Identification of a four-gene methylation biomarker panel in high-grade serous ovarian carcinoma

Characterization of candidate factors associated with the metastasis and progression of high-grade serous ovarian cancer

BACKGROUND

High-grade serous ovarian cancer (HGSOC) is the biggest cause of gynecological cancer-related mortality because of its extremely metastatic nature. This study aimed to explore and evaluate the characteristics of candidate factors associated with the metastasis and progression of HGSOC.

METHODS

Transcriptomic data of HGSOC patients' samples collected from primary tumors and matched omental metastatic tumors were obtained from three independent studies in the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were selected to evaluate the effects on the prognosis and progression of ovarian cancer using data from The Cancer Genome Atlas (TCGA) database. Hub genes' immune landscapes were estimated by the Tumor Immune Estimation Resource (TIMER) database. Finally, using 25 HGSOC patients' cancer tissues and 10 normal fallopian tube tissues, immunohistochemistry (IHC) was performed to quantify the expression levels of hub genes associated with International Federation of Gynecology and Obstetrics (FIGO) stages.

RESULTS

Fourteen DEGs, ADIPOQ , ALPK2 , BARX1 , CD37 , CNR2 , COL5A3 , FABP4 , FAP , GPR68 , ITGBL1 , MOXD1 , PODNL1 , SFRP2 , and TRAF3IP3 , were upregulated in metastatic tumors in every database while CADPS , GATA4 , STAR , and TSPAN8 were downregulated. ALPK2 , FAP , SFRP2 , GATA4 , STAR , and TSPAN8 were selected as hub genes significantly associated with survival and recurrence. All hub genes were correlated with tumor microenvironment infiltration, especially cancer-associated fibroblasts and natural killer (NK) cells. Furthermore, the expression of FAP and SFRP2 was positively correlated with the International Federation of Gynecology and Obstetrics (FIGO) stage, and their increased protein expression levels in metastatic samples compared with primary tumor samples and normal tissues were confirmed by IHC ( P = 0.0002 and P = 0.0001, respectively).

CONCLUSIONS

This study describes screening for DEGs in HGSOC primary tumors and matched metastasis tumors using integrated bioinformatics analyses. We identified six hub genes that were correlated with the progression of HGSOC, particularly FAP and SFRP2 , which might provide effective targets to predict prognosis and provide novel insights into individual therapeutic strategies for HGSOC.

PCDH17 gene promoter methylation status in a cohort of Egyptian women with epithelial ovarian cancer

BACKGROUND AND OBJECTIVE

Ovarian cancer is a leading cause of female mortality. Epigenetic changes occur in early stages of carcinogenesis and represent a marker for cancer diagnosis. Protocadherin 17 (PCDH17) is a tumor suppressor gene involved in cell adhesion and apoptosis. The methylation of PCDH17 gene promoter has been described in several cancers including ovarian cancer. The aim of the study was to compare the methylation status of PCDH17 gene promoter between females diagnosed with epithelial ovarian cancer and a control group composed of normal and benign ovarian lesions.

METHODS

Fifty female subjects were included in our study (25 ovarian cancer patients and 25 controls). DNA was extracted from Formalin-Fixed Paraffin-Embedded (FFPE) tissues of the subjects. Methylation levels for six CpG sites in the PCDH17 gene promoter were assessed by pyrosequencing.

RESULTS

The methylation levels at five out of six sites were significantly higher in females with epithelial ovarian cancer compared to the control group. Moreover, the same applies for the mean methylation level with p value 0.018.

CONCLUSION

Methylation of PCDH17 gene promoter plays a role in ovarian carcinogenesis and can be used for diagnosis and early detection.

Methylation biomarkers for early cancer detection and diagnosis: Current and future perspectives

The increase in recent scientific studies on cancer biomarkers has brought great new insights into the field. Moreover, novel technological breakthroughs such as long read sequencing and microarrays have enabled high throughput profiling of many biomarkers, while advances in bioinformatic tools have made the possibility of developing highly reliable and accurate biomarkers a reality. These changes triggered renewed interest in biomarker research and provided tremendous opportunities for enhancing cancer management and improving early disease detection. DNA methylation alterations are known to accompany and contribute to carcinogenesis, making them promising biomarkers for cancer, namely due to their stability, frequency and accessibility in bodily fluids. The advent of newer minimally invasive experimental methods such as liquid biopsies provide the perfect setting for methylation-based biomarker development and application. Despite their huge potential, accurate and robust biomarkers for the conclusive diagnosis of most cancer types are still not routinely used, hence a strong need for sustained research in this field is still needed. This review provides a brief exposition of current methylation biomarkers for cancer diagnosis and early detection, including markers already in clinical use as well as various upcoming ones. It also outlines how recent big data and novel technologies will revolutionise the next generation of cancer tests in supplementing or replacing currently existing invasive techniques.

