scholarly journals Identification and Validation of a Novel Six-lncRNA-Based Prognostic Model for Lung Adenocarcinoma

2022 ◽  
Vol 11 ◽  
Lingge Yang ◽  
Yuan Wu ◽  
Huan Xu ◽  
Jingnan Zhang ◽  
Xinjie Zheng ◽  

ObjectiveThis study was conducted in order to establish a long non-coding RNA (lncRNA)-based model for predicting overall survival (OS) in patients with lung adenocarcinoma (LUAD).MethodsOriginal RNA-seq data of LUAD samples were extracted from The Cancer Genome Atlas (TCGA) database. Univariate Cox survival analysis was performed to select lncRNAs associated with OS. The least absolute shrinkage and selection operator (LASSO) regression analysis and multivariate Cox analysis were performed for building an OS-associated lncRNA prognostic model. Moreover, receiver operating characteristic (ROC) curves were generated to assess predictive values of the hub lncRNAs. Consequently, qRT-PCR was conducted to validate its prognostic value. The potential roles of these lncRNAs in immunotherapy and anti-angiogenic therapy were also investigated.ResultsThe lncRNA-associated risk score of OS (LARSO) was established based on the LASSO coefficient of six individual lncRNAs, including CTD-2124B20.2, CTD-2168K21.1, DEPDC1-AS1, RP1-290I10.3, RP11-454K7.3, and RP11-95M5.1. Kaplan–Meier analysis revealed that LUAD patients with higher LARSO values had a shorter OS. Furthermore, a new risk score (NRS), including LARSO, stage, and N stage, could better predict the prognosis of LUAD patients compared with LARSO alone. Evaluation of the prognostic model in our cohort demonstrated that patients with higher scores had a worse prognosis. In addition, correlation analysis between these six lncRNAs and immune checkpoints or anti-angiogenic targets suggested that LUAD patients with high LARSO might not be sensitive to immunotherapy or anti-angiogenic therapy.ConclusionsThis robust six-lncRNA prognostic signature may be used as a novel and powerful prognostic biomarker for lung adenocarcinoma.

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Jie Zhu ◽  
Min Wang ◽  
Daixing Hu

Lung cancer is the most commonly diagnosed cancer and the leading cause of cancer-related death. Among these, lung adenocarcinoma (LUAD) accounts for most cases. Due to the improvement of precision medicine based on molecular characterization, the treatment of LUAD underwent significant changes. With these changes, the prognosis of LUAD becomes diverse. N6-methyladenosine (m6A) is the most predominant modification in mRNAs, which has been a research hotspot in the field of oncology. Nevertheless, little has been studied to reveal the correlations between the m6A-related genes and prognosis in LUAD. Thus, we conducted a comprehensive analysis of m6A-related gene expressions in LUAD patients based on The Cancer Genome Atlas (TCGA) database by revealing their relationship with prognosis. Different expressions of the m6A-related genes in tumor tissues and non-tumor tissues were confirmed. Furthermore, their relationship with prognosis was studied via Consensus Clustering Analysis, Principal Components Analysis (PCA), and Least Absolute Shrinkage and Selection Operator (LASSO) Regression. Based on the above analyses, a m6A-based signature to predict the overall survival (OS) in LUAD was successfully established. Among the 479 cases, we found that most of the m6A-related genes were differentially expressed between tumor and non-tumor tissues. Six genes, HNRNPC, METTL3, YTHDC2, KIAA1429, ALKBH5, and YTHDF1 were screened to build a risk scoring signature, which is strongly related to the clinical features pathological stages (p<0.05), M stages (p<0.05), T stages (p < 0.05), gender (p=0.04), and survival outcome (p=0.02). Multivariate Cox analysis indicated that risk value could be used as an independent prognostic factor, revealing that the m6A-related genes signature has great predictive value. Its efficacy was also validated by data from the Gene Expression Omnibus (GEO) database.

