Microarray analysis reveals Tmub1 as a cell cycle-associated protein in rat hepatocytes

Transmembrane and Ubiquitin-Like Domain-Containing 1 Promotes Glioma Growth and Indicates Unfavorable Prognosis

Background

Ubiquitin-related proteins have garnered increasing attention for their roles in tumorigenesis. Transmembrane and ubiquitin-like domain-containing 1 (TMUB1) is a recently discovered protein in the ubiquitin-like domain family, yet its involvement in glioma remains poorly understood. This study is aimed at investigating the functional significance and clinical relevance of TMUB1 in glioma.

Methods

We conducted a comprehensive analysis using two cohorts: a retrospective glioma cohort from our hospital and The Cancer Genome Atlas (TCGA) cohort. The mRNA levels of TMUB1 were assessed through reverse transcription-quantitative polymerase chain reaction (RT-qPCR). Clinical associations of TMUB1 in these cohorts were evaluated using correlation tests, chi-square tests, and survival analyses. Additionally, we performed TMUB1 knockdown in U87 and LN-229 human glioma cell lines, and cellular growth was assessed through the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl-2H-tetrazolium bromide (MTT) assay.

Results

Our results revealed that TMUB1 expression was elevated in glioma tissues compared to normal brain tissues. Notably, lower TMUB1 expression correlated with favorable characteristics such as lower World Health Organization (WHO) grade and 1p/19q codeletion. Moreover, patients with higher TMUB1 levels in glioma tissues exhibited worse prognosis in both TCGA cohort and our retrospective cohort, underscoring its prognostic significance in gliomas. Cellular experiments demonstrated that TMUB1 silencing suppressed the growth of glioma cells.

Conclusions

TMUB1 emerges as a novel and clinically relevant prognostic biomarker for gliomas. Targeting TMUB1 holds promise as a potential strategy for glioma treatment. This study contributes valuable insights into the multifaceted role of TMUB1 in glioma pathogenesis and its potential as a diagnostic and therapeutic target.

Label-Free Quantitative Proteomics Analysis of Adriamycin Selected Multidrug Resistant Human Lung Cancer Cells

The development of drug resistance in lung cancer is a major clinical challenge, leading to a 5-year survival rate of only 18%. Therefore, unravelling the mechanisms of drug resistance and developing novel therapeutic strategies is of crucial importance. This study systematically explores the novel biomarkers of drug resistance using a lung cancer model (DLKP) with a series of drug-resistant variants. In-depth label-free quantitative mass spectrometry-based proteomics and gene ontology analysis shows that parental DLKP cells significantly differ from drug-resistant variants, and the cellular proteome changes even among the drug-resistant subpopulations. Overall, ABC transporter proteins and lipid metabolism were determined to play a significant role in the formation of drug resistance in DKLP cells. A series of membrane-related proteins such as HMOX1, TMB1, EPHX2 and NEU1 were identified to be correlated with levels of drug resistance in the DLKP subpopulations. The study also showed enrichment in biological processes and molecular functions such as drug metabolism, cellular response to the drug and drug binding. In gene ontology analysis, 18 proteins were determined to be positively or negatively correlated with resistance levels. Overall, 34 proteins which potentially have a therapeutic and diagnostic value were identified.

Cell Sources and Influencing Factors of Liver Regeneration: A Review

Liver regeneration (LR) is a set of complicated mechanisms between cells and molecules in which the processes of initiation, maintenance, and termination of liver repair are regulated. Although LR has been studied extensively, there are still numerous challenges in gaining its full understanding. Cells for LR have a wide range of sources and the feature of plasticity, and regeneration patterns are not the same under different conditions. Many patients undergoing partial hepatectomy develop cirrhosis or steatosis. The changes of LR in these cases are not clear. Many types of cells participate in LR. Hepatocytes, biliary epithelial cells, hepatic progenitor cells, and human liver stem cells can serve as the cell sources for LR. However, different types and degrees of damage trigger the response from the most suitable cells. Exploring the cell sources of LR is of great significance for accelerating recovery of liver function under different pathological patterns and developing a cell therapy strategy to cope with the shortage of donors for liver transplantation. In clinical practice, the background of the liver influences regeneration. Fibrosis and steatosis create different LR microenvironments and signal molecule interaction patterns. In addition, factors such as partial hepatectomy, aging, platelets, nerves, hormones, bile acids, and gut microbiota are widely involved in this process. Understanding the influencing factors of LR has practical value for individualized treatment of patients with liver diseases. In this review, we have summarized recent studies and proposed our views. We discuss cell sources and the influential factors on LR to help in solving clinical problems.

