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 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 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.

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 Molecular Identification of Immunity- and Ferroptosis-related Gene Signature in Non-small Cell Lung Cancer

2021 ◽  
Author(s):  
Taisheng Liu ◽  
Jinye Zhang ◽  
Xiaoshan Hu ◽  
Tao Xie ◽  
Jian Zhang
Keyword(s):  
Lung Cancer ◽  
Overall Survival ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Risk Group ◽  
Risk Groups ◽  
Gene Signature ◽  
Small Cell ◽  
Related Gene ◽  
Small Cell Lung

Abstract Background: Lung cancer is one of the dominant causes of cancer-related deaths worldwide. Ferroptosis, an iron-dependent regulated cell death, plays an important role in the cancer immunotherapy. However, the role of immunity- and ferroptosis-related gene signature in non-small cell lung cancer (NSCLC) remains unknown.Method: The RNA sequencing (RNA-seq) expression data and clinical information of NSCLC were downloaded from The Cancer Genome Atlas (TCGA) database and performed differential analysis. Univariate and multivariate cox regressions were used to identify the ferroptosis-related gene, and receiver operating characteristic (ROC) model was established using the independent risk factors. GO and KEGG enrichment analyses were performed to investigate the biological functions of differential genes.Results: A 5-gene signature was constructed to stratify patients into high- and low-risk groups. Compared with patients in the low-risk group, patients in the high-risk group showed significantly poor overall survival (P < 0.001 in the TCGA cohort and P = 0.001 in the GSE13213 cohort). The risk score was an independent predictor for overall survival in multivariate Cox regression analyses (HR > 1, P < 0.01). The 1 year-, 2 year- and 3 year-ROCs were 0.792, 0.644 and 0.641 in TCGA and 0.623, 0.636 and 0.631 in GSE13213, respectively. Functional analysis revealed that immune-related pathways were enriched, and immune status were different between two risk groups. Conclusions: We identified differently expressed immunity- and ferroptosis-related genes that may involve in NSCLC. These genes may predict the overall survival in NSCLC and targeting ferroptosis may be an alternative for clinical therapy.

scholarly journals Premalignant Changes in the Bronchial Epithelium Are Prognostic Factors of Distant Metastasis in Non-Small Cell Lung Cancer Patients

2021 ◽  
Vol 11 ◽  
Author(s):  
Olga V. Pankova ◽  
Liubov A. Tashireva ◽  
Evgeny O. Rodionov ◽  
Sergey V. Miller ◽  
Sergey A. Tuzikov ◽  
...  
Keyword(s):  
Lung Cancer ◽  
High Risk ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Distant Metastasis ◽  
Risk Group ◽  
Morphological Changes ◽  
Distant Metastases ◽  
Small Cell ◽  
Small Cell Lung

BackgroundThe study assessed the possibility of dividing patients into groups based on the assessment of morphological changes in the epithelium of small-caliber bronchi located near the primary tumor in order to predict high and low risks of distant metastasis of non-small cell lung cancer.MethodsIn 171 patients with non-small cell lung cancer (T1-4N0-3M0) in small-caliber bronchi taken at a distance of 3–5 cm from the tumor, various variants of morphological changes in the bronchial epithelium (basal cell hyperplasia (BCH), squamous cell metaplasia (SM), and dysplasia (D)) were assessed. Long-term results of treatment, namely, distant metastasis, were assessed after 2 and 5 years.ResultsDuring the follow-up period, distant metastases were found in 35.1% (60/171) of patients. Most often, they were observed in patients of the high-risk group: BCH+SM−D− (51.6%, 40/95) and BCH−SM+D+ (54.4%, 6/11). Less often, distant metastases were observed in low-risk group patients: BCH+SM+D− (6.7%, 3/45) and BCH−SM−D− (10.0%, 2/20). Tumor size, grade, and stage were significant predictors of metastasis only in the high-risk group. The 5-year metastasis-free survival was better in the low-risk group of distant metastases.ConclusionsIsolated BCH or dysplasia in small bronchi distant from foci of tumor is associated with a high-risk distant metastasis and less 5-year metastasis-free survival.

scholarly journals Identification of a High-Risk Group for Brain Metastases in Non-Small Cell Lung Cancer Patients.