2021 ◽  
Boxuan Liu ◽  
Yun Zhao ◽  
Shuanying Yang

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.

2021 ◽  
Vol 12 ◽  
Zhi Liu ◽  
Tiezheng Qi ◽  
Xiaowen Li ◽  
Yiyan Yao ◽  
Belaydi Othmane ◽  

BackgroundThe TGF-β pathway plays critical roles in numerous malignancies. Nevertheless, its potential role in prognosis prediction and regulating tumour microenvironment (TME) characteristics require further elucidation in bladder cancer (BLCA).MethodsTGF-β-related genes were comprehensively summarized from several databases. The TCGA-BLCA cohort (training cohort) was downloaded from the Cancer Genome Atlas, and the independent validation cohorts were gathered from Xiangya Hospital (Xinagya cohort) and Gene Expression Omnibus. Initially, we identified differentially expressed TGF-β genes (DEGs) between cancer and normal tissues. Subsequently, univariate Cox analysis was applied to identify prognostic DEGs, which were further used to develop the TGF-β risk score by performing LASSO and multivariate Cox analyses. Then, we studied the role of the TGF-β risk score in predicting prognosis and the TME phenotypes. In addition, the role of the TGF-β risk score in guiding precision treatments for BLCA has also been assessed.ResultsWe successfully constructed a TGF-β risk score with an independent prognostic prediction value. A high TGF-β risk score indicated an inflamed TME, which was supported by the positive relationships between the risk score, enrichment scores of anticancer immunity steps, and the infiltration levels of tumour-infiltrating immune cells. In addition, the risk score positively correlated with the expression of several immune checkpoints and the T cell inflamed score. Consistently, the risk score was positively related to the enrichment scores of most immunotherapy-positive pathways. In addition, the sensitivities of six common chemotherapeutic drugs were positively associated with the risk score. Furthermore, higher risk score indicated higher sensitivity to radiotherapy and EGFR-targeted therapy. On the contrary, patients with low-risk scores were more sensitive to targeted therapies, including the blockade of FGFR3 and WNT-β-catenin networks.ConclusionsWe first constructed and validated a TGF-β signature that could predict the prognosis and TME phenotypes for BLCA. More importantly, the TGF-β risk score could aid in individual precision treatment for BLCA.

2020 ◽  
Ming Liu ◽  
Jiayi Xie ◽  
Xiaobei Luo ◽  
Yaxin Luo ◽  
Side Liu ◽  

Abstract Background: Gastric cancer (GC) is one of the most prevalent malignant cancers around the world. Given that abnormal RNA binding proteins (RBPs) are involved in the tumorigenesis, we aimed to explore the potential value of RBPs-associated genes in gastric cancer.Methods: RNA-seq and clinical data were retrieved from The Cancer Genome Atlas (TCGA) database and differentially expressed RBPs genes were screened. GO and KEGG pathway enrichment analyses were implemented to elucidate the roles of RBPs in GC. The protein-protein interaction (PPI) networks of RBPs were carried out, and the hub genes were determined by MCODE built in Cytoscape. The TCGA-STAD dataset was randomly divided into training and testing groups. A prognostic signature including five RBPs was developed within the training cohort after Cox regression and Lasso regression analyses. We used Kaplan–Meier (KM) and receiver operating characteristic (ROC) curves to evaluate the capacity of the model in both groups. Then, a nomogram based on hub RBPs expression was established. Gene Set Enrichment Analysis was performed between the high-risk and low-risk group.Results: A total of 166 up-regulated RBPs and 130 down-regulated RBPs were identified. Via Cox regression and Lasso regression analysis within the training group, five hub RBPs (RNASE1, SETD7, BOLL, PPARGC1B, MSI2) were screened and the prognostic model was constructed. The risk score was calculated and gastric cancer patients were divided into high-risk and low-risk groups. In multivariate analysis, risk score was still an independent prognostic indicator (HR = 1.80, 95% CI = 1.45-2.22, P < 0.01). Patients with low risk had favorable survival rate in both training and testing group compared to those at high risk (P < 0.001). The areas under the ROC curves (AUC) of the prognostic model are 0.718 in the training cohort and 0.651 in the testing cohort. The hub RBPs-based nomogram model exhibited excellent ability to predict the OS of GC. GSEA illustrated that several cancer-related signaling pathways were enriched in patients with a high-risk score.Conclusions: This study discovered a five RBPs signature which might provide a potential prognostic value to GC patients.

