scholarly journals Identification of an immune prognostic 11-gene signature for lung adenocarcinoma

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e10749
Author(s):  
Tao Yang ◽  
Lizheng Hao ◽  
Renyun Cui ◽  
Huanyu Liu ◽  
Jian Chen ◽  
...  
Keyword(s):  
High Risk ◽  
Lung Adenocarcinoma ◽  
Cox Regression ◽  
Risk Group ◽  
Stage Iv ◽  
Gene Signature ◽  
High Risk Group ◽  
Stage I ◽  
Local Progression ◽  
Immune Score

Background The immunological tumour microenvironment (TME) has occupied a very important position in the beginning and progression of non-small cell lung cancer (NSCLC). Prognosis of lung adenocarcinoma (LUAD) remains poor for the local progression and widely metastases at the time of clinical diagnosis. Our objective is to identify a potential signature model to improve prognosis of LUAD. Methods With the aim to identify a novel immune prognostic signature associated with overall survival (OS), we analysed LUADs extracted from The Cancer Genome Atlas (TCGA). Immune scores and stromal scores of TCGA-LUAD were downloaded from Estimation of STromal and Immune cells in MAlignant Tumour tissues Expression using data (ESTIMATE). LASSO COX regression was applied to build the prediction model. Then, the prognostic gene signature was validated in the GSE68465 dataset. Results The data from TCGA datasets showed patients in stage I and stage II had higher stromal scores than patients in stage IV (P < 0.05), and for immune score patients in stage I were higher than patients in stage III and stage IV (P < 0.05). The improved overall survivals were observed in high stromal score and immune score groups. Patients in the high-risk group exhibited the inferior OS (P = 2.501e − 05). By validating the 397 LUAD patients from GSE68465, we observed a better OS in the low-risk group compared to the high-risk group, which is consistent with the results from the TCGA cohort. Nomogram results showed that practical and predicted survival coincided very well, especially for 3-year survival. Conclusion We obtained an 11 immune score related gene signature model as an independent element to effectively classify LUADs into different risk groups, which might provide a support for precision treatments. Moreover, immune score may play a potential valuable sole for estimating OS in LUADs.

scholarly journals Identification of prognostic gene signature associated with microenvironment of lung adenocarcinoma

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e8128 ◽  
Author(s):  
Cheng Yue ◽  
Hongtao Ma ◽  
Yubai Zhou
Keyword(s):  
High Risk ◽  
Lung Adenocarcinoma ◽  
Prognostic Model ◽  
Cox Regression ◽  
Risk Group ◽  
Expression Profiles ◽  
Gene Signature ◽  
Low Risk ◽  
Differentially Expressed ◽  
Lasso Regression

