Metastasis is a complex multistep process that involves critical interactions between cancer cells and a variety of stromal components in the tumor microenvironment, which profoundly influence the different aspects of the metastatic cascade and organ tropism of disseminating cancer cells. Ovarian cancer is the most lethal gynecological malignancy and is characterized by peritoneal disseminated metastasis. Evidence has demonstrated that ovarian cancer possesses specific metastatic tropism for the adipose-rich omentum, which has a pivotal role in the creation of the metastatic tumor microenvironment in the intraperitoneal cavity. Considering the distinct biology of ovarian cancer metastasis, the elucidation of the cellular and molecular mechanisms underlying the reciprocal interplay between ovarian cancer cells and surrounding stromal cell types in the adipose-rich metastatic microenvironment will provide further insights into the development of novel therapeutic approaches for patients with advanced ovarian cancer. Herein, we review the biological mechanisms that regulate the highly orchestrated crosstalk between ovarian cancer cells and various cancer-associated stromal cells in the metastatic tumor microenvironment with regard to the omentum by illustrating how different stromal cells concertedly contribute to the development of ovarian cancer metastasis and metastatic tropism for the omentum.

A) Diagram shows the single-cell RNA sequencing (scRNA-seq) and analysis workflow. We collected and processed normal fallopian tube tissues from ten cancer patients. All cells (directly sorted or maintained then sorted) were processed by using the Smart-Seq2 protocol. After the initial filtering, there were 3,877 good-quality cells left for downstream analysis. We compared the cells from three conditions to select the optimal condition for scRNA-seq. Cells from cultured or cryopreserved conditions, as well as cells carrying copy number variations and non-epithelial cells, were filtered out, which left 2,132 fresh FTE cells. Next, we used differential expression-based clustering to identify secretory subtypes.