scholarly journals Identification of a Seven-Gene Signature and Establishment of a Nomogram Predicting Overall Survival in Head and Neck Squamous Cell Carcinoma

2020 ◽  
Author(s):  
Haige Zheng ◽  
Xiangkun Wu ◽  
Huixian Liu ◽  
Yumin Lu ◽  
Hengguo Li
Keyword(s):  
Overall Survival ◽  
Squamous Cell Carcinoma ◽  
Regression Analysis ◽  
High Risk ◽  
Cell Carcinoma ◽  
Cox Regression ◽  
Risk Groups ◽  
Gene Signature ◽  
Cox Regression Analysis ◽  
Kaplan Meier

Abstract Background: Head and neck squamous cell carcinoma (HNSCC) is a highly heterogeneous tumor with high incidence and poor prognosis. Therefore, effective predictive models are needed to evaluate patient outcomes and optimize treatment. Methods: Ten gene microarray datasets were obtained from the gene expression omnibus (GEO) database. Level 3 mRNA expression and clinical data were obtained in The Cancer Genome Atlas (TCGA) database. We identified highly robust differentially-expressed genes (DEGs) between HNSCC and normal tissue in nine GEO and TCGA datasets using Robust Rank Aggregation (RRA) method. Univariate Cox regression analysis and lasso Cox regression analysis were performed to identify DEGs related to the Overall-survival (OS) and to construct a prognostic gene signature. External validation was performed using GSE65858. Moreover, gene set enrichment analyses (GSEA) analysis was used to analyze significantly rich pathways in high-risk and low-risk groups, and tumor immunoassays were used to clarify immune correlation of the prognostic gene. Finally, integrate multiple forecast indicators were used to build a nomogram using the TCGA-HNSCC dataset. Kaplan–Meier analysis, receiver operating characteristic (ROC), a calibration plot, Harrell’s concordance index (C-index), and decision curve analysis (DCA) were used to test the predictive capability of the seven genetic signals and the nomogram. Results: A novel seven-gene signature (including SLURP1, SCARA5, CLDN10, MYH11, CXCL13, HLF, and ITGA3) was established to predict overall survival in HNSCC patients. ROC curve performed well in the training and validation data sets. Kaplan–Meier analysis demonstrated that low-risk groups had a longer survival time. The nomogram containing seven genetic markers and clinical prognostic factors was a good predictor of HNSCC survival and showed a certain net clinical benefit through the DCA curve. Further research demonstrated that the infiltration degree of CD8 + T cells, B cells, neutrophils, and NK cells were significantly lower in the high-risk group.Conclusion: Our analysis established a seven-gene model and nomogram to accurately predict the prognosis status of HNSCC patients, immune relevance was also described, which may provide a new possibility for individual treatment and medical decision-making.

scholarly journals Immune-Related lncRNA Signature for Predicting the Immune Landscape of Head and Neck Squamous Cell Carcinoma

2021 ◽  
Vol 8 ◽  
Author(s):  
Ji Yin ◽  
Xiaohui Li ◽  
Caifeng Lv ◽  
Xian He ◽  
Xiaoqin Luo ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Regression Analysis ◽  
High Risk ◽  
Cell Carcinoma ◽  
Cox Regression ◽  
Risk Group ◽  
Risk Groups ◽  
High Risk Group ◽  
Survival Prediction ◽  
Cox Regression Analysis

