IntroductionLysine crotonylation (Kcr) is an important post-translational modification (PTM) of proteins, playing a key role in regulating various biological processes in pathogenic fungi. However, the experimental identification of Kcr sites remains challenging due to the high cost and time-consuming nature of mass spectrometry-based techniques.MethodsTo address this limitation, we developed Fungi-Kcr, a deep learning-based model designed to predict Kcr modification sites in fungal proteins. The model integrates convolutional neural networks (CNN), gated recurrent units (GRU), and word embedding to effectively capture both local and long-range sequence dependencies.ResultsComprehensive evaluations, including ten-fold cross-validation and independent testing, demonstrate that Fungi-Kcr achieves superior predictive performance compared to conventional machine learning models. Moreover, our results indicate that a general predictive model performs better than species-specific models. DiscussionThe proposed model provides a valuable computational tool for the large-scale identification of Kcr sites, contributing to a deeper understanding of fungal pathogenesis and potential therapeutic targets. The source code and dataset for Fungi-Kcr are available at https://github.com/zayra77/Fungi-Kcr.

Endoscopic ultrasonography (EUS) is commonly utilized in preoperative staging of esophageal cancer, however with additional pain and cost as well as adverse events. Meanwhile, the accuracy of EUS is highly depend on the training and practice of operators and not universally available. Different operators would lead to high inter-observer variability. Therefore, it is desirable to explore an alternative way to determine preoperative T stage in esophageal cancer. Whether conventional endoscopy possess the ability to predict EUS T stage has never been investigated yet. In current study, with the assistance of Artificial intelligence, we have developed a deep learning model to predict EUS T stage based on 9,714 images collected from 3,333 patients. ResNet-152 pre-trained on the ImageNet dataset was trained with the appropriate transfer learning and fine-tuning strategies on the conventional endoscopic images and their corresponding labels (e.g., T1, T2, T3, T4 and Normal). Meanwhile, augmentation strategies including rotation and flipping were performed to increase the number of images to improve the prediction accuracy. Finally, 4,382 T1, 243 T2, 3,985 T3, 1,102 T4, 14,302 controls images were obtained and split into training dataset, validation dataset and independent testing dataset with the ratio of 4:1:1. Our model could achieve a satisfied performance with an area under the receiver-operating curve (AUC) were 0.9767, 0.9637, 0.9597 and 0.9442 for T1, T2, T3 and T4, respectively in independent testing dataset. In conclusion, conventional gastroscopy combined with artificial intelligence have the great potential to predict EUS T stage.

BACKGROUND:Upstream stimulatory factors (USFs) are members of the basic helix-loop-helix leucine zipper transcription factor family, including USF1, USF2, and USF3. The first two members have been well studied compared to the third member, USF3, which has received scarce attention in cancer research to date. Despite a recently reported association of its alteration with thyroid carcinoma, its expression has not been previously analyzed. METHODS:We comprehensively analyzed differential levels of USFs expression, genomic alteration, DNA methylation, and their prognostic value across different cancer types and the possible correlation with tumor-infiltrating immune cells and drug response by using different bioinformatics tools. RESULTS:Our findings established that USFs play an important role in cancers related to the urinary system and justify the necessity for further investigation. We implemented and offer a useful ShinyApp to facilitate researchers' efforts to inquire about any other gene of interest and to perform the analysis of drug response in a user-friendly fashion at http://zzdlab.com:3838/Drugdiscovery/.

Objective: The main reasons for the poor prognoses of pancreatic adenocarcinoma（PA） patients are rapid early-stage progression, advanced stage metastasis, and chemotherapy resistance. Identification of novel diagnostic and prognostic biomarkers of PA is therefore urgently needed.Methods: Three mRNA microarray datasets were obtained from the Gene Expression Omnibus database to select differentially expressed genes（DEGs）. Gene Ontology（GO） and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses for hub genes were performed using DAVID. Correlations between expression levels of hub genes and cancer-infiltrating immune cells were investigated by TIMER. Cox proportional hazard regression analyses were also performed. Serum hub genes were screened using the HPA platform and verified for diagnostic value using ELISAs.Results: We identified 59 hub genes among 752 DEGs. GO analysis indicated that these 59 hub genes were mainly involved in the defense response to viruses and the type I interferon signaling pathway. We also discovered that RSAD2 and SMC4 were associated with immune cell infiltration in the PA microenvironment. Additionally, DLGAP5 mR NA might be used as an independent risk factor for the prognoses of PA patients. Furthermore, the protein encoded by ISG15, which exists in peripheral blood, was validated as a potential diagnostic biomarker that distinguished PA patients from healthy controls（area under the curve: 0.902, 95% confidence interval: 0.819–0.961）.Conclusions: Our study suggested that RSAD2 and SMC4 were associated with immune cell infiltration in the PA microenvironment, while DLGAP5 mRNA expression might be an independent risk factor for the survival prognoses of PA patients. Moreover, ELISAs indicated that serum ISG15 could be a potential novel diagnostic biomarker for PA.

