Identification of 6 gene markers for survival prediction in osteosarcoma cases based on multi-omics analysis

2021 ◽  
pp. 153537022199201
Author(s):  
Runmin Li ◽  
Guosheng Wang ◽  
ZhouJie Wu ◽  
HuaGuang Lu ◽  
Gen Li ◽  
...  
Keyword(s):  
Feature Selection ◽  
Cox Regression ◽  
Cancer Genomics ◽  
Predictive Performance ◽  
Training Group ◽  
Gene Expression Omnibus ◽  
Survival Prediction ◽  
Screening Process ◽  
Gene Markers ◽  
Validation Set

Multiple-omics sequencing information with high-throughput has laid a solid foundation to identify genes associated with cancer prognostic process. Multiomics information study is capable of revealing the cancer occurring and developing system according to several aspects. Currently, the prognosis of osteosarcoma is still poor, so a genetic marker is needed for predicting the clinically related overall survival result. First, Office of Cancer Genomics (OCG Target) provided RNASeq, copy amount variations information, and clinically related follow-up data. Genes associated with prognostic process and genes exhibiting copy amount difference were screened in the training group, and the mentioned genes were integrated for feature selection with least absolute shrinkage and selection operator (Lasso). Eventually, effective biomarkers received the screening process. Lastly, this study built and demonstrated one gene-associated prognosis mode according to the set of the test and gene expression omnibus validation set; 512 prognosis-related genes ( P < 0.01), 336 copies of amplified genes ( P < 0.05), and 36 copies of deleted genes ( P < 0.05) were obtained, and those genes of the mentioned genomic variants display close associations with tumor occurring and developing mechanisms. This study generated 10 genes for candidates through the integration of genomic variant genes as well as prognosis-related genes. Six typical genes (i.e. MYC, CHIC2, CCDC152, LYL1, GPR142, and MMP27) were obtained by Lasso feature selection and stepwise multivariate regression study, many of which are reported to show a relationship to tumor progressing process. The authors conducted Cox regression study for building 6-gene sign, i.e. one single prognosis-related element, in terms of cases carrying osteosarcoma. In addition, the samples were able to be risk stratified in the training group, test set, and externally validating set. The AUC of five-year survival according to the training group and validation set reached over 0.85, with superior predictive performance as opposed to the existing researches. Here, 6-gene sign was built to be new prognosis-related marking elements for assessing osteosarcoma cases’ surviving state.

scholarly journals Screening the genome for HCC-specific CpG methylation signatures as biomarkers for diagnosis and prognosis evaluation

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Rui-kun Zhang ◽  
Jia-lin Liu
Keyword(s):  
Cox Regression ◽  
Malignant Tumors ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Survival Prediction ◽  
Training Set ◽  
Cpg Sites ◽  
Diagnosis And Prognosis ◽  
Patient Prognosis ◽  
Prognosis Evaluation

Abstract Background Hepatocellular carcinoma (HCC) is one of the most common and invasive malignant tumors in the world. The change in DNA methylation is a key event in HCC. Methods Methylation datasets for HCC and 17 other types of cancer were downloaded from The Cancer Genome Atlas (TCGA). The CpG sites with large differences in methylation between tumor tissues and paracancerous tissues were identified. We used the HCC methylation dataset downloaded from the TCGA as the training set and removed the overlapping sites among all cancer datasets to ensure that only CpG sites specific to HCC remained. Logistic regression analysis was performed to select specific biomarkers that can be used to diagnose HCC, and two datasets—GSE157341 and GSE54503—downloaded from GEO as validation sets were used to validate our model. We also used a Cox regression model to select CpG sites related to patient prognosis. Results We identified 6 HCC-specific methylated CpG sites as biomarkers for HCC diagnosis. In the training set, the area under the receiver operating characteristic (ROC) curve (AUC) for the model containing all these sites was 0.971. The AUCs were 0.8802 and 0.9711 for the two validation sets from the GEO database. In addition, 3 other CpG sites were analyzed and used to create a risk scoring model for patient prognosis and survival prediction. Conclusions Through the analysis of HCC methylation datasets from the TCGA and Gene Expression Omnibus (GEO) databases, potential biomarkers for HCC diagnosis and prognosis evaluation were ascertained.

