Identification of Tumor Microenvironment-Related Prognostic lncRNAs in Lung Adenocarcinoma

BackgroundLung adenocarcinoma (LUAD) is the most common type of lung cancer and is a severe threat to human health. Although many therapies have been applied to LUAD, the long-term survival rate of patients remains unsatisfactory. We aim to find reliable immune microenvironment-related lncRNA biomarkers to improve LUAD prognosis.MethodsESTIMATE analysis was performed to evaluate the degree of immune infiltration of each patient in TAGA LUAD cohort. Correlation analysis was used to identify the immune microenvironment-related lncRNAs. Univariate cox regression analysis, LASSO analysis, and Kaplan Meier analysis were used to construct and validate the prognostic model based on microenvironment-related lncRNAs.ResultsWe obtained 1,178 immune microenvironment-related lncRNAs after correlation analysis. One hundred and eighty of them are independent prognostic lncRNAs. Sixteen key lncRNAs were selected by LASSO method. This lncRNA-based model successfully predicted patients’ prognosis in validation cohort, and the risk score was related to pathological stage. Besides, we also found that TP53 had the highest frequency mutation in LUAD, and the mutation of TP53 in the high-risk group, which was identified by our survival model, has a poor prognosis. lncRNA-mRNA co-expression network further suggested that these lncRNAs play a vital role in the prognosis of LUAD.ConclusionHere, we filtered 16 key lncRNAs, which could predict the survival of LUAD and may be potential biomarkers and therapeutic targets.

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Association of masseter area and radiodensity with three-month survival after proximal anterior circulation occlusion

Journal of NeuroInterventional Surgery ◽

10.1136/neurintsurg-2020-015837 ◽

2020 ◽

Vol 13 (1) ◽

pp. 25-29 ◽

Cited By ~ 1

Author(s):

Iisa Lindström ◽

Sara Protto ◽

Niina Khan ◽

Jussi Hernesniemi ◽

Niko Sillanpää ◽

...

Keyword(s):

Regression Analysis ◽

Ischemic Stroke ◽

Acute Ischemic Stroke ◽

Cox Regression ◽

Multivariable Logistic Regression Analysis ◽

Anterior Circulation ◽

Term Survival ◽

Cox Regression Analysis ◽

Kaplan Meier

BackgroundMasseter area (MA), a surrogate for sarcopenia, appears to be useful when estimating postoperative survival, but there is lack of consensus regarding the potential predictive value of sarcopenia in acute ischemic stroke (AIS) patients. We hypothesized that MA and density (MD) evaluated from pre-interventional CT angiography scans predict postinterventional survival in patients undergoing mechanical thrombectomy (MT).Materials and methods312 patients treated with MT for acute occlusions of the internal carotid artery (ICA) or the M1 segment of the middle cerebral artery (M1-MCA) between 2013 and 2018. Median follow-up was 27.4 months (range 0–70.4). Binary logistic (alive at 3 months, OR <1) and Cox regression analyses were used to study the effect of MA and MD averages (MAavg and MDavg) on survival.ResultsIn Kaplan–Meier analysis, there was a significant inverse relationship with both MDavg and MAavg and mortality (MDavg P<0.001, MAavg P=0.002). Long-term mortality was 19.6% (n=61) and 3-month mortality 12.2% (n=38). In multivariable logistic regression analysis at 3 months, per 1-SD increase MDavg (OR 0.61, 95% CI 0.41 to 0.92, P=0.018:) and MAavg (OR 0.57, 95% CI 0.35 to 0.91, P=0.019) were the independent predictors associated with lower mortality. In Cox regression analysis, MDavg and MAavg were not associated with long-term survival.ConclusionsIn acute ischemic stroke patients, MDavg and MAavg are independent predictors of 3-month survival after MT of the ICA or M1-MCA. A 1-SD increase in MDavg and MAavg was associated with a 39%–43% decrease in the probability of death during the first 3 months after MT.

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

BioMed Research International ◽

10.1155/2020/3858373 ◽

2020 ◽

Vol 2020 ◽

pp. 1-13 ◽

Cited By ~ 1

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.

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Lepidic component identifies a subgroup of lung adenocarcinoma with a distinctive prognosis: a multicenter propensity-matched analysis

Therapeutic Advances in Medical Oncology ◽

10.1177/1758835920982845 ◽

2020 ◽

Vol 12 ◽

pp. 175883592098284

Author(s):

Erjia Zhu ◽

Chenyang Dai ◽

Huikang Xie ◽

Hang Su ◽

Xuefei Hu ◽

...

