In recent years, an increasing number of studies have focused on exploring the molecular typing of epithelial ovarian cancer to promote the realization of personalized treatment and improve the survival rate in patients; however, the achievements of molecular typing remain in the initial phase. Studies have shown that the occurrence of cancer is associated with genetic changes, and epigenetic abnormalities are also contributors. DNA MET is the major epigenetic modification mode of genomic DNA; it is an important means in regulating the functions of the genome 28 and is closely associated with the occurrence, progression, treatment, and prognosis of ovarian cancer. DNA MET-based molecular typing and subtype markers are of great significance for guiding personalized treatment and prognosis evaluation in ovarian cancer patients.
In the present study, 571 ovarian cancer MET samples were downloaded from the TCGA database, 250 MET loci related to the prognosis of ovarian cancer patients were screened by COX regression analysis, and 6 molecular subtypes were selected by clustering with k-means. There was a significant difference in MET loci among most subtypes; the highest MET level and the best prognosis were observed in Cluster 2, and the MET level in Cluster 4 and Cluster 5 was remarkably lower than that in the other subtypes, accompanied by a very poor prognosis. This suggests, to a certain degree, that the prognosis of patients with a hypomethylation subtype was worse than that of patients with a hypermethylation subtype. All samples in Cluster 5 were high-grade, and the mean age of patients in Cluster 5 was higher than that in the other subtypes. The percentage of stage IV samples in Cluster 4 was significantly greater than that in the other subtypes. The above findings suggest that these molecular subtypes can be used not only to evaluate the prognosis in ovarian cancer patients, but also to fully distinguish the tumor stage, histological grade, and age of these patients to guide subsequent treatment.
DNA MET molecular typing also plays a very important role in the diagnosis, treatment, and prognosis of other tumors. Zhang et al. 25 screened 9 molecular subtypes by clustering analysis on DNA MET data in 669 breast cancer patients, and the DNA MET mode was reflected in varying races, ages, tumor stages, subject states, histological types, metastatic states, and prognoses. In comparison with PAM50 subtypes using gene expression clustering, DNA MET subtypes are more precise and can be used for the precision treatment of specific histological subtypes of breast cancer.
Jurmeister constructed a DNA MET map using the whole genome MET data from 600 cases of primary pulmonary, colorectal, and upper gastrointestinal adenocarcinoma, and successfully distinguished between pulmonary enteric adenocarcinoma and metastatic colorectal cancer 29.
Williams et al.30measured the MET level in different histological subtypes of 154 cases of child germ cell tumors using the Illumina Infinium® Human Methylation 450K chip, identifying 4 molecular subtypes. The MET level in the germ cell tumors was low, and these molecular subtypes provided information regarding their etiology.
Again using the Illumina Infinium® Human Methylation 450K chip, Wu SP1 et al. 31 detected the DNA MET state in 482 and 421 CpG loci in 10 samples of Ewing's sarcoma, 11 samples of synovial sarcoma, and 15 samples of osteosarcoma. Moreover, they developed and validated a whole-genome DNA MET classifier to identify osteosarcoma, Ewing's sarcoma, and synovial sarcoma. MET-based molecular typing is of great significance for diagnosing, recognizing, and treating morphologically overlapping solid tumors.
Taskesen E et al. integrated the gene expression and DNA MET spectra of 344 samples of acute myeloid leukemia (AML) and established a regression model using Lasso. The results indicated that the subtype prediction of AML cytogenetics and molecular abnormalities could be significantly improved 32.
A study by Rodríguez-Rodero et al., demonstrated that thyroid carcinoma subtypes have promoter-differentiated MET features, and the molecular typing could be realized using abnormal DNA MET expression. Undifferentiated thyroid carcinoma was characterized by abnormal promoter hypomethylation, while differentiated papillary and follicular thyroid carcinoma was characterized by promoter hypermethylation 33.
To further explore the functions of the 250 screened MET loci, gene function annotation of the loci was performed and 42 genes were found to be significantly enriched to TFEC. The TFEC gene is located at 7q31.2 and encodes a polypeptide with a length of 347 amino acids, which is mainly localized in the nucleus and cytoplasm. According to a study by Chung et al., TFEC plays a role as an activating transcription factor (ATF) for the non-myosin heavy chain II-a gene 34. At present, evidence for the involvement of TFEC in cancer progression is limited; however, TFEC, MITF, TFEB, and TFE3 are important members of the MIT (microphthalmia-associated transcription factor) family, and recent studies have proven that changes in these transcription factors are related to melanoma, sarcoma, and renal cell carcinoma. With a similar structure to TFEB (another member of the MIT family), TFEC may play an important role in regulating genes related to autophagy and lysosomes 35.
