Signature In Ovarian Cancer Research Articles

Deciphering Folate Receptor alphaGene Expression and mRNA Signatures in Ovarian Cancer: Implications for Precision Therapies

Deciphering Folate Receptor alphaGene Expression and mRNA Signatures in Ovarian Cancer: Implications for Precision Therapies

Characteristics of Oxidative Phosphorylation-Related Subtypes and Construction of a Prognostic Signature in Ovarian Cancer.

Ovarian cancer is associated with a high mortality rate. Oxidative Phosphorylation (OXPHOS) is an active metabolic pathway in cancer; nevertheless, its role in ovarian cancer continues to be ambiguous. Therefore, the objective of this study was to identify the prognostic value of OXPHOS-related genes and the immune landscape in ovarian cancer. We obtained public ovarian cancer-related datasets from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases and recognized OXPHOS-related genes from the GeneCards database and literature. Cox regression analyses were conducted to identify prognostic OXPHOS-related genes and develop a prognostic nomogram based on the OXPHOS score and clinicopathological features of patients. Functional enrichment analyses were employed to identify related processes. A 12-gene signature was identified to classify the ovarian cancer patients into high- and low-risk groups. The Immunophenoscore (IPS) was higher in the OXPHOS score-high group than in the OXPHOS score-low group, suggesting a better response to immune checkpoint inhibitors. Functional enrichment analyses unveiled that OXPHOS-related genes were considerably abundant in a series of immune processes. The calibration curves of the constructed prognostic nomograms at 1, 2, and 3 years exhibited strong concordance between the anticipated and observed survival probabilities of ovarian cancer patients. We have constructed a prognostic model containing 12 OXPHOS-related genes and demonstrated its strong predictive value in ovarian cancer patients. OXPHOS has been found to be closely linked to immune infiltration and the reaction to immunotherapy, which may contribute to improving individualized treatment and prognostic evaluation in ovarian cancer.

Exploring tumor microenvironment in molecular subtyping and prognostic signatures in ovarian cancer and identification of SH2D1A as a key regulator of ovarian cancer carcinogenesis

IntroductionA deadly gynecological cancer, ovarian cancer (OV), has a poor prognosis because of late-stage diagnosis and few targeted therapies. Addressing the tumor microenvironment (TME) in solid tumors has shown promise since it is crucial in promoting cancer progression. MethodsWe obtained bulk RNA-seq data from TCGA-OV, GSE26712, GSE102073, and ICGC cohorts, as well as scRNA-seq data from EMTAB8107, GSE118828, GSE130000, and GSE154600 cohorts using the TISCH2 database. The ConsensusClusterPlus package was used to cluster the OV tumor tissues hierarchically to determine two molecularly different groups (C1 and C2). A total of ten different types of machine learning techniques with 101 combinations were used for prognostic model construction. Using eight TME algorithms integrated into the IOBR R package, the bulk RNA-seq dataset was analyzed. For in vitro experiments, OVCAR3 and SKOV3, two OV cell lines, were used. The migratory potential of the ovarian cancer cells was assessed using Transwell assay, while proliferation was assessed using CCK8 assay. ResultsBased on TME-related gene set expression, two distinct molecular subgroups (C1 and C2) were identified through consensus clustering, with C1 showing higher TME activity. Further analysis indicated that C1 had increased cancer-associated fibroblasts (CAFs), M1 macrophages, and CD8+ T cells, suggesting a more activated and pro-inflammatory TME. Drug sensitivity analysis revealed that 5-Fluorouracil might be beneficial to C1 patients. Functional differences between C1 and C2 were identified, including cell adhesion, mononuclear cell differentiation, and leukocyte migration. A machine learning model was developed to create a TME-related prognostic signature, demonstrating strong prognostic capabilities across multiple datasets. High-risk patients showed a more immune-suppressive TME and higher tumor stemness. ScRNA-seq disclosed a highly activated TME-related signature in OV. Cancer cell lines had significantly higher SH2D1A mRNA expression than normal ovarian epithelial cells. We observed that SH2D1A knockdown in 2 ovarian cancer cell lines (OVCAR3 and SKOV3) reduced migration and proliferation through a series of in-vitro experiments. ConclusionTME-associated genes were efficient in ovarian cancer molecular subtyping. A TME-based prognosis model was constructed for vigorous prognostic stratification efficacy across multiple datasets. Moreover, we identified a pivotal role of SH2D1A in promoting proliferation and migration in ovarian cancer.