USF3, a transcription factor, is a poorly studied gene in cancer research. Although the association of its alteration with thyroid carcinoma has been reported recently, its expression has never been analyzed before. Here we have comprehensively analyzed USF3 differential expression as well as its prognostic value in different types of cancer. Our results demonstrated that USF3 expression is significantly decreased in all the tumors except for Kidney Chromophobe, a rare type of kidney cancer.

目的用生物信息学一致性相关系数（CCC）法预测非小细胞肺癌一线化疗方案NP方案（长春瑞滨+顺铂）药物敏感性基因。方法使用5种统计学方法:Pearson相关分析、Spearman相关分析、Welch’s t-test、ANCOVA和rank-based ANCOVA,从NCI 60数据库筛选药物敏感性基因,通过CCC筛选非小细胞肺癌患者化疗药物敏感性基因。利用在线数据库DAVID进行KEGG通路富集分析。结果筛选出可能应用于预测非小细胞肺癌化疗药物敏感性基因:顺铂的药物敏感性基因主要富集在蛋白聚糖、细菌侵袭上皮细胞通路中;长春瑞滨的药物敏感性基因主要与癌症信号通路、蛋白聚糖有关。结论生物信息学CCC法可应用于筛选出预测非小细胞肺癌化疗药物敏感性的基因,可能为将来构建NP方案精准化疗模型提供研究基础。

Lysine crotonylation (Kcr) is a newly discovered type of protein post-translational modification and has been reported to be involved in various pathophysiological processes. High-resolution mass spectrometry is the primary approach for identification of Kcr sites. However, experimental approaches for identifying Kcr sites are often time-consuming and expensive when compared with computational approaches. To date, several predictors for Kcr site prediction have been developed, most of which are capable of predicting crotonylation sites on either histones alone or mixed histone and nonhistone proteins together. These methods exhibit high diversity in their algorithms, encoding schemes, feature selection techniques and performance assessment strategies. However, none of them were designed for predicting Kcr sites on nonhistone proteins. Therefore, it is desirable to develop an effective predictor for identifying Kcr sites from the large amount of nonhistone sequence data. For this purpose, we first provide a comprehensive review on six methods for predicting crotonylation sites. Second, we develop a novel deep learning-based computational framework termed as CNNrgb for Kcr site prediction on nonhistone proteins by integrating different types of features. We benchmark its performance against multiple commonly used machine learning classifiers (including random forest, logitboost, naïve Bayes and logistic regression) by performing both 10-fold cross-validation and independent test. The results show that the proposed CNNrgb framework achieves the best performance with high computational efficiency on large datasets. Moreover, to facilitate users' efforts to investigate Kcr sites on human nonhistone proteins, we implement an online server called nhKcr and compare it with other existing tools to illustrate the utility and robustness of our method. The nhKcr web server and all the datasets utilized in this study are freely accessible at http://nhKcr.erc.monash.edu/.

新一代测序技术的迅猛发展为个性化医学提供前所未有的潜力,生物医学和癌症基因组学在大数据时代也随之发生巨大的变革。随着生物科学技术和计算机处理数据能力的不断提高,高通量测序在多组学生物数据的获取和挖掘中发挥了关键作用。如今,它已经成为了生物学家进行科学研究不可或缺的工具,并在抗癌药物精准治疗领域做出了重要贡献。该文将针对不同测序方法在抗癌药物精准治疗中的作用进行阐述,探讨新一代测序技术在其中的指导意义。

目的:用生物信息学共表达外推（以下简称生信COXEN）法初步构建食管癌精准化疗药物（5-氟尿嘧啶、紫杉醇和顺铂）生物标志物模型。方法:本研究使用5种统计学方法（Pearson相关分析、Spearman相关分析、Welch’s t-test、ANCOVA和rank-based ANCOVA）从NCI-60数据库筛选药物敏感性基因,通过COXEN筛选食管鳞状细胞癌患者化疗药物敏感性的生物标志物。利用在线数据库DAVID进行KEGG通路富集分析。结果:筛选出可应用于预测人类食管鳞状细胞癌化疗药物敏感性的生物标志物:5-氟尿嘧啶的生物标志物主要富集在细胞周期G1期和S期（RNA和核糖体的合成、DNA复制）中;顺铂的生物标志物主要与蛋白聚糖、细菌侵袭上皮细胞和白细胞跨内皮迁移有关;紫杉醇的生物标志物主要与局部黏附、小细胞肺癌和HTLV-I感染通路有关。结论:生信COXEN法筛选出可应用于预测人类食管鳞状细胞癌单个化疗药物敏感性的基因,能为将来构建更有转化应用价值的多组合化疗方案预测模型提供研究基础。