2021 ◽  
Author(s):  
Bernardo Cacho-Díaz ◽  
D. Cuapaténcatl Laura ◽  
Jose Antonio Rodriguez ◽  
Ytel Jazmin Garcilazo-Reyes ◽  
Nancy Reynoso-Noverón ◽  
...  
Keyword(s):  
Lung Cancer ◽  
High Risk ◽  
Brain Metastases ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Risk Group ◽  
High Risk Group ◽  
Small Cell ◽  
Multivariate Model ◽  
Small Cell Lung

Abstract Purpose: Identification of a truly high-risk group of brain metastases (BM) in patients with non-small cell lung cancer (NSCLC) could lead to early interventions and probably better prognosis. The objective of the study was to identify this group by generating a multivariable model with recognized and accessible risk factors.Methods: A retrospective cohort from patients seen at a single center during 2010-2020, was divided into a training (TD) and validation (VD) datasets, associations with BM were measured in the TD with logit, variables significantly associated were used to generate a multivariate model. Model´s performance was measured by 10-Fold cross validation of the TD and in the VD with the AUC/C-statistic, Akaike information index, and Press´s Q precision test.Results: From 570 patients with NSCLC with complete records a TD and VD were formed, no significant differences were found amid both datasets. Variables associated with BM in the multivariate logit analyses were age [P 0.021, OR 0.97 (95%CI 0.94-0.99)]; male gender [P 0.010, OR 2.44 (95%CI 1.24-4.82)]; mutational status [P 0.03, OR 2.1 (95%CI 1.07-4.12); and carcinoembryonic antigen levels [P 0.002, OR 1.002 (95%CI 1.001 – 1.003). BM were diagnosed in 23% of the whole cohort. Stratification into low, medium, or high-risk groups after simplification of the model, displayed a frequency of BM in the VD of 8%, 16% and 40% respectively (P 0.027).Conclusion: A multivariate model comprising age, gender, CEA, and mutation status allowed the identification of a truly high-risk group of BM in NSCLC patients.

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 Preoperative nivolumab to evaluate pathological response in patients with stage I non-small cell lung cancer: a study protocol of phase II trial (POTENTIAL)

BMJ Open ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. e043234
Author(s):  
Atsushi Kagimoto ◽  
Yasuhiro Tsutani ◽  
Takahiro Mimae ◽  
Yoshihiro Miyata ◽  
Norihiko Ikeda ◽  
...  
Keyword(s):  
Lung Cancer ◽  
High Risk ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Phase Ii Trial ◽  
Clinical Stage ◽  
Small Cell ◽  
Pathological Response ◽  
Stage I ◽  
Small Cell Lung

IntroductionRecently, inhibition of programmed cell death 1 or its ligand has shown therapeutic effects on non-small cell lung cancer (NSCLC). However, the effectiveness of preoperative nivolumab monotherapy for stage I NSCLC remains unknown. The present study aimed to investigate the pathological response of preoperative treatment with nivolumab for clinically node negative but having a high risk of NSCLC recurrence.Methods and analysisThe Preoperative Nivolumab (Opdivo) to evaluate pathologic response in patients with stage I non-small cell lung cancer: a phase 2 trial (POTENTIAL) study is a multicentre phase II trial investigating efficacy of preoperative nivolumab for clinical stage I patients at high risk of recurrence. This study includes histologically or cytologically confirmed NSCLC patients with clinical N0 who were found on preoperative high-resolution CT to have a pure solid tumour without a ground-glass opacity component (clinical T1b, T1c or T2a) or a solid component measuring 2–4 cm in size (clinical T1c or T2a). Patients with epidermal growth factor receptor (EGFR) mutation (deletion of exon 19 or point mutation on exon21, L858R), anaplastic lymphoma kinase (ALK) translocation or c-ros oncogene 1 (ROS-1) translocation are excluded from this study. Nivolumab (240 mg/body) is administrated intravenously as preoperative therapy every 2 weeks for three cycles. Afterward, lobectomy and mediastinal lymph node dissection (ND 2a-1 or ND 2a-2) are performed. The primary endpoint is a pathological complete response in the resected specimens. The secondary endpoints are safety, response rates and major pathological response. The planed sample size is 50 patients. Patients have been enrolled since April 2019.Ethics and disseminationThis trial was approved by the Institutional Review Board of Hiroshima University Hospital and other participating institutions. This trial will help examine the efficacy of preoperative nivolumab therapy for clinical stage I NSCLC.Trial registration numberjRCT2061180016.

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