The Immune Subtypes and Landscape of Advanced-Stage Ovarian Cancer

A novel TCGA-validated programmed cell-death-related signature of ovarian cancer

BACKGROUND

Ovarian cancer (OC) is a gynecological malignancy tumor with high recurrence and mortality rates. Programmed cell death (PCD) is an essential regulator in cancer metabolism, whose functions are still unknown in OC. Therefore, it is vital to determine the prognostic value and therapy response of PCD-related genes in OC.

METHODS

By mining The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx) and Genecards databases, we constructed a prognostic PCD-related genes model and performed Kaplan-Meier (K-M) analysis and Receiver Operating Characteristic (ROC) curve for its predictive ability. A nomogram was created via Cox regression. We validated our model in train and test sets. Quantitative real-time PCR (qRT-PCR) was applied to identify the expression of our model genes. Finally, we analyzed functional analysis, immune infiltration, genomic mutation, tumor mutational burden (TMB) and drug sensitivity of patients in low- and high-risk group based on median scores.

RESULTS

A ten-PCD-related gene signature including protein phosphatase 1 regulatory subunit 15 A (PPP1R15A), 8-oxoguanine-DNA glycosylase (OGG1), HECT and RLD domain containing E3 ubiquitin protein ligase family member 1 (HERC1), Caspase-2.(CASP2), Caspase activity and apoptosis inhibitor 1(CAAP1), RB transcriptional corepressor 1(RB1), Z-DNA binding protein 1 (ZBP1), CD3-epsilon (CD3E), Clathrin heavy chain like 1(CLTCL1), and CCAAT/enhancer-binding protein beta (CEBPB) was constructed. Risk score performed well with good area under curve (AUC) (AUC3 - year =0.728, AUC5 - year = 0.730). The nomogram based on risk score has good performance in predicting the prognosis of OC patients (AUC1 - year =0.781, AUC3 - year =0.759, AUC5 - year = 0.670). Kyoto encyclopedia of genes and genomes (KEGG) analysis showed that the erythroblastic leukemia viral oncogene homolog (ERBB) signaling pathway and focal adhesion were enriched in the high-risk group. Meanwhile, patients with high-risk scores had worse OS. In addition, patients with low-risk scores had higher immune-infiltrating cells and enhanced expression of checkpoints, programmed cell death 1 ligand 1 (PD-L1), indoleamine 2,3-dioxygenase 1 (IDO-1) and lymphocyte activation gene-3 (LAG3), and were more sensitive to A.443,654, GDC.0449, paclitaxel, gefitinib and cisplatin. Finally, qRT-PCR confirmed RB1, CAAP1, ZBP1, CEBPB and CLTCL1 over-expressed, while PPP1R15A, OGG1, CASP2, CD3E and HERC1 under-expressed in OC cell lines.

CONCLUSION

Our model could precisely predict the prognosis, immune status and drug sensitivity of OC patients.

Defining the Role of Metastasis-Initiating Cells in Promoting Carcinogenesis in Ovarian Cancer

Ovarian cancer is the deadliest gynecological malignancy with a high prevalence of transcoelomic metastasis. Metastasis is a multi-step process and only a small percentage of cancer cells, metastasis-initiating cells (MICs), have the capacity to finally establish metastatic lesions. These MICs maintain a certain level of stemness that allows them to differentiate into other cell types with distinct transcriptomic profiles and swiftly adapt to external stresses. Furthermore, they can coordinate with the microenvironment, through reciprocal interactions, to invade and establish metastases. Therefore, identifying, characterizing, and targeting MICs is a promising strategy to counter the spread of ovarian cancer. In this review, we provided an overview of OC MICs in the context of characterization, identification through cell surface markers, and their interactions with the metastatic niche to promote metastatic colonization.