MCEMP1 is a potential therapeutic biomarker associated with immune infiltration in advanced gastric cancer microenvironment

The role of RETN in stomach adenocarcinoma (STAD): insights from pan-cancer analysis of RNA-seq data based on the TCGA database

OBJECTIVE

To analyze the expression and significance of resistin (RETN) in pan-cancer and determine the prospect of RETN as a biomarker for STAD, providing a new diagnostic and therapeutic target for STAD.

METHODS

Based on the RNA-seq data from the TCGA database, RETN was comprehensively analyzed from the perspective of pan-cancer. Firstly, the expression of RETN, correlation of immune infiltration, diagnostic efficacy and prognostic value in pan-cancer were investigated. Subsequently, the potential value of RETN as a biomarker for STAD was evaluated. Finally, the potential mechanisms of RETN in STAD were explored, including single-cell expression, PPI, functional enrichment analysis, single-gene correlation, gene co-expression analysis, and immune infiltration correlation.

RESULTS

RETN is upregulated in most tumors and is associated with a variety of immune cells. As a potential biomarker, RETN showed good diagnostic efficacy and prognostic value in a variety of tumors. The prediction model based on pathological stage, sex, age and RETN has a good prediction effect on the overall survival of STAD patients. Single-cell expression analysis showed that RETN was mainly expressed by DC cells and mononuclear/macrophage cells in STAD. Subsequently, RETN and related genes were analyzed from the perspective of PPI and functional enrichment, and the results showed that these genes/proteins were mainly involved in regulating hormone secretion and insulin response. Finally, RETN is also closely associated with immune cells in STAD.

CONCLUSIONS

RETN is a promising biomarker related to the diagnosis and prognosis of STAD, and is expected to become a new diagnostic and therapeutic target for STAD.

Pharmacological inhibition of tyrosine protein-kinase 2 reduces islet inflammation and delays type 1 diabetes onset in mice

BACKGROUND

Tyrosine protein-kinase 2 (TYK2) mediates inflammatory signalling through multiple cytokines, including interferon-α (IFNα), interleukin (IL)-12, and IL-23. TYK2 missense mutations protect against type 1 diabetes (T1D), and inhibition of TYK2 shows promise in other autoimmune conditions.

METHODS

We evaluated the effects of specific TYK2 inhibitors (TYK2is) in pre-clinical models of T1D, including human β cells, cadaveric islets, iPSC-derived islets, and mouse models.

FINDINGS

In vitro studies showed that TYK2is prevented IFNα-induced β cell HLA class I up-regulation, endoplasmic reticulum stress, and chemokine production. In co-culture studies, pre-treatment of β cells with TYK2i prevented IFNα-induced antigenic peptide presentation and alloreactive and autoreactive T cell degranulation. In vivo administration of BMS-986202 in two mouse models of T1D (RIP-LCMV-GP and NOD mice) reduced systemic and tissue-localised inflammation, prevented β cell death, and delayed T1D onset. Transcriptional phenotyping of pancreatic islets, pancreatic lymph nodes, and spleen highlighted a role for TYK2 inhibition in modulating signalling pathways associated with inflammation, translational control, stress signalling, secretory function, immunity, and diabetes. Additionally, TYK2i treatment changed the composition of innate and adaptive immune cell populations in the blood and disease target tissues.

INTERPRETATION

These findings indicate that TYK2i has beneficial effects on both the immune and endocrine compartments in models of T1D, thus supporting a path forward for testing TYK2is in human T1D.

FUNDING

This work was supported by the National Institutes of Health (NIH), Veteran Affairs (VA), Breakthrough T1D, and gifts from the Sigma Beta Sorority, the Ball Brothers Foundation, and the George and Frances Ball Foundation.

Low Expression MCEMP1 Promotes Lung Adenocarcinoma Progression and its Clinical Value

BACKGROUND

Lung cancer is a highly prevalent tumor with a lack of biological markers that reflect its progression. Mast cell surface membrane protein 1 (MCEMP1, also known as C19ORF59) has not been reported in lung adenocarcinoma (LUAD).

OBJECTIVE

We aimed to investigate the role of MCEMP1 in LUAD.

METHODS

MCEMP1 expression in LUAD was analyzed using The Cancer Genome Atlas (TCGA) data, and conducted univariate and multivariate Cox regression analyses to evaluate the prognostic significance of MCEMP1 expression in TCGA. Tumor Immune Estimation Resource (TIMER) was used for examining the correlation between MCEMP1 expression and immune cell infiltration in LUAD. Furthermore, proliferation, migration, invasion, and colony-forming ability were investigated using LUAD cell lines.

