Comprehensive analysis of prognostic gene signatures based on immune infiltration of ovarian cancer

Abstract

Background Ovarian cancer (OV) is one of the most common malignant tumors of gynecology oncology. The lack of effective early diagnosis methods and treatment strategies result in a low five-year survival rate. Also, immunotherapy plays an important auxiliary role in the treatment of advanced OV patient, so it is of great significance to find out effective immune-related tumor markers for the diagnosis and treatment of OV. Methods Based on the consensus clustering analysis of single-sample gene set enrichment analysis (ssGSEA) score transformed via The Cancer Genome Atlas (TCGA) mRNA profile, we obtained two groups with high and low levels of immune infiltration. Multiple machine learning methods were conducted to explore prognostic genes associated with immune infiltration. Simultaneously, the correlation between the expression of mark genes and immune cells components was explored. Results A prognostic classifier including 5 genes (CXCL11, S1PR4, TNFRSF17, FPR1 and DHRS95) was established and its robust efficacy for predicting overall survival was validated via 1129 OV samples. Some significant variations of copy number on gene loci were found between two risk groups and it showed that patients with fine chemosensitivity has lower risk score than patient with poor chemosensitivity (P = 0.013). The high and low-risk groups showed significantly different distribution (P < 0.001) of five immune cells (Monocytes, Macrophages M1, Macrophages M2, T cells CD4 menory and T cells CD8). Conclusion The present study identified five prognostic genes associated with immune infiltration of OV, which may provide some potential clinical implications for OV treatment.

NOTCH2NLA silencing inhibits ovarian carcinoma progression and oncogenic activity in vivo and in vitro

“…Similar to our study, Millstein et al (19) and Yan et al (20) used bioinformatics analysis to confirm CXCL9 and CXCL11 were associated with better OS in OC patients, respectively. Both CXCL9 and CXCL11 could recruit T and B lymphocytes and natural killer cells to inhibit tumor growth, and the expression of CXCL11 was closely related to antigen-related genes, immune checkpoint-related genes, and PARPi therapy (21).…”

Angiogenesis-Related Immune Signatures Correlate With Prognosis, Tumor Microenvironment, and Therapeutic Sensitivity in Hepatocellular Carcinoma

“…Regarding the regulatory function of lymphocytes, some evidence showed that T cell subsets (Th1, Treg ( Motz and Coukos, 2011 ) and CD4 + Th2 cells ( DeNardo et al, 2009 )) could also play pro-angiogenesis roles through different mechanisms. Previous studies showed that poor prognosis of cancer patients is greatly correlated with the proportion of M2-like TAMs ( Ni et al, 2019 ; Yan et al, 2020 ; Wang et al, 2021 ; Ye et al, 2021 ). Therefore, whether high risk score is positively correlated with M2-like macrophages needs to be further confirmed.…”

Identification of a glycolysis‐related gene signature for survival prediction of ovarian cancer patients

“…The included studies 11,35–47,54–61 used models built based on the TCGA cohort and involved all types of breast cancer. The final results showed that our signature and three other prognostic signatures, namely, a 21 immune‐related gene signature, 35 17 immune‐related gene signature 36 and 17 TF‐related gene signature 37 performed better than the other hallmark signatures in the prediction of OS in patients with OV 11,35,38–47,54–61 . Additionally, Yu et al has constructed a five GRG signature (ANGPTL4, PYGB, ISG20, SEH1L and IRS2) for patients with OV 62 .…”

“…To further explore the predictive ability of our signature, a comparison was performed among several significant molecular signatures that were employed for predicting OS in patients with OV. The included studies 11 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 used models built based on the TCGA cohort and involved all types of breast cancer. The final results showed that our signature and three other prognostic signatures, namely, a 21 immune‐related gene signature, 35 17 immune‐related gene signature 36 and 17 TF‐related gene signature 37 performed better than the other hallmark signatures in the prediction of OS in patients with OV.…”

“…The final results showed that our signature and three other prognostic signatures, namely, a 21 immune‐related gene signature, 35 17 immune‐related gene signature 36 and 17 TF‐related gene signature 37 performed better than the other hallmark signatures in the prediction of OS in patients with OV. 11 , 35 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 Additionally, Yu et al has constructed a five GRG signature (ANGPTL4, PYGB, ISG20, SEH1L and IRS2) for patients with OV. 62 The AUCs of the signature in Yu's study at 5‐years were 0.680.…”