scholarly journals An Immune-Related Long Non-Coding RNA Signature to Predict the Prognosis of Ewing’s Sarcoma Based on a Machine Learning Iterative Lasso Regression

2021 ◽  
Vol 9 ◽  
Author(s):  
En-hui Ren ◽  
Ya-jun Deng ◽  
Wen-hua Yuan ◽  
Guang-zhi Zhang ◽  
Zuo-long Wu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Immune Cell ◽  
Cox Regression ◽  
Predictive Ability ◽  
Risk Groups ◽  
Low Risk ◽  
Cell Infiltration ◽  
Lasso Regression ◽  
Non Coding Rna ◽  
Long Non Coding Rna

The aim of this study was to construct a new immune-associated long non-coding RNA (lncRNA) signature to predict the prognosis of Ewing sarcoma (ES) and explore its molecular mechanisms. We downloaded transcriptome and clinical prognosis data from the Gene Expression Omnibus (GSE17679, which included 88 ES samples and 18 matched normal skeletal muscle samples), and used it as a training set to identify immune-related lncRNAs with different expression levels in ES. Univariable Cox regression was used to screen immune-related lncRNAs related to ES prognosis, and an immune-related lncRNA signature was constructed based on machine learning iterative lasso regression. An external verification set was used to confirm the predictive ability of the signature. Clinical feature subgroup analysis was used to explore whether the signature was an independent prognostic factor. In addition, CIBERSORT was used to explore immune cell infiltration in the high- and low-risk groups, and to analyze the correlations between the lncRNA signature and immune cell levels. Gene set enrichment and variation analyses were used to explore the possible regulatory mechanisms of the immune-related lncRNAs in ES. We also analyzed the expression of 17 common immunotherapy targets in the high- and low-risk groups to identify any that may be regulated by immune-related lncRNAs. We screened 35 immune-related lncRNAs by univariate Cox regression. Based on this, an immune-related 11-lncRNA signature was generated by machine learning iterative lasso regression. Analysis of the external validation set confirmed its high predictive ability. DPP10 antisense RNA 3 was negatively correlated with resting dendritic cell, neutrophil, and γδ T cell infiltration, and long intergenic non-protein coding RNA 1398 was positively correlated with resting dendritic cells and M2 macrophages. These lncRNAs may affect ES prognosis by regulating GSE17721_CTRL_VS_PAM3CSK4_12H_BMDC_UP, GSE2770_IL4_ACT_VS_ACT_CD4_TCELL_48H_UP, GSE29615_CTRL_VS_DAY3_ LAIV_IFLU_VACCINE_PBMC_UP, complement signaling, interleukin 2-signal transducer and activator of transcription 5 signaling, and protein secretion. The immune-related 11-lncRNA signature may also have regulatory effects on the immunotherapy targets CD40 molecule, CD70 molecule, and CD276 molecule. In conclusion, we constructed a new immune-related 11-lncRNA signature that can stratify the prognoses of patients with ES.

An Autophagy-related Long Non-coding RNA Signature for Breast Cancer

2021 ◽  
Vol 24 ◽  
Author(s):  
Feng Jiang ◽  
Chuyan Wu ◽  
Ming Wang ◽  
Ke Wei ◽  
Jimei Wang
Keyword(s):  
Breast Cancer ◽  
High Risk ◽  
Cox Regression ◽  
Risk Group ◽  
Expression Profiles ◽  
Risk Groups ◽  
Low Risk ◽  
Optimal Cutoff ◽  
Non Coding Rna ◽  
Long Non Coding Rna

