scholarly journals Systematic Analysis of Survival-Associated Alternative Splicing Signatures in Thyroid Carcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Baoai Han ◽  
Minlan Yang ◽  
Xiuping Yang ◽  
Mengzhi Liu ◽  
Qiang Xie ◽  
...  
Keyword(s):  
Alternative Splicing ◽  
Prediction Models ◽  
Risk Group ◽  
Univariate Analysis ◽  
Enrichment Analysis ◽  
Underlying Mechanism ◽  
High Risk Group ◽  
Gene Set Enrichment Analysis ◽  
Prognostic Models ◽  
Systematic Analysis

Alternative splicing (AS) is a key mechanism involved in regulating gene expression and is closely related to tumorigenesis. The incidence of thyroid cancer (THCA) has increased during the past decade, and the role of AS in THCA is still unclear. Here, we used TCGA and to generate AS maps in patients with THCA. Univariate analysis revealed 825 AS events related to the survival of THCA. Five prognostic models of AA, AD, AT, ES, and ME events were obtained through lasso and multivariate analyses, and the final prediction model was established by integrating all the AS events in the five prediction models. Kaplan–Meier survival analysis revealed that the overall survival rate of patients in the high-risk group was significantly shorter than that of patients in the low-risk group. The ROC results revealed that the prognostic capabilities of each model at 3, 5, and 8 years were all greater than 0.7, and the final prognostic capabilities of the models were all greater than 0.9. By reviewing other databases and utilizing qPCR, we verified the established THCA gene model. In addition, gene set enrichment analysis showed that abnormal AS events might play key roles in tumor development and progression of THCA by participating in changes in molecular structure, homeostasis of the cell environment and in cell energy. Finally, a splicing correlation network was established to reveal the potential regulatory patterns between the predicted splicing factors and AS event candidates. In summary, AS should be considered an important prognostic indicator of THCA. Our results will help to elucidate the underlying mechanism of AS in the process of THCA tumorigenesis and broaden the prognostic and clinical application of molecular targeted therapy for THCA.

scholarly journals Role of survival-associated alternative splicing events in the prognosis of ovarian cancer

2021 ◽  
Author(s):  
Congbo Yue ◽  
Tianyi Zhao ◽  
Shoucai Zhang ◽  
Yingjie Liu ◽  
GUIXI ZHENG ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Alternative Splicing ◽  
Cox Regression ◽  
Risk Group ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
High Risk Group ◽  
Functional Enrichment ◽  
Prognostic Models

Abstract Objective Alternative splicing (AS) events play a crucial role in the tumorigenesis and progression of various cancers. In the present study, we aimed to identify specific AS events, which might be prognostic markers and therapeutic targets for ovarian cancer (OV). Methods Transcriptome data, clinical information, and Percent Spliced In (PSI) values were downloaded from TCGA database and TCGA SpliceSeq to explore the role of AS events in the prognosis of OV patients. Univariate and multivariate Cox regression analyses were performed to identify survival-associated AS events and develop multi-AS-based prognostic models. The K-M curves and ROC curves were conducted based on prognostic AS event models. Moreover, a splicing regulatory network was established according to the correlation between AS events and splicing factors (SFs). Finally, we performed functional enrichment analysis by GO terms and KEGG pathways. Results We identified 1,472 AS events that were associated with the survival of OV patients, and exon skipping (ES) was the most important type. We also found that prognostic models based on AS events were good predictors of OV prognosis, which could discriminate the high-risk group from the low-risk group (P < 0.05). Notably, the AUC value of AD, AP, AT, ES, ME, and the whole cohort was more than 0.70, indicating that these six models had valuable prediction strength. The risk score of prognostic models was identified as an independent prognostic factor. Furthermore, the AS-SF correlation network revealed several hub SF genes, including DDX39B, PNN, LUC7L3, ZC3H4 and SRSF11, and so on. Conclusions In the present study, we constructed powerful prognostic predictors for OV patients and uncovered interesting splicing networks. Collectively, our findings provided valuable insights into the underlying mechanisms of OV.

