Deubiquitinating Enzymes Orchestrate the Cancer Stem Cell-Immunosuppressive Niche Dialogue: New Perspectives and Therapeutic Potential

Cancer stem cells (CSCs) are sparks for igniting tumor recurrence and the instigators of low response to immunotherapy and drug resistance. As one of the important components of tumor microenvironment, the tumor associated immune microenvironment (TAIM) is driving force for the heterogeneity, plasticity and evolution of CSCs. CSCs create the inhibitory TAIM (ITAIM) mainly through four stemness-related signals (SRSs), including Notch-nuclear factor-κB axis, Hedgehog, Wnt and signal transducer and activator of transcription. Ubiquitination and deubiquitination in proteins related to the specific stemness of the CSCs have a profound impact on the regulation of ITAIM. In regulating the balance between ubiquitination and deubiquitination, it is crucial for deubiquitinating enzymes (DUBs) to cleave ubiquitin chains from substrates. Ubiquitin-specific peptidases (USPs) comprise the largest family of DUBs. Growing evidence suggests that they play novel functions in contribution of ITAIM, including regulating tumor immunogenicity, activating stem cell factors, upregulating the SRSs, stabilizing anti-inflammatory receptors, and regulating anti-inflammatory cytokines. These overactive or abnormal signaling may dampen antitumor immune responses. The inhibition of USPs could play a regulatory role in SRSs and reversing ITAIM, and also have great potential in improving immune killing ability against tumor cells, including CSCs. In this review, we focus on the USPs involved in CSCs signaling pathways and regulating ITAIM, which are promising therapeutic targets in antitumor therapy

Measurement of Visceral Fat: Should We Include Retroperitoneal Fat?

<div><p>Objective</p><p>Whether retroperitoneal fat should be included in the measurement of visceral fat remains controversial. We compared the relationships of fat areas in peritoneal, retroperitoneal, and subcutaneous compartments to metabolic syndrome, adipokines, and incident hypertension and diabetes.</p><p>Methods</p><p>We enrolled 432 adult participants (153 men and 279 women) in a community-based cohort study. Computed tomography at the umbilicus level was used to measure the fat areas.</p><p>Results</p><p>Retroperitoneal fat correlated significantly with metabolic syndrome (adjusted odds ratio (OR), 5.651, p<0.05) and the number of metabolic abnormalities (p<0.05). Retroperitoneal fat area was significantly associated with blood pressure, plasma glycemic indices, lipid profile, C-reactive protein, adiponectin (r = −0.244, P<0.05), and leptin (r = 0.323, p<0.05), but not plasma renin or aldosterone concentrations. During the 2.94±0.84 years of follow-up, 32 participants developed incident hypertension. Retroperitoneal fat area (hazard ration (HR) 1.62, p = 0.003) and peritoneal fat area (HR 1.62, p = 0.009), but not subcutaneous fat area (p = 0.14) were associated with incident hypertension. Neither retroperitoneal fat area, peritoneal fat area, nor subcutaneous fat areas was associated with incident diabetes after adjustment.</p><p>Conclusions</p><p>Retroperitoneal fat is similar to peritoneal fat, but differs from subcutaneous fat, in terms of its relationship with metabolic syndrome and incident hypertension. Retroperitoneal fat area should be included in the measurement of visceral fat for cardio-metabolic studies in human.</p></div

The relationship between metabolic syndrome and body fat in logistic regression models, using metabolic syndrome as the dependent variable.

<p>Body fat was logarithmically transformed for statistical analyses. Odds ratios (95% CI) were shown.</p>a<p>p<0.05.</p><p>The relationship between metabolic syndrome and body fat in logistic regression models, using metabolic syndrome as the dependent variable.</p

Hazard ratios (HRs) and 95% confidence interval (95% CI) of different fat components to predict the development of incident hypertension and incident diabetes during follow-up.

<p>Hazard ratios were normalized to show the effect of every 1 standard deviation increase in fat areas.</p>a<p>p<0.05.</p><p>Model 1: adjusted for age, sex, and family history of hypertension.</p><p>Model 2: adjusted for age, sex, and family history of diabetes.</p><p>Hazard ratios (HRs) and 95% confidence interval (95% CI) of different fat components to predict the development of incident hypertension and incident diabetes during follow-up.</p

Different fat compartments to predict the probability of incident hypertension.

<p>Kaplan-Meier failure curves for the probability of developing hypertension in subgroups divided by the median of (A) retroperitoneal fat area, (B) peritoneal fat area, and (C) subcutaneous fat area. P values by log-rank tests are shown.</p

Correlation coefficients (r) between body fat and metabolic variables in participants without medications for hypertension, diabetes, or dyslipidemia (N = 353).

<p>Body fat area was logarithmically transformed for statistical analysis.</p><p>BP, blood pressure; BMI, body mass index; WC, waist circumference; FPG, fasting plasma glucose; OGTT-2h-PG, plasma glucose at 2 h during oral glucose tolerance test; HbA1c, hemoglobin A1c; GOT, glutamic oxaloacetic transaminase; GPT, glutamic pyruvic transaminase; HDL, high-density lipoprotein; LDL, low-density lipoprotein; CRP, C-reactive protein.</p>a<p>p1: retroperitoneal fat, peritoneal fat or subcutaneous fat area vs the indicated metabolic variable.</p>b<p>p2: retroperitoneal fat vs the indicated metabolic variable, adjusted for peritoneal fat.</p>c<p>p3: compare with the correlation coefficient between retroperitoneal fat and the indicated metabolic variable.</p>d<p>log-transformed.</p><p>Correlation coefficients (r) between body fat and metabolic variables in participants without medications for hypertension, diabetes, or dyslipidemia (N = 353).</p

Clustering of metabolic abnormalities defined by MS in participants with body fat areas in the lowest, middle, or highest tertiles.

<p>(A) Retroperitoneal fat, (B) peritoneal fat, and (C) subcutaneous fat. *p<0.05 vs. lowest tertile, #p<0.05 vs. middle tertile.</p

Clinical characteristics of participants with and without metabolic syndrome (MS).

<p>Means (standard deviations) are shown.</p>a<p>Medians (interquartile ranges) of variables not normally distributed are shown. Statistical analyses were performed after log transformation.</p><p>OGTT-2h-PG, plasma glucose at 2 h during oral glucose tolerance test; HOMA, homeostasis model assessment; HDL, high-density lipoprotein; LDL, low-density lipoprotein.</p><p>Clinical characteristics of participants with and without metabolic syndrome (MS).</p

Image demonstration of determining abdominal fat distribution on a CT scan.

<p>Left, sample CT image obtained at the umbilicus level. Right, fat masks created for determining areas of subcutaneous fat (red, “S”), peritoneal fat (blue, “P”) and retroperitoneal fat (green, “R”) using methods described in the <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0112355#s2" target="_blank">Materials and Methods</a> section.</p