Affinity Isolation and Mass Spectrometry Identification of Prostacyclin Synthase (PTGIS) Subinteractome

Validating the role of PTGIS gene in colorectal cancer by bioinformatics analysis and in vitro experiments

Prostaglandin I2 synthase (PTGIS) is a member of the cytochrome P450 family. Studies have revealed that differential expression of the PTGIS gene is closely related to the pathological and physiological processes of many diseases, including breast cancer, oral squamous cell carcinoma, and head and neck cancer. However, the mechanism of action of the PTGIS gene in colorectal cancer is not fully understood. This study explored the role of PTGIS in colorectal cancer through comprehensive bioinformatics analysis and in vitro experiments, and found that the expression of PTGIS gene in colorectal cancer tissue was significantly lower than that in normal colorectal tissue (P < 0.05), and high expression of PTGIS gene was associated with poor prognosis in patients (P < 0.05). The KEGG results showed that PTGIS-related genes were mainly enriched in metabolic pathways, arachidonic acid metabolism, steroid biosynthesis, and cancer pathways. The expression of PTGIS may be related to immune infiltration. Cell experiments showed that PTGIS was expressed at a lower level in cancer. Overexpression of PTGIS inhibited apoptosis and promoted proliferation, invasion, and migration ability of SW480 colorectal cancer cells. Analysis of the PTGIS gene in this study provides a theoretical basis for further exploring the pathogenesis of colorectal cancer and finding more accurate new targets for early screening and treatment of the cancer.

Effects of Lipid Metabolism-Related Genes PTGIS and HRASLS on Phenotype, Prognosis, and Tumor Immunity in Lung Squamous Cell Carcinoma

Background

Lipid metabolism reprogramming played an important role in cancer occurrence, development, and immune regulation. The aim of this study was to identify and validate lipid metabolism-related genes (LMRGs) associated with the phenotype, prognosis, and immunological characteristics of lung squamous cell carcinoma (LUSC).

Methods

In the TCGA cohort, bioinformatics and survival analysis were used to identify lipid metabolism-related differentially expressed genes (DEGs) associated with the prognosis of LUSC. PTGIS/HRASLS knockdown and overexpression effects on the LUSC phenotype were analyzed in vitro experiments. Based on the expression distribution of PTGIS/HRASLS, LUSC patients were divided into two clusters by consensus clustering. Clinical information, prognosis, immune infiltration, expression of immune checkpoints, and tumor mutation burden (TMB) level were compared between the TCGA and GSE4573 cohorts. The genes related to clustering and tumor immunity were screened by weighted gene coexpression network analysis (WGCNA), and the target module genes were analyzed by functional enrichment analysis, protein-protein interaction (PPI) analysis, and immune correlation analysis.

Results

191 lipid metabolism-related DEGs were identified, of which 5 genes were independent prognostic genes of LUSC. PTGIS/HRASLS were most closely related to LUSC prognosis and immunity. RT-qPCR, western blot (WB) analysis, and immunohistochemistry (IHC) showed that the expression of PTGIS was low in LUSC, while HRASLS was high. Functionally, PTGIS promoted LUSC proliferation, migration, and invasion, while HRASLS inhibited LUSC proliferation, migration, and invasion. The two clusters' expression and distribution of PTGIS/HRASLS had the opposite trend. Cluster 1 was associated with lower pathological staging (pT, pN, and pTNM stages), better prognosis, stronger immune infiltration, higher expression of immune checkpoints, and higher TMB level than cluster 2. WGCNA found that 28 genes including CD4 and IL10RA were related to the expression of PTGIS/HRASLS and tumor immune infiltration. PTGIS/HRASLS in the GSE4573 cohort had the same effect on LUSC prognosis and tumor immunity as the TCGA cohort.

Conclusions

PTGIS and HRASLS can be used as new therapeutic targets for LUSC as well as biomarkers for prognosis and tumor immunity, which has positive significance for guiding the immunotherapy of LUSC.

