scholarly journals Establishment of a Macrophage Phenotypic Switch Related Prognostic Signature in Patients With Pancreatic Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Mu-xing Li ◽  
Hang-yan Wang ◽  
Chun-hui Yuan ◽  
Zhao-lai Ma ◽  
Bin Jiang ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Regression Analysis ◽  
Signaling Pathway ◽  
Risk Score ◽  
Prognostic Value ◽  
Risk Group ◽  
Gene Set Enrichment Analysis ◽  
Differentially Expressed ◽  
Prognostic Signature ◽  
Phenotypic Switch

IntroductionMacrophage phenotype switch plays a vital role in the progression of malignancies. We aimed to build a prognostic signature by exploring the expression pattern of macrophage phenotypic switch related genes (MRGs) in the Cancer Genome Atlas (TCGA)—pancreatic adenocarcinoma (PAAD), Genotype-Tissue Expression (GTEx)-Pancreas, and Gene Expression Omnibus (GEO) databases.MethodsWe identified the differentially expressed genes between the PAAD and normal tissues. We used single factor Cox proportional risk regression analysis, Least Absolute Shrinkage and Selection Operator (LASSO) analysis, and multivariate Cox proportional hazard regression analysis to establish the prognosis risk score by the MRGs. The relationships between the risk score and immune landscape, “key driver” mutations and clinicopathological factors were also analyzed. Gene-set enrichment analysis (GSEA) analysis was also performed.ResultsWe detected 198 differentially expressed MRGs. The risk score was constructed based on 9 genes (KIF23, BIN1, LAPTM4A, ERAP2, ATP8B2, FAM118A, RGS16, ELMO1, RAPGEFL1). The median overall survival time of patients in the low-risk group was significantly longer than that of patients in the high-risk group (P < 0.001). The prognostic value of the risk score was validated in GSE62452 dataset. The prognostic performance of nomogram based on risk score was superior to that of TNM stage. And GSEA analysis also showed that the risk score was closely related with P53 signaling pathway, pancreatic cancer and T cell receptor signaling pathway. qRT-PCR assay showed that the expressions of the 9 MRGs in PDAC cell lines were higher than those in human pancreatic ductal epithelium cell line.ConclusionsThe nine gene risk score could be used as an independent prognostic index for PAAD patients. Further studies validating the prognostic value of the risk score are warranted.

scholarly journals Five gene signatures were identified in the prediction of overall survival in resectable pancreatic cancer

BMC Surgery ◽  
2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Chao Wu ◽  
Zuowei Wu ◽  
Bole Tian
Keyword(s):  
Pancreatic Cancer ◽  
Overall Survival ◽  
Regression Analysis ◽  
Risk Score ◽  
Cox Regression ◽  
Risk Group ◽  
Gene Set Enrichment Analysis ◽  
Resectable Pancreatic Cancer ◽  
Cox Regression Analysis ◽  
Validation Set

Abstract Background Although genes have been previously detected in pancreatic cancer (PC), aberrant genes that play roles in resectable pancreatic cancer should be further assessed. Methods Messenger RNA samples and clinicopathological data corrected with PC were downloaded from The Cancer Genome Atlas (TCGA). Resectable PC patients were randomly divided into a primary set and a validation set. Univariable Cox regression analysis, lasso-penalized Cox regression analysis, and multivariable Cox analysis were implemented to distinguish survival-related genes (SRGs). A risk score based on the SRGs was calculated by univariable Cox regression analysis. A genomic-clinical nomogram was established by integrating the risk score and clinicopathological data to predict overall survival (OS) in resectable PC. Results Five survival-related genes (AADAC, DEF8, HIST1H1C, MET, and CHFR) were significantly correlated with OS in resectable PC. The resectable PC patients, based on risk score, were sorted into a high-risk group that showed considerably unfavorable OS (p < 0.001) than the low-risk group, in both the primary set and the validation set. The concordance index (C-index) was calculated to evaluate the predictive performance of the nomogram were respectively in the primary set [0.696 (0.608–0.784)] and the validation set [0.682 (0.606–0.758)]. Additionally, gene set enrichment Analysis discovered several meaningful enriched pathways. Conclusion Our study identified five prognostic gene biomarkers for OS prediction and which facilitate postoperative molecular target therapy for the resectable PC, especially the nomic-clinical nomogram which may be used as an effective model for the postoperative OS evaluation and also an optimal therapeutic tool for the resectable PC.

