Construction of a Prognostic Signature of Autophagy-Related lncRNAs in Non-Small Cell Lung Cancer

2021 ◽  
Author(s):  
Xinyang Zhang ◽  
Yu Cao ◽  
Li Chen
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Value At Risk ◽  
Prognostic Model ◽  
Risk Groups ◽  
Small Cell ◽  
Low Risk ◽  
Prognostic Signature ◽  
Small Cell Lung

Abstract Background: Autophagy inhibits tumorigenesis by limiting inflammation, LncRNA regulates gene expression levels in the form of RNA at various levels, so both of them are closely related to the occurrence and development of tumors.Methods: 232 autophagy-related genes were used to construct a co-expression network to extract autophagy-related lncRNAs. A prognostic signature was constructed by multivariate regression analysis. Kyoto Encyclopedia of Genes and Genomes (KEGG) was applied to analyze pathway enrichment in cancer pathways. Immunoinfiltration analysis was used to analyze the relationship between the prognostic model and the tumor.Results: Nine autophagy-related lncRNAs were used to construct a prognostic model for non-small cell lung cancer. The median value of the value at risk was used to distinguish between the high and low risk groups, and the low-risk group had better survival. Because the KEGG pathway analysis showed that the prognostic model was enriched in some immune pathways, further exploration of immune infiltration was conducted and it was found that the prognostic model did play a unique role in the immune microenvironment. And the prognostic model was associated with clinical factors.Conclusion: The prognostic model of autophagy-related lncRNAs constructed by us can predict the prognosis of non-small cell lung cancer.

scholarly journals Construction of a prognostic signature of autophagy-related lncRNAs in non-small-cell lung cancer

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Xinyang Zhang ◽  
Yu Cao ◽  
Li Chen
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Pathway Analysis ◽  
Cell Lung Cancer ◽  
Prognostic Model ◽  
Kegg Pathway ◽  
Small Cell ◽  
Prognostic Signature ◽  
Small Cell Lung ◽  
Kegg Pathway Analysis

Abstract Background Autophagy inhibits tumorigenesis by limiting inflammation. LncRNAs regulate gene expression at various levels as RNAs; thus, both autophagy and lncRNAs are closely related to the occurrence and development of tumours. Methods A total of 232 autophagy-related genes were used to construct a coexpression network to extract autophagy-related lncRNAs. A prognostic signature was constructed by multivariate regression analysis. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis was applied to analyse enrichment in cancer-related pathways. Immune infiltration analysis was used to analyse the relationship between the prognostic signature and the tumour microenvironment. Results Nine autophagy-related lncRNAs were used to construct a prognostic model for non-small-cell lung cancer. The median risk score was used to discriminate the high- and low-risk groups, and the low-risk group was found to have better survival. Because KEGG pathway analysis showed that the prognostic signature was enriched in some immune pathways, further analysis of immune infiltration was conducted, and it was found that the prognostic signature did play a unique role in the immune microenvironment. Additionally, the prognostic signature was associated with clinical factors. Conclusion We constructed a prognostic model of autophagy-related lncRNAs that can predict the prognosis of non-small-cell lung cancer.

scholarly journals A Nomogram to Predict Long-Term Survival Outcomes of Patients Who Undergo Pneumonectomy for Non-small Cell Lung Cancer With Stage I-IIIB

2021 ◽  
Vol 8 ◽  
Author(s):  
Lei-Lei Wu ◽  
Wu-Tao Chen ◽  
Xuan Liu ◽  
Wen-Mei Jiang ◽  
Yang-Yu Huang ◽  
...  
Keyword(s):  
Lung Cancer ◽  
High Risk ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Risk Groups ◽  
Small Cell ◽  
Low Risk ◽  
Small Cell Lung ◽  
Risk Patients ◽  
Nsclc Patients

Background: In this study, we aim to establish a nomogram to predict the prognosis of non-small cell lung cancer (NSCLC) patients with stage I-IIIB disease after pneumonectomy.Methods: Patients selected from the Surveillance, Epidemiology, and End Results (SEER, N = 2,373) database were divided into two cohorts, namely a training cohort (SEER-T, N = 1,196) and an internal validation cohort (SEER-V, N = 1,177). Two cohorts were dichotomized into low- and high-risk subgroups by the optimal risk prognostic score (PS). The model was validated by indices of concordance (C-index) and calibration plots. Kaplan-Meier analysis and the log-rank tests were used to compare survival curves between the groups. The primary observational endpoint was cancer-specific survival (CSS).Results: The nomogram comprised six factors as independent prognostic indictors; it significantly distinguished between low- and high-risk groups (all P < 0.05). The unadjusted 5-year CSS rates of high-risk and low-risk groups were 33 and 60% (SEER-T), 34 and 55% (SEER-V), respectively; the C-index of this nomogram in predicting CSS was higher than that in the 8th TNM staging system (SEER-T, 0.629 vs. 0.584, P < 0.001; SEER-V, 0.609 vs. 0.576, P < 0.001). In addition, the PS might be a significant negative indictor on CSS of patients with white patients [unadjusted hazard ration (HR) 1.008, P < 0.001], black patients (unadjusted HR 1.007, P < 0.001), and Asian or Pacific Islander (unadjusted HR 1.008, P = 0.008). In cases with squamous cell carcinoma (unadjusted HR 1.008, P < 0.001) or adenocarcinoma (unadjusted HR 1.008, P < 0.001), PS also might be a significant risk factor.Conclusions: For post-pneumonectomy NSCLC patients, the nomogram may predict their survival with acceptable accuracy and further distinguish high-risk patients from low-risk patients.

