scholarly journals A Four-Gene-Based Prognostic Model Predicts Overall Survival in Patients With Cutaneous Melanoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Xiaoxia Tong ◽  
Xiaofei Qu ◽  
Mengyun Wang
Keyword(s):  
Gene Expression ◽  
High Risk ◽  
Risk Score ◽  
Cutaneous Melanoma ◽  
Cox Regression ◽  
Risk Group ◽  
High Risk Group ◽  
Low Risk ◽  
Rank Test ◽  
Score Model

BackgroundCutaneous melanoma (CM) is one of the most aggressive cancers with highly metastatic ability. To make things worse, there are limited effective therapies to treat advanced CM. Our study aimed to investigate new biomarkers for CM prognosis and establish a novel risk score system in CM.MethodsGene expression data of CM from Gene Expression Omnibus (GEO) datasets were downloaded and analyzed to identify differentially expressed genes (DEGs). The overlapped DEGs were then verified for prognosis analysis by univariate and multivariate COX regression in The Cancer Genome Atlas (TCGA) datasets. Based on the gene signature of multiple survival associated DEGs, a risk score model was established, and its prognostic and predictive role was estimated through Kaplan-Meier (K-M) analysis and log-rank test. Furthermore, the correlations between prognosis related genes expression and immune infiltrates were analyzed via Tumor Immune Estimation Resource (TIMER) site.ResultsA total of 103 DEGs were obtained based on GEO cohorts, and four genes were verified in TCGA datasets. Subsequently, four genes (ADAMDEC1, GNLY, HSPA13, and TRIM29) model was developed by univariate and multivariate Cox regression analyses. The K-M plots showed that the high-risk group was associated with shortened survival than that in the low-risk group (P < 0.0001). Multivariate analysis suggested that the model was an independent prognostic factor (high-risk vs. low-risk, HR= 2.06, P < 0.001). Meanwhile, the high-risk group was prone to have larger breslow depth (P< 0.001) and ulceration (P< 0.001).ConclusionsThe four-gene risk score model functions well in predicting the prognosis and treatment response in CM and will be useful for guiding therapeutic strategies for CM patients. Additional clinical trials are needed to verify our findings.

scholarly journals A Five-Gene Risk Score Model for Predicting the Prognosis of Multiple Myeloma Patients Based on Gene Expression Profiles

2021 ◽  
Vol 12 ◽  
Author(s):  
Xiaotong Chen ◽  
Lintao Liu ◽  
Mengping Chen ◽  
Jing Xiang ◽  
Yike Wan ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
High Risk ◽  
Risk Score ◽  
Cox Regression ◽  
Risk Group ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Low Risk ◽  
Score Model

Multiple myeloma is a heterogeneous plasma cell malignancy that remains incurable because of the tendency of relapse for most patients. Survival outcomes may vary widely due to patient and disease variables; therefore, it is necessary to establish a more accurate prognostic model to improve prognostic precision and guide clinical therapy. Here, we developed a risk score model based on myeloma gene expression profiles from three independent datasets: GSE6477, GSE13591, and GSE24080. In this model, highly survival-associated five genes, including EPAS1, ERC2, PRC1, CSGALNACT1, and CCND1, are selected by using the least absolute shrinkage and selection operator (Lasso) regression and univariate and multivariate Cox regression analyses. At last, we analyzed three validation datasets (including GSE2658, GSE136337, and MMRF datasets) to examine the prognostic efficacy of this model by dividing patients into high-risk and low-risk groups based on the median risk score. The results indicated that the survival of patients in low-risk group was greatly prolonged compared with their counterparts in the high-risk group. Therefore, the five-gene risk score model could increase the accuracy of risk stratification and provide effective prediction for the prognosis of patients and instruction for individualized clinical treatment.

scholarly journals Ferroptosis-Related lncRNA Signature Predicts Prognosis and Immunotherapy Efficacy in Cutaneous Melanoma

2022 ◽  
Author(s):  
Yujian Xu ◽  
Youbai Chen ◽  
Zehao Niu ◽  
Zheng Yang ◽  
Jiahua Xing ◽  
...  
Keyword(s):  
T Cells ◽  
High Risk ◽  
Risk Score ◽  
Immune Checkpoint ◽  
Cutaneous Melanoma ◽  
Cox Regression ◽  
Risk Group ◽  
Risk Groups ◽  
High Risk Group ◽  
Low Risk