RESULTS

MCEMP1 expression in LUAD patient tissues and was correlated with lymph node metastasis, differentiation level, and tumor status. The Area under Curve (AUC) value of MCEMP1 for the Receiver Operating Characteristic (ROC) curve analysis was 0.984. The immune infiltration analysis revealed a correlation between MCEMP1 expression and the extent of macrophages and neutrophil infiltration in LUAD. Additionally, MCEMP1 has low expression in clinical samples, MCEMP1 overexpressed in LUAD cells substantially reduced cell growth, migration, and invasion of malignant cells.

CONCLUSION

Low expression MCEMP1 promotes LUAD progression, which provides new insights and a potential biological target for future LUAD therapies.

Pharmacological inhibition of tyrosine protein-kinase 2 reduces islet inflammation and delays type 1 diabetes onset in mice

Tyrosine protein-kinase 2 (TYK2), a member of the Janus kinase family, mediates inflammatory signaling through multiple cytokines, including interferon-α (IFNα), interleukin (IL)-12, and IL-23. Missense mutations in TYK2 are associated with protection against type 1 diabetes (T1D), and inhibition of TYK2 shows promise in the management of other autoimmune conditions. Here, we evaluated the effects of specific TYK2 inhibitors (TYK2is) in pre-clinical models of T1D. First, human β cells, cadaveric donor islets, and iPSC-derived islets were treated in vitro with IFNα in combination with a small molecule TYK2i (BMS-986165 or a related molecule BMS-986202). TYK2 inhibition prevented IFNα-induced β cell HLA class I up-regulation, endoplasmic reticulum stress, and chemokine production. In co-culture studies, pre-treatment of β cells with a TYK2i prevented IFNα-induced activation of T cells targeting an epitope of insulin. In vivo administration of BMS-986202 in two mouse models of T1D (RIP-LCMV-GP mice and NOD mice) reduced systemic and tissue-localized inflammation, prevented β cell death, and delayed T1D onset. Transcriptional phenotyping of pancreatic islets, pancreatic lymph nodes (PLN), and spleen during early disease pathogenesis highlighted a role for TYK2 inhibition in modulating signaling pathways associated with inflammation, translational control, stress signaling, secretory function, immunity, and diabetes. Additionally, TYK2i treatment changed the composition of innate and adaptive immune cell populations in the blood and disease target tissues, resulting in an immune phenotype with a diminished capacity for β cell destruction. Overall, these findings indicate that TYK2i has beneficial effects in both the immune and endocrine compartments in models of T1D, thus supporting a path forward for testing TYK2 inhibitors in human T1D.

Mast-cell expressed membrane protein-1 is expressed in classical monocytes and alveolar macrophages in idiopathic pulmonary fibrosis and regulates cell chemotaxis, adhesion, and migration in a TGFβ-dependent manner

Mast-cell expressed membrane protein-1 (MCEMP1) is higher in patients with idiopathic pulmonary fibrosis (IPF) with an increased risk of death. Here we aimed to establish the mechanistic role of MCEMP1 in pulmonary fibrosis. We identified increased MCEMP1 expression in classical monocytes and alveolar macrophages in IPF compared with controls. MCEMP1 is upregulated by transforming growth factor beta (TGFβ) at the mRNA and protein levels in monocytic leukemia THP-1 cells. TGFβ-mediated MCEMP1 upregulation results from the cooperation of SMAD3 and SP1 via concomitant binding to SMAD3/SP1 cis-regulatory elements within the MCEMP1 promoter. We also found that MCEMP1 regulates TGFβ-mediated monocyte chemotaxis, adhesion, and migration. Our results suggest that MCEMP1 may regulate the migration and transition of monocytes to monocyte-derived alveolar macrophages during pulmonary fibrosis development and progression.NEW & NOTEWORTHY MCEMP1 is highly expressed in circulating classical monocytes and alveolar macrophages in IPF, is regulated by TGFβ, and participates in the chemotaxis, adhesion, and migration of circulating monocytes by modulating the effect of TGFβ in RHO activity.

Mast-Cell Expressed Membrane Protein-1 (MCEMP1) is expressed in classical monocytes and alveolar macrophages in Idiopathic Pulmonary Fibrosis and regulates cell chemotaxis, adhesion, and migration in a TGFβ dependent manner

Background

Mast-Cell Expressed Membrane Protein-1 (MCEMP1) is higher in Idiopathic Pulmonary Fibrosis (IPF) patients with increased risk of death and poor outcomes. Here we seek to establish the mechanistic role of MCEMP1 in pulmonary fibrosis.

