Retina is composed of a heterogeneous population of cell types, each with a unique biological function. Even if the same type of cells, due to genetic heterogeneity will lead to cell function differences. In the past, traditional molecular biological methods cannot resolve variations in their functional roles that arise from these differences, and some cells are difficult to define due to the lack of specific molecular markers or the scarcity of numbers, which hindered the understanding and research of these cells. With the development of biotechnology, single-cell RNA sequencing can analyze and resolve differences in single-cell transcriptome expression profiles, characterize intracellular population heterogeneity, identify new and rare cell subtypes, and more definitely define the characteristics of each cell type. It clarifies the origin, function, and variations in cell phenotypes. Other attributes include pinpointing both disease-related characteristics of cell subtypes and specific differential gene expression patterns, to deepen our understanding of the causes and progression of diseases, as well as to aid clinical diagnosis and targeted therapy.
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).
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.
Objective To explore the role and clinical significance of cell-cycle dependent kinase 1 (CDK1) and its upstream and downstream molecules in the development of malignant peripheral nerve sheath tumor (MPNST) through the analysis of clinical tissue samples. Methods A total of 56 tumor samples from MPNST patients (“Tianjin” dataset) who underwent surgical resection, confirmed by histology and pathology between September 2011 and March 2020, along with 17 normal tissue samples, were selected as the research subjects. MPNST-related hub genes were identified through transcriptome sequencing, bioinformatics analysis, immunohistochemistry staining, and survival analysis, and their expression levels and prognostic associations were analyzed. Results Transcriptome sequencing and bioinformatics analysis revealed that upregulated genes in MPNST were predominantly enriched in cell cycle-related pathways, with CDK1 occupying a central position among all differentially expressed genes. Further differential analysis demonstrated that CDK1 mRNA expression in sarcoma tissues was significantly higher than in normal tissues [based on searching the cancer genome atlas (TCGA) dataset, P<0.05]. In MPNST tissues, CDK1 mRNA expression was not only significantly higher than in normal tissues (based on Tianjin, GSE141438 datasets, P<0.05), but also significantly higher than in neurofibromatosis (NF) and plexiform neurofibromas (PNF) (based on GSE66743 and GSE145064 datasets, P<0.05). Immunohistochemical staining results indicated that the expression rate of CDK1 protein in MPNST tissues was 40.31%. Survival analysis results demonstrated that CDK1 expression was associated with poor prognosis. The survival time of MPNST patients with high CDK1 mRNA expression was significantly lower than that of the low expression group (P<0.05), and the overall survival trend of patients with positive CDK1 protein expression was worse than that of patients with negative CDK1 expression. Additionally, differential analysis of CDK family genes (CDK1-8) revealed that only CDK1 was significantly upregulated in MPNST, NF, and PNF. Conclusion Increased expression of CDK1 is associated with poor prognosis in MPNST patients. Compared to other CDK family members, CDK1 exhibits a unique expression pattern, suggesting its potential as a therapeutic target for MPNST.
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.
RNA can be labeled by more than 170 chemical modifications after transcription, and these chemical modifications are collectively referred to as RNA modifications. It opened a new chapter of epigenetic research and became a major research hotspot in recent years. RNA modification regulates the expression of genes from the transcriptome level by regulating the fate of RNA, thus participating in many biological processes and disease occurrence and development. With the deepening of research, the diversity and complexity of RNA modification, as well as its physiological significance and potential as a therapeutic target, can not be ignored.