Background Lung cancer has the highest morbidity and mortality worldwide, and lung adenocarcinoma (LADC) is the most common pathological subtype. Accumulating evidence suggests the tumor microenvironment (TME) is correlated with the tumor progress and the patient’s outcome. As the major components of TME, the tumor-infiltrated immune cells and stromal cells have attracted more and more attention. In this study, differentially expressed immune and stromal signature genes were used to construct a TME-related prognostic model for predicting the outcomes of LADC patients. Methods The expression profiles of LADC samples with clinical information were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). The differentially expressed genes (DEGs) related to the TME of LADC were identified using TCGA dataset by Wilcoxon rank sum test. The prognostic effects of TME-related DEGs were analyzed using univariate Cox regression. Then, the least absolute shrinkage and selection operator (LASSO) regression was performed to reduce the overfit and the number of genes for further analysis. Next, the prognostic model was constructed by step multivariate Cox regression and risk score of each sample was calculated. Then, survival and Receiver Operating Characteristic (ROC) analyses were conducted to validate the model using TCGA and GEO datasets, respectively. The Kyoto Encyclopedia of Genes and Genomes analysis of gene signature was performed using Gene Set Enrichment Analysis (GSEA). Finally, the overall immune status, tumor purity and the expression profiles of HLA genes of high- and low-risk samples was further analyzed to reveal the potential mechanisms of prognostic effects of the model. Results A total of 93 TME-related DEGs were identified, of which 23 DEGs were up-regulated and 70 DEGs were down-regulated. The univariate cox analysis indicated that 23 DEGs has the prognostic effects, the hazard ratio ranged from 0.65 to 1.25 (p < 0.05). Then, seven genes were screened out from the 23 DEGs by LASSO regression method and were further analyzed by step multivariate Cox regression. Finally, a three-gene (ADAM12, Bruton Tyrosine Kinase (BTK), ERG) signature was constructed, and ADAM12, BTK can be used as independent prognostic factors. The three-gene signature well stratified the LADC patients in both training (TCGA) and testing (GEO) datasets as high-risk and low-risk groups, the 3-year area under curve (AUC) of ROC curves of three GEO sets were 0.718 (GSE3141), 0.646 (GSE30219) and 0.643 (GSE50081). The GSEA analysis indicated that highly expressed ADAM12, BTK, ERG mainly correlated with the activation of pathways involving in focal adhesion, immune regulation. The immune analysis indicated that the low-risk group has more immune activities and higher expression of HLA genes than that of the high-risk group. In sum, we identified and constructed a three TME-related DEGs signature, which could be used to predict the prognosis of LADC patients.

scholarly journals Identification of a Pyroptosis-Related Gene Signature for Predicting Overall Survival and Response to Immunotherapy in Hepatocellular Carcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Susu Zheng ◽  
Xiaoying Xie ◽  
Xinkun Guo ◽  
Yanfang Wu ◽  
Guobin Chen ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
High Risk ◽  
Regression Model ◽  
Risk Score ◽  
Cox Regression ◽  
Risk Group ◽  
Gene Signature ◽  
High Risk Group ◽  
Related Gene

Pyroptosis is a novel kind of cellular necrosis and shown to be involved in cancer progression. However, the diverse expression, prognosis and associations with immune status of pyroptosis-related genes in Hepatocellular carcinoma (HCC) have yet to be analyzed. Herein, the expression profiles and corresponding clinical characteristics of HCC samples were collected from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Then a pyroptosis-related gene signature was built by applying the least absolute shrinkage and selection operator (LASSO) Cox regression model from the TCGA cohort, while the GEO datasets were applied for verification. Twenty-four pyroptosis-related genes were found to be differentially expressed between HCC and normal samples. A five pyroptosis-related gene signature (GSDME, CASP8, SCAF11, NOD2, CASP6) was constructed according to LASSO Cox regression model. Patients in the low-risk group had better survival rates than those in the high-risk group. The risk score was proved to be an independent prognostic factor for overall survival (OS). The risk score correlated with immune infiltrations and immunotherapy responses. GSEA indicated that endocytosis, ubiquitin mediated proteolysis and regulation of autophagy were enriched in the high-risk group, while drug metabolism cytochrome P450 and tryptophan metabolism were enriched in the low-risk group. In conclusion, our pyroptosis-related gene signature can be used for survival prediction and may also predict the response of immunotherapy.

scholarly journals A four-gene prognostic signature for predicting the overall survival of patients with lung adenocarcinoma

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11911
Author(s):  
Lei Liu ◽  
Huayu He ◽  
Yue Peng ◽  
Zhenlin Yang ◽  
Shugeng Gao
Keyword(s):  
Overall Survival ◽  
High Risk ◽  
Lung Adenocarcinoma ◽  
Prognostic Model ◽  
Risk Group ◽  
Gene Signature ◽  
Tnm Staging ◽  
High Risk Group ◽  
Prognostic Signature ◽  
Risk Patients

