According to the 2020 global cancer data released by the World Cancer Research Fund (WCRF) International, gastric cancer (GC) is the fifth most common cancer worldwide, with yearly increasing incidence and the second-highest fatality rate in malignancies. Despite the contemporary ambiguous molecular mechanisms in GC pathogenesis, numerous in-depth studies have demonstrated that zinc finger proteins (ZFPs) are essential for the development and progression of GC. ZFPs are a class of transcription factors with finger-like domains that bind to Zn²⁺ extensively and participate in gene replication, cell differentiation and tumor development. In this review, we briefly outline the roles, molecular mechanisms and the latest advances in ZFPs in GC, including eight principal aspects, such as cell proliferation, epithelial–mesenchymal transition (EMT), invasion and metastasis, inflammation and immune infiltration, apoptosis, cell cycle, DNA methylation, cancer stem cells (CSCs) and drug resistance. Intriguingly, the myeloid zinc finger 1 (MZF1) possesses reversely dual roles in GC by promoting tumor proliferation or impeding cancer progression via apoptosis. Therefore, a thorough understanding of the molecular mechanism of ZFPs on GC progression will pave the solid way for screening the potentially effective diagnostic indicators, prognostic biomarkers and therapeutic targets of GC.

According to the global cancer burden data for 2020 issued by the World Health Organization (WHO), colorectal cancer has risen to be the third-most frequent cancer globally after breast and lung cancer. Despite advances in surgical treatment and chemoradiotherapy for colon cancer, individuals with extensive liver metastases still have depressing prognoses. Numerous studies suggest ZFPs are crucial to the development of colon cancer. The ZFP family is encoded by more than 2% of the human genome sequence and is the largest transcriptional family, all with finger-like structural domains that could combine with Zn²⁺. In this review, we summarize the functions, molecular mechanisms and recent advances of ZFPs in colon cancer. We also discuss how these proteins control the development and progression of colon cancer by regulating cell proliferation, EMT, invasion and metastasis, inflammation, apoptosis, the cell cycle, drug resistance, cancer stem cells and DNA methylation. Additionally, several investigations have demonstrated that Myeloid zinc finger 1 (MZF1) has dual functions in colon cancer, which may both promote cancer proliferation and inhibit cancer progression through apoptosis. Generally, a comprehensive understanding of the action mechanisms of ZFPs in colon cancer will not only shed light on the discovery of new diagnostic and prognosis indicators but will also facilitate the design of novel targeted therapies.

A gearbox is an indispensable component of a mechanical transmission system, and in the event of an anomaly, it can have a serious impact on the stability of the system. Graph convolutional networks have achieved some results in the study of fault diagnosis, but the way of topology construction from a single metric space is somewhat inexact and one-sided. Meanwhile, graph convolutional networks can only aggregate node features in the spatial dimension, which does not guarantee a comprehensive expression of fault information. Therefore, we propose a gearbox fault diagnosis method based on refined topology and spatio-temporal graph convolutional networks to accurately identify the gearbox health state. First, two topologies are constructed based on the distance metric and the similarity metric, respectively, and the edge structure is pruned to obtain a more lightweight topology based on the analysis of the structural similarity between the two. Then, depending on the number of common neighbors of two nodes, the weights between them are enhanced or weakened to further refine the topological information. Finally, a network model with spatio-temporal feature co-extraction is developed to mine fault features in depth in two dimensions to improve network performance. Case studies, ablation experiments, and robustness validation of the proposed method are carried out, and the results show that the accuracy of the proposed method is as high as 99.41% and 99.93% on the two datasets, respectively, which is much higher than that of other comparison methods, and at the same time, it has good noise robustness.