Development of a multi-gene-based immune prognostic signature in ovarian Cancer

Abstract

Background Various components of the immune system play a critical role in the prognosis and treatment response in ovarian cancer (OC). Immunotherapy has been recognized as a hallmark of cancer but the effect is contradictional. Reliable immune gene-based prognostic biomarkers or regulatory factors are necessary to be systematically explored to develop an individualized prediction signature. Methods This study systematically explored the gene expression profiles in patients with ovarian cancer from RNA-seq data set for The Cancer Genome Atlas (TCGA). Differentially expressed immune genes and transcription factors (TFs) were identified using the collected immune genes from ImmPort dataset and TFs from Cistoma database. Survival associated immune genes and TFs were identified in terms of overall survival. The prognostic signature was developed based on survival associated immune genes with LASSO (Least absolute shrinkage and selection operator) Cox regression analysis. Further, we performed network analysis to uncover the potential regulators of immune-related genes with the help of computational biology. Results The prognostic signature, a weighted combination of the 21 immune-related genes, performed moderately in survival prediction with AUC was 0.746, 0.735, and 0.749 for 1, 3, and 5 year overall survival, respectively. Network analysis uncovered the regulatory role of TFs in immune genes. Intriguingly, the prognostic signature reflected the immune cells landscape and infiltration of some immune cell subtypes. Conclusions We first constructed a signature with 21 immune genes of clinical significance, which showed promising predictive value in the surveillance, and prognosis of OC patients.

Immune-and Metabolism-Associated Molecular Classification of Ovarian Cancer

“…Here, the three subtypes identified showed significant differences in immune score, and the highest immune score was recorded in C1. We anticipated that C1 was the highest risk score subtype, which was in corroboration with previous findings indicating that higher risk score subtypes are correlated with higher immune score ( 32 – 34 ). This observation was further supported by the increase of immune cells in C1 subtype compared to C2 and C3.…”

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

“…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.…”

“…In our study, the AUCs of the signatures at 3, and 5 years were 0.709, 0.762, respectively, which were significantly higher than those of most hallmark predictive models. Table 4 shows that the AUCs of the other three prognostic signatures, namely, the 21 immune‐related gene signature, 35 17 immune‐related gene signature (0.754 at 3 years, 0.824 at 5 years) 36 and 17 transcription factor ‐related gene signature (0.803 at 5 years), 37 were comparable to the predictive capabilities of our predictive model and distinctly higher than those of other signatures, such as the epithelial‐mesenchymal transition related gene signature, 38 TME‐related gene signature, 39 , 40 RNA‐binding protein‐related gene signature, 41 energy metabolism‐related gene signature, 42 autophagy‐related gene signature, 43 , 44 ferroptosis‐related gene signature, 45 protein‐coding gene signature, 46 and DNA methylated gene signatures. 47 The larger the AUC value of a biomarker, the better was the predictive ability of the signature, indicating that our gene signature outperformed most of the other signatures in predicting OV prognosis.…”

“…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 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. 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.…”