A Hypoxia-related LncRNA Signature Predicts Survival of Primary Lower-grade glioma

2021 ◽  
Author(s):  
Yanjia Hu ◽  
Jing Zhang ◽  
Jing Chen
Keyword(s):  
High Risk ◽  
Risk Score ◽  
Cox Regression ◽  
Risk Group ◽  
Enrichment Analysis ◽  
High Risk Group ◽  
Gene Set Enrichment Analysis ◽  
Lower Grade ◽  
Prognostic Signature ◽  
Gene Set Enrichment

Abstract Background Hypoxia-related long non-coding RNAs (lncRNAs) have been proven to play a role in multiple cancers and can serve as prognostic markers. Lower-grade gliomas (LGGs) are characterized by large heterogeneity. Methods This study aimed to construct a hypoxia-related lncRNA signature for predicting the prognosis of LGG patients. Transcriptome and clinical data of LGG patients were obtained from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA). LGG cohort in TCGA was chosen as training set and LGG cohorts in CGGA served as validation sets. A prognostic signature consisting of fourteen hypoxia-related lncRNAs was constructed using univariate and LASSO Cox regression. A risk score formula involving the fourteen lncRNAs was developed to calculate the risk score and patients were classified into high- and low-risk groups based on cutoff. Kaplan-Meier survival analysis was used to compare the survival between two groups. Cox regression analysis was used to determine whether risk score was an independent prognostic factor. A nomogram was then constructed based on independent prognostic factors and assessed by C-index and calibration plot. Gene set enrichment analysis and immune cell infiltration analysis were performed to uncover further mechanisms of this lncRNA signature. Results LGG patients with high risk had poorer prognosis than those with low risk in both training and validation sets. Recipient operating characteristic curves showed good performance of the prognostic signature. Univariate and multivariate Cox regression confirmed that the established lncRNA signature was an independent prognostic factor. C-index and calibration plots showed good predictive performance of nomogram. Gene set enrichment analysis showed that genes in the high-risk group were enriched in apoptosis, cell adhesion, pathways in cancer, hypoxia etc. Immune cells were higher in high-risk group. Conclusion The present study showed the value of the 14-lncRNA signature in predicting survival of LGGs and these 14 lncRNAs could be further investigated to reveal more mechanisms involved in gliomas.

scholarly journals Identification of Seven-Gene Hypoxia Signature for Predicting Overall Survival of Hepatocellular Carcinoma

2020 ◽  
Author(s):  
YuPing Bai ◽  
Wenbo Qi ◽  
Le Liu ◽  
Jing Zhang ◽  
Lan Pang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
High Risk ◽  
Cox Regression ◽  
Risk Group ◽  
Enrichment Analysis ◽  
High Risk Group ◽  
Prognostic Models ◽  
Cox Regression Analysis ◽  
Significant Difference

Abstract Background: Hepatocellular carcinoma is ranked fifth among the most common cancer worldwide. Hypoxia can induce tumor growth, but the relationship with HCC prognosis remains unclear. Our study aims to construct a hypoxia-related multigene model to predict the prognosis of HCC. Methods: RNA-seq expression data and related clinical information were download from TCGA database and ICGC database, respectively. Univariate/multivariate Cox regression analysis was used to construct prognostic models. KM curve analysis, and ROC curve were used to evaluate the prognostic models, which were further verified in the clinical traits and ICGC database. GSEA analyzed pathway enrichment in high-risk groups. Nomogram was constructed to predict the personalized treatment of patients. Finally, real-time fluorescence quantitative PCR(RT-qPCR) was used to detect the expressions of KDELR3 and SCARB1 in normal hepatocytes and 4 hepatocellular carcinoma cells. Results: Through a series of analyses, 7 prognostic markers related to HCC survival were constructed. HCC patients were divided into the high and low risk group, and the results of KM curve showed that there was a significant difference between the two groups. Stratified analysis，found that there were significant differences in risk values of different ages, genders, stages and grades, which could be used as independent predictors. In addition, we assessed the risk value in the clinical traits analysis and found that it could accelerate the progression of cancer, while the results of GSEA enrichment analysis showed that the high-risk group patients were mainly distributed in the cell cycle and other pathways. Then, Nomogram was constructed to predict the overall survival of patients. Finally, RT-qPCR showed that KDELR3 and SCARB1 were highly expressed in HepG2 and L02, respectively. Conclusion: This study provides a potential diagnostic indicator for HCC patients, and help clinicians to deepen the comprehension in HCC pathogenesis so as to make personalized medical decisions.