The regulation of genes is a complex network; to investigate the effects of TFFC and its relevant factors on the occurrence and progression of tumors, function enrichment analysis was performed and these genes were found to be remarkably enriched to the following biological functions: GO:0006955 − immune response, GO:0050776 − regulation of the immune response, GO:0006954 − inflammatory response, GO:0045087 − innate immune response, and GO:0007165 − signal transduction. Currently, there are no reports of TFEC in ovarian cancer; thus, further investigation is needed.
Finally, 5 CpG loci were screened via the WGCNA co-expression network: cg27625732, cg00431050, cg22197830, cg03152385, and cg22809047. The results show that hypomethylation of these 5 CpG loci was associated with poor prognosis in ovarian cancer patients. The gene annotated by the cg22809047 locus was RPL31, and Maruyama et al. 36 have previously shown that in comparison with benign prostate tissues, RPL31 is overexpressed in prostate cancer. In RPL31 siRNA-treated LNCaP and BicR cells, there is an increase in the protein expression levels of the tumor suppressor p53 and its targets, p21 and MDM2. In addition, the inhibition of cell growth and the cell cycle by RPL31 could be recovered by p53 siRNA treatment. RPL31 could be used as the target of molecular treatment for advanced prostate cancer, and we presume that RPL31 could also be used as a target for the treatment of ovarian cancer. ELOVL3 was the gene corresponding to the gene promoter region annotated by the cg00431050 locus. ELOVL3 is a member of the ELOVL (elongase of very long-chain fatty acids) family, which contains a total of 7 members (ELOVL1 − 7). The proteins encoded by the ELOVL1 − 7 genes are involved in the elongation of fatty acid chains of different lengths, and play an important role in regulating the biological synthesis of lipids, fatty acid metabolism, and certain metabolic diseases. There exist only limited studies of the involvement of ELOVL3 in tumors, while ELOVL2 has been widely described in tumors. A study by Kang et al., revealed that breast cancer patients with low ELOVL2 expression have a poor prognosis. ELOVL2 expression has been correlated with the malignant phenotype of breast cancer, and its downregulation induced lipid metabolism reprogramming; thus, ELOVL2 is a novel prognostic biomarker 37. We suggest that ELOVL3 expression may also be involved in the occurrence and progression of ovarian cancer by inducing lipid metabolism reprogramming.
Zhang et al., 38 investigated the molecular typing of serous ovarian cancer using the multi-omics data of DNA MET and protein, miRNA, and gene expression, mainly discussing the relationship between molecular typing based on RNA-Seq data and that based on other omics data. They finally screened 9 molecular subtypes based on RNA-Seq data; these subtypes had significant overlap with the molecular subtypes of other omics, but the function analysis results showed that the subtypes based on an omics dataset could not be completely substituted by other omics data.
In the present study, the significance of MET in the molecular typing of ovarian cancer was analyzed using MET data, and the markers of subtypes closely related to the prognosis prediction of ovarian cancer were further screened. A MET data-based ovarian cancer prognosis prediction model was subsequently developed to provide a reference for clinical trials and researchers. In summary, the study by Zhang et al., and our study have different focal points, despite both involving molecular typing.
Subtyping of ovarian carcinomas based on methylation profiles has been reported in a TCGA seminal article13, in which 4 subtypes were identified to be significantly associated with differences in age, BRCA inactivation events, and survival based on consensus clustering of variable DNA methylation. The cluster associated with the worst prognosis is characterized by hypomethylation and is associated with old age, which is in accordance with the present findings; however, our approach is different from that in the aforementioned TCGA paper.
Firstly, the samples included in the TCGA paper were 489 cases of high-grade serous ovarian cancer, while the present paper included 571 cases of methylated ovarian cancer, including different clinical stages and grades. Our sample size is larger, and the results are more abundant. Secondly, a multivariate COX proportional hazards model was performed to elucidate that 250 CpG loci were significant predictors of prognosis, and 6 molecular subtypes were clustered based on the methylation level at these 250 CpG loci. The cluster that was characterized by hypomethylation was associated with a worse prognosis, stage, and grade, and an older patient age. Thirdly, weighted gene co-expression network analysis was further applied to identify the 5 most significant CpG loci, and hypomethylation of these 5 loci was demonstrated to be associated with a worse outcome.