Junqi Wang

Background: Clear cell renal cell carcinoma (ccRCC) exhibits marked heterogeneity and variable benefit from immune checkpoint inhibitors (ICIs). Post-translational modifications (PTMs) regulate immune signaling and tumor behavior, yet PTM-informed biomarkers for ccRCC remain underexplored. Methods: We intersected immune-related genes, PTM-related genes, and differentially expressed genes in TCGA-KIRC to derive candidates and built a prognostic model across TCGA and E-MTAB-1980 using multiple algorithms, selecting a random survival forest-based post-translational modification-related signature (PTMRS) with the best performance. Prognostic value and independence were evaluated by time-dependent ROC, Kaplan-Meier, and multivariate Cox analyses. Tumor immune context was profiled by immune infiltration scores, immune checkpoints, and TIDE to infer dysfunction/exclusion and ICI response. Genomic features (driver mutations, pathway alterations, tumor mutational burden) and an external ICI cohort (IMvigor210) were analyzed. Single-cell RNA-seq with CellChat, trajectory, and UCell assessed cell-cell communication and PTMRS distribution across immune subsets. Experimental validation included IHC and qPCR of IRF9, loss- and gain-of-function assays (Transwell, wound healing, CCK-8, colony, EdU), and molecular dynamics to explore IRF9 compound binding. Results: We established a five-gene PTMRS that robustly stratified prognosis in training, testing, and external cohorts and remained an independent predictor. High-PTMRS tumors displayed immunosuppressive features, including greater infiltration of Tregs/MDSCs/macrophages, higher expression of immunosuppressive checkpoints, and elevated TIDE scores with lower predicted ICI responsiveness. High PTMRS associated with alterations in oncogenic pathways and higher TMB. In IMvigor210, high PTMRS linked to inferior outcomes and non-response. Single-cell analyses showed PTMRS enrichment in Tregs and exhausted CD8 ⁺ T cells and dense immune communication networks. Among PTMRS genes, IRF9 was upregulated in ccRCC tissues and cell lines. Knockdown of IRF9 curtailed invasion, migration, and proliferation, whereas overexpression enhanced these phenotypes. Conclusion: PTMRS is a PTM-informed immune signature that reflects an immunosuppressive tumor microenvironment, improves prognostic stratification, and indicates ICI benefit in ccRCC. Experimental data pinpoint IRF9 as a functional driver and potential therapeutic target within this PTM-immunity axis.

Objective: Inguinal lymph node metastasis (ILNM) is significantly associated with poor prognosis in patients with squamous cell carcinoma of the penis (SCCP). Patient prognosis could be improved if the probability of ILNM incidence could be accurately predicted at an early stage. We developed a predictive model based on machine learning combined with big data to achieve this.Methods: Data of patients diagnosed with SCCP were obtained from the Surveillance, Epidemiology, and End Results Program Research Data. By combing variables that represented the patients' clinical characteristics, we applied five machine learning algorithms to create predictive models based on logistic regression, eXtreme Gradient Boosting, Random Forest, Support Vector Machine, and k-Nearest Neighbor. Model performance was evaluated by ten-fold cross-validation receiver operating characteristic curves, which were used to calculate the area under the curve of the five models for predictive accuracy. Decision curve analysis was conducted to estimate the clinical utility of the models. An external validation cohort of 74 SCCP patients was selected from the Affiliated Hospital of Xuzhou Medical University (February 2008 to March 2021).Results: A total of 1,056 patients with SCCP from the SEER database were enrolled as the training cohort, of which 164 (15.5%) developed early-stage ILNM. In the external validation cohort, 16.2% of patients developed early-stage ILNM. Multivariate logistic regression showed that tumor grade, inguinal lymph node dissection, radiotherapy, and chemotherapy were independent predictors of early-stage ILNM risk. The model based on the eXtreme Gradient Boosting algorithm showed stable and efficient prediction performance in both the training and external validation groups.Conclusion: The ML model based on the XGB algorithm has high predictive effectiveness and may be used to predict early-stage ILNM risk in SCCP patients. Therefore, it may show promise in clinical decision-making.

