Development and Validation of a Nomogram Based on Nutritional Indicators and Tumor Markers for Prognosis Prediction of Pancreatic Ductal Adenocarcinoma

PurposeThis study aimed to develop and validate a nomogram with preoperative nutritional indicators and tumor markers for predicting prognosis of patients with pancreatic ductal adenocarcinoma (PDAC).MethodsWe performed a bicentric, retrospective study including 155 eligible patients with PDAC. Patients were divided into a training group (n = 95), an internal validation group (n = 34), an external validation group (n = 26), and an entire validation group (n = 60). Cox regression analysis was conducted in the training group to identify independent prognostic factors to construct a nomogram for overall survival (OS) prediction. The performance of the nomogram was assessed in validation groups and through comparison with controlling nutritional status (CONUT) and prognostic nutrition index (PNI).ResultsThe least absolute shrinkage and selection operator (LASSO) regression, univariate and multivariate Cox regression analysis revealed that serum albumin and lymphocyte count were independent protective factors while CA19-9 and diabetes were independent risk factors. The concordance index (C-index) of the nomogram in the training, internal validation, external validation and entire validation groups were 0.777, 0.769, 0.759 and 0.774 respectively. The areas under curve (AUC) of the nomogram in each group were 0.861, 0.845, 0.773, and 0.814. C-index and AUC of the nomogram were better than those of CONUT and PNI in the training and validation groups. The net reclassification index (NRI), integrated discrimination improvement (IDI) and decision curve analysis showed improvement of accuracy of the nomogram in predicting OS and better net benefit in guiding clinical decisions in comparison with CONUT and PNI.ConclusionsThe nomogram incorporating four preoperative nutritional and tumor markers including serum albumin concentration, lymphocyte count, CA19-9 and diabetes mellitus could predict the prognosis more accurately than CONUT and PNI and may serve as a clinical decision support tool to determine what treatment options to choose.

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Identification of Immune-Related Genes Associated With Bladder Cancer Based on Immunological Characteristics and Their Correlation With the Prognosis

Frontiers in Genetics ◽

10.3389/fgene.2021.763590 ◽

2021 ◽

Vol 12 ◽

Author(s):

Zhen Kang ◽

Wei Li ◽

Yan-Hong Yu ◽

Meng Che ◽

Mao-Lin Yang ◽

...

Keyword(s):

Bladder Cancer ◽

Regression Analysis ◽

Prognostic Model ◽

Cox Regression ◽

External Validation ◽

Experimental Studies ◽

Training Group ◽

Validation Group ◽

Cox Regression Analysis ◽

Immune Related Genes

Background:To identify the immune-related genes of bladder cancer (BLCA) based on immunological characteristics and explore their correlation with the prognosis. Methods:We downloaded the gene and clinical data of BLCA from the Cancer Genome Atlas (TCGA) as the training group, and obtained immune-related genes from the Immport database. We downloaded GSE31684 and GSE39281 from the Gene Expression Omnibus (GEO) as the external validation group. R (version 4.0.5) and Perl were used to analyze all data. Result:Univariate Cox regression analysis and Lasso regression analysis revealed that 9 prognosis-related immunity genes (PIMGs) of differentially expressed immune genes (DEIGs) were significantly associated with the survival of BLCA patients (p < 0.01), of which 5 genes, including NPR2, PDGFRA, VIM, RBP1, RBP1 and TNC, increased the risk of the prognosis, while the rest, including CD3D, GNLY, LCK, and ZAP70, decreased the risk of the prognosis. Then, we used these genes to establish a prognostic model. We drew receiver operator characteristic (ROC) curves in the training group, and estimated the area under the curve (AUC) of 1-, 3- and 5-year survival for this model, which were 0.688, 0.719, and 0.706, respectively. The accuracy of the prognostic model was verified by the calibration chart. Combining clinical factors, we established a nomogram. The ROC curve in the external validation group showed that the nomogram had a good predictive ability for the survival rate, with a high accuracy, and the AUC values of 1-, 3-, and 5-year survival were 0.744, 0.770, and 0.782, respectively. The calibration chart indicated that the nomogram performed similarly with the ideal model. Conclusion:We had identified nine genes, including PDGFRA, VIM, RBP1, RBP1, TNC, CD3D, GNLY, LCK, and ZAP70, which played important roles in the occurrence and development of BLCA. The prognostic model based on these genes had good accuracy in predicting the OS of patients and might be promising candidates of therapeutic targets. This study may provide a new insight for the diagnosis, treatment and prognosis of BLCA from the perspective of immunology. However, further experimental studies are necessary to reveal the underlying mechanisms by which these genes mediate the progression of BLCA.