scholarly journals Identification and Validation of an Immune and Ferroptosis-Combined Index for Non–Small Cell Lung Cancer

2021 ◽  
Vol 12 ◽  
Author(s):  
Yang Teng ◽  
Bo Wang ◽  
Desi Shang ◽  
Ning Yang
Keyword(s):  
Lung Cancer ◽  
High Risk ◽  
Small Cell Lung Cancer ◽  
Risk Score ◽  
Cell Lung Cancer ◽  
Risk Group ◽  
Risk Groups ◽  
Small Cell ◽  
Low Risk ◽  
Small Cell Lung

Background: Non–small cell lung cancer (NSCLC) is among the major health problems around the world. Reliable biomarkers for NSCLC are still needed in clinical practice. We aimed to develop a novel ferroptosis- and immune-based index for NSCLC.Methods: The training and testing datasets were obtained from TCGA and GEO databases, respectively. Immune- and ferroptosis-related genes were identified and used to establish a prognostic model. Then, the prognostic and therapeutic potential of the established index was evaluated.Results: Intimate interaction of immune genes with ferroptosis genes was observed. A total of 32 prognosis-related signatures were selected to develop a predictive model for NSCLC using LASSO Cox regression. Patients were classified into the high- and low-risk group based on the risk score. Patients in the low-risk group have better OS in contrast with that in the high-risk group in independent verification datasets. Besides, patients with a high risk score have shorter OS in all subgroups (T, N, and M0 subgroups) and pathological stages (stage I, II, and III). The risk score was positively associated with Immune Score, Stromal Score, and Ferroptosis Score in TCGA and GEO cohorts. A differential immune cell infiltration between the high-risk and the low-risk groups was also observed. Finally, we explored the significance of our model in tumor-related pathways, and different enrichment levels in the therapeutic pathway were observed between the high- and low-risk groups.Conclusion: The present study developed an immune and ferroptosis-combined index for the prognosis of NSCLC.

scholarly journals Using machine learning modeling to explore new immune-related prognostic markers in non-small cell lung cancer

2020 ◽  
Author(s):  
Jiasheng Xu ◽  
Kaili Liao ◽  
Han Nie ◽  
xiaozhong wang
Keyword(s):  
Machine Learning ◽  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Prognostic Markers ◽  
Risk Groups ◽  
Small Cell ◽  
Low Risk ◽  
Immune Checkpoints ◽  
Small Cell Lung

Abstract BACKGROUND To find new immune-related prognostic markers for non-small cell lung cancer (NSCLC) METHODS We found suitable data chip (GSE14814) related to NSCLC in geo database. The non-small cell lung cancer observation (NSCLC-OBS) group was evaluated for immunity, and the NSCLC-OBS were divided into high and low groups for differential gene screening according to the score of immune evaluation.A single factor COX regression analysis was performed to select the genes related to prognosis. A prognostic model was constructed by machine learning, and the Receiver Operating Characteristic (ROC) model was analyzed to test whether the model has a test efficacy for prognosis, and then test the association between the selected prognostic genes and the patient's prognosis. A chip-in-chip non-small cell lung cancer chemotherapy (NSCLC-ACT) sample was used as a validation dataset for the same validation and prognostic analysis of the model. The relative infiltration scores of 24 immune cells in NSCLC-ACT patients were compared with those of high and low risk groups. The coexpression genes of hub genes were obtained by pearson analysis and gene enrichment, function enrichment and protein interaction analysis were carried out and the correlation between prognostic genes and immune checkpoints was further analyzed. The tumor samples of patients with different clinical stages were detected by immunohistochemistry and the expression difference of prognostic genes in tumor tissues of patients with different stages was compared. RESULTS By screening, we found that LYN、C3、COPG2IT1、HLA.DQA1、TNFRSF17 is closely related to prognosis. After machine learning we found that the immune prognosis model constructed from these 5 genes was ROC analyzed, and the AUC values were greater than 0.9 at three time periods of 1,3, and 5 years; the total survival period of the low-risk group containing these 5 hubgene was significantly better than that of the high-risk group.The Kaplan–Meier curve showed that the increase of COPG2IT1、HLA.DQA1 expression and the decrease of LYN、C3、TNFRSF17 expression were significantly related to the shortening of survival time.The results of prognosis analysis and ROC analysis in ACT samples were consistent with those of OBS groups. Hubgene was most expressed in fibroblasts, but there was no significant difference in immune infiltration in the high and low risk groups in 24 immune cells.The coexpression genes are mainly involved B cell receptor signaling pathway and mainly enriched in biological processes such as apoptotic cell clearance、Intestinal immune network for IgA Production. Prognostic key genes are highly correlated with PDCD1、PDCD1LG2、LAG3、CTLA4 immune checkpoints (p < 0.05). The immunohistochemical results showed that the expression of COPG2IT1 and HLA.DQA1 in stage III increased significantly and the expression of LYN、C3 and TNFRSF17 in stage III decreased significantly compared with that of stage I. The experimental results are consistent with the previous analysis. CONCLUSION LYN、C3、COPG2IT1、HLA.DQA1、TNFRSF17 may be a new immune marker to judge the prognosis of patients with non-small cell lung cancer.