Keyword(s):

Multivariate Analysis ◽

Lung Adenocarcinoma ◽

Cox Regression ◽

Survival Rates ◽

Prognostic Impact ◽

Data Set ◽

Cox Regression Analysis ◽

T Stage ◽

Kaplan Meier ◽

T Classification

Background: Our aim was to investigate the prognostic impact of the lepidic component on T stage in patients with lung adenocarcinoma (LUAD). Methods: A retrospective data set including 863 cases of LUAD with lepidic component and 856 cases without lepidic component was used to identify matched lepidic-positive and lepidic-negative cohorts ( n = 376 patients per group) using a propensity-score matching. Primary outcome variables included recurrence-free survival (RFS) and overall survival (OS). Prognostic factors were assessed by Cox regression analysis and Kaplan–Meier estimates. Results: Multivariate analysis revealed that lepidic component presence was an independent prognostic factor for prolonged RFS ( p < 0.001) and OS ( p < 0.001). Furthermore, lepidic ratio (LR) >25% or ⩽25% were confirmed to be independent prolonged survival predictors. No survival differences were observed between patients with LUAD with LR >25% or ⩽25% (RFS p = 0.333; OS p = 0.078). The 5-year OS rates of patients with LUAD with a lepidic component were 90% regardless of the T stage, and these survival rates were significantly better than those of patients with LUAD without a lepidic component in the corresponding T stage. Multivariate analysis confirmed that T stage was associated with survival only in patients with LUAD without a lepidic component. Conclusions: Lepidic component presence identifies a LUAD subgroup with an excellent prognosis independent of the LR, pathological T classification. Considering the lepidic component presence may improve prognostic predictions for patients with LUAD.

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Expression Status and Prognostic Value of m6A RNA Methylation Regulators in Lung Adenocarcinoma

Life ◽

10.3390/life11070619 ◽

2021 ◽

Vol 11 (7) ◽

pp. 619

Author(s):

Xiuhong Li ◽

Zian Feng ◽

Rui Wang ◽

Jie Hu ◽

Xiaodong He ◽

...

Keyword(s):

Regression Analysis ◽

Survival Analysis ◽

Lung Adenocarcinoma ◽

Roc Curve ◽

Cox Regression ◽

Cox Regression Analysis ◽

Rna Methylation ◽

Kaplan Meier ◽

M6a Rna Methylation ◽

Prediction Efficiency

N6-methyladenosine (m6A) RNA modification is the most abundant modification method in mRNA, and it plays an important role in the occurrence and development of many cancers. This paper mainly discusses the role of m6A RNA methylation regulators in lung adenocarcinoma (LUAD) to identify novel prognostic biomarkers. The gene expression data of 19 m6A methylation regulators in LUAD patients and its relevant clinical parameters were extracted from The Cancer Genome Atlas (TCGA) database. We selected three significantly differentially expressed m6A regulators in LUAD to construct the risk signature, and evaluated its prognostic prediction efficiency using the receiver operating characteristic (ROC) curve. Kaplan–Meier survival analysis and Cox regression analysis were used to identify the independent prognostic significance of the risk signature. The ROC curve indicated that the area under the curve (AUC) was 0.659, which means that the risk signature had a good prediction efficiency. The results of the Kaplan–Meier survival analysis and Cox regression analysis showed that the risk score can be used as an independent prognostic factor for LUAD. In addition, we explored the differential signaling pathways and cellular processes related to m6A methylation regulators in LUAD.

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Comprehensive Analysis of Inflammatory Response–Related Genes, and Prognosis and Immune Infiltration in Patients With Low-Grade Glioma

Frontiers in Pharmacology ◽

10.3389/fphar.2021.748993 ◽

2021 ◽

Vol 12 ◽

Author(s):

Tao Han ◽

Zhifan Zuo ◽

Meilin Qu ◽

Yinghui Zhou ◽

Qing Li ◽

...

