A Novel Glycosyltransferase-Related Gene Signature for Overall Survival Prediction in Patients with Ovarian Cancer

A 19-Gene Signature of Serous Ovarian Cancer Identified by Machine Learning and Systems Biology: Prospects for Diagnostics and Personalized Medicine

Ovarian cancer is a major cause of cancer deaths among women. Early diagnosis and precision/personalized medicine are essential to reduce mortality and morbidity of ovarian cancer, as with new molecular targets to accelerate drug discovery. We report here an integrated systems biology and machine learning (ML) approach based on the differential coexpression analysis to identify candidate systems biomarkers (i.e., gene modules) for serous ovarian cancer. Accordingly, four independent transcriptome datasets were statistically analyzed independently and common differentially expressed genes (DEGs) were identified. Using these DEGs, coexpressed gene pairs were unraveled. Subsequently, differential coexpression networks between the coexpressed gene pairs were reconstructed so as to identify the differentially coexpressed gene modules. Based on the established criteria, "SOV-module" was identified as being significant, consisting of 19 genes. Using independent datasets, the diagnostic capacity of the SOV-module was evaluated using principal component analysis (PCA) and ML techniques. PCA showed a sensitivity and specificity of 96.7% and 100%, respectively, and ML analysis showed an accuracy of up to 100% in distinguishing phenotypes in the present study sample. The prognostic capacity of the SOV-module was evaluated using survival and ML analyses. We found that the SOV-module's performance for prognostics was significant (p-value = 1.36 × 10-4) with an accuracy of 63% in discriminating between survival and death using ML techniques. In summary, the reported genomic systems biomarker candidate offers promise for personalized medicine in diagnosis and prognosis of serous ovarian cancer and warrants further experimental and translational clinical studies.

Glycosyltransferase-related prognostic and diagnostic biomarkers of uterine corpus endometrial carcinoma

Uterine corpus endometrial carcinoma (UCEC) has a strong ability of invasion and metastasis, high recurrence rate, and poor survival. Glycosyltransferases are one of the most important enzymes that coordinate the glycosylation process, and abnormal modification of proteins by glycosyltransferases is closely related to the occurrence and development of cancer. However, there were fewer reports on glycosyltransferase related biomarkers in UCEC. In this paper, based on the UCEC transcriptome data published on The Cancer Genome Atlas (TCGA), we predicted the relationship between the expression of glycosyltransferase-related genes (GTs) and the diagnosis and prognosis of UCEC using bioinformatics methods. And validation of model genes by clinical samples. We used 4 methods: generalized linear model (GLM), random forest (RF), support vector machine (SVM) and extreme gradient boosting (XGB) to screen biomarkers with diagnostic significance, and the binary logistic regression was used to establish a diagnostic model for the 2-GTs (AUC = 0.979). And the diagnostic model was validated using a GEO external database (AUC = 0.978). Moreover, a prognostic model for the 6-GTs was developed using univariate, Lasso, and multivariate Cox regression analyses, and the model was made more stable by internal validation using the bootstrap. In addition, risk score is closely related to immune microenvironment (TME), immune infiltration, mutation, immunotherapy and chemotherapy. Overall, this study provides novel biomarkers for the diagnosis and prognosis of UCEC, and the models established by these biomarkers can also provide a good reference for individualized and precision medicine in UCEC.

A Prognostic Model Based on Metabolism-Related Genes for Patients with Ovarian Cancer

Metabolism-associated genes (MAGs) are important regulators of tumor progression and can affect a variety of physiological processes. In this study, we focused on the relationship between MAGs and Ovarian cancer (OC) prognosis.

METHOD

Metabolism-related genes were extracted from the Cancer Genome Atlas (TCGA) database. Through univariate COX and lasso regression models, a dynamic risk model based on MAGs was established. Compared with other clinical factors, demonstrated the ability of the model to predict the prognosis of patients with OC. The clinical samples were used to verify the expression of these MAGs.

RESULTS

A metabolism-associated gene signature was constructed by LASSO Cox regression analysis in OC, which was composed of 3-MAGs (PTGIS, AOC3, and IDO1). The signature was used to classify the OC patients into high-risk and low-risk groups. The overall survival of the low-risk group was significantly better than that of the high-risk group. The analysis of the therapeutic effect of bevacizumab showed that bevacizumab was not conducive to improving the prognosis of the low-risk group.

CONCLUSIONS

We constructed a prognostic model of MAGs in OC, which can be used to predict the prognosis of OC patients and may have a good guiding significance in the individualized treatment of patients.

A novel glycosyltransferase-related lncRNA signature correlates with lung adenocarcinoma prognosis

Background

Lung adenocarcinoma (LUAD) is one of the most fatal cancers in the world. Previous studies have shown the increase in glycosylation level, and abnormal expressions of related enzymes are closely related to various cancers. Long non-coding RNAs (lncRNAs) play an important role in the proliferation, metabolism, and migration of cancer cells, but the underlying role of glycosyltransferase (GT)-related lncRNAs in LUAD remains to be elucidated.

Methods

We abstracted 14,056 lncRNAs from The Cancer Genome Atlas (TCGA) dataset and 257 GT-related genes from the Gene Set Enrichment Analysis (GSEA) database. Univariate, LASSO-penalized, and multivariate Cox regression analyses were conducted to construct a GT-related lncRNA prognosis model.

Results

A total of 2,726 GT-related lncRNAs were identified through Pearson’s correlation analysis, and eight of them were utilized to construct a GT-related lncRNA model. The overall survival (OS) of the low-risk group continued to be superior to that of the high-risk group according to the subgroups classified by clinical features. The risk model was proved to have independent prognostic characteristics for LUAD by univariate and multivariate Cox regression analyses. The status of the tumor immune microenvironment and the relevant immunotherapy response was significantly different between the two risk groups. The candidate drugs aimed at LUAD subtype differentiation were identified.

Conclusion

We constructed a risk model comprising eight GT-related lncRNAs which was identified as an independent predictor of prognoses to predict patient survival and guide-related treatments for patients with LUAD.