Identification and validation of a prognostic index based on a metabolic-genomic landscape analysis of ovarian cancer

An 11-gene glycosyltransferases-related model for the prognosis of patients with bladder urothelial carcinoma: development and validation based on TCGA and GEO datasets

Background

Bladder urothelial carcinoma (BLCA) is a highly heterogeneous cancer with a wide range of prognoses, ranging from low-grade non-muscle-invasive bladder cancer (NMIBC), which has a good prognosis but a high recurrence rate, to high-grade muscle-invasive bladder cancer (MIBC), which has a poor prognosis. Glycosylation dysregulation plays a significant role in cancer development. Therefore, this study aimed to investigate the role of glycosyltransferases (GT)-related genes in the prognosis of BLCA and to develop a prognostic model based on these genes to predict overall survival (OS) and assess its clinical application.

Methods

The Cancer Genome Atlas (TCGA)-BLCA dataset, comprising 411 tumor and 19 normal samples. The validation set, GSE13507 from the Gene Expression Omnibus (GEO) database, included 165 primary bladder cancer samples with survival data. Differentially expressed GT-related genes (DEGRGs) in BLCA were identified in the training set. Predictive DEGRGs were used to construct risk score models by univariate Cox regression, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression. The predictive value of the models was assessed using Kaplan-Meier survival analysis and receiver operating characteristic (ROC) analysis in the training and validation sets. A nomogram was developed and its performance was evaluated with calibration curves. In addition, the relationship between the risk score and the tumor immune microenvironment was explored, and tumor immune dysfunction score (TIDE) and immune signature scores were used to predict the response to immunotherapy in BLCA patients.

Results

Thirty-three DEGRGs were identified in the comparison of BLCA patients with control samples. A risk score model was constructed based on 11 of these genes (GYS2, GALNTL6, GLT8D2, PYGB, B3GALNT2, GALNT15, ST6GALNAC3, ST8SIA6, CHPF, ALG9 and B3GALT2). The model performed well in predicting 3-, 5-, and 7-year overall survival (OS), with areas under the curve (AUC) of 0.65, 0.67, and 0.68, respectively. In addition, patients in the high-risk group had significantly lower survival than those in the low-risk group, and there were significant differences in immune status between the two groups. Based on age, tumor stage, T stage, and risk score, a Nomogram was constructed to predict the probability of OS, and the results of the calibration curves showed that the model had high predictive accuracy. Further analysis showed that the rejection score and TIDE were higher in the high-risk group, while the GT-related pathway was significantly upregulated in the high-risk group.

Conclusions

The 11 GT-related genes identified were associated with OS in BLCA patients, suggesting that the model has potential predictive value. At the same time, further research is needed to explore its role in clinical practice.

Role of ENPP1 in cancer pathogenesis: Mechanisms and clinical implications (Review)

Cancer is a significant societal, public health and economic challenge in the 21st century, and is the primary cause of death from disease globally. Ectonucleotide pyrophosphatase/phosphodiesterase (ENPP) serves a crucial role in several biochemical processes, including adenosine triphosphate hydrolysis, purine metabolism and regulation of signaling pathways. Specifically, ENPP1, a type II transmembrane glycoprotein and key member of the ENPP family, may be upregulated in tumor cells and implicated in the pathogenesis of multiple human cancers. The present review provides an overview of the structural, pathological and physiological roles of ENPP1 and discusses the potential mechanisms of ENPP1 in the development of cancers such as breast, colon, gallbladder, liver and lung cancers, and also summarizes the four major signaling pathways in tumors. Furthermore, the present review demonstrates that ENPP1 serves a crucial role in cell migration, proliferation and invasion, and that corresponding inhibitors have been developed and associated with clinical characterization.