Comprehensive single-cell and bulk RNA-seq analyses reveal a novel CD8+ T cell-associated prognostic signature in ovarian cancer.

CD8+ T cells play pivotal roles in combating intracellular pathogens and eliminating malignant cells in cancer. However, the prognostic role of CD8+ T cells in ovarian carcinoma is insufficiently exploited. Herein, through univariate Cox regression along with least absolute shrinkage and selection operator (LASSO) regression analyses, we developed a novel prognostic model based on CD8+ T cell markers identified by single-cell sequencing (scRNA-seq) analyses. Patient grouping by the median risk score reveals an excellent prognostic efficacy of this model in both training and validation cohorts. Of note, patients classified as low-risk group exhibit a dramatically improved prognosis. In addition, higher enrichment level of immune-related pathways and increased infiltration level of multiple immune cells are found in patients with lower risk score. Importantly, low-risk patients also exhibited higher response rate to immunotherapies. Summarily, this developed CD8+ T cell-associated prognostic model serves as an excellent predictor for clinical outcomes and aids in guiding therapeutic strategy choices for ovarian cancer patients.

A cuproptosis-related signature predicts prognosis and indicates cross-talk with immunocyte in ovarian cancer

PurposeCuproptosis, programmed cell death by intracellular copper-mediated lipoylated protein aggregation, is involved in various tumorigenesis and drug resistance abilities by mediating the tumor microenvironment. Previous studies have demonstrated that serum copper levels are higher in OC patients than in normal subjects. However, the exact relationship between cuproptosis and ovarian cancer progression remains to be further elucidated.MethodsThe Cancer Genome Atlas (TCGA) and gene expression omnibus (GEO) datasets were utilized to establish a cuproptosis-related prognostic signature in ovarian cancer. Subsequently, the bulk RNA-seq analysis and single-cell RNA-seq analysis were used to identify the relationship between signature with immune cell infiltration, chemotherapy, and cuproptosis-related scoring (CuRS) system. Finally, the potential biological functional roles of target genes in cuproptosis were validated in vitro.ResultsBy using LASSO-Cox regression analysis to establish the cuproptosis-related prognostic model, our works demonstrated the accuracy and efficiency of our model in the TCGA (583 OC patients) and GEO (260 OC patients) OC cohorts, and the high-scoring groups showed worse survival outcomes. Notably, there were substantial differences between the high and low-risk groups in extensive respects, such as the activating transcription factors, cell pseudotime features, cell intercommunication patterns, immunocytes infiltration, chemotherapy response, and potential drug resistance. KIF26B was selected to construct a prognostic model from the identified 33 prognosis-related genes, and high expression of KIF26B predicted poorer prognosis in ovarian cancer. Ultimately, further in vitro experiments demonstrated that KIF26B participated in the proliferation and cisplatin resistance of OC cells. Knockdown of KIF26B increased the sensitivity of OC cells to elesclomol, a cuproptosis agonists.ConclusionThis study constructed a new cuproptosis-related gene signature that has a good prognostic capacity in assessing the outcome of OC patients. This study enhances our understanding of cuproptosis associated with ovarian cancer aggressiveness, cross-talk with immunocytes, and serves as a novel chemotherapy strategy.

Prognostic and immunotherapeutic potential of regulatory Tcell-associated signature in ovarian cancer.

