ARMCX Family Gene Expression Analysis and Potential Prognostic Biomarkers for Prediction of Clinical Outcome in Patients with Gastric Carcinoma

Identification of angiogenesis-related subtypes, the development of a prognosis model, and features of tumor microenvironment in colon cancer

Angiogenesis is associated with tumor progression, prognosis, and treatment effect. However, the angiogenesis' underlying mechanisms in the tumor microenvironment (TME) still remain unclear. Understanding the dynamic interactions between angiogenesis and TME in colon adenocarcinoma (COAD) is necessary. We downloaded the transcriptome data and corresponding clinical data of colon cancer patients from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases, respectively. We identified two distinct angiogenesis-related molecular subtypes (subtype A and subtype B) and assessed the clinical features, prognosis, and infiltrating immune cells of patients in the two subtypes. According to the prognostic differential genes, we defined two different gene clusters to further explore the correlation between angiogenesis and tumor heterogeneity. Then, we construct the prognostic risk scoring model angiogenesis-related gene (ARG-score) including seven genes (ARMCX2, latent transforming growth factor β binding protein 1, ADAM8, FABP4, CCL11, CXCL11, ITLN1) using Lasso-multivariate cox method. We analyzed the correlation between ARG-score and prognosis, clinicopathological features, TME, molecular feature, cancer stem cells (CSCs), and microsatellite instability (MSI) status. To assess the application value of ARG-score in clinical treatment, immunophenotype score was used to predict patients' immunotherapy response in colon cancer. We found the mutations of ARGs in TCGA-COAD dataset from genetic levels and discussed their expression patterns based on TCGA and GEO datasets. We observed important differences in clinicopathological features, prognosis, immune feature, molecular feature between the two molecular subgroups. Then, we established an ARG-score for predicting OS and validated its predictive capability. A high ARG-score characterized by higher transcription level of ARGs, suggested lower MSI-high (MSI-H), lower immune score, and worse clinical stage and survival outcome. Additionally, the ARG-score was remarkably related to the CSCs index and immunotherapy sensitivity. We found two new molecular subtypes and two gene clusters based on ARGs and established an ARG-score. Multilayered analysis revealed that ARGs were remarkably correlated to the heterogeneity of colon cancer patients and explained the process of tumorigenesis and progression better. The ARG-score can help us better assess patients' survival outcomes and provide guidance for individualized treatment.

The Expression of ARMCX1 in Gastric Cancer Contributes to Prognosis and Influences Chemotherapy

The altered expression of ARMCX1 in patients with gastric cancer has been reported frequently, yet its correlation to prognosis and chemotherapy needs to be unveiled. In combination of the gene expression data retrieved from TCGA database and bioinformatic analysis, this study discovered 590 differentially expressed genes in the cancerous biopsies isolated from gastric patients, compared with controls. Among which, ARMCX1 exhibited great potential to serve as a prognostic biomarker for gastric patients; furthermore, patients with low expression of ARMCX1 could be more sensitive to these 9 chemotherapeutic agents: A-770041, AMG-706, ATRA, BEZ235, bortezomib, CGP60474, dasatinib, HG-64-1, and pazopanib, rather than the other chemotherapeutic agents. This study helps the improvement of evaluating the prognosis of gastric cancer patients, and would help optimize chemotherapeutic strategies in consideration of the expression of ARMCX1.

Screening of crosstalk and pyroptosis-related genes linking periodontitis and osteoporosis based on bioinformatics and machine learning

Background and objective

This study aimed to identify crosstalk genes between periodontitis (PD) and osteoporosis (OP) and potential relationships between crosstalk and pyroptosis-related genes.

Methods

PD and OP datasets were downloaded from the GEO database and were performed differential expression analysis to obtain DEGs. Overlapping DEGs got crosstalk genes linking PD and OP. Pyroptosis-related genes were obtained from literature reviews. Pearson coefficients were used to calculate crosstalk and pyroptosis-related gene correlations in the PD and OP datasets. Paired genes were obtained from the intersection of correlated genes in PD and OP. PINA and STRING databases were used to conduct the crosstalk-bridge-pyroptosis genes PPI network. The clusters in which crosstalk and pyroptosis-related genes were mainly concentrated were defined as key clusters. The key clusters' hub genes and the included paired genes were identified as key crosstalk-pyroptosis genes. Using ROC curve analysis and XGBoost screened key genes. PPI subnetwork, gene-biological process and gene-pathway networks were constructed based on key genes. In addition, immune infiltration was analyzed on the PD dataset using the CIBERSORT algorithm.

Results

A total of 69 crosstalk genes were obtained. 13 paired genes and hub genes TNF and EGFR in the key clusters (cluster2, cluster8) were identified as key crosstalk-pyroptosis genes. ROC and XGBoost showed that PRKCB, GSDMD, ARMCX3, and CASP3 were more accurate in predicting disease than other key crosstalk-pyroptosis genes while better classifying properties as a whole. KEGG analysis showed that PRKCB, GSDMD, ARMCX3, and CASP3 were involved in neutrophil extracellular trap formation and MAPK signaling pathway pathways. Immune infiltration results showed that all four key genes positively correlated with plasma cells and negatively correlated with T cells follicular helper, macrophages M2, and DCs.

