Hypoxanthine Guanine Phosphoribosyltransferase expression is negatively correlated with immune activity through its regulation of purine synthesis

Identification of Metabolism-Related Prognostic Biomarkers and Immune Features of Head and Neck Squamous Cell Carcinoma

We aimed to identify an effective metabolic subtype and risk score to predict survival and immunotherapy response in head and neck squamous cell carcinoma (HNSCC). Data were obtained from an online database. We screened significant prognostic metabolism-related genes between the normal and tumor groups using a series of bioinformatics methods. Based on the selected prognostic genes, we conducted a subtype analysis to identify significantly different subtypes in HNSCC. We then investigated survival, immune features, and hallmark differences among different subtypes. LASSO was utilized to identify optimal genes for the risk score model construction. Finally, distribution of the risk score samples was analyzed for different subtypes. A total of 32 significantly prognostic metabolism-related genes were screened, and all samples were grouped into two subtypes: cluster 1 and cluster 2. Cluster 1 had worse survival. Different immune cell infiltration (CD8 T cells, macrophages, and regulatory T cells) and immune checkpoint gene expression (PD-1 and CLAT-4) were observed between the two clusters. Twelve optimal genes were involved in risk score model, and high-risk group had poorer survival. Cluster 1 contained more high-risk samples (60%). Finally, four genes CAV1, GGT6, PYGL, and HS3ST1 were identified as significantly related to immune cells, and these genes were differentially expressed in the normal oral epithelial cells and HNSCC cells. The subtypes and risk score model in the study provide a promising biomarker for prognosis and immunotherapy response.

A novel TNFRSF1A mutation associated with TNF-receptor-associated periodic syndrome and its metabolic signature

OBJECTIVE

We describe a family with a novel mutation in the TNF Receptor Superfamily Member 1A (TNFRSF1A) gene causing TNF receptor-associated periodic syndrome (TRAPS) with renal AA amyloidosis.

METHODS

Case series of affected family members. We further investigated the plasma metabolome of these patients in comparison with healthy controls using mass spectrometry.

RESULTS

In all symptomatic family members, we detected the previously undescribed variant c.332A>G (p.Q111R) in the TNFRSF1A gene. Canakinumab proved an effective treatment option leading to remission in all treated patients. One patient with suspected renal amyloidosis showed near normalization of proteinuria under treatment. Analysis of the metabolome revealed 31 metabolic compounds to be upregulated and 35 compounds to be downregulated compared with healthy controls. The most dysregulated metabolites belonged to pathways identified as arginine biosynthesis, phenylalanine, tyrosine and tryptophan biosynthesis, and cysteine and methionine metabolism. Interestingly, the metabolic changes observed in all three TRAPS patients seemed independent of treatment with canakinumab and subsequent remission.

CONCLUSION

We present a novel mutation in the TNFRSF1A gene associated with amyloidosis. Canakinumab is an effective treatment for individuals with this new likely pathogenic variant. Alterations in the metabolome were most prominent in the pathways related to arginine biosynthesis, tryptophan metabolism, and metabolism of cysteine and methionine, and seemed to be unaffected by treatment with canakinumab. Further investigation is needed to determine the role of these metabolomic changes in the pathophysiology of TRAPS.

Metabolic reprogramming in cancer: Mechanisms and therapeutics

Cancer cells characterized by uncontrolled growth and proliferation require altered metabolic processes to maintain this characteristic. Metabolic reprogramming is a process mediated by various factors, including oncogenes, tumor suppressor genes, changes in growth factors, and tumor–host cell interactions, which help to meet the needs of cancer cell anabolism and promote tumor development. Metabolic reprogramming in tumor cells is dynamically variable, depending on the tumor type and microenvironment, and reprogramming involves multiple metabolic pathways. These metabolic pathways have complex mechanisms and involve the coordination of various signaling molecules, proteins, and enzymes, which increases the resistance of tumor cells to traditional antitumor therapies. With the development of cancer therapies, metabolic reprogramming has been recognized as a new therapeutic target for metabolic changes in tumor cells. Therefore, understanding how multiple metabolic pathways in cancer cells change can provide a reference for the development of new therapies for tumor treatment. Here, we systemically reviewed the metabolic changes and their alteration factors, together with the current tumor regulation treatments and other possible treatments that are still under investigation. Continuous efforts are needed to further explore the mechanism of cancer metabolism reprogramming and corresponding metabolic treatments.