With the development of life sciences and informatics, bioinformatics is developing as an interdisciplinary subject. Its main application is the relationship between genes and proteins and their expression. With the help of genomics, proteomics, transcriptomics, and metabolomics, researchers introduce bioinformatics research methods into fundus disease research. A series of gratifying research results have been achieved including the screening of genetic susceptibility genes, the screening of diagnostic markers, and the exploration of pathogenesis. Genomics has the characteristics of high efficiency and accuracy. It has been used to detect new mutation sites in retinoblastoma and retinal pigment degeneration research, which helps to further improve the pathogenesis of retinal genetic diseases. Transcriptomics, proteomics, and metabolomics have high throughput characteristics. They are used to analyze changes in the expression profiles of RNA, proteins, and metabolites in intraocular fluid or isolated cells in disease states, which help to screen biomarkers and further elucidate the pathogenesis. With the advancement of technology, bioinformatics will provide new ideas for the study of ocular fundus diseases.
ObjectiveTo analyze the early changes of gene expression levels and signaling pathways in 661W cell line under hypoxic conditions and to find potential functional target genes.MethodsThe cultured mouse 661W cells were divided into hypoxia treatment group and normoxia control group. Cells in the hypoxia treatment group were cultured in a three-gas incubator with volume fraction of 1% and 5% CO2 at 37 ℃. Cells in the normoxia control group were cultured in an incubator at 37 ℃ with volume fraction of 5% CO2. High-throughput sequencing technology was used to sequence the transcriptome of 661W cell treated with hypoxia and normoxia for 4 hours to screen for differentially expressed genes (DEG). Clustering heat map analysis, gene ontology (GO) functional enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis and protein-protein interaction network (PPI) analysis were performed. The reverse transcription-polymerase chain reaction (RT-PCR) was used to verify the accuracy of the sequencing results.ResultsA total of 506 differentially expressed genes were screened, including 459 up-regulated genes and 47 down-regulated genes. GO functional enrichment analysis showed that the main biological processes of DEG were the cell's response to hypoxia, glycolysis, negative regulation of cell proliferation and apoptosis. hypoxia inducible factor (HIF)-1α pathway, glycolysis, Forkhead box O (FoxO) pathway, Insulin signaling pathway and Adenosine 5'-monophosphate-activated protein kinase (AMPK) pathway were involved in the above process. PPI analysis results showed that hub genes related to hypoxia were Aldoa, Aldoc, Gpi1, Hk2, Hk1, Pfkl, Pfkp, Vhl, Fbxo10 and Fbxo27. The RT-PCR results showed that the relative expression levels of 15 DEG mRNA in the hypoxic treatment group were higher than that of the normoxic control group, and the difference was statistically significant (P＜0.05). The mRNA expression levels of N-myc downstream-regulated gene-1 (Ndrg1), Mt1, and vascular endothelial growth factor A (VEGFA) were time-dependent on hypoxia.ConclusionsUnder hypoxia, DEG is mainly related to glucose metabolism, cell response to hypoxia, regulation of proliferation and apoptosis. HIF-1α pathway, glycolysis, FoxO pathway and AMPK pathway are involved in the early changes of 661W cells under hypoxia. Aldoa, Aldoc, Gpi1, Hk2, Hk1, Pfkl, Pfkp, Vhl, Fbxo10, Fbxo27 may play key roles in the response of 661W cells to hypoxia. Ndrg1, Mt1 and VEGFA could be potential functional target genes for the study of ischemia and hypoxia-related fundus diseases.
目的 研究糖尿病、阳痿、慢性肾炎患者中筛选出的典型肾阳虚证病例的转录组学特征，揭示出肾阳虚证“同证异治”的生物学基础。 方法 分别对9例肾阳虚证患者进行Agilent人444k表达谱芯片实验，对差异表达基因进行基因本休论（GO）、Pathway分析。 结果 找出332条共同差异表达基因，其中有注释的基因为181条。通过GO分析发现肾阳虚证在免疫系统、氨基酸分解和合成、脂类代谢、生殖、能量代谢及肿瘤相关的基因有密切联系，通过Pathway分析发现与肾阳虚证相关的信号通路有39个。 结论 肾阳虚证可能导致免疫系统低下，氨基酸分解和合成、脂类代谢、生殖、能量代谢功能降低，以及与肿瘤形成相关。