Establishment of a 5-gene risk model related to regulatory T cells for predicting gastric cancer prognosis

ADAMTS12, a novel prognostic predictor, promotes cell proliferation, migration, and invasion in head and neck squamous cell carcinoma

OBJECTIVES

The prognostic significance and potential carcinogenic mechanism of ADAM metallopeptidase with thrombospondin type 1 motif 12 (ADAMTS12) in head and neck squamous cell carcinoma (HNSC) remain unclear.

MATERIALS AND METHODS

Immunohistochemistry was used to analyze the correlation between ADAMTS12 protein expression and clinicopathological factors in tumor samples from 195 patients with HNSC. Based on clinicopathological data of patients, Cox regression and Kaplan-Meier analysis were used to identify the prognostic significance of the ADAMTS12 expression. The carcinogenicity of the ADAMTS12 in HNSC cells was analyzed by CCK-8 assay, the wound-healing assay, and transwell assays after transfection of ADAMTS12 overexpression or knock-down vector.

RESULTS

The expression of ADAMTS12 was up-regulated in HNSC compared with normal tissue, related to pathology grade and lymph node metastasis of patients with HNSC, which was an independent prognostic factor. ADAMTS12 overexpression facilitated cell viability, invasion, and migration of HNSC cells, while ADAMTS12 knock-down had inverse results. Moreover, enrichment analysis, ADAMTS12 overexpression assay, and ADAMTS12 knock-down assay confirmed that ADAMTS12 mediated the activation of P13K/Akt pathway in HNSC.

CONCLUSIONS

Our studies indicated that ADAMTS12 was a novel prognostic biomarker and potentially therapeutic target in HNSC.

ADAMTS12 is a stromal modulator in chronic liver disease

Adamalysins, a family of metalloproteinases containing a disintegrin and metalloproteinases (ADAMs) and ADAM with thrombospondin motifs (ADAMTSs), belong to the matrisome and play important roles in various biological and pathological processes, such as development, immunity and cancer. Using a liver cancer dataset from the International Cancer Genome Consortium, we developed an extensive in silico screening that identified a cluster of adamalysins co-expressed in livers from patients with hepatocellular carcinoma (HCC). Within this cluster, ADAMTS12 expression was highly associated with recurrence risk and poorly differentiated HCC signatures. We showed that ADAMTS12 was expressed in the stromal cells of the tumor and adjacent fibrotic tissues of HCC patients, and more specifically in activated stellate cells. Using a mouse model of carbon tetrachloride-induced liver injury, we showed that Adamts12 was strongly and transiently expressed after a 24 h acute treatment, and that fibrosis was exacerbated in Adamts12-null mice submitted to carbon tetrachloride-induced chronic liver injury. Using the HSC-derived LX-2 cell line, we showed that silencing of ADAMTS12 resulted in profound changes of the gene expression program. In particular, genes previously reported to be induced upon HSC activation, such as PAI-1, were mostly down-regulated following ADAMTS12 knock-down. The phenotype of these cells was changed to a less differentiated state, showing an altered actin network and decreased nuclear spreading. These phenotypic changes, together with the down-regulation of PAI-1, were offset by TGF-β treatment. The present study thus identifies ADAMTS12 as a modulator of HSC differentiation, and a new player in chronic liver disease.

Analysis of Zinc and Stromal Immunity in Disuse Osteoporosis: Mendelian Randomization and Transcriptomic Analysis

OBJECTIVE

Disuse osteoporosis is known to be primarily caused by a lack of exercise. However, the causal relationships between zinc and immunity and disuse osteoporosis remain unknown. This study investigated these relationships and their potential mechanisms.

METHODS

This study was an integrative study combining genome-wide association studies and transcriptomics. Two-sample Mendelian randomization analysis (MR) was used to analyze the causal relationships between exposures (zinc, immunity, physical activity) and the outcome (osteoporosis) with the aid of single-nucleotide polymorphisms (SNPs) as instrumental variables (IVs). Four models, MR-Egger, inverse variance weighted, weighted median and MR-Pleiotrophy RESidual Sum and Outlier (MRPRESSO), were used to calculate odds ratio values. Sensitivity and heterogeneity analyses were also performed using MRPRESSO and MR-Egger methods. The mRNA transcriptomic analysis was subsequently conducted. Zinc metabolism scores were acquired through single-sample Gene Set Enrichment Analysis algorithms. Stromal scores were obtained using the R Package "estimate" algorithms. Important Kyoto Encyclopedia of Genes and Genomes and Gene Ontology pathways were also derived through gene set variation analysis. Cytoscape software helped construct the transcription factor (TF)-mRNA-microRNA (miRNA) network. Virtual screening and molecular docking were performed. Polymerase chain reaction validation was also carried out in vivo.