Urine Metabolome Dynamics Discriminate Influenza Vaccination Response

Influenza represents a major and ongoing public health hazard. Current collaborative efforts are aimed toward creating a universal flu vaccine with the goals of both improving responses to vaccination and increasing the breadth of protection against multiple strains and clades from a single vaccine. As an intermediate step toward these goals, the current work is focused on evaluating the systemic host response to vaccination in both normal and high-risk populations, such as the obese and geriatric populations, which have been linked to poor responses to vaccination. We therefore employed a metabolomics approach using a time-course (n = 5 time points) of the response to human vaccination against influenza from the time before vaccination (pre) to 90 days following vaccination. We analyzed the urinary profiles of a cohort of subjects (n = 179) designed to evenly sample across age, sex, BMI, and other demographic factors, stratifying their responses to vaccination as “High”, “Low”, or “None” based on the seroconversion measured by hemagglutination inhibition assay (HAI) from plasma samples at day 28 post-vaccination. Overall, we putatively identified 15,903 distinct, named, small-molecule structures (4473 at 10% FDR) among the 895 samples analyzed, with the aim of identifying metabolite correlates of the vaccine response, as well as prognostic and diagnostic markers from the periods before and after vaccination, respectively. Notably, we found that the metabolic profiles could unbiasedly separate the high-risk High-responders from the high-risk None-responders (obese/geriatric) within 3 days post-vaccination. The purine metabolites Guanine and Hypoxanthine were negatively associated with high seroconversion (p = 0.0032, p < 0.0001, respectively), while Acetyl-Leucine and 5-Aminovaleric acid were positively associated. Further changes in Cystine, Glutamic acid, Kynurenine and other metabolites implicated early oxidative stress (3 days) after vaccination as a hallmark of the High-responders. Ongoing efforts are aimed toward validating these putative markers using a ferret model of influenza infection, as well as an independent cohort of human seasonal vaccination and human challenge studies with live virus.

A Metabolism-Related Gene Prognostic Index Bridging Metabolic Signatures and Antitumor Immune Cycling in Head and Neck Squamous Cell Carcinoma

Background

In the era of immunotherapy, predictive or prognostic biomarkers for head and neck squamous cell carcinoma (HNSCC) are urgently needed. Metabolic reprogramming in the tumor microenvironment (TME) is a non-negligible reason for the low therapeutic response to immune checkpoint inhibitor (ICI) therapy. We aimed to construct a metabolism-related gene prognostic index (MRGPI) for HNSCC bridging metabolic characteristics and antitumor immune cycling and identified the immunophenotype, genetic alternations, potential targeted inhibitors, and the benefit of immunotherapy in MRGPI-defined subgroups of HNSCC.

Methods

Based on The Cancer Genome Atlas (TCGA) HNSCC dataset (n = 502), metabolism-related hub genes were identified by the weighted gene co-expression network analysis (WGCNA). Seven genes were identified to construct the MRGPI by using the Cox regression method and validated with an HNSCC dataset (n = 270) from the Gene Expression Omnibus (GEO) database. Afterward, the prognostic value, metabolic activities, genetic alternations, gene set enrichment analysis (GSEA), immunophenotype, Connectivity map (cMAP), and benefit of immunotherapy in MRGPI-defined subgroups were analyzed.

Results

The MRGPI was constructed based on HPRT1, AGPAT4, AMY2B, ACADL, CKM, PLA2G2D, and ADA. Patients in the low-MRGPI group had better overall survival than those in the high-MRGPI group, consistent with the results in the GEO cohort (cutoff value = 1.01). Patients with a low MRGPI score display lower metabolic activities and an active antitumor immunity status and more benefit from immunotherapy. In contrast, a higher MRGPI score was correlated with higher metabolic activities, more TP53 mutation rate, lower antitumor immunity ability, an immunosuppressive TME, and less benefit from immunotherapy.

