TMSB10 acts as a biomarker and promotes progression of clear cell renal cell carcinoma

CDX2 dose-dependently influences the gene regulatory network underlying human extraembryonic mesoderm development

Loss of Cdx2 in vivo leads to stunted development of the allantois, an extraembryonic mesoderm-derived structure critical for nutrient delivery and waste removal in the early embryo. Here, we investigate how CDX2 dose-dependently influences the gene regulatory network underlying extraembryonic mesoderm development. By engineering human induced pluripotent stem cells (hiPSCs) consisting of wild-type (WT), heterozygous (CDX2-Het), and homozygous null CDX2 (CDX2-KO) genotypes, differentiating these cells in a 2D gastruloid model, and subjecting these cells to single-nucleus RNA and ATAC sequencing, we identify several pathways that are dose-dependently regulated by CDX2 including VEGF and non-canonical WNT. snATAC-seq reveals that CDX2-Het cells retain a WT-like chromatin accessibility profile, suggesting accessibility alone is not sufficient to drive this variability in gene expression. Because the loss of CDX2 or TBXT phenocopy one another in vivo, we compared differentially expressed genes in our CDX2-KO to those from TBXT-KO hiPSCs differentiated in an analogous experiment. This comparison identifies several communally misregulated genes that are critical for cytoskeletal integrity and tissue permeability. Together, these results clarify how CDX2 dose-dependently regulates gene expression in the extraembryonic mesoderm and reveal pathways that may underlie the defects in vascular development and allantoic elongation seen in vivo.

CDX2 dose-dependently influences the gene regulatory network underlying human extraembryonic mesoderm development

Proper regulation of gene dosage is critical for the development of the early embryo and the extraembryonic tissues that support it. Specifically, loss of Cdx2 in vivo leads to stunted development of the allantois, an extraembryonic mesoderm-derived structure critical for nutrient delivery and waste removal in the early embryo. In this study, we investigate how CDX2 dose-dependently influences the gene regulatory network underlying extraembryonic mesoderm development. We generate an allelic series for CDX2 in human induced pluripotent stem cells (hiPSCs) consisting of WT, heterozygous, and homozygous null CDX2 genotypes, differentiate these cells in a 2D gastruloid model, and subject these cells to multiomic single nucleus RNA and ATAC sequencing. We identify several genes that CDX2 dose-dependently regulate cytoskeletal integrity and adhesiveness in the extraembryonic mesoderm population, including regulators of the VEGF, canonical WNT, and non-canonical WNT signaling pathways. Despite these dose-dependent gene expression patterns, snATAC-seq reveals that heterozygous CDX2 expression is capable of inducing a WT-like chromatin accessibility profile, suggesting accessibility is not sufficient to drive gene expression when the CDX2 dosage is reduced. Finally, because the loss of CDX2 or TBXT phenocopy one another in vivo, we compare differentially expressed genes in our CDX2 knock-out model to those from TBXT knock-out hiPSCs differentiated in an analogous experiment. This comparison identifies several communally misregulated genes that are critical for cytoskeletal integrity and tissue permeability, including ANK3 and ANGPT1. Together, these results clarify how CDX2 dose-dependently regulates gene expression in the extraembryonic mesoderm and suggest these genes may underlie the defects in vascular development and allantoic elongation seen in the absence or reduction of CDX2 in vivo.

The Clinical Relevance and Functional Implications of Thymosin Beta-10 in Glioma

Glioma is a highly aggressive form of brain cancer characterized by limited treatment options and poor patient prognosis. In this study, we aimed to elucidate the oncogenic role of thymosin beta-10 (TMSB10) in glioma through comprehensive analyses of patient data from the TCGA and GTEx databases. Our investigation encompassed several key aspects, including the analysis of patients' clinical characteristics, survival analysis, in vitro and in vivo functional experiments, and the exploration of correlations between TMSB10 expression and immune cell infiltration. Our findings revealed a significant upregulation of TMSB10 expression in glioma tissues compared to normal brain tissues, with higher expression levels observed in tumors of advanced histological grades. Moreover, we observed positive correlations between TMSB10 expression and patient age, while no significant association with gender was detected. Additionally, TMSB10 exhibited marked elevation in gliomas with wild-type IDH and noncodeletion of 1p/19q. Survival analysis indicated that high TMSB10 expression was significantly associated with worse overall survival, disease-specific survival, and progression-free survival in glioma patients. Functionally, knockdown of TMSB10 in glioma cells resulted in reduced cellular growth rates and impaired tumor growth in xenograft models. Furthermore, our study revealed intriguing correlations between TMSB10 expression and immune cell infiltration within the tumor microenvironment. Specifically, TMSB10 showed negative associations with plasmacytoid dendritic cells (pDC) and γδ T cells (Tgd), while displaying positive correlations with neutrophils and macrophages. These findings collectively provide valuable insights into the oncogenic properties of TMSB10 in glioma, suggesting its potential as a therapeutic target and a biomarker for patient stratification.

Pan-cancer analysis identifies the correlations of Thymosin Beta 10 with predicting prognosis and immunotherapy response

Introduction

The biological function and prognosis roles of thymosin β(TMSB) 10 are still unclear in pan-cancer.

Methods

We retrieved The Cancer Genome Atlas and Genotype-tissue expression datasets to obtain the difference of TMSB10 expression between pan-cancer and normal tissues, and analyzed the biological function and prognosis role of TMSB10 in pan-cancer by using cBioPortal Webtool.

