scholarly journals A novel DNA methylation-based model that effectively predicts prognosis in hepatocellular carcinoma

2021 ◽  
Vol 41 (3) ◽  
Author(s):  
Xiang-Yong Hao ◽  
An-Qiang Li ◽  
Hao Shi ◽  
Tian-Kang Guo ◽  
Yan-Fei Shen ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Dna Methylation ◽  
Predictive Model ◽  
Risk Score ◽  
Methylation Level ◽  
Pathway Enrichment Analysis ◽  
Validation Group ◽  
Cox Regression Analysis ◽  
Differentially Methylated Genes ◽  
Cox Analysis

Abstract Purpose: To build a novel predictive model for hepatocellular carcinoma (HCC) patients based on DNA methylation data. Methods: Four independent DNA methylation datasets for HCC were used to screen for common differentially methylated genes (CDMGs). Gene Ontology (GO) enrichment, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were used to explore the biological roles of CDMGs in HCC. Univariate Cox analysis and least absolute shrinkage and selection operator (LASSO) Cox analysis were performed to identify survival-related CDMGs (SR-CDMGs) and to build a predictive model. The importance of this model was assessed using Cox regression analysis, propensity score-matched (PSM) analysis and stratification analysis. A validation group from the Cancer Genome Atlas (TCGA) was constructed to further validate the model. Results: Four SR-CDMGs were identified and used to build the predictive model. The risk score of this model was calculated as follows: risk score = (0.01489826 × methylation level of WDR69) + (0.15868618 × methylation level of HOXB4) + (0.16674959 × methylation level of CDKL2) + (0.16689301 × methylation level of HOXA10). Kaplan–Meier analysis demonstrated that patients in the low-risk group had a significantly longer overall survival (OS; log-rank P-value =0.00071). The Cox model multivariate analysis and PSM analysis identified the risk score as an independent prognostic factor (P<0.05). Stratified analysis results further confirmed this model performed well. By analyzing the validation group, the results of receiver operating characteristic (ROC) curve analysis and survival analysis further validated this model. Conclusion: Our DNA methylation-based prognosis predictive model is effective and reliable in predicting prognosis for patients with HCC.

scholarly journals Comprehensive Analysis of the Prognostic Values of the TRIM Family in Hepatocellular Carcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Weiyu Dai ◽  
Jing Wang ◽  
Zhi Wang ◽  
Yizhi Xiao ◽  
Jiaying Li ◽  
...  
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Survival Analysis ◽  
Risk Score ◽  
Roc Curves ◽  
Genetic Alterations ◽  
Pathway Enrichment Analysis ◽  
Family Risk ◽  
Cox Regression Analysis ◽  
Family Gene

BackgroundAccumulating studies have demonstrated the abnormal expressions and prognostic values of certain members of the tripartite motif (TRIM) family in diverse cancers. However, comprehensive prognostic values of the TRIM family in hepatocellular carcinoma (HCC) are yet to be clearly defined.MethodsThe prognostic values of the TRIM family were evaluated by survival analysis and univariate Cox regression analysis based on gene expression data and clinical data of HCC from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The expression profiles, protein–protein interaction among the TRIM family, prediction of transcription factors (TFs) or miRNAs, genetic alterations, correlations with the hallmarks of cancer and immune infiltrates, and pathway enrichment analysis were explored by multiple public databases. Further, a TRIM family gene-based signature for predicting overall survival (OS) in HCC was built by using the least absolute shrinkage and selection operator (LASSO) regression. TCGA–Liver Hepatocellular Carcinoma (LIHC) cohort was used as the training set, and GSE76427 was used for external validation. Time-dependent receiver operating characteristic (ROC) and survival analysis were used to estimate the signature. Finally, a nomogram combining the TRIM family risk score and clinical parameters was established.ResultsHigh expressions of TRIM family members including TRIM3, TRIM5, MID1, TRIM21, TRIM27, TRIM32, TRIM44, TRIM47, and TRIM72 were significantly associated with HCC patients’ poor OS. A novel TRIM family gene-based signature (including TRIM5, MID1, TRIM21, TRIM32, TRIM44, and TRIM47) was built for OS prediction in HCC. ROC curves suggested the signature’s good performance in OS prediction. HCC patients in the high-risk group had poorer OS than the low-risk patients based on the signature. A nomogram integrating the TRIM family risk score, age, and TNM stage was established. The ROC curves suggested that the signature presented better discrimination than the similar model without the TRIM family risk score.ConclusionOur study identified the potential application values of the TRIM family for outcome prediction in HCC.

