Regular gastroscopy and colonoscopy during the evaluation of urachal cancer: do we really need them?

PURPOSE: Urachal cancer is similar to gastrointestinal adenocarcinoma in histology, and gastroscopy/colonoscopy is often administered during perioperative evaluation. However, gastroscopy and colonoscopy have corresponding disadvantages. This study discusses whether gastroscopy/colonoscopy is truly necessary for patients with urachal cancer. PATIENTS AND METHODS: A total of 166 bladder adenocarcinoma cases diagnosed at Sun Yat-sen University Cancer Center were retrospectively reviewed and divided into two groups (urachal cancer and nonurachal cancer), and perioperative evaluations were retrieved. RESULTS: There were 78 patients with urachal cancer, the median age was 48 years, and 59 were male. Perioperative gastroscopy/colonoscopy revealed 5 intestinal polyps and 1 adenoma during these evaluations, and no primary gastrointestinal cancer was found. Meanwhile, preoperative imaging evaluation did not detect significant gastrointestinal lesions. For 88 patients with nonurachal cancer, including primary bladder adenocarcinoma and metastatic tumors from gastrointestinal cancer, the median age was 56 years, and 64 were male. Preoperative imaging evaluation demonstrated 36 cases of gastrointestinal lesions, and 32 were confirmed by gastroscopy/colonoscopy; the other 4 were negative. Another 4 cases of colon cancer were detected by regular colonoscopy for suspected primary bladder adenocarcinoma. In all, 35 cases of colon cancer and 1 case of gastric cancer were identified by endoscopic examination. The diagnostic consistency of imaging and gastrointestinal endoscopy was favorable (P < 0.001), and the negative predictive value and diagnostic efficiency of imaging were 96.9% and 94.6%, respectively. CONCLUSIONS: The vast majority of gastrointestinal cancer cases can be identified by assessment of the patient's clinical symptoms, meticulous physical examination, and imaging evaluation. We recommend that gastroscopy/colonoscopy only be applied to patients with urachal cancer when the above examinations are positive.

Plasma cell subtypes analyzed using artificial intelligence algorithm for predicting biochemical recurrence, immune escape potential, and immunotherapy response of prostate cancer.

Background: Plasma cells as an important component of immune microenvironment plays a crucial role in immune escape and are closely related to immune therapy response. However, its role for prostate cancer is rarely understood. In this study, we intend to investigate the value of a new plasma cell molecular subtype for predicting the biochemical recurrence, immune escape and immunotherapy response in prostate cancer. Methods: Gene expression and clinicopathological data were collected from 481 prostate cancer patients in the Cancer Genome Atlas. Then, the immune characteristics of the patients were analyzed based on plasma cell infiltration fractions. The unsupervised clustering based machine learning algorithm was used to identify the molecular subtypes of the plasma cell. And the characteristic genes of plasma cell subtypes were screened out by three types of machine learning models to establish an artificial neural network for predicting plasma cell subtypes. Finally, the prediction artificial neural network of plasma cell infiltration subtypes was validated in an independent cohort of 449 prostate cancer patients from the Gene Expression Omnibus. Results: The plasma cell fraction in prostate cancer was significantly decreased in tumors with high T stage, high Gleason score and lymph node metastasis. In addition, low plasma cell fraction patients had a higher risk of biochemical recurrence. Based on the differential genes of plasma cells, plasma cell infiltration status of PCa patients were divided into two independent molecular subtypes(subtype 1 and subtype 2). Subtype 1 tends to be immunosuppressive plasma cells infiltrating to the PCa region, with a higher likelihood of biochemical recurrence, more active immune microenvironment, and stronger immune escape potential, leading to a poor response to immunotherapy. Subsequently, 10 characteristic genes of plasma cell subtype were screened out by three machine learning algorithms. Finally, an artificial neural network was constructed by those 10 genes to predict the plasma cell subtype of new patients. This artificial neural network was validated in an independent validation set, and the similar results were gained. Conclusions: Plasma cell infiltration subtypes could provide a potent prognostic predictor for prostate cancer and be an option for potential responders to prostate cancer immunotherapy.