METTL3-mediated m6A methylation of PYGB facilitates pancreatic ductal adenocarcinoma progression through the activation of NF-κB signaling

Brain Type Glycogen Phosphorylase (PYGB) has been revealed to participate in the progression of multiple human cancers. Nevertheless, the clinical significance and biological function of PYGB in pancreatic ductal adenocarcinoma (PAAD) remains unclarified. This study first analyzed the expression pattern, diagnostic value, and prognostic significance of PYGB in PAAD using the TCGA database. Subsequently, western blot assessed the protein expression of genes in PAAD cells. The viability, apoptosis, migration, and invasion of PAAD cells were assessed by CCK-8, TUNEL, and Transwell assays. Finally, in vivo experiment evaluated the effect of PYGB on PAAD tumor growth and metastasis. Through our investigation, it was revealed that PYGB had extremely high expression in PAAD and predicted a worse prognosis in patients with PAAD. Besides, the aggressiveness of PAAD cells could be suppressed or enhanced by depleting or supplementing PYGB. In addition, we demonstrated that METTL3 enhanced the translation of PYGB mRNA in an m6A-YTHDF1-dependent manner. Moreover, PYGB was revealed to regulate the malignant behaviors of PAAD cells by the mediation of the NF-κB signaling. Finally, PYGB depletion suppressed the growth and distant metastasis of PAAD in vivo. To conclude, our results indicated that METTL3-mediated m6A modification of PYGB exerted the tumor-promotive effect on PAAD through NF-κB signaling, suggesting PYGB is a potential therapeutic target in PAAD.

Identification of an Individualized Prognostic Biomarker for Serous Ovarian Cancer: A Qualitative Model

Serous ovarian cancer is the most common type of ovarian epithelial cancer and usually has a poor prognosis. The objective of this study was to construct an individualized prognostic model for predicting overall survival in serous ovarian cancer. Based on the relative expression orderings (Ea > Eb/Ea ≤ Eb) of gene pairs closely associated with serous ovarian prognosis, we tried constructing a potential individualized qualitative biomarker by the greedy algorithm and evaluated the performance in independent validation datasets. We constructed a prognostic biomarker consisting of 20 gene pairs (SOV-P20). The overall survival between high- and low-risk groups stratified by SOV-P20 was statistically significantly different in the training and independent validation datasets from other platforms (p < 0.05, Wilcoxon test). The average area under the curve (AUC) values of the training and three validation datasets were 0.756, 0.590, 0.630, and 0.680, respectively. The distribution of most immune cells between high- and low-risk groups was quite different (p < 0.001, Wilcoxon test). The low-risk patients tended to show significantly better tumor response to chemotherapy than the high-risk patients (p < 0.05, Fisher’s exact test). SOV-P20 achieved the highest mean index of concordance (C-index) (0.624) compared with the other seven existing prognostic signatures (ranging from 0.511 to 0.619). SOV-P20 is a promising prognostic biomarker for serous ovarian cancer, which will be applicable for clinical predictive risk assessment.

HPGDS is a novel prognostic marker associated with lipid metabolism and aggressiveness in lung adenocarcinoma

BACKGROUND

Lung adenocarcinoma (LUAD) is the most common respiratory globallywith a poor prognosis. Lipid metabolism is extremely important for the occurrence and development of cancer. However, the role of genes involved in lipid metabolism in LUAD development is unclear. We aimed to identify the abnormal lipid metabolism pathway of LUAD, construct a novel prognostic model of LUAD, and discover novel biomarkers involved in lipid metabolism in LUAD.

METHODS

Based on differentially expressed genes involved in lipid metabolism in LUAD samples from The Cancer Genome Atlas (TCGA), abnormal lipid metabolism pathways in LUAD were analyzed. The lasso penalized regression analysis was performed on the TCGA cohort (training set) to construct a risk score formula. The predictive ability of the risk score was validated in the Gene Expression Omnibus (GEO) dataset (validation set) using Kaplan-Meier analysis and ROC curves. Finally, based on CRISPR gene editing technology, hematopoietic prostaglandin D synthase (HPGDS) was knocked out in A549 cell lines, the changes in lipid metabolism-related markers were detected by western blotting, and the changes in cell migration were detected by transwell assay.

RESULTS

Based on the differential genes between lung cancer tissue and normal tissue, we found that the arachidonic acid metabolism pathway is an abnormal lipid metabolism pathway in both lung adenocarcinoma and lung squamous cell carcinoma. Based on the sample information of TCGA and abnormally expressed lipid metabolism-related genes, a 9-gene prognostic risk score was successfully constructed and validated in the GEO dataset. Finally, we found that knockdown of HPGDS in A549 cell lines promoted lipid synthesis and is more invasive than in control cells. Rescue assays showed that ACSL1 knockdown reversed the pro-migration effects of HPGDS knockdown. The knockdown of HPGDS promoted migration response by upregulating the expression of the lipid metabolism key enzymes ACSL1 and ACC.