scholarly journals A Novel Six-mRNA Signature Predicts Survival of Patients With Glioblastoma Multiforme

2021 ◽  
Vol 12 ◽  
Author(s):  
Zhentao Liu ◽  
Hao Zhang ◽  
Hongkang Hu ◽  
Zheng Cai ◽  
Chengyin Lu ◽  
...  
Keyword(s):  
Glioblastoma Multiforme ◽  
Cox Regression ◽  
Methylation Status ◽  
Survival Rates ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Survival Prediction ◽  
Lasso Regression ◽  
Cox Regression Analysis ◽  
Score Model

Glioblastoma multiforme (GBM) is a devastating brain tumor and displays divergent clinical outcomes due to its high degree of heterogeneity. Reliable prognostic biomarkers are urgently needed for improving risk stratification and survival prediction. In this study, we analyzed genome-wide mRNA profiles in GBM patients derived from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases to identify mRNA-based signatures for GBM prognosis with survival analysis. Univariate Cox regression model was used to evaluate the relationship between the expression of mRNA and the prognosis of patients with GBM. We established a risk score model that consisted of six mRNA (AACS, STEAP1, STEAP2, G6PC3, FKBP9, and LOXL1) by the LASSO regression method. The six-mRNA signature could divide patients into a high-risk and a low-risk group with significantly different survival rates in training and test sets. Multivariate Cox regression analysis confirmed that it was an independent prognostic factor in GBM patients, and it has a superior predictive power as compared with age, IDH mutation status, MGMT, and G-CIMP methylation status. By combining this signature and clinical risk factors, a nomogram can be established to predict 1-, 2-, and 3-year OS in GBM patients with relatively high accuracy.

scholarly journals Tumor Grade and Overall Survival Prediction of Gliomas Using Radiomics

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Jianming Ye ◽  
He Huang ◽  
Weiwei Jiang ◽  
Xiaomei Xu ◽  
Chun Xie ◽  
...  
Keyword(s):  
Machine Learning ◽  
Overall Survival ◽  
Feature Selection ◽  
Model Performance ◽  
Predictive Performance ◽  
Tumor Grade ◽  
Survival Prediction ◽  
Lower Grade ◽  
Feature Selection Technique ◽  
Mri Scans

Glioma is one of the most common and deadly malignant brain tumors originating from glial cells. For personalized treatment, an accurate preoperative prognosis for glioma patients is highly desired. Recently, various machine learning-based approaches have been developed to predict the prognosis based on preoperative magnetic resonance imaging (MRI) radiomics, which extract quantitative features from radiographic images. However, major challenges remain for methodologic developments to optimize feature extraction and provide rapid information flow in clinical settings. This study investigates two machine learning-based prognosis prediction tasks using radiomic features extracted from preoperative multimodal MRI brain data: (i) prediction of tumor grade (higher-grade vs. lower-grade gliomas) from preoperative MRI scans and (ii) prediction of patient overall survival (OS) in higher-grade gliomas (<12 months vs. > 12 months) from preoperative MRI scans. Specifically, these two tasks utilize the conventional machine learning-based models built with various classifiers. Moreover, feature selection methods are applied to increase model performance and decrease computational costs. In the experiments, models are evaluated in terms of their predictive performance and stability using a bootstrap approach. Experimental results show that classifier choice and feature selection technique plays a significant role in model performance and stability for both tasks; a variability analysis indicates that classification method choice is the most dominant source of performance variation for both tasks.

scholarly journals Identification of a Five-Gene Signature Derived From MYCN Amplification and Establishment of a Nomogram for Predicting the Prognosis of Neuroblastoma