Tmub1 Suppresses Hepatocellular Carcinoma by Promoting the Ubiquitination of ΔNp63 Isoforms

Transmembrane and ubiquitin-like domain-containing 1 (Tmub1) inhibits hepatocyte proliferation during liver regeneration, but its role in hepatocellular carcinoma (HCC) has yet to be revealed. In this study, we show that the levels of Tmub1 were significantly lower in HCC tissues and cells than they were in adjacent tissues and normal hepatic cells, and the low levels of Tmub1 indicated a poor prognosis in HCC patients. Xenograft growth assay revealed that Tmub1 represses HCC growth in vivo. In addition, Tmub1 formed a protein complex with apoptosis-associated protein tumor protein 63 (p63), especially with the ΔN isoforms (ΔNp63α, β, and γ). Further loss- and gain-of-function analyses indicated that Tmub1 promotes apoptosis of Hep3B and MHCC-LM3 cells. Tmub1 decreased the protein expression of ΔNp63, and the pro-apoptotic effect of Tmub1 can be reversed by ΔNp63 isoforms (α, β, and γ). Additionally, we report that Tmub1 promotes the ubiquitination and degradation of ΔNp63 proteins. Finally, we confirmed in HCC tissues that Tmub1 is negatively correlated with ΔNp63 and positively correlated with the level of apoptosis. Taken together, Tmub1 suppresses HCC by enhancing the ubiquitination and degradation of ΔNp63 isoforms to induce HCC cell apoptosis. These findings provide a potential strategy for the management of HCC.

Transmembrane and Ubiquitin-Like Domain Containing 1 Protein (TMUB1) Negatively Regulates Hepatocellular Carcinoma Proliferation via Regulating Signal Transducer and Activator of Transcription 1 (STAT1)

Background

Hepatocellular carcinoma (HCC) is a common malignancy, but the pathogenesis of HCC is unclear. TMUB1 has an inhibitory effect on normal hepatocytes, but its role in HCC has not been reported.

Material/Methods

We used immunohistochemistry to observe the expression of transmembrane and ubiquitin-like domain containing 1 protein (TMUB1) and signal transducer and activator of transcription 1 (STAT1) in 132 HCC tissue specimens. The expression of TMUB1, STAT1, and CCND1 in HCC cells were detected by quantitative polymerase chain reaction (qPCR) and western blotting. Cell Counting Kit-8 (CCK-8) and 5-ethynyl-2′-deoxyuridine (EdU) assays were used for detecting HCC cells proliferation, and Transwell assays were used for observing the invasion and migration of HCC cells.

Results

TMUB1 was negatively correlated with HCC pathological malignancy; low expression of TMUB1 indicated poor prognosis. TMUB1 inhibited proliferation but not metastasis in HCC cells. TMUB1 expression was positively correlated with STAT1 in 132 HCC tissues, TMUB1 promoted the expression of STAT1, and suppressed the expression of CCND1 in HCC cells.

Conclusions

TMUB1 negatively regulates hepatocellular carcinoma proliferation via regulating STAT1.

Analysis of differentially expressed genes among human hair follicle-derived iPSCs, induced hepatocyte-like cells, and primary hepatocytes

Background

Differentiation of human induced pluripotent stem cells (hiPSCs) into hepatocytes has important clinical significance in providing a new stem cell source for cell therapy of terminal liver disease. The differential gene expression analysis of hiPSCs, induced hepatocyte-like cells (HLCs), and primary human hepatocytes (PHHs) provides valuable information for optimization of an induction scheme and exploration of differentiation mechanisms.

Methods

Human hair follicle-derived iPSCs (hHF-iPSCs) were induced in vitro by mimicking the environment of a developing liver for 19 days. Expression of specific proteins was determined by immunofluorescence staining; the function of HLCs in storage and metabolism was identified by detecting periodic acid–Schiff, indocyanine green, and low-density lipoprotein. Based on the transcriptomics data, the differential gene expression profiles of hHF-iPSCs, HLCs, and PHHs were analyzed by Gene Ontology, Kyoto Encyclopedia of Genes and Genomes pathway, FunRich, and network analysis methods.

Results

HLCs were able to express albumin (ALB), alpha-fetoprotein, CYP3A4, and CYP7A1, and exhibited matured liver cell functions such as glycogen synthesis and storage. Complement and coagulation cascades and metabolic pathways ranked top in the downregulated list of HLCs/PHHs, while the cell cycle ranked top in the upregulated list of HLCs/PHHs. In the protein–protein interaction network, according to the degree rankings, TOP2A, CDK1, etc. were the important upregulated differentially expressed genes (DEGs), while ALB, ACACB, etc. were the major downregulated DEGs in HLCs/PHHs; the module analysis indicated that CDCA8, AURKB, and AURKA were the top upregulated DEGs in HLCs/PHHs.

Conclusions

We presented the differences in gene expression among hHF-iPSCs, HLCs, and PHHs through transcriptome array data and provided new ideas for the optimization of induction.

Electronic supplementary material

The online version of this article (10.1186/s13287-018-0940-z) contains supplementary material, which is available to authorized users.