Background: Increased expression of transcriptional coactivator p300 has been observed in a variety of human cancers. However, the expression status of p300 protein/mRNA in nasopharyngeal carcinoma (NPC) tissues and its clinicopathologic/prognostic implication are poorly understood.Methods: In our study, mRNA and protein expression levels of p300 was explored by reverse transcription-polymerase chain reaction (RT-PCR), Western blotting (WB) and immunohistochemistry (IHC) in nasopharyngeal mucosal and NPC tissues. The data were analyzed by receiver operating characteristic (ROC) curve analysis, spearman’s rank correlation, Kaplan-Meier plots and Cox proportional hazards regression model.Results: Up-regulated expression ofp300mRNA/p300 protein was detected in NPC tissues by RT-PCR and WB, when compared to nasopharyngeal mucosal tissues. Based on ROC curve analysis, the cutoff score for p300 high expression was defined when more than 35% of the tumor cells were positively stained. High expression of p300 was observed in 127/209 (60.7%) of NPCs. In NPCs, high expression of p300 was positively associated with later T classification, later N classification, distant metastasis and later clinical stage (P < 0.05). In univariate survival analysis, overexpression of p300 was found to be an indicator of progression-free (P = 0.002) and overall survival (P = 0.001) in NPCs. More importantly, p300 expression was evaluated as an independent prognostic factor for NPC in multivariate analysis (P = 0.036).Conclusions: Our findings support that high expression of p300 protein might be important in conferring a more aggressive behavior, and is an independent molecular marker for shortened survival time of patients with NPC.

Objective: This study was to explore the most appropriate radiomics modeling method to predict the progression-free survival of EGFR-TKI treatment in advanced non-small cell lung cancer with EGFR mutations. Different machine learning methods may vary considerably and the selection of a proper model is essential for accurate treatment outcome prediction. Our study were established 176 discrimination models constructed with 22 feature selection methods and 8 classifiers. The predictive performance of each model were evaluated using the AUC, ACC, sensitivity and specificity, where the optimal model was identified. Results: There were totally 107 radiomics features and 7 clinical features obtained from each patient. After feature selection, the top-ten most relevant features were fed to train 176 models. Significant performance variations were observed in the established models, with the best performance achieved by the logistic regression model using gini-index feature selection (AUC = 0.797, ACC = 0.722, sensitivity = 0.758, specificity = 0.693). The median R-score was 0.518 (IQR, 0.023–0.987), and the patients were divided into high-risk and low-risk groups based on this cut-off value. The KM survival curves of the two groups demonstrated evident stratification results (p = 0.000).

Background Gastric neuroendocrine carcinoma (G-NEC) presents with clinical and pathological features that closely resemble those of gastric adenocarcinoma (GC), often complicating differential diagnosis. However, G-NEC is markedly more aggressive and associated with a significantly poorer prognosis, necessitating accurate and timely identification to guide appropriate therapeutic interventions. Methods In response to this clinical need, we developed G-NECNet, a deep convolutional neural network tailored to detect G-NEC from histopathological whole-slide images. Results The model demonstrates excellent diagnostic performance, yielding an average area under the receiver operating curve (AUROC) of 0.993 in the internal validation cohort, 0.985 on an external single-institutional dataset, and 1.000 on an external multi-institutional consultation dataset. These consistently high AUROC values highlight the robustness, accuracy, and generalizability of G-NECNet across diverse clinical settings. Conclusions The integration of G-NECNet into routine diagnostic workflows may not only improve the precision of G-NEC classification but also reduce misdiagnosis-related healthcare costs, offering a practical and scalable solution for clinical application.

Objective:This study was to explore the most appropriate radiomics modeling method to predict the progression-free survival of EGFR-TKI treatment in advanced non-small cell lung cancer with EGFR mutations. Different machine learning methods may vary considerably and the selection of a proper model is essential for accurate treatment outcome prediction. Our study were established 176 discrimination models constructed with 22 feature selection methods and 8 classifiers. The predictive performance of each model was evaluated using the AUC, ACC,sensitivity and specificity,where the optimal model was identified. Results:There were totally 107 radiomics features and 7 clinical features obtained from each patient. After feature selection, the top-ten most relevant features were fed to train 176 models.Significant performance variations were observed in the established models, with the best performance achieved by the logistic regression model using gini-index feature selection (AUC=0.797, ACC=0.722, sensitivity=0.758, specificity=0.693).The median R-score was 0.518 (IQR,0.023-0.987), and the patients were divided into high-risk and low-risk groups based on this cut-off value. The KM survival curves of the two groups demonstrated evident stratification results (p=0.000).

Lung squamous cell carcinoma (LUSC) is a heterogeneous cancer characterized by complex cellular interactions. Plasma cells, a critical immune population, e