ABSTRACT
BACKGROUND: According to clinical practice guidelines, transarterial chemoembolization (TACE) is the standard treatment modality for patients with intermediate-stage hepatocellular carcinoma (HCC). Early prediction of treatment response can help patients choose a reasonable treatment plan. This study aimed to investigate the value of the radiomic-clinical model in predicting the efficacy of the first TACE treatment for HCC to prolong patient survival. METHODS: A total of 164 patients with HCC who underwent the first TACE from January 2017 to September 2021 were analyzed. The tumor response was assessed by modified response evaluation criteria in solid tumors (mRECIST), and the response of the first TACE to each session and its correlation with overall survival were evaluated. The radiomic signatures associated with the treatment response were identified by the least absolute shrinkage and selection operator (LASSO), and four machine learning models were built with different types of regions of interest (ROIs) (tumor and corresponding tissues) and the model with the best performance was selected. The predictive performance was assessed with receiver operating characteristic (ROC) curves and calibration curves. RESULTS: Of all the models, the random forest (RF) model with peritumor (+10 mm) radiomic signatures had the best performance [area under ROC curve (AUC) = 0.964 in the training cohort, AUC = 0.949 in the validation cohort]. The RF model was used to calculate the radiomic score (Rad-score), and the optimal cutoff value (0.34) was calculated according to the Youden's index. Patients were then divided into a high-risk group (Rad-score > 0.34) and a low-risk group (Rad-score ≤ 0.34), and a nomogram model was successfully established to predict treatment response. The predicted treatment response also allowed for significant discrimination of Kaplan-Meier curves. Multivariate Cox regression identified six independent prognostic factors for overall survival, including male [hazard ratio (HR) = 0.500, 95% confidence interval (CI): 0.260-0.962, P = 0.038], alpha-fetoprotein (HR = 1.003, 95% CI: 1.002-1.004, P < 0.001), alanine aminotransferase (HR = 1.003, 95% CI: 1.001-1.005, P = 0.025), performance status (HR = 2.400, 95% CI: 1.200-4.800, P = 0.013), the number of TACE sessions (HR = 0.870, 95% CI: 0.780-0.970, P = 0.012) and Rad-score (HR = 3.480, 95% CI: 1.416-8.552, P = 0.007). CONCLUSIONS: The radiomic signatures and clinical factors can be well-used to predict the response of HCC patients to the first TACE and may help identify the patients most likely to benefit from TACE.
ABSTRACT
There are no effective therapies for advanced renal cell carcinoma (RCC), except for VEGFR inhibitors with only ~50% response rate. To identify novel targets and biomarkers for RCC is of great importance in treating RCC. In this study, we observed that eukaryotic initiation factor 3d (EIF3D) expression was significantly increased in RCC compared with paracarcinoma tissue using immunohistochemistry staining and western blot analysis. Furthermore, bioinformatics meta-analysis using ONCOMINE microarray datasets showed that EIF3D mRNA expressions in CCRCC tissue specimens were significantly higher than that in normal tissue specimens. In addition, RCC tissue microarray demonstrated that elevated EIF3D expression was positively correlated with TNM stage and tumor size. EIF3D silencing in human 786-O and ACHN CCRCC cell lines by RNA interference demonstrated that EIF3D knockdown obviously inhibited cell proliferation and colony formation, caused G2/M arrest through downregulation of Cyclin B1 and Cdk1 and upregulation of p21, and induced apoptosis shown by sub-G1 accumulation and RARP cleavage. Moreover, correlation analysis using ONCOMINE microarray datasets indicated that increased EIF3D mRNA expression was positively correlated to PCNA, Cyclin B1 and CDK1 mRNA expression in RCC. Collectively, these results provide reasonable evidences that EIF3D may function as a potential proto-oncogene that participates in the occurrence and progression of RCC.
