scholarly journals Combination of Immune-Related Genomic Alterations Reveals Immune Characterization and Prediction of Different Prognostic Risks in Ovarian Cancer

2021 ◽  
Vol 9 ◽  
Author(s):  
Xibo Zhao ◽  
Shanshan Cong ◽  
Qiuyan Guo ◽  
Yan Cheng ◽  
Tian Liang ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Overall Survival ◽  
Risk Score ◽  
Immune Cell ◽  
Cox Regression ◽  
Case Fatality ◽  
The Cancer Genome Atlas ◽  
Genomic Alterations ◽  
Predictive Values ◽  
Score Model

With the highest case-fatality rate among women, the molecular pathological alterations of ovarian cancer (OV) are complex, depending on the diversity of genomic alterations. Increasing evidence supports that immune infiltration in tumors is associated with prognosis. Therefore, we aim to assess infiltration in OV using multiple methods to capture genomic signatures regulating immune events to identify reliable predictions of different outcomes. A dataset of 309 ovarian serous cystadenocarcinoma patients with overall survival >90 days from The Cancer Genome Atlas (TCGA) was analyzed. Multiple estimations and clustering methods identified and verified two immune clusters with component differences. Functional analyses pointed out immune-related alterations underlying internal genomic variables potentially. After extracting immune genes from a public database, the LASSO Cox regression model with 10-fold cross-validation was used for selecting genes associated with overall survival rate significantly, and a risk score model was then constructed. Kaplan–Meier survival and Cox regression analyses among cohorts were performed systematically to evaluate prognostic efficiency among the risk score model and other clinical pathological parameters, establishing a predictive ability independently. Furthermore, this risk score model was compared among identified signatures in previous studies and applied to two external cohorts, showing better prediction performance and generalization ability, and also validated as robust in association with immune cell infiltration in bulk tissues. Besides, a transcription factor regulation network suggested upper regulatory mechanisms in OV. Our immune risk score model may provide gyneco-oncologists with predictive values for the prognosis and treatment management of patients with OV.

scholarly journals Risk score model of autophagy-related genes in osteosarcoma

2021 ◽  
Author(s):  
Wentao Qin ◽  
Mingyang Jiang ◽  
Yang Hu ◽  
Mingjing Xie ◽  
Yiji Jike ◽  
...  
Keyword(s):  
Risk Score ◽  
Immune Cell ◽  
Cox Regression ◽  
Mapk Pathway ◽  
Pi3k Pathway ◽  
The Cancer Genome Atlas ◽  
Sensitivity Analyses ◽  
Risk Scores ◽  
Primary Malignancy ◽  
Score Model

Abstract Background Osteosarcoma (OS) is the most common primary malignancy in children and adolescents, with a high mortality and disability rate. Autophagy plays an important role in the regulation of apoptosis, invasion and metastasis of tumor cells. Hence, construction of a risk score model of autophagy related genes (ARGs) of OS would benefit the treatment and prognosis evaluation. Methods We downloaded a dataset of OS from The Cancer Genome Atlas (TCGA) database, and found the OS-related ARGs through Human Autophagy Database (HADb). Five hub ARGs (CCL2, AMBRA1, VEGFA, MYC and EGFR) were obtained by using multivariate Cox regression model. Then we calculated the risk scores and constructed a prediction model. Another two datasets downloaded from GEO were combined to verify the accuracy and validity of the model. The role of immune cell infiltration was systematically explored, and prediction of response to targeted drugs was assessed. Immunohistochemistry was carried out to verify the expression of the key ARGs. Results Based on these five hub ARGs, we constructed a risk score model related to OS. High accuracy and validity were demonstrated by datasets downloaded from GEO. These five ARGs played a role in cancer-related biological processes, such as MAPK pathway and PI3K pathway. The results of targeted drug sensitivity analyses coincided with the pathway analysis. Immunohistochemistry showed that the expression of 5 ARGs in OS group was more obvious than that in paracancerous group. Conclusion This study constructs a risk score model related to autophagy of OS, explores the prognostic value of autophagy related genes, and finds possible therapeutic targets.

