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 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 Comprehensive Analysis of the Immune-Oncology Targets and Immune Infiltrates of N6-Methyladenosine-Related Long Noncoding RNA Regulators in Breast Cancer

2021 ◽  
Vol 9 ◽  
Author(s):  
Xiaoqiang Zhang ◽  
Li Shen ◽  
Ruyu Cai ◽  
Xiafei Yu ◽  
Junzhe Yang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
High Risk ◽  
Risk Score ◽  
Noncoding Rna ◽  
Immune Cell ◽  
Cox Regression ◽  
High Risk Group ◽  
The Cancer Genome Atlas ◽  
Risk Scores ◽  
Immune Oncology

Breast cancer (BRCA) has become the highest incidence of cancer due to its heterogeneity. To predict the prognosis of BRCA patients, sensitive biomarkers deserve intensive investigation. Herein, we explored the role of N6-methyladenosine-related long non-coding RNAs (m6A-related lncRNAs) as prognostic biomarkers in BRCA patients acquired from The Cancer Genome Atlas (TCGA; n = 1,089) dataset and RNA sequencing (RNA-seq) data (n = 196). Pearson’s correlation analysis, and univariate and multivariate Cox regression were performed to select m6A-related lncRNAs associated with prognosis. Twelve lncRNAs were identified to construct an m6A-related lncRNA prognostic signature (m6A-LPS) in TCGA training (n = 545) and validation (n = 544) cohorts. Based on the 12 lncRNAs, risk scores were calculated. Then, patients were classified into low- and high-risk groups according to the median value of risk scores. Distinct immune cell infiltration was observed between the two groups. Patients with low-risk score had higher immune score and upregulated expressions of four immune-oncology targets (CTLA4, PDCD1, CD274, and CD19) than patients with high-risk score. On the contrary, the high-risk group was more correlated with overall gene mutations, Wnt/β-catenin signaling, and JAK-STAT signaling pathways. In addition, the stratification analysis verified the ability of m6A-LPS to predict prognosis. Moreover, a nomogram (based on risk score, age, gender, stage, PAM50, T, M, and N stage) was established to evaluate the overall survival (OS) of BRCA patients. Thus, m6A-LPS could serve as a sensitive biomarker in predicting the prognosis of BRCA patients and could exert positive influence in personalized immunotherapy.

scholarly journals Immunogenomic Analyses of the Prognostic Predictive Model for Patients With Renal Cancer

2021 ◽  
Vol 12 ◽  
Author(s):  
Tao Feng ◽  
Jiahui Zhao ◽  
Dechao Wei ◽  
Pengju Guo ◽  
Xiaobing Yang ◽  
...  
Keyword(s):  
Cell Carcinoma ◽  
Predictive Model ◽  
Risk Score ◽  
Immune Cell ◽  
The Cancer Genome Atlas ◽  
Risk Scores ◽  
Immune Checkpoints ◽  
Survival Outcomes ◽  
Prognostic Signature ◽  
Therapeutic Benefits

BackgroundRenal cell carcinoma (RCC) is associated with poor prognostic outcomes. The current stratifying system does not predict prognostic outcomes and therapeutic benefits precisely for RCC patients. Here, we aim to construct an immune prognostic predictive model to assist clinician to predict RCC prognosis.MethodsHerein, an immune prognostic signature was developed, and its predictive ability was confirmed in the kidney renal clear cell carcinoma (KIRC) cohorts based on The Cancer Genome Atlas (TCGA) dataset. Several immunogenomic analyses were conducted to investigate the correlations between immune risk scores and immune cell infiltrations, immune checkpoints, cancer genotypes, tumor mutational burden, and responses to chemotherapy and immunotherapy.ResultsThe immune prognostic signature contained 14 immune-associated genes and was found to be an independent prognostic factor for KIRC. Furthermore, the immune risk score was established as a novel marker for predicting the overall survival outcomes for RCC. The risk score was correlated with some significant immunophenotypic factors, including T cell infiltration, antitumor immunity, antitumor response, oncogenic pathways, and immunotherapeutic and chemotherapeutic response.ConclusionsThe immune prognostic, predictive model can be effectively and efficiently used in the prediction of survival outcomes and immunotherapeutic responses of RCC patients.

scholarly journals Integrated analysis of the underlying immunomodulator and survival signature of STON1 gene in kidney renal clear cell carcinoma

2021 ◽  
Author(s):  
Axiu Zheng ◽  
Jianrong Bai ◽  
Yanping Ha ◽  
Bingshu Wang ◽  
Yuan Zou ◽  
...  
Keyword(s):  
Cell Carcinoma ◽  
Risk Score ◽  
Immune Cell ◽  
Cox Regression ◽  
Clear Cell ◽  
Clear Cell Carcinoma ◽  
Renal Clear Cell Carcinoma ◽  
Clinicopathological Characteristics ◽  
Risk Scores ◽  
Integrated Analysis

