Seema Khadirnaikar1,2, Pranjal Kumar1, Sudhanshu Kumar Shukla1. 1. Department of Biosciences and Bioengineering, Indian Institute of Technology Dharwad, Dharwad, India. 2. Department of Electrical Engineering, Indian Institute of Technology Dharwad, Dharwad, India.
Abstract
BACKGROUND: Ovarian cancer (OC) causes a significant proportion of cancer-related deaths in women. Recently, immunotherapy has emerged as a substantial player in cancer treatment. Lymphocyte infiltration, an important indicator of immune activity and disease aggressiveness, can be identified by gene expression profiling of immune-related genes of tumours which may prove useful in prognosis of patients. AIMS: The aim of this study is to identify and validate a novel immune gene-based prognostic signature for OC. METHODS AND RESULTS: Here, we extracted the expression of immune-related genes and performed the Cox regression analysis and identified five genes with significant correlation with survival in training cohort of patients (n = 286). We utilised regression coefficient and expression level of five genes to calculate immune prognostic signature (IPS) score for OC patients. In univariate and multivariate Cox regression analysis with other clinicopathological factors, we showed that IPS is an independent predictor of survival (P value <0.01). More importantly, we utilised 404 patients from TCGA dataset as the validation cohort and validated the survival capability of IPS in the univariate and multivariate analysis (P value <0.001). Interestingly, KM analysis showed a significant difference in survival of patients with high and low IPS score in both datasets (training dataset P value <0.01, validation dataset P value <0.01). Further, we showed that all the five genes are differentially expressed and involved in immune modulation among other pathways. Interestingly, GSEA analysis showed that high IPS patients had low immune activity and activated EMT and other oncogenic pathways. CONCLUSION: In summary, we have developed and validated robust immune-related gene-based prognostic signature to identify the OC patients with high immune activity who can be taken for immunotherapy.
BACKGROUND: Ovarian cancer (OC) causes a significant proportion of cancer-related deaths in women. Recently, immunotherapy has emerged as a substantial player in cancer treatment. Lymphocyte infiltration, an important indicator of immune activity and disease aggressiveness, can be identified by gene expression profiling of immune-related genes of tumours which may prove useful in prognosis of patients. AIMS: The aim of this study is to identify and validate a novel immune gene-based prognostic signature for OC. METHODS AND RESULTS: Here, we extracted the expression of immune-related genes and performed the Cox regression analysis and identified five genes with significant correlation with survival in training cohort of patients (n = 286). We utilised regression coefficient and expression level of five genes to calculate immune prognostic signature (IPS) score for OC patients. In univariate and multivariate Cox regression analysis with other clinicopathological factors, we showed that IPS is an independent predictor of survival (P value <0.01). More importantly, we utilised 404 patients from TCGA dataset as the validation cohort and validated the survival capability of IPS in the univariate and multivariate analysis (P value <0.001). Interestingly, KM analysis showed a significant difference in survival of patients with high and low IPS score in both datasets (training dataset P value <0.01, validation dataset P value <0.01). Further, we showed that all the five genes are differentially expressed and involved in immune modulation among other pathways. Interestingly, GSEA analysis showed that high IPSpatients had low immune activity and activated EMT and other oncogenic pathways. CONCLUSION: In summary, we have developed and validated robust immune-related gene-based prognostic signature to identify the OC patients with high immune activity who can be taken for immunotherapy.
Authors: Cole Trapnell; Adam Roberts; Loyal Goff; Geo Pertea; Daehwan Kim; David R Kelley; Harold Pimentel; Steven L Salzberg; John L Rinn; Lior Pachter Journal: Nat Protoc Date: 2012-03-01 Impact factor: 13.491
Authors: Richard W Tothill; Anna V Tinker; Joshy George; Robert Brown; Stephen B Fox; Stephen Lade; Daryl S Johnson; Melanie K Trivett; Dariush Etemadmoghadam; Bianca Locandro; Nadia Traficante; Sian Fereday; Jillian A Hung; Yoke-Eng Chiew; Izhak Haviv; Dorota Gertig; Anna DeFazio; David D L Bowtell Journal: Clin Cancer Res Date: 2008-08-15 Impact factor: 12.531