scholarly journals The prognostic value of an autophagy-related lncRNA signature in hepatocellular carcinoma

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Shiming Yang ◽  
Yaping Zhou ◽  
Xiangxin Zhang ◽  
Lu Wang ◽  
Jianfeng Fu ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Risk Score ◽  
Rna Splicing ◽  
Cox Regression ◽  
Survival Rates ◽  
Treatment Strategies ◽  
Cox Regression Analysis ◽  
Risk Patients ◽  
Prognostic Prediction ◽  
Cox Analysis

Abstract Background lncRNA may be involved in the occurrence, metastasis, and chemical reaction of hepatocellular carcinoma (HCC) through various pathways associated with autophagy. Therefore, it is urgent to reveal more autophagy-related lncRNAs, explore these lncRNAs’ clinical significance, and find new targeted treatment strategies. Methods The corresponding data of HCC patients and autophagy genes were obtained from the TCGA database, and the human autophagy database respectively. Based on the co-expression and Cox regression analysis to construct prognostic prediction signature. Results Finally, a signature containing seven autophagy-related lncRNAs (PRRT3-AS1, RP11-479G22.8, RP11-73M18.8, LINC01138, CTD-2510F5.4, CTC-297N7.9, RP11-324I22.4) was constructed. Based on the risk score of signature, Overall survival (OS) curves show that the OS of high-risk patients is significantly lower than that of low-risk patients (P = 2.292e−10), and the prognostic prediction accuracy of risk score (AUC = 0.786) is significantly higher than that of ALBI (0.532), child_pugh (0.573), AFP (0.5751), and AJCC_stage (0.631). Moreover, multivariate Cox analysis and Nomogram of risk score are indicated that the 1-year and 3-year survival rates of patients are obviously accuracy by the combined analysis of the risk score, child_pugh, age, M_stage, and Grade (The AUC of 1- and 3-years are 0.87, and 0.855). Remarkably, the 7 autophagy-related lncRNAs may participate in Spliceosome, Cell cycle, RNA transport, DNA replication, and mRNA surveillance pathway and be related to the biological process of RNA splicing and mRNA splicing. Conclusion In conclusion, the 7 autophagy-related lncRNAs might be promising prognostic and therapeutic targets for HCC.

scholarly journals Mining prognostic markers of Asian hepatocellular carcinoma patients based on the apoptosis-related genes

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Junbin Yan ◽  
Jielu Cao ◽  
Zhiyun Chen
Keyword(s):  
Hepatocellular Carcinoma ◽  
Regression Analysis ◽  
Risk Score ◽  
Roc Curve ◽  
Prognostic Model ◽  
Cox Regression ◽  
Cox Regression Analysis ◽  
Kaplan Meier ◽  
Cox Analysis ◽  
Kaplan Meier Plotter

