Transcriptomic Concordance Between Borderline Ovarian Tumors and Endometrioid Carcinoma: A Research Study

Background Introduction

Borderline ovarian tumors (BOTs) are ovarian neoplasms that lie between benign and malignant tumors, typically occurring in young women. Although BOTs are clinically non-invasive, they can occasionally progress to malignancy. BOTs account for 10%-15% of epithelial ovarian tumors, with approximately 2,000 women diagnosed annually in Japan. Histologically, BOTs are classified into several types, including serous BOTs (SBOTs), mucinous BOTs (MBOTs), endometrioid BOTs (EBOTs), clear-cell BOTs, and seromucinous BOTs (SMBOTs). Despite their relatively favorable prognosis, the molecular characteristics of BOTs remain poorly understood, particularly their molecular relationship with high-grade serous carcinoma (HGSC), endometrioid carcinoma (EC), and clear-cell carcinoma (CCC).

This study aims to elucidate the molecular features of BOTs by integrating whole-exome sequencing (WES) and RNA sequencing (RNA-seq) technologies, comparing BOTs with HGSC, EC, and CCC to reveal their similarities and differences.

Source of the Paper

This paper was authored by Mio Takahashi, Kohei Nakamura, and colleagues from the Department of Obstetrics and Gynecology at Keio University School of Medicine, the Center for Cancer Genomics, the Department of Biomedical Informatics at Mitsubishi Electric Software Co., Ltd., and the Department of Obstetrics and Gynecology at Kagoshima University Faculty of Medicine. The study was published in 2025 in the journal Cancer Medicine with the DOI 10.1002/cam4.70601.

Research Process

1. Patient Selection and Sample Collection

The study included 44 ovarian tumor patients, comprising 14 cases of HGSC, 13 cases of EC, 10 cases of CCC, and 7 cases of BOTs (4 SBOTs, 1 SMBOT, 1 MBOT, and 1 EBOT). All BOT cases were diagnosed as stage IA according to the International Federation of Gynecology and Obstetrics (FIGO) 2014 staging system. The median age of the patients was 48 years (range: 36-67 years).

2. DNA Extraction and Whole-Exome Sequencing (WES)

Tissue samples collected during surgery were fixed using the PAXgene Tissue System and embedded in paraffin. Pathologists evaluated tumor cell content by examining hematoxylin-eosin-stained slides and performed macrodissection when necessary. Genomic DNA was quantified using a Qubit4 Fluorometer, and DNA quality was assessed using the DNA Integrity Number (DIN) score on the Agilent 4150 Tapestation. A minimum of 150 ng of DNA was extracted, and samples with a DIN score above 2.0 were used for genomic sequencing.

Whole-exome sequencing libraries were prepared using the xGen Exome Research Panel v2 and sequenced on the Illumina NovaSeq 6000 system in 150 bp paired-end mode. Sequencing data were analyzed using the GenomeJack bioinformatics pipeline, which included filtering low-quality reads, aligning reads to the human reference genome (UCSC human genome 19), and identifying single-nucleotide variants (SNVs) and insertions/deletions (indels). Tumor mutation burden (TMB) was defined as the number of nonsynonymous variants across the entire region, with TMB-high (TMB-H) tumors defined as those containing at least 200 nonsynonymous variants.

3. RNA Sequencing (RNA-seq)

Total RNA was extracted from tissue samples using TRIzol reagent, and RNA integrity and concentration were assessed using the Agilent 2100 Bioanalyzer and Qubit RNA HS Assay Kit. rRNA was depleted using the NEBNext rRNA Depletion Kit, and sequencing libraries were prepared using the NEBNext Ultra II Directional RNA Library Prep Kit. Sequencing was performed on the Illumina platform, with each sample yielding at least 30 million 150 bp paired-end reads.

Raw reads were trimmed for adapters and low-quality bases, aligned to the reference genome using STAR, and analyzed for differential expression using DESeq2. Genes with an adjusted p-value < 0.05 and a fold change > 2 were considered significantly differentially expressed. Multiple hypothesis testing was corrected using Tukey’s HSD test, with q-values < 0.001 indicating highly significant differential expression between groups.

Key Findings

1. Genomic Analysis

WES analysis revealed the genomic mutation profiles of different ovarian tumor types. The most common mutation in HGSC was TP53 (100% of cases), followed by BRCA1 (36%) and BRCA2 (14%) mutations. CCC exhibited high frequencies of ARID1A (70%) and PIK3CA (50%) mutations, with KRAS mutations present in 30% of cases. In EC, ARID1A (54%), PTEN (46%), and PIK3CA (46%) mutations were common. In BOTs, the most frequent mutations were KRAS (43%) and BRAF (57%), with ARID1A, PIK3CA, CTNNB1, PTEN, and MSH2 mutations occurring at a frequency of 14%.

2. RNA-seq Analysis

RNA-seq analysis demonstrated significant transcriptional similarities between BOTs and EC. Principal component analysis (PCA) and hierarchical clustering revealed overlapping gene expression patterns between BOTs and EC. Differential expression analysis identified only 2 differentially expressed genes (DEGs) between BOTs and EC, compared to 108 and 87 DEGs between BOTs and HGSC and CCC, respectively.

Conclusions and Significance

This study is the first to reveal significant transcriptional similarities between BOTs and EC, challenging traditional classifications of ovarian tumors. These findings suggest that BOTs and EC may share certain oncogenic pathways or tumor microenvironmental factors, indicating that BOTs may be molecularly more similar to EC than to other ovarian cancer types. This discovery not only provides new insights into the molecular classification of BOTs but also opens new avenues for research into their pathogenesis and treatment strategies.

Research Highlights

1. Transcriptomic Similarity: The study is the first to identify significant transcriptional similarities between BOTs and EC, uncovering potential shared molecular mechanisms.
2. Genomic Analysis: Comprehensive genomic and transcriptomic profiling of BOTs and other ovarian cancer types was conducted using WES and RNA-seq technologies.
3. Clinical Implications: The findings may influence the diagnosis and treatment strategies for BOTs, suggesting that BOTs may benefit from treatment approaches similar to those used for EC.

Additional Valuable Information

The limitations of this study include the relatively small sample size and high tumor heterogeneity. Future research should expand the sample size and further explore the molecular mechanisms and clinical relevance of the similarities between BOTs and EC.