Conclusion

The MRGPI is a promising indicator to distinguish the prognosis, the metabolic, molecular, and immune phenotype, and the benefit from immunotherapy in HNSCC.

HPRT1 Promotes Chemoresistance in Oral Squamous Cell Carcinoma via Activating MMP1/PI3K/Akt Signaling Pathway

Simple Summary

Hypoxanthine phosphoribosyl transferase 1 (HPRT1) is traditionally believed to be a housekeeping gene, however, we found that highly expressed HPRT1 was associated with a poor prognosis and could promote resistance to cisplatin (CDDP) in OSCC cells in both in vitro and in vivo. Hence, HPRT1 can no longer be simply believed to be a housekeeping gene. HPRT1 over- expression indicates a worse prognosis and can improve CDDP resistance for patients with OSCC by promoting the MMP1/PI3K/Akt axis, and it may be a potential prognostic biomarker and thera- peutic target in OSCC.

Abstract

Hypoxanthine phosphoribosyl transferase 1 (HPRT1) is traditionally believed to be a housekeeping gene. However, recent reports have indicated that HPRT1 overexpression is associated with a poor prognosis in various types of cancers. Using The Cancer Genome Atlas (TCGA), HPRT1 was found to be highly expressed in various cancer types, especially in head and neck squamous cell carcinoma (HNSCC). Therefore, we measured HPRT1 expression in human cancer tissues and adjacent non-carcinoma tissues (ANT) and explored the relationship between HPRT1 expression and clinical pathological factors and prognosis in patients with oral squamous cell carcinoma (OSCC), a common type of HNSCC. We built OSCC cells with stable knockdown and overexpression of HPRT1 to observe its influence on chemoresistance and malignancy in vitro and vivo. We found that highly expressed HPRT1 was associated with a poor prognosis and could promote resistance to cisplatin (CDDP) in OSCC cells in both in vitro and in vivo. An RNA sequence assay was carried out to explore the mechanism of function of HPRT1, we found that HPRT1 could positively regulate the expression of MMP1 and the activation of the PI3K/AKT pathway, to regulate the resistance to CDDP of OSCC. In conclusion, HPRT1 can no longer be simply believed to be a housekeeping gene. HPRT1 overexpression indicates a worse prognosis and can improve CDDP resistance for patients with OSCC by promoting the MMP1/PI3K/Akt axis. HPRT1 may be a potential prognostic biomarker and therapeutic target in OSCC.

Overexpression and surface localization of HPRT in prostate cancer provides a potential target for cancer specific antibody mediated cellular cytotoxicity

We chose to evaluate Hypoxanthine Guanine Phosphoribosyltransferase (HPRT) as a possible biomarker for prostate cancer due to its involvement in nucleotide synthesis and cell cycle progression. We utilized two prostate cancer cell lines (PC3 and DU145) along with patient tissue and knockdowns to evaluate overall HPRT expression. The surface localization of HPRT was determined utilizing flow cytometry, confocal microscopy, and scanning electron microscopy followed by ADCC to evaluate targeting potential. We found significant upregulation of HPRT within malignant samples with approximately 47% of patients had elevated levels of HPRT compared to normal controls. We also observed a significant association between HPRT and the plasma membrane of DU145 cells (p = 0.0004), but found no presence on PC3 cells (p = 0.14). This was confirmed with scanning electron microscopy and confocal microscopy. ADCC experiments were performed to determine whether HPRT could be used as a target antigen for selective cell-mediated killing. We found that DU145 cells treated with HPRT antibodies had a significantly higher incidence of cell death than both isotype treated samples and PC3 cells treated with the same concentrations of HPRT antibody. Finally, we determined that p53 had a significant impact on HPRT expression both internally and on the surface of cancer cells. These results suggest HPRT as a possible biomarker target for the treatment of patients with prostate cancer.