Tumour-induced immunosuppressive microenvironments facilitate oncogenesis, with regulatory T cells (Tregs) serving as a crucial component. The significance of Treg-associated genes within the context of ovarian cancer (OC) remains elucidated insufficiently. Utilizing single-cell RNA sequencing (scRNA-Seq) for the identification of Treg-specific biomarkers, this investigation employed single-sample gene set enrichment analysis (ssGSEA) for the derivation of a Treg signature score. Weighted gene co-expression network analysis (WGCNA) facilitated the identification of Treg-correlated genes. Machine learning algorithms were employed to determine an optimal prognostic model, subsequently exploring disparities across risk strata in terms of survival outcomes, immunological infiltration, pathway activation and responsiveness to immunotherapy. Through WGCNA, a cohort of 365 Treg-associated genes was discerned, with 70 implicated in the prognostication of OC. A Tregs-associated signature (TAS), synthesized from random survival forest (RSF) and Least Absolute Shrinkage and Selection Operator (LASSO) algorithms, exhibited robust predictive validity across both internal and external cohorts. Low TAS OC patients demonstrated superior survival outcomes, augmented by increased immunological cell infiltration, upregulated immune checkpoint expression, distinct pathway enrichment and differential response to immunotherapeutic interventions. The devised TAS proficiently prognosticates patient outcomes and delineates the immunological milieu within OC, offering a strategic instrument for the clinical stratification and selection of patients.

Clinical significance and immune infiltration analyses of a novel coagulation-related signature in ovarian cancer

Ovarian cancer (OV) is the most lethal gynecological malignancies worldwide. The coagulation cascade could induce tumor cell infiltration and contribute to OV progression. However, coagulation-related gene (CRG) signature for OV prognosis hasn’t been determined yet. In this study, we evaluated the prognostic value of coagulation scores through receiver operating characteristics (ROC) analysis and K-M curves, among OV patients at our institution. Based on the transcriptome data of TCGA-OV cohort, we stratified two coagulation-related subtypes with distinct differences in prognosis and tumor immune microenvironment (p < 0.05). Moreover, from the 6406 differentially-expressed genes (DEGs) between the GTEx (n = 180) and TCGA-OV cohorts (n = 376), we identified 138 potential CRGs. Through LASSO-Cox algorithm, we finally distinguished a 3-gene signature (SERPINA10, CD38, and ZBTB16), with promising prognostic ability in both TCGA (p < 0.001) and ICGC cohorts (p = 0.040). Stepwise, we constructed a nomogram based on the clinical features and coagulation-related signature for overall survival prediction, with the C-index of 0.6761, which was evaluated by calibration curves. Especially, based on tissue microarrays analysis, Quantitative real-time fluorescence PCR (qRT-PCR), and Western Blot, we found that aberrant upregulation of CRGs was related to poor prognosis in OV at both mRNA and protein level (p < 0.05). Collectively, the coagulation-related signature was a robust prognostic biomarker, which could provide therapeutic benefits for chemotherapy/immunotherapy and assist clinical decision in OV patients.

Integrative analysis from multi-center studies identifies a weighted gene co-expression network analysis-based Tregs signature in ovarian cancer.

Ovarian cancer (OC) is a malignancy associated with poor prognosis and has been linked to regulatory T cells (Tregs) in the immune microenvironment. Nevertheless, the association between Tregs-related genes (TRGs) and OC prognosis remains incompletely understood. The xCell algorithm was used to analyze Tregs scores across multiple cohorts. Weighted gene co-expression network analysis (WGCNA) was utilized to identify potential TRGs and molecular subtypes. Furthermore, we used nine machine learning algorithms to create risk models with prognostic indicators for patients. Reverse transcription-quantitative polymerase chain reaction and immunofluorescence staining were used to demonstrate the immunosuppressive ability of Tregs and the expression of key TRGs in clinical samples. Our study found that higher Tregs scores were significantly correlated with poorer overall survival. Recurrent patients exhibited increased Tregs infiltration and reduced CD8+ T cell. Moreover, molecular subtyping using seven key TRGs revealed that subtype B exhibited higher enrichment of multiple oncogenic pathways and had a worse prognosis. Notably, subtype B exhibited high Tregs levels, suggesting immune suppression. In addition, we validated machine learning-derived prognostic models across multiple platform cohorts to better distinguish patient survival and predict immunotherapy efficacy. Finally, the differential expression of key TRGs was validated using clinical samples. Our study provides novel insights into the role of Tregs in the immune microenvironment of OC. We identified potential therapeutic targets derived from Tregs (CD24, FHL2, GPM6A, HOXD8, NAP1L5, REN, and TOX3) for personalized treatment and created a machining learning-based prognostic model for OC patients, which could be useful in clinical practice.