Background: Long non-coding RNA (lncRNA) plays a significant role in the development, establishment, and progression of head and neck squamous cell carcinoma (HNSCC). This article aims to develop an immune-related lncRNA (irlncRNA) model, regardless of expression levels, for risk assessment and prognosis prediction in HNSCC patients.Methods: We obtained clinical data and corresponding full transcriptome expression of HNSCC patients from TCGA, downloaded GTF files to distinguish lncRNAs from Ensembl, discerned irlncRNAs based on co-expression analysis, distinguished differentially expressed irlncRNAs (DEirlncRNAs), and paired these DEirlncRNAs. Univariate Cox regression analysis, LASSO regression analysis, and stepwise multivariate Cox regression analysis were then performed to screen lncRNA pairs, calculate the risk coefficient, and establish a prognosis model. Finally, the predictive power of this model was validated through the AUC and the ROC curves, and the AIC values of each point on the five-year ROC curve were calculated to select the maximum inflection point, which was applied as a cut-off point to divide patients into low- or high-risk groups. Based on this methodology, we were able to more effectively differentiate between these groups in terms of survival, clinico-pathological characteristics, tumor immune infiltrating status, chemotherapeutics sensitivity, and immunosuppressive molecules.Results: A 13-irlncRNA-pair signature was built, and the ROC analysis demonstrated high sensitivity and specificity of this signature for survival prediction. The Kaplan–Meier analysis indicated that the high-risk group had a significantly shorter survival rate than the low-risk group, and the chi-squared test certified that the signature was highly related to survival status, clinical stage, T stage, and N stage. Additionally, the signature was further proven to be an independent prognostic risk factor via the Cox regression analyses, and immune infiltrating analyses showed that the high-risk group had significant negative relationships with various immune infiltrations. Finally, the chemotherapeutics sensitivity and the expression level of molecular markers were also significantly different between high- and low-risk groups.Conclusion: The signature established by paring irlncRNAs, with regard to specific expression levels, can be utilized for survival prediction and to guide clinical therapy in HNSCC.

scholarly journals Identification of a Novel Signature Predicting Overall Survival in Head and Neck Squamous Cell Carcinoma

2021 ◽  
Vol 8 ◽  
Author(s):  
Haige Zheng ◽  
Huixian Liu ◽  
Yumin Lu ◽  
Hengguo Li
Keyword(s):  
Overall Survival ◽  
Squamous Cell Carcinoma ◽  
Regression Analysis ◽  
High Risk ◽  
Cell Carcinoma ◽  
Poor Prognosis ◽  
Cox Regression ◽  
Risk Group ◽  
High Risk Group ◽  
Cox Regression Analysis

Background: Head and neck squamous cell carcinoma (HNSCC) is a highly heterogeneous tumor with a high incidence and poor prognosis. Therefore, effective predictive models are needed to evaluate patient outcomes and optimize treatment.Methods: Robust Rank Aggregation (RRA) method was used to identify highly robust differentially-expressed genes (DEGs) between HNSCC and normal tissue in 9 GEO and TCGA datasets. Univariate Cox regression analysis and Lasso Cox regression analysis were performed to identify DEGs related to the Overall survival (OS) and to construct a prognostic gene signature (HNSCCSig). External validation was performed using GSE65858 dataset. Moreover, comprehensive bioinformatics analyses were used to identify the association between HNSCCSig and tumor immune environment.Results: A total of 257 reliable DEGs were identified by differentially analysis result of TCGA and GSE65858 datasets. The HNSCCSig including 7 mRNAs (SLURP1, SCARA5, CLDN10, MYH11, CXCL13, HLF, and ITGA3) were developed and validated to identify high-risk group who had a worse OS than low-risk group in TCGA and GSE65858 datasets. Cox regression analysis showed that the HNSCCSig could independently predict OS in both the TCGA and the GSE65858 datasets. Further research demonstrated that the infiltration bundance of CD8 + T cells, B cells, neutrophils, and NK cells were significantly lower in the high-risk group. A nomogram was also constructed by combining the HNSCCSig and clinical characters.Conclusion: We established and validated the HNSCCSig consisting of SLURP1, SCARA5, CLDN10, MYH11, CXCL13, HLF, and ITGA3. A nomogram combining HNSCCSig and some clinical parameters was constructed to identify high-risk HNSCC-patients with poor prognosis.

scholarly journals Prognostic Value of an Immune-Related Gene Signature in Oral Squamous Cell Carcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Chao Zhu ◽  
Liqun Gu ◽  
Mianfeng Yao ◽  
Jiang Li ◽  
Changyun Fang
Keyword(s):  
Overall Survival ◽  
Squamous Cell Carcinoma ◽  
Regression Analysis ◽  
Oral Squamous Cell Carcinoma ◽  
Risk Score ◽  
Cox Regression ◽  
Gene Signature ◽  
Related Gene ◽  
Cox Regression Analysis ◽  
Immune Related Gene