2021 ◽  
Vol 8 ◽  
Author(s):  
Yuren Xia ◽  
Xin Li ◽  
Xiangdong Tian ◽  
Qiang Zhao
Keyword(s):  
Gene Expression ◽  
Cox Regression ◽  
External Validation ◽  
Clinical Manifestations ◽  
Staging System ◽  
Predictive Performance ◽  
Gene Signature ◽  
Gene Expression Omnibus ◽  
Mycn Amplification ◽  
Regression Analyses

Background: Neuroblastoma (NB), the most common solid tumor in children, exhibits vastly different genomic abnormalities and clinical behaviors. While significant progress has been made on the research of relations between clinical manifestations and genetic abnormalities, it remains a major challenge to predict the prognosis of patients to facilitate personalized treatments.Materials and Methods: Six data sets of gene expression and related clinical data were downloaded from the Gene Expression Omnibus (GEO) database, ArrayExpress database, and Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database. According to the presence or absence of MYCN amplification, patients were divided into two groups. Differentially expressed genes (DEGs) were identified between the two groups. Enrichment analyses of these DEGs were performed to dig further into the molecular mechanism of NB. Stepwise Cox regression analyses were used to establish a five-gene prognostic signature whose predictive performance was further evaluated by external validation. Multivariate Cox regression analyses were used to explore independent prognostic factors for NB. The relevance of immunity was evaluated by using algorithms, and a nomogram was constructed.Results: A five-gene signature comprising CPLX3, GDPD5, SPAG6, NXPH1, and AHI1 was established. The five-gene signature had good performance in predicting survival and was demonstrated to be superior to International Neuroblastoma Staging System (INSS) staging and the MYCN amplification status. Finally, a nomogram based on the five-gene signature was established, and its clinical efficacy was demonstrated.Conclusion: Collectively, our study developed a novel five-gene signature and successfully built a prognostic nomogram that accurately predicted survival in NB. The findings presented here could help to stratify patients into subgroups and determine the optimal individualized therapy.

scholarly journals Comprehensive Analysis of the Immune and Prognostic Implication of TRIM8 in Breast Cancer

2021 ◽  
Author(s):  
Cheng Yan ◽  
Qingling Liu ◽  
Mingkun Nie ◽  
Wei Hu ◽  
Ruoling Jia
Keyword(s):  
Breast Cancer ◽  
Cox Regression ◽  
Molecular Taxonomy ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Survival Prediction ◽  
Mrna Levels ◽  
Breast Cancer Patients ◽  
Prognostic Signature ◽  
Normal Tissues

Abstract Background: Breast cancer remains one of most lethal illnesses for female and the most common malignancies among women, making it important to discover novel biomarkers and therapeutic targets for breast cancer. Immunotherapy has become a promising therapeutic tool for breast cancer. The role of TRIM8 in breast cancer has rarely been reported. Method: Here we identified TRIM8 expression and its potential functions on survival in patients with breast cancer using TCGA (The cancer genome atlas), GEO (Gene expression omnibus) database and METABRIC (Molecular Taxonomy of Breast Cancer International Consortium). Then, TIMER and TISIDB databases were used to investigate the correlations between TRIM8 mRNA levels and immune characteristics. Using stepwise cox regression, we established an immune prognostic signature based on five differentially expression immune-related genes (DE-IRGs). Finally, a nomogram, accompanied by a calibration curve was proposed to predict 1-, 3-, and 5-year survival for breast cancer patients. Results: We found that TRIM8 expression was dramatically lower in breast cancer tissues in comparison with normal tissues. Lower TRIM8 expression was related with worse prognosis in breast cancer. TIMER and TISIDB analysis showed that there were strong correlations between TRIM8 expression and immune characteristics. The receiver operating characteristic (ROC) curve confirmed the good performance in survival prediction, showing good accuracy of the immune prognostic signature. We demonstrated the model usefulness of predictions by nomogram and calibration curves. Our findings indicated that TRIM8 might be a potential link between progression and prognosis survival of breast cancer.Conclusion: This is a comprehensive study to reveal that TRIM8 may serve as a potential prognostic biomarker associating with immune characteristics and provide a novel therapeutic target for the treatment of breast cancer.

