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 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.

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 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 Development and validation of a prognostic model for non-lung cancer death in elderly patients treated with stereotactic body radiotherapy for non-small cell lung cancer

2021 ◽  
Author(s):  
Hideki Hanazawa ◽  
Yukinori Matsuo ◽  
Atsuya Takeda ◽  
Yuichiro Tsurugai ◽  
Yusuke Iizuka ◽  
...  
Keyword(s):  
Lung Cancer ◽  
High Risk ◽  
Elderly Patients ◽  
Prognostic Model ◽  
Risk Groups ◽  
Small Cell ◽  
Tumor Diameter ◽  
Cancer Death ◽  
Small Cell Lung ◽  
Lung Cancer Death

Abstract This study sought to develop and validate a prognostic model for non-lung cancer death (NLCD) in elderly patients with non-small cell lung cancer (NSCLC) treated with stereotactic body radiotherapy (SBRT). Patients aged ≥65 diagnosed with NSCLC (Tis-4N0M0), tumor diameter ≤5 cm and SBRT between 1998 and 2015 were retrospectively registered from two independent institutions. One institution was used for model development (arm D, 353 patients) and the other for validation (arm V, 401 patients). To identify risk factors for NLCD, multiple regression analysis on age, sex, performance status (PS), body mass index (BMI), Charlson comorbidity index (CCI), tumor diameter, histology and T-stage was performed on arm D. A score calculated using the regression coefficient was assigned to each factor and three risk groups were defined based on total score. Scores of 1.0 (BMI ≤18.4), 1.5 (age ≥ 5), 1.5 (PS ≥2), 2.5 (CCI 1 or 2) and 3 (CCI ≥3) were assigned, and risk groups were designated as low (total ≤ 3), intermediate (3.5 or 4) and high (≥4.5). The cumulative incidences of NLCD at 5 years in the low, intermediate and high-risk groups were 6.8, 23 and 40% in arm D, and 23, 19 and 44% in arm V, respectively. The AUC index at 5 years was 0.705 (arm D) and 0.632 (arm V). The proposed scoring system showed usefulness in predicting a high risk of NLCD in elderly patients treated with SBRT for NSCLC.

scholarly journals A fourteen-lncRNA risk score system for prognostic prediction of patients with non-small cell lung cancer

2020 ◽  
Vol 29 (4) ◽  
pp. 493-508
Author(s):  
Jia-Yi Song ◽  
Xiao-Ping Li ◽  
Xiu-Jiao Qin ◽  
Jing-Dong Zhang ◽  
Jian-Yu Zhao ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Overall Survival ◽  
Small Cell Lung Cancer ◽  
Risk Score ◽  
Cell Lung Cancer ◽  
Small Cell ◽  
Small Cell Lung ◽  
Cox Regression Analysis ◽  
Score Model ◽  
Nsclc Patients

Growing evidence has underscored long non-coding RNAs (lncRNAs) serving as potential biomarkers for cancer prognosis. However, systematic tracking of a lncRNA signature for prognosis prediction in non-small cell lung cancer (NSCLC) has not been accomplished yet. Here, comprehensive analysis with differential gene expression analysis, univariate and multivariate Cox regression analysis based on The Cancer Genome Atlas (TCGA) database was performed to identify the lncRNA signature for prediction of the overall survival of NSCLC patients. A risk-score model based on a 14-lncRNA signature was identified, which could classify patients into high-risk and low-risk groups and show poor and improved outcomes, respectively. The receiver operating characteristic (ROC) curve revealed that the risk-score model has good performance with high AUC value. Multivariate Cox’s regression model and stratified analysis indicated that the risk-score was independent of other clinicopathological prognostic factors. Furthermore, the risk-score model was competent for the prediction of metastasis-free survival in NSCLC patients. Moreover, the risk-score model was applicable for prediction of the overall survival in the other 30 caner types of TCGA. Our study highlighted the significant implications of lncRNAs as prognostic predictors in NSCLC. We hope the lncRNA signature could contribute to personalized therapy decisions in the future.

