Comprehensive intratumoral heterogeneity landscaping of liver hepatocellular carcinoma and discerning of APLP2 in cancer progression

Comprehensive analysis of juvenile idiopathic arthritis patients' immune characteristics based on bulk and single-cell sequencing data

Background

The pathogenesis of juvenile idiopathic arthritis (JIA) is strongly influenced by an impaired immune system. However, the molecular mechanisms underlying its development and progression have not been elucidated. In this study, the computational methods TRUST4 were used to construct a T-cell receptor (TCR) and B-cell receptor (BCR) repertoire from the peripheral blood of JIA patients via bulk RNA-seq data, after which the clonality and diversity of the immune repertoire were analyzed.

Results

Our findings revealed significant differences in the frequency of clonotypes between the JIA and healthy control groups in terms of the TCR and BCR repertoires. This work identified specific V genes and J genes in TCRs and BCRs that could be used to expand our understanding of JIA. After single-cell RNA analysis, the relative percentages of CD14 monocytes were significantly greater in the JIA group. Cell-cell communication analysis revealed the significant role of the MIF signaling pathway in JIA.

Conclusion

In conclusion, this work describes the immune features of both the TCR and BCR repertoires under JIA conditions and provides novel insight into immunotherapy for JIA.

Exploring the metastasis-related biomarker and carcinogenic mechanism in liver cancer based on single cell technology

Background

Hepatocellular carcinoma (HCC) is a fatal primary malignancy characterized by high invasion and migration. We aimed to explore the underlying metastasis-related mechanism supporting the development of HCC.

Methods

The dataset of single cell RNA-seq (GSE149614) were collected for cell clustering by using the Seurat R package, the FindAllMarkers function was used to find the highly expression and defined the cell cluster. The WebGestaltR package was used for the GO and KEGG function analysis of shared genes, the Gene Set Enrichment Analysis (GSVA) was performed by clusterProfiler R package, the hTFtarget database was used to identify the crucial transcription factors (TFs), the Genomics of Drug Sensitivity in Cancer (GDSC) database was used for the drug sensitivity analysis. Finally, the overexpression and trans-well assay was used for gene function analysis.

Results

We obtained 9 cell clusters from the scRNA-seq data, including the nature killer (NK)/T cells, Myeloid cells, Hepatocytes, Epithelial cells, Endothelial cells, Plasma B cells, Smooth muscle cells, B cells, Liver bud hepatic cells. Further cell ecological analysis indicated that the Hepatocytes and Endothelial cell cluster were closely related to the cancer metastasis. Subsequently, the NDUFA4L2-Hepatocyte, GTSE1-Hepatocyte, ENTPD1-Endothelial and NDUFA4L2-Endothelial were defined as metastasis-supporting cell clusters, in which the NDUFA4L2-Hepatocyte cells was closely related to angiogenesis, while the NDUFA4L2-Endothelial was related with the inflammatory response and complement response. The overexpression and trans-well assay displayed that NDUFA4L2 exhibited clearly metastasis-promoting role in HCC progression.

Conclusion

We identified and defined 4 metastasis-supporting cell clusters by using the single cell technology, the specify shared gene was observed and played crucial role in promoting cancer progression, our findings were expected to provide new insight in control cancer metastasis.

Transcriptomics analysis reveals distinct mechanism of breast cancer stem cells regulation in mammospheres from MCF-7 and T47D cells

Luminal A breast cancer, constituting 70 % of breast cancer cases, presents a challenge due to the development of resistance and recurrence caused by breast cancer stem cells (BCSC). Luminal breast tumors are characterized by TP53 expression, a tumor suppressor gene involved in maintaining stem cell attributes in cancer. Although a previous study successfully developed mammospheres (MS) from MCF-7 (with wild-type TP53) and T47D (with mutant TP53) luminal breast cancer cells for BCSC enrichment, their transcriptomic profiles remain unclear. We aimed to elucidate the transcriptomic disparities between MS of MCF-7 and T47D cells using bioinformatics analyses of differentially expressed genes (DEGs), including the KEGG pathway, Gene Ontology (GO), drug-gene association, disease-gene association, Gene Set Enrichment Analysis (GSEA), DNA methylation analysis, correlation analysis of DEGs with immune cell infiltration, and association analysis of genes and small-molecule compounds via the Connectivity Map (CMap). Upregulated DEGs were enriched in metabolism-related KEGG pathways, whereas downregulated DEGs were enriched in the MAPK signaling pathway. Drug-gene association analysis revealed that both upregulated and downregulated DEGs were associated with fostamatinib. The KEGG pathway GSEA results indicated that the DEGs were enriched for oxidative phosphorylation, whereas the downregulated DEGs were negatively enriched for the p53 signaling pathway. Examination of DNA methylation revealed a noticeable disparity in the expression patterns of the PKM2, ERO1L, SLC6A6, EPAS1, APLP2, RPL10L, and NEDD4 genes when comparing cohorts with low- and high-risk breast cancer. Furthermore, a significant positive correlation was identified between SLC6A6 expression and macrophage presence, as well as MSN, and AKR1B1 expression and neutrophil and dentritic cell infiltration. CMap analysis unveiled SA-83851 as a potential candidate to counteract the effects of DEGs, specifically in cells harbouring mutant TP53. Further research, including in vitro and in vivo validations, is warranted to develop drugs targeting BCSCs.

