Loss of FBP1 promotes proliferation, migration, and invasion by regulating fatty acid metabolism in esophageal squamous cell carcinoma

Decoding host-microbiome interactions through co-expression network analysis within the non-human primate intestine

The gut microbiome affects the health status of the host through complex interactions with the host's intestinal wall. These host-microbiome interactions may spatially vary along the physical and chemical environment of the intestine, but these changes remain unknown. This study investigated these intricate relationships through a gene co-expression network analysis based on dual transcriptome profiling of different intestinal sites-cecum, transverse colon, and rectum-of the primate common marmoset. We proposed a gene module extraction algorithm based on the graph theory to find tightly interacting gene modules of the host and the microbiome from a vast co-expression network. The 27 gene modules identified by this method, which include both host and microbiome genes, not only produced results consistent with previous studies regarding the host-microbiome relationships, but also provided new insights into microbiome genes acting as potential mediators in host-microbiome interplays. Specifically, we discovered associations between the host gene FBP1, a cancer marker, and polysaccharide degradation-related genes (pfkA and fucI) coded by Bacteroides vulgatus, as well as relationships between host B cell-specific genes (CD19, CD22, CD79B, and PTPN6) and a tryptophan synthesis gene (trpB) coded by Parabacteroides distasonis. Furthermore, our proposed module extraction algorithm surpassed existing approaches by successfully defining more functionally related gene modules, providing insights for understanding the complex relationship between the host and the microbiome.IMPORTANCEWe unveiled the intricate dynamics of the host-microbiome interactions along the colon by identifying closely interacting gene modules from a vast gene co-expression network, constructed based on simultaneous profiling of both host and microbiome transcriptomes. Our proposed gene module extraction algorithm, designed to interpret inter-species interactions, enabled the identification of functionally related gene modules encompassing both host and microbiome genes, which was challenging with conventional modularity maximization algorithms. Through these identified gene modules, we discerned previously unrecognized bacterial genes that potentially mediate in known relationships between host genes and specific bacterial species. Our findings underscore the spatial variations in host-microbiome interactions along the colon, rather than displaying a uniform pattern throughout the colon.

Leptin signalling regulates transcriptional differences in granulosa cells from genetically obese mice but not the activation of NLRP3 inflammasome

Obesity is associated with increased ovarian inflammation and the establishment of leptin resistance. We presently investigated the role of impaired leptin signalling on transcriptional regulation in granulosa cells (GCs) collected from genetically obese mice. Furthermore, we characterised the association between ovarian leptin signalling, the activation of the NOD-like receptor protein 3 (NLRP3) inflammasome and macrophage infiltration in obese mice. After phenotype characterisation, ovaries were collected from distinct group of animals for protein and mRNA expression analysis: (i) mice subjected to a diet-induced obesity (DIO) protocol, where one group was fed a high-fat diet (HFD) and another a standard chow diet (CD) for durations of 4 or 16 weeks; (ii) mice genetically deficient in the long isoform of the leptin receptor (ObRb; db/db); (iii) mice genetically deficient in leptin (ob/ob); and (iv) mice rendered pharmacologically hyperleptinemic (LEPT). Next, GCs from antral follicles isolated from db/db and ob/ob mice were subjected to transcriptome analysis. Transcriptional analysis revealed opposing profiles in genes associated with steroidogenesis and prostaglandin action between the genetic models, despite the similarities in body weight. Furthermore, we observed no changes in the mRNA and protein levels of NLRP3 inflammasome components in the ovaries of db/db mice or in markers of M1 and M2 macrophage infiltration. This contrasted with the downregulation of NLRP3 inflammasome components and M1 markers in ob/ob and 16-wk HFD-fed mice. We concluded that leptin signalling regulates NLRP3 inflammasome activation and the expression of M1 markers in the ovaries of obese mice in an ObRb-dependent and ObRb-independent manner. Furthermore, we found no changes in the expression of leptin signalling and NLRP3 inflammasome genes in GCs from db/db and ob/ob mice, which was associated with no effects on macrophage infiltration genes, despite the dysregulation of genes associated with steroidogenesis in homozygous obese db/db. Our results suggest that: (i) the crosstalk between leptin signalling, NLRP3 inflammasome and macrophage infiltration takes place in ovarian components other than the GC compartment; and (ii) transcriptional changes in GCs from homozygous obese ob/ob mice suggest structural rearrangement and organisation, whereas in db/db mice the impairment in steroidogenesis and secretory activity.