Prognostic value of nicotinamide adenine dinucleotide (NAD+) metabolic genes in patients with stomach adenocarcinoma based on bioinformatics analysis

Background

Because stomach adenocarcinoma (STAD) has a poor prognosis, it is necessary to explore new prognostic genes to stratify patients to guide existing individualized treatments.

Methods

Survival and clinical information, RNA-seq data and mutation data of STAD were acquired from The Cancer Genome Atlas (TCGA) database. Fifty-one nicotinamide adenine dinucleotide (NAD⁺) metabolism-related genes (NMRGs) were obtained from the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Reactome databases. Differentially expressed NMRGs (DE-NMRGs) between STAD and normal samples were screened, and consistent clustering analysis of STAD patients was performed based on the DE-NMRGs. Survival analysis, Gene Set Enrichment Analysis (GSEA), mutation frequency analysis, immune microenvironment analysis and drug prediction were performed among different clusters. Additionally, the differentially expressed genes (DEGs) among different clusters were selected, and the intersections of DEGs and DE-NMRGs were selected as the prognostic genes. Finally, quantitative real-time polymerase chain reaction (qRT-PCR) was performed on a human gastric mucosa epithelial cell line and cancer cell line to verify the expression of the prognostic genes.

Results

A total of 27 DE-NMRGs and two clusters were selected. There was a difference in survival between clusters 1 and 2. Furthermore, 18 DE-NMRGs were significantly different between clusters 1 and 2. The different Gene Ontology (GO) biological processes and KEGG pathways between clusters 1 and 2 were mainly enriched in cyclic nucleotide mediated signaling, synaptic signaling and hedgehog signaling pathway, etc. The somatic mutation frequencies were different between the two clusters, and TTN was the highest mutated gene in the patients of the clusters 1 and 2. Additionally, eight immune cells, immune score, stromal score, and estimate score were different between clusters 1 and 2. The patients in cluster 2 were sensitive to CTLA4 inhibitor treatment. Furthermore, the top five drugs (AP.24534, BX.795, Midostaurin, WO2009093927 and CCT007093) were significantly higher in cluster 1 than in cluster 2. Finally, three genes (AOX1, NNMT and PTGIS) were acquired as prognostic, and their expressions were consistent with the results of bioinformatics analysis.

Conclusions

Three prognostic genes related to NAD⁺ metabolism in STAD were screened out, which provides a theoretical basis and reference value for future treatment and prognosis of STAD.

Prostanoid Signaling in Cancers: Expression and Regulation Patterns of Enzymes and Receptors

Cancer-associated disturbance of prostanoid signaling provides an aberrant accumulation of prostanoids. This signaling consists of 19 target genes, encoding metabolic enzymes and G-protein-coupled receptors, and prostanoids (prostacyclin, thromboxane, and prostaglandins E2, F2α, D2, H2). The study addresses the systems biology analysis of target genes in 24 solid tumors using a data mining pipeline. We analyzed differential expression patterns of genes and proteins, promoter methylation status as well as tissue-specific master regulators and microRNAs. Tumor types were clustered into several groups according to gene expression patterns. Target genes were characterized as low mutated in tumors, with the exception of melanoma. We found at least six ubiquitin ligases and eight protein kinases that post-translationally modified the most connected proteins PTGES3 and PTGIS. Models of regulation of PTGIS and PTGIR gene expression in lung and uterine cancers were suggested. For the first time, we found associations between the patient’s overall survival rates with nine multigene transcriptomics signatures in eight tumors. Expression patterns of each of the six target genes have predictive value with respect to cytostatic therapy response. One of the consequences of the study is an assumption of prostanoid-dependent (or independent) tumor phenotypes. Thus, pharmacologic targeting the prostanoid signaling could be a probable additional anticancer strategy.

