptome datasets and the five methylome datasets.

RESULTS

The experimental data suggested that the regular RFE framework did not perform well, and dRFE outperformed the existing feature selection algorithms in most cases. The dRFE-detected features achieved Acc=1.0000 for the two methylome datasets GSE53045 and GSE66695. The best prediction accuracies of the dRFE-detected features were 0.9259, 0.9424, and 0.8601 for the other three methylome datasets GSE74845, GSE103186, and GSE80970, respectively. Four transcriptome datasets received Acc=1.0000 using the dRFE-detected features, and the prediction accuracies for the other six newly collected transcriptome datasets were between 0.6301 and 0.9917.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Role of DNA Methylation in the Resistance to Therapy in Solid Tumors

Despite the recent advances in chemotherapeutic treatments against cancer, some types of highly aggressive and invasive cancer develop drug resistance against conventional therapies, which continues to be a major problem in the fight against cancer. In recent years, studies of alterations of DNA methylome have given us a better understanding of the role of DNA methylation in the development of tumors. DNA methylation (DNAm) is an epigenetic change that promotes the covalent transfer of methyl groups to DNA. This process suppresses gene expression through the modulation of the transcription machinery access to the chromatin or through the recruitment of methyl binding proteins. DNAm is regulated mainly by DNA methyltransferases. Aberrant DNAm contributes to tumor progression, metastasis, and resistance to current anti-tumoral therapies. Aberrant DNAm may occur through hypermethylation in the promoter regions of tumor suppressor genes, which leads to their silencing, while hypomethylation in the promoter regions of oncogenes can activate them. In this review, we discuss the impact of dysregulated methylation in certain genes, which impact signaling pathways associated with apoptosis avoidance, metastasis, and resistance to therapy. The analysis of methylome has revealed patterns of global methylation, which regulate important signaling pathways involved in therapy resistance in different cancer types, such as breast, colon, and lung cancer, among other solid tumors. This analysis has provided gene-expression signatures of methylated region-specific DNA that can be used to predict the treatment outcome in response to anti-cancer therapy. Additionally, changes in cancer methylome have been associated with the acquisition of drug resistance. We also review treatments with demethylating agents that, in combination with standard therapies, seem to be encouraging, as tumors that are in early stages can be successfully treated. On the other hand, tumors that are in advanced stages can be treated with these combination schemes, which could sensitize tumor cells that are resistant to the therapy. We propose that rational strategies, which combine specific demethylating agents with conventional treatment, may improve overall survival in cancer patients.

ABCC9, NKAPL, and TMEM132C are potential diagnostic and prognostic markers in triple-negative breast cancer

Triple-negative breast cancer (TNBC) is a highly heterogeneous disease. The aim of this study is to identify the diagnostic and poor prognostic signatures in TNBC by exploring the aberrant DNA methylation and gene expression. Differential expression and methylation analysis of the TNBC and paracancer samples from The Cancer Genome Atlas were performed. Gene set enrichment and protein-protein interaction (PPI) network analysis was used to explore the mechanisms of TNBC. Methylation-gene expression correlation analysis was performed, and multivariate Cox analysis and receiver operating characteristics analysis were used to further screen the hub genes for TNBC. We identified 1,525 differentially expressed genes and 150 differentially methylated genes between TNBC and paracancer samples. About 96.64% of the methylation sites were located on the CpG island. A total of 17 Gene Ontology biological process terms and 18 signal pathways were significantly enriched. GNG4, GNG11, PENK, MAOA, and AOX1 were identified as the core genes of the PPI network. Methylation-expression correlations revealed that ABCC9 (cg06951626), NKAPL (cg18675097, cg01031101, and cg17384889), and TMEM132C (cg03530754) showed promise as diagnostic and prognostic markers in TNBC. ABCC9 (cg06951626), NKAPL (cg18675097, cg01031101, and cg17384889), and TMEM132C (cg03530754) were potential diagnostic and prognostic markers in TNBC.