CircHIPK3 regulates fatty acid metabolism through miR-637/FASN axis to promote esophageal squamous cell carcinoma

The oncogenic role of circRNA in cancers including esophageal cancer (EC) has been well studied. However, whether and how circRNAs are involved in cancer cell metabolic processes remains largely unknown. Here, we reported that circRNA, circHIPK3, is highly expressed in ESCC cell lines and tissues. Knockdown of circHIPK3 significantly restrained cell proliferation, colony formation, migration, and invasion in vitro and inhibited tumor growth in vivo. Mechanistically, circHIPK3 was found to act as a ceRNA by sponging miR-637 to regulate FASN expression and fatty acid metabolism in ESCC cells. Anti-sense oligonucleotide (ASO) targeting circHIPK3 substantially inhibited ESCC both in vitro and in vivo. Therefore, these results uncover a modulatory axis constituting of circHIPK3/miR-637/FASN may be a potential biomarker and therapeutic target for ESCC in the clinic.

Metabolic subtypes and immune landscapes in esophageal squamous cell carcinoma: prognostic implications and potential for personalized therapies

BACKGROUND

This study aimed to identify metabolic subtypes in ESCA, explore their relationship with immune landscapes, and establish a metabolic index for accurate prognosis assessment.

METHODS

Clinical, SNP, and RNA-seq data were collected from 80 ESCA patients from the TCGA database and RNA-seq data from the GSE19417 dataset. Metabolic genes associated with overall survival (OS) and progression-free survival (PFS) were selected, and k-means clustering was performed. Immune-related pathways, immune infiltration, and response to immunotherapy were predicted using bioinformatic algorithms. Weighted gene co-expression network analysis (WGCNA) was conducted to identify metabolic genes associated with co-expression modules. Lastly, cell culture and functional analysis were performed using patient tissue samples and ESCA cell lines to verify the identified genes and their roles.

RESULTS

Molecular subtypes were identified based on the expression profiles of metabolic genes, and univariate survival analysis revealed 163 metabolic genes associated with ESCA prognosis. Consensus clustering analysis classified ESCA samples into three distinct subtypes, with MC1 showing the poorest prognosis and MC3 having the best prognosis. The subtypes also exhibited significant differences in immune cell infiltration, with MC3 showing the highest scores. Additionally, the MC3 subtype demonstrated the poorest response to immunotherapy, while the MC1 subtype was the most sensitive. WGCNA analysis identified gene modules associated with the metabolic index, with SLC5A1, NT5DC4, and MTHFD2 emerging as prognostic markers. Gene and protein expression analysis validated the upregulation of MTHFD2 in ESCA. MTHFD2 promotes the progression of ESCA and may be a potential therapeutic target for ESCA.

CONCLUSION

The established metabolic index and identified metabolic genes offer potential for prognostic assessment and personalized therapeutic interventions for ESCA, underscoring the importance of targeting metabolism-immune interactions in ESCA. MTHFD2 promotes the progression of ESCA and may be a potential therapeutic target for ESCA.

Post-treatment serum triglyceride: An effective biomarker for body fat mass and overall survival in esophageal squamous cell cancer patients treated with chemoradiotherapy

OBJECTIVES

Although lipids have been assessed for their possible roles in cancer survival prediction, studies on the association between serum triglyceride (TG) levels and the prognosis of esophageal squamous cell carcinoma (ESCC) patients are limited. This study aimed to evaluate whether serum TG is associated with outcomes in patients with ESCC and investigate any interaction between serum TG and clinical parameters, especially body fat mass.