Background Bone metastasis has been suggested to be a significant impactor on the prognosis of newly diagnosed de novo metastatic hormone-sensitive prostate cancer (mHSPC), and some risk stratification models have been proposed on the basis of this hypothesis. However, the effectiveness of these risk stratification criteria has not been fully evaluated in China. This study aimed to evaluate the effectiveness of the risk stratification models in China. Methods A total of 140 patients who were newly diagnosed with metastatic prostate cancer followed by primary androgen deprivation-based therapy from January 2008 to June 2021 at our institution were enrolled in this study. The patients were divided into different groups on the basis of high- and low-volume disease (H/LVD) criteria, high-and low-risk disease (H/LRD) criteria, extremity bone metastasis criteria (EBM), and extent of disease (EOD) criteria. The area under the receiver operating characteristic (ROC) curve (AUC) and decision curve analysis (DCA) were used to compare the validity and net benefit of these models. Using the Cox proportional hazards model, we performed univariable and multivariable analyses of the factors influencing overall survival (OS) and the time of progression to metastatic castration-resistant prostate cancer (CRPC). Results The median patient age was 72 years. Most patients had a Gleason score ≥8 (102 cases, 72.9%) and clinical T stage >2 (75 cases, 53.6%). The median follow-up time was 25 months (range, 2–95 months). Ninety-two patients progressed to CRPC and fifty-seven patients died during the follow-up. The AUC of OS and CRPC showed that the EOD model had higher validity than the other risk stratification models. DCA shows that the net benefit of the EOD model on OS was better than that of the other risk stratification models. As for CRPC, the net benefit of the EOD model was second only to that of the H/LRD model when the threshold was 0.5, the EOD model outperformed the other models. The effectiveness of EOD as an independent prognostic variable was verified through univariable and multivariable analyses. Conclusion The EOD model yields reasonable risk stratification for use in Chinese mHSPC patients, providing further evidence supporting its role in clinical decision-making.

Purpose: The objective of this study aimed to explore whether the original IVC regimen should be continued after the second TURBT or whether the IVC induction phase should be restarted from the beginning. Methods: A retrospective analysis was performed on 137 patients who underwent a second TURBT at the Affiliated Hospital of Xuzhou Medical University between April 2014 and June 2022. Based on the pathological findings, patients were divided into two groups: group A patients, who did not have a residual tumor on pathological examination after the second TURBT; and group B patients, who had residual tumor. Recurrence was determined using cystoscopy and imaging every three months. The endpoint was recurrence-free survival. Result: In the entire cohort, there was a statistically significant difference in the RFS between patients in the two IVC regimens (p = 0.029). The RFS of patients in group B1 was significantly lower than that of patients in group B2 (p = 0.009). There was no significant difference in RFS between the subgroups A1 and A2 (p = 0.560). Multivariate Cox regression analysis confirmed that the IVC regimen after a second TURBT (p = 0.012) and T stage after a second TURBT (p = 0.005) were both independent predictors for patient RFS. Conclusion: If the pathological findings of the second TURBT specimen is benign, patients can continue their previous treatment regimen without restarting an IVC induction phase. Unnecessary IVC can be avoided in these patients. In contrast, for patients with residual tumors in the second TURBT specimen, the need to restart the IVC induction phase should be emphasized to improve patient prognosis.

Renal cyst development and expansion in autosomal dominant polycystic kidney disease (ADPKD) involves over‐proliferation of cyst‐lining epithelial cells and excessive cystic fluid secretion. While metformin effectively inhibits renal cyst growth in mouse models of ADPKD it exhibits low potency, and thus an adenosine monophosphate‐activated protein kinase (AMPK) activator with higher potency is required. Herein, we adopted a drug repurposing strategy to explore the potential of PF‐06409577, an AMPK activator for diabetic nephropathy, in cellular, ex vivo and in vivo models of ADPKD. Our results demonstrated that PF‐06409577 effectively down‐regulated mammalian target of rapamycin pathway‐mediated proliferation of cyst‐lining epithelial cells and reduced cystic fibrosis transmembrane conductance regulator‐regulated cystic fluid secretion. Overall, our data suggest that PF‐06409577 holds therapeutic potential for ADPKD treatment.

