MC1R regulates T regulatory cell differentiation through metabolic reprogramming to promote colon cancer.

BACKGROUND: Until 2021, colon cancer was a leading cancer globally. Early detection improves outcomes; however, advanced cases still having poor prognosis. Therefore, an understanding of associated molecular mechanisms is crucial for developing new preventive and therapeutic strategies for colon cancer. METHODS: The TCGA database was analyzed to assess melanocortin 1receptor (MC1R) expression in colon cancer and its link with patient prognosis. Further, models and diverse experimental techniques were employed to investigate the impact of MC1R on colon cancer progression and its underlying mechanism was elucidated. RESULTS: In a follow-up study of clinical patients, the important role of MC1R was identified in the development of colon cancer. First, MC1R was expressed more highly in colon tumor tissues than in adjacent tissues. In addition, MC1R was associated with colon cancer prognosis, and higher expression of MC1R tended to predict a worse prognosis. This conclusion was verified in MC1R-/- mice, which showed a greater resistance to tumor growth than wild-type mice, as expected. Further investigation revealed a significant change in the portion of Tregs in MC1R-/- mice, while the portion of CD4 + and CD8 + T cells remained unchanged. The in vitro experiments revealed a weaker ability of the MC1R-/- T cells to differentiate into Tregs. Previous studies report that the functional integrity of Tregs is interwoven with cellular metabolism. Therefore, MC1R was deduced to regulate the differentiation of Tregs by reprogramming the metabolism. As expected, MC1R-/- T cells exhibited weaker mitochondrial function and a lower aerobic oxidation capacity. Concurrently, the MC1R-/- T cells had stronger limiting effects on colon cancer cells. According to these results, the MC1R inhibitor was hypothesized as a potential therapeutic agent to suppress colon cancer. The results showed that upon MC1R suppression, the tumors in the mice developed more slowly, and the mice survived longer, potentially providing a novel strategy to treat clinical colon cancer. CONCLUSION: By regulating Tregs differentiation, MC1R overexpression in colon cancer correlates with poor prognosis, while MC1R inhibition shows potential as a therapeutic approach to slow tumor growth and enhance survival.

Leptin receptor Gln223Arg and Lys109Arg polymorphisms may be associated with HBV-related hepatocellular carcinoma risk: A system review and meta-analysis.

Increasing evidence has suggested a strong association of hepatocellular carcinoma (HCC) susceptibility and Gln223Arg (rs1137101) and Lys109Arg (rs1137100) polymorphisms in leptin receptor (LEPR) genes. To provide a quantitative assessment for such correlation, we reviewed all related systems and conducted meta-analysis for case and control researches. A literature search of Web of Science, EMBASE, PubMed, Scopus as well as China National Knowledge Infrastructure databases was collected. 95% confidence intervals (95% CIs) together with odds ratios (ORs) were calculated. Five case-control researches consisting of 1323 cases and 1919 control cases were incorporated into meta-analysis. Researches indicated A-allelic and AA genotype of rs1137101 were substantially related to boosted susceptibility of hepatitis B virus (HBV)-related HCC (mutant model, OR = 1.81, 95% CI = 1.36-2.41, p < .001; allelic model, OR = 1.55, 95% CI = 1.32-1.83, p < .001). On the contrary, we observed GG genotype of rs1137101 substantially related to reduced risk of HBV-related HCC (wild model, OR 0.59, 95%CI = 0.46-0.75, p < .001). We observed AA genotype of rs1137100 relevant to boosted HCC risk (mutant model, OR = 1.51, 95%CI = 1.14-2.01, p = .005) as well as in those with HBV-related HCCs (homozygous model, OR = 2.12, 95%CI = 1.49-3.02, p < .001; mutant model, OR = 1.67, 95%CI = 1.23-2.26, p = .001). G-allele and AA genotype of rs1137101 might be in connection with boosted HBV-related HCC susceptibility, and wild-type GG genotype might prevent diseases. AA genotype of rs1137100 might also improve HBV-related HCC susceptibility. Such conclusions ought to be validated by larger and better-designed researches.

Combining bulk and scRNA-seq to explore the molecular mechanisms governing the distinct efferocytosis activities of a macrophage subpopulation in PDAC.