scholarly journals Targeting UCHL1 Induces Cell Cycle Arrest in High-Risk Multiple Myeloma with t(4;14)

2021 ◽  
Vol 27 ◽  
Author(s):  
Parin Kamseng ◽  
Teerapong Siriboonpiputtana ◽  
Teeraya Puavilai ◽  
Suporn Chuncharunee ◽  
Karan Paisooksantivatana ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Multiple Myeloma ◽  
High Risk ◽  
Molecular Mechanism ◽  
Risk Group ◽  
Enrichment Analysis ◽  
High Risk Group ◽  
Gene Set Enrichment Analysis ◽  
Functional Enrichment ◽  
Newly Diagnosed

Multiple myeloma (MM) patients considered to be at high cytogenetic risk commonly fail to respond to standard treatment. A thorough understanding of the molecular mechanism of MM development is, therefore, needed. We endeavored to explore the transcriptional signature among different subgroups of newly diagnosed MM using gene chip-based expression microarray. Bone marrow samples of 15 newly diagnosed Thai MM patients were included. The chromosomal translocation t(4;14) was the most frequently identified genetic alteration in the high-risk subgroup. Cluster analysis from expression profiling demonstrated that high-risk MM have a distinctly different expression pattern compared to standard-risk patients. The most significant differentially expressed gene was UCHL1. Functional enrichment analysis by Gene Set Enrichment Analysis, FUNRICH, and Gene Ontology Panther pathway revealed the gene sets involved in cell cycle control to be enriched in the t(4;14) high-risk group. Interestingly, among the well-established downstream targets of UCHL1, only CCND2 was significantly expressed in the t(4;14) high-risk group. Suppression of UCHL1 protein level by LDN-5744 inhibitor could arrest the cell cycle in G1 phase in cell lines. These findings shed light on the molecular mechanism of UCHL1 in t(4;14) high-risk MM and support the evidence that alteration of the UCHL1 pathway may play a role in the pathogenesis of high-risk MM.

scholarly journals Development and Validation of a Lncrnas-Based Nomogram for Survival Prediction in Neuroblastoma Patients

2021 ◽  
Author(s):  
Yong Lv ◽  
ShuGuang Jin ◽  
Bo Xiang
Keyword(s):  
High Risk ◽  
Risk Score ◽  
Cox Regression ◽  
Validation Cohort ◽  
Risk Group ◽  
Enrichment Analysis ◽  
High Risk Group ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Gene Set Enrichment

Abstract BackgroundTreatment of neuroblastoma is evolving toward precision medicine. LncRNAs can be used as prognostic biomarkers in many types of cancer.MethodsBased on the RNA-seq data from GSE49710, we built a lncRNAs-based risk score using the least absolute shrinkage and selection operation (LASSO) regression. Cox regression, receiver operating characteristic curves were used to evaluate the association of the LASSO risk score with overall survival. Nomograms were created and then validated in an external cohort from TARGET database. Gene set enrichment analysis was performed to identify the significantly changed biological pathways. ResultsThe 16-lncRNAs-based LASSO risk score was used to separate patients into high-risk and low-risk groups. In GSE49710 cohort, the high-risk group exhibited a poorer OS than those in the low-risk group (P<0.001). Moreover, multivariate Cox regression analysis demonstrated that LASSO risk score was an independent risk factor (HR=6.201;95%CI:2.536-15.16). The similar prognostic powers of the 16-lncRNAs were also achieved in the external cohort and in stratified analysis. In addition, a nomogram was established and worked well both in the internal validation cohort (C-index=0.831) and external validation cohort (C-index=0.773). The calibration plot indicated the good clinical utility of the nomogram. Gene set enrichment analysis (GSEA) indicated that high-risk group was related with cancer recurrence, metastasis and inflammatory associated pathways.ConclusionThe lncRNA-based LASSO risk score is a promising and potential prognostic tool in predicting the survival of patients with neuroblastoma. The nomogram combined the lncRNAs and clinical parameters allows for accurate risk assessment in guiding clinical management.