The prognosis and immunotherapy response rates are unfavorable in patients with oral squamous cell carcinoma (OSCC). The tumor microenvironment is associated with tumor prognosis and progression, and the underlying mechanisms remain unclear. We obtained differentially expressed immune-related genes from OSCC mRNA data in The Cancer Genome Atlas (TCGA) database. Overall survival-related risk signature was constructed by univariate Cox regression analysis and LASSO Cox regression analysis. The prognostic performance was validated with receiver operating characteristic (ROC) analysis and Kaplan–Meier survival curves in the TCGA and Gene Expression Omnibus (GEO) datasets. The risk score was confirmed to be an independent prognostic factor and a nomogram was built to quantify the risk of outcome for each patient. Furthermore, a negative correlation was observed between the risk score and the infiltration rate of immune cells, as well as the expression of immunostimulatory and immunosuppressive molecules. Functional enrichment analysis between different risk score subtypes detected multiple immune-related biological processes, metabolic pathways, and cancer-related pathways. Thus, the immune-related gene signature can predict overall survival and contribute to the personalized management of OSCC patients.

scholarly journals A Novel Ferroptosis-related Gene Signature for Prognosis Prediction in Glioma

2021 ◽  
Author(s):  
Tian Lan ◽  
Die Wu ◽  
Wei Quan ◽  
Donghu Yu ◽  
Sheng Li ◽  
...  
Keyword(s):  
Overall Survival ◽  
High Risk ◽  
Cox Regression ◽  
Risk Group ◽  
Risk Groups ◽  
Gene Signature ◽  
High Risk Group ◽  
Differential Analysis ◽  
Related Gene ◽  
Cox Regression Analysis

Abstract Background: Glioma is a fatal brain tumor characterized by invasive nature, rapidly proliferation and tumor recurrence. Despite aggressive surgical resection followed by concurrent radiotherapy and chemotherapy, the overall survival (OS) of Glioma patients remains poor. Ferroptosis is a unique modality to regulate programmed cell death and associated with multiple steps of tumorigenesis of a variety of tumors.Methods: In this study, ferroptosis-related genes model was identified by differential analysis and Cox regression analysis. GO, KEGG and GSVA analysis were used to detect the potential biological functions and signaling pathway. The infiltration of immune cells was quantified by Cibersort.Results: The patients’ samples are stratified into two risk groups based on 4-gene signature. High-risk group has poorer overall survival. The results of functional analysis indicated that the extracellular matrix-related biologic functions and pathways were enriched in high-risk group, and that the infiltration of immunocytes is different in two groups.Conclusion: In summary, a novel ferroptosis-related gene signature can be used for prognostic prediction in glioma. The filtered genes related to ferroptosis in clinical could be a potential extra method to assess glioma patients’ prognosis and therapeutic.

scholarly journals Prognostic Signatures of Metabolic Genes and Metabolism-Related Long Non-coding RNAs Accurately Predict Overall Survival for Osteosarcoma Patients

2021 ◽  
Vol 9 ◽  
Author(s):  
Gong Chao-yang ◽  
Tang Rong ◽  
Shi Yong-qiang ◽  
Liu Tai-cong ◽  
Zhou Kai-sheng ◽  
...  
Keyword(s):  
Overall Survival ◽  
Regression Analysis ◽  
High Risk ◽  
Cox Regression ◽  
Gene Signature ◽  
Survival Outcomes ◽  
Data Set ◽  
Cox Regression Analysis ◽  
Metabolic Gene ◽  
Metabolic Genes

In this study, we identified eight survival-related metabolic genes in differentially expressed metabolic genes by univariate Cox regression analysis based on the therapeutically applicable research to generate effective treatments (n = 84) data set and genotype tissue expression data set (n = 396). We also constructed a six metabolic gene signature to predict the overall survival of osteosarcoma (OS) patients using least absolute shrinkage and selection operator (Lasso) Cox regression analysis. Our results show that the six metabolic gene signature showed good performance in predicting survival of OS patients and was also an independent prognostic factor. Stratified correlation analysis showed that the metabolic gene signature accurately predicted survival outcomes in high-risk and low-risk OS patients. The six metabolic gene signature was also verified to perform well in predicting survival of OS patients in an independent cohort (GSE21257). Then, using univariate Cox regression and Lasso Cox regression analyses, we identified an eight metabolism-related long noncoding RNA (lncRNA) signature that accurately predicts overall survival of OS patients. Gene set variation analysis showed that the apical surface and bile acid metabolism, epithelial mesenchymal transition, and P53 pathway were activated in the high-risk group based on the eight metabolism-related lncRNA signature. Furthermore, we constructed a competing endogenous RNA (ceRNA) network and conducted immunization score analysis based on the eight metabolism-related lncRNA signature. These results showed that the six metabolic gene signature and eight metabolism-related lncRNA signature have good performance in predicting the survival outcomes of OS patients.