Results

The expression of TMSB10 in tumor tissues was significantly higher than normal tissues, and showed the potential ability to predict the prognosis of patients in Pan-cancer. It was found that TMSB10 was significantly correlated with tumor microenvironment, immune cell infiltration and immune regulatory factor expression. TMSB10 is involved in the regulation of cellular signal transduction pathways in a variety of tumors, thereby mediating the occurrence of tumor cell invasion and metastasis. Finally, TMSB10 can not only effectively predict the anti-PD-L1 treatment response of cancer patients, but also be used as an important indicator to evaluate the sensitivity of chemotherapy. In vitro, low expression of TMSB10 inhibited clonogenic formation ability, invasion, and migration in glioma cells. Furthermore, TMSB10 may involve glioma immune regulation progression by promoting PD-L1 expression levels via activating STAT3 signaling pathway.

Conclusions

Our results show that TMSB10 is abnormally expressed in tumor tissues, which may be related to the infiltration of immune cells in the tumor microenvironment. Clinically, TMSB10 is not only an effective prognostic factor for predicting the clinical treatment outcome of cancer patients, but also a promising biomarker for predicting the effect of tumor immune checkpoint inhibitors (ICIs) and chemotherapy in some cancers.

The single-cell landscape of cystic echinococcosis in different stages provided insights into endothelial and immune cell heterogeneity

Introduction

Hydatid cysts and angiogenesis are the key characteristics of cystic echinococcosis, with immune cells and endothelial cells mediating essential roles in disease progression. Recent single-cell analysis studies demonstrated immune cell infiltration after Echinococcus granulosus infection, highlighting the diagnostic and therapeutic potential of targeting certain cell types in the lesion microenvironment. However, more detailed immune mechanisms during different periods of E. granulosus infection were not elucidated.

Methods

Herein, we characterized immune and endothelial cells from the liver samples of mice in different stages by single-cell RNA sequencing.

Results

We profiled the transcriptomes of 45,199 cells from the liver samples of mice at 1, 3, and 6 months after infection (two replicates) and uninfected wild-type mice. The cells were categorized into 26 clusters with four distinct cell types: natural killer (NK)/T cells, B cells, myeloid cells, and endothelial cells. An SPP1⁺ macrophage subset with immunosuppressive and pro-angiogenic functions was identified in the late infection stage. Single-cell regulatory network inference and clustering (SCENIC) analysis suggested that Cebpe, Runx3, and Rora were the key regulators of the SPP1⁺ macrophages. Cell communication analysis revealed that the SPP1⁺ macrophages interacted with endothelial cells and had pro-angiogenic functions. There was an obvious communicative relationship between SPP1⁺ macrophages and endothelial cells via Vegfa-Vegfr1/Vegfr2, and SPP1⁺ macrophages interacted with other immune cells via specific ligand-receptor pairs, which might have contributed to their immunosuppressive function.

Discussion

Our comprehensive exploration of the cystic echinococcosis ecosystem and the first discovery of SPP1⁺ macrophages with infection period specificity provide deeper insights into angiogenesis and the immune evasion mechanisms associated with later stages of infection.

A bioinformatic analysis study of m7G regulator-mediated methylation modification patterns and tumor microenvironment infiltration in glioblastoma

Background

Glioblastoma is one of the most common brain cancers in adults, and is characterized by recurrence and little curative effect. An effective treatment for glioblastoma patients remains elusive worldwide. 7-methylguanosine (m7G) is a common RNA modification, and its role in tumors has become a research hotspot.

Methods

By searching for differentially expressed genes related to m7G, we generated a prognostic signature via cluster analysis and established classification criteria of high and low risk scores. The effectiveness of classification was validated using the Non-negative matrix factorization (NMF) algorithm, and repeatedly verified using training and test groups. The dimension reduction method was used to clearly show the difference and clinical significance of the data. All analyses were performed via R (version 4.1.2).

Results

According to the signature that included four genes (TMOD2, CACNG2, PLOD3, and TMSB10), glioblastoma patients were divided into high and low risk score groups. The survival rates between the two groups were significantly different, and the predictive abilities for 1-, 3-, and 5-year survivals were effective. We further established a Nomogram model to further examine the signature,as well as other clinical factors, with remaining significant results. Our signature can act as an independent prognostic factor related to immune-related processes in glioblastoma.

Conclusions

Our research addresses the gap in knowledge in the m7G and glioblastoma research fields. The establishment of a prognostic signature and the extended analysis of the tumor microenvironment, immune correlation, and tumor mutation burden further suggest the important role of m7G in the development and development of this disease. This work will provide support for future research.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09791-y.

Visualization and Analysis of Single cell RNA-seq Data by Maximizing Correntropy based Non-negative Low Rank Representation

The exploration of single cell RNA-sequencing (scRNA-seq) technology generates a new perspective to analyze biological problems. One of the major applications of scRNA-seq data is to discover subtypes of cells by cell clustering. Nevertheless, it is challengeable for traditional methods to handle scRNA-seq data with high level of technical noise and notorious dropouts. To better analyze single cell data, a novel scRNA-seq data analysis model called Maximum correntropy criterion based Non-negative and Low Rank Representation (MccNLRR) is introduced. Specifically, the maximum correntropy criterion, as an effective loss function, is more robust to the high noise and large outliers existed in the data. Moreover, the low rank representation is proven to be a powerful tool for capturing the global and local structures of data. Therefore, some important information, such as the similarity of cells in the subspace, is also extracted by it. Then, an iterative algorithm on the basis of the half-quadratic optimization and alternating direction method is developed to settle the complex optimization problem. Before the experiment, we also analyze the convergence and robustness of MccNLRR. At last, the results of cell clustering, visualization analysis, and gene markers selection on scRNA-seq data reveal that MccNLRR method can distinguish cell subtypes accurately and robustly.