Tumor size as a significant prognostic factor in T1 gastric cancer: a Surveillance, Epidemiology, and End Results (SEER) database analysis.

BACKGROUND: It has previously been observed that the prognostic value of tumor size varied according to different stages patients enrolled in gastric cancer. We aimed to investigate the influence of T stage on the prognostic and predicting value of tumor size. MATERIAL AND METHODS: A total of 13,585 patients with stage I-III gastric cancer were selected from the Surveillance, Epidemiology, and End Results Program (SEER) database. Univariate and multivariate cox regression analysis stratified by T stage were performed. C-index and time-dependent receiver operating characteristic curve (ROC) curve were applied to assess discrimination ability of tumor size and other factors. Nomograms were constructed to further assess the performance of tumor size in a specific model. Calibration ability, discrimination ability, reclassification ability and clinical benefits were executed to judge the performance of models. RESULTS: Stratified analyses according to T stage illustrated that with the increase of T stage, the effect of tumor size on overall survival (OS) and cancer-specific survival (CSS) significantly decreased. Moreover, tumor size showed superior discrimination ability in T1 gastric cancer, outperformed other prognostic factors in predicting both CSS (C-index: 0.666, AUC: 0.687) and OS (C-index: 0.635, AUC: 0.660). The cox regression model included tumor size showed better performance than the model excluded tumor size in every aspect. CONCLUSION: T stage had a negative impact on the predicting value of tumor size. Tumor size showed significant prognostic value in T1 gastric cancer, which may be effective in clinical practice.

Identification of a Gene Signature to Aid Treatment Decisions by Integrated Analysis of Mutated Genes Between Primary and Metastatic Prostate Cancer.

Prostate cancer is one of the most common malignancies in males. Despite the recent development of advanced diagnostic platforms and treatment, patients with metastatic disease still have a poor five-year survival rate. Cancer metastasis is correlated with the characteristics of the tumor microenvironment and is significantly associated with patient prognosis. In this study, we obtained mutated genes with significant differences between primary and metastatic prostate cancer from the COSMIC database. Unsupervised consensus clustering was used based on the 1,051 genes obtained, and two PCa clusters were identified, which exhibited different prognostic outcomes and immune characteristics. Next, we generated a scoring system and evaluated the prognostic value of riskscore and its potential to aid treatment decisions in clinical practice. The riskscore could be applied to predict patients' response to immunotherapy and sensitivity to Docetaxel. In conclusion, this study performed an integrated analysis of mutated genes between primary and metastatic prostate cancer and provides a novel assessment scheme to precisely select treatment strategies.

A predictive signature based on enhancer RNA associates with immune infiltration and aids treatment decision in clear cell renal cell carcinoma.

Clear cell renal cell carcinoma (ccRCC) is a prevalent urinary malignancy. Despite the recent development of better diagnostic tools and therapy, the five-year survival rate for individuals with advanced and metastatic ccRCC remains dismal. Unfortunately, ccRCC is less susceptible to radiation and chemotherapy. Consequently, targeted therapy and immunotherapy play a crucial role in the treatment of ccRCC. Enhancer RNAs (eRNAs) are noncoding RNAs transcribed by enhancers. Extensive research has shown that eRNAs are implicated in a variety of cancer signaling pathways. However, the biological functions of eRNAs have not been systematically investigated in ccRCC. In this study, we conducted a comprehensive investigation of the role of eRNAs in the onset and management of ccRCC. Patient prognosis-influencing eRNAs and target genes were chosen to construct a predictive signature. On the basis of the median riskscore, ccRCC patients were split into high- and low-risk subgroups. The prediction efficiency was assessed in several cohorts, and multi-omics analysis was carried out to investigate the differences and underlying mechanisms between the high- and low-risk groups. In addition, we investigated its potential to facilitate clinical treatment choices. The riskscore might be used to forecast a patient's response to immunotherapy and targeted therapy, giving a revolutionary method for selecting treatment regimens with pinpoint accuracy.

Molecular Imaging of Ulex Europaeus Agglutinin in Colorectal Cancer Using Confocal Laser Endomicroscopy (With Video).

BACKGROUND AND STUDY AIMS: Previous studies have identified that colorectal cancer has different fucosylation levels compared to the normal colon. Ulex europaeus agglutinin-I (UEA-I), which specifically combines with α1-2 fucose glycan, is usually used to detect fucosylation levels. Therefore, we used confocal laser endomicroscopy (CLE) to investigate fluorescently labeled UEA-Fluorescein isothiocyanate (FITC) for detecting colonic cancer. PATIENTS AND METHODS: We stained frozen mouse colon tissue sections of normal, adenoma, and adenocarcinoma species with UEA-FITC to detect fucosylation levels in different groups. White light endoscopy and endocytoscopy were first used to detect the lesions. The UEA-FITC was then stained in the mice and human colon tissues in vitro. The CLE was used to detect the UEA-FITC levels of the corresponding lesions, and videos were recorded for quantitation analysis. The diagnostic accuracy of UEA-FITC using CLE was evaluated in terms of sensitivity and specificity. RESULTS: The UEA expression level in colorectal cancer was lower than that in normal intestinal epithelium. The fluorescence intensity ratio of UEA-FITC in colorectal cancer was significantly lower than that in normal tissue detected by CLE in both mice and humans. The combination of UEA-FITC and CLE presented a good diagnostic accuracy with a sensitivity of 95.6% and a specificity of 97.7% for detecting colorectal cancer. The positive and negative predictive values were 91.6% and 95.6%, respectively. Overall, 95.6% of the sites were correctly classified by CLE. CONCLUSIONS: We developed a new imaging strategy to improve the diagnostic efficacy of CLE by using UEA-FITC.