Abstract Background Stonin 1 (STON1) is an endocytic protein but its role in cancer remains unclear. Here, we investigated the role of STON1 in kidney renal clear cell carcinoma (KIRC). Methods We undertook bioinformatics analyses of a series of public databases to determine the expression and clinical significance of STON1 in KIRC and focused on STON1-related immunomodulator and survival signatures. A nomogram model integrating clinicopathological characteristics and risk scores for KIRC was established and validated. Results Through TGCA and GEO databases, STON1 mRNA was found to be significantly downregulated in KIRC compared with normal controls, and decreased STON1 was related to grade, TNM stage, distant metastasis, and vital status of KIRC. Furthermore, OncoLnc, UALCAN, Kaplan–Meier, and GEPIA2 analyses supported that KIRC patients with high STON1 expression had better overall survival. STON1 was positively associated with mismatch proteins including MLH1, PMS2, MSH2, MSH6 and EpCAM, and was negatively correlated with tumor mutational burden. Interestingly, arm-level deletion of STON1 was clearly related to the abundance of immune cells and the infiltration abundance in the majority of 26 immune cell types that were positively related to STON1 mRNA level in the TIMER database. The TISIDB database revealed 21 immunostimulators and 10 immunoinhibitors that were obviously related to STON1 in KIRC. In univariate and multivariate Cox regression analyses, CTLA4 , KDR , LAG3 , PDCD1 , HHLA2 , TNFRSF25 , and TNFSF14 were screened to establish a risk score model, and the low-risk group had a better prognosis for KIRC. Furthermore, a nomogram integrating clinicopathological characteristics and risk score had better accuracy and practicability in predicating the survival of KIRC patients. Conclusions Decreased STON1 expression in KIRC leads to clinical progression and poor survival. Mechanically, loss of STON1 is associated with the aberrant immune microenvironment in KIRC. Integrated clinicopathological characteristics and risk score derived from STON1 -related immunomodulators can aid the prediction of KIRC survival.

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 < 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 Identification and validation of a novel ferroptosis-related gene model for predicting the prognosis of gastric cancer patients

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0254368
Author(s):  
Gang Liu ◽  
Jian-ying Ma ◽  
Gang Hu ◽  
Huan Jin
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Risk Score ◽  
Cox Regression ◽  
Predictive Accuracy ◽  
Expression Profiles ◽  
Critical Role ◽  
Clinicopathological Features ◽  
The Cancer Genome Atlas ◽  
Risk Scores

Background Ferroptosis is a novel form of regulated cell death that plays a critical role in tumorigenesis. The purpose of this study was to establish a ferroptosis-associated gene (FRG) signature and assess its clinical outcome in gastric cancer (GC). Methods Differentially expressed FRGs were identified using gene expression profiles from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. Univariate and least absolute shrinkage and selection operator (LASSO) Cox regression analyses were performed to construct a prognostic signature. The model was validated using an independent GEO dataset, and a genomic-clinicopathologic nomogram integrating risk scores and clinicopathological features was established. Results An 8-FRG signature was constructed to calculate the risk score and classify GC patients into two risk groups (high- and low-risk) according to the median value of the risk score. The signature showed a robust predictive capacity in the stratification analysis. A high-risk score was associated with advanced clinicopathological features and an unfavorable prognosis. The predictive accuracy of the signature was confirmed using an independent GSE84437 dataset. Patients in the two groups showed different enrichment of immune cells and immune-related pathways. Finally, we established a genomic-clinicopathologic nomogram (based on risk score, age, and tumor stage) to predict the overall survival (OS) of GC patients. Conclusions The novel FRG signature may be a reliable tool for assisting clinicians in predicting the OS of GC patients and may facilitate personalized treatment.

scholarly journals An immune-related pseudogene signature to improve prognosis prediction of endometrial carcinoma patients

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Shanshan Tang ◽  
Yiyi Zhuge
Keyword(s):  
Cancer Treatment ◽  
Risk Score ◽  
Immune Cell ◽  
Cox Regression ◽  
Risk Group ◽  
Prognostic Significance ◽  
Low Risk ◽  
The Cancer Genome Atlas ◽  
Prognostic Signature ◽  
Multiple Cancer

