A signature based on glycosyltransferase genes provides a promising tool for the prediction of prognosis and immunotherapy responsiveness in ovarian cancer.

Ovarian cancer (OC) is the most fatal gynaecological malignancy and has a poor prognosis. Glycosylation, the biosynthetic process that depends on specific glycosyltransferases (GTs), has recently attracted increasing importance due to the vital role it plays in cancer. In this study, we aimed to determine whether OC patients could be stratified by glycosyltransferase gene profiles to better predict the prognosis and efficiency of immune checkpoint blockade therapies (ICBs). We retrieved transcriptome data across 420 OC and 88 normal tissue samples using The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases, respectively. An external validation dataset containing 185 OC samples was downloaded from the Gene Expression Omnibus (GEO) database. Knockdown and pathway prediction of B4GALT5 were conducted to investigate the function and mechanism of B4GALT5 in OC proliferation, migration and invasion. A total of 50 differentially expressed GT genes were identified between OC and normal ovarian tissues. Two clusters were stratified by operating consensus clustering, but no significant prognostic value was observed. By applying the least absolute shrinkage and selection operator (LASSO) Cox regression method, a 6-gene signature was built that classified OC patients in the TCGA cohort into a low- or high-risk group. Patients with high scores had a worse prognosis than those with low scores. This risk signature was further validated in an external GEO dataset. Furthermore, the risk score was an independent risk predictor, and a nomogram was created to improve the accuracy of prognostic classification. Notably, the low-risk OC patients exhibited a higher degree of antitumor immune cell infiltration and a superior response to ICBs. B4GALT5, one of six hub genes, was identified as a regulator of proliferation, migration and invasion in OC. Taken together, we established a reliable GT-gene-based signature to predict prognosis, immune status and identify OC patients who would benefit from ICBs. GT genes might be a promising biomarker for OC progression and a potential therapeutic target for OC.

Xu X ，Wu Y ，Jia G ，Zhu Q ，Li D ，Xie K ... -  《Journal of Ovarian Research》

A signature based on anoikis-related genes for the evaluation of prognosis, immunoinfiltration, mutation, and therapeutic response in ovarian cancer.

Ovarian cancer (OC) is a highly lethal and aggressive gynecologic cancer, with an overall survival rate that has shown little improvement over the decades. Robust models are urgently needed to distinguish high-risk cases and predict reliable treatment options for OC. Although anoikis-related genes (ARGs) have been reported to contribute to tumor growth and metastasis, their prognostic value in OC remains unknown. The purpose of this study was to construct an ARG pair (ARGP)-based prognostic signature for patients with OC and elucidate the potential mechanism underlying the involvement of ARGs in OC progression. The RNA-sequencing and clinical information data of OC patients were obtained from The Center Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. A novel algorithm based on pairwise comparison was utilized to select ARGPs, followed by the Least Absolute Shrinkage and Selection Operator Cox analysis to construct a prognostic signature. The predictive ability of the model was validated using an external dataset, a receiver operating characteristic curve, and stratification analysis. The immune microenvironment and the proportion of immune cells were analyzed in high- and low-risk OC cases using seven algorithms. Gene set enrichment analysis and weighted gene co-expression network analysis were performed to investigate the potential mechanisms of ARGs in OC occurrence and prognosis. The 19-ARGP signature was identified as an important prognostic predictor for 1-, 2-, and 3-year overall survival of patients with OC. Gene function enrichment analysis showed that the high-risk group was characterized by the infiltration of immunosuppressive cells and the enrichment of adherence-related signaling pathway, suggesting that ARGs were involved in OC progression by mediating immune escape and tumor metastasis. We constructed a reliable ARGP prognostic signature of OC, and our findings suggested that ARGs exerted a vital interplay in OC immune microenvironment and therapeutic response. These insights provided valuable information regarding the molecular mechanisms underlying this disease and potential targeted therapies.

Duan Y ，Xu X 《Frontiers in Endocrinology》

Identification of an Autophagy-Related Signature for Prognosis and Immunotherapy Response Prediction in Ovarian Cancer.

Ding J ，Wang C ，Sun Y ，Guo J ，Liu S ，Cheng Z ... -  《Biomolecules》

Identification of STEAP3-based molecular subtype and risk model in ovarian cancer.