Conclusion

This study shows a joint mechanism between PD and OP through crosstalk and pyroptosis-related genes. The key genes PRKCB, GSDMD, ARMCX3, and CASP3 are involved in the neutrophil extracellular trap formation and MAPK signaling pathway, affecting both diseases. These findings may point the way to future research.

Neuregulin (NRG-1β) Is Pro-Myogenic and Anti-Cachectic in Respiratory Muscles of Post-Myocardial Infarcted Swine

Neuregulin-1β (NRG-1β) is a growth and differentiation factor with pleiotropic systemic effects. Because NRG-1β has therapeutic potential for heart failure and has known growth effects in skeletal muscle, we hypothesized that it might affect heart failure-associated cachexia, a severe co-morbidity characterized by a loss of muscle mass. We therefore assessed NRG-1β's effect on intercostal skeletal muscle gene expression in a swine model of heart failure using recombinant glial growth factor 2 (USAN-cimaglermin alfa), a version of NRG-1β that has been tested in humans with systolic heart failure. Animals received one of two intravenous doses (0.67 or 2 mg/kg) of NRG-1β bi-weekly for 4 weeks, beginning one week after infarct. Based on paired-end RNA sequencing, NRG-1β treatment altered the intercostal muscle gene expression of 581 transcripts, including genes required for myofiber growth, maintenance and survival, such as MYH3, MYHC, MYL6B, KY and HES1. Importantly, NRG-1β altered the directionality of at least 85 genes associated with cachexia, including myostatin, which negatively regulates myoblast differentiation by down-regulating MyoD expression. Consistent with this, MyoD was increased in NRG-1β-treated animals. In vitro experiments with myoblast cell lines confirmed that NRG-1β induces ERBB-dependent differentiation. These findings suggest a NRG-1β-mediated anti-atrophic, anti-cachexia effect that may provide additional benefits to this potential therapy in heart failure.

ARMC Subfamily: Structures, Functions, Evolutions, Interactions, and Diseases

Armadillo repeat-containing proteins (ARMCs) are widely distributed in eukaryotes and have important influences on cell adhesion, signal transduction, mitochondrial function regulation, tumorigenesis, and other processes. These proteins share a similar domain consisting of tandem repeats approximately 42 amino acids in length, and this domain constitutes a substantial platform for the binding between ARMCs and other proteins. An ARMC subfamily, including ARMC1∼10, ARMC12, and ARMCX1∼6, has received increasing attention. These proteins may have many terminal regions and play a critical role in various diseases. On the one hand, based on their similar central domain of tandem repeats, this ARMC subfamily may function similarly to other ARMCs. On the other hand, the unique domains on their terminals may cause these proteins to have different functions. Here, we focus on the ARMC subfamily (ARMC1∼10, ARMC12, and ARMCX1∼6), which is relatively conserved in vertebrates and highly conserved in mammals, particularly primates. We review the structures, biological functions, evolutions, interactions, and related diseases of the ARMC subfamily, which involve more than 30 diseases and 40 bypasses, including interactions and relationships between more than 100 proteins and signaling molecules. We look forward to obtaining a clearer understanding of the ARMC subfamily to facilitate further in-depth research and treatment of related diseases.

Validation of the Combined Biomarker for Prediction of Response to Checkpoint Inhibitor in Patients with Advanced Cancer

Although immune checkpoint inhibitors can induce durable responses in patients with multiple types of advanced cancer, only a limited number of patients have a known reliable biomarker. This study aimed to validate the IMmunotherapy Against GastrIc Cancer (IMAGiC) model, which was developed based on a previous study of four-gene and PD-L1 level, to predict immunotherapy response. We developed a clinical assay for formalin-fixed paraffin-embedded samples using quantitative real-time polymerase chain reaction to measure the expression level of the previously published four-gene set. The predictive performance was validated in a cohort of 89 patients with several advanced tumor types. The IMAGiC score was derived from tumor samples of 89 patients consisting of eight cancer types, and 73 out of 89 patients available for clinical response were analyzed with clinicopathological factors. The IMAGiC group (responder vs. non-responder) was determined with a specific value of the IMAGiC score as a cutoff, which was set by log-rank statistics for progression-free survival (PFS) divided the patients into 56 (76.7%) non-responders and 17 (23.3%) responders. Clinical responders (complete response/partial response) were higher in the IMAGiC responder group than in the non-responder group (70.6 vs. 21.4%). The median PFS of the IMAGiC responder group and non-responder was 20.8 months (95% CI 9.1-not reached) and 6.7 months (95% CI 4.9-11.1, p = 0.007), respectively. Among the 17 IMAGiC responders, 11 patients had tumor mutation burden-low and microsatellite-stable tumors. This study validated a predictive model based on a four-gene expression signature. Along with conventional biomarkers, our model could be useful for predicting response to immunotherapy in patients with advanced cancer.

Corrigendum to "ARMCX Family Gene Expression Analysis and Potential Prognostic Biomarkers for Prediction of Clinical Outcome in Patients with Gastric Carcinoma"

[This corrects the article DOI: 10.1155/2020/3575038.].