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Sterol O-Acyl Transferase 1 as a Prognostic Marker of Adrenocortical Carcinoma.

ABSTRACT: Adrenocortical carcinoma (ACC) is a rare endocrine malignancy with an unfavorable prognosis. Despite the poor prognosis in the majority of patients, no improvements in treatment strategies have been achieved. Therefore, the discovery of new prognostic biomarkers is of enormous interest. Sterol-O-acyl transferase 1 (SOAT1) is involved in cholesterol esterification and lipid droplet formation. Recently, it was demonstrated that SOAT1 inhibition leads to impaired steroidogenesis and cell viability in ACC. To date, no studies have addressed the impact of SOAT1 expression on ACC prognosis and clinical outcomes. We evaluated SOAT1 expression by quantitative real-time polymerase chain reaction and immunohistochemistry in a tissue microarray of 112 ACCs (Weiss score ? 3) from adults treated in a single tertiary center in Brazil. Two independent pathologists evaluated the immunohistochemistry results through a semiquantitative approach (0-4). We aimed to evaluate the correlation between SOAT1 expression and clinical, biochemical and anatomopathological parameters, recurrence-free survival (RFS), progression-free survival (PFS), and overall survival (OS). SOAT1 protein expression was heterogeneous in this cohort, 37.5% of the ACCs demonstrated a strong SOAT1 protein expression (score > 2), while 62.5% demonstrated a weak or absent protein expression (score ? 2). Strong SOAT1 protein expression correlated with features of high aggressiveness in ACC, such as excessive tumor cortisol secretion (p = 0.01), an advanced disease stage [European Network for the Study of Adrenal Tumors (ENSAT) staging system 3 and 4 (p = 0.011)] and a high Ki67 index (p = 0.002). In multivariate analysis, strong SOAT1 protein expression was an independent predictor of a reduced OS (hazard ratio (HR) 2.15, confidence interval (CI) 95% 1.26-3.66; p = 0.005) in all patients (n = 112), and a reduced RFS (HR 2.1, CI 95% 1.09-4.06; p = 0.027) in patients with localized disease at diagnosis (n = 83). Our findings demonstrated that SOAT1 protein expression has prognostic value in ACC and reinforced the importance of investigating SOAT1 as a possible therapeutic target for patients with ACC.

SUBMITTER: Lacombe AMF

PROVIDER: S-EPMC7016635 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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Project description:Adrenocortical carcinoma (ACC) is an aggressive malignancy with a low but variable overall survival rate. Even after complete tumor resection, over half of patients develop recurrent disease. Long noncoding RNAs (lncRNAs) have been found to be involved in cancer initiation/progression and as markers of cancer prognosis. The role of lncRNAs in ACC is poorly understood. Thus, in this study we performed lncRNA expression profiling in ACC, adrenocortical adenoma (ACA) and normal adrenal cortex (NAC). Total RNA was extracted from fresh frozen tissue samples (11 ACA, nine ACC and five NAC samples) with histopathological confirmed diagnosis. ArrayStar Human LncRNA/mRNA Expression Microarray V3.0 was used for transcriptome analysis. Differentially expressed lncRNAs were validated using TaqMan real-time quantitative PCR in a validation cohort including an additional 10 ACCs. The ACC dataset from the Cancer Genome Atlas (TCGA) project was used to evaluate the prognostic utility of lncRNAs found to be differentially expressed in ACC. Survival curves were plotted by Kaplan-Meier analysis, and differences in survival rates were assessed using a log-rank test. P < 0.05 was considered significant. Gene Set Enrichment Analysis (GSEA) was performed to identify lncRNA-associated biological signaling pathways. Unsupervised hierarchical and heat map clustering showed distinct clustering of ACC samples compared with NAC and ACA samples by lncRNA expression profiles. A total of 874 lncRNAs were differentially expressed between ACC and NAC, including known carcinogenesis-related lncRNAs such as HOTTIP, HOXA11-AS1, CRNDE, LINC00271 and TBXAS1. One thousand seventy-six lncRNAs were differentially expressed between ACC and ACA. LINC00271 was a prognostic marker, with patients with low LINC00271 expression surviving significantly shorter than patients with a high LINC00271 expression. Median survival time (MST) for the low-expression group was 1788 days, whereas the MST was not reached for the high-expression group (P < 0.019). Importantly, low LINC00271 expression was positively associated with WNT signaling, cell cycle, chromosome segregation and tissue morphogenesis pathways. ACC has a distinct lncRNA expression profile. LINC00271 is a prognostic marker in ACC and is involved in biological pathways commonly dysregulated in ACC.