2021 ◽  
Vol 2021 ◽  
pp. 1-23
Zhixiao Xu ◽  
Chengshui Chen

Background. Lung adenocarcinoma (LUAD) is one of the most life-threatening malignancies. The crucial role of bone morphogenetic protein (BMP)/BMP receptors reveals the significance of exploring BMP protein-related prognostic predictors in LUAD. Methods. The mRNA expression of BMPs/BMP receptors was investigated in LUAD and normal lung tissues. Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes pathway analyses were performed, and the prognostic values were assessed by Kaplan-Meier Plotter. Univariate and multivariate Cox regression analyses were executed to ascertain the correlation between overall survival (OS) and the mRNA expression of BMPs/BMP receptors. The receiver operating characteristic (ROC) curves were implemented to evaluate the predictive power of the prognostic model. Then, the prognostic model was validated in the GEO cohort. Furthermore, a nomogram comprising the prognostic model was established. Results. The mRNA expression of BMP2/5/6/R2, ACVRL1, and TGFBR2/3 was lower in LUAD tissues than in normal lung tissues. High expression of BMP2/4/5/R1A/R2, ACVR1/2A/L1, and TGFBR1/3 was associated with better OS, while BMP7 and ACVR1C/2B were associated with poorer OS. Three genes (BMP5, BMP7, and ACVR2A) were screened by univariate and multivariate Cox regression analyses to develop the prognostic model in TCGA. Significantly better survival was observed in LUAD patients with a low-risk score than those with a high-risk score. The ROC curves confirmed the good performance of the prognostic model, then, the prognostic model was validated in the GSE31210 dataset. A nomogram was constructed (AUCs>0.7). And hub genes were further evaluated, including gene set enrichment analysis and immune cell infiltration. Conclusions. BMP5, BMP7, and ACVR2A are potential therapeutic targets in LUAD. The three-gene prognostic model and the nomogram are reliable tools for predicting the OS of LUAD patients.

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Ting Zhou ◽  
Ping Yang ◽  
Sanyuan Tang ◽  
Zhongshan Zhu ◽  
Xiaobing Li ◽  

Aims. Lung adenocarcinoma (LUAD) cells could escape from the monitoring of immune cells and metastasize rapidly through immune escape. Therefore, we aimed to develop a method to predict the prognosis of LUAD patients based on immune checkpoints and their associated genes, thus providing guidance for LUAD treatment. Methods. Gene sequencing data were downloaded from the Cancer Genome Atlas (TCGA) and analyzed by R software and R Bioconductor software package. Based on immune checkpoint genes, kmdist clustering in ConsensusClusterPlus R software package was utilized to classify LUAD. CIBERSORT was used to quantify the abundance of immune cells in LUAD samples. LM22 signature was performed to distinguish 22 phenotypes of human infiltrating immune cells. Gene set variation analysis (GSVA) was performed on immune checkpoint cluster and immune checkpoint score using GSVA R software package. The risk score was calculated by LASSO regression coefficient. Gene Ontology (GO), Hallmark, and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed. PROC was performed to generate the ROC curve and calculate the area under the curve (AUC). Results. According to the immune checkpoint, LUAD was classified into clusters 1 and 2. Survival rate, immune infiltration patterns, TMB, and immune score were significantly different between the two clusters. Functional prediction showed that the functions of cluster 1 focused on apoptosis, JAK/STAT signaling pathway, TNF-α/NFκB signaling pathway, and STAT5 signaling pathway. The risk score model was constructed based on nine genes associated with immune checkpoints. Survival analysis and ROC analysis showed that patients with high-risk score had poor prognosis. The risk score was significantly correlated with cancer status (with tumor), male proportion, status, tobacco intake, and cancer stage. With the increase of the risk score, the enrichment of 22 biological functions increased, such as p53 signaling pathway. The signature was verified in IMvigor immunotherapy dataset with excellent diagnostic accuracy. Conclusion. We established a nine-gene signature based on immune checkpoints, which may contribute to the diagnosis, prognosis, and clinical treatment of LUAD.

