Jinhui Liu

Single‐Cell and Spatial Transcriptomics Explore Purine Metabolism–Related Prognostic Risk Model and Tumor Immune Microenvironment Modulation in Ovarian Cancer

Background: Ovarian cancer (OC) ranks as the second leading cause of gynecological cancer–related deaths in women globally. Single‐cell and spatial transcriptomics could precisely describe the heterogeneity of OC that affect the clinical treatment.Methods: Single‐cell sequencing and spatial transcriptomics information were from different public datasets. A pseudotime analysis of cellular developmental pathways, score single‐cell gene sets, and cell activity ratings in each metabolic pathway were performed. A prognostic model was created using univariate regression analysis, LASSO, and multivariate regression analysis. Finally, the immune microenvironment and immunotherapeutic effects were analyzed for their association with purine metabolism activity. Finally, RT‐qPCR was used to estimate the mRNA level of OC in cell lines.Results: We observed a higher purine metabolism score by a signature of 12 purine metabolism–related gene in tumor cells. When compared with fibroblasts, epithelial cells with high scores displayed more intense TGF‐β signaling pathway activity. Forty‐four differentially expressed purine metabolism–related genes were identified to be substantially expressed in the tumor’s core region and were closely linked to purine and pyrimidine metabolic activities. Low‐risk population had higher immune infiltration level and immunotherapy results. The NME6+ epithelial cell high‐expression group had a greater prognosis and showed a negative connection with the tumor immune dysfunction and exclusion score and cancer‐associated fibroblast cell concentration.Conclusion: Purine metabolism was a predictor for OC patients’ prognosis. The presence of positive NME6 expression in epithelial cells emerges as a protective factor for OC patients, presenting a possible therapeutic target for personalized treatment.

A Hypoxia Molecular Signature-Based Prognostic Model for Endometrial Cancer Patients

Endometrial cancer has the highest incidence of uterine corpus cancer, the sixth most typical cancer in women until 2020. High recurrence rate and frequent adverse events were reported in either standard chemotherapy or combined therapy. Hence, developing precise diagnostic and prognostic approaches for endometrial cancer was on demand. Four hypoxia-related genes were screened for the EC prognostic model by the univariate, LASSO, and multivariate Cox regression analysis from the TCGA dataset. QT-PCR and functional annotation analysis were performed. Associations between predicted risk and immunotherapy and chemotherapy responses were investigated by evaluating expressions of immune checkpoint inhibitors, infiltrated immune cells, m6a regulators, and drug sensitivity. The ROC curve and calibration plot indicated a fair predictability of our prognostic nomogram model. NR3C1 amplification, along with IL-6 and SRPX suppressions, were detected in tumor. High stromal score and enriched infiltrated aDCs and B cells in the high-risk group supported the hypothesis of immune-deserted tumor. Hypoxia-related molecular subtypes of EC were then identified via the gene signature. Cluster 2 patients showed a significant sensitivity to Vinblastine. In summary, our hypoxia signature model accurately predicted the survival outcome of EC patients and assessed translational and transcriptional dysregulations to explore targets for precise medical treatment.

Identification of cuproptosis-related subtypes, establishment of a prognostic model and tumor immune landscape in endometrial carcinoma

Cuproptosis, the mechanism of copper-dependent cell death, is distinct from all other known forms of regulated cell death and dependents on mitochondrial respiration. Cuproptosis promises to be a novel treatment, especially for tumors resistant to conventional therapies. We investigated the changes in cuproptosis-related genes (CRGs) in endometrial cancer (EC) cohorts from the merged Gene Expression Omnibus and the Cancer Genome Atlas databases, which could be divided into three distinct CRGclusters. Patients in CRGcluster C would have higher survival probability (P = 0.007), and higher levels of tumor microenvironment (TME) cell infiltration than other CRGclusters. CRG score was calculated via the results of univariate, multivariate cox analysis and least absolute shrinkage and selection operator regression analysis. Patients were divided into two risk subgroups according to the median risk score. Low-risk patients exhibited a more favorable prognosis, higher immunogenicity, and greater immunotherapy efficacy. Besides, CRG scores were strongly correlated to copy number variation, immunophenoscore, tumor mutation load, cancer stem cell index, microsatellite instability, and chemosensitivity. The c-index of our model is 0.702, which is higher than other four published model. The results proved that our model can distinguish EC patients with high-risk and low-risk and accurately predict the prognosis of EC patients. It will provide new ideas for clinical prognosis and precise treatments.