scholarly journals A New Ferroptosis-Related lncRNA Signature Predicts the Prognosis of Bladder Cancer Patients

2021 ◽  
Vol 9 ◽  
Author(s):  
Mei Chen ◽  
Zhenyu Nie ◽  
Yan Li ◽  
Yuanhui Gao ◽  
Xiaohong Wen ◽  
...  
Keyword(s):  
High Risk ◽  
Immune Status ◽  
Risk Group ◽  
Enrichment Analysis ◽  
Risk Groups ◽  
High Risk Group ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Gene Set Enrichment ◽  
Clinicopathological Variables

Background: Ferroptosis is closely related to the occurrence and development of cancer. An increasing number of studies have induced ferroptosis as a treatment strategy for cancer. However, the predictive value of ferroptosis-related lncRNAs in bladder cancer (BC) still need to be further elucidated. The purpose of this study was to construct a predictive signature based on ferroptosis-related long noncoding RNAs (lncRNAs) to predict the prognosis of BC patients.Methods: We downloaded RNA-seq data and the corresponding clinical and prognostic data from The Cancer Genome Atlas (TCGA) database and performed univariate and multivariate Cox regression analyses to obtain ferroptosis-related lncRNAs to construct a predictive signature. The Kaplan-Meier method was used to analyze the overall survival (OS) rate of the high-risk and low-risk groups. Gene set enrichment analysis (GSEA) was performed to explore the functional differences between the high- and low-risk groups. Single-sample gene set enrichment analysis (ssGSEA) was used to explore the relationship between the predictive signature and immune status. Finally, the correlation between the predictive signature and the treatment response of BC patients was analyzed.Results: We constructed a signature composed of nine ferroptosis-related lncRNAs (AL031775.1, AL162586.1, AC034236.2, LINC01004, OCIAD1-AS1, AL136084.3, AP003352.1, Z84484.1, AC022150.2). Compared with the low-risk group, the high-risk group had a worse prognosis. The ferroptosis-related lncRNA signature could independently predict the prognosis of patients with BC. Compared with clinicopathological variables, the ferroptosis-related lncRNA signature has a higher diagnostic efficiency, and the area under the receiver operating characteristic curve was 0.707. When patients were stratified according to different clinicopathological variables, the OS of patients in the high-risk group was shorter than that of those in the low-risk group. GSEA showed that tumor- and immune-related pathways were mainly enriched in the high-risk group. ssGSEA showed that the predictive signature was significantly related to the immune status of BC patients. High-risk patients were more sensitive to anti-PD-1/L1 immunotherapy and the conventional chemotherapy drugs sunitinib, paclitaxel, cisplatin, and docetaxel.Conclusion: The predictive signature can independently predict the prognosis of BC patients, provides a basis for the mechanism of ferroptosis-related lncRNAs in BC and provides clinical treatment guidance for patients with BC.

scholarly journals A Risk Signature Established From Three Coagulation-fibrinolysis Genes Predicts Prognosis in Digestive System Pancancer

2022 ◽  
Author(s):  
Xingyun Wang ◽  
Jinli Ji ◽  
Ying Jiang ◽  
Yiyang Zhao ◽  
Zheyao Song ◽  
...  
Keyword(s):  
High Risk ◽  
Poor Prognosis ◽  
Digestive System ◽  
Risk Group ◽  
Epigenetic Modification ◽  
Enrichment Analysis ◽  
Risk Groups ◽  
High Risk Group ◽  
Gene Set Enrichment Analysis ◽  
Cox Analysis