2021 ◽  
Vol 12 ◽  
Min Liang ◽  
Mafeng Chen ◽  
Yinghua Zhong ◽  
Shivank Singh ◽  
Shantanu Singh

Background: Lung adenocarcinoma is one of the most common malignant tumors of the respiratory system, ranking first in morbidity and mortality among all cancers. This study aims to establish a ferroptosis-related gene-based prognostic model to investigate the potential prognosis of lung adenocarcinoma.Methods: We obtained gene expression data with matching clinical data of lung adenocarcinoma from the The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The ferroptosis-related genes (FRGs) were downloaded from three subgroups in the ferroptosis database. Using gene expression differential analysis, univariate Cox regression, and LASSO regression analysis, seven FRGs with prognostic significance were identified. The result of multivariate Cox analysis was utilized to calculate regression coefficients and establish a risk-score formula that divided patients with lung adenocarcinoma into high-risk and low-risk groups. The TCGA results were validated using GEO data sets. Then we observed that patients divided in the low-risk group lived longer than the overall survival (OS) of the other. Then we developed a novel nomogram including age, gender, clinical stage, TNM stage, and risk score.Results: The areas under the curves (AUCs) for 3- and 5-years OS predicted by the model were 0.823 and 0.852, respectively. Calibration plots and decision curve analysis also confirmed the excellent predictive performance of the model. Subsequently, gene function enrichment analysis revealed that the identified FRGs are important in DNA replication, cell cycle regulation, cell adhesion, chromosomal mutation, oxidative phosphorylation, P53 signaling pathway, and proteasome processes.Conclusions: Our results verified the prognostic significance of FRGs in patients with lung adenocarcinoma, which may regulate tumor progression in a variety of pathways.

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Dongsheng He ◽  
Shengyin Liao ◽  
Lifang Cai ◽  
Weiming Huang ◽  
Xuehua Xie ◽  

Abstract Background The potential reversibility of aberrant DNA methylation indicates an opportunity for oncotherapy. This study aimed to integrate methylation-driven genes and pretreatment prognostic factors and then construct a new individual prognostic model in hepatocellular carcinoma (HCC) patients. Methods The gene methylation, gene expression dataset and clinical information of HCC patients were downloaded from The Cancer Genome Atlas (TCGA) database. Methylation-driven genes were screened with a Pearson’s correlation coefficient less than − 0.3 and a P value less than 0.05. Univariable and multivariable Cox regression analyses were performed to construct a risk score model and identify independent prognostic factors from the clinical parameters of HCC patients. The least absolute shrinkage and selection operator (LASSO) technique was used to construct a nomogram that might act to predict an individual’s OS, and then C-index, ROC curve and calibration plot were used to test the practicability. The correlation between clinical parameters and core methylation-driven genes of HCC patients was explored with Student’s t-test. Results In this study, 44 methylation-driven genes were discovered, and three prognostic signatures (LCAT, RPS6KA6, and C5orf58) were screened to construct a prognostic risk model of HCC patients. Five clinical factors, including T stage, risk score, cancer status, surgical method and new tumor events, were identified from 13 clinical parameters as pretreatment-independent prognostic factors. To avoid overfitting, LASSO analysis was used to construct a nomogram that could be used to calculate the OS in HCC patients. The C-index was superior to that from previous studies (0.75 vs 0.717, 0.676). Furthermore, LCAT was found to be correlated with T stage and new tumor events, and RPS6KA6 was found to be correlated with T stage. Conclusion We identified novel therapeutic targets and constructed an individual prognostic model that can be used to guide personalized treatment in HCC patients.