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

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

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

scholarly journals A Risk Signature of Nine Autophagy-Related Genes Associated With Immune Microenvironment and Clinical Prognosis in Wilms Tumor

2021 ◽  
Author(s):  
Shaopei Ye ◽  
Wenbin Tang ◽  
Ke Huang
Keyword(s):  
Regression Analysis ◽  
High Risk ◽  
Risk Score ◽  
Cox Regression ◽  
Wilms Tumor ◽  
Risk Group ◽  
High Risk Group ◽  
Differentially Expressed ◽  
Clinical Prognosis ◽  
Cox Regression Analysis

Abstract Background: Autophagy is a biological process to eliminate dysfunctional organelles, aggregates or even long-lived proteins. . Nevertheless, the potential function and prognostic values of autophagy in Wilms Tumor (WT) are complex and remain to be clarifed. Therefore, we proposed to systematically examine the roles of autophagy-associated genes (ARGs) in WT.Methods: Here, we obtained differentially expressed autophagy-related genes (ARGs) between healthy and Wilms tumor from Therapeutically Applicable Research To Generate Effective Treatments(TARGET) and The Cancer Genome Atlas (TCGA) database. The functionalities of the differentially expressed ARGs were analyzed using Gene Ontology. Then univariate COX regression analysis and multivariate COX regression analysis were performed to acquire nine autophagy genes related to WT patients’ survival. According to the risk score, the patients were divided into high-risk and low-risk groups. The Kaplan-Meier curve demonstrated that patients with a high-risk score tend to have a poor prognosis.Results: Eighteen DEARGs were identifed, and nine ARGs were fnally utilized to establish the FAGs based signature in the TCGA cohort. we found that patients in the high-risk group were associated with mutations in TP53. We further conducted CIBERSORT analysis, and found that the infiltration of Macrophage M1 was increased in the high-risk group. Finally, the expression levels of crucial ARGs were verifed by the experiment, which were consistent with our bioinformatics analysis.Conclusions: we emphasized the clinical significance of autophagy in WT, established a prediction system based on autophagy, and identified a promising therapeutic target of autophagy for WT.

scholarly journals Construction and validation of a novel prognostic signature of microRNAs in lung adenocarcinoma

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e10470
Author(s):  
Wanzhen Li ◽  
Shiqing Liu ◽  
Shihong Su ◽  
Yang Chen ◽  
Gengyun Sun
Keyword(s):  
Lung Adenocarcinoma ◽  
Signaling Pathway ◽  
Prognostic Value ◽  
Target Genes ◽  
Predictive Accuracy ◽  
Differentially Expressed ◽  
Prognostic Signature ◽  
Tissue Samples ◽  
Gene Set ◽  
Differentially Expressed Mirnas