Enzymes in the Cholesterol Synthesis Pathway: Interactomics in the Cancer Context

A global protein interactome ensures the maintenance of regulatory, signaling and structural processes in cells, but at the same time, aberrations in the repertoire of protein-protein interactions usually cause a disease onset. Many metabolic enzymes catalyze multistage transformation of cholesterol precursors in the cholesterol biosynthesis pathway. Cancer-associated deregulation of these enzymes through various molecular mechanisms results in pathological cholesterol accumulation (its precursors) which can be disease risk factors. This work is aimed at systematization and bioinformatic analysis of the available interactomics data on seventeen enzymes in the cholesterol pathway, encoded by HMGCR, MVK, PMVK, MVD, FDPS, FDFT1, SQLE, LSS, DHCR24, CYP51A1, TM7SF2, MSMO1, NSDHL, HSD17B7, EBP, SC5D, DHCR7 genes. The spectrum of 165 unique and 21 common protein partners that physically interact with target enzymes was selected from several interatomic resources. Among them there were 47 modifying proteins from different protein kinases/phosphatases and ubiquitin-protein ligases/deubiquitinases families. A literature search, enrichment and gene co-expression analysis showed that about a quarter of the identified protein partners was associated with cancer hallmarks and over-represented in cancer pathways. Our results allow to update the current fundamental view on protein-protein interactions and regulatory aspects of the cholesterol synthesis enzymes and annotate of their sub-interactomes in term of possible involvement in cancers that will contribute to prioritization of protein targets for future drug development.

A new insight into subinteractomes of functional antagonists: Thromboxane (CYP5A1) and prostacyclin (CYP8A1) synthases

The current article aims to summarize all possible spectrum of protein-protein interactions for thromboxane A synthase (CYP5A1) and prostacyclin synthase (CYP8A1). These enzymes metabolize the same substrate (prostaglandin H₂ ) and can participate in cardiovascular, inflammatory, immune processes, and apoptosis modulation, as well as significantly influence the risk of cancers. Binary protein-protein and multiprotein complexes are of great importance in enzyme-regulating and signal-transduction pathways. However, protein partners of CYP5A1 and CYP8A1 are not yet fully identified, although both synthases are considered as prospective drug targets. At least 36 novel protein partners of CYP5A1 and CYP8A1 were revealed from different tissue types using an approach based on affinity isolation and mass spectrometry. Enrichment analysis showed that these proteins have different molecular functions: folding (refolding), unfolded protein and chaperon binding, protein transport (export/import), posttranslational modification, protein domain-specific binding, antioxidant activity, and glutathione homeostasis. A significant part of them, belonging to molecular chaperones, were common partners for CYP5A1 and CYP8A1, while other proteins were unique with the tissue-dependent distribution. New aspects of CYP5A1 and CYP8A1 interactomics and hetero-complex formation with different protein partners, including cytochrome P450s are discussed.

Prognostic value of prostaglandin I2 synthase and its correlation with tumor-infiltrating immune cells in lung cancer, ovarian cancer, and gastric cancer

BACKGROUND

Prostaglandin I2 synthase (PTGIS) is a crucial gene for the synthesis of prostaglandin I2, which has multiple roles in inflammation and immune modulation. However, studies on the prognostic value of PTGIS and its correlation with tumor-infiltrating immune cells in multiple cancers are still rare.

RESULTS

Multiple datasets of the Oncomine database showed that PTGIS was expressed at low levels in lung cancer and ovarian cancer compared to the levels in normal tissues. Kaplan-Meier plotter showed that high PTGIS was associated with poor overall survival and progression-free survival in lung, ovarian, and gastric cancers. Moreover, PTGIS expression was significantly positively correlated with infiltrating levels of macrophages and was strongly associated with a variety of immune markers, especially tumor-associated macrophages (TAMs) and T-regulatory cells (Tregs).

CONCLUSIONS

High expression of PTGIS could promote the infiltration of TAMs and Tregs in the tumor microenvironment and deteriorate outcomes of patients with lung, ovarian, and gastric cancers. These findings suggest that PTGIS could be taken as a potential biomarker of prognosis and tumor-infiltrating immune cells.

METHODS

PTGIS expression was investigated in different datasets of the Oncomine database, and its expression levels in various tumors and corresponding normal tissues were analyzed by the Tumor Immune Estimation Resource (TIMER). Then, the clinical prognostic value of PTGIS was assessed with online public databases. In addition, we initially explored the correlation between PTGIS and tumor-infiltrating immune cells by TIMER and Gene Expression Profiling Interactive Analysis (GEPIA).