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

Abstract

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.

Figure 1

Data exhibition and processing of four cohorts. (a). Row principal coordinates analysis (PCA) for the combined expression profile from the GSE19750, GSE33371, and GSE49278 cohorts. (b) PCA for the combined expression profile from the three cohorts after removing batch effects. (c) Heatmap showed the distributions of clinical information and 49 necroptosis-associated genes in four cohorts.

Figure 2

LASSO Cox regression selected necroptosis-associated genes with the best optimal lambda value, the association between NAGs and clinical features, and the survival analysis among NAGs groups. (a) The coefficients of 49 necroptosis-associated genes. (b) The seven necroptosis-associated genes selected with the best optimal lambda value, including LEF1, MAPK8, CYLD, TRAF2, DNMT1, PLK1, and GATA3. (c) The negative correlation between NAGs and OS in ACC. (d) Distribution of clinicopathological, status of survival, and tumor stage among NAGs groups. (e) Predictive accuracy of necroptosis-related gene signature at 1 year, 3 years, and 5 years, and the accuracy was equal to the corresponding AUC value. (f) Two distinct subgroups divided by the mean NAGs. (g) Different survival probabilities between the two defined subgroups are showed in K-M curves.

Figure 3

Multivariate Cox regression was performed to validate independent prognostic factors in ACC, and a nomogram model was established to predict progression risk for each patient. (a) Forest plot of corresponding multivariate HR for six algorithms, including age, gender, stage, laterality, and NAGs. Tumor stages assessed at stage I, stage II, stage III, stage IV, and unknown because of the pivotal effects on prognosis. Lines that do not cross the dashed line are considered as independent prognostic factors. (b) Discriminative power of four clinicopathological features as well as the nomogram and NAGs; the accuracy was equal to corresponding AUC value, and the value more than 0.75 represents high stability. (c) Establishment of a nomogram combining tumor stage and NAGs. For a given patient, find patient's tumor stage on stage axis, find patient's NAGs on NAGs axis, each time draw straight line upward toward points axis, total points were the sum of each predictor point, find the total point on total point on total point axis, draw straight line to the bottom 3-year progression probability and 5-year progression probability axis, and the points in progression line represented the progression probability. (d) Calibration plot for the nomogram. The dashed line represents the ideal nomogram, the solid line represents our nomogram, and a P value of 0.865 indicates that our nomogram is very close to the ideal nomogram. (e) DCA showed that our nomogram had the greatest net benefit among the four policies. (f) The clinical impact curve for the predictive value of the nomogram model, the orange solid line represents the predictive number of patients with high risk, and the black dashed line represents the actual number of patients with high risk.

Figure 4

Pathway activation and gene mutations among NAG groups were revealed. (a) Different metabolic pathways between high and low NAGs groups; the significant activation pathways were marked with purple and green. (b) Different gene alterations between the two groups, and different spectral color represent different types of genetic mutation. (c) Correlations between NAGs and the steps of the cancer immunity cycle and correlations between NAGs and the enrichment scores of immunotherapy-predicted pathways. The dashed lines represent a negative correlation, and the solid lines represent a positive correlation.

Figure 5

The immunocyte infiltration landscape of ACC was revealed by ssGSEA, and a necroptosis-associated gene signature was validated as a predictor for both immunotherapy and chemotherapy in ACC. (a) Immunocyte infiltration landscape for each patient depiction via a heatmap. (b) Response prediction of immunotherapy via SubMap analysis with an ACC cohort containing both patients who received and did not receive anti-PD1 or anti-CTLA4 therapy. (c) Comparison of NAGs among patients with CP/RP, PD, and SD in a checkmate cohort. (d) Comparison of chemotherapy response between high and low NAGs groups. The IC50 was employed as the evaluation indicator, and a higher IC50 represented lower drug sensitivity. (e) Drug-induced change in NAGs across NCI-60 cell lines after exposure to cisplatin for 2, 6, and 24 h. (f) Drug-induced change in NAGs across NCI-60 cell lines after exposure to gemcitabine for 2, 6, and 24 h.

Figure 6

Clinical relevance and survival analyses in the GEO combined cohort. (a) Correlations between NAGs and OS. (b) Comparisons of clinicopathological feature distributions between the high and low NAGs groups. (c). Discriminative accuracy validation in GEO combined cohort; ROC analysis was conducted to calculate the AUC value at 1 year, 3 years, and 5 year, respectively. (d). High and low NAGs patients in GEO combined cohort. (e) Comparisons of OS between patients with high NAGs and low NAGs, the results were depicted via K-M curves. (f) Multivariate Cox regression analysis.

Figure 7

Pathway enrichment analysis and therapeutic response to immunotherapy and chemotherapy in ACC based on the GEO combined cohort. (a) Enrichment analysis of metabolic pathways in the low NAGs group and high NAGs group via GSVA algorithms and comparison of enrichment scores. (b) Tumor immunocyte infiltration landscape of patients in GEO combined cohort. (c) Assessment of therapeutic response to anti-CTLA4 and anti-PD-1 in ACC D. Comparison of response to four chemotherapy agents, including cisplatin, gemcitabine, paclitaxel, and etoposide.