Abstract 1355: Inflammation-related gene signature as a predictor of survival in adrenocortical carcinoma: machine learning and bioinformatics analysis

Abstract Background: Adrenocortical carcinoma (ACC) is a rare malignancy with poor outcomes. Inflammation is increasingly recognized to play a significant role in the tumorigenesis of ACC. This study examines the relationship between inflammation-related genes and survival outcomes in patients with ACC. Methods: We analyzed the RNA-seq and clinical data of 79 patients with ACC obtained from TCGA. Principal component (PC) analysis was performed on the expression data of 222 inflammation-related genes (MSigDB) to categorize patients into high and low inflammation groups (IG) based on the median of the first PC. Differentially expressed genes (DEGs) related to inflammation between the two groups were identified. Stepwise AIC identified the best model based on all DEGs, then genes were fitted to LASSO to mitigate multicollinearity. Uni- and multivariate Cox proportional hazards were used to identify independent predictors of survival. PC analysis was then conducted on the independent predictors of survival genes to compute the inflammation score (IS), with a cutoff point determined based on survival data. The correlation between IS and overall survival (OS) and disease-free survival (DFS) was assessed using the log-rank test. Clinical variables were investigated for possible association with the IS using Welch's t-, chi-square, Fisher’s exact, and Wilcoxon tests. Immune infiltration analysis was conducted using CIBERSORT. Results: Five inflammation-related DEGs associated with survival were identified, namely ITGB3, MARCO, MSR1, SLAMF1, and SELE with the first 2 identified as independent predictors. ITGB3 is associated with worse OS (HR= 1.29, P= 0.036) and MARCO is linked to worse DFS (HR= 1.17, P= 0.048). Patients were split into high (n=39) and low (n=40) IGs based on inflammation-related DEGs. We calculated IS based on independent predictors of survival genes and the patients were grouped into high and low IS with a cutoff point of - 0.47 determined based on OS data (low IS n= 20, high IS n= 59). Log-rank test revealed significantly lower OS and DFS in the high IS group in comparison to the low IS group. No significant associations were detected between the IS groups and sex, age, T, N, & M stages, or the disease stage. The absolute immune fraction score was significantly higher in the high IS group compared to the low IS group, with means of 26.59 and 11.96, respectively (P < 0.00001, Welch's t-test). Gene Ontology (GO) analysis of the DEGs showed overrepresentation in biological processes associated with cell adhesion in high IS group and overrepresentation of processes related to mitochondrial function and energy production in the low IS group. Conclusions: Our findings underscore the potential link between inflammation and survival outcomes in ACC and support the use of an inflammation-related gene signature as a prognostication tool for survival in this aggressive cancer type. Citation Format: Jaber H. Jaradat, Lujia Chen, Anwaar Saeed, Azhar Saeed. Inflammation-related gene signature as a predictor of survival in adrenocortical carcinoma: machine learning and bioinformatics analysis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 1355.