scholarly journals A glycolysis-based three-gene signature predicts survival in patients with lung squamous cell carcinoma

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Guichuan Huang ◽  
Jing Zhang ◽  
Ling Gong ◽  
Yi Huang ◽  
Daishun Liu
Keyword(s):  
Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Risk Score ◽  
Squamous Cell ◽  
Cox Regression ◽  
Lung Squamous Cell Carcinoma ◽  
Gene Signature ◽  
Gene Set Enrichment Analysis ◽  
Related Gene ◽  
Cox Regression Analysis

Abstract Background Lung cancer is one of the most lethal and most prevalent malignant tumors worldwide, and lung squamous cell carcinoma (LUSC) is one of the major histological subtypes. Although numerous biomarkers have been found to be associated with prognosis in LUSC, the prediction effect of a single gene biomarker is insufficient, especially for glycolysis-related genes. Therefore, we aimed to develop a novel glycolysis-related gene signature to predict survival in patients with LUSC. Methods The mRNA expression files and LUSC clinical information were obtained from The Cancer Genome Atlas (TCGA) dataset. Results Based on Gene Set Enrichment Analysis (GSEA), we found 5 glycolysis-related gene sets that were significantly enriched in LUSC tissues. Univariate and multivariate Cox proportional regression models were performed to choose prognostic-related gene signatures. Based on a Cox proportional regression model, a risk score for a three-gene signature (HKDC1, ALDH7A1, and MDH1) was established to divide patients into high-risk and low-risk subgroups. Multivariate Cox regression analysis indicated that the risk score for this three-gene signature can be used as an independent prognostic indicator in LUSC. Additionally, based on the cBioPortal database, the rate of genomic alterations in the HKDC1, ALDH7A1, and MDH1 genes were 1.9, 1.1, and 5% in LUSC patients, respectively. Conclusion A glycolysis-based three-gene signature could serve as a novel biomarker in predicting the prognosis of patients with LUSC and it also provides additional gene targets that can be used to cure LUSC patients.

scholarly journals Prognostic Significance of PD-L1 and mTOR Expression in Oral Squamous Cell Carcinoma

2020 ◽  
Author(s):  
Nattinee Charoen ◽  
Kitti Jantharapattana ◽  
Paramee Thongsuksai
Keyword(s):  
Squamous Cell Carcinoma ◽  
Regression Analysis ◽  
Prognostic Factors ◽  
Oral Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Cox Regression ◽  
Prognostic Significance ◽  
P Value ◽  
Cox Regression Analysis

Objective: Programmed cell death ligand 1 (PD-L1) and mammalian target of rapamycin (mTOR) are key players in host immune evasion and oncogenic activation, respectively. Evidence of the prognostic role in oral squamous cell carcinoma (OSCC) is conflicting. This study examined the associations of PD-L1 and mTOR expression with 5-year overall survival in OSCC patients. Material and Methods: The expressions of PD-L1 and mTOR proteins were immunohistochemically evaluated on tissue microarrays of 191 patients with OSCC who were treated by surgery at Songklanagarind Hospital, Thailand from 2008 to 2011. Cox regression analysis was used to determine independent prognostic factors. Results: PD-L1 expression was observed in 14.1% of cases while mTOR expression was present in 74.3% of cases. Females were more likely to have tumors with PD-L1 (p-value=0.007) and mTOR expressions (p-value=0.003) than males. In addition, lower clinical stage and well differentiated tumor are more likely to have mTOR expression (p-value= 0.038 and p-value<0.001, respectively). Cox regression analysis showed that age, tumor stage, nodal stage, combined surgical treatment with radiation or chemoradiation therapy, surgical margin status, PD-L1 expression and mTOR expression are independent prognostic factors. High PD-L1 expression (hazard ratio (HR) 3.14, 95% confidence interval (CI), 1.26–7.79) and high mTOR expression (HR 1.69, 95% CI, 1.00–2.84) are strong predictors of poor outcome. Conclusion: A proportion of OSCC expressed PD-L1 and mTOR proteins. Expression of PD-L1 and mTOR proteins are strong prognostic factors of OSCC.

