Immune-and Metabolism-Associated Molecular Classification of Ovarian Cancer

Abstract

Ovarian cancer (OV) is a complex gynecological disease, and its molecular characteristics are not clear. In this study, the molecular characteristics of OV subtypes based on metabolic genes were explored through the comprehensive analysis of genomic data. A set of transcriptome data of 2752 known metabolic genes was used as a seed for performing non negative matrix factorization (NMF) clustering. Three subtypes of OV (C1, C2 and C3) were found in analysis. The proportion of various immune cells in C1 was higher than that in C2 and C3 subtypes. The expression level of immune checkpoint genes TNFRSF9 in C1 was higher than that of other subtypes. The activation scores of cell cycle, RTK-RAS, Wnt and angiogenesis pathway and ESTIMATE immune scores in C1 group were higher than those in C2 and C3 groups. In the validation set, grade was significantly correlated with OV subtype C1. Functional analysis showed that the extracellular matrix related items in C1 subtype were significantly different from other subtypes. Drug sensitivity analysis showed that C2 subtype was more sensitive to immunotherapy. Survival analysis of differential genes showed that the expression of PXDN and CXCL11 was significantly correlated with survival. The results of tissue microarray immunohistochemistry showed that the expression of PXDN was significantly correlated with tumor size and pathological grade. Based on the genomics of metabolic genes, a new OV typing method was developed, which improved our understanding of the molecular characteristics of human OV.

Analyzing the extracellular matrix-dominated immune landscape of high-grade serous ovarian cancer to determine prognosis and guide therapy

“…ECMs are a very active component of the TME, in uencing the behavior and metastatic potential of tumor cells. Clarifying the function of ECM in HGSOC is essential for the development of effective biomarkers and innovative therapeutics [17,54]. In order to improve prognostic prediction and therapy selection, we developed a classi cation strategy based on ECM genes and established a 14-gene signature (MGP, COL8A2, PAPPA, NYX, PLXNA1, CST6, LOXL4, SERPINA10, TGM7, CXCL11, CXCL13, HCFC2, LTA, WNT9A) of HGSOC.…”

“…ECM and its modi cation by cancer cells, stromal cells, and immune cells have been found to increase cancer cell proliferation, survival, and metastasis [11][12][13][14]. The deregulation of ECM components has also been connected to HGSOC development and progression [15][16][17][18][19][20]. However, it is currently unknown how the ECM environment of HGSOC differs from the normal ovarian environment and whether ECM modi cation can give diagnostic and therapeutic methods for this cancer with a poor prognosis.…”

Prognostic and Predictive Role of Tumor-Infiltrating Lymphocytes (TILs) in Ovarian Cancer

“…In particular, some researchers performed investigations which allowed the classification of HGSC into four different subtypes, called C1 (mesenchymal), C2 (immunoreactive), C4 (differentiated), and C5 (proliferative) [ 110 ]. Chen and colleagues [ 111 ], instead, identified three OC subtypes (C1, C2, and C3) which showed differences in immune score with significant impact on prognosis and therapy response. In particular, an increased quantity of B cells, CD8+ T cells, NK cells, cytotoxic lymphocytes, neutrophils, monocytes, Th2 cells, effective memory T cells (Tem), and Treg cells was observed in the C1 subtype [ 111 ].…”

“…Chen and colleagues [ 111 ], instead, identified three OC subtypes (C1, C2, and C3) which showed differences in immune score with significant impact on prognosis and therapy response. In particular, an increased quantity of B cells, CD8+ T cells, NK cells, cytotoxic lymphocytes, neutrophils, monocytes, Th2 cells, effective memory T cells (Tem), and Treg cells was observed in the C1 subtype [ 111 ]. Additionally, Liu et al [ 112 ] performed combination analyses between PD-1/PD-L1 and TILs in order to characterize a unique molecular subtype of HGSC with higher APC infiltration and greater PD-1/PD-L1 expression which is able to benefit from a specific immunotherapy.…”

Ovarian cancer subtypes based on the regulatory genes of RNA modifications: Novel prediction model of prognosis

“…According to 426 immune lncRNA pair data, OC samples could be classified into 2 molecular subtypes ( 36 ). Similarly, based on the expression profiles of 177 metabolism-related genes after a filtration of prognostic association, 3 different molecular subtypes of OC were obtained ( 37 ). Based on the immune cell infiltration in OC tumor microenvironment (TME), all OC samples were defined into 4 TME clusters, after which 2 genomic OC subtypes were identified according to differential expression genes among TME clusters ( 38 ).…”