The mechanistic, diagnostic and therapeutic novel nucleic acids for hepatocellular carcinoma emerging in past score years

Despite The Central Dogma states the destiny of gene as 'DNA makes RNA and RNA makes protein', the nucleic acids not only store and transmit genetic information but also, surprisingly, join in intracellular vital movement as a regulator of gene expression. Bioinformatics has contributed to knowledge for a series of emerging novel nucleic acids molecules. For typical cases, microRNA (miRNA), long noncoding RNA (lncRNA) and circular RNA (circRNA) exert crucial role in regulating vital biological processes, especially in malignant diseases. Due to extraordinarily heterogeneity among all malignancies, hepatocellular carcinoma (HCC) has emerged enormous limitation in diagnosis and therapy. Mechanistic, diagnostic and therapeutic nucleic acids for HCC emerging in past score years have been systematically reviewed. Particularly, we have organized recent advances on nucleic acids of HCC into three facets: (i) summarizing diverse nucleic acids and their modification (miRNA, lncRNA, circRNA, circulating tumor DNA and DNA methylation) acting as potential biomarkers in HCC diagnosis; (ii) concluding different patterns of three key noncoding RNAs (miRNA, lncRNA and circRNA) in gene regulation and (iii) outlining the progress of these novel nucleic acids for HCC diagnosis and therapy in clinical trials, and discuss their possibility for clinical applications. All in all, this review takes a detailed look at the advances of novel nucleic acids from potential of biomarkers and elaboration of mechanism to early clinical application in past 20 years.

Recent advances in molecular biomarkers for patients with hepatocellular carcinoma

Introduction: Hepatocellular carcinoma (HCC) is a leading cause of cancer death worldwide and recurrence rate after curative resection remains high. To improve HCC prognosis, novel sensitive biomarkers and targeted molecular therapies are needed. Accumulation of multiple genetic aberrations caused by pathologically derived liver damage results in HCC carcinogenesis. Elucidating the genes associated with tumorigenesis and progression of HCC may lead to the development of early detection and prognosis markers and to the identification of therapeutic targets. Areas covered: We review recently reported (January 2017-March 2019) HCC-associated molecules, including protein-coding genes, microRNAs, long non-coding RNAs, and methylated gene promoters. Expert opinion: The molecules reviewed have the potential to be clinical biomarkers and therapeutic targets for HCC. The accumulation and understanding of genetic and epigenetic data are essential to improve the management of HCC patients.

Epigenetic alterations caused by aflatoxin b1: a public health risk in the induction of hepatocellular carcinoma

Aflatoxin B1 (AFB1) is currently the most commonly studied mycotoxin due to its great toxicity, its distribution in a wide variety of foods such as grains and cereals and its involvement in the development of + (hepatocellular carcinoma; HCC). HCC is one of the main types of liver cancer, and has become a serious public health problem, due to its high incidence mainly in Southeast Asia and Africa. Studies show that AFB1 acts in synergy with other risk factors such as hepatitis B and C virus leading to the development of HCC through genetic and epigenetic modifications. The genetic modifications begin in the liver through the biomorphic AFB1, the AFB1-exo-8.9-Epoxy active, which interacts with DNA to form adducts of AFB1-DNA. These adducts induce mutation in codon 249, mediated by a transversion of G-T in the p53 tumor suppressor gene, causing HCC. Thus, this review provides an overview of the evidence for AFB1-induced epigenetic alterations and the potential mechanisms involved in the development of HCC, focusing on a critical analysis of the importance of severe legislation in the detection of aflatoxins.

Screening of differentially methylated genes in breast cancer and risk model construction based on TCGA database

Differentially methylated genes in breast cancer were screened out and a prognostic risk model of breast cancer was constructed. RNA-seq data and methylation data for breast cancer-related level 3 were downloaded from The Cancer Genome Atlas (TCGA), and MethylMix R package was used to screen out differentially methylated genes in cancer tissues and normal tissues. DAVID was used to analyze the GO enrichment of differentially methylated genes, ConsensusPathDB to analyze the PATHWAY pathways of differentially methylated genes, the single factor, multivariate Cox analysis and Akaike Information Criterion (AIC) to construct the prognostic risk model of breast cancer, and the ROC curve to judge the clinical application value of the risk model. Two hundred and fifty-seven differentially methylated genes were successfully screened out in cancer tissues and normal tissues; 39 related to GO enrichments and 19 related to PATHWAY pathways were found; the best prognostic risk model was obtained, risk score = QRFP (degree of methylation) × (−3.657) + S100A16 × (−3.378) + TDRD1 × (−4.001) + SMO × (3.548); it was determined from each sample that the median value of the risk score was 0.936; using it as the cut-off value, the five-year survival rate in high-risk group of patients was 72.4% (95% CI, 62.7–83.6%), and that in low-risk group of patients was 86.6% (95% CI, 78.6–95.3%). The difference in the survival rate between the high-risk and low-risk groups was significant (P<0.001). The AUC of ROC curve was 0.791, so the model had a good clinical application value. This study successfully found multiple breast cancer-related methylation genes, the relationship between them and the course and prognosis of breast cancer was analyzed. Moreover, a prognostic risk model was constructed, which facilitated the expansion of the current study on the role of methylation in the occurrence and development of breast cancer.