Abstract Background Apoptosis-related genes(Args)play an essential role in the occurrence and progression of hepatocellular carcinoma(HCC). However, few studies have focused on the prognostic significance of Args in HCC. In the study, we aim to explore an efficient prognostic model of Asian HCC patients based on the Args. Methods We downloaded mRNA expression profiles and corresponding clinical data of Asian HCC patients from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases. The Args were collected from Deathbase, a database related to cell death, combined with the research results of GeneCards、National Center for Biotechnology Information (NCBI) databases and a lot of literature. We used Wilcoxon-test and univariate Cox analysis to screen the differential expressed genes (DEGs) and the prognostic related genes (PRGs) of HCC. The intersection genes of DEGs and PGGs were seen as crucial Args of HCC. The prognostic model of Asian HCC patients was constructed by least absolute shrinkage and selection operator (lasso)- proportional hazards model (Cox) regression analysis. Kaplan-Meier curve, Principal Component Analysis (PCA) analysis, t-distributed Stochastic Neighbor Embedding (t-SNE) analysis, risk score curve, receiver operating characteristic (ROC) curve, and the HCC data of ICGC database and the data of Asian HCC patients of Kaplan-Meier plotter database were used to verify the model. Results A total of 20 of 56 Args were differentially expressed between HCC and adjacent normal tissues (p < 0.05). Univariate Cox regression analysis showed that 10 of 56 Args were associated with survival time and survival status of HCC patients (p < 0.05). There are seven overlapping genes of these 20 and 10 genes, including BAK1, BAX, BNIP3, CRADD, CSE1L, FAS, and SH3GLB1. Through Lasso-Cox analysis, an HCC prognostic model composed of BAK1, BNIP3, CSE1L, and FAS was constructed. Kaplan-Meier curve, PCA, t-SNE analysis, risk score curve, ROC curve, and secondary verification of ICGC database and Kaplan-Meier plotter database all support the reliability of the model. Conclusions Lasso-Cox regression analysis identified a 4-gene prognostic model, which integrates clinical and gene expression and has a good effect. The expression of Args is related to the prognosis of HCC patients, but the specific mechanism remains to be further verified.

scholarly journals Construction and Validation of a Metabolic Reprogramming Related Prognostic Model for Hepatocellular Carcinoma

2020 ◽  
Author(s):  
Xiaohong - Liu ◽  
Qian - Xu ◽  
Zi-Jing - Li ◽  
Bin - Xiong
Keyword(s):  
Hepatocellular Carcinoma ◽  
Regression Analysis ◽  
Risk Score ◽  
Prognostic Model ◽  
Cox Regression ◽  
Expression Profiles ◽  
Prognostic Significance ◽  
Metabolic Reprogramming ◽  
Cox Regression Analysis ◽  
Metabolic Genes

Abstract BackgroundMetabolic reprogramming is an important hallmark in the development of malignancies. Numerous metabolic genes have been demonstrated to participate in the progression of hepatocellular carcinoma (HCC). However, the prognostic significance of the metabolic genes in HCC remains elusive. MethodsWe downloaded the gene expression profiles and clinical information from the GEO, TCGA and ICGC databases. The differently expressed metabolic genes were identified by using Limma R package. Univariate Cox regression analysis and LASSO (Least absolute shrinkage and selection operator) Cox regression analysis were utilized to uncover the prognostic significance of metabolic genes. A metabolism-related prognostic model was constructed in TCGA cohort and validated in ICGC cohort. Furthermore, we constructed a nomogram to improve the accuracy of the prognostic model by using the multivariate Cox regression analysis.ResultsThe high-risk score predicted poor prognosis for HCC patients in the TCGA cohort, as confirmed in the ICGC cohort (P < 0.001). And in the multivariate Cox regression analysis, we observed that risk score could act as an independent prognostic factor for the TCGA cohort (HR (hazard ratio) 3.635, 95% CI (confidence interval)2.382-5.549) and the ICGC cohort (HR1.905, 95%CI 1.328-2.731). In addition, we constructed a nomogram for clinical use, which suggested a better prognostic model than risk score.ConclusionsOur study identified several metabolic genes with important prognostic value for HCC. These metabolic genes can influence the progression of HCC by regulating tumor biology and can also provide metabolic targets for the precise treatment of HCC.

scholarly journals Prognostic model of head and neck squamous cell carcinoma based on 12 ferroptosis-related genes

2021 ◽  
Author(s):  
Sijia Li ◽  
Hongyang Zhang ◽  
Wei Li
Keyword(s):  
Regression Analysis ◽  
Risk Score ◽  
Cox Regression ◽  
Risk Group ◽  
High Risk Group ◽  
Risk Scores ◽  
Cox Regression Analysis ◽  
Normal Tissues ◽  
Model Based ◽  
Cox Analysis