Background: The most prevalent malignant tumor in women is breast cancer (BC). Autophagic therapies have been identified for their contribution in BC cell death. Therefore, the potential prognostic role of long non-coding RNA (lncRNA) related to autophagy in patients with BC was examined. Methods: The lncRNAs expression profiles were derived from The Cancer Genome Atlas (TCGA) database. Throughout univariate Cox regression and multivariate Cox regression test, lncRNA with BC prognosis have been differentially presented. We then defined the optimal cutoff point between high and low-risk groups. The receiver operating characteristic (ROC) curves were drawn to test this signature. In order to examine possible signaling mechanisms linked to these lncRNAs, the Gene Set Enrichment Analysis (GSEA) has been carried out. Results: Based on the lncRNA expression profiles for BC, a 9 lncRNA signature associated with autophagy was developed. The optimal cutoff value for high-risk and low-risk groups was used. The high-risk group had less survival time than the low-risk group. The result of this lncRNA signature was highly sensitive and precise. GSEA study found that the gene sets have been greatly enriched in many cancer pathways. Conclusions: Our signature of 9 lncRNAs related to autophagy has prognostic value for BC, and these lncRNAs related to autophagy may play an important role in BC biology.

scholarly journals Identification and validation of a pyroptosis-related prognostic signature for thyroid cancer

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Pu Wu ◽  
Jinyuan Shi ◽  
Wei Sun ◽  
Hao Zhang
Keyword(s):  
Thyroid Cancer ◽  
Risk Score ◽  
Prognostic Model ◽  
Immune Cell ◽  
Cox Regression ◽  
Checkpoint Inhibitors ◽  
Risk Groups ◽  
Low Risk ◽  
Prognostic Signature ◽  
Cox Regression Analysis

Abstract Background Pyroptosis is a form of programmed cell death triggered by inflammasomes. However, the roles of pyroptosis-related genes in thyroid cancer (THCA) remain still unclear. Objective This study aimed to construct a pyroptosis-related signature that could effectively predict THCA prognosis and survival. Methods A LASSO Cox regression analysis was performed to build a prognostic model based on the expression profile of each pyroptosis-related gene. The predictive value of the prognostic model was validated in the internal cohort. Results A pyroptosis-related signature consisting of four genes was constructed to predict THCA prognosis and all patients were classified into high- and low-risk groups. Patients with a high-risk score had a poorer overall survival (OS) than those in the low-risk group. The area under the curve (AUC) of the receiver operator characteristic (ROC) curves assessed and verified the predictive performance of this signature. Multivariate analysis showed the risk score was an independent prognostic factor. Tumor immune cell infiltration and immune status were significantly higher in low-risk groups, which indicated a better response to immune checkpoint inhibitors (ICIs). Of the four pyroptosis-related genes in the prognostic signature, qRT-PCR detected three of them with significantly differential expression in THCA tissues. Conclusion In summary, our pyroptosis-related risk signature may have an effective predictive and prognostic capability in THCA. Our results provide a potential foundation for future studies of the relationship between pyroptosis and the immunotherapy response.

scholarly journals A Ferroptosis-Related Signature Robustly Predicts Clinical Outcomes and Associates With Immune Microenvironment for Thyroid Cancer

2021 ◽  
Vol 8 ◽  
Author(s):  
Mingqin Ge ◽  
Jie Niu ◽  
Ping Hu ◽  
Aihua Tong ◽  
Yan Dai ◽  
...  
Keyword(s):  
Thyroid Cancer ◽  
High Risk ◽  
Risk Score ◽  
Immune Cell ◽  
Cox Regression ◽  
Low Risk ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Immune Microenvironment ◽  
Cox Regression Analysis

Objective: This study aimed to construct a prognostic ferroptosis-related signature for thyroid cancer and probe into the association with tumor immune microenvironment.Methods: Based on the expression profiles of ferroptosis-related genes, a LASSO cox regression model was established for thyroid cancer. Kaplan-Meier survival analysis was presented between high and low risk groups. The predictive performance was assessed by ROC. The predictive independency was validated via multivariate cox regression analysis and stratified analysis. A nomogram was established and verified by calibration curves. The enriched signaling pathways were predicted via GSEA. The association between the signature and immune cell infiltration was analyzed by CIBERSORT. The ferroptosis-related genes were validated in thyroid cancer tissues by immunohistochemistry and RT-qPCR.Results: A ferroptosis-related eight gene model was established for predicting the prognosis of thyroid cancer. Patients with high risk score indicated a poorer prognosis than those with low risk score (p = 1.186e-03). The AUCs for 1-, 2-, and 3-year survival were 0.887, 0.890, and 0.840, respectively. Following adjusting other prognostic factors, the model could independently predict the prognosis (p = 0.015, HR: 1.870, 95%CI: 1.132–3.090). A nomogram combining the signature and age was constructed. The nomogram-predicted probability of 1-, 3-, and 5-year survival approached the actual survival time. Several ferroptosis-related pathways were enriched in the high-risk group. The signature was distinctly associated with the immune cell infiltration. After validation, the eight genes were abnormally expressed between thyroid cancer and control tissues.Conclusion: Our findings established a prognostic ferroptosis-related signature that was associated with the immune microenvironment for thyroid cancer.