Pancreatic ductal adenocarcinoma (PDAC), a very aggressive tumour, is currently the third leading cause of cancer-related deaths. Unfortunately, many patients face the issue of inoperability at the diagnostic phase leading to a quite dismal prognosis. The onset of metastatic processes has a crucial role in the elevated mortality rates linked to PDAC. Individuals with metastatic advances receive only palliative therapy and have a grim prognosis. It is essential to carefully analyse the intricacies of the metastatic process to enhance the prognosis for individuals with PDAC. Malignancy development is greatly impacted by the process of macrophage efferocytosis. Our current knowledge about the complete range of macrophage efferocytosis activities in PDAC and their intricate interactions with tumour cells is still restricted. This work aims to resolve communication gaps and pinpoint the essential transcription factor that is vital in the immunological response of macrophage populations. We analysed eight PDAC tissue samples sourced from the gene expression omnibus. We utilized several software packages such as Seurat, DoubletFinder, Harmony, Pi, GSVA, CellChat and Monocle from R software together with pySCENIC from Python, to analyse the single-cell RNA sequencing (scRNA-seq) data collected from the PDAC samples. This study involved the analysis of a comprehensive sample of 22,124 cells, which were classified into distinct cell types. These cell types encompassed endothelial and epithelial cells, PDAC cells, as well as various immune cells, including CD4+ T cells, CD8+ T cells, NK cells, B cells, plasma cells, mast cells, monocytes, DC cells and different subtypes of macrophages, namely C0 macrophage TGM2+, C1 macrophage PFN1+, C2 macrophage GAS6+ and C3 macrophage APOC3+. The differentiation between tumour cells and epithelial cells was achieved by the implementation of CopyKat analysis, resulting in the detection and categorization of 1941 PDAC cells. The amplification/deletion patterns observed in PDAC cells on many chromosomes differ significantly from those observed in epithelial cells. The study of Pseudotime Trajectories demonstrated that the C0 macrophage subtype expressing TGM2+ had the lowest level of differentiation. Additionally, the examination of gene set scores related to efferocytosis suggested that this subtype displayed higher activity during the efferocytosis process compared to other subtypes. The most active transcription factors for each macrophage subtype were identified as BACH1, NFE2, TEAD4 and ARID3A. In conclusion, the examination of human PDAC tissue samples using immunofluorescence analysis demonstrated the co-localization of CD68 and CD11b within regions exhibiting the presence of keratin (KRT) and alpha-smooth muscle actin (α-SMA). This observation implies a spatial association between macrophages, fibroblasts, and epithelial cells. There is variation in the expression of efferocytosis-associated genes between C0 macrophage TGM2+ and other macrophage cell types. This observation implies that the diversity of macrophage cells might potentially influence the metastatic advancement of PDAC. Moreover, the central transcription factor of different macrophage subtypes offers a promising opportunity for targeted immunotherapy in the treatment of PDAC.

LGTRL-DE: Local and Global Temporal Representation Learning with Demographic Embedding for in-hospital mortality prediction.

Predicting the patient's in-hospital mortality from the historical Electronic Medical Records (EMRs) can assist physicians to make clinical decisions and assign medical resources. In recent years, researchers proposed many deep learning methods to predict in-hospital mortality by learning patient representations. However, most of these methods fail to comprehensively learn the temporal representations and do not sufficiently mine the contextual knowledge of demographic information. We propose a novel end-to-end approach based on Local and Global Temporal Representation Learning with Demographic Embedding (LGTRL-DE) to address the current issues for in-hospital mortality prediction. LGTRL-DE is enabled by (1) a local temporal representation learning module that captures the temporal information and analyzes the health status from a local perspective through a recurrent neural network with the demographic initialization and the local attention mechanism; (2) a Transformer-based global temporal representation learning module that extracts the interaction dependencies among clinical events; (3) a multi-view representation fusion module that fuses temporal and static information and generates the final patient's health representations. We evaluate our proposed LGTRL-DE on two public real-world clinical datasets (MIMIC-III and e-ICU). Experimental results show that LGTRL-DE achieves area under receiver operating characteristic curve of 0.8685 and 0.8733 on the MIMIC-III and e-ICU datasets, respectively, outperforming several state-of-the-art approaches.

Correlation of DEPDC5 rs1012068 and rs5998152 Polymorphisms with Risk of Hepatocellular Carcinoma: A Systematic Review and Meta-Analysis.

Background: Emerging evidence has shown that two common genetic polymorphisms within the pleckstrin domain-containing protein 5 (DEPDC5), rs1012068 and rs5998152, may be associated with the risk of hepatocellular carcinoma (HCC), especially in those individuals chronically infected with the hepatitis C virus (HCV) or the hepatitis B virus (HBV). However, these findings have not been consistently replicated in the literature due to limited sample sizes or different etiologies of HCC. Thus, the present systematic review and meta-analysis were performed to resolve this inconsistency. Methods: The databases PubMed, Embase, Web of Science, the China National Knowledge Infrastructure, and Scopus were searched up to December 12, 2022. Data from relevant studies were pooled, and odds ratios and 95% confidence intervals were calculated. Results: A total of 11 case-control studies encompassing 2,609 cases and 8,171 controls on rs1012068 and three encompassing 411 cases and 1,448 controls on rs5998152 were included. Results indicated that the DEPDC5 rs1012068 polymorphism did not significantly increase HCC risk in the total population (allelic model (OR = 1.32, 95% CI = 1.04-1.67, P = 0.02); the recessive model (OR = 1.42, 95% CI = 0.96-2.10, P = 0.08); the dominant model (OR = 1.43, 95% CI = 1.09-1.87, P = 0.01); the homozygous model (OR = 1.61, 95% CI = 1.01-2.57, P = 0.05); the heterozygous model (OR = 1.39, 95% CI = 1.09-1.79, P = 0.009)). Subgroup analyses based on ethnicity and etiology revealed that the rs1012068 polymorphism, under all five genetic models, was associated with increased HCC risk in Asians or in individuals with chronic HBV infection but not in individuals with chronic HCV infection. A significant association was also observed between rs5998152 and HCV-related HCC risk in Asians chronically infected with HCV under allelic, dominant, and heterozygous models. Conclusion: Our study suggests that the DEPDC5 rs1012068 polymorphism increases HCC risk, especially in Asians with chronic HBV infection, while the rs5998152 polymorphism increases HCC risk in Asians with chronic HCV infection.