N1‐Methyladenosine‐Related lncRNAs Are Potential Biomarkers for Predicting Prognosis and Immune Response in Uterine Corpus Endometrial Carcinoma

Uterine corpus endometrial carcinoma (UCEC) is a malignant disease that, at present, has no well‐characterised prognostic biomarker. In this study, two clusters were identified based on 28 N1‐methyladenosine‐ (m1A‐) related long noncoding RNAs (lncRNAs), of which cluster 1 was related to immune pathways according to the results of an enrichment analysis. We further observed better prognosis in patients with higher levels of immune cell infiltration, tumor mutation burden, microsatellite instability, and immune checkpoint gene expression. In addition, through Cox regression analysis and least absolute shrinkage and selection operator regression analysis, 10 m1A‐related lncRNAs (mRLs) were employed to build a prognosis model. We found that people in higher risk categories had a poorer survival probability than those in lower risk. Low‐risk samples were enriched with immune‐related pathways, while the high‐risk group was similar to the definition of the “immune desert” phenotype, which was associated with decreased immune infiltration, T cell failure, and decreased tumor mutation burden, while also being insensitive to immunotherapy and chemotherapy. This mRL‐based model has the ability to accurately predict the prognosis of UCEC patients, and the mRLs could become promising therapeutic targets in enhancing the response of immunotherapy.

MiRNA based tumor mutation burden diagnostic and prognostic prediction models for endometrial cancer

Uterus Corpus Endometrial cancer (UCEC) is the sixth most common malignant tumor worldwide. In this research, we identified diagnostic and prognostic biomarkers to reflect patients' immune microenvironment and prognostic. Various data of UCEC patients from the TCGA database were obtained. Firstly, patients were divided into a high tumor mutation burden (TMB) level group and a low TMB level group according to the level of TMB. Then, differentially expressed miRNAs between the two groups were obtained. LASSO logistic regression analysis was used to construct a diagnostic model to predict the level of TMB. Univariate, multivariate, and LASSO regression analysis were used to construct a prognostic risk signature (PRS) to predict the prognosis of UCEC patients. Twenty-one miRNAs were used to construct a diagnostic model for predicting TMB levels. The AUC values of ROC curves for 21-miRNA-based diagnostic models were 0.911 in the training set, 0.827 in the test set, and 0.878 in the entire set. This diagnostic model showed positive correlation with TMB, PDL1 expression, and the infiltration of immune cells. In addition, three prognostic miRNAs were finally used to construct the PRS. The PRS was related to the expression of multiple immune checkpoints and the infiltration of multiple immune cells. Furthermore, the PRS can also reflect the response to some commonly used chemotherapy regimens. We have established a miRNA-based diagnostic model and a prognostic model that can predict the prognosis of UCEC patients and their response to chemotherapy and immunotherapy, thus providing valuable information on the choice of treatment regimen.

Exosome-Associated Gene Signature for Predicting the Prognosis of Ovarian Cancer Patients

