Prognosis and Therapeutic Efficacy Prediction of Adrenocortical Carcinoma Based on a Necroptosis-Associated Gene Signature

Background: Adrenocortical carcinoma (ACC) is a rare and poor prognosis malignancy. Necroptosis is a special type of cell apoptosis, which is regulated in caspase-independent pathways and mainly induced through the activation of receptor-interacting protein kinase 1, receptor-interacting protein kinase 3, and mixed lineage kinase domain-like pseudokinase. A precise predictive tool based on necroptosis is needed to improve the level of diagnosis and treatment.

Method: Four ACC cohorts were enrolled in this study. The Cancer Genome Atlas ACC (TCGA-ACC) cohort was used as the training cohort; three datasets (GSE19750, GSE33371, and GSE49278) from Gene Expression Omnibus (GEO) platform were combined as the GEO testing cohort after removing of batch effect. Forty-nine necroptosis-associated genes were obtained from a prior study and further filtered by least absolute shrinkage and selection operator Cox regression analysis; corresponding coefficients were used to calculate the necroptosis-associated gene score (NAGs). Patients in the TCGA-ACC cohort were equally divided into two groups with the mean value of NAGs. We investigated the associations between NAGs groups and clinicopathological feature distribution and overall survival (OS) in ACC, the molecular mechanisms, and the value of NAGs in therapy prediction. A nomogram risk model was established to quantify risk stratification for ACC patients. Finally, the results were confirmed in the GEO-combined cohort.

Result: Patients in the TCGA-ACC cohort were divided into high and low NAGs groups. The high NAGs group had more fatal cases and advanced stage patients than the low NAGs group (P < 0.001, hazard ratio (HR) = 13.97, 95% confidence interval (95% CI): 4.168-46.844; survival rate: low NAGs, 7.69% vs. high NAGs, 61.53%). NAGs were validated to be negatively correlated with OS (R = -0.48, P < 0.001) and act as an independent factor in ACC with high discriminative efficacy (P < 0.001, HR = 11.76, 95% CI: 2.86-48.42). In addition, a high predictive efficacy nomogram risk model was established combining NAGs with tumor stage. Higher mutation rates were observed in the high NAGs group, and the mutation of TP53 may lead to a high T cell infiltration level among the NAGs groups. Patients belonged to the high NAGs are more sensitive to the chemotherapy of cisplatin, gemcitabine, paclitaxel, and etoposide (all P < 0.05). Ultimately, the same statistical algorithms were conducted in the GEO-combined cohort, and the crucial role of NAGs prediction value was further validated.

Conclusion: We constructed a necroptosis-associated gene signature, revealed the prognostic value between ACC and it, systematically explored the molecular alterations among patients with different NAGs, and manifested the value of drug sensitivity prediction in ACC.