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A Prognostic Nomogram based on Risk Assessment for Invasive Micropapillary Carcinoma of the Breast after Surgery

10.21203/rs.3.rs-960394/v1 ◽

2021 ◽

Author(s):

Yuyuan Chen ◽

Caixian Yu ◽

Yiyin Tang ◽

Dedian Chen ◽

Keying Zhu ◽

...

Keyword(s):

Regression Analysis ◽

Cox Regression ◽

External Validation ◽

Training Group ◽

Cox Proportional Hazards ◽

Invasive Micropapillary Carcinoma ◽

Validation Group ◽

Micropapillary Carcinoma ◽

Cox Regression Analysis ◽

Calibration Curves

Abstract Background: Invasive micropapillary carcinoma (IMPC) is one of the rare subtypes of breast cancer. This study aimed to explore a novel predictive nomogram model for IMPC prognosis.Methods: A total of 1855 IMPC patients diagnosed after surgery between 2004 and 2014 were identified from the Surveillance, Epidemiology and End Results (SEER) database to build and validate nomograms. All the patients included were divided into a training group (n=1300) and a validation group (n=555). A nomogram was created based on univariate and multivariate Cox proportional hazards regression analysis. In addition, receiver operating characteristic (ROC) curves were used to demonstrate the accuracy of the prognostic model. Decision curve analysis (DCA) was performed to evaluate the safety of the model in the range of clinical applications, while calibration curves were used to validate the prediction consistency.Results: Cox regression analysis indicated that age ≥62 at diagnosis, negative ER status, and tumor stage were considered adverse independent factors for overall survival (OS), while patients who were married, white or of other races, received chemotherapy or radiotherapy, had a better postoperative prognosis. The nomogram accurately predicted OS with high internal and external validation consistency index (C index) (0.756 and 0.742, respectively). The areas under the ROC curve (AUCs) of the training group were 0.787, 0.774 and 0.764 for 3 years, 5 years and 10 years, respectively, while those of the validation group were 0.756, 0.766 and 0.762, respectively. The results of both DCA and calibration curves demonstrated the good performance of the model.Conclusion: A novel nomogram for IMPC of the breast patients after surgery was developed to estimate 3- and 5-OS based on independent risk factors. This model has good accuracy and consistency in predicting prognosis and has clinical application value.

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Identification and Validation of Immune-Related Gene Prognostic Signature for breast cancer

10.21203/rs.3.rs-386385/v1 ◽

2021 ◽

Author(s):

Bing Zhou ◽

Linyi Zhang ◽

Qinyu Wang ◽

Changqin Pu ◽

Sixuan Guo ◽

...

Keyword(s):

Breast Cancer ◽

Regression Analysis ◽

Cox Regression ◽

Validation Cohort ◽

External Validation ◽

Enrichment Analysis ◽

Risk Groups ◽

Prognostic Signature ◽

Internal Validation ◽

Cox Regression Analysis

Abstract Background Although the outcome of breast cancer patients has been improved by advances in early detection, diagnosis and treatment. Due to the heterogeneity of the disease, prognostic assessment still faces challenges. The accumulated data indicate that there is a clear correlation between the tumor immune microenvironment and clinical outcomes. Objective Construct immune-related gene pairs to evaluate the prognosis of breast cancer and patient survival rate. Methods From the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database, the Gene expression profiles and clinical data of breast cancer samples were downloaded. TCGA cohort were further divided into a training set (n = 764) and internal validation sets (n = 325). The GEO cohort was analyzed as an external validation cohort (n = 327). In the training set, differently expressed immune-relevant genes (IRGs) were screened firstly, and they were used to construct immune-relevant gene pairs (IRGPs). Then, the prognostic IRGPs were identified via univariate Cox regression analysis. Finally, least absolute shrinkage and selection operator (LASSO) Cox regression analysis was used to constituted the IRGP prognostic signature. Kaplan-Meier (KM) survival curves, receiver operating characteristic (ROC) curve analysis, univariate and multivariate Cox regression analysis were used to estimate the predictive value of the IRGP prognostic signature. And the IRGP prognostic signature was validated in the internal validation cohort and external validation cohort. We used gene set enrichment analysis (GSEA) to elucidate the biological functions of the IRGP prognostic signature. Results A total of 474 differently expressed IRGs and 2942 prognostic IRGPs were identified. Finally, we generated a IRGP prognostic signature consisting of 33 IRGPs. Subsequently, the 33 IRGPs grouped BRCA patients into high- and low-risk groups. Kaplan-Meier curves shown a significantly different overall survival in risk groups. Time-dependent ROC curves indicated that the IRGP prognostic signature possessed a high specificity and sensitivity in all the sets. Univariate and multivariate Cox regression analysis showed a statistical significance for the prognostic value of IRGP prognostic signature and the IRGP prognostic signature was a strong independent risk factor. The functional enrichment analysis indicated that low IRGP value was correlated with biological processes related to immune. Immune cell infiltration analysis indicated a significant difference in percentage of M2 macrophages between high- and low-risk groups. Conclusion The 33-IRGPs prognostic signature was developed to provide new insights for the identification of high-risk breast cancer and the evaluation of the possibility of immunotherapy in personalized breast cancer treatment.