Abstract Ferroptosis-related lncRNAs are promising biomarkers for predicting the prognosis of many cancers. However, a ferroptosis-related signature to predict the prognosis of cutaneous melanoma (CM) has not been identified. The purpose of our study was to construct a ferroptosis-related lncRNA signature to predict prognosis and immunotherapy efficacy in CM. Ferroptosis-related differentially expressed genes (FDEGs) and lncRNAs (FDELs) were identified using TCGA, GTEx, and FerrDb datasets. We performed Cox and LASSO regressions to identify key FDELs, and constructed a risk score to stratify patients into high- and low-risk groups. A nomogram was developed for clinical use. We performed gene set enrichment analyses (GSEA) to identify significantly enriched pathways. Differences in the tumor microenvironment (TME) between the 2 groups were assessed using 7 algorithms. To predict the efficacy of immune checkpoint inhibitors (ICI), we analyzed the association between PD1 and CTLA4 expression and the risk score. Finally, differences in Tumor Mutational Burden (TMB) and molecular drugs Sensitivity between the 2 groups were performed. Here, we identified 5 lncRNAs (AATBC, AC145423.2, LINC01871, AC125807.2, and AC245041.1) to construct the risk score. The AUC of the lncRNA signature was 0.743 in the training cohort and was validated in the testing and entire cohorts. Kaplan-Meier analyses revealed that the high-risk group had poorer prognosis. Multivariate Cox regression showed that the lncRNA signature was an independent predictor of OS with higher accuracy than traditional clinicopathological features. The 1-, 3-, and 5-year survival probabilities for CM patients were 92.7%, 57.2%, and 40.2% with an AUC of 0.804, indicating a good accuracy and reliability of the nomogram. GSEA showed that the high-risk group had lower ferroptosis and immune response. TME analyses confirmed that the high-risk group had lower immune cell infiltration (e.g., CD8+ T cells, CD4+ memory-activated T cells, and M1 macrophages) and lower immune functions (e.g., immune checkpoint activation). Low-risk patients whose disease expressed PD1 or CTLA4 were likely to respond better to ICIs. The analysis demonstrated that the TMB had significantly difference between low- and high- risk groups. Chemotherapy drugs, such as sorafenib, Imatinib, ABT.888 (Veliparib), Docetaxel, and Paclitaxel showed Significant differences in the estimated IC50 between the two risk groups. Overall, our novel ferroptosis-related lncRNA signature was able to accurately predict the prognosis and ICI outcomes of CM patients. These ferroptosis-related lncRNAs might be potential biomarkers and therapeutic targets for CM.

scholarly journals Identification of the 11-lncRNA signatures associated with the prognosis of endometrial carcinoma

2021 ◽  
Vol 104 (1) ◽  
pp. 003685042110065
Author(s):  
Jing Wan ◽  
Peigen Chen ◽  
Yu Zhang ◽  
Jie Ding ◽  
Yuebo Yang ◽  
...  
Keyword(s):  
High Risk ◽  
Endometrial Carcinoma ◽  
Risk Score ◽  
Cox Regression ◽  
Risk Group ◽  
High Risk Group ◽  
Low Risk ◽  
Training Set ◽  
Lncrna Expression ◽  
Robust Likelihood

Endometrial carcinoma (EC) is the fourth most common cancer in women. Some long non-coding RNAs (lncRNAs) are regarded as potential prognostic biomarkers or targets for treatment of many types of cancers. We aim to screen prognostic-related lncRNAs and build a possible lncRNA signature which can effectively predict the survival of patients with EC. We obtained lncRNA expression profiling from the TCGA database. The patients were classified into training set and verification set. By performing Univariate Cox regression model, Robust likelihood-based survival analysis, and Cox proportional hazards model, we developed a risk score with the Cox co-efficient of individual lncRNAs in the training set. The optimum cut-off point was selected by ROC analysis. Patients were effectively divided into high-risk group and low-risk group according to the risk score. The OS of the low-risk patients was significantly prolonged compared with that of the high-risk group. At last, we validated this 11-lncRNA signature in the verification set and the complete set. We identified an 11-lncRNA expression signature with high stability and feasibility, which can predict the survival of patients with EC. These findings provide new potential biomarkers to improve the accuracy of prognosis prediction of EC.

