Zhijun Dai

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

Abstract 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.

HPV‐related methylation‐based reclassification and risk stratification of cervical cancer

Human papillomavirus (HPV) is a clear etiology of cervical cancer (CC). However, the associations between HPV infection and DNA methylation have not been thoroughly investigated. Additionally, it remains unknown whether HPV‐related methylation signatures can identify subtypes of CC and stratify the prognosis of CC patients. DNA methylation profiles were obtained from The Cancer Genome Atlas to identify HPV‐related methylation sites. Unsupervised clustering analysis of HPV‐related methylation sites was performed to determine the different CC subtypes. CC patients were categorized into cluster 1 (Methylation‐H), cluster 2 (Methylation‐M), and cluster 3 (Methylation‐L). Compared to Methylation‐M and Methylation‐L, Methylation‐H exhibited a significantly improved overall survival (OS). Gene set enrichment analysis (GSEA) was conducted to investigate the functions that correlated with different CC subtypes. GSEA indicated that the hallmarks of tumors, including KRAS signaling, TNFα signaling via NF‐κB, inflammatory response, epithelial–mesenchymal transition, and interferon‐gamma response, were enriched in Methylation‐M and Methylation‐L. Based on mutation and copy number variation analyses, we found that aberrant mutations, amplifications, and deletions among the MYC, Notch, PI3K‐AKT, and RTK‐RAS pathways were most frequently detected in Methylation‐H. Additionally, mutations, amplifications, and deletions within the Hippo, PI3K‐AKT, and TGF‐β pathways were presented in Methylation‐M. Genes within the cell cycle, Notch, and Hippo pathways possessed aberrant mutations, amplifications, and deletions in Methylation‐L. Moreover, the analysis of tumor microenvironments revealed that Methylation‐H was characterized by a relatively low degree of immune cell infiltration. Finally, a prognostic signature based on six HPV‐related methylation sites was developed and validated. Our study revealed that CC patients could be classified into three heterogeneous clusters based on HPV‐related methylation signatures. Additionally, we derived a prognostic signature using six HPV‐related methylation sites that stratified the OS of patients with CC into high‐ and low‐risk groups.

Role of Methylation Test Triage in HPV Positive Women

The pathological results were used as the gold standard in this study and the investigators analyze the diagnostic value of six gene methylation status (ASTN1 DLX1, ITGA4, RXFP3, SOX17, ZNF671) in triaging high-risk human papillomavirus infection. The sensitivity and specificity of methylation test and cytology in the diagnosis of high-grade cervical lesions are compared in order to providing new methods and basis in improving the accuracy of cervical cancer screening.