Abstract Background: The purpose of our study is establishing a model based on ferroptosis-related genes predicting the prognosis of patients with head and neck squamous cell carcinoma (HNSCC).Methods: In our study, transcriptome and clinical data of HNSCC patients were from The Cancer Genome Atlas, ferroptosis-related genes and pathways were from Ferroptosis Signatures Database. Differentially expressed genes (DEGs) were screened by comparing tumor and adjacent normal tissues. Functional enrichment analysis of DEGs, protein-protein interaction network and gene mutation examination were applied. Univariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) regression were used to identified DEGs. The model was constructed by multivariate Cox regression analysis and verified by Kaplan-Meier analysis. The relationship between risk scores and other clinical features was also analyzed. Univariate and multivariate Cox analysis was used to verified the independence of our model. The model was evaluated by receiver operating characteristic analysis and calculation of the area under the curve (AUC). A nomogram model based on risk score, age, gender and TNM stages was constructed.Results: We analyzed data including 500 tumor tissues and 44 adjacent normal tissues and 259 ferroptosis-related genes, then obtained 73 DEGs. Univariate Cox regression analysis screened out 16 genes related to overall survival, and LASSO analysis fingered out 12 of them with prognostic value. A risk score model based on these 12 genes was constructed by multivariate Cox regression analysis. According to the median risk score, patients were divided into high-risk group and low-risk group. The survival rate of high-risk group was significantly lower than that of low-risk group in Kaplan-Meier curve. Risk scores were related to T and grade. Univariate and multivariate Cox analysis showed our model was an independent prognostic factor. The AUC was 0.669. The nomogram showed high accuracy predicting the prognosis of HNSCC patients.Conclusion: Our model based on 12 ferroptosis-related genes performed excellently in predicting the prognosis of HNSCC patients. Ferroptosis-related genes may be promising biomarkers for HNSCC treatment and prognosis.

scholarly journals Prognostic Value of DNA Methylation-Driven Genes in Clear Cell Renal Cell Carcinoma: A Study Based on Methylation and Transcriptome Analyses

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Maolin Hu ◽  
Jiangling Xie ◽  
Huiming Hou ◽  
Ming Liu ◽  
Jianye Wang
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Risk Score ◽  
Renal Cell ◽  
Methylation Level ◽  
Cox Regression ◽  
Clear Cell ◽  
Cell Renal Cell Carcinoma

Background. Few previous studies have comprehensively explored the level of DNA methylation and gene expression in ccRCC. The purpose of this study was to identify the key clear cell renal cell carcinoma- (ccRCC-) related DNA methylation-driven genes (MDG) and to build a prognostic model based on the level of DNA methylation. Methods. RNA-seq transcriptome data and DNA methylation data were obtained from The Cancer Genome Atlas. Based on the MethylMix algorithm, we obtain ccRCC-related MDG. The univariate and multivariate Cox regression analyses were employed to investigate the correlation between patient overall survival and the methylation level of each MDG. Finally, a prognosis risk score was established based on a linear combination of the regression coefficient derived from the multivariate Cox regression model (β) multiplied with the methylation level of the gene. Results. 19 ccRCC-related MDG were identified. Three MDG (NCKAP1L, EVI2A, and BATF) were further screened and integrated into a prognostic risk score model, risk score=3.710∗methylation level of NCKAP1L+−3.892∗methylation level of EVI2A+−3.907∗methylation level of BATF. The risk model was independent from conventional clinical characteristics as a prognostic factor for ccRCC (HR=1.221, 95% confidence interval: 1.063–1.402, and P=0.005). The joint survival analysis showed that the gene expression and methylation levels of the prognostic genes EVI2A and BATF were significantly related with prognosis. Conclusion. This study provided an important bioinformatics foundation for in-depth studies of ccRCC DNA methylation.

scholarly journals Systematic Characterization of Novel Immune Gene Signatures Predicts Prognostic Factors in Hepatocellular Carcinoma

2021 ◽  
Vol 9 ◽  
Author(s):  
Dafeng Xu ◽  
Yu Wang ◽  
Jincai Wu ◽  
Yuliang Zhang ◽  
Zhehao Liu ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Risk Score ◽  
Cox Regression ◽  
Expression Profiles ◽  
Gene Signature ◽  
Clinical Samples ◽  
Immune Gene ◽  
Cox Regression Analysis ◽  
Immune Related Genes ◽  
Cancer Tissues