Keyword(s):

Regression Analysis ◽

High Risk ◽

Immune Cell ◽

Risk Model ◽

Cox Regression ◽

Risk Group ◽

High Risk Group ◽

Low Grade ◽

Immune Microenvironment ◽

Cox Regression Analysis

Background: Although low-grade glioma (LGG) has a good prognosis, it is prone to malignant transformation into high-grade glioma. It has been confirmed that the characteristics of inflammatory factors and immune microenvironment are closely related to the occurrence and development of tumors. It is necessary to clarify the role of inflammatory genes and immune infiltration in LGG.Methods: We downloaded the transcriptome gene expression data and corresponding clinical data of LGG patients from the TCGA and GTEX databases to screen prognosis-related differentially expressed inflammatory genes with the difference analysis and single-factor Cox regression analysis. The prognostic risk model was constructed by LASSO Cox regression analysis, which enables us to compare the overall survival rate of high- and low-risk groups in the model by Kaplan–Meier analysis and subsequently draw the risk curve and survival status diagram. We analyzed the accuracy of the prediction model via ROC curves and performed GSEA enrichment analysis. The ssGSEA algorithm was used to calculate the score of immune cell infiltration and the activity of immune-related pathways. The CellMiner database was used to study drug sensitivity.Results: In this study, 3 genes (CALCRL, MMP14, and SELL) were selected from 9 prognosis-related differential inflammation genes through LASSO Cox regression analysis to construct a prognostic risk model. Further analysis showed that the risk score was negatively correlated with the prognosis, and the ROC curve showed that the accuracy of the model was better. The age, grade, and risk score can be used as independent prognostic factors (p < 0.001). GSEA analysis confirmed that 6 immune-related pathways were enriched in the high-risk group. We found that the degree of infiltration of 12 immune cell subpopulations and the scores of 13 immune functions and pathways in the high-risk group were significantly increased by applying the ssGSEA method (p < 0.05). Finally, we explored the relationship between the genes in the model and the susceptibility of drugs.Conclusion: This study analyzed the correlation between the inflammation-related risk model and the immune microenvironment. It is expected to provide a reference for the screening of LGG prognostic markers and the evaluation of immune response.

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N6-Methyladenosine-Regulated mRNAs: Potential Prognostic Biomarkers for Patients With Lung Adenocarcinoma

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.705962 ◽

2021 ◽

Vol 9 ◽

Author(s):

Junjun Sun ◽

Yili Ping ◽

Jingjuan Huang ◽

Bingjie Zeng ◽

Ping Ji ◽

...

Keyword(s):

Lung Adenocarcinoma ◽

Mrna Expression ◽

Cox Regression ◽

Interaction Network ◽

Prognostic Biomarkers ◽

Cox Regression Analysis ◽

Receiver Operating Characteristic Curves ◽

Network Analyses ◽

Kaplan Meier ◽

Selection Operator

Aberrant regulation of m6A mRNA modification can lead to changes in gene expression, thus contributing to tumorigenesis in several types of solid tumors. In this study, by integrating analyses of m6A methylation and mRNA expression, we identified 84 m6A-regulated mRNAs in lung adenocarcinoma (LUAD). Although the m6A methylation levels of total RNA in LUAD patient tumor tissue were reduced, the majority (75.2%) of m6A-regulated mRNAs were hypermethylated. The m6A-hypermethylated mRNAs were mainly enriched in terms related to transcription factor activity. We established a 10-m6A-regulated-mRNA signature score system through least absolute shrinkage and selection operator Cox regression analysis, with its predictive value validated by Kaplan–Meier curve and time-dependent receiver operating characteristic curves. RFXAP and KHDRBS2 from the signature also exhibited an independent prognostic value. The co-expression and interaction network analyses demonstrated the strong correlation between m6A regulators and the genes in the signature, further supporting the results of the m6A methylation modification patterns. These findings highlight the potential utility of integrating multi-omics data (m6A methylation level and mRNA expression) to accurately obtain potential prognostic biomarkers, which may provide important insights into developing novel and effective therapies for LUAD.

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Establishment of a 5 DNA Methylation Site Prognostic Signature Associated with N6-Methyladenosine in Lung Adenocarcinoma

10.21203/rs.3.rs-1071199/v1 ◽

2021 ◽

Author(s):

Liqiang Yuan ◽

Wei Jiang ◽

Zhanyu Xu ◽

Kung Deng ◽

Yu Sun ◽

...