scholarly journals Identification of a Liver Progenitor Cell-Related Genes Signature Predicting Overall Survival for Hepatocellular Carcinoma

2021 ◽  
Vol 20 ◽  
pp. 153303382110414
Author(s):  
Xiaoyong Li ◽  
Jiaqong Lin ◽  
Yuguo pan ◽  
Peng Cui ◽  
Jintang Xia
Keyword(s):  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
Regression Analysis ◽  
Prognostic Model ◽  
Cox Regression ◽  
Risk Group ◽  
Gene Signature ◽  
Regression Analyses ◽  
Related Gene ◽  
Cox Regression Analysis

Background: Liver progenitor cells (LPCs) play significant roles in the development and progression of hepatocellular carcinoma (HCC). However, no studies on the value of LPC-related genes for evaluating HCC prognosis exist. We developed a gene signature of LPC-related genes for prognostication in HCC. Methods: To identify LPC-related genes, we analyzed mRNA expression arrays from a dataset (GSE57812 & GSE 37071) containing LPCs, mature hepatocytes, and embryonic stem cell samples. HCC RNA-Seq data from The Cancer Genome Atlas (TCGA) were used to explore the differentially expressed genes (DEGs) related to prognosis through DEG analysis and univariate Cox regression analysis. Lasso and multivariate Cox regression analyses were performed to construct the LPC-related gene prognostic model in the TCGA training dataset. This model was validated in the TCGA testing set and an external dataset (International Cancer Genome Consortium [ICGC] dataset). Finally, we investigated the relationship between this prognostic model with tumor-node-metastasis stage, tumor grade, and vascular invasion of HCC. Results: Overall, 1770 genes were identified as LPC-related genes, of which 92 genes were identified as DEGs in HCC tissues compared with normal tissues. Furthermore, we randomly assigned patients from the TCGA dataset to the training and testing cohorts. Twenty-six DEGs correlated with overall survival (OS) in the univariate Cox regression analysis. Lasso and multivariate Cox regression analyses were performed in the TCGA training set, and a 3-gene signature was constructed to stratify patients into 2 risk groups: high-risk and low-risk. Patients in the high-risk group had significantly lower OS than those in the low-risk group. Receiver operating characteristic curve analysis confirmed the signature's predictive capacity. Moreover, the risk score was confirmed to be an independent predictor for patients with HCC. Conclusion: We demonstrated that the LPC-related gene signature can be used for prognostication in HCC. Thus, targeting LPCs may serve as a therapeutic alternative for HCC.

scholarly journals Analysis of Immune-Related Signatures Related to CD4+ T Cell Infiltration With Gene Co-Expression Network in Pancreatic Adenocarcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Zhen Tan ◽  
Yubin Lei ◽  
Bo Zhang ◽  
Si Shi ◽  
Jiang Liu ◽  
...  
Keyword(s):  
Overall Survival ◽  
Molecular Mechanisms ◽  
Cox Regression ◽  
Risk Groups ◽  
Gene Signature ◽  
Gene Expression Omnibus ◽  
Ductal Adenocarcinoma ◽  
Rank Test ◽  
Cancer Center ◽  
Cox Regression Analysis

