Coexpression Pattern Analysis of NPM1-Associated Genes in Chronic Myelogenous Leukemia

Overexpression of MTFR1 promotes cancer progression and drug-resistance on cisplatin and is related to the immune microenvironment in lung adenocarcinoma

OBJECTIVE

The roles of MTFR1 in the drug resistance of lung adenocarcinoma (LAC) to cisplatin remain unexplored. In this study, the expression, clinical values and mechanisms of MTFR1 were explored, and the relationship between MTFR1 expression and immune microenvironment was investigated in LAC using bioinformatics analysis, cell experiments, and meta-analysis.

METHODS

MTFR1 expression and clinical values, and the relationship between MTFR1 expression and immunity were explored, through bioinformatics analysis. The effects of MTFR1 on the growth, migration and cisplatin sensitivity of LAC cells were identified using cell counting kit-8, wound healing and Transwell experiments. Additionally, the mechanisms of drug resistance of LAC cells involving MTFR1 were investigated using western blotting.

RESULTS

MTFR1 was elevated in LAC tissues. MTFR1 overexpression was associated with sex, age, primary therapy outcome, smoking, T stage, unfavourable prognosis and diagnostic value and considered an independent risk factor for an unfavourable prognosis in patients with LAC. MTFR1 co-expressed genes involved in the cell cycle, oocyte meiosis, DNA replication and others. Moreover, interfering with MTFR1 expression inhibited the proliferation, migration and invasion of A549 and A549/DDP cells and promoted cell sensitivity to cisplatin, which was related to the inhibition of p-AKT, p-P38 and p-ERK protein expression. MTFR1 overexpression was associated with stromal, immune and estimate scores along with natural killer cells, pDC, iDC and others in LAC.

CONCLUSIONS

MTFR1 overexpression was related to the unfavourable prognosis, diagnostic value and immunity in LAC. MTFR1 also participated in cell growth and migration and promoted the drug resistance of LAC cells to cisplatin via the p-AKT and p-ERK/P38 signalling pathways.

Use of pain-related gene features to predict depression by support vector machine model in patients with fibromyalgia

The prevalence rate of depression is higher in patients with fibromyalgia syndrome, but this is often unrecognized in patients with chronic pain. Given that depression is a common major barrier in the management of patients with fibromyalgia syndrome, an objective tool that reliably predicts depression in patients with fibromyalgia syndrome could significantly enhance the diagnostic accuracy. Since pain and depression can cause each other and worsen each other, we wonder if pain-related genes can be used to differentiate between those with major depression from those without. This study developed a support vector machine model combined with principal component analysis to differentiate major depression in fibromyalgia syndrome patients using a microarray dataset, including 25 fibromyalgia syndrome patients with major depression, and 36 patients without major depression. Gene co-expression analysis was used to select gene features to construct support vector machine model. The principal component analysis can help reduce the number of data dimensions without much loss of information, and identify patterns in data easily. The 61 samples available in the database were not enough for learning based methods and cannot represent every possible variation of each patient. To address this issue, we adopted Gaussian noise to generate a large amount of simulated data for training and testing of the model. The ability of support vector machine model to differentiate major depression using microarray data was measured as accuracy. Different structural co-expression patterns were identified for 114 genes involved in pain signaling pathway by two-sample KS test (p < 0.001 for the maximum deviation D = 0.11 > Dcritical = 0.05), indicating the aberrant co-expression patterns in fibromyalgia syndrome patients. Twenty hub gene features were further selected based on co-expression analysis to construct the model. The principal component analysis reduced the dimension of the training samples from 20 to 16, since 16 components were needed to retain more than 90% of the original variance. The support vector machine model was able to differentiate between those with major depression from those without in fibromyalgia syndrome patients with an average accuracy of 93.22% based on the expression levels of the selected hub gene features. These findings would contribute key information that can be used to develop a clinical decision-making tool for the data-driven, personalized optimization of diagnosing depression in patients with fibromyalgia syndrome.

Characterization of the Ovine Vaginal Microbiome and Inflammation Patterns as an Improved Testing Model of Human Vaginal Irritation

The development of therapies targeted to improve the health of women has utilized direct vaginal delivery as a more effective and less toxic method of protection from HIV and other pathogens. Vaginal applicants and delivery devices that provide sustained effects have been met with increasing acceptability, but the efficacy and toxicity outcomes have not been successfully predicted by preclinical in vitro studies and animal modeling. We have explored the utilization of sheep as a model for testing the safety of vaginal applicants and devices based on spatial and structural similarities to the human vagina. As recently noted by the FDA, an additional safety measure is an impact on the vaginal microbiome (VMB) that is known to contribute to vaginal health and influence pathogen susceptibility and drug metabolism. To advance the utility of the sheep vaginal model, we completed a thorough molecular characterization of the ovine VMB utilizing both next-generation sequencing (NGS) and PCR methods. The process also created a custom PCR array to quantify ovine VMB community profiles in an affordable, higher throughput fashion. The results from vaginal swabs (>475 samples) collected from non-pregnant crossbred Dorset and Merino ewes treated with selected vaginal applicants or collected as sham samples established 16 VMB community types (VMB CTs). To associate VMB CTs with eubiosis or dysbiosis, we also completed custom ELISAs for six cytokines identifying IL1B, IL8, TNFa, and CXCL10 as useful markers to support the characterization of ovine vaginal inflammation. The results indicated that Pasteurella, Actinobacillus, Pseudomonas, Bacteroides, Leptotrichia, and E. coli were common markers of eubiosis (low inflammatory marker expression), and that Haemophilus, Ureaplasma, and Corynebacterium were associated with dysbiosis (high cytokine levels). Utilizing the optimized workflow, we also confirmed the utility of three commonly used vaginal applicants for impact on the VMB and inflammatory state, producing a dataset that supports the recommendation for the use of sheep for testing of vaginal applicants and devices as part of preclinical pipelines.