Background: The prognosis of patients with hepatocellular carcinoma (HCC) is negatively affected by the lack of effective prognostic indicators. The change of tumor immune microenvironment promotes the development of HCC. This study explored new markers and predicted the prognosis of HCC patients by systematically analyzing immune characteristic genes.Methods: Immune-related genes were obtained, and the differentially expressed immune genes (DEIGs) between tumor and para-cancer samples were identified and analyzed using gene expression profiles from TCGA, HCCDB, and GEO databases. An immune prognosis model was also constructed to evaluate the predictive performance in different cohorts. The high and low groups were divided based on the risk score of the model, and different algorithms were used to evaluate the tumor immune infiltration cell (TIIC). The expression and prognosis of core genes in pan-cancer cohorts were analyzed, and gene enrichment analysis was performed using clusterProfiler. Finally, the expression of the hub genes of the model was validated by clinical samples.Results: Based on the analysis of 730 immune-related genes, we identified 64 common DEIGs. These genes were enriched in the tumor immunologic related signaling pathways. The first 15 genes were selected using RankAggreg analysis, and all the genes showed a consistent expression trend across multi-cohorts. Based on lasso cox regression analysis, a 5-gene signature risk model (ATG10, IL18RAP, PRKCD, SLC11A1, and SPP1) was constructed. The signature has strong robustness and can stabilize different cohorts (TCGA-LIHC, HCCDB18, and GSE14520). Compared with other existing models, our model has better performance. CIBERSORT was used to assess the landscape maps of 22 types of immune cells in TCGA, GSE14520, and HCCDB18 cohorts, and found a consistent trend in the distribution of TIIC. In the high-risk score group, scores of Macrophages M1, Mast cell resting, and T cells CD8 were significantly lower than those of the low-risk score group. Different immune expression characteristics, lead to the different prognosis. Western blot demonstrated that ATG10, PRKCD, and SPP1 were highly expressed in cancer tissues, while IL18RAP and SLC11A1 expression in cancer tissues was lower. In addition, IL18RAP has a highly positive correlation with B cell, macrophage, Neutrophil, Dendritic cell, CD8 cell, and CD4 cell. The SPP1, PRKCD, and SLC11A1 genes have the strongest correlation with macrophages. The expression of ATG10, IL18RAP, PRKCD, SLC11A1, and SPP1 genes varies among different immune subtypes and between different T stages.Conclusion: The 5-immu-gene signature constructed in this study could be utilized as a new prognostic marker for patients with HCC.

scholarly journals Epigenome-Wide Association Study of Prostate Cancer in African Americans Identifies DNA Methylation Biomarkers for Aggressive Disease

Biomolecules ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 1826
Author(s):  
Yifan Xu ◽  
Chia-Wen Tsai ◽  
Wen-Shin Chang ◽  
Yuyan Han ◽  
Maosheng Huang ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Dna Methylation ◽  
African American ◽  
African Americans ◽  
Zinc Finger Protein ◽  
African American Men ◽  
Pathway Enrichment Analysis ◽  
Cpg Sites ◽  
Differentially Methylated Genes ◽  
Incidence And Mortality

DNA methylation plays important roles in prostate cancer (PCa) development and progression. African American men have higher incidence and mortality rates of PCa than other racial groups in U.S. The goal of this study was to identify differentially methylated CpG sites and genes between clinically defined aggressive and nonaggressive PCa in African Americans. We performed genome-wide DNA methylation profiling in leukocyte DNA from 280 African American PCa patients using Illumina MethylationEPIC array that contains about 860K CpG sties. There was a slight increase of overall methylation level (mean β value) with the increasing Gleason scores (GS = 6, GS = 7, GS ≥ 8, P for trend = 0.002). There were 78 differentially methylated CpG sites with P < 10−4 and 9 sites with P < 10−5 in the trend test. We also found 77 differentially methylated regions/genes (DMRs), including 10 homeobox genes and six zinc finger protein genes. A gene ontology (GO) molecular pathway enrichment analysis of these 77 DMRs found that the main enriched pathway was DNA-binding transcriptional factor activity. A few representative DMRs include HOXD8, SOX11, ZNF-471, and ZNF-577. Our study suggests that leukocyte DNA methylation may be valuable biomarkers for aggressive PCa and the identified differentially methylated genes provide biological insights into the modulation of immune response by aggressive PCa.