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 Prognostic Model for Hepatocellular Carcinoma Patients Based on Signature Ferroptosis-related Genes

2021 ◽  
Author(s):  
Sizhe Wan ◽  
Yiming Lei ◽  
Mingkai Li ◽  
Bin Wu
Keyword(s):  
Overall Survival ◽  
T Cells ◽  
High Risk ◽  
Risk Score ◽  
Cox Regression ◽  
Risk Group ◽  
High Risk Group ◽  
Low Risk ◽  
Tnm Stage ◽  
Functional Enrichment

Abstract BackgroundWith the increasing number of HCC patients, it is necessary to accurately predicting the prognosis of these patients. Ferroptosis has been confirmed to be closely related to HCC progression. However, there is still a challenge in predicting the survival of HCC patients through ferroptosis-related genes.MethodThe RNA-seq data and corresponding clinical data of HCC from TCGA database were downloaded to establish a prognosis model, and data of ICGC and GSE14520 as the validation set. The risk score was constructed with 5 genes identified by univariate and LASSO Cox regression analysis. Then, risk score, TNM stage and cirrhosis were included to construct a nomogram, through univariate and multivariate Cox regression analysis.Results5 genes were identified from 70 ferroptosis-related DEGs to construct a gene signature to predict HCC patient survival from TCGA cohort. PCA and heatmap results show that there are obvious differences in patients with different score groups. Then, we included risk score, TNM stage and cirrhosis to construct a nomogram to further predict the overall survival of the patients. Survival analysis indicates that overall survival of the low- risk group is significantly higher than that of the high-risk group. Similarly, the data in the GSE14520 cohort also confirmed good performance for the nomogram. Furthermore, KEGG and GO functional enrichment analyses indicates the difference in overall survival between groups is closely related to immune-related pathways. Finally, through analyzing the immune status of all patients, we found that compared with patients in the low-risk group, “Macrophages M0”, “T cells CD8”, and “T cells regulatory” of the high-risk group were significantly higher.ConclusionThe nomogram based on ferroptosis-related genes has a good performance for the prognosis of HCC patients. The model may provide a reference for evaluation of HCC patients by targeting ferroptosis.

scholarly journals A Novel Signature Based on Pyroptosis-Related Genes for Predicting Breast Cancer

2021 ◽  
Author(s):  
Shuang Shen ◽  
Xin Chen ◽  
Rui Qu ◽  
Youming Guo ◽  
Yingying Su ◽  
...  
Keyword(s):  
Breast Cancer ◽  
High Risk ◽  
Risk Score ◽  
Cox Regression ◽  
Risk Group ◽  
High Risk Group ◽  
Low Risk ◽  
The Cancer Genome Atlas ◽  
Independent Predictive Factor ◽  
Tumour Immunity

Abstract Background: Breast cancer (BC) surpassed lung cancer as the most frequent malignant tumour in women. In recent years, pyroptosis has revealed itself as an inflammatory form of programmed cell death. However, it is unclear as to the expression of genes associated with pyroptosis in BC and its relationship to prognosis. Results: In this study, we identified 31 pyroptosis regulators that are differentially expressed between BC and normal breast. The differently expressed genes (DEG) allow BC patients to be divided into three subtypes. Through single-factor and multi-factor COX regression and the application of least absolute contraction and selection operator (LASSO) Cox regression method, the survival prognostic value of each gene related to pyroptosis in The Cancer Genome Atlas (TCGA) cohort was evaluated, and a 4-gene signature was constructed. BC patients of the TCGA cohort are divided into low-risk or high-risk groups by risk score. The survival of the low-risk group was significantly higher than the high-risk group (P <0.001). Using the median risk score from the TCGA cohort, BC patients from the Gene Expression Omnibus (GEO) cohort were divided into two risk sub-groups and similar conclusions were drawn. In combination with clinicopathological characteristics, the risk score is an independent predictive factor of OS in BC patients. Gene ontology (GO) and Kyoto Encylopedia of Genes and Genomes (KEGG) indicated that the high-risk group's immune genes were enriched and immune status was reduced. Conclusions: In conclusion, pyroptosis-related genes are important for tumour immunity and can be used to predict the prognosis of BC.

scholarly journals Identification of a novel autophagy signature for predicting survival in patients with lung adenocarcinoma

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11074
Author(s):  
Jin Duan ◽  
Youming Lei ◽  
Guoli Lv ◽  
Yinqiang Liu ◽  
Wei Zhao ◽  
...  
Keyword(s):  
Gene Expression ◽  
High Risk ◽  
Immune Cell ◽  
Cox Regression ◽  
Risk Group ◽  
Risk Groups ◽  
High Risk Group ◽  
Low Risk ◽  
Cell Analysis ◽  
Prognostic Signature