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Prognostic Nomogram for Patients With Pancreatic Ductal Adenocarcinoma of Pancreatic Head After Pancreaticoduodenectomy

Clinical Medicine Insights Oncology ◽

10.1177/11795549211024149 ◽

2021 ◽

Vol 15 ◽

pp. 117955492110241

Author(s):

Hongkai Zhuang ◽

Zixuan Zhou ◽

Zuyi Ma ◽

Shanzhou Huang ◽

Yuanfeng Gong ◽

...

Keyword(s):

Regression Analysis ◽

Pancreatic Ductal Adenocarcinoma ◽

Perineural Invasion ◽

Cox Regression ◽

Area Under The Curve ◽

Pancreatic Head ◽

Ductal Adenocarcinoma ◽

R0 Resection ◽

Cox Regression Analysis ◽

Ajcc Stage

Background: The prognosis of patients with pancreatic ductal adenocarcinoma (PDAC) of pancreatic head remains poor, even after potentially curative R0 resection. The aim of this study was to develop an accurate model to predict patients’ prognosis for PDAC of pancreatic head following pancreaticoduodenectomy. Methods: We retrospectively reviewed 112 patients with PDAC of pancreatic head after pancreaticoduodenectomy in Guangdong Provincial People’s Hospital between 2014 and 2018. Results: Five prognostic factors were identified using univariate Cox regression analysis, including age, histologic grade, American Joint Committee on Cancer (AJCC) Stage 8th, total bilirubin (TBIL), CA19-9. Using all subset analysis and multivariate Cox regression analysis, we developed a nomogram consisted of age, AJCC Stage 8th, perineural invasion, TBIL, and CA19-9, which had higher C-indexes for OS (0.73) and RFS (0.69) compared with AJCC Stage 8th alone (OS: 0.66; RFS: 0.67). The area under the curve (AUC) values of the receiver operating characteristic (ROC) curve for the nomogram for OS and RFS were significantly higher than other single parameter, which are AJCC Stage 8th, age, perineural invasion, TBIL, and CA19-9. Importantly, our nomogram displayed higher C-index for OS than previous reported models, indicating a better predictive value of our model. Conclusions: A simple and practical nomogram for patient prognosis in PDAC of pancreatic head following pancreaticoduodenectomy was established, which shows satisfactory predictive efficacy and deserves further evaluation in the future.

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Dipeptidyl peptidase like 6 promoter methylation is a potential prognostic biomarker for pancreatic ductal adenocarcinoma

Bioscience Reports ◽

10.1042/bsr20200214 ◽

2020 ◽

Vol 40 (7) ◽

Author(s):

Xin Zhao ◽

Di Cao ◽

Zhangyong Ren ◽

Zhe Liu ◽

Shaocheng Lv ◽

...

Keyword(s):

