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Abstract The study aims to investigate the predictive capability of machine learning algorithms for omental metastasis in locally advanced gastric cancer (LAGC) and to compare the performance metrics of various machine learning predictive models. A retrospective collection of 478 pathologically confirmed LAGC patients was undertaken, encompassing both clinical features and arterial phase computed tomography images. Radiomic features were extracted using 3D Slicer software. Clinical and radiomic features were further filtered through lasso regression. Selected clinical and radiomic features were used to construct omental metastasis predictive models using support vector machine (SVM), decision tree (DT), random forest (RF), K-nearest neighbors (KNN), and logistic regression (LR). The models’ performance metrics included accuracy, area under the curve (AUC) of the receiver operating characteristic curve, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). In the training cohort, the RF predictive model surpassed LR, SVM, DT, and KNN in terms of accuracy, AUC, sensitivity, specificity, PPV, and NPV. Compared to the other four predictive models, the RF model significantly improved PPV. In the test cohort, all five machine learning predictive models exhibited lower PPVs. The DT model demonstrated the most significant variation in performance metrics relative to the other models, with a sensitivity of 0.231 and specificity of 0.990. The LR-based predictive model had the lowest PPV at 0.210, compared to the other four models. In the external validation cohort, the performance metrics of the predictive models were generally consistent with those in the test cohort. The LR-based model for predicting omental metastasis exhibited a lower PPV. Among the machine learning algorithms, the RF predictive model demonstrated higher accuracy and improved PPV relative to LR, SVM, KNN, and DT models.

Abstract Background Homologous recombination plays a vital role in the occurrence and drug resistance of gastric cancer. This study aimed to screen new gastric cancer diagnostic biomarkers in the homologous recombination pathway and then used radiomic features to construct a prediction model of biomarker expression to guide the selection of chemotherapy regimens. Methods Gastric cancer transcriptome data were downloaded from The Cancer Genome Atlas database. Machine learning methods were used to screen for diagnostic biomarkers of gastric cancer and validate them experimentally. Computed Tomography image data of gastric cancer patients and corresponding clinical data were downloaded from The Cancer Imaging Archive and our imaging centre, and then the Computed Tomography images were subjected to feature extraction, and biomarker expression prediction models were constructed to analyze the correlation between the biomarker radiomics scores and clinicopathological features. Results We screened RAD51D and XRCC2 in the homologous recombination pathway as biomarkers for gastric cancer diagnosis by machine learning, and the expression of RAD51D and XRCC2 was significantly positively correlated with pathological T stage, N stage, and TNM stage. Homologous recombination pathway blockade inhibits gastric cancer cell proliferation, promotes apoptosis, and reduces the sensitivity of gastric cancer cells to chemotherapeutic drugs. Our predictive RAD51D and XRCC2 expression models were constructed using radiomics features, and all the models had high accuracy. In the external validation cohort, the predictive models still had decent accuracy. Moreover, the radiomics scores of RAD51D and XRCC2 were also significantly positively correlated with the pathologic T, N, and TNM stages. Conclusions The gastric cancer diagnostic biomarkers RAD51D and XRCC2 that we screened can, to a certain extent, reflect the expression status of genes through radiomic characteristics, which is of certain significance in guiding the selection of chemotherapy regimens for gastric cancer patients.

BackgroundColorectal cancer (CRC) has the third-highest incidence and second-highest mortality rate of all cancers worldwide. Early diagnosis and screening of CRC have been the focus of research in this field. With the continuous development of artificial intelligence (AI) technology, AI has advantages in many aspects of CRC, such as adenoma screening, genetic testing, and prediction of tumor metastasis.ObjectiveThis study uses bibliometrics to analyze research in AI in CRC, summarize the field’s history and current status of research, and predict future research directions.MethodWe searched the SCIE database for all literature on CRC and AI. The documents span the period 2002-2022. we used bibliometrics to analyze the data of these papers, such as authors, countries, institutions, and references. Co-authorship, co-citation, and co-occurrence analysis were the main methods of analysis. Citespace, VOSviewer, and SCImago Graphica were used to visualize the results.ResultThis study selected 1,531 articles on AI in CRC. China has published a maximum number of 580 such articles in this field. The U.S. had the most quality publications, boasting an average citation per article of 46.13. Mori Y and Ding K were the two authors with the highest number of articles. Scientific Reports, Cancers, and Frontiers in Oncology are this field’s most widely published journals. Institutions from China occupy the top 9 positions among the most published institutions. We found that research on AI in this field mainly focuses on colonoscopy-assisted diagnosis, imaging histology, and pathology examination.ConclusionAI in CRC is currently in the development stage with good prospects. AI is currently widely used in colonoscopy, imageomics, and pathology. However, the scope of AI applications is still limited, and there is a lack of inter-institutional collaboration. The pervasiveness of AI technology is the main direction of future housing development in this field.

Enhancer of zeste homolog 2 (EZH2) is a significant epigenetic regulator that plays a critical role in the development and progression of cancer. However, the multiomics features and immunological effects of EZH2 in pan-cancer remain unclear. Transcriptome and clinical raw data of pan-cancer samples were acquired from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, and subsequent data analyses were conducted by using R software (version 4.1.0). Furthermore, numerous bioinformatics analysis databases also reapplied to comprehensively explore and elucidate the oncogenic mechanism and therapeutic potential of EZH2 from pan-cancer insight. Finally, quantitative reverse transcription polymerase chain reaction and immunohistochemical assays were performed to verify the differential expression of EZH2 gene in various cancers at the mRNA and protein levels. EZH2 was widely expressed in multiple normal and tumor tissues, predominantly located in the nucleoplasm. Compared with matched normal tissues, EZH2 was aberrantly expressed in most cancers either at the mRNA or protein level, which might be caused by genetic mutations, DNA methylation, and protein phosphorylation. Additionally, EZH2 expression was correlated with clinical prognosis, and its up-regulation usually indicated poor survival outcomes in cancer patients. Subsequent analysis revealed that EZH2 could promote tumor immune evasion through T-cell dysfunction and T-cell exclusion. Furthermore, expression of EZH2 exhibited a strong correlation with several immunotherapy-associated responses (i.e., immune checkpoint molecules, tumor mutation burden (TMB), microsatellite instability (MSI), mismatch repair (MMR) status, and neoantigens), suggesting that EZH2 appeared to be a novel target for evaluating the therapeutic efficacy of immunotherapy. [ABSTRACT FROM AUTHOR]

Ion transport properties are critical to some emerging technologies. Here, the authors show the controllable 2D movement and redistribution of Li-ions in metal oxides such as WO3 by a magnetic field. The success is based on a current-driving model which also works for other cations including H+, Na+, Zn2+ and Ca2+.