Abstract Venous thromboembolism (VTE) is one of the major complications of digestive system cancer, and coagulation-fibrinolysis genes play an important role in VTE. We used univariate Cox analysis, least absolute shrinkage and selection operator (LASSO), and multivariate Cox analysis to construct 3-PCFGs (prognostic coagulation-fibrinolysis genes) model based on six prognostic coagulation-fibrinolysis genes. Gene set enrichment analysis (GSEA) was used to analyze the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways of the high- and low-risk groups. In addition, we classified digestive system pancancer patients into three clusters A, B, and C based on 3-PCFGs by K means. High-risk group and cluster C were associated with poor prognosis in digestive system pancancer. The m6A-related genes ALKBH5, FTO, RBM15, YTHDC1, and YTHDC2 (P<0.001) were highly expressed in the high-risk group and cluster C. The risk score was positively correlated with cancer-associated fibroblasts and endothelial cells. Cluster C had the highest immune score and stromal score. The poor prognosis in the high-risk group and cluster C may be affected by m6A epigenetic modification and immune microenvironment components in the digestive system pancancer.

scholarly journals A Cluster-Based Approach for Identifying Prognostic microRNA Signatures in Digestive System Cancers

2021 ◽  
Vol 22 (4) ◽  
pp. 1529
Author(s):  
Jun Zhou ◽  
Xiang Cui ◽  
Feifei Xiao ◽  
Guoshuai Cai
Keyword(s):  
Prognostic Value ◽  
Digestive System ◽  
Prediction Models ◽  
Risk Group ◽  
High Risk Group ◽  
Prognostic Models ◽  
P Value ◽  
Aberrant Expression ◽  
Distinct Cluster ◽  
Cancer Cluster

Cancer remains the second leading cause of death all over the world. Aberrant expression of miRNA has shown diagnostic and prognostic value in many kinds of cancer. This study aims to provide a novel strategy to identify reliable miRNA signatures and develop improved cancer prognostic models from reported cancer-associated miRNAs. We proposed a new cluster-based approach to identify distinct cluster(s) of cancers and corresponding miRNAs. Further, with samples from TCGA and other independent studies, we identified prognostic markers and validated their prognostic value in prediction models. We also performed KEGG pathway analysis to investigate the functions of miRNAs associated with the cancer cluster of interest. A distinct cluster with 28 cancers and 146 associated miRNAs was identified. This cluster was enriched by digestive system cancers. Further, we screened out 8 prognostic miRNA signatures for STAD, 5 for READ, 18 for PAAD, 24 for LIHC, 12 for ESCA and 18 for COAD. These identified miRNA signatures demonstrated strong abilities in discriminating the overall survival time between high-risk group and low-risk group (p-value < 0.05) in both TCGA training and test datasets, as well as four independent Gene Expression Omnibus (GEO) validation datasets. We also demonstrated that these cluster-based miRNA signatures are superior to signatures identified in single cancers for prognosis. Our study identified significant miRNA signatures with improved prognosis accuracy in digestive system cancers. It also provides a novel method/strategy for cancer prognostic marker selection and offers valuable methodological directions to similar research topics.

scholarly journals Immune Cell as a Promising Biomarker in the Diagnosis and Prognosis of Cutaneous Melanoma by Using Machine Learning

2021 ◽  
Author(s):  
Jing Tang ◽  
Hongqaun Ye ◽  
Wan qi
Keyword(s):  
Machine Learning ◽  
Immune Cells ◽  
Immune Cell ◽  
Risk Group ◽  
Enrichment Analysis ◽  
High Risk Group ◽  
Gene Set Enrichment Analysis ◽  
Gene Set Enrichment ◽  
Diagnostic Models ◽  
Melanoma Patients