NPM1 Mutated, BCR-ABL1 Positive Myeloid Neoplasms: Review of the Literature

Breakpoint cluster region - Abelson (BCR-ABL1) chimeric protein and mutated Nucleophosmin (NPM1) are often present in hematological cancers, but they rarely coexist in the same disease. Both anomalies are considered founder mutations that inhibit differentiation and apoptosis, but BCR-ABL1 could act as a secondary mutation conferring a proliferative advantage to a pre-neoplastic clone. The 2016 World Health Organization (WHO) classification lists the provisional acute myeloid leukemia (AML) with BCR-ABL1, which must be diagnosed differentially from the rare blast phase (BP) onset of chronic myeloid leukemia (CML), mainly because of the different therapeutic approach in the use of tyrosine kinase inhibitors (TKI). Here we review the BCR/ABL1 plus NPMc+ published cases since 1975 and describe a case from our institution in order to discuss the clinical and molecular features of this rare combination, and report the latest acquisition about an occurrence that could pertain either to the rare AML BCR-ABL1 positive or the even rarer CML-BP with mutated NPM1 at the onset. Differential diagnosis is based on careful analysis of genotypic and phenotypic features and anamnestic, clinical evolution, and background data. Therapeutic decisions must consider the broader clinical aspects, the comparatively mild effects of TKI therapy versus the great benefit that might bring to most of the patients, as may be incidentally demonstrated by our case history.

Evaluation of the Prognostic Value of STEAP1 in Lung Adenocarcinoma and Insights Into Its Potential Molecular Pathways via Bioinformatic Analysis

Background

Upregulation of the six-transmembrane epithelial antigen of prostate-1 (STEAP1) is closely associated with prognosis of numerous malignant cancers. However, its role in lung adenocarcinoma (LUAD), the most common type of lung cancer, remains unknown. This study aimed to investigate the role of STEAP1 in the occurrence and progression of LUAD and the potential mechanisms underlying its regulatory effects.

Methods

STEAP1 mRNA and protein expression were analyzed in 40 LUAD patients via real-time PCR and western blotting, respectively. We accessed the clinical data of 522 LUAD patients from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) to investigate the expression and prognostic role of STEAP1 in LUAD. Further, we performed gene ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, and gene set enrichment analysis (GSEA) to elucidate the potential mechanism underlying the role of STEAP1 in LUAD. The protein-protein interaction (PPI) network of STEAP1 was analyzed using the Search Tool for the Retrieval of Interacting Genes (STRING) database, and hub genes with significant positive and negative associations with STEAP1 were identified and their role in LUAD prognosis was predicted.

Results

STEAP1 was significantly upregulated in LUAD patients and associated with LUAD prognosis. Further, TCGA data indicated that STEAP1 upregulation is correlated with the clinical prognosis of LUAD. GO and KEGG analysis revealed that the genes co-expressed with STEAP1 were primarily involved in cell division, DNA replication, cell cycle, apoptosis, cytokine signaling, NF-kB signaling, and TNF signaling. GSEA revealed that homologous recombination, p53 signaling pathway, cell cycle, DNA replication, apoptosis, and toll-like receptor signaling were highly enriched upon STEAP1 upregulation. Gene Expression Profiling Interactive Analysis (GEPIA) analysis revealed that the top 10 hub genes associated with STEAP1 expression were also associated with the LUAD prognosis.

Conclusion

STEAP1 upregulation potentially influences the occurrence and progression of LUAD and its co-expressed genes via regulation of homologous recombination, p53 signaling, cell cycle, DNA replication, and apoptosis. STEAP1 is a potential prognostic biomarker for LUAD.

Co-expression Pattern Analysis of miR-17-92 Target Genes in Chronic Myelogenous Leukemia

MicroRNAs (miRNAs) are post-transcriptional regulators that regulate gene expression by binding to the 3' untranslated region of target mRNAs. Mature miRNAs transcribed from the miR-17-92 cluster have an oncogenic activity, which are overexpressed in chronic-phase chronic myelogenous leukemia (CML) patients compared with normal individuals. Besides, the tyrosine kinase activity of BCR-ABL oncoprotein from the Philadelphia chromosome in CML can affect this miRNA cluster. Genes with similar mRNA expression profiles are likely to be regulated by the same regulators. We hypothesize that target genes regulated by the same miRNA are co-expressed. In this study, we aim to explore the difference in the co-expression patterns of those genes potentially regulated by miR-17-92 cluster between the normal and the CML groups. We applied a statistical method for gene pair classification by identifying a disease-specific cutoff point that classified the co-expressed gene pairs into strong and weak co-expression classes. The method effectively identified the differences in the co-expression patterns from the overall structure. Functional annotation for co-expressed gene pairs showed that genes involved in the metabolism processes were more likely to be co-expressed in the normal group compared to the CML group. Our method can identify the co-expression pattern difference from the overall structure between two different distributions using the distribution-based statistical method. Functional annotation further provides the biological support. The co-expression pattern in the normal group is regarded as the inter-gene linkages, which represents the healthy pathological balance. Dysregulation of metabolism may be related to CML pathology. Our findings will provide useful information for investigating the novel CML mechanism and treatment.