scholarly journals A Novel Overall Survival Prediction Signature Based on Cancer Stem Cell-Related Genes in Osteosarcoma

2021 ◽  
Vol 9 ◽  
Author(s):  
Bo Xiao ◽  
Liyan Liu ◽  
Zhuoyuan Chen ◽  
Aoyu Li ◽  
Yu Xia ◽  
...  
Keyword(s):  
Stem Cell ◽  
Cox Regression ◽  
Gene Expression Omnibus ◽  
Survival Prediction ◽  
Related Gene ◽  
Gene Model ◽  
Cox Regression Analysis ◽  
Kaplan Meier ◽  
Geo Dataset ◽  
Auc Value

Background: Osteosarcoma is the most general bone malignancy that mostly affects children and adolescents. Numerous stem cell-related genes have been founded in distinct forms of cancer. This study aimed at identifying a stem cell-related gene model for the expected assessment of the prognosis of osteosarcoma patients.Methods: We obtained the genes expression data and relevant clinical materials from Therapeutically Applicable Research to Generate Effective Treatments (TARGET) and Gene Expression Omnibus (GEO) databases. We identified differentially expressed genes (DEGs) from the GEO dataset, whereas prognostic stem cell-related genes were obtained from the TARGET database. Subsequently, univariate, LASSO and multivariate Cox regression analyses were applied to establish the stem cell-related signature. Finally, the prognostic value of the signature was validated in the GEO dataset.Results: Twenty-five genes were prognostic ferroptosis-related DEGs. Consequently, we identified eight stem cell-related genes as a signature of prognosis of osteosarcoma patients. Then, the Kaplan–Meier (K-M) curve, the AUC value of ROC, and Cox regression analysis verified that the eight stem cell-related gene model were a new and substantial prognostic marker independent of other clinical traits. Moreover, the nomogram on the foundation of risk score and other clinical traits was established for predicting the survival rate of osteosarcoma patients. Biological function analyses displayed that tumor related pathways were affluent.Conclusion: The expression level of stem cell-related genes offers novel prognostic markers as well as underlying therapeutic targets for the therapy and prevention of osteosarcoma.

scholarly journals A Nomogram Based on CENPP Expression for Survival Prediction in Breast Cancer

2020 ◽  
Author(s):  
Heyan Chen ◽  
Shengyu Pu ◽  
Shibo Yu ◽  
Xiaoqin Liao ◽  
Jianjun He ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cox Regression ◽  
Malignant Tumors ◽  
Roc Curves ◽  
Her2 Status ◽  
Survival Prediction ◽  
Cox Regression Analysis ◽  
Kaplan Meier ◽  
Validation Set ◽  
Prognostic Prediction

Abstract Introduction: In recent years, it has been found that the expression of 17 centromere proteins (CENPs) is closely related to malignant tumors. This study intends to investigate the prognostic value of CENPs in breast cancer (BC). Methods: A total of 800 BC patients were included from the TCGA database. The Cox proportional regression models was used to develop a CENPs-related prognostic signature. Furthermore, the mRNA expression and overall survival (OS) of CENPP (centromere protein P) in BC patients with different clinicopathological featureswas analyzed via GEPIA, bcGenExMiner v4.4 and Kaplan-Meier plotter. Finally, the nomogram was established based on the independent predictors recognized by multivariate Cox regression analysis and further validated by receiver-operating characteristic (ROC) curves and calibration plots internally and externally. Results: The result shown that age, Her2 status, pathologic_T stage, pathologic_M stage and CENPP expression with independent prognostic values for BC. CENPP was overexpressed in BC tissues and CENPP high expression was associated with better OS. We then established a nomogram based on those independent predictors, and the calibration curve demonstrated good fitness of the nomogram for OS prediction. In the training set, the AUC of 3−year and 5−year survival were 0.757 and 0.797, respectively. In the validation set, the AUC of 3−year and 5−year survival were 0.727 and 0.71. Conclusion: Our study showed that CENPP is a novel prognostic factor for patients with BC, and the established nomogram can provide valuable information on prognostic prediction for patients with BC.