The synergistic effect of EMT regulators and m6A modification on prognosis-related immunological signatures for ovarian cancer

Recently, there has been growing interest among researchers in exploring the effects of epithelial-mesenchymal transformation (EMT) or N6-Methyladenosine (m6A) modification regulators on tumor development. However, the synergistic efficiency of these regulators in relation to ovarian cancer development remains unclear. This study aims to explore the transcription patterns of main regulators, including 19 EMT and 22 m6A, in ovarian cancer samples from TCGA datasets and normal samples from GTEx datasets. After conducting a LASSO regression analysis, ten prognostic signatures were identified, namely KIAA1429, WTAP, SNAI1, AXL, IGF2BP1, ELAVL1, CBLL1, CDH2, NANOG and ALKBH5. These signatures were found to have a comprehensive effect on immune infiltrating signatures and the final prognostic outcome. Next, utilizing the ssGSEA algorithm and conducting overall survival analyses, we have identified the key prognosis-related immunological signatures in ovarian cancer to be ALKBH5, WTAP, ELAVL1, and CDH2 as the regulators. The characteristic immune response and related genetic expression have revealed a significant correlation between the alteration of m6A regulators and EMT regulators, indicating a synergistic effect between these two factors in the development of ovarian cancer. In summary, our research offers a novel perspective and strategy to enhance the occurrence, progression, and prognosis of ovarian cancer.

Identification of peripheral blood immune infiltration signatures and construction of monocyte-associated signatures in ovarian cancer and Alzheimer's disease using single-cell sequencing

BackgroundOvarian cancer (OC) is a common tumor of the female reproductive system, while Alzheimer's disease (AD) is a prevalent neurodegenerative disease that primarily affects cognitive function in the elderly. Monocytes are immune cells in the blood that can enter tissues and transform into macrophages, thus participating in immune and inflammatory responses. Overall, monocytes may play an important role in Alzheimer's disease and ovarian cancer. MethodsThe CIBERSORT algorithm results indicate a potential crucial role of monocytes/macrophages in OC and AD. To identify monocyte marker genes, single-cell RNA-seq data of peripheral blood mononuclear cells (PBMCs) from OC and AD patients were analyzed. Enrichment analysis of various cell subpopulations was performed using the “irGSEA” R package. The estimation of cell cycle was conducted with the “tricycle” R package, and intercellular communication networks were analyzed using “CellChat”. For 134 monocyte-associated genes (MRGs), bulk RNA-seq data from two diseased tissues were obtained. Cox regression analysis was employed to develop risk models, categorizing patients into high-risk (HR) and low-risk (LR) groups. The model's accuracy was validated using an external GEO cohort. The different risk groups were evaluated in terms of immune cell infiltration, mutational status, signaling pathways, immune checkpoint expression, and immunotherapy. To identify characteristic MRGs in AD, two machine learning algorithms, namely random forest and support vector machine (SVM), were utilized. ResultsBased on Cox regression analysis, a risk model consisting of seven genes was developed in OC, indicating a better prognosis for patients in the LR group. The LR group had a higher tumor mutation burden, immune cell infiltration abundance, and immune checkpoint expression. The results of the TIDE algorithm and the IMvigor210 cohort showed that the LR group was more likely to benefit from immunotherapy. Finally, ZFP36L1 and AP1S2 were identified as characteristic MRGs affecting OC and AD progression. ConclusionThe risk profile containing seven genes identified in this study may help further guide clinical management and targeted therapy for OC. ZFP36L1 and AP1S2 may serve as biomarkers and new therapeutic targets for patients with OC and AD.

Construction of a prognostic signature for serous ovarian cancer based on lactate metabolism-related genes.