scholarly journals Construction and Validation of an Autophagy-Related Prognostic Model for Osteosarcoma Patients

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Hu Qian ◽  
Ting Lei ◽  
Pengfei Lei ◽  
Yihe Hu
Keyword(s):  
Prognostic Value ◽  
Risk Model ◽  
Cox Regression ◽  
Bone Development ◽  
Treatment Strategies ◽  
Risk Groups ◽  
Low Risk ◽  
Lasso Regression ◽  
Prognostic Accuracy ◽  
Selection Operator

While the prognostic value of autophagy-related genes (ARGs) in OS patients remains scarcely known, increasing evidence is indicating that autophagy is closely associated with the development and progression of osteosarcoma (OS). Therefore, we explored the prognostic value of ARGs in OS patients and illuminate associated mechanisms in this study. When the OS patients in the training/validation cohort were stratified into high- and low-risk groups according to the risk model established using least absolute shrinkage and selection operator (LASSO) regression analysis, we observed that patients in the low-risk group possessed better prognosis ( P < 0.0001 ). Univariate/Multivariate COX regression and subgroup analysis demonstrated that the ARGs-based risk model was an independent survival indicator for OS patients. The nomogram incorporating the risk model and clinical features exhibited excellent prognostic accuracy. GO, KEGG, and GSVA analyses collectively indicated that bone development-associated pathway mediated the contribution of ARGs to the malignance of OS. Immune infiltration analysis suggested the potential pivotal role of macrophage in OS. In summary, the risk model based on 12 ARGs possessed potent capacity in predicting the prognosis of OS patients. Our work may assist clinicians to map out more reasonable treatment strategies and facilitate individual-targeted therapy in osteosarcoma.

scholarly journals Prognostic Value of Immune-Related genes in the Tumor Microenvironment of Glioma - Analyzed by Integrated Bioinformatics

2021 ◽  
Author(s):  
Yali Zhong ◽  
Xiaobin Luo ◽  
Fubing Yang ◽  
Xinling Song
Keyword(s):  
T Cells ◽  
High Risk ◽  
Risk Prediction ◽  
Immune Cell ◽  
Risk Group ◽  
Risk Groups ◽  
High Risk Group ◽  
Low Risk ◽  
Lasso Regression ◽  
Immune Related Genes