Background The prognosis of patients for lung adenocarcinoma (LUAD) is known to vary widely; the 5-year overall survival rate is just 63% even for the pathological IA stage. Thus, in order to identify high-risk patients and facilitate clinical decision making, it is vital that we identify new prognostic markers that can be used alongside TNM staging to facilitate risk stratification. Methods We used mRNA expression from The Cancer Genome Atlas (TCGA) cohort to identify a prognostic gene signature and combined this with clinical data to develop a predictive model for the prognosis of patients for lung adenocarcinoma. Kaplan-Meier curves, Lasso regression, and Cox regression, were used to identify specific prognostic genes. The model was assessed via the area under the receiver operating characteristic curve (AUC-ROC) and validated in an independent dataset (GSE50081) from the Gene Expression Omnibus (GEO). Results Our analyses identified a four-gene prognostic signature (CENPH, MYLIP, PITX3, and TRAF3IP3) that was associated with the overall survival of patients with T1-4N0-2M0 in the TCGA dataset. Multivariate regression suggested that the total risk score for the four genes represented an independent prognostic factor for the TCGA and GEO cohorts; the hazard ratio (HR) (high risk group vs low risk group) were 2.34 (p < 0.001) and 2.10 (p = 0.017). Immune infiltration estimations, as determined by an online tool (TIMER2.0) showed that CD4+ T cells were in relative abundance in the high risk group compared to the low risk group in both of the two cohorts (both p < 0.001). We established a composite prognostic model for predicting OS, combined with risk-grouping and clinical factors. The AUCs for 1-, 3-, 5- year OS in the training set were 0.750, 0.737, and 0.719; and were 0.645, 0.766, and 0.725 in the validation set. The calibration curves showed a good match between the predicted probabilities and the actual probabilities. Conclusions We identified a four-gene predictive signature which represents an independent prognostic factor and can be used to identify high-risk patients from different TNM stages of LUAD. A new prognostic model that combines a prognostic gene signature with clinical features exhibited better discriminatory ability for OS than traditional TNM staging.

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

2020 ◽  
Vol 2020 ◽  
pp. 1-13 ◽  
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.

scholarly journals Development of an Oncogenic Driver Alteration Associated Immune-Related Prognostic Model for Stage I-II Lung Adenocarcinoma

2021 ◽  
Vol 10 ◽  
Author(s):  
Jian-Zhao Xu ◽  
Chen Gong ◽  
Zheng-Fu Xie ◽  
Hua Zhao
Keyword(s):  
High Risk ◽  
Mast Cells ◽  
Tumor Microenvironment ◽  
Lung Adenocarcinoma ◽  
Prognostic Model ◽  
Risk Group ◽  
High Risk Group ◽  
Low Risk ◽  
Stage I ◽  
Oncogenic Driver

Lung adenocarcinoma (LUAD) needs to be stratified for its heterogeneity. Oncogenic driver alterations such as EGFR mutation, ALK translocation, ROS1 translocation, and BRAF mutation predict response to treatment for LUAD. Since oncogenic driver alterations may modulate immune response in tumor microenvironment that may influence prognosis in LUAD, the effects of EGFR, ALK, ROS1, and BRAF alterations on tumor microenvironment remain unclear. Immune-related prognostic model associated with oncogenic driver alterations is needed. In this study, we performed the Cox-proportional Hazards Analysis based on the L1-penalized (LASSO) Analysis to establish an immune-related prognostic model (IPM) in stage I-II LUAD patients, which was based on 3 immune-related genes (PDE4B, RIPK2, and IFITM1) significantly enriched in patients without EGFR, ALK, ROS1, and BRAF alterations in The Cancer Genome Atlas (TCGA) database. Then, patients were categorized into high-risk and low-risk groups individually according to the IPM defined risk score. The predicting ability of the IPM was validated in GSE31210 and GSE26939 downloaded from the Gene Expression Omnibus (GEO) database. High-risk was significantly associated with lower overall survival (OS) rates in 3 independent stage I-II LUAD cohorts (all P &lt; 0.05). Moreover, the IPM defined risk independently predicted OS for patients in TCGA stage I-II LUAD cohort (P = 0.011). High-risk group had significantly higher proportions of macrophages M1 and activated mast cells but lower proportions of memory B cells, resting CD4 memory T cells and resting mast cells than low-risk group (all P &lt; 0.05). In addition, the high-risk group had a significantly lower expression of CTLA-4, PDCD1, HAVCR2, and TIGIT than the low-risk group (all P &lt; 0.05). In summary, we established a novel IPM that could provide new biomarkers for risk stratification of stage I-II LUAD patients.

