Lymph node metastasis, the leading cause of mortality in esophageal squamous carcinoma (ESCC) with a highly complex tumor microenvironment, remains underexplored. Here, the transcriptomes of 85 263 single cells are analyzed from four ESCC patients with lymph node metastases. Strikingly, it is observed that the metastatic microenvironment undergoes the emergence or expansion of interferon induced IFIT3+ T, B cells, and immunosuppressive cells such as APOC1+APOE+ macrophages and myofibroblasts with highly expression of immunoglobulin genes (IGKC) and extracellular matrix component and matrix metallopeptidase genes. A poor‐prognostic epithelial‐immune dual expression program regulating immune effector processes, whose activity is significantly enhanced in metastatic malignant epithelial cells and enriched in CD74+CXCR4+ and major histocompatibility complex (MHC) class II genes upregulated malignant epithelia cells is discovered. Comparing with primary tumor, differential intercellular communications of metastatic ESCC microenvironment are revealed and furtherly validated via multiplexed immunofluorescence and immunohistochemistry staining, which mainly rely on the crosstalk of APOC1+APOE+ macrophages with tumor and stromal cell. The data highlight potential molecular mechanisms that shape the lymph‐node metastatic microenvironment and may inform drug discovery and the development of new strategies to target these prometastatic nontumor components for inhibiting tumor growth and overcoming metastasis to improve clinical outcomes.

The present study aimed to identify differentially regulated genes between the peritumoral brain zone (PBZ) and tumor core (TC) of glioblastoma (GBM), to elucidate the underlying molecular mechanisms and provide a target for the treatment of tumors. The GSE13276 and GSE116520 datasets were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) for the PBZ and TC were obtained using the GEO2R tool. The bioinformatics and evolutionary genomics online tool Venn was used to identify common DEGs between the two datasets. The Database for Annotation, Visualization, and Integrated Discovery online tool was used to analyze enriched pathways of the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. The Search Tool for the Retrieval of Interacting Genes/Proteins online tool was used to construct a protein-protein interaction (PPI) network of DEGs. Hub genes were identified using Cytohubba, a plug-in for Cytoscape. The Gene Expression Profiling Interactive Analysis (GEPIA) database was utilized to perform survival analysis. In total, 75 DEGs, including 12 upregulated and 63 downregulated genes, were identified. In the GO term analysis, these DEGs were mainly enriched in ‘regulation of angiogenesis’ and ‘central nervous system development’. Furthermore, in the KEGG pathway analysis, the DEGs were mainly enriched in ‘bladder cancer’ and ‘endocytosis’. When filtering the results of the PPI network analysis using Cytohubba, a total of 10 hub genes, including proteolipid protein 1, myelin associated oligodendrocyte basic protein, contactin 2, myelin oligodendrocyte glycoprotein, myelin basic protein, myelin associated glycoprotein, SRY-box transcription factor 10, C-X-C motif chemokine ligand 8 (CXCL8), vascular endothelial growth factor A (VEGFA) and plasmolipin, were identified. These hub genes were further subjected to GO term and KEGG pathway analysis, and were revealed to be enriched in ‘central nervous system development’, ‘bladder cancer’ and ‘rheumatoid arthritis’. These hub genes were used to perform survival analysis using the GEPIA database, and it was determined that VEGFA and CXCL8 were significantly associated with a reduction in the overall survival of patients with GBM. In conclusion, the results suggest that the recurrence of GBM is associated with high gene expression levels VEGFA and CXCL8, and the development of the central nervous system.

Glioblastoma accounts for 45.2% of central nervous system tumors. Despite the availability of multiple treatments (e.g., surgery, radiotherapy, chemotherapy, biological therapy, immunotherapy, and electric field therapy), glioblastoma has a poor prognosis, with a 5-year survival rate of approximately 5%. The pathogenesis and prognostic markers of this cancer are currently unclear. To this end, this study aimed to explore the pathogenesis of glioblastoma and identify potential prognostic markers. We used data from the GEO and TCGA databases and identified five genes (ITGA5, MMP9, PTPRN, PTX3, and STX1A) that could affect the survival rate of glioblastoma patients and that were differentially expressed between glioblastoma patients and non-tumors groups. Based on a variety of bioinformatics tools for reverse prediction of target genes associated with the prognosis of GBM, a ceRNA network of messenger RNA (STX1A, PTX3, MMP9)-microRNA (miR-9-5p)-long non-coding RNA (CRNDE) was constructed. Finally, we identified five potential therapeutic drugs (bacitracin, hecogenin, clemizole, chrysin, and gibberellic acid) that may be effective treatments for glioblastoma.

