Predicting Panel of Metabolism and Immune-Related Genes for the Prognosis of Human Ovarian Cancer

Kynurenine in IDO1high cancer cell-derived extracellular vesicles promotes angiogenesis by inducing endothelial mitophagy in ovarian cancer

BACKGROUND

Mitophagy, a prominent cellular homeostasis process, has been implicated in modulating endothelial cell function. Emerging evidence suggests that extracellular vesicles (EVs) participate in intercellular communication, which could modulate tumor angiogenesis, a hallmark of ovarian cancer (OC) progression. However, the underlying mechanisms through how EVs regulate endothelial mitophagy associated with tumor angiogenesis during OC development remain obscure.

METHODS

The effect of cancer cell-derived EVs on endothelial mitophagy and its correlation with tumor angiogenesis and OC development were explored by in vitro and in vivo experiments. Multi-omics integration analysis was employed to identify potential regulatory mechanisms of cancer cell-derived EVs on endothelial mitophagy, which is involved in tumor angiogenesis associated with OC development. These insights were then further corroborated through additional experiments. An orthotopic OC mouse model was constructed to assess the antiangiogenic and therapeutic potential of the Indoleamine 2,3 dioxygenase-1 (IDO1) inhibitor.

RESULTS

Cancer cell-derived EVs promoted tumor angiogenesis via the activation of endothelial mitophagy, contributing to the growth and metastasis of OC. The aberrantly high expression of IDO1 mediated abnormal tryptophan metabolism in cancer cells and promoted the secretion of L-kynurenine (L-kyn)-enriched EVs, with associated high levels of L-kyn in EVs isolated from both the tumor tissues and patient plasma in OC. EVs derived from IDO1high ovarian cancer cells elevated nicotinamide adenine dinucleotide (NAD +) levels in endothelial cells via delivering L-kyn. Besides, IDO1high ovarian cancer cell-derived EVs upregulated sirt3 expression in endothelial cells by increasing acetylation modification. These findings are crucial for promoting endothelial mitophagy correlated with tumor angiogenesis. Notably, both endothelial mitophagy and tumor angiogenesis could be suppressed by the IDO1 inhibitor in the orthotopic OC mouse model.

CONCLUSIONS

Together, our findings unveil a mechanism of mitophagy in OC angiogenesis and indicate the clinical relevance of EV enriched L-kyn as a potential biomarker for tumorigenesis and progression. Additionally, IDO1 inhibitors might become an alternative option for OC adjuvant therapy.

Tryptophan metabolism enzymes are potential targets in ovarian clear cell carcinoma

AIM

As the second most prevalent subtype of epithelial ovarian cancers, ovarian clear cell carcinoma (OCCC) is known for its chemoresistance to conventional platinum-based therapy. In this work, we examined the tryptophan (Trp) metabolism enzymes' differential expression in patients with OCCC to assess the potential for personalised treatment.

METHODS

A total of 127 OCCC tissues were used to construct tissue microarrays, and immunohistochemistry (IHC) staining of the Trp enzymes IDO1, IDO2, TDO2 and IL4I1 was performed. The correlations between Trp enzyme expression and clinical characteristics were analysed.

RESULTS

Positive IDO1, IDO2, TDO2 and IL4I1 staining was identified in 26.8%, 94.5%, 75.6% and 82.7% of OCCC respectively. IDO1-positive samples were more common in the chemoresistant group than in the platinum-sensitive group (46.7% vs. 19.8%). Moreover, positive expression of IDO1, TDO2 and IL4I1 was related to advanced stage, metastasis, bilateral tumours, endometriosis and tumour rupture (p < 0.05) respectively. Univariate analysis revealed a significant association between bilateral tumours, lymph node metastasis, advanced stage, distant metastasis and aberrant cytology with a poor prognosis for OCCC, while the absence of residual tumour was correlated with a favourable outcome (p < 0.05). However, only bilateral tumours and lymph node metastases were related to a poor prognosis after multivariate analysis.

CONCLUSION

This is the first study to investigate the expression of the Trp enzymes IDO1, IDO2, TDO2 and IL4I1 in OCCC tissues. IDO2, TDO2 and IL4I1 were detected in the majority of OCCC. Clinical traits were correlated with IDO1, IDO2, TDO2 and IL4I1 expression. IDO1 may be used as a therapeutic target given the large percentage of chemoresistant cases with IDO1 expression. These results will aid the development of personalised therapies for OCCC.

