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BackgroundAnoikis is a form of programmed cell death essential for preventing cancer metastasis. In some solid cancer, anoikis resistance can facilitate tumor progression. However, this phenomenon is underexplored in clear-cell renal cell carcinoma (ccRCC).MethodsUsing SVM machine learning, we identified core anoikis-related genes (ARGs) from ccRCC patient transcriptomic data. A LASSO Cox regression model stratified patients into risk groups, informing a prognostic model. GSVA and ssGSEA assessed immune infiltration, and single-cell analysis examined ARG expression across immune cells. Quantitative PCR and immunohistochemistry validated ARG expression differences between immune therapy responders and non-responders in ccRCC.ResultsARGs such as CCND1, CDKN3, PLK1, and BID were key in predicting ccRCC outcomes, linking higher risk with increased Treg infiltration and reduced M1 macrophage presence, indicating an immunosuppressive environment facilitated by anoikis resistance. Single-cell insights showed ARG enrichment in Tregs and dendritic cells, affecting immune checkpoints. Immunohistochemical analysis reveals that ARGs protein expression is markedly elevated in ccRCC tissues responsive to immunotherapy.ConclusionThis study establishes a novel anoikis resistance gene signature that predicts survival and immunotherapy response in ccRCC, suggesting that manipulating the immune environment through these ARGs could improve therapeutic strategies and prognostication in ccRCC.

BackgroundA model to predict preoperative outcomes after percutaneous nephrolithotomy (PCNL) with renal staghorn stones is developed to be an essential preoperative consultation tool.ObjectiveIn this study, we constructed a predictive model for one-time stone clearance after PCNL for renal staghorn calculi, so as to predict the stone clearance rate of patients in one operation, and provide a reference direction for patients and clinicians.MethodsAccording to the 175 patients with renal staghorn stones undergoing PCNL at two centers, preoperative/postoperative variables were collected. After identifying characteristic variables using PCA analysis to avoid overfitting. A predictive model was developed for preoperative outcomes after PCNL in patients with renal staghorn stones. In addition, we repeatedly cross-validated their model’s predictive efficacy and clinical application using data from two different centers.ResultsThe study included 175 patients from two centers treated with PCNL. We used a training set and an external validation set. Radionics characteristics, deep migration learning, clinical characteristics, and DTL+Rad-signature were successfully constructed using machine learning based on patients’ pre/postoperative imaging characteristics and clinical variables using minimum absolute shrinkage and selection operator algorithms. In this study, DTL-Rad signal was found to be the outstanding predictor of stone clearance in patients with renal deer antler-like stones treated by PCNL. The DTL+Rad signature showed good discriminatory ability in both the training and external validation groups with AUC values of 0.871 (95% CI, 0.800-0.942) and 0.744 (95% CI, 0.617-0.871). The decision curve demonstrated the radiographic model’s clinical utility and illustrated specificities of 0.935 and 0.806, respectively.ConclusionWe found a prediction model combining imaging characteristics, neural networks, and clinical characteristics can be used as an effective preoperative prediction method.

Since 2017, the new H7N9 highly pathogenic avian influenza viruses (HPAIVs) have been responsible for more than 200,000 cases of chicken infection and more than 120,000 chicken deaths in China. Our previous study found that the Q26 was chicken-origin H7N9 HPAIV. In this study, we analyzed the genetic characterization of Q24, Q65, Q66, Q85, and Q102 H7N9 avian influenza viruses isolated from Guangdong, China in 2017. Our results showed that these viruses were highly pathogenic and belonged to two different genotypes, which suggested they occurred genetic reassortant. To investigate the pathogenicity, transmission, and host immune responses of H7N9 virus in chickens, we selected Q24 and Q26 viruses to inoculate chickens. The Q24 and Q26 viruses killed all inoculated chickens within 3 days and replicated effectively in all tested tissues. They were efficiently transmitted to contact chickens and killed them within 4 days through direct contact. Furthermore, we found that the expressions of several immune-related genes (e.g., TLR3, TLR7, MDA5, MAVS, IFN-β, IL-6, IL-8, OAS, Mx1, MHC I, and MHC II) were upregulated obviously in the lungs and spleen of chickens inoculated with the two H7N9 viruses at 24 h post-inoculation (HPI). Among these, IL-6 and IFN-β in lungs were the most upregulated (by 341.02–381.48-fold and 472.50–500.56-fold, respectively). These results suggest that the new H7N9 viruses isolated in 2017, can replicate and transmit effectively and trigger strong immune responses in chickens, which helps us understand the genetic and pathogenic variations of H7N9 HPAIVs in China.

BackgroundBladder cancer (BCa) is a remarkably malignant and heterogeneous neoplastic disease, and its prognosis prediction is still challenging. Even with the mounting researches on the mechanisms of tumor immunotherapy, the prognostic value of T-cell proliferation regulators in bladder cancer remains elusive.MethodsHerein, we collected mRNA expression profiles and relevant clinical information of bladder cancer sufferers from a publicly available data base. Then, the LASSO Cox regression model was utilized to establish a multi-gene signature for the TCGA cohort to predict the prognosis and staging of bladder cancer. Eventually, the predictive power of the model was validated by randomized grouping.ResultsThe outcomes revealed that most genes related to T-cell proliferation in the TCGA cohort exhibited different expressions between BCa cells and neighboring healthy tissues. Univariable Cox regressive analyses showed that four DEGs were related to OS in bladder cancer patients (p