A novel three-long noncoding RNA risk score system for the prognostic prediction of triple-negative breast cancer

2021 ◽  
Vol 15 (1) ◽  
pp. 43-55
Author(s):  
Chao Yuan ◽  
Hongjun Yuan ◽  
Li Chen ◽  
Miaomiao Sheng ◽  
Wenru Tang
Keyword(s):  
Breast Cancer ◽  
Risk Score ◽  
Triple Negative Breast Cancer ◽  
Long Noncoding Rna ◽  
Noncoding Rna ◽  
Triple Negative ◽  
Cox Regression ◽  
Characteristic Curve ◽  
Gene Expression Omnibus ◽  
Prognostic Prediction

Background: Triple-negative breast cancer (TNBC) is characterized by fast tumor increase, rapid recurrence and natural metastasis. We aimed to identify a genetic signature for predicting the prognosis of TNBC. Materials & methods: We conducted a weighted correlation network analysis of datasets from the Gene Expression Omnibus. Multivariate Cox regression was used to construct a risk score model. Results: The multi-factor risk scoring model was meaningfully associated with the prognosis of patients with TBNC. The predictive power of the model was demonstrated by the time-dependent receiver operating characteristic curve and Kaplan–Meier curve, and verified using a validation set. Conclusion: We established a long noncoding RNA-based model for the prognostic prediction of TNBC.

scholarly journals Multi-view feature selection for identifying gene markers: a diversified biological data driven approach

2020 ◽  
Vol 21 (S18) ◽  
Author(s):  
Sudipta Acharya ◽  
Laizhong Cui ◽  
Yi Pan
Keyword(s):  
Gene Expression ◽  
Feature Selection ◽  
Gene Selection ◽  
Marker Gene ◽  
Biological Data ◽  
Protein Interaction Data ◽  
Marker Genes ◽  
Data Sets ◽  
Gene Markers ◽  
Multi Objective

Abstract Background In recent years, to investigate challenging bioinformatics problems, the utilization of multiple genomic and proteomic sources has become immensely popular among researchers. One such issue is feature or gene selection and identifying relevant and non-redundant marker genes from high dimensional gene expression data sets. In that context, designing an efficient feature selection algorithm exploiting knowledge from multiple potential biological resources may be an effective way to understand the spectrum of cancer or other diseases with applications in specific epidemiology for a particular population. Results In the current article, we design the feature selection and marker gene detection as a multi-view multi-objective clustering problem. Regarding that, we propose an Unsupervised Multi-View Multi-Objective clustering-based gene selection approach called UMVMO-select. Three important resources of biological data (gene ontology, protein interaction data, protein sequence) along with gene expression values are collectively utilized to design two different views. UMVMO-select aims to reduce gene space without/minimally compromising the sample classification efficiency and determines relevant and non-redundant gene markers from three cancer gene expression benchmark data sets. Conclusion A thorough comparative analysis has been performed with five clustering and nine existing feature selection methods with respect to several internal and external validity metrics. Obtained results reveal the supremacy of the proposed method. Reported results are also validated through a proper biological significance test and heatmap plotting.

scholarly journals POS0859 DEEP PHENOTYPING OF DERMATOMYOSITIS BASED ON LIPID FERROPTOSIS-RELATED GENES BY MACHINE LEARNING