scholarly journals Reverse engineering a predictive signature characterized by proliferation, DNA damage, and immune escape from stage I lung adenocarcinoma recurrence

2020 ◽  
Vol 52 (6) ◽  
pp. 638-653
Author(s):  
Jiannan Yao ◽  
Xinying Xue ◽  
Dongfeng Qu ◽  
C Benedikt Westphalen ◽  
Yang Ge ◽  
...  
Keyword(s):  
High Risk ◽  
Lung Adenocarcinoma ◽  
Cox Regression ◽  
Immune Escape ◽  
Early Stage ◽  
Gene Signature ◽  
Response To Therapy ◽  
Stage I ◽  
Cox Regression Analysis ◽  
Patients At Risk

Abstract Identifying early-stage cancer patients at risk for progression is a major goal of biomarker research. This report describes a novel 19-gene signature (19-GCS) that predicts stage I lung adenocarcinoma (LAC) recurrence and response to therapy and performs comparably in pancreatic adenocarcinoma (PAC), which shares LAC molecular traits. Kaplan–Meier, Cox regression, and cross-validation analyses were used to build the signature from training, test, and validation sets comprising 831 stage I LAC transcriptomes from multiple independent data sets. A statistical analysis was performed using the R language. Pathway and gene set enrichment were used to identify underlying mechanisms. 19-GCS strongly predicts overall survival and recurrence-free survival in stage I LAC (P=0.002 and P&lt;0.001, respectively) and in stage I–II PAC (P&lt;0.0001 and P&lt;0.0005, respectively). A multivariate cox regression analysis demonstrated the independence of 19-GCS from significant clinical factors. Pathway analyses revealed that 19-GCS high-risk LAC and PAC tumors are characterized by increased proliferation, enhanced stemness, DNA repair deficiency, and compromised MHC class I and II antigen presentation along with decreased immune infiltration. Importantly, high-risk LAC patients do not appear to benefit from adjuvant cisplatin while PAC patients derive additional benefit from FOLFIRINOX compared with gemcitabine-based regimens. When validated prospectively, this proof-of-concept biomarker may contribute to tailoring treatment, recurrence reduction, and survival improvements in early-stage lung and pancreatic cancers.

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 A Mitophagy-Related Gene Signature Associated With Prognosis and Immune Microenvironment in Colorectal Cancer

2022 ◽  
Author(s):  
Cong Zhang ◽  
Cailing Zeng ◽  
Shaoquan Xiong ◽  
Zewei Zhao ◽  
Guoyu Wu
Keyword(s):  
Gene Expression ◽  
Colorectal Cancer ◽  
Regression Analysis ◽  
High Risk ◽  
Cox Regression ◽  
Risk Group ◽  
Gene Signature ◽  
High Risk Group ◽  
Related Gene ◽  
Prognostic Signature