CONCLUSION

The genes involved in lipid metabolism are associated with the occurrence and development of LUAD. HPGDS can be a therapeutic target of a potential lipid metabolism pathway in LUAD, and the therapeutic target of lipid metabolism genes in LUAD should be studied further.

Glutathione S-Transferases S1, Z1 and A1 Serve as Prognostic Factors in Glioblastoma and Promote Drug Resistance through Antioxidant Pathways

The glutathione S-transferase (GST) family of detoxification enzymes can regulate the malignant progression and drug resistance of various tumors. Hematopoietic prostaglandin D synthase (HPGDS, also referred to as GSTS1), GSTZ1, and GSTA1 are abnormally expressed in multiple cancers, but their roles in tumorigenesis and development remain unclear. In this study, we used bioinformatics tools to analyze the connections of HPGDS, GSTZ1, and GSTA1 to a variety of tumors in genetic databases. Then, we performed biochemical assays in GBM cell lines to investigate the involvement of HPGDS in proliferation and drug resistance. We found that HPGDS, GSTZ1, and GSTA1 are abnormally expressed in a variety of tumors and are associated with prognoses. The expression level of HPGDS was significantly positively correlated with the grade of glioma, and high levels of HPGDS predicted a poor prognosis. Inhibiting HPGDS significantly downregulated GBM proliferation and reduced resistance to temozolomide by disrupting the cellular redox balance and inhibiting the activation of JNK signaling. In conclusion, this study suggested that HPGDS, GSTZ1, and GSTA1 are related to the progression of multiple tumors, and HPGDS is expected to be a prognostic factor in GBM.

Identification of an Amino Acid Metabolism Signature Participating in Immunosuppression in Ovarian Cancer

Ovarian cancer is one of the most fatal gynecologic cancer types, and its heterogeneity in the microenvironment limited the efficacy of the current treatment. In this study, we aimed at building a risk score to predict patient survival based on the amino acid metabolic genes and TCGA RNA-seq dataset (n = 376). We first used univariate analysis and PCA to select and test the survival-related genes, and the LASSO regression was applied to build the risk score signature with prediction accuracy estimation by survival analysis and ROC. We then conducted GSEA and GSVA to investigate the biological roles of the signature and run ESTIMATE and 4 different immunocyte infiltration algorithms to investigate the immunological diversity between the risk groups. Furthermore, the immune checkpoint expression was compared. We finally explored the cMap and PRISM database to screen out sensitive drugs for high-risk patients and analyzed the oncogenic role of TPH1 by clone formation and transwell migration assays. As a result, the risk score predicted patients' survival and stage with high accuracy. We found that the signature mainly affected the extracellular activities and cancer immunity by functional enrichment. We further discovered that the high-risk OV harbored a high level of stromal cell infiltration and was associated with highly infiltrated fibroblasts and decreased CD8+ T cells. The immune checkpoint analyses showed that TGFB1 and CD276 were upregulated. Finally, we screened out 4 PRISM drugs with lower IC₅₀ in the high-risk group and validated the oncogenic role of TPH1 in OV cancers. We believe this research offered a novel understanding of the interplay between amino acid metabolism and immunity in OV and will benefit patients with better prognostic management and therapeutic strategy development.

Identification of key genes associated with polycystic ovary syndrome (PCOS) and ovarian cancer using an integrated bioinformatics analysis

BACKGROUND

Accumulating evidence suggests a strong association between polycystic ovary syndrome (PCOS) and ovarian cancer (OC), but the potential molecular mechanism remains unclear. In this study, we identified previously unrecognized genes that are significantly correlated with PCOS and OC via bioinformatics.

MATERIALS AND METHODS

Multiple bioinformatic analyses, such as differential expression analysis, univariate Cox analysis, functional and pathway enrichment analysis, protein-protein interaction (PPI) network construction, survival analysis, and immune infiltration analysis, were utilized. We further evaluated the effect of OGN on FSHR expression via immunofluorescence.