scholarly journals Tumor Immune Microenvironment Related Gene-Based Model to Predict Prognosis and Response to Compounds in Ovarian Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Jiang Yang ◽  
Shasha Hong ◽  
Xiaoyi Zhang ◽  
Jingchun Liu ◽  
Ying Wang ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Risk Score ◽  
Immune Cell ◽  
Cox Regression ◽  
Killer Cell ◽  
Figo Stage ◽  
The Cancer Genome Atlas ◽  
Immune Microenvironment ◽  
Natural Killer Cell Cytotoxicity ◽  
Tumor Immune Microenvironment

BackgroundThe tumor immune microenvironment (TIME) has been recognized to be an imperative factor facilitating the acquisition of many cancer-related hallmarks and is a critical target for targeted biological therapy. This research intended to construct a risk score model premised on TIME-associated genes for prediction of survival and identification of potential drugs for ovarian cancer (OC) patients.Methods and ResultsThe stromal and immune scores were computed utilizing the ESTIMATE algorithm in OC patient samples from The Cancer Genome Atlas (TCGA) database. Weighted gene co-expression network and differentially expressed genes analyses were utilized to detect stromal-and immune-related genes. The Least Absolute Shrinkage and Selection Operator (LASSO)-Cox regression was utilized for additional gene selection. The genes that were selected were utilized as the input for a stepwise regression to construct a TIME-related risk score (TIMErisk), which was then validated in Gene Expression Omnibus (GEO) database. For the evaluation of the protein expression levels of TIME regulators, the Human Protein Atlas (HPA) dataset was utilized, and for their biological functions, the TIMER and CIBERSORT algorithm, immunoreactivity, and Immune Cell Abundance Identifier (ImmuCellAI) were used. Possible OC medications were forecasted utilizing the Genomics of Drug Sensitivity in Cancer (GDSC) database and connectivity map (CMap). TIMErisk was developed based on ALPK2, CPA3, PTGER3, CTHRC1, PLA2G2D, CXCL11, and ZNF683. High TIMErisk was recognized as a poor factor for survival in the GEO and TCGA databases; subgroup analysis with FIGO stage, grade, lymphatic and venous invasion, debulking, and tumor site also indicated similar results. Functional immune cells corresponded to more incisive immune reactions, including secretion of chemokines and interleukins, natural killer cell cytotoxicity, TNF signaling pathway, and infiltration of activated NK cells, eosinophils, and neutrophils in patients with low TIMErisk. Several small molecular medications which may enhance the prognosis of patients in the TIMErisk subgroup were identified. Lastly, an enhanced predictive performance nomogram was constructed by compounding TIMErisk with the FIGO stage and debulking.ConclusionThese findings may offer a valuable indicator for clinical stratification management and personalized therapeutic options for OC patients and may be a foundation for future mechanistic research of their association.

Wnt Signaling and Survival of Women With High-Grade Serous Ovarian Cancer: A Brief Report

2016 ◽  
Vol 26 (6) ◽  
pp. 1078-1080 ◽  
Author(s):  
Brandon-Luke L. Seagle ◽  
Monica Dandapani ◽  
Judy Y. Yeh ◽  
Shohreh Shahabi
Keyword(s):  
Ovarian Cancer ◽  
Overall Survival ◽  
Wnt Signaling ◽  
Cancer Survival ◽  
Cox Regression ◽  
Case Fatality ◽  
Progression Free Survival ◽  
The Cancer Genome Atlas ◽  
Chemotherapy Response ◽  
Free Survival