Yongmei Wang ◽  
Guimin Zhang ◽  
Ruixian Wang

Background: This study aims to explore the prognostic values of CT83 and CT83-related genes in lung adenocarcinoma (LUAD). Methods: We downloaded the mRNA profiles of 513 LUAD patients (RNA sequencing data) and 246 NSCLC patients (Affymetrix Human Genome U133 Plus 2.0 Array) from TCGA and GEO databases. According to the median expression of CT83, the TCGA samples were divided into high and low expression groups, and differential expression analysis between them was performed. Functional enrichment analysis of differential expression genes (DEGs) was conducted. Univariate Cox regression analysis and LASSO Cox regression analysis were performed to screen the optimal prognostic DEGs. Then we established the prognostic model. A Nomogram model was constructed to predict the overall survival (OS) probability of LUAD patients. Results: CT83 expression was significantly correlated to the prognosis of LUAD patients. A total of 59 DEGs were identified, and a predictive model was constructed based on six optimal CT83-related DEGs, including CPS1, RHOV, TNNT1, FAM83A, IGF2BP1, and GRIN2A, could effectively predict the prognosis of LUAD patients. The nomogram could reliably predict the OS of LUAD patients. Moreover, the six important immune checkpoints (CTLA4, PD1, IDO1, TDO2, LAG3, and TIGIT) were closely correlated with the Risk Score, which was also differentially expressed between the LUAD samples with high and low-Risk Scores, suggesting that the poor prognosis of LUAD patients with high-Risk Score might be due to the immunosuppressive microenvironments. Conclusion: A prognostic model based on six optimal CT83 related genes could effectively predict the prognosis of LUAD patients.

Minling Liu ◽  
Lei Li ◽  
Shan Huang ◽  
Xiaofen Pan ◽  
Huiru Dai ◽  

Background: Triple-negative breast cancer (TNBC) is a highly aggressive malignancy with poor prognosis. Therefore, it is imperative to develop new prognostic or therapeutic biomarkers for TNBC. Objective: To explore the prognostic and therapeutic values of autophagy-related genes (ARGs) in TNBC. Methods: Overall, 157 TNBC patients’ data were obtained from The Cancer Genome Atlas database, and the ARGs were acquired from the Human Autophagy Database. Differentially expressed ARGs (DEGs) between tumor and normal tissues were identified and the prognostic ARGs were developed using R software. Kaplan–Meier survival curves and receiver operating characteristic (ROC) curves were both used to evaluate the accuracy of the signature. Patents about prognostic ARGs were reviewed through Worldwide Espacenet® and Patentscope®. Results: We obtained 28 DEGs and two prognostic ARGs (EIF4EBP1 and PARP1). The Kaplan–Meier survival curves showed that the survival rate of patients with low 2-ARG signature risk score was significantly higher than that of patients with high risk score (P=0.003). ROC at 5 years indicated that the signature had good prognostic accuracy (AUC=0.929). The signature was independent of T, N, M, and TNM stage (P<0.05). Patent review suggested that many mTOR inhibitors alone or in combination with another anticancer agent have been provided for treatment of many cancers and shown promising results. No drug patents about PARP1 overexpression were disclosed. Conclusion: We developed a 2-ARG signature (EIF4EBP1 and PARP1) which was an independent prognostic biomarker for TNBC. As EIF4EBP1 was upregulated in TNBC, mTOR inhibitors which blocked the mTOR/4EBP1/eIF4E pathway may be a promising therapeutic strategy for TNBC.

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