Patients with biliary tract cancer (BTC) were associated with poor prognosis and limited therapeutic options after first-line therapy currently. In this study, we sought to evaluate the feasibility and tolerability of sintilimab plus anlotinib as the second-line treatment for patients with advanced BTC. Eligible patients had histologically confirmed locally advanced unresectable or metastatic BTC and failed after the first-line treatment were recruited. The primary endpoint was overall survival (OS). Simultaneously, association between clinical outcomes and genomic profiling and gut microbiome were explored to identify the potential biomarkers for this regimen. Twenty patients were consecutively enrolled and received study therapy. The trail met its primary endpoint with a median OS of 12.3 months (95%

Glioblastoma (GBM), the primary malignant brain tumor, is typically associated with a poor prognosis and poor quality of life, mainly due to the lack of early diagnostic biomarkers and therapeutic targets. However, gene sequencing technologies and bioinformatics analysis are currently being actively utilized to explore potential targets for the diagnosis and management of malignancy. Herein, based on a variety of bioinformatics tools for the reverse prediction of target genes associated with the prognosis of GBM, a ceRNA network of AGAP2-AS1-miR-9-5p-MMP2/MMP9 was constructed, and a potential therapeutic target for GBM was identified. Enrichment analysis predicted that the ceRNA regulatory network participates in the processes of cell proliferation, differentiation, and migration.

Object: Immune related genes play an important role in the process of tumor genesis and development. Therefore, we aim to find the Immune genes which are related to the prognosis of glioma patients, and to explore the infiltration of Immune cells in glioma microenvironment. Methods We downloaded the data of the glioma samples from the CGGA database, and performed batch correction to screen the primary glioma samples for subsequent analysis. Then the ESTIMATE algorithm was used to deal with the Stromal scores and Immune scores of the primary glioma samples, and the difference was analyzed. Then the common Immune related genes (IRGs) were obtained by intersecting with the Immune genes in the ImmPort database. Moreover, we used common IRGs to construct protein-protein interaction (PPI) networks, from which we screened the top 30 genes with high connectivity, and Lasso regression was used to screen the IRGs. Lastly, we obtained the combined genes, which were overlapped both in the top 30 high-connection genes and Lasso regression genes. The final genes were used to construct COX risk prediction models. The accuracy of the model were verified by the TCGA glioma data, and the model genes were analyzed for Immune-related pathways, as well as the Hallmark and KEGG enrichment. Additionally, we used CIBERSOFT algorithm to estimate the Immune cell content of the samples, and analyzed the differences, correlations and survival of the Immune cells in high and low risk groups. Results Firstly, a total of 117 IRGs were obtained from the gene sets, which were overlapped in the data of Stromal score, Immune score and ImmPort database. Secondly, the top 30 genes were selected after the PPI network, and another 26 genes were screened out after the Lasso regression algorithm. And then, six coexist IRGs were obtained from the intersecting sets. Furthermore, the COX risk prediction model was constructed and tested, showing that the overall survival rate of the high-risk group was about 50% of that of the low-risk group. We observed that the high-risk group were enriched in Immune response and Immune process. Most importantly, in KEGG pathways, the high-risk groups were mainly enriched in p53 signaling pathway, JAK-STAT signaling pathway, pathways in cancer and cell cycle. By estimating the Immune cell contents, we also found that the Immune cell Plasma cells, T cells CD8, T cells CD4 naïve, T cells regulatory (Tregs), Macrophages M0 and Neutrophils were higher in high-risk groups, when compared to the low-risk group, with significant difference. Finally, the correlation analysis showed that the degree of Immune infiltration in high-risk groups was related to T cells regulatory (Tregs), Macrophages M0 and Neutrophils. Conclusion A COX risk prediction model of 6 genes was successfully constructed, which was enriched in Immune-related pathways. Meanwhile, survival analysis and TCGA data validation revealed significant differences in the model genes in the overall survival of the glioma patients, and the degree of Immune infiltration in the model was associated with T cells regulatory (Tregs), Macrophages M0 and Neutrophils.