ObjectiveOvarian cancer is the gynecologic malignancy with the highest case-fatality rate due to the development of chemotherapy resistance. Predictors of chemotherapy response are needed to guide chemotherapy selection and improve survival for patients with ovarian cancer. Wnt signaling may impact chemoresistance in ovarian cancer.MethodsWe studied The Cancer Genome Atlas patients with ovarian cancer treated with intraperitoneal or intravenous-only adjuvant chemotherapy. Cox regression tested associations of expression of 26 Wnt pathway genes with progression-free survival and overall survival. Permutation tests compared survival between chemotherapy groups stratified by expression.Pvalues are two-tailed.ResultsIncreasedFZD3was associated with increased survival (intraperitoneal group, overall survival: hazard ratio [HR], 0.25; 95% confidence interval [CI], 0.11–0.72,P= 0.009; progression-free survival: HR, 0.58; 95% CI, 0.37–0.92,P= 0.020) (intravenous-only group, overall survival: HR, 0.85; 95% CI, 0.72–0.99,P= 0.039; progression-free survival: HR, 0.83; 95% CI, 0.73–0.95,P= 0.006). LowFZD3predicted decreased overall survival after intraperitoneal versus intravenous-only chemotherapy (21.7 vs 33.3 months,P< 0.0001). IncreasedAPC2was associated with decreased overall survival (HR, 1.22; 95% CI, 1.05–1.42;P= 0.009) and progression-free survival (HR, 1.28; 95% CI, 1.12–1.45;P= 0.0002).ConclusionsUp-regulated tumor Wnt signaling predicts increased ovarian cancer survival.FZD3may predict benefit from intraperitoneal chemotherapy.

scholarly journals Development and Validation of a Prognostic Nomogram Based on DNA Methylation-Driven Genes for Patients With Ovarian Cancer

2021 ◽  
Vol 12 ◽  
Author(s):  
Min Zhou ◽  
Shasha Hong ◽  
Bingshu Li ◽  
Cheng Liu ◽  
Ming Hu ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Dna Methylation ◽  
Mrna Expression ◽  
Risk Score ◽  
Cox Regression ◽  
Expression Profiles ◽  
Figo Stage ◽  
Prognostic Indicator ◽  
Tissue Expression ◽  
The Cancer Genome Atlas

Background: DNA methylation affects the development, progression, and prognosis of various cancers. This study aimed to identify DNA methylated-differentially expressed genes (DEGs) and develop a methylation-driven gene model to evaluate the prognosis of ovarian cancer (OC).Methods: DNA methylation and mRNA expression profiles of OC patients were downloaded from The Cancer Genome Atlas, Genotype-Tissue Expression, and Gene Expression Omnibus databases. We used the R package MethylMix to identify DNA methylation-regulated DEGs and built a prognostic signature using LASSO Cox regression. A quantitative nomogram was then drawn based on the risk score and clinicopathological features.Results: We identified 56 methylation-related DEGs and constructed a prognostic risk signature with four genes according to the LASSO Cox regression algorithm. A higher risk score not only predicted poor prognosis, but also was an independent poor prognostic indicator, which was validated by receiver operating characteristic (ROC) curves and the validation cohort. A nomogram consisting of the risk score, age, FIGO stage, and tumor status was generated to predict 3- and 5-year overall survival (OS) in the training cohort. The joint survival analysis of DNA methylation and mRNA expression demonstrated that the two genes may serve as independent prognostic biomarkers for OS in OC.Conclusion: The established qualitative risk score model was found to be robust for evaluating individualized prognosis of OC and in guiding therapy.

Clinicopathological signature and prognostic value of RNA:m5C methyltransferases in gliomas

2020 ◽  
Author(s):  
Peng Wang ◽  
Kai Huang ◽  
Miaojing Wu ◽  
Qing Hu ◽  
Chuming Tao ◽  
...  
Keyword(s):  
Overall Survival ◽  
Risk Score ◽  
Prognostic Value ◽  
Cox Regression ◽  
Roc Curves ◽  
Clinicopathological Features ◽  
Gene Set Enrichment Analysis ◽  
Consensus Clustering ◽  
Score Model ◽  
Genome Atlas