BackgroundThe key biochemical feature of malignant tumor is the conversion of energy metabolism from oxidative phosphorylation to glycolysis, which provides sufficient capacity and raw materials for tumor cell rapid growth. Our study aims to construct a prognostic signature for ovarian cancer based on lactate metabolism-related genes (LMRGs).MethodsData of ovarian cancer and non-diseased ovarian data were downloaded from TCGA and the GTEx database, respectively. LMRGs were obtained from GeneCards and MSigDB databases, and the differentially expressed LMRGs were identified using limma and DESeq2 R packages. Cox regression analysis and LASSO were performed to determine the LMRGs associated with OS and develop the prognostic signature. Then, clinical significance of the prognostic signature in ovarian cancer was assessed.ResultsA total of 485 differentially expressed LMRGs in ovarian tissue were selected for subsequent analysis, of which 324 were up-regulated and 161 were down regulated. We found that 22 LMRGs were most significantly associated with OS by using the univariate regression analysis. The prognostic scoring model was consisted of 12 LMRGs (SLCO1B3, ERBB4, SLC28A1, PDSS1, BDH1, AIFM1, TSFM, PPARGC1A, HGF, FGFR1, ABCC8, TH). Kaplan-Meier survival analysis indicated that poorer overall survival (OS) in the high-risk group patients (P<0.0001). This prognostic signature could be an independent prognostic indicator after adjusting to other clinical factors. The calibration curves of nomogram for the signature at 1, 2, and 3 years and the ROC curve demonstrated good agreement between the predicted and observed survival rates of ovarian cancer patients. Furthermore, the high-risk group patients have much lower expression level of immune checkpoint-TDO2 compared with the low-risk group (P=0.024).ConclusionsWe established a prognostic signature based on LMRGs for ovarian cancer, and highlighted emerging evidence indicating that this prognostic signature is a promising approach for predicting ovarian cancer prognosis and guiding clinical therapy.

Pharmacologic inhibition of beta-adrenergic receptors decreases PD-L1 mediated immunosuppression and improves anti-tumor immune signature in ovarian cancer (126)

<b>Objectives:</b> The immune system is affected by stress hormones, but also plays an important role in activating and suppressing mechanisms controlling epithelial ovarian cancer (EOC). Although EOC is a "cold tumor" the trafficking of tumor infiltrating lymphocytes in the tumor mass and the expression of programmed cell death ligand (PD-L1) are prognostic indicators in EOC. To date PD-1/PD-L1 inhibitors have had limited benefit as single agents or in combination with chemotherapy. This study aimed to explore the impact of adrenergic stress and its blockade with propranolol in both <i>in vitro</i> and <i>in vivo</i> EOC models. <b>Methods:</b> Two high grade serous cell lines SKOV3ip1 and OVCAR8 were used in co-culture experiments and exosome experiments. Western blot was used to detect changes in PD-L1 protein expression in exosomes treated with noradrenaline, propranolol, and interferon-gamma (IFN-g). C57bl/6 mice were injected with ID8 cells and exposed to chronic behavioral stress to activate the beta-adrenergic signalling pathway. Mice were treated with the following combinations: control (saline), propranolol (2mg/Kg/d via Alzet pump for 6 weeks), PD-1/PD-L1 inhibitor (200μg/mouse IP, three times a week for two weeks) or a combination of propranolol and PD-1/PD-L1 inhibitor. CD3, CD4, CD8 and CXCL10 mRNA expression were evaluated in the tumors with qPCR. GraphPad Prism v.7 was used as well as one-way ANOVA with Bonferroni multiple comparison test correction. <b>Results:</b> EOC cell lines were treated with noradrenaline (NA), propranolol, IFN-g or in combination (NA+ IFN-g). NA+IFN-g caused an increase in PD-L1 mRNA and protein expression (p<0.05). IFN-g causes a statistically significant increase in the production of PD-L1 carrying exosomes (p<0.05) while propranolol does not. Combination of propranolol and PD-1/PD-L1 inhibitor significantly reduced tumour weight compared to the cancer control group (p<0.05). Chronic behavioural stress significantly increased the number of organs with metastatic nodules (p<0.05) while propranolol monotherapy and the combined therapy showed a significant decrease in the number of organs affected by metastatic nodules (p<0.001). VEGF expression was significantly reduced in the combined therapy group compared to the propranolol and PD-1/PD-L1 monotherapy (p<0.001). CD8 CD3, CD4 mRNA expression increased significantly in the combined therapy group compared to PD-1/PD-L1 inhibitor monotherapy and the control group (p<0.05). Propranolol also decreased IDO1 and CXCL10 mRNA as well. <b>Conclusions:</b> Propranolol decreases upstream production of IFN-g and the IFN-g-mediated PD-L1 expression on cancer cells. Therefore the combination of propranolol with PD-L1 inhibitors is a promising therapy in this preclinical EOC model.