Methods

MCEMP1 expression was analyzed by single-cell RNA sequencing, immunofluorescence in Peripheral Blood Mononuclear Cells (PBMC) as well as in lung tissues from IPF patients and controls. Chromatin Immunoprecipitation (ChiP) and Proximity Ligation Assay (PLA) were used to study the transcriptional regulation of MCEMP1 . Transient RNA interference and lentivirus transduction were used to knockdown and knock-in MCEMP1 in THP-1 cells to study chemotaxis, adhesion, and migration. Bulk RNA sequencing was used to identify the mechanisms by which MCEMP1 participates in monocyte function. Active RHO pull-down assay was used to validate bulk RNA sequencing results.

Results

We identified increased MCEMP1 expression in classical monocytes and alveolar macrophages in IPF compared to controls. MCEMP1 was upregulated by TGFβ at the mRNA and protein levels in THP-1. TGFβ-mediated MCEMP1 upregulation results from the cooperation of SMAD3 and SP1 via concomitant binding to SMAD3/SP1 cis -regulatory elements within the MCEMP1 promoter. In terms of its function, we found that MCEMP1 regulates TGFβ-mediated monocyte chemotaxis, adhesion, and migration. 400 differentially expressed genes were found to increase after TGFβ stimulation of THP-1, further increased in MCEMP1 knock-in cells treated with TGFβ and decreased in MCEMP1 knockdown cells treated with TGFβ. GO annotation analysis of these genes showed enrichment for positive regulation of RHO GTPase activity and signal transduction. While TGFβ enhanced RHO GTPase activity in THP-1 cells, this effect was attenuated following MCEMP1 knockdown.

Conclusion

MCEMP1 is highly expressed in circulating classical monocytes and alveolar macrophages in IPF. MCEMP1 is regulated by TGFβ and participates in the chemotaxis, adhesion, and migration of circulating monocytes by modulating the effect of TGFβ in RHO activity. Our results suggest that MCEMP1 may regulate the migration and transition of monocytes to monocyte-derived alveolar macrophages during pulmonary fibrosis development and progression.

A prognostic model based on regulatory T-cell-related genes in gastric cancer: Systematic construction and validation

Human gastrointestinal tumours have been shown to contain massive numbers of tumour infiltrating regulatory T cells (Tregs), the presence of which are closely related to tumour immunity. This study was designed to develop new Treg-related prognostic biomarkers to monitor the prognosis of patients with gastric cancer (GC). Treg-related prognostic genes were screened from Treg-related differentially expressed genes in GC patients by using Cox regression analysis, based on which a prognostic model was constructed. Then, combined with RiskScore, survival curve, survival status assessment and ROC analysis, these genes were used to verify the accuracy of the model, whose independent prognostic ability was also evaluated. Six Treg-related prognostic genes (CHRDL1, APOC3, NPTX1, TREML4, MCEMP1, GH2) in GC were identified, and a 6-gene Treg-related prognostic model was constructed. Survival analysis revealed that patients had a higher survival rate in the low-risk group. Combining clinicopathological features, we performed univariate and multivariate regression analyses, with results establishing that the RiskScore was an independent prognostic factor. Predicted 1-, 3- and 5-year survival rates of GC patients had a good fit with the actual survival rates according to nomogram results. In addition patients in the low-risk group had higher tumour mutational burden (TMB) values. Gene Set Enrichment Analysis (GSEA) demonstrated that genes in the high-risk group were significantly enriched in pathways related to immune inflammation, tumour proliferation and migration. In general, we constructed a 6-gene Treg-associated GC prognostic model with good prediction accuracy, where RiskScore could act as an independent prognostic factor. This model is expected to provide a reference for clinicians to estimate the prognosis of GC patients.