MicroRNA (miRNA, miR) has been reported to be highly implicated in a wide range of biological processes in lung cancer (LC), and identification of differentially expressed miRNAs between normal and LC samples has been widely used in the discovery of prognostic factors for overall survival (OS) and response to therapy. The present study was designed to develop and evaluate a miRNA-based signature with prognostic value for the OS of lung adenocarcinoma (LUAD), a common histologic subtype of LC. In brief, the miRNA expression profiles and clinicopathological factors of 499 LUAD patients were collected from The Cancer Genome Atlas (TCGA) database. Kaplan–Meier (K-M) survival analysis showed significant correlations between differentially expressed miRNAs and LUAD survival outcomes. Afterward, 1,000 resample LUAD training matrices based on the training set was applied to identify the potential prognostic miRNAs. The least absolute shrinkage and selection operator (LASSO) cox regression analysis was used to constructed a six-miRNA based prognostic signature for LUAD patients. Samples with different risk scores displayed distinct OS in K-M analysis, indicating considerable predictive accuracy of this signature in both training and validation sets. Furthermore, time-dependent receiver operating characteristic (ROC) analysis demonstrated the nomogram achieved higher predictive accuracy than any other clinical variables after incorporating the clinical information (age, sex, stage, and recurrence). In the stratification analysis, the prognostic value of this classifier in LUAD patients was validated to be independent of other clinicopathological variables, such as age, gender, tumor recurrence, and early stage. Gene set annotation analyses were also conducted through the Hallmark gene set and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, indicating target genes of the six miRNAs were positively related to various molecular pathways of cancer, such as hallmark UV response, Wnt signaling pathway and mTOR signaling pathway. In addition, fresh cancer tissue samples and matched adjacent tissue samples from 12 LUAD patients were collected to verify the expression of miR-582’s target genes in the model, further revealing the potential relationship between SOX9, RASA1, CEP55, MAP4K4 and LUAD tumorigenesis, and validating the predictive value of the model. Taken together, the present study identified a robust signature for the OS prediction of LUAD patients, which could potentially aid in the individualized selection of therapeutic approaches for LUAD patients.

scholarly journals Prognostic value of an autophagy-related gene expression signature for endometrial cancer patients

2020 ◽  
Author(s):  
Hui Wang ◽  
Xiaoling Ma ◽  
Jinhui Liu ◽  
Yicong Wan ◽  
Yi Jiang ◽  
...  
Keyword(s):  
Endometrial Cancer ◽  
High Risk ◽  
Risk Score ◽  
Risk Group ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Prognostic Signature ◽  
Gene Set Enrichment ◽  
Test Set ◽  
Gene Set

Abstract Background: Autophagy is associated with cancer development. Autophagy-related genes play significant roles in endometrial cancer (EC), a major gynecological malignancy worldwide, but little was known about their value as prognostic markers. Here we evaluated the value of a prognostic signature based on autophagy-related genes for EC.Methods: First, various autophagy-related genes were obtained via the Human Autophagy Database and their expression profiles were downloaded from The Cancer Genome Atlas. Second, key prognostic autophagy-related genes were identified via univariat, LASSO, and multivariate Cox regression analyses. Finally, a risk score to predict the prognosis of EC was calculated and validated by using the test and the entire data sets. Besides, gene set enrichment and somatic mutation analyses were also used for these prognostic autophagy-related genes. Results: A total of 40 differentially expressed autophagy-related genes in EC were screened and five of them were prognosis-related (CDKN1B, DLC1, EIF4EBP1, ERBB2 and GRID1). A prognostic signature was constructed based on these five genes using the train set, which stratified EC patients into high-risk and low-risk groups (P<0.05). In terms of overall survival, the analyses of the test set and the entire set yielded consistent results (test set: p < 0.05; entire set: p < 0.05). Time-dependent ROC analysis suggested that the risk score predicted EC prognosis accurately and independently (0.674 at 1 year, 0.712 at 3 years and 0.659 at 5 years). A nomogram with clinical utility was built. Patients in the high-risk group displayed distinct mutation signatures compared with those in the low-risk group. Gene set enrichment analysis revealed high risk score was associated with tumor initiation and progression associated pathways.Conclusions: Based on five autophagy-related genes (CDKN1B, DLC1, EIF4EBP1, ERBB2 and GRID1), our model can independently predict the OS of EC patients by combining molecular signature and clinical characteristics.

scholarly journals Prognostic value of eight immune gene signatures in pancreatic cancer patients