RESULTS

Causal relationships were demonstrated between zinc and exercise (95% confidence interval [CI] = 1.30-2.95, p = 0.001), exercise and immunity (95% CI = 0.36-0.80, p = 0.002), exercise and osteoporosis (95% CI = 0.97-0.99, p = 0.0007), and immunity disorder and osteoporosis (95% CI = 1.30-2.03, p = 0.00002). One hundred and seventy-nine mRNAs in important modules were screened. Combining the differential expressional genes (DEGs) and the Boruta selection, six DEGs were screened (AHNAK, CSF2, ADAMTS12, SRA1, RUNX2, and SLC39A14). TF HOXC10 and miRNA hsa-miR-204 were predicted. Then, the TF-mRNA-miRNA network was successfully constructed. RUNX2 and SLC39A14 were identified as hub mRNAs in the TF-mRNA-miRNA network. Eventually, the novel small drug C6O4NH5 was designed according to the pharmacophore structure of SLC39A14. The docking energy for the novel drug was -5.83 kcal/mol. SLC39A14 and RUNX2 were downregulated (of statistical significance p-value < 0.05) in our animal experiment.

CONCLUSION

This study revealed that zinc had a protective causal relationship with disuse osteoporosis by promoting exercise and immunity. SLC39A14 and RUNX2 mRNA participated in this zinc-related mechanism.

Six potential biomarkers in septic shock: a deep bioinformatics and prospective observational study

Background

Septic shock occurs when sepsis is related to severe hypotension and leads to a remarkable high number of deaths. The early diagnosis of septic shock is essential to reduce mortality. High-quality biomarkers can be objectively measured and evaluated as indicators to accurately predict disease diagnosis. However, single-gene prediction efficiency is inadequate; therefore, we identified a risk-score model based on gene signature to elevate predictive efficiency.

Methods

The gene expression profiles of GSE33118 and GSE26440 were downloaded from the Gene Expression Omnibus (GEO) database. These two datasets were merged, and the differentially expressed genes (DEGs) were identified using the limma package in R software. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichments of DEGs were performed. Subsequently, Lasso regression and Boruta feature selection algorithm were combined to identify the hub genes of septic shock. GSE9692 was then subjected to weighted gene co-expression network analysis (WGCNA) to identify the septic shock-related gene modules. Subsequently, the genes within such modules that matched with septic shock-related DEGs were identified as the hub genes of septic shock. To further understand the function and signaling pathways of hub genes, we performed gene set variation analysis (GSVA) and then used the CIBERSORT tool to analyze the immune cell infiltration pattern of diseases. The diagnostic value of hub genes in septic shock was determined using receiver operating characteristic (ROC) analysis and verified using quantitative PCR (qPCR) and Western blotting in our hospital patients with septic shock.

Results

A total of 975 DEGs in the GSE33118 and GSE26440 databases were obtained, of which 30 DEGs were remarkably upregulated. With the use of Lasso regression and Boruta feature selection algorithm, six hub genes (CD177, CLEC5A, CYSTM1, MCEMP1, MMP8, and RGL4) with expression differences in septic shock were screened as potential diagnostic markers for septic shock among the significant DEGs and were further validated in the GSE9692 dataset. WGCNA was used to identify the co-expression modules and module-trait correlation. Enrichment analysis showed significant enrichment in the reactive oxygen species pathway, hypoxia, phosphatidylinositol 3-kinases (PI3K)/Protein Kinase B (AKT)/mammalian target of rapamycin (mTOR) signaling, nuclear factor-κβ/tumor necrosis factor alpha (NF-κβ/TNF-α), and interleukin-6 (IL-6)/Janus Kinase (JAK)/Signal Transducers and Activators of Transcription 3 (STAT3) signaling pathways. The receiver operating characteristic curve (ROC) of these signature genes was 0.938, 0.914, 0.939, 0.956, 0.932, and 0.914, respectively. In the immune cell infiltration analysis, the infiltration of M0 macrophages, activated mast cells, neutrophils, CD8 T cells, and naive B cells was more significant in the septic shock group. In addition, higher expression levels of CD177, CLEC5A, CYSTM1, MCEMP1, MMP8, and RGL4 messenger RNA (mRNA) were observed in peripheral blood mononuclear cells (PBMCs) isolated from septic shock patients than from healthy donors. Higher expression levels of CD177 and MMP8 proteins were also observed in the PBMCs isolated from septic shock patients than from control participants.