MATERIALS AND METHODS

We conducted a prospective case study on patients diagnosed with ESCC between March 2012 and November 2018. We measured patients' serum TG levels before and after treatment. The association between serum TG and overall survival (OS) was evaluated using hazard ratios. We sought to determine a threshold point using optimal stratification. Survival analysis was performed using Kaplan-Meier curves and a Cox proportional hazards model.

RESULTS

Of the 257 participants diagnosed with ESCC, 200 (77.8%) were men. Median follow-up time was 22.4 months (range 3.3-92.4 months). Using univariate Cox proportional hazard analysis and subsequent multivariate analysis, post-TG levels, Karnofsky performance scores, T stages, and chemotherapy cycles were shown to be independent prognostic factors for OS (p < 0.05). The post-TG cut-off point to best classify patients with respect to time to mortality was 1.47 mmol/L. A post-TG level of ≥ 1.47 mmol/L could independently predict a better OS (hazard ratio: 0.55, 95% confidence interval: 0.37-0.79). The associations were consistent across the subtypes of clinical parameters. Furthermore, the post-body mass index, post-subcutaneous adipose tissue area, post-visceral adipose tissue area, post-total adiposity tissue area, and post-total adipose density exhibited a strong positive association with post-TG levels.

CONCLUSION

Post-TG levels were found to be a significant positive prognostic biomarker for body fat mass and OS in ESCC patients.

Seven Fatty Acid Metabolism-Related Genes as Potential Biomarkers for Predicting the Prognosis and Immunotherapy Responses in Patients with Esophageal Cancer

Background: Esophageal cancer (ESCA) is a major cause of cancer-related mortality worldwide. Altered fatty acid metabolism is a hallmark of cancer. However, studies on the roles of fatty acid metabolism-related genes (FRGs) in ESCA remain limited. Method: We identified differentially expressed FRGs (DE-FRGs). Then, the DE-FRGs prognostic model was constructed and validated using a comprehensive analysis. Moreover, the correlation between the risk model and clinical characteristics was investigated. A nomogram for predicting survival was established and evaluated. Subsequently, the difference in tumor microenvironment (TME) was compared between two risk groups. The sensitivity of key DE-FRGs to chemotherapeutic interventions and their correlation with immune cells were investigated. Finally, DEGs between two risk groups were measured and the prognostic value of key DE-FRGs in ESCA was confirmed in other databases. Results: A prognostic model was constructed based on seven selected DEG-FRGs. TNM staging and CD8+ T cells were significantly correlated with high-risk groups. Low-risk groups exhibited more infiltrated M0 macrophages, an activation of type II interferon (IFN-γ) responses, and were found to be more suitable for immunotherapy. Seven key DE-FRGs with prognostic value were found to be considerably influenced by different chemotherapy drugs. Conclusion: A prognostic model based on seven DE-FRGs may efficiently predict patient prognosis and immunotherapy response, helping to develop individualized treatment strategies in ESCA.

Targeting Strategies for Aberrant Lipid Metabolism Reprogramming and the Immune Microenvironment in Esophageal Cancer: A Review

Esophageal cancer is of high importance to occurrence, development, and treatment resistance. As evidenced by recent studies, pathways (e.g., Wnt/β-catenin, AMPK, and Hippo) are critical to the proliferation, differentiation, and self-renewal of esophageal cancer. In addition, the above pathways play a certain role in regulating esophageal cancer and act as potential therapeutic targets. Over the past few years, the function of lipid metabolism in controlling tumor cells and immune cells has aroused extensive attention. It has been reported that there are intricate interactions between lipid metabolism reprogramming between immune and esophageal cancer cells, whereas molecular mechanisms should be studied in depth. Immune cells have been commonly recognized as a vital player in the esophageal cancer microenvironment, having complex crosstalk with cancer cells. It is increasingly evidenced that the function of immune cells in the tumor microenvironment (TME) is significantly correlated with abnormal lipid metabolism. In this review, the latest findings in lipid metabolism reprogramming in TME are summarized, and the above findings are linked to esophageal cancer progression. Aberrant lipid metabolism and associated signaling pathways are likely to serve as a novel strategy to treat esophageal cancer through lipid metabolism reprogramming.