BackgroundPancreatic ductal adenocarcinoma (PDAC) is one of the most invasive solid malignancies. Immunotherapy and targeted therapy confirmed an existing certain curative effect in treating PDAC. The aim of this study was to develop an immune-related molecular marker to enhance the ability to predict Stages III and IV PDAC patients.MethodIn this study, weighted gene co-expression network (WGCNA) analysis and a deconvolution algorithm (CIBERSORT) that evaluated the cellular constituent of immune cells were used to evaluate PDAC expression data from the GEO (Gene Expression Omnibus) datasets, and identify modules related to CD4+ T cells. LASSO Cox regression analysis and Kaplan–Meier curve were applied to select and build prognostic multi-gene signature in TCGA Stages III and IV PDAC patients (N = 126). This was followed by independent Stages III and IV validation of the gene signature in the International Cancer Genome Consortium (ICGC, N = 62) and the Fudan University Shanghai Cancer Center (FUSCC, N = 42) cohort. Inherited germline mutations and tumor immunity exploration were applied to elucidate the molecular mechanisms in PDAC. Univariate and Multivariate Cox regression analyses were applied to verify the independent prognostic factors. Finally, a prognostic nomogram was created according to the TCGA-PDAC dataset.ResultsA four-gene signature comprising NAPSB, ZNF831, CXCL9 and PYHIN1 was established to predict overall survival of PDAC. This signature also robustly predicted survival in two independent validation cohorts. The four-gene signature could divide patients into high and low-risk groups with disparity overall survival verified by a Log-rank test. Expression of four genes positively correlated with immunosuppression activity (PD-L1 and PD1). Immune-related genes nomogram and corresponding calibration curves showed significant performance for predicting 3-year survival in TCGA-PDAC dataset.ConclusionWe constructed a novel four-gene signature to predict the prognosis of Stages III and IV PDAC patients by applying WGCNA and CIBERSORT algorithm scoring to transcriptome data different from traditional methods of filtrating for differential genes in cancer and healthy tissues. The findings may provide reference to predict survival and was beneficial to individualized management for advanced PDAC patients.

scholarly journals Prognostic Nomogram Based on Circular RNA-Associated Competing Endogenous RNA Network for Patients with Lung Adenocarcinoma

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Yang Li ◽  
Rongrong Sun ◽  
Rui Li ◽  
Yonggang Chen ◽  
He Du
Keyword(s):  
Overall Survival ◽  
Regression Analysis ◽  
Lung Adenocarcinoma ◽  
Regulatory Mechanism ◽  
Cox Regression ◽  
Gene Signature ◽  
Differentially Expressed ◽  
Prognostic Signature ◽  
Cox Regression Analysis ◽  
Cerna Network

Evidence is increasingly indicating that circular RNAs (circRNAs) are closely involved in tumorigenesis and cancer progression. However, the function and application of circRNAs in lung adenocarcinoma (LUAD) are still unknown. In this study, we constructed a circRNA-associated competitive endogenous RNA (ceRNA) network to investigate the regulatory mechanism of LUAD procession and further constructed a prognostic signature to predict overall survival for LUAD patients. Differentially expressed circRNAs (DEcircRNAs), differentially expressed miRNAs (DEmiRNAs), and differentially expressed mRNAs (DEmRNAs) were selected to construct the ceRNA network. Based on the TargetScan prediction tool and Pearson correlation coefficient, we constructed a circRNA-associated ceRNA network including 11 DEcircRNAs, 8 DEmiRNAs, and 49 DEmRNAs. GO and KEGG enrichment indicated that the ceRNA network might be involved in the regulation of GTPase activity and endothelial cell differentiation. After removing the discrete points, a PPI network containing 12 DEmRNAs was constructed. Univariate Cox regression analysis showed that three DEmRNAs were significantly associated with overall survival. Therefore, we constructed a three-gene prognostic signature for LUAD patients using the LASSO method in the TCGA-LUAD training cohort. By applying the signature, patients could be categorized into the high-risk or low-risk subgroups with significant survival differences (HR: 1.62, 95% CI: 1.12-2.35, log-rank p = 0.009 ). The prognostic performance was confirmed in an independent GEO cohort (GSE42127, HR: 2.59, 95% CI: 1.32-5.10, log-rank p = 0.004 ). Multivariate Cox regression analysis proved that the three-gene signature was an independent prognostic factor. Combining the three-gene signature with clinical characters, a nomogram was constructed. The primary and external verification C -indexes were 0.717 and 0.716, respectively. The calibration curves for the probability of 3- and 5-year OS showed significant agreement between nomogram predictions and actual observations. Our findings provided a deeper understanding of the circRNA-associated ceRNA regulatory mechanism in LUAD pathogenesis and further constructed a useful prognostic signature to guide personalized treatment of LUAD patients.

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