Abstract Background: Glioma is the most common primary intracranial tumor, accounting for the vast majority of intracranial malignant tumors. Aberrant expression of RNA:5-methylcytosine(m5C) methyltransferases has recently been the focus of research relating to the occurrence and progression of tumors. However, the prognostic value of RNA:m5C methyltransferases in glioma remains unclear. This study investigated RNA: m5C methyltransferase expression and defined its clinicopathological signature and prognostic value in gliomas. Methods: We systematically studied the RNA-sequence data of RNA:m5C methyltransferases underlying gliomas in the Chinese Glioma Genome Atlas (CGGA) and The Cancer Genome Atlas (TCGA) datasets and identified different subtypes using Consensus clustering analysis. Gene Ontology (GO) and Gene Set Enrichment analysis (GSEA) was used to annotate the function of these genes. Univariate Cox regression and the least absolute shrinkage and selection operator (LASSO) Cox regression algorithm analyses were performed to construct the risk score model. Kaplan-Meier method and Receiver operating characteristic (ROC) curves were used to assess the overall survival of glioma patients. Additionally, Cox proportional regression model analysis was developed to address the connections between the risk scores and clinical factors. Results: Consensus clustering of RNA:m5C methyltransferases identified three clusters of gliomas with different prognostic and clinicopathological features. Meanwhile, Functional annotations demonstrated that RNA:m5C methyltransferases were significantly associated with the malignant progression of gliomas. Thereafter, five RNA:m5C methyltransferase genes were screened to construct a risk score model which can be used to predict not only overall survival but also clinicopathological features in gliomas. ROC curves revealed the significant prognostic ability of this signature. In addition, Multivariate Cox regression analyses indicated that the risk score was an independent prognostic factor for glioma outcome. Conclusion: We demonstrated the role of RNA:m5C methyltransferases in the initiation and progression of glioma. We have expanded on the understanding of the molecular mechanism involved, and provided a unique approach to predictive biomarkers and targeted therapy.

scholarly journals A Novel Risk-Score Model With Eight MiRNA Signatures for Overall Survival of Patients With Lung Adenocarcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Jun Wu ◽  
Yuqing Lou ◽  
Yi-Min Ma ◽  
Jun Xu ◽  
Tieliu Shi
Keyword(s):  
Overall Survival ◽  
Lung Adenocarcinoma ◽  
Risk Score ◽  
Early Stage ◽  
Differential Expression Analysis ◽  
The Cancer Genome Atlas ◽  
Clinical Staging ◽  
Risk Scores ◽  
Regulatory Relationship ◽  
Score Model

Lung adenocarcinoma (LUAD) is the most common subtype of lung cancer with heterogeneous outcomes and diverse therapeutic responses. To classify patients into different groups and facilitate the suitable therapeutic strategy, we first selected eight microRNA (miRNA) signatures in The Cancer Genome Atlas (TCGA)-LUAD cohort based on multi-strategy combination, including differential expression analysis, regulatory relationship, univariate survival analysis, importance clustering, and multivariate combinations analysis. Using the eight miRNA signatures, we further built novel risk scores based on the predefined cutoff and beta coefficients and divided the patients into high-risk and low-risk groups with significantly different overall survival time (p-value &lt; 2 e−16). The risk-score model was confirmed with an independent dataset (p-value = 4.71 e−4). We also observed that the risk scores of early-stage patients were significantly lower than those of late-stage patients. Moreover, our model can also provide new insights into the current clinical staging system and can be regarded as an alternative system for patient stratification. This model unified the variable value as the beta coefficient facilitating the integration of biomarkers obtained from different omics data.

scholarly journals Comprehensive Characterization of Alternative mRNA Splicing Events in Glioblastoma: Implications for Prognosis, Molecular Subtypes, and Immune Microenvironment Remodeling

2021 ◽  
Vol 10 ◽  
Author(s):  
Liang Zhao ◽  
Jiayue Zhang ◽  
Zhiyuan Liu ◽  
Yu Wang ◽  
Shurui Xuan ◽  
...  
Keyword(s):  
Risk Score ◽  
Immune Cell ◽  
Cox Regression ◽  
Malignant Tumors ◽  
Cox Model ◽  
Molecular Classification ◽  
The Cancer Genome Atlas ◽  
Consensus Clustering ◽  
Cox Regression Analysis ◽  
Alternative Mrna Splicing

