No abstract

Background: Breast cancer is one of the malignant tumors with a high incidence and mortality rate among women worldwide, and its prevalence is increasing year by year, posing a serious health risk to women. UTP23 (UTP23 Small Subunit Processome Component) is a nucleolar protein that is essential for ribosome production. As we all know, disruption of ribosome structure and function results in improper protein function, affecting the body's normal physiological processes and promoting cancer growth. However, little research has shown a connection between UTP23 and cancer. Methods: We analyzed the mRNA expression of UTP23 in normal tissue and breast cancer using The Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO) database, and the protein expression of UTP23 using The Human Protein Atlas (HPA) database. Next, we examined the relationship between UTP23 high expression and Overall Survival (OS) using Kaplan-Meier Plotters and enriched 980 differentially expressed genes in UTP23 high and low expression samples using GO/KEGG and GSEA to identify potential biological functions of UTP23 and signaling pathways that it might influence. Finally, we also investigated the relationship between UTP23 and immune infiltration and examined the effect of UTP23 on the proliferation of human breast cancer cell lines by knocking down UTP23. Results:We found that UTP23 levels in breast cancer patient samples were noticeably greater than those in healthy individuals and that high UTP23 levels were strongly linked with poor prognoses (P=0.008). Functional enrichment analysis revealed that UTP23 expression was connected to the humoral immune response. Besides, UTP23 expression was found to be positively correlated with immune cell infiltration. Furthermore, UTP23 knockdown has been shown to inhibit the proliferation of human breast cancer cells MDA-MB-231 and HCC-1806. Conclusions: Taken together, our study demonstrated that UTP23 is a promising target in detecting and treating breast cancer and is intimately linked to immune infiltration.

Background Breast cancer is one of the malignant tumors with a high incidence and mortality rate among women worldwide, and its prevalence is increasing year by year, posing a serious health risk to women. UTP23 (UTP23 Small Subunit Processome Component) is a nucleolar protein that is essential for ribosome production. As we all know, disruption of ribosome structure and function results in improper protein function, affecting the body's normal physiological processes and promoting cancer growth. However, little research has shown a connection between UTP23 and cancer. Methods We analyzed the mRNA expression of UTP23 in normal tissue and breast cancer using The Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO) database, and the protein expression of UTP23 using The Human Protein Atlas (HPA) database. Next, we examined the relationship between UTP23 high expression and Overall Survival (OS) using Kaplan-Meier Plotters and enriched 980 differentially expressed genes in UTP23 high and low expression samples using GO/KEGG and GSEA to identify potential biological functions of UTP23 and signaling pathways that it might influence. Finally, we also investigated the relationship between UTP23 and immune infiltration and examined the effect of UTP23 on the proliferation of human breast cancer cell lines by knocking down UTP23. Results We found that UTP23 levels in breast cancer patient samples were noticeably greater than those in healthy individuals and that high UTP23 levels were strongly linked with poor prognoses (P = 0.008). Functional enrichment analysis revealed that UTP23 expression was connected to the humoral immune response. Besides, UTP23 expression was found to be positively correlated with immune cell infiltration. Furthermore, UTP23 knockdown has been shown to inhibit the proliferation of human breast cancer cells MDA-MB-231 and HCC-1806. Conclusion Taken together, our study demonstrated that UTP23 is a promising target in detecting and treating breast cancer and is intimately linked to immune infiltration.

Effective treatment of lung cancer requires accurate diagnosis of mediastinal lymph node metastasis (LNM). In the current clinical practices, invasive examination is considered the gold standard, but it is inefficient and probably causes complications to the patient. Therefore, the automatic diagnosis of LNM from computed tomography (CT) images based on Deep Learning (DL) methods has become important research in aided diagnosis. DL methods require a large number of high-quality data to achieve good results. However, obtaining labels for LNM is difficult, the lack of annotations for LNM limits the accuracy of deep learning network classification. In this paper, we propose a semi-supervised multiple image transformation network (MITNet) for LNM prediction in CT images. We perform multiple image transformations on the images and input them to the feature extractors to extract multi-dimensional features, then use an attention-based module (ABM) to adaptively fuse the features to accurately predict LNM. In addition, in order to solve the problem of insufficient data volume, we introduce a semi-supervised learning strategy to train the network with CT image containing only lymph node (LN) segmentation annotations to improve its generalization ability. Experimental results show that our proposed method has an accuracy of 92.45% and outperforms several state-of-the-art methods.