Bevacizumab reduces peritumoral brain edema in lung cancer brain metastases after radiotherapy.

BACKGROUND: The aim of this study was to investigate the efficacy of bevacizumab (Bev) in reducing peritumoral brain edema (PTBE) after stereotactic radiotherapy (SRT) for lung cancer brain metastases. METHODS: A retrospective analysis was conducted on 44 patients with lung cancer brain metastases (70 lesions) who were admitted to our oncology and Gamma Knife center from January 2020 to May 2022. All patients received intracranial SRT and had PTBE. Based on treatment with Bev, patients were categorized as SRT + Bev and SRT groups. Follow-up head magnetic resonance imaging was performed to calculate PTBE and tumor volume changes. The edema index (EI) was used to assess the severity of PTBE. Additionally, the extent of tumor reduction and intracranial progression-free survival (PFS) were compared between the two groups. RESULTS: The SRT + Bev group showed a statistically significant difference in EI values before and after radiotherapy (p = 0.0115), with lower values observed after treatment, but there was no difference in the SRT group (p = 0.4008). There was a difference in the distribution of EI grades in the SRT + Bev group (p = 0.0186), with an increased proportion of patients at grades 1-2 after radiotherapy, while there was no difference in the SRT group (p > 0.9999). Both groups demonstrated a significant reduction in tumor volume after radiotherapy (p < 0.05), but there was no difference in tumor volume changes between the two groups (p = 0.4089). There was no difference in intracranial PFS between the two groups (p = 0.1541). CONCLUSION: Bevacizumab significantly reduces the severity of PTBE after radiotherapy for lung cancer. However, its impact on tumor volume reduction and intracranial PFS does not reach statistical significance.

The prognostic value of cancer stage-associated genes in clear cell renal cell carcinoma.

OBJECTIVES: Clear cell renal cell carcinoma (ccRCC) is a highly prevalent subtype of malignant renal tumor, but unfortunately, the survival rate remains unsatisfactory. The aim of the present study is to explore genomic features that are correlated with cancer stage, allowing for the identification of subgroups of ccRCC patients with high risk of unfavorable outcomes and enabling prompt intervention and treatment. METHODS: We compared the gene expression levels across ccRCC patients with diverse cancer stages from The Cancer Genome Atlas (TCGA) database, which revealed characteristic genes associated with tumor stage. We then extracted prognostic genes and used least absolute shrinkage selection operator (LASSO) regression to select four genes for feature extraction and the construction of a prognostic risk model. RESULTS: We have identified a total of 171 differentially expressed genes (DEGs) that are closely linked to the tumor stage of ccRCC through difference analysis. A prognostic risk model constructed based on the expression levels of ZIC2, TFAP2A-AS1, ITPKA, and SLC16A12 holds significant prognostic value in ccRCC. The results of the functional enrichment analysis imply that the DEGs are mainly involved in the regulation of immune-related signaling pathways, and therefore may have a significant function in immune system regulation of ccRCC. CONCLUSIONS: Our study has successfully identified significant DEGs between high- and low-staging groups of ccRCC using bioinformatics methods. The construction of a prognostic risk model based on the expression levels of ZIC2, TFAP2A-AS1, ITPKA, and SLC16A12 has displayed promising prognostic significance, indicating its valuable potential for clinical application.

Prognostic nomograms for predicting overall survival and cause-specific survival of signet ring cell carcinoma in colorectal cancer patients.

BACKGROUND: Signet ring cell carcinoma (SRCC) is an uncommon subtype in colorectal cancer (CRC), with a short survival time. Therefore, it is imperative to establish a useful prognostic model. As a simple visual predictive tool, nomograms combining a quantification of all proven prognostic factors have been widely used for predicting the outcomes of patients with different cancers in recent years. Until now, there has been no nomogram to predict the outcome of CRC patients with SRCC. AIM: To build effective nomograms for predicting overall survival (OS) and cause-specific survival (CSS) of CRC patients with SRCC. METHODS: Data were extracted from the Surveillance, Epidemiology, and End Results database between 2004 and 2015. Multivariate Cox regression analyses were used to identify independent variables for both OS and CSS to construct the nomograms. Performance of the nomograms was assessed by concordance index, calibration curves, and receiver operating characteristic (ROC) curves. ROC curves were also utilized to compare benefits between the nomograms and the tumor-node-metastasis (TNM) staging system. Patients were classified as high-risk, moderate-risk, and low-risk groups using the novel nomograms. Kaplan-Meier curves were plotted to compare survival differences. RESULTS: In total, 1230 patients were included. The concordance index of the nomograms for OS and CSS were 0.737 (95% confidence interval: 0.728-0.747) and 0.758 (95% confidence interval: 0.738-0.778), respectively. The calibration curves and ROC curves demonstrated good predictive accuracy. The 1-, 3-, and 5-year area under the curve values of the nomogram for predicting OS were 0.796, 0.825 and 0.819, in comparison to 0.743, 0.798, and 0.803 for the TNM staging system. In addition, the 1-, 3-, and 5-year area under the curve values of the nomogram for predicting CSS were 0.805, 0.847 and 0.863, in comparison to 0.740, 0.794, and 0.800 for the TNM staging system. Based on the novel nomograms, stratified analysis showed that the 5-year probability of survival in the high-risk, moderate-risk, and low-risk groups was 6.8%, 37.7%, and 67.0% for OS (P < 0.001), as well as 9.6%, 38.5%, and 67.6% for CSS (P < 0.001), respectively. CONCLUSION: Convenient and visual nomograms were built and validated to accurately predict the OS and CSS rates for CRC patients with SRCC, which are superior to the conventional TNM staging system.