Regression Analysis ◽

Pancreatic Ductal Adenocarcinoma ◽

Promoter Methylation ◽

Methylation Level ◽

Cox Regression ◽

Prognostic Biomarker ◽

Dipeptidyl Peptidase ◽

Ductal Adenocarcinoma ◽

Gene Promoters ◽

Cox Regression Analysis

Abstract Background: Hypermethylation of gene promoters plays an important role in tumorigenesis. The present study aimed to identify and validate promoter methylation-driven genes (PMDGs) for pancreatic ductal adenocarcinoma (PDAC). Methods: Based on GSE49149 and the PDAC cohort of The Cancer Genome Atlas (TCGA), differential analyses of promoter methylation, correlation analysis, and Cox regression analysis were performed to identify PMDGs. The promoter methylation level was assessed by bisulfite sequencing polymerase chain reaction (BSP) in paired tumor and normal tissues of 72 PDAC patients. Kaplan−Meier survival analyses were performed to evaluate the clinical value of PMDGs. Results: In GSE49149, the β-value of the dipeptidyl peptidase like 6 (DPP6) promoter was significantly higher in tumor compared with normal samples (0.50 vs. 0.24, P<0.001). In the PDAC cohort of TCGA, the methylation level of the DPP6 promoter was negatively correlated with mRNA expression (r = −0.54, P<0.001). In a multivariate Cox regression analysis, hypermethylation of the DPP6 promoter was an independent risk factor for PDAC (hazard ratio (HR) = 543.91, P=0.002). The results of BSP revealed that the number of methylated CG sites in the DPP6 promoter was greater in tumor samples than in normal samples (7.43 vs. 2.78, P<0.001). The methylation level of the DPP6 promoter was moderately effective at distinguishing tumor from normal samples (area under ROC curve (AUC) = 0.74, P<0.001). Hypermethylation of the DPP6 promoter was associated with poor overall (HR = 3.61, P<0.001) and disease-free (HR = 2.01, P=0.016) survivals for PDAC patients. Conclusion: These results indicate that DPP6 promoter methylation is a potential prognostic biomarker for PDAC.

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A Novel DNA Replication-Related Signature Predicting Recurrence After R0 Resection of Pancreatic Ductal Adenocarcinoma: Prognostic Value and Clinical Implications

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.619549 ◽

2021 ◽

Vol 9 ◽

Author(s):

Zengyu Feng ◽

Kexian Li ◽

Jianyao Lou ◽

Mindi Ma ◽

Yulian Wu ◽

...

Keyword(s):

Dna Replication ◽

Pancreatic Ductal Adenocarcinoma ◽

Risk Model ◽

Cox Regression ◽

External Validation ◽

Gene Signature ◽

Ductal Adenocarcinoma ◽

R0 Resection ◽

Pathway Enrichment Analysis ◽

Cox Regression Analysis

The aim of any surgical resection for pancreatic ductal adenocarcinoma (PDAC) is to achieve tumor-free margins (R0). R0 margins give rise to better outcomes than do positive margins (R1). Nevertheless, postoperative morbidity after R0 resection remains high and prognostic gene signature predicting recurrence risk of patients in this subgroup is blank. Our study aimed to develop a DNA replication-related gene signature to stratify the R0-treated PDAC patients with various recurrence risks. We conducted Cox regression analysis and the LASSO algorithm on 273 DNA replication-related genes and eventually constructed a 7-gene signature. The predictive capability and clinical feasibility of this risk model were assessed in both training and external validation sets. Pathway enrichment analysis showed that the signature was closely related to cell cycle, DNA replication, and DNA repair. These findings may shed light on the identification of novel biomarkers and therapeutic targets for PDAC.

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PPARG, GNG12, and CD19 are Potential Independent Predictors of Central Nerve Recurrence in Childhood Acute Lymphoblastic Leukemia

10.21203/rs.3.rs-963371/v1 ◽

2021 ◽

Author(s):

Shan Zhang ◽

Yansong Tu ◽

Qianmiao Wu ◽

Huijun Chen ◽

Huaijun Tu ◽

...

Keyword(s):