MAVS integrates glucose metabolism and RIG-I-like receptor signaling

MAVS is an adapter protein involved in RIG-I-like receptor (RLR) signaling in mitochondria, peroxisomes, and mitochondria-associated ER membranes (MAMs). However, the role of MAVS in glucose metabolism and RLR signaling cross-regulation and how these signaling pathways are coordinated among these organelles have not been defined. This study reports that RLR action drives a switch from glycolysis to the pentose phosphate pathway (PPP) and the hexosamine biosynthesis pathway (HBP) through MAVS. We show that peroxisomal MAVS is responsible for glucose flux shift into PPP and type III interferon (IFN) expression, whereas MAMs-located MAVS is responsible for glucose flux shift into HBP and type I IFN expression. Mechanistically, peroxisomal MAVS interacts with G6PD and the MAVS signalosome forms at peroxisomes by recruiting TNF receptor-associated factor 6 (TRAF6) and interferon regulatory factor 1 (IRF1). By contrast, MAMs-located MAVS interact with glutamine-fructose-6-phosphate transaminase, and the MAVS signalosome forms at MAMs by recruiting TRAF6 and TRAF2. Our findings suggest that MAVS mediates the interaction of RLR signaling and glucose metabolism.

What is new in cancer-associated fibroblast biomarkers?

The tumor microenvironment is one of the important drivers of tumor development. Cancer-associated fibroblasts (CAFs) are a major component of the tumor stroma and actively participate in tumor development, invasion, metastasis, drug resistance, and other biological behaviors. CAFs are a highly heterogeneous group of cells, a reflection of the diversity of their origin, biomarkers, and functions. The diversity of CAF origin determines the complexity of CAF biomarkers, and CAF subpopulations expressing different biomarkers may play contrasting roles in tumor progression. In this review, we provide an overview of these emerging CAF biomarkers and the biological functions that they suggest, which may give a better understanding of the relationship between CAFs and tumor cells and be of great significance for breakthroughs in precision targeted therapy for tumors. Video Abstract.

Prognostic factors and Doxorubicin involved in malignant progression of meningioma

Meningioma was the most primary intracranial tumor, but the molecular characteristics and the treatment of malignant meningioma were still unclear. Nine malignant progression-related genes based prognostic signatures were identified by transcriptome analysis between benign meningioma and malignant meningioma. The external dataset GEO136661 and quantitative Real-time Polymerase Chain Reaction were used to verify the prognostic factors. has-miR-3605-5p, hsa-miR-664b-5p, PNRC2, BTBD8, EXTL2, SLFN13, DGKD, NSD2, and BVES were closed with malignant progression. Moreover, Doxorubicin was identified by Connectivity Map website with the differential malignant progression-related genes. CCK-8 assay, Edu assay, wound healing assay, and trans-well experiment were used to reveal that Doxorubicin could inhibit proliferation, migration and invasion of IOMM-Lee Cells.

Protein expression profiling identifies a prognostic model for ovarian cancer

Background

Owing to the high morbidity and mortality, ovarian cancer has seriously endangered female health. Development of reliable models can facilitate prognosis monitoring and help relieve the distress.

Methods

Using the data archived in the TCPA and TCGA databases, proteins having significant survival effects on ovarian cancer patients were screened by univariate Cox regression analysis. Patients with complete information concerning protein expression, survival, and clinical variables were included. A risk model was then constructed by performing multiple Cox regression analysis. After validation, the predictive power of the risk model was assessed. The prognostic effect and the biological function of the model were evaluated using co-expression analysis and enrichment analysis.

Results

394 patients were included in model construction and validation. Using univariate Cox regression analysis, we identified a total of 20 proteins associated with overall survival of ovarian cancer patients (p < 0.01). Based on multiple Cox regression analysis, six proteins (GSK3α/β, HSP70, MEK1, MTOR, BAD, and NDRG1) were used for model construction. Patients in the high-risk group had unfavorable overall survival (p < 0.001) and poor disease-specific survival (p = 0.001). All these six proteins also had survival prognostic effects. Multiple Cox regression analysis demonstrated the risk model as an independent prognostic factor (p < 0.001). In receiver operating characteristic curve analysis, the risk model displayed higher predictive power than age, tumor grade, and tumor stage, with an area under the curve value of 0.789. Analysis of co-expressed proteins and differentially expressed genes based on the risk model further revealed its prognostic implication.