Conclusions

CD177, CLEC5A, CYSTM1, MCEMP1, MMP8, and RGL4 were identified as hub genes, which were of considerable value in the early diagnosis of septic shock patients. These preliminary findings are of great significance for studying immune cell infiltration in the pathogenesis of septic shock, which should be further validated in clinical studies and basic studies.

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.

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

BACKGROUND

Over the last decade, breakthroughs have been made in cancer immunotherapy. However, for advanced gastric cancer (AGC), the complexity and heterogeneity of the tumor microenvironment (TME) has been the biggest challenge for immunotherapy. Therefore, an intensive study on TME of AGC is necessary.

METHODS

ESTIMATE and CIBERSORT algorithms were applied to analyze the transcriptome data of AGC using TCGA database systematically. We identified mast cell-expressed membrane protein 1 (MCEMP1) as a potential prognostic marker by protein-protein interaction (PPI) and Univariate Cox regression. The expression of MCEMP1 was evaluated by immunohistochemistry (IHC) and quantitative real time PCR. We assessed prognostic values of MCEMP1 with use of Kaplan-Meier and Multivariate Cox regression analysis. Gene set enrichment analysis (GSEA) was used to analyze the molecular mechanism of MCEMP1. The correlation between MCEMP1 expression and tumor immune infiltration was analyzed by the TIMER database and CIBERSORT algorithm, which was confirmed by IHC.

RESULTS

The mRNA and protein expression of MCEMP1 was up-regulated substantially and related to poor survival in AGC. GSEA analysis revealed that MCEMP1 was involved in the immune-related signaling pathways. We further demonstrated that the expression of MCEMP1 was correlated with multiple immune cells and immune checkpoints. The results of IHC indicated that there was a positive correlation between PD-L1 expression and MCEMP1, suggesting that MCEMP1 may affect the prognosis of AGC patients by regulating immune infiltration and the function of immune cells.

CONCLUSION

MCEMP1 may serve as a biomarker associated with immune infiltration in TME and could be a potential therapeutic target for AGC patients.

The Expression, Prognostic Value, and Immunological Correlation of MCEMP1 and its Potential Role in Gastric Cancer

Purpose

Gastric cancer (GC) is a lethal cancer with a poor 5-year relative survival, which requires a new research perspective. Our study aims to explore the biological impact of the mast cell-expressed membrane protein 1 (MCEMP1) in GC, which includes its expression and potential biological functions.

Methods

The expression of MCEMP1 was assessed through public databases. The GO, KEGG, and GESA analyses were conducted to explore the biofunction of MCEMP1. And ssGSEA was used to analyze the infiltration of the immune cells for MCEMP1. The proliferation, migration, and invasion of GC cells were analyzed through CCK8, colony-forming, wound healing, Transwell, and Western blot assay.

Results

The expression of MCEMP1 was higher in GC tissues. Further, we found a close relationship between MCEMP1 and poorer prognosis of gastric cancer by prognostic analysis. The functional analysis showed that MCEMP1 is involved in immune, inflammation, and metabolism-related pathways. The ssGSEA analysis indicated MCEMP1 mRNA expression was associated with immune infiltration of multiple immune cells. In cellular experiments, the invasion and metastasis of gastric cancer cells could be promoted by regulating the rise of MCEMP1 expression. Western blot analysis showed that regulation of MCEMP1 expression can affect EMT-related protein expression and that NF-κB expression is involved in this process.

Conclusion

MCEMP1 shows a potential value for the prognosis in GC. And, abnormal expression of MCEMP1 in GC is correlated with tumor immune cell infiltration. In in vitro experiments, MCEMP1 can affect the proliferation, migration, and invasion of GC cells by regulating EMT, in which TLR4/NOD2/NF-κB was involved.