Abstract Background Pseudogenes show multiple functions in various cancer types, and immunotherapy is a promising cancer treatment. Therefore, this study aims to identify immune-related pseudogene signature in endometrial cancer (EC). Methods Gene transcriptome data of EC tissues and corresponding clinical information were downloaded from The Cancer Genome Atlas (TCGA) through UCSC Xena browser. Spearman correlation analysis was performed to identify immune-related pseudogenes (IRPs) between the immune genes and pseudogenes. Univariate Cox regression, LASSO, and multivariate were performed to develop a risk score signature to investigate the different overall survival (OS) between high- and low-risk groups. The prognostic significance of the signature was assessed by the Kaplan–Meier curve, time-dependent receiver operating characteristic (ROC) curve. The abundance of 22 immune cell subtypes of EC patients was evaluated using CIBERSORT. Results Nine IRPs were used to build a prognostic signature. Survival analysis revealed that patients in the low-risk group presented longer OS than those in the high-risk group as well as in multiple subgroups. The signature risk score was independent of other clinical covariates and was associated with several clinicopathological variables. The prognostic signature reflected infiltration by multiple types of immune cells and revealed the immunotherapy response of patients with anti-programmed death-1 (PD-1) and anti-programmed cell death 1 ligand 1 (PD-L1) therapy. Function enrichment analysis revealed that the nine IRPs were mainly involved in multiple cancer-related pathways. Conclusion We identified an immune-related pseudogene signature that was strongly correlated with the prognosis and immune response to EC. The signature might have important implications for improving the clinical survival of EC patients and provide new strategies for cancer treatment.

scholarly journals Identification of a thirteen-gene signature predicting overall survival for hepatocellular carcinoma

2021 ◽  
Author(s):  
Xiaohan Zhou ◽  
Chengdong Liu ◽  
Hanyi Zeng ◽  
Dehua Wu ◽  
Li Liu
Keyword(s):  
Hepatocellular Carcinoma ◽  
Risk Score ◽  
Cox Regression ◽  
R Package ◽  
Gene Signature ◽  
The Cancer Genome Atlas ◽  
Prognostic Indicators ◽  
Pathway Enrichment Analysis ◽  
Score Model ◽  
Predictive Efficiency

Background: Hepatocellular carcinoma (HCC) is a malignant tumor of the digestive system characterized by mortality rate and poor prognosis. To indicate the prognosis of HCC patients, lots of genes have been screened as prognostic indicators. However, the predictive efficiency of single gene is not enough. Therefore, it is essential to identify a risk-score model based on gene signature to elevate predictive efficiency. Methods: lasso regression analysis followed by univariate cox regression was employed to establish a risk-score model for HCC prognosis prediction based on The Cancer Genome Atlas (TCGA) dataset and Gene Expression Omnibus (GEO) dataset GSE14520. R package “clusterProfiler” was used to conduct function and pathway enrichment analysis. The infiltration level of various immune and stromal cells in the tumor microenvironment (TME) were evaluated by ssGSEA of R package “GSVA”. Results: This prognostic model is an independent prognostic factor for predicting the prognosis of HCC patients and can be more effective combining with clinical data through the construction of nomogram model. Further analysis showed patients in high-risk group possess more complex TME and immune cell composition. Conclusions: Taken together, our research suggests the thirteen-gene signature to possess potential prognostic value for HCC patients and provide new information for immunological research and treatment in HCC.

scholarly journals Prognostic Signature of Osteosarcoma Based on 14 Autophagy-Related Genes

2021 ◽  
Vol 27 ◽  
Author(s):  
Wei Qi ◽  
Qian Yan ◽  
Ming Lv ◽  
Delei Song ◽  
Xianbin Wang ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Risk Score ◽  
Clinical Decision Making ◽  
Immune Cell ◽  
Cox Regression ◽  
Clinical Decision ◽  
Cell Types ◽  
Survival Outcome ◽  
Risk Scores ◽  
Cox Regression Analysis

Background: Osteosarcoma is a common malignancy of bone with inferior survival outcome. Autophagy can exert multifactorial influence on tumorigenesis and tumor progression. However, the specific function of genes related to autophagy in the prognosis of osteosarcoma patients remains unclear. Herein, we aimed to explore the association of genes related to autophagy with the survival outcome of osteosarcoma patients.Methods: The autophagy-associated genes that were related to the prognosis of osteosarcoma were optimized by LASSO Cox regression analysis. The survival of osteosarcoma patients was forecasted by multivariate Cox regression analysis. The immune infiltration status of 22 immune cell types in osteosarcoma patients with high and low risk scores was compared by using the CIBERSORT tool.Results: The risk score model constructed according to 14 autophagy-related genes (ATG4A, BAK1, BNIP3, CALCOCO2, CCL2, DAPK1, EGFR, FAS, GRID2, ITGA3, MYC, RAB33B, USP10, and WIPI1) could effectively predict the prognosis of patients with osteosarcoma. A nomogram model was established based on risk score and metastasis.Conclusion: Autophagy-related genes were identified as pivotal prognostic signatures, which could guide the clinical decision making in the treatment of osteosarcoma.

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