Abstract Background: Colorectal cancer (CRC) is a heterogeneous disease and one of the most common malignancies in the world. Previous studies have found that mitophagy plays an important role in the progression of colorectal cancer. This study is aimed to investigate the relationship between mitophagy-related genes and the prognosis of patients with CRC.Methods: Gene expression profiles and clinical information of CRC patients were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Univariate Cox regression and the least absolute shrinkage and selection operator (LASSO) regression analysis were used to establish the prognostic signature composed of mitophagy related genes. Kaplan-Meier curve and receiver operating characteristic (ROC) curve were used to analyze patient survival and verify the predictive accuracy of the signature, respectively. Construction of a nomogram prognostic prediction model was based on risk scores and clinicopathological parameters. Using the Genomics of Drug Sensitivity in Cancer (GDSC) database and Tumor Immune Dysfunction and Exclusion (TIDE) algorithm to estimate the sensitivity of chemotherapy, targeted therapy and immunotherapy. Results: A total of 44 mitophagy-driven genes connected with CRC survival were identified, and prognostic signature was established based on the expression of 10 of them (AMBRA1, ATG14, MAP1LC3A, MAP1LC3B, OPTN, VDAC1, ATG5, CSNK2A2, MFN1, TOMM22). Patients were divided into high-risk and low-risk groups based on the median risk score, and the survival of patients in the high-risk group was significantly shorter than that of the low-risk group among the TCGA cohort (median OS 67.3 months vs not reached, p=0.00059) and two independent cohorts from GEO (median OS in GSE17536: 54.0 months vs not reached, p=0.0082; in GSE245: 7.7 months vs not reached, p=0.025). ROC curve showed that the area under the curves (AUC) of 1-, 3- and 5-year survival were 0.66, 0.66 and 0.64, respectively. Multivariate Cox regression analysis confirmed the independent prognostic value of the signature. Then we constructed a nomogram combining the risk score, age and M stage, which had a concordance index of survival prediction of 0.77 (95% CI=0.71-0.83) and more robust predictive sensitivity and specificity. Results showed that CD8+ T cells, regulatory T cells and activated NK cells were significantly more abundant in the high-risk group. Furthermore, patients in the high-risk group were more sensitive to potential targeted therapies, including Motesanib, ATRA, Olaparib, Selumetinib, AZD8055 and immunotherapy. Conclusion: In conclusion, we constructed and validated a novel mitophagy related gene signature that can be used as an independent prognostic biomarker for CRC, and may lead to better stratification and selection of precise treatment for CRC patients.

scholarly journals Development and Validation of a Seven-Gene Signature for Predicting the Prognosis of Lung Adenocarcinoma

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Yingqing Zhang ◽  
Xiaoping Zhang ◽  
Xiaodong Lv ◽  
Ming Zhang ◽  
Xixi Gao ◽  
...  
Keyword(s):  
Lung Adenocarcinoma ◽  
Risk Score ◽  
Cox Regression ◽  
Risk Group ◽  
Clinical Information ◽  
R Package ◽  
Tumor Stage ◽  
Gene Signature ◽  
High Risk Group ◽  
Kaplan Meier

Background. Prognosis is a main factor affecting the survival of patients with lung adenocarcinoma (LUAD), yet no robust prognostic model of high effectiveness has been developed. This study is aimed at constructing a stable and practicable gene signature-based model via bioinformatics methods for predicting the prognosis of LUAD sufferers. Methods. The mRNA expression data were accessed from the TCGA-LUAD dataset, and paired clinical information was collected from the GDC website. R package “edgeR” was employed to select the differentially expressed genes (DEGs), which were then used for the construction of a gene signature-based model via univariate COX, Lasso, and multivariate COX regression analyses. Kaplan-Meier and ROC survival analyses were conducted to comprehensively evaluate the performance of the model in predicting LUAD prognosis, and an independent dataset GSE26939 was accessed for further validation. Results. Totally, 1,655 DEGs were obtained, and a 7-gene signature-based risk score was developed and formulated as risk_score=0.000245∗NTSR1+7.13E−05∗RHOV+0.000505∗KLK8+7.01E−05∗TNS4+0.000288∗C1QTNF6+0.00044∗IVL+0.000161∗B4GALNT2. Kaplan-Meier survival curves revealed that the survival rate of patients in the high-risk group was lower in both the TCGA-LUAD dataset and GSE26939 relative to that of patients in the low-risk group. The relationship between the risk score and clinical characteristics was further investigated, finding that the model was effective in prognosis prediction in the patients with different age (age>65, age<65) and TNM stage (N0&N1, T1&T2, and tumor stage I/II). In sum, our study provides a robust predictive model for LUAD prognosis, which boosts the clinical research on LUAD and helps to explore the mechanism underlying the occurrence and progression of LUAD.

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