Comprehensive analysis of prognostic genes in gastric cancer

Background: Gastric cancer is associated with high mortality, and effective methods for predicting prognosis are lacking. We aimed to identify potential prognostic markers associated with the development of gastric cancer through bioinformatic analyses.

Methods: Gastric cancer-associated gene expression profiles were obtained from The Cancer Genome Atlas and Gene Expression Omnibus databases. The key genes involved in the development of gastric cancer were obtained by differential expression analysis, coexpression analysis, and short time-series expression miner (STEM) analysis. The potential prognostic value of differentially expressed genes was further evaluated using a Cox regression model and risk scores. Hierarchical clustering was applied to validate the impact of key genes on the overall survival of gastric cancer patients.

Results: A total of 1381 genes were consistently dysregulated in the development of gastric cancer. Among them, 186 genes affected the overall survival of gastric cancer patients. The following genes had areas under the receiver operating characteristic curve greater than 0.9 in both datasets and were therefore considered key genes: ADAM12, CEP55, LRFN4, INHBA, ADH1B, DPT, FAM107A, and LOC100506388. LRFN4, DPT, and LOC100506388 were identified as potential prognostic genes for gastric cancer through a nomogram. Overexpression of LRFN4 and LOC100506388 was associated with a higher risk of gastric cancer. Finally, we found that tumors were infiltrated with high levels of Th2 cells and mast cells, and the infiltration levels were associated with overall survival in gastric cancer patients.

Conclusions: We found that key dysregulated genes may have a prognostic value for the development of gastric cancer.

Early B-cell factors involve in the tumorigenesis and predict the overall survival of gastric cancer

Gastric cancer (GC) is a heavy health burden around the world, which is the fifth most frequent tumor and leads to the third most common cancer-related deaths. It is urgent to identify prognostic markers as the guideline for personalized treatment and follow-up. We accessed the prognostic value of Early B-cell factors (EBFs) in GC. A total of 415 GC tissues and 34 normal tissues from The Cancer Genome Atlas Stomach Adenocarcinoma (TCGA-STAD) cohort, 616 external patients from GSE15459, GSE22377, GSE51105, GSE62245 were enrolled for analysis. Univariate and multivariate Cox regression analyses were employed to evaluate the sole and integrative prognostic value of EBFs, respectively. Genetic alterations, DNA methylation of EBFs were also evaluated, as well as the involved signaling pathways. We revealed that increased EBFs associated with the poor prognosis of GC patients, the prognostic model was established in TCGA-STAD cohort, and validated in Gene Expression Omnibus (GEO) cohorts, with effectiveness in both HER2 positive and negative patients. DNA methylation was involved in the impact on prognosis. Cell cycle, immune-associated, and MAPK pathways were influenced by EBFs. Anti-CTLA4 immunotherapy is more suitable for EBFs determining high-risk groups, but not anti-PD-1/PD-L1 therapy. 5-Fluorouracil, methotrexate, vorinostat are suitable to inhibit the function of EBFs. Our new findings provide novel insight into the prediction of prognosis and clinical treatment of GC patients based on EBFs.

ADAMTS-12: Functions and Challenges for a Complex Metalloprotease

Nineteen members of the ADAMTS family of secreted zinc metalloproteinases are present in the human degradome. A wide range of different functions are being attributed to these enzymes and the number of their known substrates is considerably increasing in recent years. ADAMTSs can participate in processes such as fertility, inflammation, arthritis, neuronal and behavioral disorders, as well as cancer. Since its first annotation in 2001, ADAMTS-12 has been described to participate in different processes displayed by members of this family of proteinases. In this sense, ADAMTS-12 performs essential roles in modulation and recovery from inflammatory processes such as colitis, endotoxic sepsis and pancreatitis. ADAMTS-12 has also been involved in cancer development acting either as a tumor suppressor or as a pro-tumoral agent. Furthermore, participation of ADAMTS-12 in arthritis or in neuronal disorders has also been suggested through degradation of components of the extracellular matrix. In addition, ADAMTS-12 proteinase activity can also be modified by interaction with other proteins and thus, can be an alternative way of modulating ADAMTS-12 functions. In this review we revised the most relevant findings about ADAMTS-12 function on the 20th anniversary of its identification.