ObjectiveTo study the abnormal biological pathways of intrahepatic cholangiocarcinoma (ICC) from the transcriptomics level and identify genes associated with the prognosis of ICC.MethodsThe differentially expressed genes were screened by t test and fold change method, then KEGG functional enrichment analysis was performed on related genes. The STRING database was applied to construct protein interaction network and find the hub nodes of the network by calculating the degree, betweenness, and closeness of each node. Kaplan-Meier survival analysis was performed using log-rank test to identify prognostic genes related to ICC.ResultsAll of 1 134 differentially expressed genes were overlapped in 3 datasets, which were mainly involved in 15 pathways, including DNA replication, cell cycle, drug metabolism, RNA transport, etc. signaling pathways and amino acid synthesis. According to protein interaction network analysis, TAF1, GRB2, E2F4, HNF4A, MYC, and TP53 genes were hub nodes. As GRB2 and TP53 genes were also the death related genes of ICC, it was found that patients with lower GRB2 gene expression had a better overall survival than those with higher GRB2 gene expression (P=0.040 9), while patients with lower TP53 had a worse overall survival than those with higher TP53 gene expression (P=0.027 3), which were also verified in the TCGA database.ConclusionsThe abnormal cell metabolism is notably related to the tumorigenesis of ICC. TAF1, GRB2, E2F4, HNF4A, MYC, and TP53 are the key genes in the carcinogenesis and progression of ICC. Expressions of GRB2 and TP53 genes are associated with the prognosis of ICC.
Non-small cell lung cancer is one of the primary types of cancer that leads to brain metastases. Approximately 10% of patients with non-small cell lung cancer have brain metastases at the time of diagnosis, and 26%-53% of patients develop brain metastases during the progression of their disease. However, the underlying mechanisms of lung cancer brain metastasis have not been fully elucidated. With the continuous development of single-cell and spatial transcriptomics, the genomic and transcriptomic characteristics of lung cancer brain metastasis are gradually being revealed. In February 2025, the journal Nature Medicine published an article titled "Single-cell and spatial genomic landscape of non-small cell lung cancer brain metastases". This article aims to provide a brief interpretation of the paper for colleagues in research and clinical practice.
Due to the high dimensionality and complexity of the data, the analysis of spatial transcriptome data has been a challenging problem. Meanwhile, cluster analysis is the core issue of the analysis of spatial transcriptome data. In this article, a deep learning approach is proposed based on graph attention networks for clustering analysis of spatial transcriptome data. Our method first enhances the spatial transcriptome data, then uses graph attention networks to extract features from nodes, and finally uses the Leiden algorithm for clustering analysis. Compared with the traditional non-spatial and spatial clustering methods, our method has better performance in data analysis through the clustering evaluation index. The experimental results show that the proposed method can effectively cluster spatial transcriptome data and identify different spatial domains, which provides a new tool for studying spatial transcriptome data.
ObjectiveTo analyze the correlation between the molecular biological information of SMARCA4-deficient non-small cell lung cancer (SMARCA4-dNSCLC) and its clinical prognosis, and to explore the spatial features and molecular mechanisms of interactions between cells in the tumor microenvironment (TME) of SMARCA4-dNSCLC. MethodsUsing data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), this study conducted functional enrichment analysis on differentially expressed genes (DEGs) in SMARCA4-dNSCLC and depicted its genomic variation landscape. Through weighted gene co-expression network analysis (WGCNA) and a combination of 10 different machine learning algorithms, patients in the training group were divided into a low-risk group and a high-risk group based on a median risk score (RiskScore). A corresponding prognostic prediction model was established, and on this basis, a nomogram was constructed to predict the 1, 3, and 5-year survival rates of patients. K-M survival curves, receiver operating characteristic (ROC) curves, and time-dependent ROC curves were drawn to evaluate the predictive ability of the model. External datasets from GEO further validated the prognostic value of the prediction model. In addition, we also evaluated the immunological characteristics of the TME of the prognostic model. Finally, using single-cell RNA sequencing (scRNA-seq) and spatial transcriptome (ST), we explored the spatial features of interactions between cells in the TME of SMARCA4-dNSCLC, intercellular communication, and molecular mechanisms. ResultsA total of 56 patients were included in the training group, including 38 males and 18 females, with a median age of 62 (56-70) years. There were 28 patients in both the low-risk and high-risk groups. A total of 474 patients were included in the training group, including 265 males and 209 females, with a median age of 65 (58-70) years. A risk score model composed of 8 prognostic feature genes (ELANE, FSIP2, GFI1B, GPR37, KRT81, RHOV, RP1, SPIC) was established. Compared with patients in the low-risk group, those in the high-risk group showed a more unfavorable prognostic outcome. Immunological feature analysis revealed differences in the infiltration of various immune cells between the low-risk and high-risk groups. ScRNA-seq and ST analyses found that interactions between cells were mainly through macrophage migration inhibitory factor (MIF) signaling pathways (MIF-CD74+CXCR4 and MIF-CD74+CD44) via ligand-receptor pairs, while also describing the niche interactions of the MIF signaling pathway in tissue regions. ConclusionThe 8-gene prognostic model constructed in this study has certain predictive accuracy in predicting the survival of SMARCA4-dNSCLC. Combining the ScRNA-seq and ST analyses, cell-to-cell crosstalk and spatial niche interaction may occur between cells in the TME via the MIF signaling pathway (MIF-CD74+CXCR4 and MIF-CD74+CD44).