2020 ◽  
Author(s):  
Wenting Wang ◽  
Zhijian Xu ◽  
Ning Wang ◽  
Ruyong Yao ◽  
Tao Qin ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Regression Analysis ◽  
Prediction Model ◽  
Cancer Immunotherapy ◽  
Cancer Patients ◽  
Risk Score ◽  
Prognostic Value ◽  
Risk Scores ◽  
Feasible Method ◽  
Immune Genes

Abstract BackgroundPancreatic cancer is one of the most common malignant tumors of the digestive tract, and it has a poor prognosis. Traditional methods are not effective to accurately assess the prognosis of patients with pancreatic cancer. Immunotherapy is a new promising approach for the treatment of pancreatic cancer; however, some patients do not respond well to immunotherapy, which may be related to tumor microenvironment regulation. In this study, we use gene expression database to mine important immune genes and establish a prognostic prediction model for pancreatic cancer patients. We hope to provide a feasible method to evaluate the prognosis of pancreatic cancer and provide valuable targets for pancreatic cancer immunotherapy.ResultsWe used univariate Cox proportional hazard regression analysis, the least absolute shrinkage and selection operator (LASSO), and multivariate COX regression analysis to screen 8 genes related to prognosis from the 314 immune-related genes, and used them to construct a new clinical prediction model in the TCGA pancreatic cancer cohort. Subsequently, we evaluated the prognostic value of the model. The Kaplan–Meier cumulative curve showed that patients with low risk scores survived significantly longer than patients with high risk scores. The area under the ROC curve (AUC value) of the risk score was 0.755.The univariate COX analysis showed that the risk score was significantly related to overall survival (OS) (HR = 1.406, 95% CI = 1.237 ~ 1.598, P <0.001), and multivariate analysis showed that the risk score was an independent prognostic factor (HR = 1.400 95% CI = 1.287 ~ 1.522, P <0.001). Correlation analysis found that immune genes are closely related to tumor immune microenvironment.ConclusionsBased on the TCGA-PAAD cohort, we identified immune-related markers with independent prognostic significance, validated, and analyzed their biological functions, to provide a feasible method for the prognosis of pancreatic cancer and provide potentially valuable targets for pancreatic cancer immunotherapy.

scholarly journals Prognostic value of an autophagy-related gene expression signature for endometrial cancer patients

2020 ◽  
Author(s):  
Hui Wang ◽  
Xiaoling Ma ◽  
Jinhui Liu ◽  
Yicong Wan ◽  
Yi Jiang ◽  
...  
Keyword(s):  
Endometrial Cancer ◽  
High Risk ◽  
Risk Score ◽  
Risk Group ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Prognostic Signature ◽  
Gene Set Enrichment ◽  
Test Set ◽  
Gene Set

Abstract Background: Autophagy is associated with cancer development. Autophagy-related genes play significant roles in endometrial cancer (EC), a major gynecological malignancy worldwide, but little was known about their value as prognostic markers. Here we evaluated the value of a prognostic signature based on autophagy-related genes for EC. Methods: First, various autophagy-related genes were obtained via the Human Autophagy Database and their expression profiles were downloaded from The Cancer Genome Atlas. Second, key prognostic autophagy-related genes were identified via univariat, LASSO, and multivariate Cox regression analyses. Finally, a risk score to predict the prognosis of EC was calculated and validated by using the test and the entire data sets. Besides, gene set enrichment and somatic mutation analyses were also used for these prognostic autophagy-related genes. Results: A total of 40 differentially expressed autophagy-related genes in EC were screened and five of them were prognosis-related (CDKN1B, DLC1, EIF4EBP1, ERBB2 and GRID1). A prognostic signature was constructed based on these five genes using the train set, which stratified EC patients into high-risk and low-risk groups (P<0.05). In terms of overall survival, the analyses of the test set and the entire set yielded consistent results (test set: p < 0.05; entire set: p < 0.05). Time-dependent ROC analysis suggested that the risk score predicted EC prognosis accurately and independently (0.674 at 1 year, 0.712 at 3 years and 0.659 at 5 years). A nomogram with clinical utility was built. Patients in the high-risk group displayed distinct mutation signatures compared with those in the low-risk group. Gene set enrichment analysis revealed high risk score was associated with tumor initiation and progression associated pathways. Conclusions: Based on five autophagy-related genes (CDKN1B, DLC1, EIF4EBP1, ERBB2 and GRID1), our model can independently predict the OS of EC patients by combining molecular signature and clinical characteristics.