Background Lung adenocarcinoma (LUAD) is the most commonhistological lung cancer subtype, with an overall five-year survivalrate of only 17%. In this study, we aimed to identify autophagy-related genes (ARGs) and develop an LUAD prognostic signature. Methods In this study, we obtained ARGs from three databases and downloaded gene expression profiles from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. We used TCGA-LUAD (n = 490) for a training and testing dataset, and GSE50081 (n = 127) as the external validation dataset.The least absolute shrinkage and selection operator (LASSO) Cox and multivariate Cox regression models were used to generate an autophagy-related signature. We performed gene set enrichment analysis (GSEA) and immune cell analysis between the high- and low-risk groups. A nomogram was built to guide the individual treatment for LUAD patients. Results We identified a total of 83 differentially expressed ARGs (DEARGs) from the TCGA-LUAD dataset, including 33 upregulated DEARGs and 50 downregulated DEARGs, both with thresholds of adjusted P < 0.05 and |Fold change| > 1.5. Using LASSO and multivariate Cox regression analyses, we identified 10 ARGs that we used to build a prognostic signature with areas under the curve (AUCs) of 0.705, 0.715, and 0.778 at 1, 3, and 5 years, respectively. Using the risk score formula, the LUAD patients were divided into low- or high-risk groups. Our GSEA results suggested that the low-risk group were enriched in metabolism and immune-related pathways, while the high-risk group was involved in tumorigenesis and tumor progression pathways. Immune cell analysis revealed that, when compared to the high-risk group, the low-risk group had a lower cell fraction of M0- and M1- macrophages, and higher CD4 and PD-L1 expression levels. Conclusion Our identified robust signature may provide novel insight into underlying autophagy mechanisms as well as therapeutic strategies for LUAD treatment.

scholarly journals Development of a Novel Autophagy-Related Prognostic Signature and Nomogram for Hepatocellular Carcinoma

2020 ◽  
Vol 10 ◽  
Author(s):  
Qiongxuan Fang ◽  
Hongsong Chen
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
High Risk ◽  
Cox Regression ◽  
Risk Group ◽  
High Risk Group ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Regression Analyses ◽  
Gene Model

BackgroundHepatocellular carcinoma (HCC) is the seventh most common malignancy and the second most common cause of cancer-related deaths. Autophagy plays a crucial role in the development and progression of HCC.MethodsUnivariate and Lasso Cox regression analyses were performed to determine a gene model that was optimal for overall survival (OS) prediction. Patients in the GSE14520 and GSE54236 datasets of the Cancer Genome Atlas (TCGA) were divided into the high-risk and low-risk groups according to established ATG models. Univariate and multivariate Cox regression analyses were used to identify risk factors for OS for the purpose of constructing nomograms. Calibration and receiver operating characteristic (ROC) curves were used to evaluate model performance. Real-time PCR was used to validate the effects of the presence or absence of an autophagy inhibitor on gene expression in HepG2 and Huh7 cell lines.ResultsOS in the high-risk group was significantly shorter than that in the low-risk group. Gene set enrichment analysis (GSEA) indicated that the association between the low-risk group and autophagy- as well as immune-related pathways was significant. ULK2, PPP3CC, and NAFTC1 may play vital roles in preventing HCC progression. Furthermore, tumor environment analysis via ESTIMATION indicated that the low-risk group was associated with high immune and stromal scores. Based on EPIC prediction, CD8+ T and B cell fractions in the TCGA and GSE54236 datasets were significantly higher in the low-risk group than those in the high-risk group. Finally, based on the results of univariate and multivariate analyses three variables were selected for nomogram development. The calibration plots showed good agreement between nomogram prediction and actual observations. Inhibition of autophagy resulted in the overexpression of genes constituting the gene model in HepG2 and Huh7 cells.ConclusionsThe current study determined the role played by autophagy-related genes (ATGs) in the progression of HCC and constructed a novel nomogram that predicts OS in HCC patients, through a combined analysis of TCGA and gene expression omnibus (GEO) databases.

scholarly journals Construction of an immune-related lncRNA signature pair for predicting oncologic outcomes and the sensitivity of drugs to treat adverse reactions to immunotherapy in lung adenocarcinoma