Keyword(s):

Dna Methylation ◽

Lung Adenocarcinoma ◽

Cox Regression ◽

Clinicopathological Parameters ◽

Main Body ◽

Methylation Site ◽

Cox Regression Analysis ◽

Site Model ◽

Kaplan Meier ◽

Cox Analysis

Abstract Background: There is a high incidence of lung adenocarcinoma (LUAD). Even with surgery, targeted therapy and immunotherapy, the survival rate of LUAD patients is still low. N6-methyladenosine (m6A) and DNA methylation markers can help with the diagnosis and treatment of LUAD patients. Therefore, it is necessary to identify a novel m6A-related DNA methylation sites signature to predict the survival of patients with LUAD. Methods: In this study, we screened 15 m6A-related genes and their 217 methylation sites. RNA sequencing data of 15 genes and the clinicopathological parameters of TCGA-LUAD were obtained from the TCGA database (http://cancergenome.nih.gov/). The LUAD-DNA CpG site information was obtained from the Illumina Human Methylation 450 BeadChip (Illumina, San Diego, CA, United States). The methylation sites related to prognosis were screened using univariate COX analysis, and the independent predictors of LUAD patients were identified using multivariate COX analysis of least absolute shrinkage and selection operator (LASSO) Cox regression analysis. Finally, a model with 5 methylation sites as the main body to predict the prognosis of OS in patients with LUAD was obtained. According to the risk grouping of the prediction model, Kaplan-Meier curve and the receiver operating characteristic (ROC) curve were performed in the test and training sets to assess the predicted capacity of the model. In addition, a nomogram constructed by combining the risk score of methylation group and other related clinicopathological factors to verify the reliability of our model.Results: We constructed a m6A-related 5-DNA methylation site model to predict OS in LUAD patients. According to the results of the Kaplan-Meier curve, both the test set and the training set, the high-risk group showed a worse prognosis. The AUCs of the 5 DNA methylation signature at 1, 5 and 10 years in test datasets were 0.730, 0.649 and 0.726, respectively, and 0.679, 0.656 and 0.732 in training datasets. Finally, we constructed a nomogram to further verify the reliability of the model.Conclusion: In this study, we analyzed the methylation sites of m6A-related genes and established a m6A-related 5-DNA methylation site model to predict OS in LUAD patients.

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A Ferroptosis-Related lncRNAs Signature Predicts Prognosis and Immune Microenvironment for Breast Cancer

Frontiers in Molecular Biosciences ◽

10.3389/fmolb.2021.678877 ◽

2021 ◽

Vol 8 ◽

Author(s):

Kaiming Zhang ◽

Liqin Ping ◽

Tian Du ◽

Gehao Liang ◽

Yun Huang ◽

...

Keyword(s):

Breast Cancer ◽

High Risk ◽

Prognostic Value ◽

Cox Regression ◽

Validation Cohort ◽

Risk Group ◽

High Risk Group ◽

Breast Cancer Patients ◽

Immune Microenvironment ◽

Cox Regression Analysis

Background: Ferroptosis, a regulated cell death which is driven by the iron-dependent peroxidation of lipids, plays an important role in cancer. However, studies about ferroptosis-related Long non-coding RNAs (lncRNAs) in breast cancer (BC) are limited. Besides, the prognostic role of ferroptosis-related lncRNAs and their relationship to immune microenvironment in breast cancer remain unclear. This study aimed to explore the potential prognostic value of ferroptosis-related lncRNAs and their relationship to immune microenvironment in breast cancer.Methods: RNA-sequencing data of female breast cancer patients were downloaded from TCGA database. 937 patients were randomly separated into training or validation cohort in 2:1 ratio. Ferroptosis-related lncRNAs were screened by Pearson correlation analysis with 239 reported ferroptosis-related genes. A ferroptosis-related lncRNAs signature was constructed with univariate and multivariate Cox regression analyses in the training cohort, and its prognostic value was further tested in the validation cohort.Results: An 8-ferroptosis-related-lncRNAs signature was developed by multivariate Cox regression analysis to divide patients into two risk groups. Patients in the high-risk group had worse prognosis than patients in the low-risk group. Multivariate Cox regression analysis showed the risk score was an independent prognostic indicator. Receiver operating characteristic curve (ROC) analysis proved the predictive accuracy of the signature. The area under time-dependent ROC curve (AUC) reached 0.853 at 1 year, 0.802 at 2 years, 0.740 at 5 years in the training cohort and 0.791 at 1 year, 0.778 at 2 years, 0.722 at 5 years in the validation cohort. Further analysis demonstrated that immune-related pathways were significantly enriched in the high-risk group. Analysis of the immune cell infiltration landscape showed that breast cancer in the high-risk group tended be immunologically “cold”.Conclusion: We identified a novel ferroptosis-related lncRNA signature which could precisely predict the prognosis of breast cancer patients. Ferroptosis-related lncRNAs may have a potential role in the process of anti-tumor immunity and serve as therapeutic targets for breast cancer.