Ovarian cancer (OC) is one of the most common malignancies in women. It has a poor prognosis owing to its recurrence and metastasis. Unfortunately, reliable markers for early diagnosis and prognosis of OC are lacking. Our research aimed to investigate the value of the six-transmembrane epithelial antigen of prostate family member 3 (STEAP3) as a prognostic predictor and therapeutic target in OC using bioinformatics analysis. STEAP3 expression and clinical data were acquired from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and Gene Expression Omnibus (GEO). Unsupervised clustering was used to identify molecular subtypes. Prognosis, tumor immune microenvironment (TIME), stemness indexes, and functional enrichment analysis were compared between two definite clusters. Through the least absolute shrinkage and selection operator (LASSO) regression analysis, a STEAP3-based risk model was developed, and the predictive effectiveness of this signature was confirmed using GEO datasets. A nomogram was used to predict the survival possibility of patients. Additionally, TIME, tumor immune dysfunction and exclusion (TIDE), stemness indexes, somatic mutations, and drug sensitivity were evaluated in different risk groups with OC. STEAP3 protein expression was detected using immunohistochemistry (IHC). STEAP3 displayed marked overexpression in OC. STEAP3 is an independent risk factor for OC. Based on the mRNA levels of STEAP3-related genes (SRGs), two distinct clusters were identified. Patients in the cluster 2 (C2) subgroup had a considerably worse prognosis, higher immune cell infiltration, and lower stemness scores. Pathways involved in tumorigenesis and immunity were highly enriched in the C2 subgroup. A prognostic model based on 13 SRGs was further developed. Kaplan-Meier analysis indicated that the overall survival (OS) of high-risk patients was poor. The risk score was significantly associated with TIME, TIDE, stemness indexes, tumor mutation burden (TMB), immunotherapy response, and drug sensitivity. Finally, IHC revealed that STEAP3 protein expression was noticeably elevated in OC, and overexpression of STEAP3 predicted poor OS and relapse-free survival (RFS) of patients. In summary, this study revealed that STEAP3 reliably predicts patient prognosis and provides novel ideas for OC immunotherapy.

Zhao Z ，Sun C ，Hou J ，Yu P ，Wei Y ，Bai R ，Yang P ... -  《Journal of Ovarian Research》

Integrative analysis of cuproptosis-associated genes for predicting immunotherapy response in single-cell and multi-cohort studies.

The role of genes associated with the cuproptosis cell signaling pathway in prognosis and immunotherapy in ovarian cancer (OC) has been extensively investigated. In this study, we aimed to explore these mechanisms and establish a prognostic model for patients with OC using bioinformatics techniques. We obtained the single cell sequencing data of ovarian cancer from the Gene Expression Omnibus (GEO) database and preprocessed the data. We analyzed a variety of factors including cuproptosis cell signal score, transcription factors, tumorigenesis and progression signals, gene set variation analysis (GSVA) and intercellular communication. Differential gene analysis was performed between groups with high and low cuproptosis cell signal scores, as well as Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses. Using bulk RNA sequencing data from The Cancer Genome Atlas, we used the least absolute shrinkage and selection operator (LASSO)-Cox algorithm to develop cuproptosis cell signaling pathword-related gene signatures and validated them with GEO ovarian cancer datasets. In addition, we analyzed the inherent rules of the genes involved in building the model using a variety of bioinformatics methods, including immune-related analyses and single nucleotide polymorphisms. Molecular docking is used to screen potential therapeutic drugs. To confirm the analysis results, we performed various wet experiments such as western blot, cell counting kit 8 (CCK8) and clonogenesis tests to verify the role of the Von Willebrand Factor (VWF) gene in two ovarian cancer cell lines. Based on single-cell data analysis, we found that endothelial cells and fibroblasts showed active substance synthesis and signaling pathway activation in OC, which further promoted immune cell suppression, cancer cell proliferation and metastasis. Ovarian cancer has a high tendency to metastasize, and cancer cells cooperate with other cells to promote disease progression. We developed a signature consisting of eight cuproptosis-related genes (CRGs) (MAGEF1, DNPH1, RARRES1, NBL1, IFI27, VWF, OLFML3 and IGFBP4) that predicted overall survival in patients with ovarian cancer. The validity of this model is verified in an external GEO validation set. We observed active infiltrating states of immune cells in both the high- and low-risk groups, although the specific cells, genes and pathways of activation differed. Gene mutation analysis revealed that TP53 is the most frequently mutated gene in ovarian cancer. We also predict small molecule drugs associated with CRGs and identify several potential candidates. VWF was identified as an oncogene in ovarian cancer, and the protein was expressed at significantly higher levels in tumor samples than in normal samples. The high-score model of the cuproptosis cell signaling pathway was associated with the sensitivity of OC patients to immunotherapy. Our study provides greater insight into the mechanisms of action of genes associated with the cuproptosis cell signaling pathway in ovarian cancer, highlighting potential targets for future therapeutic interventions.

Li H ，Wang Y ，Li G ，Xiong J ，Qin L ，Wen Q ，Yue C ... -  《-》