scholarly journals Human Nuclear Receptors (NRs) Genes have Prognostic Significance in Hepatocellular Carcinoma Patients

2020 ◽  
Author(s):  
Guangtao Sun ◽  
Kejian Sun ◽  
Chao Shen
Keyword(s):  
Hepatocellular Carcinoma ◽  
Nuclear Receptors ◽  
Risk Score ◽  
Prognostic Model ◽  
Cox Regression ◽  
Prognostic Significance ◽  
Prognostic Signature ◽  
Term Survival ◽  
Cox Regression Analysis

Abstract Background: Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related mortality in the world. Human nuclear receptors (NRs) have been identified to closely related to various cancer. However, the prognostic significance of NRs on HCC patients has not been studied in detail.Method: We downloaded the mRNA profiles and clinical information of 371 HCC patients from TCGA database and analyzed the expression of 48 NRs. The consensus clustering analysis with the mRNA levels of 48 NRs was performed by the "ConsensusClusterPlus". The Univariate cox regression analysis was performed to predict the prognostic significance of NRs on HCC. The risk score was calculated by the prognostic model constructed based on eight optimal NRs which were selected. Then Multivariate Cox regression analysis was performed to determine whether the risk score is an independent prognostic signature. Finally, the nomogram based on multiple independent prognostic factors including risk score and TNM Stage was used to predict the long-term survival of HCC patients.Results: NRs could effectively separate HCC samples with different prognosis. The prognostic model constructed based on the eight optimal NRs (NR1H3, ESR1, NR1I2, NR2C1, NR6A1, PPARD, PPARG and VDR) could effectively predict the prognosis of HCC patients as an independent prognostic signature. Moreover, the nomogram was constructed based on multiple independent prognostic factors including risk score and TNM Stage and could better predict the long-term survival for 3- and 5-year of HCC patients.Conclusion: Our results provided novel evidences that NRs could act as the potential prognostic signatures for HCC patients.

scholarly journals A Five Autophagy-Related Long Non-Coding RNA Prognostic Model for Patients with Lung Adenocarcinoma

2021 ◽  
Author(s):  
Boxuan Liu ◽  
Yun Zhao ◽  
Shuanying Yang
Keyword(s):  
Lung Adenocarcinoma ◽  
Risk Score ◽  
Prognostic Model ◽  
Risk Model ◽  
Cox Regression ◽  
Predictive Ability ◽  
Enrichment Analysis ◽  
Pathway Enrichment Analysis ◽  
Lasso Regression ◽  
Cox Regression Analysis

Abstract Background: Lung adenocarcinoma is the most occurred pathological type among non-small cell lung cancer. Although huge progress has been made in terms of early diagnosis, precision treatment in recent years, the overall 5-year survival rate of a patient remains low. In our study, we try to construct an autophagy-related lncRNA prognostic signature that may guide clinical practice.Methods: The mRNA and lncRNA expression matrix of lung adenocarcinoma patients were retrieved from TCGA database. Next, we constructed a co-expression network of lncRNAs and autophagy-related genes. Lasso regression and multivariate Cox regression were then applied to establish a prognostic risk model. Subsequently, a risk score was generated to differentiate high and low risk group and a ROC curve and Nomogram to visualize the predictive ability of current signature. Finally, gene ontology and pathway enrichment analysis were executed via GSEA.Results: A total of 1,703 autophagy-related lncRNAs were screened and five autophagy-related lncRNAs (LINC01137, AL691432.2, LINC01116, AL606489.1 and HLA-DQB1-AS1) were finally included in our signature. Judging from univariate(HR=1.075, 95% CI: 1.046–1.104) and multivariate(HR =1.088, 95%CI = 1.057 − 1.120) Cox regression analysis, the risk score is an independent factor for LUAD patients. Further, the AUC value based on the risk score for 1-year, 3-year, 5-year, was 0.735, 0.672 and 0.662 respectively. Finally, the lncRNAs included in our signature were primarily enriched in autophagy process, metabolism, p53 pathway and JAK/STAT pathway. Conclusions: Overall, our study indicated that the prognostic model we generated had certain predictability for LUAD patients’ prognosis.

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