Alternative splicing (AS) of pre-mRNA has been widely reported to be associated with the progression of malignant tumors. However, a systematic investigation into the prognostic value of AS events in glioblastoma (GBM) is urgently required. The gene expression profile and matched AS events data of GBM patients were obtained from The Cancer Genome Atlas Project (TCGA) and TCGA SpliceSeq database, respectively. 775 AS events were identified as prognostic factors using univariate Cox regression analysis. The least absolute shrinkage and selection operator (LASSO) cox model was performed to narrow down candidate AS events, and a risk score model based on several AS events were developed subsequently. The risk score-based signature was proved as an efficient predictor of overall survival and was closely related to the tumor purity and immunosuppression in GBM. Combined similarity network fusion and consensus clustering (SNF-CC) analysis revealed two distinct GBM subtypes based on the prognostic AS events, and the associations between this novel molecular classification and clinicopathological factors, immune cell infiltration, as well as immunogenic features were further explored. We also constructed a regulatory network to depict the potential mechanisms that how prognostic splicing factors (SFs) regulate splicing patterns in GBM. Finally, a nomogram incorporating AS events signature and other clinical-relevant covariates was built for clinical application. This comprehensive analysis highlights the potential implications for predicting prognosis and clinical management in GBM.

scholarly journals Ovarian Cancer Risk Scores Based on Immune-Related Pseudogenes to Predict Overall Survival and Guide Immunotherapy and Chemotherapy

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Jie Zhao ◽  
Rixiang Zhao ◽  
Xiaocen Wei ◽  
Xiaojing Jiang ◽  
Fan Su
Keyword(s):  
Ovarian Cancer ◽  
Overall Survival ◽  
High Risk ◽  
Cox Regression ◽  
High Risk Group ◽  
The Cancer Genome Atlas ◽  
Risk Scores ◽  
Ovarian Cancer Risk ◽  
Cox Regression Analysis ◽  
The Impact

Background. Ovarian cancer (OC) is the top of the aggressive malignancies in females with a poor survival rate. However, the roles of immune-related pseudogenes (irPseus) in the immune infiltration of OC and the impact on overall survival (OS) have not been adequately studied. Therefore, this study aims to identify a novel model constructed by irPseus to predict OS in OC and to determine its significance in immunotherapy and chemotherapy. Methods. In this study, with the use of The Cancer Genome Atlas (TCGA) combined with Genotype-Tissue Expression (GTEx), 55 differentially expressed irPseus (DEirPseus) were identified. Then, we constructed 10 irPseus pairs with the help of univariate, Lasso, and multivariate Cox regression analysis. The prognostic performance of the model was determined and measured by the Kaplan–Meier curve, a time-dependent receiver operating characteristic (ROC) curve. Results. After dividing OC subjects into high- and low-risk subgroups via the cut-off point, it was revealed that subjects in the high-risk group had a shorter OS. The multivariate Cox regression performed between the model and multiple clinicopathological variables revealed that the model could effectively and independently predict the prognosis of OC. The prognostic model characterized infiltration by various kinds of immune cells and demonstrated the immunotherapy response of subjects with cytotoxic lymphocyte antigen 4 (CTLA4), anti-programmed death-1 (PD-1), and anti-PD-ligand 1 (PD-L1) therapy. A high risk score was related to a higher inhibitory concentration (IC50) for etoposide ( P = 0.0099 ) and mitomycin C ( P = 0.0013 ). Conclusion. It was the first study to identify a novel signature developed by DEirPseus pairs and verify the role in predicting OS, immune infiltrates, immunotherapy, and chemosensitivity. The irPseus are vital factors predicting the prognosis of OC and could act as a novel potential treatment target.

scholarly journals Systemic characterization of alternative splicing related to prognosis and immune infiltration in malignant mesothelioma

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Jinzhi Lai ◽  
Hainan Yang ◽  
Tianwen Xu
Keyword(s):  
Overall Survival ◽  
Regression Analysis ◽  
Risk Score ◽  
Regulatory Network ◽  
Immune Cell ◽  
Cox Regression ◽  
Risk Group ◽  
High Risk Group ◽  
Immune Cell Infiltration ◽  
Cox Regression Analysis

