KLHL4, a novel p53 target gene, inhibits cell proliferation by activating p21WAF/CDKN1A

Transcriptome analysis reveals genes associated with stem cell activation by physical exercise in the dentate gyrus of aged p16Ink4a knockout mice

Throughout adulthood neural stem cells divide in neurogenic niches-the dentate gyrus of the hippocampus and the subventricular zone-producing progenitor cells and new neurons. Stem cells self-renew, thus preserving their pool. Furthermore, the number of stem/progenitor cells in the neurogenic niches decreases with age. We have previously demonstrated that the cyclin-dependent kinase inhibitor p16Ink4a maintains, in aged mice, the pool of dentate gyrus stem cells by preventing their activation after a neurogenic stimulus such as exercise (running). We showed that, although p16Ink4a ablation by itself does not activate stem/progenitor cells, exercise strongly induced stem cell proliferation in p16Ink4a knockout dentate gyrus, but not in wild-type. As p16Ink4a regulates stem cell self-renewal during aging, we sought to profile the dentate gyrus transcriptome from p16Ink4a wild-type and knockout aged mice, either sedentary or running for 12 days. By pairwise comparisons of differentially expressed genes and by correlative analyses through the DESeq2 software, we identified genes regulated by p16Ink4a deletion, either without stimulus (running) added, or following running. The p16Ink4a knockout basic gene signature, i.e., in sedentary mice, involves upregulation of apoptotic, neuroinflammation- and synaptic activity-associated genes, suggesting a reactive cellular state. Conversely, another set of 106 genes we identified, whose differential expression specifically reflects the pattern of proliferative response of p16 knockout stem cells to running, are involved in processes that regulate stem cell activation, such as synaptic function, neurotransmitter metabolism, stem cell proliferation control, and reactive oxygen species level regulation. Moreover, we analyzed the regulation of these stem cell-specific genes after a second running stimulus. Surprisingly, the second running neither activated stem cell proliferation in the p16Ink4a knockout dentate gyrus nor changed the expression of these genes, confirming that they are correlated to the stem cell reactivity to stimulus, a process where they may play a role regulating stem cell activation.

Diepoxybutane induces the p53-dependent transactivation of the CCL4 gene that mediates apoptosis in exposed human lymphoblasts

Diepoxybutane (DEB) is the most toxic metabolite of the environmental chemical 1,3-butadiene. We previously demonstrated the occurrence of DEB-induced p53-mediated apoptosis in human lymphoblasts. The p53 protein functions as a master transcriptional regulator in orchestrating the genomic response to a variety of stress signals. Transcriptomic analysis indicated that C-C chemokine ligand 4 (CCL4) gene expression was elevated in a p53-dependent manner in DEB-exposed p53-proficient TK6 cells, but not in DEB-exposed p53-deficient NH32 cells. Thus, the objective of this study was to determine whether the CCL4 gene is a transcriptional target of p53 and deduce its role in DEB-induced apoptosis in human lymphoblasts. Endogenous and exogenous wild-type p53 transactivated the activity of the CCL4 promoter in DEB-exposed lymphoblasts, but mutant p53 activity on this promoter was reduced by ∼80% under the same experimental conditions. Knockdown of the upregulated CCL4 mRNA levels in p53-proficient TK6 cells inhibited DEB-induced apoptosis by ∼45%-50%. Collectively, these observations demonstrate for the first time that the CCL4 gene is upregulated by wild-type p53 at the transcriptional level, and this upregulation mediates apoptosis in DEB-exposed human lymphoblasts.

A prognostic gene signature for gastric cancer and the immune infiltration-associated mechanism underlying the signature gene, PLG

BACKGROUND

Globally, gastric cancer (GC) is a common and lethal solid malignant tumor. Identifying the molecular signature and its functions can provide mechanistic insights into GC development and new methods for targeted therapy.

METHODS

Differentially expressed genes (DEGs) and prognostic genes (from univariate Cox regression analysis) were overlapped to obtain prognostic DEGs. Subsequently, molecular modules and the functions of these prognostic DEGs were identified by Metascape and Gene Ontology (GO)/Kyoto Encyclopedia of Genes and Genomes (KEGG)/Gene Set Enrichment Analysis (GSEA) enrichment analyses, respectively. Protein-protein interaction (PPI) networks of up- and down-regulated prognostic DEGs in GC were analyzed using the MCC algorithm of the Cytohubba plug-in in Cytoscape. The prognostic gene signature was defined on hub genes of the PPI networks by least absolute shrinkage and selection operator (LASSO)-Cox regression analysis. Furthermore, the expressional level of PLG in our clinical GC samples was validated by quantitative PCR (qPCR), western blotting, and immunohistochemistry (IHC). Subsequently, the PLG expression-correlation analysis was performed to assess the role of PLG in GC progression. Immune infiltration analysis was performed by single-sample gene set enrichment analysis (ssGSEA) to assess the inhibitory effect of PLG on immune infiltration.

RESULTS

Firstly, Up- and down-regulated prognostic DEGs and hub genes in protein-protein interaction (PPI) networks in GC were identified. A prognostic five-gene signature (i.e., PLG, SPARC, FGB, SERPINE1, and KLHL41) was identified. Among the five genes, the relationship between plasminogen (PLG) and GC remains largely unclear. Moreover, the functions of PLG-correlated genes in GC, like 'fibrinolysis', 'hemostasis', 'ion channel complex', and 'transporter complex' were identified. In addition, PLG expression correlated negatively with the infiltration of almost all immune cell types. Interestingly, the expression of PLG was significantly and highly correlated with that of CD160, an immune checkpoint inhibitor.