scholarly journals Identification of Aging-Related Genes Associated With Clinical and Prognostic Features of Hepatocellular Carcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Xingte Chen ◽  
Lei Wang ◽  
Liang Hong ◽  
Zhixiong Su ◽  
Xiaohong Zhong ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Regression Analysis ◽  
High Risk ◽  
Risk Score ◽  
Cancer Genome ◽  
Gene Set Enrichment Analysis ◽  
Differentially Expressed ◽  
Survival Prediction ◽  
Lasso Regression ◽  
Prognostic Features

Background: Aging is a well-studied concept, but no studies have comprehensively analyzed the association between aging-related genes (AGs) and hepatocellular carcinoma (HCC) prognosis.Methods: Gene candidates were selected from differentially expressed genes and prognostic genes in The Cancer Genome Atlas (TCGA) database. A gene risk score for overall survival prediction was established using the least absolute shrinkage and selection operator (LASSO) regression analysis, and this was validated using data from the International Cancer Genome Consortium (ICGC) database. Functional analysis was conducted using gene ontology enrichment, Kyoto Encyclopedia of Genes and Genomes analysis, gene set enrichment analysis, and immune microenvironment and tumor stemness analyses.Results: Initially, 72 AGs from the TCGA database were screened as differentially expressed between normal and tumor tissues and as genes associated with HCC prognosis. Then, seven AGs (POLA1, CDK1, SOCS2, HDAC1, MAPT, RAE1, and EEF1E1) were identified using the LASSO regression analysis. The seven AGs were used to develop a risk score in the training set, and the risk was validated to have a significant prognostic value in the ICGC set (p &lt; 0.05). Patients with high risk scores had lower tumor differentiation, higher stage, and worse prognosis (all p &lt; 0.05). Multivariate Cox regression analyses also confirmed that the risk score was an independent prognostic factor for HCC in both the TCGA and ICGC sets (all p &lt; 0.05). Further analysis showed that a high risk score was correlated with the downregulation of metabolism and tumor immunity.Conclusion: The risk score predicts HCC prognosis and could thus be used as a biomarker not only for predicting HCC prognosis but also for deciding on treatment.

scholarly journals Identification of A Novel Six Autophagy-Related Genes Signature for The Prognostic and A miRNA-Related Autophagy Predictor for Anti-PD-1 Therapy Responses in Prostate Cancer

2020 ◽  
Author(s):  
Lei Wu ◽  
Guojun Yue ◽  
Wen Quan ◽  
Qiong Luo ◽  
Dongxu Peng ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
High Risk ◽  
Regression Model ◽  
Risk Group ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Differentially Expressed ◽  
Prognostic Signature ◽  
Potential Biomarker ◽  
Risk Patients