Tissue-Specific Methylation Biosignatures for Monitoring Diseases: An In Silico Approach

Tissue-specific gene methylation events are key to the pathogenesis of several diseases and can be utilized for diagnosis and monitoring. Here, we established an in silico pipeline to analyze high-throughput methylome datasets to identify specific methylation fingerprints in three pathological entities of major burden, i.e., breast cancer (BrCa), osteoarthritis (OA) and diabetes mellitus (DM). Differential methylation analysis was conducted to compare tissues/cells related to the pathology and different types of healthy tissues, revealing Differentially Methylated Genes (DMGs). Highly performing and low feature number biosignatures were built with automated machine learning, including: (1) a five-gene biosignature discriminating BrCa tissue from healthy tissues (AUC 0.987 and precision 0.987), (2) three equivalent OA cartilage-specific biosignatures containing four genes each (AUC 0.978 and precision 0.986) and (3) a four-gene pancreatic β-cell-specific biosignature (AUC 0.984 and precision 0.995). Next, the BrCa biosignature was validated using an independent ccfDNA dataset showing an AUC and precision of 1.000, verifying the biosignature's applicability in liquid biopsy. Functional and protein interaction prediction analysis revealed that most DMGs identified are involved in pathways known to be related to the studied diseases or pointed to new ones. Overall, our data-driven approach contributes to the maximum exploitation of high-throughput methylome readings, helping to establish specific disease profiles to be applied in clinical practice and to understand human pathology.

DNA methylation-based profiling reveals distinct clusters with survival heterogeneity in high-grade serous ovarian cancer

High-grade serous ovarian cancer (HGSOC) is the most common type of epigenetically heterogeneous ovarian cancer. Methylation typing has previously been used in many tumour types but not in HGSOC. Methylation typing in HGSOC may promote the development of personalized care. The present study used DNA methylation data from The Cancer Genome Atlas database and identified four unique methylation subtypes of HGSOC. With the poorest prognosis and high frequency of residual tumours, cluster 4 featured hypermethylation of a panel of genes, which indicates that demethylation agents may be tested in this group and that neoadjuvant chemotherapy may be used to reduce the possibility of residual lesions. Cluster 1 and cluster 2 were significantly associated with metastasis genes and metabolic disorders, respectively. Two feature CpG sites, cg24673765 and cg25574024, were obtained through Cox proportional hazards model analysis of the CpG sites. Based on the methylation level of the two CpG sites, the samples were classified into high- and low-risk groups to identify the prognostic information. Similar results were obtained in the validation set. Taken together, these results explain the epigenetic heterogeneity of HGSOC and provide guidance to clinicians for the prognosis of HGSOC based on DNA methylation sites.

Host Genetic Risk Factors for Chlamydia trachomatis-Related Infertility in Women

BACKGROUND

Chlamydia trachomatis (Ct) infection ascending to the upper genital tract can cause infertility. Direct association of genetic variants as contributors is challenging because infertility may not be diagnosed until years after infection. Investigating the intermediate trait of ascension bridges this gap.

METHODS

We identified infertility genome-wide association study (GWAS) loci using deoxyribonucleic acid from Ct-seropositive cisgender women in a tubal factor infertility study and Ct-infected cisgender women from a longitudinal pelvic inflammatory disease cohort with known fertility status. Deoxyribonucleic acid and blood messenger ribonucleic acid from 2 additional female cohorts with active Ct infection and known endometrial infection status were used to investigate the impact of infertility single-nucleotide polymorphisms (SNPs) on Ct ascension. A statistical mediation test examined whether multiple infertility SNPs jointly influenced ascension risk by modulating expression of mediator genes.

RESULTS

We identified 112 candidate infertility GWAS loci, and 31 associated with Ct ascension. The SNPs altered chlamydial ascension by modulating expression of 40 mediator genes. Mediator genes identified are involved in innate immune responses including type I interferon production, T-cell function, fibrosis, female reproductive tract health, and protein synthesis and degradation.

CONCLUSIONS

We identified Ct-related infertility loci and their potential functional effects on Ct ascension.

A comprehensive analysis of the expression, epigenetic and genetic changes of HNF1B and ECI2 in 122 cases of high-grade serous ovarian carcinoma

High-grade serous ovarian cancer (HGSC) is the most common subtype of ovarian cancer, with a poor prognosis; however, most studies concerning ovarian carcinoma have focused mainly on clear cell carcinoma. The involvement of hepatocyte nuclear factor 1β (HNF1B) in the carcinogenesis of HGSC has not yet been fully elucidated. To the best of our knowledge, the present study is the first to analyse the expression of the possible downstream target of HNF1B, enoyl-CoA (Δ) isomerase 2 (ECI2), in HGSC. The present study performed a comprehensive analysis of HNF1B mRNA and protein expression, and epigenetic and genetic changes, as well as an analysis of ECI2 mRNA and protein expression in 122 cases of HGSC. HNF1B protein expression was detected in 28/122 cases, and was positively associated with lymphovascular invasion (P=0.025). Protein expression of ECI2 was detected in 115/122 cases, but no associations with clinicopathological variables were revealed. Therefore, ECI2 does not seem to function as a suitable prognostic marker for HGSC. In the sample set, a positive correlation between HNF1B and ECI2 protein expression was detected (P=0.005). HNF1B mRNA was also positively correlated with HNF1B protein expression (P=0.001). HNF1B promoter methylation was detected in 26/67 (38.8%) of cases. A novel pathogenic somatic HNF1B mutation was detected in 1/61 (1.6%) of the analysed HGSC cases. No other correlations between the examined SNPs (rs4430796, rs757210 and rs7405776), HNF1B promoter methylation, HNF1B/ECI2 expression or clinicopathological characteristics were found.