Identification of prognostic metabolic genes in adrenocortical carcinoma and establishment of a prognostic nomogram

Abstract

Adrenocortical carcinoma is an invasive malignancy with poor prognosis, high recurrence rate and limited therapeutic options. Therefore, it is necessary to establish an effective method to diagnose and evaluate the prognosis of patients, so as to realize individualized treatment and improve their survival rate.This study investigated metabolic genes that may be potential therapeutic targets for Adrenocortical carcinoma (ACC). Level 3 gene expression data from the ACC cohort and the relevant clinical information were obtained from The Cancer Genome Atlas (TCGA) database. To verify, other ACC datasets (GSE76021, GSE19750) were downloaded from the Gene Expression Omnibus (GEO) database. The ACC datasets from TCGA and GEO were used to screen metabolic genes through the Molecular Signatures Database using gene set enrichment analysis. Then, the overlapping metabolic genes of the 2 datasets were identified.A signature of five metabolic genes (CYP11B1, GSTM2, IRF9, RPL31, and UBE2C) was identified in patients with ACC. The signature could be used to divide the patients with ACC into high-and low-risk groups based on their median risk score. Multivariate Cox regression analysis was performed to determine the independent prognostic factors of ACC. Time-dependent receiver operating characteristic (ROC) curve analysis was conducted to assess the prediction accuracy of the prognostic signature. Last, a nomogram was established to assess the individualized prognosis prediction model.The results indicated that the signature of 5 metabolic genes had excellent predictive value for ACC. These findings might help improve personalized treatment and medical decisions.Abbreviations: ACC = adrenocortical carcinoma, GO = gene oncology, HR = hazard ratio, KEGG = Kyoto Encyclopedia of Genes and Genomes, OS = overall survival, ROC = receiver operating characteristic, TCGA = the cancer genome atlas.

Diagnostic and prognostic assessments of adrenocortical carcinomas by pathological features, immunohistochemical markers and reticular histochemistry staining

“…Our results showed statistical difference between two groups but the AUC value was relatively low, suggesting that CYP11B1 had potential for distinguishing carcinomas from adenomas. Additionally, Qing Chen et al [ 23 ] reported the identification of a CYP11B1 signature in ACCs and its prognostic value. However, our study showed no prognostic significance of CYP11B1.…”

“…Ki-67 is a classical marker that reflects the activity of cell proliferation and is widely used in various tumors. ACC, as a proliferation-driven malignancy, typically displays a Ki-67 labeling index greater than 5%, which significantly exceeds that of ACA [ 3 , 5 , 23 ]. Notably, ACCs exhibit proliferative heterogeneity, requiring the accurate identification of hot spots to precisely assess the Ki-67 labeling index.…”

“…PHH3 has advantages in assessing mitotic count compared to visual observation [ 9 , 25 , 26 ]. PHH3 can distinguish mitotic figures from apoptotic bodies, which are sometimes difficult to identify through microscopic observation of H&E slides, thereby improving the accuracy of mitosis identification [ 23 , 24 ]. Additionally, the predominant staining of PHH3 by immunohistochemistry is time-saving and reproducible among observers.…”

Comparative efficacy of unilateral biportal endoscopy and micro-endoscopic discectomy in the treatment of degenerative lumbar spinal stenosis: a systematic review and meta-analysis

“…Our study rigorously analyzes the efficacy of surgical interventions for DLSS, focusing on pain relief, functional outcomes, and operative time. In contrast, Chen et al [ 26 ] delve into the genomic landscape of Adrenocortical carcinoma (ACC), targeting metabolic gene signatures for prognostic evaluation. These distinct studies underscore different clinical challenges, rendering direct comparisons non-applicable, and emphasizing the breadth of ongoing medical research.…”