The Comprehensive Analysis of Interferon-Related Prognostic Signature with regard to Immune Features in Ovarian Cancer.

Interferon plays an important role in immune response of ovarian cancer. However, the expression pattern of interferon in ovarian cancer remains unclear. This study is aimed at exploring the expression profile of interferon-relate genes and constructing an interferon-based prognostic signature in ovarian cancer. The ovarian cancer samples collected from TCGA database were viewed as the training set, and ovarian cancer samples collected from GEO datasets were used as the independent validation sets. Univariate Cox regression analysis and multivariate Cox regression analysis were used to construct interferon-related signature, which worked as independent prognostic factor. Bioinformatics based on David software, GSEA, and R software were used to investigate the relationship between immune status and the signature in ovarian cancer. The signature showed close correlation with the status for ovarian cancer immune microenvironment, which might provide the possibility for clinical targeted therapy.

Integrated analysis of ascites and plasma extracellular vesicles identifies a miRNA-based diagnostic signature in ovarian cancer

Ovarian cancer is mostly diagnosed at advantaged stages due to the lack of early diagnostic biomarkers. The common metastasis pattern is characterized by peritoneal dissemination with a formation of malignant ascites. Extracellular vesicles (EVs) are emerging as promising clinical biomarkers in liquid biopsy. Here, we aimed to investigate robust liquid biopsy-based EV miRNA biomarkers for ovarian cancer diagnosis and metastasis regulation. EVs were isolated from malignant ascites and plasma of ovarian cancer patients as well as the benign control counterparts of patients with benign gynecologic diseases. EV small RNA sequencing identified a panel of eight miRNAs (miR-1246, miR-1290, miR-483, miR-429, miR-34b-3p, miR-34c-5p, miR-145–5p, miR-449a) based on dysregulated miRNAs overlapped in the ascites and plasma subset. The ovarian cancer EV miRNA (OCEM) signature developed based on these eight miRNAs demonstrated high diagnostic accuracy in our in-house dataset and multiple public datasets across diverse clinical samples (blood, tissue and urine). In addition, malignant ascites-derived EVs could significantly facilitate the aggressive property of ovarian cancer cells and boost the growth of ascites-derived organoids. Notably, miR-1246 and miR-1290 shuttled in malignant ascites-derived EVs were identified to promote the invasion and migration of ovarian cancer cells through regulating a common target RORα.

Construction and validation of a transcription factors-based prognostic signature for ovarian cancer

BackgroundOvarian cancer (OC) is one of the most common and lethal malignant tumors worldwide and the prognosis of OC remains unsatisfactory. Transcription factors (TFs) are demonstrated to be associated with the clinical outcome of many types of cancers, yet their roles in the prognostic prediction and gene regulatory network in patients with OC need to be further investigated.MethodsTFs from GEO datasets were collected and analyzed. Differential expression analysis, WGCNA and Cox-LASSO regression model were used to identify the hub-TFs and a prognostic signature based on these TFs was constructed and validated. Moreover, tumor-infiltrating immune cells were analyzed, and a nomogram containing age, histology, FIGO_stage and TFs-based signature were established. Potential biological functions, pathways and the gene regulatory network of TFs in signature was also explored.ResultsIn this study, 6 TFs significantly associated with the prognosis of OC were identified. These TFs were used to build up a TFs-based signature for predicting the survival of patients with OC. Patients with OC in training and testing datasets were divided into high-risk and low-risk groups, according to the median value of risk scores determined by the signature. The two groups were further used to validate the performance of the signature, and the results showed the TFs-based signature had effective prediction ability. Immune infiltrating analysis was conducted and abundance of B cells naïve, T cells CD4 memory resting, Macrophages M2 and Mast cells activated were significantly higher in high-risk group. A nomogram based on the signature was established and illustrated good predictive efficiencies for 1, 2, and 3-year overall survival. Furthermore, the construction of the TFs-target gene regulatory network revealed the potential mechanisms of TFs in OC.ConclusionsTo our best knowledge, it is for the first time to develop a prognostic signature based on TFs in OC. The TFs-based signature is proven to be effective in predicting the survival of patients with OC. Our study may facilitate the clinical decision-making for patients with OC and help to elucidate the underlying mechanism of TFs in OC.