Conclusions

The risk model composed of GSK3α/β, HSP70, MEK1, MTOR, BAD, and NDRG1 could predict survival prognosis of ovarian cancer patients efficiently and help disease management.

Identification of Crucial Genes and Infiltrating Immune Cells Underlying Sepsis-Induced Cardiomyopathy via Weighted Gene Co-Expression Network Analysis

Sepsis-induced cardiomyopathy (SIC), with a possibly reversible cardiac dysfunction, is a potential complication of septic shock. Despite quite a few mechanisms including the inflammatory mediator, exosomes, and mitochondrial dysfunction, having been confirmed in the existing research studies we still find it obscure about the overall situation of gene co-expression that how they can affect the pathological process of SIC. Thus, we intended to find out the crucial hub genes, biological signaling pathways, and infiltration of immunocytes underlying SIC. It was weighted gene co-expression network analysis that worked as our major method on the ground of the gene expression profiles: hearts of those who died from sepsis were compared to hearts donated by non-failing humans which could not be transplanted for technical reasons (GSE79962). The top 25 percent of variant genes were abstracted to identify 10 co-expression modules. In these modules, brown and green modules showed the strongest negative and positive correlation with SIC, which were primarily enriched in the bioenergy metabolism, immunoreaction, and cell death. Next, nine genes (LRRC39, COQ10A, FSD2, PPP1R3A, TNFRSF11B, IL1RAP, DGKD, POR, and THBS1) including two downregulated and seven upregulated genes which were chosen as hub genes that meant the expressive level of which was higher than the counterparts in control groups. Then, the gene set enrichment analysis (GSEA) demonstrated a close relationship of hub genes to the cardiac metabolism and the necroptosis and apoptosis of cells in SIC. Concerning immune cells infiltration, a higher level of neutrophils and B cells native and a lower level of mast cells resting and plasma cells had been observed in patients with SIC. In general, nine candidate biomarkers were authenticated as a reliable signature for deeper exploration of basic and clinical research studies on SIC.

Tumor Immune Microenvironment Related Gene-Based Model to Predict Prognosis and Response to Compounds in Ovarian Cancer

BACKGROUND

The tumor immune microenvironment (TIME) has been recognized to be an imperative factor facilitating the acquisition of many cancer-related hallmarks and is a critical target for targeted biological therapy. This research intended to construct a risk score model premised on TIME-associated genes for prediction of survival and identification of potential drugs for ovarian cancer (OC) patients.

METHODS AND RESULTS

The stromal and immune scores were computed utilizing the ESTIMATE algorithm in OC patient samples from The Cancer Genome Atlas (TCGA) database. Weighted gene co-expression network and differentially expressed genes analyses were utilized to detect stromal-and immune-related genes. The Least Absolute Shrinkage and Selection Operator (LASSO)-Cox regression was utilized for additional gene selection. The genes that were selected were utilized as the input for a stepwise regression to construct a TIME-related risk score (TIMErisk), which was then validated in Gene Expression Omnibus (GEO) database. For the evaluation of the protein expression levels of TIME regulators, the Human Protein Atlas (HPA) dataset was utilized, and for their biological functions, the TIMER and CIBERSORT algorithm, immunoreactivity, and Immune Cell Abundance Identifier (ImmuCellAI) were used. Possible OC medications were forecasted utilizing the Genomics of Drug Sensitivity in Cancer (GDSC) database and connectivity map (CMap). TIMErisk was developed based on ALPK2, CPA3, PTGER3, CTHRC1, PLA2G2D, CXCL11, and ZNF683. High TIMErisk was recognized as a poor factor for survival in the GEO and TCGA databases; subgroup analysis with FIGO stage, grade, lymphatic and venous invasion, debulking, and tumor site also indicated similar results. Functional immune cells corresponded to more incisive immune reactions, including secretion of chemokines and interleukins, natural killer cell cytotoxicity, TNF signaling pathway, and infiltration of activated NK cells, eosinophils, and neutrophils in patients with low TIMErisk. Several small molecular medications which may enhance the prognosis of patients in the TIMErisk subgroup were identified. Lastly, an enhanced predictive performance nomogram was constructed by compounding TIMErisk with the FIGO stage and debulking.

CONCLUSION

These findings may offer a valuable indicator for clinical stratification management and personalized therapeutic options for OC patients and may be a foundation for future mechanistic research of their association.