Abstract Background Malignant mesothelioma (MM) is a relatively rare and highly lethal tumor with few treatment options. Thus, it is important to identify prognostic markers that can help clinicians diagnose mesothelioma earlier and assess disease activity more accurately. Alternative splicing (AS) events have been recognized as critical signatures for tumor diagnosis and treatment in multiple cancers, including MM. Methods We systematically examined the AS events and clinical information of 83 MM samples from TCGA database. Univariate Cox regression analysis was used to identify AS events associated with overall survival. LASSO analyses followed by multivariate Cox regression analyses were conducted to construct the prognostic signatures and assess the accuracy of these prognostic signatures by receiver operating characteristic (ROC) curve and Kaplan–Meier survival analyses. The ImmuCellAI and ssGSEA algorithms were used to assess the degrees of immune cell infiltration in MM samples. The survival-related splicing regulatory network was established based on the correlation between survival-related AS events and splicing factors (SFs). Results A total of 3976 AS events associated with overall survival were identified by univariate Cox regression analysis, and ES events accounted for the greatest proportion. We constructed prognostic signatures based on survival-related AS events. The prognostic signatures proved to be an efficient predictor with an area under the curve (AUC) greater than 0.9. Additionally, the risk score based on 6 key AS events proved to be an independent prognostic factor, and a nomogram composed of 6 key AS events was established. We found that the risk score was significantly decreased in patients with the epithelioid subtype. In addition, unsupervised clustering clearly showed that the risk score was associated with immune cell infiltration. The abundances of cytotoxic T (Tc) cells, natural killer (NK) cells and T-helper 17 (Th17) cells were higher in the high-risk group, whereas the abundances of induced regulatory T (iTreg) cells were lower in the high-risk group. Finally, we identified 3 SFs (HSPB1, INTS1 and LUC7L2) that were significantly associated with MM patient survival and then constructed a regulatory network between the 3 SFs and survival-related AS to reveal potential regulatory mechanisms in MM. Conclusion Our study provided a prognostic signature based on 6 key events, representing a better effective tumor-specific diagnostic and prognostic marker than the TNM staging system. AS events that are correlated with the immune system may be potential therapeutic targets for MM.

scholarly journals A signature of seven immune‐related genes predicts overall survival in male gastric cancer patients

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Xin Xu ◽  
Yida Lu ◽  
Youliang Wu ◽  
Mingliang Wang ◽  
Xiaodong Wang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Overall Survival ◽  
Risk Score ◽  
Cox Regression ◽  
External Validation ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed ◽  
Survival Prediction ◽  
Immune Related Genes

Abstract Background Gastric cancer (GC) has a high mortality rate and is one of the most fatal malignant tumours. Male sex has been proven as an independent risk factor for GC. This study aimed to identify immune-related genes (IRGs) associated with the prognosis of male GC. Methods RNA sequencing and clinical data were obtained from The Cancer Genome Atlas (TCGA) database. Differentially expressed IRGs between male GC and normal tissues were identified by integrated bioinformatics analysis. Univariate and multivariate Cox regression analyses were applied to screen survival-associated IRGs. Then, GC patients were separated into high- and low-risk groups based on the median risk score. Furthermore, a nomogram was constructed based on the TCGA dataset. The prognostic value of the risk signature model was evaluated by Kaplan-Meier curve, receiver operating characteristic (ROC), Harrell’s concordance index and calibration curves. In addition, the gene expression dataset from the Gene Expression Omnibus (GEO) was also downloaded for external validation. The relative proportions of 22 types of infiltrating immune cells in each male GC sample were evaluated using CIBERSORT. Results A total of 276 differentially expressed IRGs were screened, including 189 up-regulated and 87 down-regulated genes. Subsequently, a seven-IRGs signature (LCN12, CCL21, RNASE2, CGB5, NRG4, AGTR1 and NPR3) was identified to be significantly associated with the overall survival (OS) of male GC patients. Survival analysis indicated that patients in the high-risk group exhibited a poor clinical outcome. The results of multivariate analysis revealed that the risk score was an independent prognostic factor. The established nomogram could be used to evaluate the prognosis of individual male GC patients. Further analysis showed that the prognostic model had excellent predictive performance in both TCGA and validated cohorts. Besides, the results of tumour-infiltrating immune cell analysis indicated that the seven-IRGs signature could reflect the status of the tumour immune microenvironment. Conclusions Our study developed a novel seven-IRGs risk signature for individualized survival prediction of male GC patients.

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