Regression Analysis ◽

Acute Lymphoblastic Leukemia ◽

Phase I ◽

Cox Regression ◽

Lymphoblastic Leukemia ◽

Validation Group ◽

Hub Genes ◽

Cox Regression Analysis ◽

Cox Analysis ◽

Target Database

Abstract Objective: To identify biomarkers that can predict the recurrence of the central nervous system (CNS) in children with acute lymphoblastic leukemia (ALL), and establish a prediction model. Materials and Methods: The transcriptome and clinical data collected by the Children's Oncology Group (COG) collaboration group in the Phase II study (use for test group) and Phase I study (use for validation group) of ALL in children were downloaded from the TARGET database. Transcriptome data were analyzed by bioinformatics method to identify core (hub) genes and establish a risk assessment model. Univariate Cox analysis was performed on each clinical data, and multivariate Cox regression analysis was performed on the obtained results and risk score. The children ALL phase I samples collected by the COG collaboration group in the TARGET database were used for verification. Results: A total of 1230 differentially expressed genes were screened out between the CNS relapsed and non-relapsed groups. Univariate multivariate Cox analysis of 10 hub genes identified showed that PPARG (HR=0.78, 95%CI=0.67-0.91, p=0.007), CD19 (HR=1.15, 95%CI=1.05-1.26, p=0.003) and GNG12 (HR=1.25, 95%CI=1.04-1.51, p=0.017) had statistical differences. The risk score was statistically significant in univariate (HR=3.06, 95%CI=1.30-7.19, p=0.011) and multivariate (HR=1.81, 95%CI=1.16-2.32, p=0.046) Cox regression analysis. The survival analysis results of the high and low-risk groups were different when the validation group was substituted into the model (p=0.018). In addition, the CNS involvement grading status at first diagnosis CNS3 vs. CNS1 (HR=5.74, 95%CI=2.01-16.4, p=0.001), T cell vs B cell (HR=1.63, 95% CI=1.06-2.49, p=0.026) were also statistically significant. Conclusions: PPARG, GNG12, and CD19 may be predictors of CNS relapse in childhood ALL.

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Effect of Aging-Related Genes on the Prognosis of Colon Cancer

10.21203/rs.3.rs-984927/v1 ◽

2021 ◽

Author(s):

Yushu Liu ◽

Jiantao Gong ◽

Yanyi Huang ◽

Qunguang Jiang

Keyword(s):

Colon Cancer ◽

Cancer Patients ◽

Clinical Characteristics ◽

Cox Regression ◽

Validation Cohort ◽

External Validation ◽

Predictive Ability ◽

Lasso Regression ◽

Internal Validation ◽

Cox Regression Analysis

Abstract Background:Colon cancer is a common malignant cancer with high incidence and poor prognosis. Cell senescence and apoptosis are important mechanisms of tumor occurrence and development, in which aging-related genes(ARGs) play an important role. This study aimed to establish a prognostic risk model based on ARGs for diagnosis and prognosis prediction of colon cancer .Methods: We downloaded transcriptome data and clinical information of colon cancer patients from the Cancer Genome Atlas(TCGA) database and the microarray dataset(GSE39582) from the Gene Expression Omnibus(GEO) database. Univariate COX, least absolute shrinkage and selection operator(LASSO) regression algorithm and multivariate COX regression analysis were used to construct a 6-ARG prognosis model and calculated the riskScore. The prognostic signatures is validated by internal validation cohort and external validation cohort(GSE39582).In addition, functional enrichment pathways and immune microenvironment of aging-related genes(ARGs) were also analyzed. We also analyzed the correlation between rsikScore and clinical features and constructed a nomogram based on riskScore. We are the first to construct prognostic nomogram based on ARGs.Results: Through univariate COX,LASSO regression algorithm and multivariate COX regression analysis,6 prognostic ARGs (PDPK1,RAD52,GSR,IL7,BDNF and SERPINE1) were screened out and riskScore was constructed. We have verified that riskScore has good prognostic value in both internal validation cohort and external validation cohort. Pathway enrichment and immunoanalysis of ARGs provide a direction for the treatment of colon cancer patients. We also found that riskScore was closely related to the clinical characteristics of patients. Based on riskScore and related clinical features, we constructed a nomogram, which has good predictive performance.Conclusion: The 6-ARG prognostic signature we constructed has a certain clinical predictive ability. Its riskScore is also closely related to clinical characteristics, and nomogram based on this has stronger predictive ability than a single indicator. ARGs and the nomogram we constructed may provide a promising treatment for colon cancer patients.

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Dynamic changes of D-Dimer and Neutrophil-Lymphocyte Count Ratio as prognostic biomarkers in COVID-19

10.21203/rs.3.rs-21984/v1 ◽

2020 ◽

Cited By ~ 1

Author(s):

Wenjing Ye ◽

Guoxi Chen ◽

Xiaopan Li ◽

Xing Lan ◽

Chen Ji ◽

...