Abstract Background: Autophagy is a highly conserved homeostatic process in the human body that is responsible for the elimination of aggregated proteins and damaged organelles. Several autophagy-related genes (ARGs) contribute to the process of tumorigenesis and metastasis of prostate cancer (PCa). Also, miRNAs have been proven to modulate autophagy by targeting some ARGs. However, their potential role in PCa still remains unclear.Methods: An univariate Cox proportional regression model was used to identify 17 ARGs associated with the overall survival (OS) of PCa. Then, a multivariate Cox proportional regression model was used to construct a 6 autophagy-related prognostic genes signature. Patients were divided into low-risk group and high-risk group using the median risk score as a cutoff value. High-risk patients had shorter OS than low-risk patients. Furthermore, the signature was validated by ROC curves. Regarding mRNA and miRNA, 12 differentially expressed miRNAs (DEMs) and 1073 differentially expressed genes (DEGs) were detected via the GEO database. We found that miR-205, one of the DEMs, was negatively regulated the expression of ARG (NKX2-3). Based on STRING analysis results, we found that the NKX2-3 was moderately related to the part of genes among the 6 autophagy-related genes prognostic signature. Further, NKX 2-3 was significantly correlated with OS and some clinical parameters of PCa by cBioProtal. By gene set enrichment analysis (GSEA). Lastly, we demonstrated that the association between NKX2-3 and tumor mutation burden (TMB) and PDCD1 (programmed cell death 1) of PCa.Results: We identified that the six ARGs expression patterns are independent predictors of OS in PCa patients. Furthermore, our results suggest that ARGs and miRNAs are inter-related. MiR-205 was negatively regulated the expression of ARG (NKX2-3). Further analysis demonstrated that NKX2-3 may be a potential biomarker for predicting the efficacy of anti-PD-1 therapy in PCa.Conclusions: The current study may offer a novel autophagy-related prognostic signature and may identify a promising miRNA-ARG pathway for predicting the efficacy of anti-PD-1 therapy in PCa.

scholarly journals Identification of a nine ferroptosis-related lncRNA prognostic signature for lung adenocarcinoma

2021 ◽  
Author(s):  
Xiwen Tong ◽  
Yujiao Zhang ◽  
Guodong Yang ◽  
Guanghui Yi
Keyword(s):  
Lung Adenocarcinoma ◽  
Signaling Pathways ◽  
Cox Regression ◽  
Risk Group ◽  
Gene Set Enrichment Analysis ◽  
Differentially Expressed ◽  
Rank Test ◽  
Lymph Node Status ◽  
Prognostic Signature ◽  
Test Set

Abstract Background Recently, mounting of studies has shown that lncRNA affects tumor progression through the regulation of ferroptosis. The current study aims to construct a robust ferroptosis-related lncRNAs signature to increase the predicted value of lung adenocarcinoma (LUAD) by bioinformatics analysis. Methods The transcriptome data were abstracted from The Cancer Genome Atlas (TCGA). Differentially expressed lncRNAs were screened by comparing 535 LAUD tissues with 59 adjacent non-LAUD tissues. Univariate Cox regression, lasso regression, multivariate Cox regression were conducted to design a ferroptosis-related lncRNA signature. This signature’s prognosis was verified by the log-rank test of Kaplan-Meier curve and the area under curve (AUC) of receiver operating characteristic (ROC) in train set, test set, and entire set. Furthermore, univariate and multivariate Cox regression were used to analyze its independent prognostic ability. The relationship of the ferroptosis-linked lncRNAs' expression and clinical variables was demonstrated by Wilcoxon rank-sum test and Kruskal-Wallis test. Gene set enrichment analysis (GSEA) was performed to signaling pathways it may involve. Results 1224 differentially expressed lncRNAs were idendified, of which 195 are ferroptosis-related lncRNAs. A nine ferroptosis-related lncRNAs (AC099850.3, NAALADL2-AS2, AL844908.1, AL365181.2, SMIM25, FAM83A-AS1, LINC01116, AL049836.1, C20orf197) prognostic signature was constucted. This model's prognosis in the high-risk group is obviously worse than that of the low-risk group in train set, test set, and entire set. The AUC of ROC predicting the three years survival in the train set, test set, and entire set was 0.754, 0.716, and 0.738, respectively. Moreover, the designed molecular signature was found to be an independent prognostic variable. The expression of these lncRNAs and the lncRNA signature are related to clinical stage, T stage, Lymph-node status, distant metastasis. Finally, GSEA analysis results show that the signature is involved in eight tumor-related and metabolism-related signaling pathways Conclusion The current study constructed, validated, and evaluated a nine ferroptosis-related lncRNA signature which can independently be used to predict the prognosis of LAUD patients, and may become a new therapeutic target.

Sign in / Sign up

Export Citation Format

Share Document