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 ◽  
...  

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.

2020 ◽  
Author(s):  
Bo Jia ◽  
Qiwen Zheng ◽  
Jingjing Wang ◽  
Hongyan Sun ◽  
Jun Zhao ◽  
...  

Abstract Background This study aimed to establish a novel nomogram prognostic model to predict death probability for non-small cell lung cancer (NSCLC) patients who received surgery. Methods We collected data from the Surveillance, Epidemiology, and End Results (SEER) database of the National Cancer Institute in the United States. A nomogram prognostic model was constructed to predict mortality of NSCLC patients who received surgery. Results A total of 44,880 NSCLC patients who received surgery from 2004 to 2014 were included in this study. Gender, race, tumor anatomic sites, histologic subtype, tumor differentiation, clinical stage, tumor size, tumor extent, lymph node stage, examined lymph node, positive lymph node, type of surgery showed significant associations with lung cancer related death rate (P<0.001). Patients who received chemotherapy and radiotherapy had significant higher lung cancer related death rate but were associated with significant lower non-cancer related mortality (P<0.001). A nomogram model was established based on multivariate models of training data set. In the validation cohort, the unadjusted C-index was 0.73 (95% CI, 0.72-0.74), 0.71 (95% CI, 0.66-0.75) and 0.69 (95% CI, 0.68-0.70) for lung cancer related death, other cancer related death and non-cancer related death. Conclusions A prognostic nomogram model was constructed to predict death rate for NSCLC patients who received surgery. This novel prognostic model may be helpful for physicians to develop the most appropriate treatment strategies for resected NSCLC patients. Parts of these results were presented at the 2018 American Society of Clinical Oncology Annual Meeting (Abstract #8525)


2019 ◽  
Vol 21 (Supplement_3) ◽  
pp. iii91-iii91
Author(s):  
P Mir Seyed Nazari ◽  
C Ay ◽  
A Steindl ◽  
B Gatterbauer ◽  
J M Frischer ◽  
...  

Abstract BACKGROUND Venous thromboembolism (VTE) is a common complication in patients with cancer. In general, patients with metastatic disease are at highest risk. Lung cancer belong to those tumor entities with a particularly high risk of VTE, ranging between 3–13.8%. However, little is known about the VTE rate in lung cancer patients with brain metastases. MATERIAL AND METHODS Our study was conducted in the framework of the Vienna Brain Metastasis Registry. Clinical data and VTE events during the course of the disease were recorded via retrospective chart review. In this analysis, non-small cell lung cancer (NSCLC) patients with a resection of brain metastases at the Medical University of Vienna between 2006 and 2010 were included. RESULTS In total, 69 NSCLC patients with brain metastases were analyzed. Overall, 69.6% (48/69) patients had an adenocarcinoma, 13% (9/69) a squamous cell carcinoma, 8.7% (6/69) a large cell carcinoma and 8.7% (6/69) other primary tumor histologies. After cancer diagnosis, 20.3% (14/69) patients developed VTE during the course of the disease. Of those, 85.7% (12/14) thromboembolic events occurred after the diagnosis of brain metastases. CONCLUSION Based on our data, patients with brain metastases from NSCLC have a very high VTE risk. Further investigations are needed in order to identify patients with distinct VTE risk profiles. Patients at high risk might potentially benefit from primary thromboprophylaxis over the high risk of intracerebral bleeding.


2021 ◽  
Vol 12 ◽  
Author(s):  
Qiang Guo ◽  
Dan Li ◽  
Xiangyu Luo ◽  
Ye Yuan ◽  
Tian Li ◽  
...  

BackgroundThe occurrence and development of cancer could be promoted by abnormally competing endogenous RNAs (ceRNA) network. This article aims to determine the prognostic biomarker of ceRNA for non-small-cell lung cancer (NSCLC) prognosis.MethodsThe expression and clinical significance of LINC00973 in NSCLC tissues were analyzed via the The Cancer Genome Atlas (TCGA), Gene Expression Profiling Interactive Analysis (GEPIA), lnCAR, and clinical samples in Taihe Hospital. The biological functions and signaling pathways involved in target genes of ceRNA network were analyzed via Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Survival analysis, univariate and multivariate Cox regression analysis were used for prognostic-related mRNA.ResultsExpression of LINC00973 was increased in NSCLC tissues. High expression of LINC00973 was associated with poor prognosis of NSCLC patients. There were 15 miRNA and 238 differential mRNA in the INC00973-miRNA-mRNA ceRNA network, involving cell migration, endothelial cell proliferation, tumor growth factor (TGF)-β, cellular senescence, phosphatidylinositol 3-hydroxy kinase (PI3K)-Akt, Hippo, Rap1, mitogen-activated protein kinase (MAPK), cell cycle signaling pathway, etc. The expression levels of RTKN2, NFIX, PTX3, BMP2 and LOXL2 were independent risk factors for the poor prognosis of NSCLC patients.ConclusionsLINC00973-miRNA-mRNA ceRNA network might be the basis for determining pivotal post-translational regulatory mechanisms in the progression of NSCLC. BMP2, LOXL2, NFIX, PTX3 and RTKN2 might be valuable prognostic markers and potential therapeutic targets.


2021 ◽  
Vol 8 ◽  
Author(s):  
Lei-Lei Wu ◽  
Wu-Tao Chen ◽  
Xuan Liu ◽  
Wen-Mei Jiang ◽  
Yang-Yu Huang ◽  
...  

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 &lt; 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 &lt; 0.001; SEER-V, 0.609 vs. 0.576, P &lt; 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 &lt; 0.001], black patients (unadjusted HR 1.007, P &lt; 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 &lt; 0.001) or adenocarcinoma (unadjusted HR 1.008, P &lt; 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.


2021 ◽  
Author(s):  
Ke Han ◽  
Ju Kun Kun Wang ◽  
Kun Qian ◽  
Teng Zhao ◽  
Xing Sheng Liu ◽  
...  

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.


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