2021 ◽  
Vol 80 (Suppl 1) ◽  
pp. 684.1-684
Author(s):  
J. Q. Zhang ◽  
S. X. Zhang ◽  
R. Zhao ◽  
J. Qiao ◽  
M. T. Qiu ◽  
...  
Keyword(s):  
Gene Expression ◽  
T Cells ◽  
Expression Profiles ◽  
Inflammatory Myopathy ◽  
Shanxi Province ◽  
Survival Prediction ◽  
Training Set ◽  
Redox Biology ◽  
Score Model ◽  
Validation Set

Background:Dermatomyositis (DM) is an idiopathic inflammatory myopathy with heterogeneous clinical manifestation that raise challenges regarding diagnosis and therapy1. Ferroptosis is a newly discovered form of regulated cell death that is the nexus between metabolism, redox biology, and rheumatic immune diseases2. However, how ferroptosis maintains the balance of lymphocyte T cells and affect disease activity in DM is unclear.Objectives:To investigate an ferroptosis-related multiple gene expression signature for classification by assessing the global gene expression profile, and calculate the lymphocyte T cells status in the different subsets.Methods:Gene expression profiles of skeletal muscle from DM samples were acquired from GEO database. GSE143323 (30 patients and 20 HCs) was selected as the training set. The GSE3307 contained 21 DM patients and was selected as the validation set. The 60 ferroptosis genes were obtained from previous literature3. The intersection of the global gene and ferroptosis genes was considered the set of significant G-Ferroptosis genes for further analysis. The “NMF” (R-package) was applied as an unsupervised clustering method for sample classification by using G-Ferroptosis genes expression microarray data from the training datasets. An ferroptosis score model was constructed. The performance of the ferroptosis genes-based risk score model constructed by the DM training set was validated in the batch-1 and batch-2 DM sets. Normalized ferroptosis genes training data was used to compare the ssGSEA scores of gene sets between the high risk and low risk group. The statistical software package R (version 4.0.3) was used for all analyses. P value < 0.05 were considered statistically significant.Results:We selected 54 significant G-Ferroptosis genes for further analysis in training set. There were 2 distinct subtypes (high-ferroptosis-score groups and low-ferroptosis-score groups) identified in G-Ferroptosis genes cohort which were also identified in validation datasets (Fig.1A, C, D). Metallothionein 1G (MT1G) was a characteristic gene of low-ferroptosis-score group. The characteristic genes of high-ferroptosis-score group were acyl-CoA synthetase family member 2(ACSF2) and aconitase 1(ACO1) (Fig.1B). Patients in high-ferroptosis-score group had a lower level of Tregs compared with that of low-ferroptosis-score patients in both training and validation set (P <0.05, Fig.1E).Conclusion:The biological process of ferroptosis is associated with the lever of Tregs, suggesting the process of ferroptosis may be involved in the disease progression of DM. Identificating ferroptosis-related features for DM might provide a new idea for clinical treatment.References:[1]DeWane ME, Waldman R, Lu J. Dermatomyositis: Clinical features and pathogenesis. Journal of the American Academy of Dermatology 2020;82(2):267-81. doi: 10.1016/j.jaad.2019.06.1309 [published Online First: 2019/07/08].[2]Liang C, Zhang X, Yang M, et al. Recent Progress in Ferroptosis Inducers for Cancer Therapy. Advanced materials (Deerfield Beach, Fla) 2019;31(51):e1904197. doi: 10.1002/adma.201904197 [published Online First: 2019/10/09].[3]Liang JY, Wang DS, Lin HC, et al. A Novel Ferroptosis-related Gene Signature for Overall Survival Prediction in Patients with Hepatocellular Carcinoma. International journal of biological sciences 2020;16(13):2430-41. doi: 10.7150/ijbs.45050 [published Online First: 2020/08/08].Acknowledgements:This project was supported by National Science Foundation of China (82001740).Open Fund from the Key Laboratory of Cellular Physiology (Shanxi Medical University) (KLCP2019) and Innovation Plan for Postgraduate Education in Shanxi Province (2020BY078).Disclosure of Interests:None declared

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