Project description:Importance:The risk stratification of adrenocortical carcinoma (ACC) based on tumor proliferation index and stage is limited. Adjuvant therapy after surgery is recommended for most patients. Pan-genomic studies have identified distinct molecular groups closely associated with outcome. Objective:To compare the molecular classification for prognostic assessment of ACC with other known prognostic factors. Design, Setting, and Participants:In this retrospective biomarker analysis, ACC tumor samples from 368 patients who had undergone surgical tumor removal were collected from March 1, 2005, to September 30, 2015 (144 in the training cohort and 224 in the validation cohort) at 21 referral centers with a median follow-up of 35 months (interquartile range, 18-74 months). Data were analyzed from March 2016 to March 2018. Exposures:Meta-analysis of pan-genomic studies (transcriptome, methylome, chromosome alteration, and mutational profiles) was performed on the training cohort. Targeted biomarker analysis, including targeted gene expression (BUB1B and PINK1), targeted methylation (PAX5, GSTP1, PYCARD, and PAX6), and targeted next-generation sequencing, was performed on the training and validation cohorts. Main Outcomes and Measures:Disease-free survival. Cox proportional hazards regression and C indexes were used to assess the prognostic value of each model. Results:Of the 368 patients (mean [SD] age, 49 [16] years), 144 were in the training cohort (100 [69.4%] female) and 224 were in the validation cohort (142 [63.4%] female). In the training cohort, pan-genomic measures classified ACC into 3 molecular groups (A1, A2, and A3-B), with 5-year survival of 9% for group A1, 45% for group A2, and 82% for group A3-B (log-rank P?<?.001). Molecular class was an independent prognostic factor of recurrence in stage I to III ACC after complete surgery (hazard ratio, 55.91; 95% CI, 8.55-365.40; P?<?.001). The combination of European Network for the Study of Adrenal Tumors (ENSAT) stage, tumor proliferation index, and molecular class provided the most discriminant prognostic model (C index, 0.88). In the validation cohort, the molecular classification, determined by targeted biomarker measures, was confirmed as an independent prognostic factor of recurrence (hazard ratio, 5.96 [95% CI, 1.81-19.58], P?=?.003 for the targeted classifier combining expression, methylation, and chromosome alterations; and 2.61 [95% CI, 1.31-5.19], P?=?.006 for the targeted classifier combining methylation, chromosome alterations, and mutational profile). The prognostic value of the molecular markers was limited for patients with stage IV ACC. Conclusions and Relevance:The findings suggest that in localized ACC, targeted classifiers may be used as independent markers of recurrence. The determination of molecular class may improve individual prognostic assessment and thus may spare unnecessary adjuvant treatment.

Project description:Objective: Adrenocortical carcinoma (ACC) is a rare but aggressive malignant cancer that has been attracting growing attention over recent decades. This study aims to integrate protein interaction networks with gene expression profiles to identify potential biomarkers with prognostic value in silico. Methods: Three microarray data sets were downloaded from the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs) according to the normalization annotation information. Enrichment analyses were utilized to describe biological functions. A protein-protein interaction network (PPI) of the DEGs was developed, and the modules were analyzed using STRING and Cytoscape. LASSO Cox regression was used to identify independent prognostic factors. The Kaplan-Meier method for the integrated expression score was applied to analyze survival outcomes. A receiver operating characteristic (ROC) curve was constructed with area under curve (AUC) analysis to determine the diagnostic ability of the candidate biomarkers. Results: A total of 150 DEGs and 24 significant hub genes with functional enrichment were identified as candidate prognostic biomarkers. LASSO Cox regression suggested that ZWINT, PRC1, CDKN3, CDK1 and CCNA2 were independent prognostic factors in ACC. In multivariate Cox analysis, the integrated expression scores of the modules showed statistical significance in predicting disease-free survival (DFS, P = 0.019) and overall survival (OS, P < 0.001). Meanwhile, ROC curves were generated to validate the ability of the Cox model to predict prognosis. The AUC index for the integrated genes scores was 0.861 (P < 0.0001). Conclusion: In conclusion, the present study identifies DEGs and hub genes that may be involved in poor prognosis and early recurrence of ACC. The expression levels of ZWINT, PRC1, CDKN3, CDK1 and CCNA2 are of high prognostic value, and may help us understand better the underlying carcinogenesis or progression of ACC. Further studies are required to elucidate molecular pathogenesis and alteration in signaling pathways for these genes in ACC.

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Sterol O-Acyl Transferase 1 as a Prognostic Marker of Adrenocortical Carcinoma.

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