2021 ◽  
Author(s):  
Jinman Zhuang ◽  
Zhongwu Chen ◽  
Zishan Chen ◽  
Jin Chen ◽  
Maolin Liu ◽  
...  
Keyword(s):  
Gene Expression ◽  
High Risk ◽  
Lung Adenocarcinoma ◽  
Risk Score ◽  
Scoring System ◽  
Cox Regression ◽  
Risk Group ◽  
High Risk Group ◽  
Oncologic Outcomes ◽  
Molecular Characteristics

Abstract Background Although immunotherapy has shown clinical activity in lung adenocarcinoma (LUAD), LUAD prognosis has been a perplexing problem. We aimed to construct an immune-related lncRNA pair (IRLPs) score for LUAD and identify the best drugs to treat immune-related adverse events (ir AEs). Methods Based on The Cancer Genome Atlas (TCGA) LUAD dataset, IRLPs were identified to construct an IRLPs scoring system by Cox regression and were validated in the Gene Expression Omnibus (GEO) dataset. Next, immune and molecular characteristics were explored in different IRLP subgroups. The “pRRophetic” package was used to predict the sensitivity of drugs used to treat ir AEs. Results A total of 477 LUAD patients in TCGA with gene expression and mutation data with complete clinicopathological features were found in our study and used as a training set. The study also included 318 patients from three GEO datasets. The IRLPs score was constructed based on eight IRLPs, and patients with a high IRLP risk score had a better overall survival (OS). Immune score (Cor=-0.18893, P<0.001), stoma score (Cor=-0.24804, P<0.001), and microenvironment score (Cor=-0.22338, P<0.001) were significantly decreased in the patients with the highest IRLP risk score. The high-risk group was found enriched in molecular changes in DNA and chromosomes, and in this group the tumor mutation burden (TMB) was higher than in the low-risk group (P=0.0015). Immunosuppressor methotrexate sensitivity was higher in the high-risk group (P=0.0052), whereas parthenolide (P<0.001) and rapamycin (P=0.013) sensitivity were lower in the high-risk group. Conclusions Our study established an IRLPs scoring system as a biomarker to help in the prognosis, the identification of molecular and immune characteristics, and the patient-tailored selection of the most suitable drugs for ir AEs therapy.

scholarly journals Molecular Analysis of Prognosis and Immune Pathways of Pancreatic Cancer Based on TNF Family Members

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Zemin Zhu ◽  
Caixi Tang ◽  
Tao Xu ◽  
Zhijian Zhao
Keyword(s):  
High Risk ◽  
Risk Score ◽  
Risk Group ◽  
Family Members ◽  
Enrichment Analysis ◽  
High Risk Group ◽  
Low Risk ◽  
Risk Scores ◽  
Score Model ◽  
Vesicle Cycle

Background. Tumor necrosis factor (TNF) family members play a vital role in anticancer therapy. This study aimed to screen the critical markers for the prognostic analysis of pancreatic adenocarcinoma (PAAD) by analyzing the clustering patterns of TNF family members in PAAD. Methods. In this study, the NMF clustering method was adopted to cluster samples from The Cancer Genome Atlas (TCGA) to acquire the clustering pattern of the TNF family in PAAD. Differential gene analysis was performed according to TNF family gene clusters. The support vector machine (SVM) method was conducted for further gene screening, and the risk score model of the screened genes was constructed by Lasso. The single sample gene set enrichment analysis (ssGSEA) method was adopted for immunoenrichment analysis and tumor immune cycle analysis. Genes associated with risk scores were analyzed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Results. We clustered PAAD into two groups based on TNF family genes. Nineteen TNF family genes were significantly associated with the clinical characteristics of PAAD patients. The risk score formula was composed of RHOD, UBE2C, KLHDC7b, MSLN, ADAM8, NME3, GNG2, and MCOLN3. GSE57495 and GSE62452 datasets verified that patients in the high-risk group had a worse prognosis than those in the low-risk group. The risk score-related genes analyzed by GO and KEGG were mainly involved in the modulation of chemical synaptic transmission and synaptic vesicle cycle pathway. There were significant differences in the expression of 15 immune cells between the high-risk group and the low-risk group. The risk score was positively correlated with HCK, interferon, MHC-I, and STAT1. The expression of genes relevant to chemokine, immunostimulator, MHC, and receptor was strongly associated with the risk score. Conclusion. The risk score model based on the TNF family can predict the prognosis and immune status of PAAD patients. Further research is needed to verify the clinical prognostic value of risk scores.

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