scholarly journals Construction of a prognostic model for non-small-cell lung cancer based on ferroptosis-related genes

2021 ◽  
Author(s):  
Ke Han ◽  
Ju Kun Kun Wang ◽  
Kun Qian ◽  
Teng Zhao ◽  
Xing Sheng Liu ◽  
...  
Keyword(s):  
Lung Cancer ◽  
High Risk ◽  
Small Cell Lung Cancer ◽  
Risk Score ◽  
Prognostic Model ◽  
Risk Groups ◽  
Small Cell ◽  
Low Risk ◽  
Small Cell Lung ◽  
Score Model

We wished to construct a prognostic model based on ferroptosis-related genes and to simultaneously evaluate the performance of the prognostic model and analyze differences between high-risk and low-risk groups at all levels. The gene-expression profiles and relevant clinical data of patients with non-small-cell lung cancer (NSCLC) were downloaded from public databases. Differentially expressed genes (DEGs) were obtained by analyzing differences between cancer tissues and paracancerous tissues, and common genes between DEGs and ferroptosis-related genes were identified as candidate ferroptosis-related genes. Next, a risk-score model was constructed using univariate Cox analysis and least absolute shrinkage and selection operator (Lasso) analysis. According to the median risk score, samples were divided into high-risk and low-risk groups, and a series of bioinformatics analyses were conducted to verify the predictive ability of the model. Single-sample gene set enrichment analysis (ssGSEA) was used to investigate differences in immune status between high-risk and low-risk groups, and differences in gene mutations between the two groups were investigated. A risk-score model was constructed based on 21 ferroptosis-related genes. A Kaplan–Meier curve and receiver operating characteristic curve showed that the model had good prediction ability. Univariate and multivariate Cox analyses revealed that ferroptosis-related genes associated with the prognosis may be used as independent prognostic factors for the overall survival time of NSCLC patients. The pathways enriched with DEGs in low-risk and high-risk groups were analyzed, and the enriched pathways were correlated significantly with immunosuppressive status.

scholarly journals Recursive partitioning analysis of patients with oligometastatic non-small cell lung cancer: a retrospective study

BMC Cancer ◽  
2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Jia-Tao Zhang ◽  
Si-Yang Liu ◽  
Hong-Hong Yan ◽  
Yi-Long Wu ◽  
Qiang Nie ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Recursive Partitioning ◽  
Risk Groups ◽  
Group Iv ◽  
Small Cell ◽  
Recursive Partitioning Analysis ◽  
Small Cell Lung ◽  
Never Smokers

Abstract Background Local consolidative treatment (LCT) is important for oligometastasis, defined as the restricted metastatic capacity of a tumor. This study aimed to determine the effects and prognostic heterogeneity of LCT in oligometastatic non-small cell lung cancer. Methods This retrospective study identified 436 eligible patients treated for oligometastatic disease at the Guangdong Provincial People’s Hospital during 2009–2016. A Cox regression analysis was used to identify potential predictors of overall survival (OS). After splitting cases randomly into training and testing sets, risk stratification was performed using recursive partitioning analysis with a training dataset. The findings were confirmed using a validation dataset. The effects of LCT in different risk groups were evaluated using the Kaplan-Meier method. Results The T stage (p = 0.001), N stage (p = 0.008), number of metastatic sites (p = 0.031), and EGFR status (p = 0.043) were identified as significant predictors of OS. A recursive partitioning analysis was used to establish a prognostic risk model with the following four risk groups: Group I included never smokers with N0 disease (3-year OS: 55.6%, median survival time [MST]: 42.8 months), Group II included never smokers with N+ disease (3-year OS: 32.8%, MST: 26.5 months), Group III included smokers with T0–2 disease (3-year OS: 23.3%, MST: 19.4 months), and Group IV included smokers with T3/4 disease (3-year OS: 12.5%, MST: 11.1 months). Significant differences in OS according to LCT status were observed in all risk groups except Group IV (p = 0.45). Conclusions Smokers with T3/4 oligometastatic non-small cell lung cancer may not benefit from LCT.

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