Abstract Object: Immune related genes play an important role in the process of tumor genesis and development. Therefore, we aim to find the Immune genes which are related to the prognosis of glioma patients, and to explore the infiltration of Immune cells in glioma microenvironment. Methods We downloaded the data of the glioma samples from the CGGA database, and performed batch correction to screen the primary glioma samples for subsequent analysis. Then the ESTIMATE algorithm was used to deal with the Stromal scores and Immune scores of the primary glioma samples, and the difference was analyzed. Then the common Immune related genes (IRGs) were obtained by intersecting with the Immune genes in the ImmPort database. Moreover, we used common IRGs to construct protein-protein interaction (PPI) networks, from which we screened the top 30 genes with high connectivity, and Lasso regression was used to screen the IRGs. Lastly, we obtained the combined genes, which were overlapped both in the top 30 high-connection genes and Lasso regression genes. The final genes were used to construct COX risk prediction models. The accuracy of the model were verified by the TCGA glioma data, and the model genes were analyzed for Immune-related pathways, as well as the Hallmark and KEGG enrichment. Additionally, we used CIBERSOFT algorithm to estimate the Immune cell content of the samples, and analyzed the differences, correlations and survival of the Immune cells in high and low risk groups. Results Firstly, a total of 117 IRGs were obtained from the gene sets, which were overlapped in the data of Stromal score, Immune score and ImmPort database. Secondly, the top 30 genes were selected after the PPI network, and another 26 genes were screened out after the Lasso regression algorithm. And then, six coexist IRGs were obtained from the intersecting sets. Furthermore, the COX risk prediction model was constructed and tested, showing that the overall survival rate of the high-risk group was about 50% of that of the low-risk group. We observed that the high-risk group were enriched in Immune response and Immune process. Most importantly, in KEGG pathways, the high-risk groups were mainly enriched in p53 signaling pathway, JAK-STAT signaling pathway, pathways in cancer and cell cycle. By estimating the Immune cell contents, we also found that the Immune cell Plasma cells, T cells CD8, T cells CD4 naïve, T cells regulatory (Tregs), Macrophages M0 and Neutrophils were higher in high-risk groups, when compared to the low-risk group, with significant difference. Finally, the correlation analysis showed that the degree of Immune infiltration in high-risk groups was related to T cells regulatory (Tregs), Macrophages M0 and Neutrophils. Conclusion A COX risk prediction model of 6 genes was successfully constructed, which was enriched in Immune-related pathways. Meanwhile, survival analysis and TCGA data validation revealed significant differences in the model genes in the overall survival of the glioma patients, and the degree of Immune infiltration in the model was associated with T cells regulatory (Tregs), Macrophages M0 and Neutrophils.

scholarly journals Construction of a prognostic model for lung squamous cell carcinoma based on seven N6-methylandenosine-related autophagy genes

2021 ◽  
Vol 18 (5) ◽  
pp. 6709-6723
Author(s):  
Xin Yu ◽  
◽  
Jun Liu ◽  
Ruiwen Xie ◽  
Mengling Chang ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Prognostic Model ◽  
Immune Cell ◽  
Cox Regression ◽  
Risk Group ◽  
Lung Squamous Cell Carcinoma ◽  
Risk Groups ◽  
Low Risk ◽  
Immune Cell Infiltration ◽  
Cox Regression Analysis

<abstract> <sec><title>Objective</title><p>We aimed to construct a novel prognostic model based on N6-methyladenosine (m6A)-related autophagy genes for predicting the prognosis of lung squamous cell carcinoma (LUSC).</p> </sec> <sec><title>Methods</title><p>Gene expression profiles and clinical information of Patients with LUSC were downloaded from The Cancer Genome Atlas (TCGA) database. In addition, m6A- and autophagy-related gene profiles were obtained from TCGA and Human Autophagy Database, respectively. Pearson correlation analysis was performed to identify the m6A-related autophagy genes, and univariate Cox regression analysis was conducted to screen for genes associated with prognosis. Based on these genes, LASSO Cox regression analysis was used to construct a prognostic model. The corresponding prognostic score (PS) was calculated, and patients with LUSC were assigned to low- and high-risk groups according to the median PS value. An independent dataset (GSE37745) was used to validate the prognostic ability of the model. CIBERSORT was used to calculate the differences in immune cell infiltration between the high- and low-risk groups.</p> </sec> <sec><title>Results</title><p>Seven m6A-related autophagy genes were screened to construct a prognostic model: <italic>CASP4</italic>, <italic>CDKN1A</italic>, <italic>DLC1</italic>, <italic>ITGB1</italic>, <italic>PINK1</italic>, <italic>TP63</italic>, and <italic>EIF4EBP1</italic>. In the training and validation sets, patients in the high-risk group had worse survival times than those in the low-risk group; the areas under the receiver operating characteristic curves were 0.958 and 0.759, respectively. There were differences in m6A levels and immune cell infiltration between the high- and low-risk groups.</p> </sec> <sec><title>Conclusions</title><p>Our prognostic model of the seven m6A-related autophagy genes had significant predictive value for LUSC; thus, these genes may serve as autophagy-related therapeutic targets in clinical practice.</p> </sec> </abstract>

scholarly journals Identification and validation of ADME genes as prognosis and therapy markers for hepatocellular carcinoma patients