Construction of a ceRNA network of hub genes affecting immune infiltration in ovarian cancer identified by WGCNA

BackgroundOvarian cancer is the leading cause of death among gynecological malignancies. Immunotherapy has demonstrated potential effects in ovarian cancer. However, few studies on immune-related prognostic signatures in ovarian cancer have been reported. This study aimed to identify hub genes associated with immune infiltrates to provide insight into the immune regulatory mechanisms in ovarian cancer.MethodsRaw data and clinical information were downloaded from The Cancer Genome Atlas (TCGA) and University of California, Santa Cruz (UCSC) Xena websites. Single-sample gene set enrichment analysis (ssGSEA) and weighted gene co-expression network analysis (WGCNA) were used to identify hub genes. Kaplan-Meier analysis and differential expression analysis were applied to explore the real hub genes.ResultsThrough ssGSEA and WGCNA, 7 hub genes (LY9, CD5, CXCL9, IL2RG, SLAMF1, SLAMF6, and SLAMF7) were identified. Finally, LY9 and SLAMF1 were recognized as the real hub genes in immune infiltrates of ovarian cancer. LY9 and SLAMF1 are classified as SLAM family receptors involved in the activation of hematopoietic cells and the pathogenesis of multiple malignancies. Furthermore, 12 lncRNAs and 43 miRNAs significantly related to the 2 hub genes were applied to construct a lncRNA-miRNA-mRNA ceRNA network. The lncRNA-miRNA-mRNA ceRNA network shows upstream regulatory sites of the 2 hub genes.ConclusionsThese findings improve our understanding of the regulatory mechanism of and reveal potential immune checkpoints for immunotherapy for ovarian cancer.

Comprehensive Analysis of Tumor Microenvironment Identified Prognostic Immune-Related Gene Signature in Ovarian Cancer.

BackgroundAccumulating evidence demonstrated that tumor microenvironmental cells played important roles in predicting clinical outcomes and therapeutic efficacy. We aimed to develop a reliable immune-related gene signature for predicting the prognosis of ovarian cancer (OC).MethodsSingle sample gene-set enrichment analysis (ssGSEA) of immune gene-sets was used to quantify the relative abundance of immune cell infiltration and develop high- and low-abundance immune subtypes of 308 OC samples. The presence of infiltrating stromal/immune cells in OC tissues was calculated as an estimate score. We estimated the correlation coefficients among the immune subtype, clinicopathological feature, immune score, distribution of immune cells, and tumor mutation burden (TMB). The differentially expressed immune-related genes between high- and low-abundance immune subtypes were further used to construct a gene signature of a prognostic model in OC with lasso regression analysis.ResultsThe ssGSEA analysis divided OC samples into high- and low-abundance immune subtypes based on the abundance of immune cell infiltration, which was significantly related to the estimate score and clinical characteristics. The distribution of immune cells was also significantly different between high- and low-abundance immune subtypes. The correlation analysis showed the close relationship between TMB and the estimate score. The differentially expressed immune-related genes between high- and low-abundance immune subtypes were enriched in multiple immune-related pathways. Some immune checkpoints (PDL1, PD1, and CTLA-4) were overexpressed in the high-abundance immune subtype. Furthermore, the five-immune-related-gene-signature prognostic model (CCL18, CXCL13, HLA-DOB, HLA-DPB2, and TNFRSF17)-based high-risk and low-risk groups were significantly related to OC overall survival.ConclusionImmune-related genes were the promising predictors of prognosis and survival, and the comprehensive landscape of tumor microenvironmental cells of OC has potential for therapeutic schedule monitoring.

Development and verification of an immune-related gene pairs prognostic signature in ovarian cancer.