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Macrophage Polarization-Related Gene Signature for Risk Stratification and Prognosis of Survival in Gliomas

10.21203/rs.3.rs-691799/v1 ◽

2021 ◽

Author(s):

Jingwei Zhang ◽

Shuwang Li ◽

Fangkun Liu

Keyword(s):

Essential Role ◽

Cox Regression ◽

Macrophage Polarization ◽

Gene Signature ◽

High Risk Group ◽

M2 Macrophage ◽

Related Gene ◽

Sequencing Data ◽

Cox Regression Analysis ◽

Kaplan Meier

Abstract Macrophage polarization plays an essential role in tumor immune cells infiltration and tumor growth. We selected a series of genes distinguishing between M1 and M2 macrophage and explored their prognostic value in gliomas. A total of 170 genes were included in our study. CGGA database was used as the training cohort, and the TCGA database as the validation cohort. The biological processes and functions were identified by GO and KEGG analysis. Kaplan-Meier analysis was used to compare survival differences between groups. Finally, GEPIA was applied to explore immune infiltrates in the tumor microenvironment. Importantly, we re-verified the results using our sequencing data. We build a risk score model using Cox regression analysis based on the CGGA and verified in the TCGA database and our sequencing data. Patients with gliomas in the high-risk group were associated with high grade, IDH WT status, MGMT promoter unmethylation, 1p19q non-codeletion, and prone to have a poor outcome. Moreover, these genes play an essential role in immune infiltrations in LGG and GBM microenvironments. Macrophage polarization-related gene signature can predict the malignancy and outcome of patients with gliomas and might act as a promising target for glioma immunotherapy in the future.

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Abstract 111: In-hospital Cardiac Arrest: Predictors Of Long Term Survival After Being Discharged Alive

Circulation ◽

10.1161/circ.116.suppl_16.ii_946-b ◽

2007 ◽

Vol 116 (suppl_16) ◽

Author(s):

Wessel Keuper ◽

Hendrik-Jan Dieker ◽

Marc A Brouwer ◽

Freek W Verheugt

Keyword(s):

Cardiac Arrest ◽

Cox Regression ◽

Term Survival ◽

Discharge Data ◽

Long Term Survival ◽

Cox Regression Analysis ◽

Kaplan Meier ◽

Initial Rhythm ◽

Hospital Cardiac Arrest

Background Long term survival of patients discharged alive after cardiopulmonary resuscitation (CPR) for an in-hospital cardiac arrest (IHCA) has not been extensively studied. It is also largely unknown which of these patients are at high risk for poor survival. Therefore we studied survival and predictors of survival for these patients. Methods We retrospectively studied patients who suffered from an IHCA between 1997–2004 and who survived to discharge. Data were collected using an Utstein form. A Kaplan Meier curve was calculated for survival. Survivors were compared with non-survivors and Cox regression analysis was performed to determine predictors of survival. Results In this period 222 patients had an IHCA and 19% (n=42) was discharged alive. Known predictors of survival to discharge were confirmed, primarily initial rhythm. In the discharged patients, survival after a median follow-up of 2.9 years (IQR 1.5–7.2) was 57% (n=24). Non-survivors were significantly older, median age 69.3 (IQR 59.6 –75.2) versus 56.7 (IQR 48.1– 68.8) years and had significantly more often diabetes mellitus, arrhythmias, valvular disease and cancer in their medical history than survivors. Initial rhythm did not differ between groups. After adjustment for baseline differences it was found that cancer independently predicted a lower chance of survival (HR 2.8; 95% CI 1.1–7.5). Older age tended to predict a lower chance of survival as well. Conclusion Whenever a patient is discharged alive after an IHCA, the chance of survival is evidently reduced. Only cancer independently predicted a lower chance of survival. Long term survival seems to be determined more by comorbidity than arrest variables.

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