Fructose-1,6-bisphosphatase 1 (FBP1) is an independent biomarker associated with a favorable prognosis in esophageal adenocarcinoma

Introduction

Despite modern multimodal therapeutic regimens, the prognosis of esophageal adenocarcinoma (EAC) is still poor and there is a lack of biological markers estimating the patients’ prognosis. Fructose-1,6-biphosphatase (FBP1) is a key enzyme in gluconeogenesis and is associated with tumor initiation in several cancers. Therefore, this study aims to characterize its implication for EAC patients.

Methods and materials

A total of 571 EAC patients who underwent multimodal treatment between 1999 and 2017 were analyzed for FBP1 expression using immunohistochemistry.

Results

82.5% of the EACs show FBP1 expression in the tumor albeit with different intensities categorizing specimens accordingly into score 0 (no expression), score 1 (weak expression), score 2 (moderate expression) and score 3 (strong expression) (score 1 = 25.0%, score 2 = 35.9%, score 3 = 21.5%). Intratumoral FBP1 expression was significantly associated with a better prognosis (p = 0.024). This observation was particularly relevant among patients who received primary surgery without neoadjuvant treatment (p = 0.004). In multivariate analysis, elevated FBP1 expression was an independent biomarker associated with a favorable prognosis.

Discussion

Despite being associated with a favorable prognosis, the majority of patients with high FBP1 expression also require individualized therapy options to ensure long-term survival. Recently, it has been shown that the presence of the FBP1 protein increases the sensitivity of pancreatic cancer cells to the bromodomain and extraterminal domain (BET) inhibitor JQ1.

Conclusion

We described for the first time the prognostic and possibly therapeutic relevance of FBP1 in EAC. The efficiency of the BET inhibitor in EAC should be verified in clinical studies and special attention should be paid to the effects of neoadjuvant therapy on FBP1 expression.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00432-022-04025-x.

Establishment of a novel glycolysis-related prognostic gene signature for ovarian cancer and its relationships with immune infiltration of the tumor microenvironment

Background

Glycolysis affects tumor growth, invasion, chemotherapy resistance, and the tumor microenvironment. In this study, we aimed to construct a glycolysis-related prognostic model for ovarian cancer and analyze its relationship with the tumor microenvironment’s immune cell infiltration.

Methods

We obtained six glycolysis-related gene sets for gene set enrichment analysis (GSEA). Ovarian cancer data from The Cancer Genome Atlas (TCGA) database and two Gene Expression Omnibus (GEO) datasets were divided into two groups after removing batch effects. We compared the tumor environments' immune components in high-risk and low-risk groups and analyzed the correlation between glycolysis- and immune-related genes. Then, we generated and validated a predictive model for the prognosis of ovarian cancer using the glycolysis-related genes.

Results

Overall, 27/329 glycolytic genes were associated with survival in ovarian cancer, 8 of which showed predictive value. The tumor cell components in the tumor microenvironment did not differ between the high-risk and low-risk groups; however, the immune score differed significantly between groups. In total, 13/24 immune cell types differed between groups, including 10 T cell types and three other immune cell types. Eight glycolysis-related prognostic genes were related to the expression of multiple immune-related genes at varying degrees, suggesting a relationship between glycolysis and immune response.

Conclusions

We identified eight glycolysis-related prognostic genes that effectively predicted survival in ovarian cancer. To a certain extent, the newly identified gene signature was related to the tumor microenvironment, especially immune cell infiltration and immune-related gene expression. These findings provide potential biomarkers and therapeutic targets for ovarian cancer.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-021-03057-0.