2021 ◽  
Author(s):  
Ju Kun Wang ◽  
Ke Han ◽  
Chao Zhang ◽  
Xin Chen ◽  
Yu Li ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Prediction Model ◽  
Cox Regression ◽  
Predictive Ability ◽  
Risk Groups ◽  
Cancer Genome ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Bioinformatics Analyses

Purpose: ADME genes are genes involved in drug absorption, distribution, metabolism, and excretion (ADME). Previous studies report that expression levels of ADME-related genes correlate with prognosis of hepatocellular carcinoma (HCC) patients. However, the role of ADME gene expression on HCC prognosis has not been fully explored. This study sought to construct a prediction model using ADME-related genes for prognosis of HCC. Methods: Transcriptome and clinical data were retrieved from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC), which were used as training and validation cohorts, respectively. A prediction model was constructed using univariate Cox regression and LASSO analysis. Patients were divided into high- and low-risk groups based on the median risk score. The predictive ability of the risk signature was estimated through bioinformatics analyses. Results: Six ADME-related genes (CYP2C9, ABCB6, ABCC5, ADH4, DHRS13, and SLCO2A1) were used to construct the prediction model with a good predictive ability. Univariate and multivariate Cox regression analyses showed the risk signature was an independent predictor of overall survival. A single-sample gene set enrichment analysis (ssGSEA) strategy showed a significant relationship between risk signature and immune status. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses showed differentially expressed genes in the high- and low-risk groups were enriched in biological process associated with metabolic and cell cycle pathways. Conclusion: A prediction model was constructed using six ADME-related genes for prediction of HCC prognosis. This signature can be used to improve HCC diagnosis, treatment, and prognosis in clinical use.

A novel Immunoscore signature to predict survival in patients with gastric cancer: Implications for immunotherapy.

2018 ◽  
Vol 36 (5_suppl) ◽  
pp. 84-84 ◽  
Author(s):  
Dongqiang Zeng ◽  
Yunfang Yu ◽  
Rui Zhou ◽  
Wangjun Liao
Keyword(s):  
Gastric Cancer ◽  
Risk Score ◽  
Ad Hoc ◽  
Immune Cell ◽  
Cox Regression ◽  
Risk Groups ◽  
Low Risk ◽  
Entire Cohort ◽  
Potent Predictor ◽  
Cell Fractions

84 Background: Immunotherapies are transforming the treatment of gastric cancer (GC). Unfortunately, the majority of patients do not respond to immunotherapy. Thus, a novel risk score is needed to estimate tumor-infiltrating immune cells in predicting survival of GC and guide of more effective immunotherapy strategies. Methods: In total, 700 patients with GC from Gene Expression Ominus were sorted by training (490 patients) and validation (210 patients) cohorts.Immune risk score (IRS) signature based on the association between the expression of every immune cell and the duration of individual patients' survival was constructed using the LASSO Cox regression model. The relation between immunomodulatory genes, microsatellite instability and IRS value in GC was explored. Results: We established an IRS signature consisting of 11 types of immune cell fractions selected by the LASSO model, an IRS signature for GC was significantly different developed to classify patients into high- and low-risk groups in three cohorts. In the initial training cohort of patients, an IRS signature for GC was developed to classify patients into high- and low-risk groups in 8-year overall survival (hazard ratio [HR] = 2.93, P < 0.0001). The performance of IRS was validated in the validation cohort (HR = 1.77, P = 0.011) and in the entire cohort (HR = 2.54, P < 0.0001). In an ad-hoc analysis, we found the IRS value was substantially correlated with the expression of immunomodulatory genes including PD-1, PD-L1 and PD-L2, a prominent lower IRS was observed in microsatellite-instable (MSI) tumors compare with the microsatellite-stable (MSS) tumors. We developed nomograms for clinical use that integrated IRS signature and four clinicopathological risk factors was generated to predict which patients might have better survival with GC, and it performed well in the three cohorts (concordance index: 0.775, 0.682, and 0.752, respectively). Conclusions: These findings indicate that the assessment of the immune status via Immunoscore provides a potent predictor of survival in patients with GC and this resource may help facilitate the development of precision immunotherapy.