CONCLUSION

Our findings defined a new five-gene signature for predicting GC prognosis, but more validation is required to assess the effects and mechanism of the five genes, especially PLG, for the development of new GC therapies.

The Dual Roles of MAGE-C2 in p53 Ubiquitination and Cell Proliferation Through E3 Ligases MDM2 and TRIM28

The tumor suppressor p53 is critical for the maintenance of genome stability and protection against tumor malignant transformation, and its homeostasis is usually regulated by ubiquitination. MDM2 is a major E3 ligase of p53 ubiquitination, and its activity is enhanced by TRIM28. TRIM28 also independently ubiquitinates p53 as an E3 ligase activated by MAGE-C2. Moreover, MAGE-C2 is highly expressed in various cancers, but the detailed mechanisms of MAGE-C2 involved in MDM2/TRIM28-mediated p53 ubiquitination remain unknown. Here, we found that MAGE-C2 directly interacts with MDM2 through its conserved MHD domain to inhibit the activity of MDM2 on p53 ubiquitination. Furthermore, TRIM28 acts as an MAGE-C2 binding partner and directly competes with MAGE-C2 for MDM2 interaction, thus releasing the inhibitory role of MAGE-C2 and promoting p53 ubiquitination. MAGE-C2 suppresses cell proliferation in TRIM28-deficient cells, but the overexpression of TRIM28 antagonizes the inhibitory role of MAGE-C2 and accumulates p53 ubiquitination to promote cell proliferation. This study clarified the molecular link of MAGE-C2 in two major E3 systems MDM2 and TRIM28 on p53 ubiquitination. Our results revealed the molecular function of how MAGE-C2 and TRIM28 contribute to p53 ubiquitination and cell proliferation, in which MAGE-C2 acts as a potential inhibitor of MDM2 and TRIM28 is a vital regulator for MAGE-C2 function in p53 protein level and cell proliferation. This work would be helpful to understand the regulation mechanism of tumor suppressor p53.

Nuclear Mechanisms Involved in Endocrine Resistance

Endocrine therapy is a standard treatment offered to patients with ERα (estrogen receptor α)-positive breast cancer. In endocrine therapy, ERα is either directly targeted by anti-estrogens or indirectly by aromatase inhibitors which cause estrogen deficiency. Resistance to these drugs (endocrine resistance) compromises the efficiency of this treatment and requires additional measures. Endocrine resistance is often caused by deregulation of the PI3K/AKT/mTOR pathway and/or cyclin-dependent kinase 4 and 6 activities allowing inhibitors of these factors to be used clinically to counteract endocrine resistance. The nuclear mechanisms involved in endocrine resistance are beginning to emerge. Exploring these mechanisms may reveal additional druggable targets, which could help to further improve patients' outcome in an endocrine resistance setting. This review intends to summarize our current knowledge on the nuclear mechanisms linked to endocrine resistance.

A Ten-N6-Methyladenosine (m6A)-Modified Gene Signature Based on a Risk Score System Predicts Patient Prognosis in Rectum Adenocarcinoma

OBJECTIVES

The study aims to analyze the expression of N6-methyladenosine (m6A)-modified genes in rectum adenocarcinoma (READ) and identify reliable prognostic biomarkers to predict the prognosis of READ.

MATERIALS AND METHODS

RNA sequence data of READ and corresponding clinical survival data were obtained from The Cancer Genome Atlas (TCGA) database. N6-methyladenosine (m6A)-modified genes in READ were downloaded from the "m6Avar" database. Differentially expressed m6A-modified genes in READ stratified by different clinicopathological characteristics were identified using the "limma" package in R. Protein-protein interaction (PPI) network and co-expression analysis of differentially expressed genes (DEGs) were performed using "STRING" and Cytoscape, respectively. Principal component analysis (PCA) was done using R. In addition, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were used to functionally annotate the differentially expressed genes in different subgroups. Univariate Cox regression analyses were conducted to identify the powerful independent prognostic factors in READ associated with overall survival (OS). A robust likelihood-based survival model was built using the "rbsurv" package to screen for survival-associated signature genes. The Support Vector Machine (SVM) was used to predict the prognosis of READ through the risk score of survival-associated signature genes. Correlation analysis were carried out using GraphPad prism 8.

RESULTS

We screened 974 differentially expressed m6A-modified genes among four types of READ samples. Two READ subgroups (group 1 and group 2) were identified by K means clustering according to the expression of DEGs. The two subgroups were significantly different in overall survival and pathological stages. Next, 118 differentially expressed genes between the two subgroups were screened and the expression of 112 genes was found to be related to the prognosis of READ. Next, a panel of 10 survival-associated signature genes including adamtsl1, csmd2, fam13c, fam184a, klhl4, olfml2b, pdzd4, sec14l5, setbp1, tmem132b was constructed. The signature performed very well for prognosis prediction, time-dependent receiver-operating characteristic (ROC) analysis displaying an area under the curve (AUC) of 0.863, 0.8721, and 0.8752 for 3-year survival rate, prognostic status, and pathological stage prediction, respectively. Correlation analysis showed that the expression levels of the 10 m6A-modified genes were positively correlated with that of m6A demethylase FTO and ALKBH5.

CONCLUSION

This study identified potential m6A-modified genes that may be involved in the pathophysiology of READ and constructed a novel gene expression panel for READ risk stratification and prognosis prediction.