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 Galectin-9-based immune risk score model helps to predict relapse in stage I–III small cell lung cancer

2020 ◽  
Vol 8 (2) ◽  
pp. e001391
Author(s):  
Peixin Chen ◽  
Liping Zhang ◽  
Wei Zhang ◽  
Chenglong Sun ◽  
Chunyan Wu ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Risk Score ◽  
Cell Lung Cancer ◽  
Predictive Performance ◽  
Small Cell ◽  
Stage I ◽  
Small Cell Lung ◽  
Immune Microenvironment ◽  
Score Model

BackgroundFor small cell lung cancer (SCLC) therapy, immunotherapy might have unique advantages to some extent. Galectin-9 (Gal-9) plays an important role in antitumor immunity, while little is known of its function in SCLC.Materials and methodsBy mean of immunohistochemistry (IHC), we tested the expression level of Gal-9 and other immune markers on both tumor cells and tumor-infiltrating lymphocytes (TILs) in 102 surgical-resected early stage SCLC clinical samples. On the basis of statistical analysis and machine learning results, the Gal-9-based immune risk score model was constructed and its predictive performance was evaluated. Then, we thoroughly explored the effects of Gal-9 and immune risk score on SCLC immune microenvironment and immune infiltration in different cohorts and platforms.ResultsIn the SCLC cohort for IHC, the expression level of Gal-9 on TILs was statistically correlated with the levels of program death-1 (p=0.001), program death-ligand 1 (PD-L1) (p<0.001), CD3 (p<0.001), CD4 (p<0.001), CD8 (p<0.001), and FOXP3 (p=0.047). High Gal-9 protein expression on TILs indicated better recurrence-free survival (30.4 months, 95% CI: 23.7–37.1 vs 39.4 months, 95% CI: 31.6–47.3, p=0.009). The immune risk score model which consisted of Gal-9 on TILs, CD4, and PD-L1 on TILs was established and validated so as to differentiate high-risk or low-risk patients with SCLC. The prognostic predictive performance of immune risk score model was better than single immune biomarker (area under the curve 0.671 vs 0.621–0.644). High Gal-9-related enrichment pathways in SCLC were enriched in immune system diseases and rheumatic disease. Furthermore, we found that patients with SCLC with low immune risk score presented higher fractions of activated memory CD4 T cells than patients with high immune risk score (p=0.048).ConclusionsGal-9 is markedly related to tumor-immune microenvironment and immune infiltration in SCLC. This study emphasized the predictive value and promising clinical applications of Gal-9 in stage I–III SCLC.

scholarly journals Comprehensive analysis of an immune infiltrate-related competitive endogenous RNA network reveals potential prognostic biomarkers for non-small cell lung cancer

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0260720
Author(s):  
Cai-Zhi Yang ◽  
Lei-Hao Hu ◽  
Zhong-Yu Huang ◽  
Li Deng ◽  
Wei Guo ◽  
...  
Keyword(s):  
Lung Cancer ◽  
High Risk ◽  
Small Cell Lung Cancer ◽  
Prognostic Model ◽  
Comprehensive Analysis ◽  
Small Cell ◽  
Small Cell Lung ◽  
Cerna Network ◽  
Nsclc Patients ◽  
Competitive Endogenous Rna

Globally, non-small cell lung cancer (NSCLC) is the most common malignancy and its prognosis remains poor because of the lack of reliable early diagnostic biomarkers. The competitive endogenous RNA (ceRNA) network plays an important role in the tumorigenesis and prognosis of NSCLC. Tumor immune microenvironment (TIME) is valuable for predicting the response to immunotherapy and determining the prognosis of NSCLC patients. To understand the TIME-related ceRNA network, the RNA profiling datasets from the Genotype-Tissue Expression and The Cancer Genome Atlas databases were analyzed to identify the mRNAs, microRNAs, and lncRNAs associated with the differentially expressed genes. Weighted gene co-expression network analysis revealed that the brown module of mRNAs and the turquoise module of lncRNAs were the most important. Interactions among microRNAs, lncRNAs, and mRNAs were prognosticated using miRcode, miRDB, TargetScan, miRTarBase, and starBase databases. A prognostic model consisting of 13 mRNAs was established using univariate and multivariate Cox regression analyses and validated by the receiver operating characteristic (ROC) curve. The 22 immune infiltrating cell types were analyzed using the CIBERSORT algorithm, and results showed that the high-risk score of this model was related to poor prognosis and an immunosuppressive TIME. A lncRNA–miRNA–mRNA ceRNA network that included 69 differentially expressed lncRNAs (DElncRNAs) was constructed based on the five mRNAs obtained from the prognostic model. ROC survival analysis further showed that the seven DElncRNAs had a substantial prognostic value for the overall survival (OS) in NSCLC patients; the area under the curve was 0.65. In addition, the high-risk group showed drug resistance to several chemotherapeutic and targeted drugs including cisplatin, paclitaxel, docetaxel, gemcitabine, and gefitinib. The differential expression of five mRNAs and seven lncRNAs in the ceRNA network was supported by the results of the HPA database and RT-qPCR analyses. This comprehensive analysis of a ceRNA network identified a set of biomarkers for prognosis and TIME prediction in NSCLC.

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