Background. The exosome is of vital importance throughout the entire progression of cancer. Because of the lack of effective biomarkers in ovarian cancer (OV), we intend to investigate the connection between exosomes and tumor immune microenvironment to verify that exosome-related genes (ERGs) can precisely forecast the prognosis of OV patients. Methods. First, 117 ERGs in The Cancer Genome Atlas (TCGA) dataset were recognized. Afterwards, the risk signature consisting of four ERGs with prognostic significance was built by univariate Cox, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analysis. We also validated the risk signature by Kaplan-Meier analysis, receiver operating characteristic curve analysis and principal component analysis. Furthermore, gene set enrichment analysis was performed to compare the enrichment patterns between the two risk subgroups. The connections between the exosome-related gene risk score (ERGRS) and clinical features, immune infiltration, immune checkpoint-related genes, copy number variation, and drug sensitivity were explored. We also assessed the function of the ERGRS to forecast immunotherapeutic efficacy by immunophenoscore (IPS). Results. The high-risk group had a worse prognosis than the group with low risk. We verified that the established model possessed a relatively good prognostic value. Pathway enrichment analysis indicated that the genome-wide group with low risk was enriched in immune-related pathways. We discovered that resting dendritic cells and stromal scores were upregulated in patients with high risk in the TCGA and Gene Expression Omnibus (GEO) cohorts. Moreover, the expression of six common immune checkpoint inhibitor targets was assessed, which revealed that the expression levels of CD274 (PD-L1), PDCD1 (PD-1), and IDO1 in patients with high risk were lower than those in patients with low risk. Afterwards, the low-risk group had higher IPS across the four immunotherapies, implying that it had better effects of immunotherapies. Conclusion. Our study demonstrates that the exosome-related gene risk model is closely associated with immune infiltration. It can well forecast the prognosis of OV patients and guide the selection of immunotherapeutic strategies.

Necroptosis-Related Modification Patterns Depict the Tumor Microenvironment, Redox Stress Landscape, and Prognosis of Ovarian Cancer

Necroptosis is one of programmed cell death discovered recently, which involves in tumorigenesis, cancer metastasis, and immune reaction. We studied the necroptosis-related genes (NRGs) in ovarian cancer (OV) tissues using data from public databases, which separated into two NRGclusters. Patients in cluster A would have severe clinical characteristics, poor prognosis, and worse tumor microenvironment infiltration characteristics. The NRG score was achieved through the Cox analysis, along with a construction of a prognostic model. People with lower risk score would have better prognosis, lower expression of redox related genes, higher immunogenicity, and better effect on immunotherapy. In addition, the NRG score was closely related to cancer stem cell index, copy number variations, tumor mutation load, and chemosensitivity. We built a nomogram to enhance clinical application of the signature. These outcomes can help use know the function of NRGs in OV and provide new ideas for evaluating clinical outcome and developing more effective treatment protocols.

m6A RNA methylation regulators were associated with the malignancy and prognosis of ovarian cancer

N6-methyladenosine (m6A) RNA methylation regulators play a regulatory role in tumor pathogenesis and development. However, the role of m6A regulator genes in ovarian cancer (OC) has not been fully elucidated. This study aims to investigate the mRNA expressions, clinicopathological features, and prognostic values of m6A regulators in OC. Here, we demonstrate that the 17 m6A RNA methylation regulators are differentially expressed in ovarian cancer and normal tissues. By using consensus clustering, all ovarian cancer patients can be divided into two subgroups (cluster 1 and 2) based on the expression of 17 m6A RNA methylation regulators. Using Gene Set Enrichment Analysis, we identified that cluster 1 was most connected to oxidative phosphorylation pathways. Regression models identified that prognosis is associated with HNRNPA2B1, KIAA1429, and WTAP. qRT-PCR result show that the expression trends of HNRNPA2B1 and KIAA1429 are consistent with the predicted results. Multivariate Cox regression analysis results show that the risk score was an independent predictive factor in OV. The overall survival of high-risk patients was significantly shorter than that of low-risk patients. ROC curve analysis showed that the prognostic signature precisely predicted the 5-year survival of OV patients. A nomogram was developed to predict each patient's survival probability and well calibrated and showed a satisfactory discrimination. Dendritic fraction, macrophage fraction, and neutrophil fraction showed higher fraction in high-risk patients. In conclusion, m6A RNA methylation regulators are vital participants in ovarian cancer pathology, and three-gene mRNA levels are valuable factors for prognosis predictions.