scholarly journals Potential Biomarkers for Predicting the Overall Survival of Lung squamous cell carcinoma: A analysis of Ferroptosis-Related lncRNAs

2021 ◽  
Author(s):  
zixuan Wu ◽  
Xuyan Huang ◽  
Min-jie Cai ◽  
Peidong Huang ◽  
Zunhui Guan
Keyword(s):  
Squamous Cell Carcinoma ◽  
High Risk ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Immune Cell ◽  
Lung Squamous Cell Carcinoma ◽  
Risk Groups ◽  
Low Risk ◽  
Cell Infiltration ◽  
Immune Cell Infiltration

Abstract Background In 502 Lung squamous cell carcinoma (LUSC) samples from The Cancer Genome Atlas (TCGA) datasets, the predictive significance of ferroptosis-related long non-coding RNAs (lncRNAs) was investigated. In LUSC, we meant to express how ferroptosis-associated lncRNAs interact with immune cell infiltration. Methods Gene expression enrichment was investigated using gene set enrichment analysis in the Kyoto Encyclopedia of Genes and Genomes. The prognostic model was constructed using Lasso regression. To better understand immune cell infiltration in different risk groups and its relationship to clinical outcome, researchers analyzed by modifications in the tumor microenvironment (TME) and immunological association. The expression of lncRNA was intimately connected to that of ferroptosis, according to co-expression analyses. Ferroptosis-related lncRNAs were shown to be partially overexpressed in high-risk patients in the absence of additional clinical signs, suggesting that they may be incorporated into a prediction model to predict LUSC prognosis. GSEA revealed the immunological and tumor-related pathways in the low-risk group. Results According to TCGA, CCR and inflammation-promoting genes were considered to be significantly different between the low-risk and high-risk groups. The expression of C10orf55, AC016924.1, AL161431.1, LUCAT1, AC104248.1, and MIR3945HG were likewise different in the two risk groups. Conclusion LncRNAs linked to ferroptosis are connected to the occurrence and development of LUSC. With the use of matching prognostic models, the prognosis of LUSC patients can be predicted. In LUSC, ferroptosis-related lncRNAs and immune cell infiltration in the TME might be novel therapeutic targets that should be investigated further.

scholarly journals Autophagy‐related Long Non-coding RNA Signature Associated with Prognosis of Lung Adenocarcinoma

2021 ◽  
Author(s):  
Guofei Zhang ◽  
Jiayi Shen ◽  
Zipu Yu ◽  
Gang Shen ◽  
Chengxiao Liang
Keyword(s):  
Lung Adenocarcinoma ◽  
Molecular Mechanisms ◽  
Cox Regression ◽  
Microarray Dataset ◽  
Risk Groups ◽  
The Cancer Genome Atlas ◽  
Non Coding Rna ◽  
Cancer Genome Atlas ◽  
Non Coding Rnas ◽  
Long Non Coding Rna

Abstract BackgroundEvidence suggests that long non-coding RNAs (lncRNAs) are involved in various cancers. Here, we developed and evaluated an autophagy-related prognostic lncRNA signature for lung adenocarcinoma (LUAD). ResultsUsing a publicly available microarray dataset from The Cancer Genome Atlas, we analyzed the lncRNA expression profile in a cohort of 439 LUAD patients. The lncRNA-mRNA co-expression network along with univariate and multivariate Cox regression analyses were used to determine 15 autophagy-related lncRNA signatures that were significantly correlated with patient overall survival. Autophagy-related lncRNA signatures stratified patients into high- and low-risk groups with significantly different survival (hazard ratio = 3.256, 95% confidence interval = 2.858–4.101, P < 0.001). The lncRNA signature was further confirmed in other independent datasets. Moreover, the lncRNA signature had prognostic value independent of routine clinical factors. Functional analysis indicated that autophagy-related lncRNA signatures may be involved in LUAD via known autophagy-related pathways. ConclusionsThis newly identified autophagy-related lncRNA signature is a more powerful prognostic tool than the clinicopathological factors routinely used to predict patient survival, and can provide further insights into the molecular mechanisms underlying LUAD.

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