Comprehensive analysis of scRNA-Seq and bulk RNA-Seq data reveals dynamic changes in tumor-associated neutrophils in the tumor microenvironment of hepatocellular carcinoma and leads to the establishment of a neutrophil-related prognostic model

BACKGROUND

Analysis of hepatocellular carcinoma (HCC) single-cell sequencing data was conducted to explore the role of tumor-associated neutrophils in the tumor microenvironment.

METHODS

Analysis of single-cell sequencing data from 12 HCC tumor cores and five HCC paracancerous tissues identified cellular subpopulations and cellular marker genes. The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases were used to establish and validate prognostic models. xCELL, TIMER, QUANTISEQ, CIBERSORT, and CIBERSORT-abs analyses were performed to explore immune cell infiltration. Finally, the pattern of tumor-associated neutrophil roles in tumor microenvironmental components was explored.

RESULTS

A total of 271 marker genes for tumor-associated neutrophils were identified based on single-cell sequencing data. Prognostic models incorporating eight genes were established based on TCGA data. Immune cell infiltration differed between the high- and low-risk groups. The low-risk group benefited more from immunotherapy. Single-cell analysis indicated that tumor-associated neutrophils were able to influence macrophage, NK cell, and T-cell functions through the IL16, IFN-II, and SPP1 signaling pathways.

CONCLUSION

Tumor-associated neutrophils regulate immune functions by influencing macrophages and NK cells. Models incorporating tumor-associated neutrophil-related genes can be used to predict patient prognosis and immunotherapy responses.

Single-cell transcriptomics reveals heterogeneity in esophageal squamous epithelial cells and constructs models for predicting patient prognosis and immunotherapy

Background

Esophageal squamous cell carcinoma (ESCC), characterized by its high invasiveness and malignant potential, has long been a formidable challenge in terms of treatment.

Methods

A variety of advanced analytical techniques are employed, including single-cell RNA sequencing (scRNA-seq), cell trajectory inference, transcription factor regulatory network analysis, GSVA enrichment analysis, mutation profile construction, and the inference of potential immunotherapeutic drugs. The purpose is to conduct a more comprehensive exploration of the heterogeneity among malignant squamous epithelial cell subgroups within the ESCC microenvironment and establish a model for predicting the prognosis and immunotherapy outcomes of ESCC patients.

Results

An analysis was conducted through scRNA-seq, and three Cluster of malignant epithelial cells were identified using the infer CNV method. Cluster 0 was found to exhibit high invasiveness, whereas Cluster 1 displayed prominent characteristics associated with epithelial-mesenchymal transition. Confirmation of these findings was provided through cell trajectory analysis, which positioned Cluster 0 at the initiation stage of development and Cluster 1 at the final developmental stage. The abundance of Cluster 0-2 groups in TCGA-LUAD samples was assessed using ssGSEA and subsequently categorized into high and low-expression groups. Notably, it was observed that Cluster 0-1 had a significant impact on survival (p<0.05). Furthermore, GSVA enrichment analysis demonstrated heightened activity in hallmark pathways for Cluster 0, whereas Cluster 1 exhibited notable enrichment in pathways related to cell proliferation. It is noteworthy that a prognostic model was established utilizing feature genes from Cluster 0-1, employing the Lasso and stepwise regression methods. The results revealed that in TCGA and GSE53624 cohorts, the low-risk group demonstrated significantly higher overall survival and increased levels of immune infiltration. An examination of four external immunotherapy cohorts unveiled that the low-risk group exhibited improved immunotherapeutic efficacy. Additionally, more meaningful treatment options were identified for the low-risk group.

Conclusion

The findings revealed distinct interactions between malignant epithelial cells of ESCC and subgroups within the tumor microenvironment. Two cell clusters, strongly linked to survival, were pinpointed, and a signature was formulated. This signature is expected to play a crucial role in identifying and advancing precision medicine approaches for the treatment of ESCC.