Use of machine learning-based integration to develop an immune-related signature for improving prognosis in patients with gastric cancer

Gastric cancer is one of the most common malignancies. Although some patients benefit from immunotherapy, the majority of patients have unsatisfactory immunotherapy outcomes, and the clinical significance of immune-related genes in gastric cancer remains unknown. We used the single-sample gene set enrichment analysis (ssGSEA) method to evaluate the immune cell content of gastric cancer patients from TCGA and clustered patients based on immune cell scores. The Weighted Correlation Network Analysis (WGCNA) algorithm was used to identify immune subtype-related genes. The patients in TCGA were randomly divided into test 1 and test 2 in a 1:1 ratio, and a machine learning integration process was used to determine the best prognostic signatures in the total cohort. The signatures were then validated in the test 1 and the test 2 cohort. Based on a literature search, we selected 93 previously published prognostic signatures for gastric cancer and compared them with our prognostic signatures. At the single-cell level, the algorithms "Seurat," "SCEVAN", "scissor", and "Cellchat" were used to demonstrate the cell communication disturbance of high-risk cells. WGCNA and univariate Cox regression analysis identified 52 prognosis-related genes, which were subjected to 98 machine-learning integration processes. A prognostic signature consisting of 24 genes was identified using the StepCox[backward] and Enet[alpha = 0.7] machine learning algorithms. This signature demonstrated the best prognostic performance in the overall, test1 and test2 cohort, and outperformed 93 previously published prognostic signatures. Interaction perturbations in cellular communication of high-risk T cells were identified at the single-cell level, which may promote disease progression in patients with gastric cancer. We developed an immune-related prognostic signature with reliable validity and high accuracy for clinical use for predicting the prognosis of patients with gastric cancer.

Early peripheral blood MCEMP1 and HLA-DRA expression predicts COVID-19 prognosis

BACKGROUND

Mass vaccination has dramatically reduced the incidence of severe COVID-19, with most cases now presenting as self-limiting upper respiratory tract infections. However, those with co-morbidities, the elderly and immunocompromised, as well as the unvaccinated, remain disproportionately vulnerable to severe COVID-19 and its sequelae. Furthermore, as the effectiveness of vaccination wanes with time, immune escape SARS-CoV-2 variants could emerge to cause severe COVID-19. Reliable prognostic biomarkers for severe disease could be used as early indicator of re-emergence of severe COVID-19 as well as for triaging of patients for antiviral therapy.

METHODS

We performed a systematic review and re-analysis of 7 publicly available datasets, analysing a total of 140 severe and 181 mild COVID-19 patients, to determine the most consistent differentially regulated genes in peripheral blood of severe COVID-19 patients. In addition, we included an independent cohort where blood transcriptomics of COVID-19 patients were prospectively and longitudinally monitored previously, to track the time in which these gene expression changes occur before nadir of respiratory function. Single cell RNA-sequencing of peripheral blood mononuclear cells from publicly available datasets was then used to determine the immune cell subsets involved.

FINDINGS

The most consistent differentially regulated genes in peripheral blood of severe COVID-19 patients were MCEMP1, HLA-DRA and ETS1 across the 7 transcriptomics datasets. Moreover, we found significantly heightened MCEMP1 and reduced HLA-DRA expression as early as four days before the nadir of respiratory function, and the differential expression of MCEMP1 and HLA-DRA occurred predominantly in CD14+ cells. The online platform which we developed is publicly available at https://kuanrongchan-covid19-severity-app-t7l38g.streamlitapp.com/, for users to query gene expression differences between severe and mild COVID-19 patients in these datasets.

INTERPRETATION

Elevated MCEMP1 and reduced HLA-DRA gene expression in CD14+ cells during the early phase of disease are prognostic of severe COVID-19.

FUNDING

K.R.C is funded by the National Medical Research Council (NMRC) of Singapore under the Open Fund Individual Research Grant (MOH-000610). E.E.O. is funded by the NMRC Senior Clinician-Scientist Award (MOH-000135-00). J.G.H.L. is funded by the NMRC under the Clinician-Scientist Award (NMRC/CSAINV/013/2016-01). S.K. is funded by the NMRC under the Transition Award. This study was sponsored in part by a generous gift from The Hour Glass.

Integrative analysis of the proteome and transcriptome in gastric cancer identified LRP1B as a potential biomarker

Background: The aim of this study was to discover unique membrane proteins associated with gastric cancer (GC) in proteomics analysis. Methods: Using a data-independent acquisition strategy, we compared the relative expression levels of membrane proteins in GC. Results: A total of 2774 differentially expressed membrane proteins were identified between GC and normal cell lines. Conjoint analysis of transcriptomes and proteomes provided 11 potential biomarkers (GPRC5A, PSAT1, NUDCD1, RCC2, IPO4, FAM91A1, KANK2, PRADC1, NME4, METTL7A and LRP1B) for further exploration. Downregulation of LRP1B in GC was validated by immunohistochemistry. Moreover, LRP1B demonstrated an area under the receiver operating characteristic curve of 0.917 in differentiating GC from normal tissues. Conclusion: LRP1B was identified as a meaningful indicator assisting in GC detection and labeling of tumor boundaries.