Abstract Background: Tumor infiltration, is known to associate with various cancer initiations and progressions, is potential therapeutic target for this aggressive skin cancer.Methods: single sample gene set enrichment analysis (ssGSEA) algorithm was applied to assess the relative expression of 24 types of immune cell from public database. Firstly, the differentially expressed immune cells between melanomas and normal samples were identified. Next, multiple machine learning algorithms were performed to evaluate the efficiency of immune cells in diagnosis of melanoma. In addition, the feature selection in machine learning methods was used to figure out the most important prognostic immune cells for developing biomarker to predict the prognosis of melanoma.Results: In comparison with the expression of immune cells in tumors and normal controls, we built the immune diagnostic models in training dataset, which can accurately classify melanoma patients from normal (LR AUC= 0.965, RF AUC= 0.99, SVM AUC=0.963, LASSO AUC= 0.964 and NNET AUC=0.989). These diagnostic models also validated in three outside datasets and suggested over 90% sensitivity and specificity to distinguish melanomas from normal patients. Moreover, we also developed a robust immune cell biomarker which could estimate the prognosis of melanoma. This biomarker also further validated in internal and external datasets. Next, we constructed nomogram combined risk score of biomarker and clinical characteristics, which showed good accuracies in predicting 3 and 5 years’ survival. The decision curve of nomogram model manifested a higher net benefit than tumor stage. In addition, melanoma patients divided into high and low risk subgroups by applied risk score system. The high risk group have a significantly shorter survival time than the low risk subgroup. Gene Set Enrichment Analysis (GSEA) analysis revealed that complement, epithelial mesenchymal transition and inflammatory response and so on significantly activated in high risk group. Conclusions: We constructed immune cell related diagnostic and prognostic models, which could provide new clinical applications for diagnosing and predicting the survival of melanoma patients.

scholarly journals Identification and characterization of a 25-lncRNA prognostic signature for early recurrence in hepatocellular carcinoma

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Yi Fu ◽  
Xindong Wei ◽  
Qiuqin Han ◽  
Jiamei Le ◽  
Yujie Ma ◽  
...  
Keyword(s):  
Risk Factors ◽  
High Risk ◽  
Cox Regression ◽  
Risk Group ◽  
Enrichment Analysis ◽  
Response Prediction ◽  
Early Recurrence ◽  
High Risk Group ◽  
Gene Set Enrichment Analysis ◽  
Prognostic Signature

Abstract Background Early recurrence is the major cause of poor prognosis in hepatocellular carcinoma (HCC). Long non-coding RNAs (lncRNAs) are deeply involved in HCC prognosis. In this study, we aimed to establish a prognostic lncRNA signature for HCC early recurrence. Methods The lncRNA expression profile and corresponding clinical data were retrieved from total 299 HCC patients in TCGA database. LncRNA candidates correlated to early recurrence were selected by differentially expressed gene (DEG), univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses. A 25-lncRNA prognostic signature was constructed according to receiver operating characteristic curve (ROC). Kaplan-Meier and multivariate Cox regression analyses were used to evaluate the performance of this signature. ROC and nomogram were used to evaluate the integrated models based on this signature with other independent clinical risk factors. Gene set enrichment analysis (GSEA) was used to reveal enriched gene sets in the high-risk group. Tumor infiltrating lymphocytes (TILs) levels were analyzed with single sample Gene Set Enrichment Analysis (ssGSEA). Immune therapy response prediction was performed with TIDE and SubMap. Chemotherapeutic response prediction was conducted by using Genomics of Drug Sensitivity in Cancer (GDSC) pharmacogenomics database. Results Compared to low-risk group, patients in high-risk group showed reduced disease-free survival (DFS) in the training (p < 0.0001) and validation cohort (p = 0.0132). The 25-lncRNA signature, AFP, TNM and vascular invasion could serve as independent risk factors for HCC early recurrence. Among them, the 25-lncRNA signature had the best predictive performance, and combination of those four risk factors further improves the prognostic potential. Moreover, GSEA showed significant enrichment of “E2F TARGETS”, “G2M CHECKPOINT”, “MYC TARGETS V1” and “DNA REPAIR” pathways in the high-risk group. In addition, increased TILs were observed in the low-risk group compared to the high-risk group. The 25-lncRNA signature negatively associates with the levels of some types of antitumor immune cells. Immunotherapies and chemotherapies prediction revealed differential responses to PD-1 inhibitor and several chemotherapeutic drugs in the low- and high-risk group. Conclusions Our study proposed a 25-lncRNA prognostic signature for predicting HCC early recurrence, which may guide postoperative treatment and recurrence surveillance in HCC patients.

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