RESULTS

TCGA-OC, GSE140082 (for OC) and GSE34526 (for PCOS) datasets were downloaded. Twelve genes, including RNF144B, LPAR3, CRISPLD2, JCHAIN, OR7E14P, IL27RA, PTPRD, STAT1, NR4A1, OGN, GALNT6 and CXCL11, were identified as signature genes. Drug sensitivity analysis showed that OGN might represent a hub gene in the progression of PCOS and OC. Experimental analysis found that OGN could increase FSHR expression, indicating that OGN could regulate the hormonal response in PCOS and OC. Furthermore, correlation analysis indicated that OGN function might be closely related to m6A and ferroptosis.

CONCLUSIONS

Our study identified a 12-gene signature that might be involved in the prognostic significance of OC. Furthermore, the hub gene OGN represent a significant gene involved in OC and PCOS progression by regulating the hormonal response.

Identification of a glycolysis-related gene signature for survival prediction of ovarian cancer patients

Background

Ovarian cancer (OV) is deemed the most lethal gynecological cancer in women. The aim of this study was to construct an effective gene prognostic model for predicting overall survival (OS) in patients with OV.

Methods

The expression profiles of glycolysis‐related genes (GRGs) and clinical data of patients with OV were extracted from The Cancer Genome Atlas (TCGA) database. Univariate, multivariate, and least absolute shrinkage and selection operator Cox regression analyses were conducted, and a prognostic signature based on GRGs was constructed. The predictive ability of the signature was analyzed using training and test sets.

Results

A gene risk signature based on nine GRGs (ISG20, CITED2, PYGB, IRS2, ANGPTL4, TGFBI, LHX9, PC, and DDIT4) was identified to predict the survival outcome of patients with OV. The signature showed a good prognostic ability for OV, particularly high‐grade OV, in the TCGA dataset, with areas under the curve (AUC) of 0.709 and 0.762 for 3‐ and 5‐year survival, respectively. Similar results were found in the test sets, and the AUCs of 3‐, 5‐year OS were 0.714 and 0.772 in the combined test set. And our signature was an independent prognostic factor. Moreover, a nomogram combining the prediction model and clinical factors was developed.

Conclusion

Our study established a nine‐GRG risk model and nomogram to better predict OS in patients with OV. The risk model represents a promising and independent prognostic predictor for patients with OV. Moreover, our study on GRGs could offer guidance for the elucidation of underlying mechanisms in future studies.

Identification and validation of a novel glycolysis-related gene signature for predicting the prognosis in ovarian cancer

Background

Ovarian cancer (OC) is the most lethal gynaecological tumor. Changes in glycolysis have been proven to play an important role in OC progression. We aimed to identify a novel glycolysis-related gene signature to better predict the prognosis of patients with OC.

Methods

mRNA and clinical data were obtained from The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC) and Genotype Tissue Expression (GTEx) database. The “limma” R package was used to identify glycolysis-related differentially expressed genes (DEGs). Then, a multivariate Cox proportional regression model and survival analysis were used to develop a glycolysis-related gene signature. Furthermore, the TCGA training set was divided into two internal test sets for validation, while the ICGC dataset was used as an external test set. A nomogram was constructed in the training set, and the relative proportions of 22 types of tumor-infiltrating immune cells were evaluated using the “CIBERSORT” R package. The enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were determined by single-sample gene set enrichment analysis (ssGSEA) with the “GSVA” R package. Finally, the expression and function of the unreported signature genes ISG20 and SEH1L were explored using immunohistochemistry, western blotting, qRT-PCR, proliferation, migration, invasion and xenograft tumor assays.

Results

A five-gene signature comprising ANGPTL4, PYGB, ISG20, SEH1L and IRS2 was constructed. This signature could predict prognosis independent of clinical factors. A nomogram incorporating the signature and three clinical features was constructed, and the calibration plot suggested that the nomogram could accurately predict the survival rate. According to ssGSEA, the signature was associated with KEGG pathways related to axon guidance, mTOR signalling, tight junctions, etc. The proportions of tumor-infiltrating immune cells differed significantly between the high-risk group and the low-risk group. The expression levels of ISG20 and SEH1L were lower in tumor tissues than in normal tissues. Overexpression of ISG20 or SEH1L suppressed the proliferation, migration and invasion of Caov3 cells in vitro and the growth of xenograft tumors in vivo.

Conclusion

Five glycolysis-related genes were identified and incorporated into a novel risk signature that can effectively assess the prognosis and guide the treatment of OC patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-02045-0.