Ovarian cancer (OV) is the most common gynaecological cancer worldwide. Immunotherapy has recently been proven to be an effective treatment strategy. The work here attempts to produce a prognostic immune‐related gene pair (IRGP) signature to estimate OV patient survival. The Gene Expression Omnibus (GEO) and Cancer Genome Atlas (TCGA) databases provided the genetic expression profiles and clinical data of OV patients. Based on the InnateDB database and the least absolute shrinkage and selection operator (LASSO) regression model, we first identified a 17‐IRGP signature associated with survival. The average area under the curve (AUC) values of the training, validation, and all TCGA sets were 0.869, 0.712, and 0.778, respectively. The 17‐IRGP signature noticeably split patients into high‐ and low‐risk groups with different prognostic outcomes. As suggested by a functional study, some biological pathways, including the Toll‐like receptor and chemokine signalling pathways, were significantly negatively correlated with risk scores; however, pathways such as the p53 and apoptosis signalling pathways had a positive correlation. Moreover, tumour stage III, IV, grade G1/G2, and G3/G4 samples had significant differences in risk scores. In conclusion, an effective 17‐IRGP signature was produced to predict prognostic outcomes in OV, providing new insights into immunological biomarkers.

Development of a multi-gene-based immune prognostic signature in ovarian Cancer

BackgroundVarious components of the immune system play a critical role in the prognosis and treatment response in ovarian cancer (OC). Immunotherapy has been recognized as a hallmark of cancer but the effect is contradictional. Reliable immune gene-based prognostic biomarkers or regulatory factors are necessary to be systematically explored to develop an individualized prediction signature.MethodsThis study systematically explored the gene expression profiles in patients with ovarian cancer from RNA-seq data set for The Cancer Genome Atlas (TCGA). Differentially expressed immune genes and transcription factors (TFs) were identified using the collected immune genes from ImmPort dataset and TFs from Cistoma database. Survival associated immune genes and TFs were identified in terms of overall survival. The prognostic signature was developed based on survival associated immune genes with LASSO (Least absolute shrinkage and selection operator) Cox regression analysis. Further, we performed network analysis to uncover the potential regulators of immune-related genes with the help of computational biology.ResultsThe prognostic signature, a weighted combination of the 21 immune-related genes, performed moderately in survival prediction with AUC was 0.746, 0.735, and 0.749 for 1, 3, and 5 year overall survival, respectively. Network analysis uncovered the regulatory role of TFs in immune genes. Intriguingly, the prognostic signature reflected the immune cells landscape and infiltration of some immune cell subtypes.ConclusionsWe first constructed a signature with 21 immune genes of clinical significance, which showed promising predictive value in the surveillance, and prognosis of OC patients.

A Five-Gene Expression Signature Predicts Ovarian Cancer Metastasis.

Ovarian cancer represents one of the most malignant gynecological tumors. Despite recent advances in treatment, ovarian cancer remains to be highly susceptible to metastasis. However, information concerning genome-wide gene expression profiles is limited to develop a metastasis-specific gene signature in ovarian cancer. In this work, we try to identify changes in gene expression profile that underlie ovarian cancer metastasis. The dataset GSE73168 deposited in the Gene Expression Omnibus (GEO) database was processed to identify differentially expressed genes (DEGs) between primary tumor and metastatic tumor samples. The weighted gene correlation network analysis (WGCNA) was conducted for modules related to ovarian cancer metastasis. Modular genes associated with ovarian cancer metastasis were summarized for the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Receiver operating characteristic (ROC) curves were plotted to estimate the superiority of candidate genes in detecting ovarian cancer metastasis. The WGCNA yielded 25 co-expression network modules in the dataset GSE73168, and highly correlated genes with ovarian cancer metastasis were identified in the blue module. Twenty-two genes demonstrated differential expression between primary tumor and metastatic tumor samples, and two downregulated genes (P2RY13 and NKX6-1) and three upregulated genes (CD36, LOC57399 and RP11-587D21.4) of these 22 DEGs was also shown to correlate with ovarian cancer metastasis in the blue module. The area under the ROC curve verified these five DEGs as metastasis-specific genes for ovarian cancer. These results show P2RY13, NKX6-1, CD36, LOC57399 and RP11-587D21.4 serve as metastasis-specific genes for ovarian cancer.