Computed tomography-based radiomics predicts the fibroblast-related gene EZH2 expression level and survival of hepatocellular carcinoma

Construction of enhanced MRI-based radiomics models using machine learning algorithms for non-invasive prediction of IL7R expression in high-grade gliomas and its prognostic value in clinical practice

BACKGROUND

High-grade gliomas are among the most aggressive and deadly brain tumors, highlighting the critical need for improved prognostic markers and predictive models. Recent studies have identified the expression of IL7R as a significant risk factor that affects the prognosis of patients diagnosed with high-grade gliomas (HGG). This research focuses on investigating the prognostic significance of Interleukin 7 Receptor (IL7R) expression and aims to develop a noninvasive predictive model based on radiomics for HGG.

METHODS

We conducted an analysis using data from The Cancer Genome Atlas (TCGA) and The Cancer Imaging Archive (TCIA), focusing on a group of 310 patients diagnosed with high-grade gliomas. To evaluate prognosis, we applied both univariate and multivariate Cox regression analyses alongside Kaplan-Meier survival analysis. Radiomics features were extracted from specific regions of interest, which were outlined by two physicians using 3D Slicer software. For selecting the most relevant features, we utilized the Minimum Redundancy Maximum Relevance (mRMR) and Recursive Feature Elimination (RFE) algorithms. Following this, we developed and assessed Support Vector Machine (SVM) and Logistic Regression (LR) models, measuring their performance through various metrics such as accuracy, specificity, sensitivity, positive predictive value, calibration curves, the Hosmer-Lemeshow goodness-of-fit test, decision curve analysis (DCA), and Kaplan-Meier survival analysis.

RESULTS

The survival analysis encompassed a total of 310 patients diagnosed with high-grade glioma, sourced from the TCGA database. Patients were stratified into high and low expression groups based on the levels of IL7R expression. Kaplan-Meier survival curves and Cox regression analysis revealed that an increase in IL7R expression correlated with a decline in overall survival (OS). The median Intraclass Correlation Coefficient (ICC) for the assessed radiomic features was determined to be 0.869, with 93 features exhibiting an ICC of 0.75 or greater. Utilizing the mRMR and RFE methodologies led to the identification of a final set comprising eight features. The Support Vector Machine (SVM) model recorded an Area Under the Curve (AUC) value of 0.805, whereas the AUC derived from fivefold cross-validation was noted to be 0.768. Conversely, the Logistic Regression (LR) model produced an AUC of 0.85, with an internal fivefold cross-validation AUC of 0.779, indicating a more robust predictive capability. We developed Support Vector Machine (SVM) and Logistic Regression (LR) models, with the LR model demonstrating a more robust predictive capability. Further Kaplan-Meier analysis underscored a significant association between elevated risk scores from the LR model and OS malignancy, with a P value of less than 0.001. GSVA analysis showed the enrichment pathway of KEGG and Hallmark genes in the high RS group. Moreover, expression levels of the LOX gene and the infiltration of M0 macrophages were significantly heightened in the high-risk score group, alongside an increase in tumor mutation burden (TMB). Interestingly, the mutation frequencies of TP53 and PIK3CA were found to be lower in the high-risk score group when compared to their low-risk counterparts.

CONCLUSION

IL7R expression is a vital prognostic marker in high-grade gliomas. The radiomics-based LR models demonstrate strong predictive capabilities for patient outcomes. Future investigations should aim to incorporate these insights into clinical practice to enhance personalized treatment approaches for patients with high-grade glioma.

Radiomics and molecular analysis: Bridging the gap for predicting hepatocellular carcinoma prognosis

This editorial examines a recent study that used radiomics based on computed tomography (CT) to predict the expression of the fibroblast-related gene enhancer of zeste homolog 2 (EZH2) and its correlation with the survival of patients with hepatocellular carcinoma (HCC). By integrating radiomics with molecular analysis, the study presented a strategy for accurately predicting the expression of EZH2 from CT scans. The findings demonstrated a strong link between the radiomics model, EZH2 expression, and patient prognosis. This noninvasive approach provides valuable insights into the therapeutic management of HCC.

Gene targets with therapeutic potential in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related deaths worldwide. Major treatments include liver transplantation, resection, and chemotherapy, but the 5-year recurrence rate remains high. Late diagnosis often prevents surgical intervention, contributing to poor patient survival rates. Carcinogenesis in HCC involves genetic alterations that drive the transformation of normal cells into malignant ones. Enhancer of zeste homolog 2 (EZH2), a key regulator of cell cycle progression, is frequently upregulated in HCC and is associated with advanced stages and poor prognosis, making it a potential biomarker. Additionally, signal transducer and activator of transcription 3, which binds to EZH2, affects disease staging and outcomes. Targeting EZH2 presents a promising therapeutic strategy. On the other hand, abnormal lipid metabolism is a hallmark of HCC and impacts prognosis. Fatty acid binding protein 5 is highly expressed in HCC tissues and correlates with key oncogenes, suggesting its potential as a biomarker. Other genes such as guanine monophosphate synthase, cell division cycle associated 5, and epidermal growth factor receptor provide insights into the molecular mechanisms of HCC, offering potential as biomarkers and therapeutic targets.