A multiomics comparison between endometrial cancer and serous ovarian cancer

Abstract

Background Endometrial carcinoma (EC) and serous ovarian carcinoma (OvCa) are both among the common cancer types in women. EC can be divided into two subtypes, endometroid EC and serous-like EC, with distinct histological characterizations and molecular phenotypes. There is an increasing awareness that serous-like EC resembles serous OvCa in genetic landscape, but a clear relationship between them is still lacking. Methods Here, we took advantage of the large-scale molecular profiling of The Cancer Genome Atlas(TCGA) to compare the two EC subtypes and serous OvCa. We used bioinformatics data analytic methods to systematically examine the somatic mutation (SM) and copy number alteration (SCNA), gene expression, pathway activities, survival gene signatures and immune infiltration. Based on these quantifiable molecular characterizations, we asked whether serous-like EC should be grouped more closely to serous OvCa, based on the context of being serous-like; or if should be grouped more closely to endometroid EC, based on the same organ origin. Results We found that although serous-like EC and serous OvCa share some common genotypes, including mutation and copy number alteration, they differ in molecular phenotypes such as gene expression and signaling pathway activity. Moreover, no shared prognostic gene signature was found, indicating that they use unique genes governing tumor progression. Finally, although the endometrioid EC and serous OvCa are both highly immune infiltrated, the immune cell composition in serous OvCa is mostly immune suppressive, whereas endometrioid EC has a higher level of cytotoxic immune cells. Overall, our genetic aberration and molecular phenotype characterizations indicated that serous-like EC and serous OvCa cannot be simply treated as a simple “serous” cancer type. In particular, additional attention should be paid to their unique gene activities and tumor microenvironments for novel targeted therapy development.

An Integrative Computational Approach Based on Expression Similarity Signatures to Identify Protein–Protein Interaction Networks in Female-Specific Cancers

“…Oncogenic signaling pathways found in our study are in agreement with previous literature findings (Zhang, 2018;Zhong et al, 2020). To the best of our knowledge, we carried out for the first time a transcriptomic integrated PCA-based feature extraction approach to evaluate intertumor expression signatures and putative protein-protein interaction networks.…”

“…Integrative analyses of these female-specific and hormonesensitive cancers may decipher still cryptic molecular features. Several integrative analyses showed the co-occurrence of driver genes across distinct tumor entities and the existence of common molecular mechanisms for their progression also between breast and gynecological malignancy entities (Neapolitan et al, 2015;Berger et al, 2018;Hoadley et al, 2018;Liu et al, 2018;Sanchez-Vega et al, 2018;Bhyan et al, 2019;Zhong et al, 2020).…”

Ligustrazine inhibits the proliferation and migration of ovarian cancer cells via regulating miR-211

“…Ovarian cancer (OC) is a commonly diagnosed female cancer worldwide (1)(2)(3). Like most types of cancer, OC lacks clinical symptoms at its early stages, therefore, most patients have proceeded to the advanced stage at the time of diagnosis (4)(5)(6).…”

Deep Learning Model for Tumor Type Prediction using Targeted Clinical Genomic Sequencing Data

“…For example, 10.1% of all high-confidence misclassifications arise between endometrial and ovarian cancer (37/366 predictions). It is well established that high-grade serous or endemetrioid ovarian cancers have very similar genomic characteristics as endometrial cancers, making these cancer types particularly challenging to distinguish 25 . Despite these errors, GDD-ENS correctly identified at least one endometrial cancer that was initially misannotated as ovarian cancer.…”