Objective: Distant metastasis other than non-regional lymph nodes and lung (i.e., M1b stage) significantly contributes to the poor survival prognosis of patients with germ cell testicular cancer (GCTC). The aim of this study was to develop a machine learning (ML) algorithm model to predict the risk of patients with GCTC developing the M1b stage, which can be used to assist in early intervention of patients.Methods: The clinical and pathological data of patients with GCTC were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. Combing the patient's characteristic variables, we applied six machine learning (ML) algorithms to develop the predictive models, including logistic regression(LR), eXtreme Gradient Boosting (XGBoost), light Gradient Boosting Machine (lightGBM), random forest (RF), multilayer perceptron (MLP), and k-nearest neighbor (kNN). Model performances were evaluated by 10-fold cross-receiver operating characteristic (ROC) curves, which calculated the area under the curve (AUC) of models for predictive accuracy. A total of 54 patients from our own center (October 2006 to June 2021) were collected as the external validation cohort.Results: A total of 4,323 patients eligible for inclusion were screened for enrollment from the SEER database, of which 178 (4.12%) developing M1b stage. Multivariate logistic regression showed that lymph node dissection (LND), T stage, N stage, lung metastases, and distant lymph node metastases were the independent predictors of developing M1b stage risk. The models based on both the XGBoost and RF algorithms showed stable and efficient prediction performance in the training and external validation groups.Conclusion: S-stage is not an independent factor for predicting the risk of developing the M1b stage of patients with GCTC. The ML models based on both XGBoost and RF algorithms have high predictive effectiveness and may be used to predict the risk of developing the M1b stage of patients with GCTC, which is of promising value in clinical decision-making. Models still need to be tested with a larger sample of real-world data.

Objective: We aimed to establish nomograms to predict the overall survival (OS) and cancer-specific survival (CSS) of patients with primary urachal carcinoma (UrC). Methods: Information on patients diagnosed with UrC from 1975 to 2018 was collected from the Surveillance, Epidemiology, and End Results (SEER) Program Research Data. The independent prognostic factors were determined using univariate and multivariate Cox regression. Backward variable elimination according to the Akaike information criterion (AIC) identified the most accurate and parsimonious model. Nomograms were built based on regression coefficients. The C-index, calibration plot, Brier score, integrated discrimination improvement (IDI), area under the receiver operating curve (AUC), and decision curve analysis (DCA) curve were used to evaluate the efficiency of models. Results: In total, 236 patients obtained from SEER were divided randomly into training and validation cohorts in a 70:30 ratio (166 and 70 patients, respectively). In the training cohort, multivariate Cox regression analysis indicated that pTNM/Sheldon/Mayo staging systems (included respectively), age, and tumor grade were independent prognostic factors for OS. A similar result was also found in CSS. While other variables, such as radiotherapy and chemotherapy, did not identify significant correlations. In predicting OS and CSS at 3- and 5- years, the nomograms based on pTNM showed superior discriminative and calibration capabilities in comparison to multiple statistical tools. The C-index values for the training cohort were 0.770 for OS and 0.806 for CSS, and similar outcomes were shown in further internal validation (C-index 0.693 for OS and 0.719 for CSS). We also discovered that the link between age at diagnosis and survival follows a U-shaped curve, indicating that the risk of poor prognosis decreases first and then increases with age. Conclusion: The efficacy of pTNM in predicting the prognosis of patients with UrC was greater than that of the Sheldon and Mayo staging system. Therefore, we recommend pTNM as the preferred system to stage UrC. The novel constructed nomograms based on pTNM, age, and tumor grade showed high accuracy and specificity and could be applied clinically to predict the prognosis of patients with UrC.

Ovotesticular disorder of sex development (DSD), previously known as true hermaphroditism, is a disorder in which individuals have both testicular and ovarian tissues. Instances of tumors arising in the gonads of individuals with 46,XX ovotesticular DSD are uncommon. We report a case of a 36-year-old phenotypical male with a chief complaint of an abdominal mass for 3 months. He reported normal erections and regular menses. Computerized tomography showed a large tumor measuring 15 × 10 cm in size, a uterus, and a cystic ovary. 46, XX ovotesticular DSD with seminoma. The patient was treated with neochemotherapy (etoposide and cisplatin), surgery, chemotherapy, and testosterone replacement. At the 13-month follow-up, the patient reported satisfactory erections, and no evidence of disease was found. Cases of 46,XX ovotesticular DSD with seminoma are uncommon. Our case reveals the importance of surgery combined with neochemotherapy, chemotherapy, and testosterone replacement in these patients to improve the prognosis.