Keyword(s):

Regression Analysis ◽

Lymphocyte Count ◽

Cox Regression ◽

Roc Curves ◽

Critical Value ◽

Dynamic Changes ◽

Cox Regression Analysis ◽

Risk Of Death ◽

Count Ratio ◽

D Dimer

Abstract Background Since December 2019, the outbreak of COVID-19 caused a large number of hospital admissions in China. Many patients with COVID-19 have symptoms of acute respiratory distress syndrome, even are in danger of death. This is the first study to evaluate dynamic changes of D-Dimer and Neutrophil-Lymphocyte Count Ratio (NLR) as a prognostic utility in patients with COVID-19 for clinical use.Methods In a retrospective study, we collected data from 349 hospitalized patients who diagnosed as the infection of the COVID-19 in Wuhan Pulmonary Hospital. We used ROC curves and Cox regression analysis to explore critical value (optimal cut-off point associated with Youden index) and prognostic role of dynamic changes of D-Dimer and NLR.Results 349 participants were enrolled in this study and the mortality rate of the patients with laboratory diagnosed COVID-19 was 14.9%. The initial and peak value of D-Dimer and NLR in deceased patients were higher statistically compared with survivors (P<0.001). There was a more significant upward trend of D-Dimer and NLR during hospitalization in the deceased patients, initial D-Dimer and NLR were lower than the peak tests (MD) -25.23, 95% CI:-31.81- -18.64, P <0.001; (MD) -43.73, 95% CI:-59.28- -31.17, P <0.001. The test showed a stronger correlation between hospitalization days, PCT and peak D-Dimer than initial D-Dimer. The areas under the ROC curves of peak D-Dimer and peak NLR tests were higher than the initial tests (0.94(95%CI: 0.90-0.98) vs. 0.80 (95% CI: 0.73-0.87); 0.93 (95%CI:0.90-0.96) vs. 0.86 (95%CI:0.82-0.91). The critical value of initial D-Dimer, peak D-Dimer, initial NLR and peak NLR was 0.73 mg/L, 3.78 mg/L,7.13 and 14.31 respectively. The multivariable Cox regression analysis showed that age (HR 1.04, 95% CI 1.00-1.07, P=0.01), the peak D-Dimer (HR 1.03, 95% CI 1.01-1.04, P<0.001) were prognostic factors for COVID-19 patients’ death.Conclusions To dynamically observe the ratio of D-Dimer and NLR was more valuable during the prognosis of COVID-19. The rising trend in D-Dimer and NLR, or the test results higher than the critical values may indicate a risk of death for participants with COVID-19.

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A Robust Signature Based on Autophagy-Associated LncRNAs for Predicting Prognosis in Lung Adenocarcinoma

BioMed Research International ◽

10.1155/2020/3858373 ◽

2020 ◽

Vol 2020 ◽

pp. 1-13 ◽

Cited By ~ 1

Author(s):

Mi Zhou ◽

Weihua Shao ◽

Haiyun Dai ◽

Xin Zhu

Keyword(s):

High Risk ◽

Lung Adenocarcinoma ◽

Cox Regression ◽

Risk Group ◽

Surgical Margin ◽

Training Group ◽

High Risk Group ◽

Low Risk ◽

Validation Group ◽

Cox Regression Analysis

Objective. To construct a predictive signature based on autophagy-associated lncRNAs for predicting prognosis in lung adenocarcinoma (LUAD). Materials and Methods. Differentially expressed autophagy genes (DEAGs) and differentially expressed lncRNAs (DElncRNAs) were screened between normal and LUAD samples at thresholds of ∣log2Fold Change∣>1 and P value < 0.05. Univariate Cox regression analysis was conducted to identify overall survival- (OS-) associated DElncRNAs. The total cohort was randomly divided into a training group (n=229) and a validation group (n=228) at a ratio of 1 : 1. Multivariate Cox regression analysis was used to build prognostic models in the training group that were further validated by the area under curve (AUC) values of the receiver operating characteristic (ROC) curves in both the validation and total cohorts. Results. A total of 30 DEAGs and 2997 DElncRNAs were identified between 497 LUAD tissues and 54 normal tissues; however, only 1183 DElncRNAs were related to the 30 DEAGs. A signature consisting of 13 DElncRNAs was built to predict OS in lung adenocarcinoma, and the survival analysis indicated a significant OS advantage of the low-risk group over the high-risk group in the training group, with a 5-year OS AUC of 0.854. In the validation group, survival analysis also indicated a significantly favorable OS for the low-risk group over the high-risk group, with a 5-year OS AUC of 0.737. Univariate and multivariate Cox regression analyses indicated that only positive surgical margin (vs negative surgical margin) and high-risk group (vs low-risk group) based on the predictive signature were independent risk factors predictive of overall mortality in LUAD. Conclusions. This study investigated the association between autophagy-associated lncRNAs and prognosis in LUAD and built a robust predictive signature of 13 lncRNAs to predict OS.

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