scholarly journals Functional implications of aging-related lncRNAs for predicting prognosis and immune status in glioma patients

2021 ◽  
Author(s):  
Guangying Zhang ◽  
Yanyan Li ◽  
Na Li ◽  
Liangfang Shen ◽  
Zhanzhan N Li
Keyword(s):  
Expression Profiles ◽  
Survival Curve ◽  
Pearson Correlation ◽  
Critical Role ◽  
Risk Groups ◽  
Low Risk ◽  
The Cancer Genome Atlas ◽  
Individual Treatment ◽  
Missense Mutations ◽  
Genome Atlas

Glioma, is the most prevalent intracranial tumor with high recurrence and mortality rate. Long noncoding RNAs (lncRNAs) play a critical role in the occurrence and progression of tumors as well as in aging regulation. Our study aimed to establish a new glioma prognosis model by integrating aging-related lncRNAs expression profiles and clinical parameters in glioma patients from the Chinese Glioma Genome Atlas (CGGA) and the Cancer Genome Atlas (TCGA) datasets. The Pearson correlation analysis ( |R|> 0.6, P＜0.001) was performed to explore the aging-related lncRNAs, and univariate cox tregresion and least absolute shrinkage and selection operator (LASSO) regression were used to screening prognostic signature in glioma patients. Based on the fifteen lncRNAs, we can divide glioma patients into three subtypes, and developed a prognostic model. Kaplan-Meier survival curve analysis showed that low-risk patients had longer survival time than high-risk group. Principal component analysis indicated that aging-related lncRNAs signature had a clear distinction between high- and low-risk groups. We also found that fifteen target lncRNAs were closely correlated with 119 genes by establishing a co-expression network. In addition, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis displayed different function and pathways enrichment in high-and low-risk groups. The different missense mutations were observed in two groups, and the most frequent variant types were single nucleotide polymorphism (SNP). This study demonstrated that the novel aging-related lncRNAs signature had an important prognosis prediction and may contribute to individual treatment for glioma.

scholarly journals Development of a Prognostic Five-Gene Signature for Diffuse Lower-Grade Glioma Patients

2021 ◽  
Vol 12 ◽  
Author(s):  
Qiang Zhang ◽  
Wenhao Liu ◽  
Shun-Bin Luo ◽  
Fu-Chen Xie ◽  
Xiao-Jun Liu ◽  
...  
Keyword(s):  
Expression Profiles ◽  
Clinical Information ◽  
Predictive Performance ◽  
Risk Groups ◽  
Gene Signature ◽  
High Risk Group ◽  
The Cancer Genome Atlas ◽  
Lower Grade ◽  
Good Prospect ◽  
Genome Atlas

Background: Diffuse lower-grade gliomas (LGGs) are infiltrative and heterogeneous neoplasms. Gene signature including multiple protein-coding genes (PCGs) is widely used as a tumor marker. This study aimed to construct a multi-PCG signature to predict survival for LGG patients.Methods: LGG data including PCG expression profiles and clinical information were downloaded from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA). Survival analysis, receiver operating characteristic (ROC) analysis, and random survival forest algorithm (RSFVH) were used to identify the prognostic PCG signature.Results: From the training (n = 524) and test (n = 431) datasets, a five-PCG signature which can classify LGG patients into low- or high-risk group with a significantly different overall survival (log rank P < 0.001) was screened out and validated. In terms of prognosis predictive performance, the five-PCG signature is stronger than other clinical variables and IDH mutation status. Moreover, the five-PCG signature could further divide radiotherapy patients into two different risk groups. GO and KEGG analysis found that PCGs in the prognostic five-PCG signature were mainly enriched in cell cycle, apoptosis, DNA replication pathways.Conclusions: The new five-PCG signature is a reliable prognostic marker for LGG patients and has a good prospect in clinical application.

scholarly journals Identification of a ferroptosis-related gene signature predictive model in colon cancer

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Ye Wang ◽  
Heng-bo Xia ◽  
Zhang-ming Chen ◽  
Lei Meng ◽  
A-man Xu
Keyword(s):  
Colon Cancer ◽  
Cox Regression ◽  
Risk Group ◽  
Expression Profiles ◽  
Risk Groups ◽  
High Risk Group ◽  
Low Risk ◽  
The Cancer Genome Atlas ◽  
Lasso Regression ◽  
Cox Regression Analysis

Abstract Background The prognosis of colon cancer (CC) is challenging to predict due to its highly heterogeneous nature. Ferroptosis, an iron-dependent form of cell death, has roles in various cancers; however, the correlation between ferroptosis-related genes (FRGs) and prognosis in CC remains unclear. Methods The expression profiles of FRGs and relevant clinical information were retrieved from the Cancer Genome Atlas (TCGA) database. Cox regression analysis and the least absolute shrinkage and selection operator (LASSO) regression model were performed to build a prognostic model in TCGA cohort. Results Ten FRGs, five of which had mutation rates ≥ 3%, were found to be related to the overall survival (OS) of patients with CC. Patients were divided into high- and low-risk groups based on the results of Cox regression and LASSO analysis. Patients in the low-risk group had a significantly longer survival time than patients in the high-risk group (P < 0.001). Enrichment analyses in different risk groups showed that the altered genes were associated with the extracellular matrix, fatty acid metabolism, and peroxisome. Age, risk score, T stage, N stage, and M stage were independent predictors of patient OS based on the results of Cox analysis. Finally, a nomogram was constructed to predict 1-, 3-, and 5-year OS of patients with CC based on the above five independent factors. Conclusion A novel FRG model can be used for prognostic prediction in CC and may be helpful for individualized treatment.

Prognosis-associated methylation subtypes in endometrial carcinoma patients and the role of magnetic nanoparticles in gene extraction

2021 ◽  
Vol 11 (8) ◽  
pp. 1288-1298
Author(s):  
Liang Wang ◽  
Fengxia Xue
Keyword(s):  
Endometrial Cancer ◽  
Magnetic Nanoparticles ◽  
Endometrial Carcinoma ◽  
High Throughput Sequencing ◽  
Risk Groups ◽  
Vital Role ◽  
Low Risk ◽  
The Cancer Genome Atlas ◽  
Consensus Clustering ◽  
Tumor Subtypes

Endometrial cancer is one of the most common gynecological malignancies, and DNA methylation plays a vital role in its occurrence and development. In this study, we collected the relevant data on endometrial cancer from the Cancer Genome Atlas database and UCSC website. By screening and processing the data, we obtained 410 samples and 16,381 methylation sites. Endometrial carcinoma can be divided into seven molecular subtypes using consensus clustering method. Based on the analysis of the differences among subtypes, the methylation degree of different sites was obtained, and the prognosis model of methylation sites was established. Based on the median value of the train group, the train and test groups were divided into high and low-risk groups. The survival between the high and low-risk groups was different. It also showed that this model can predict the survival of patients, with better accuracy. In conclusion, the tumor subtypes based on methylation sites can provide a better guidance for treatment, relapse, and prognosis of endometrial cancer. In this study, magnetic nanoparticles can be used to extract genomic DNA and total RNA due to their paramagnetism and biocompatibility, then transcriptome high-throughput sequencing was performed. It may serve as potential cancer immune biomarker targets for developing future oncological treatments.

scholarly journals A lipid metabolism-related genes prognosis biomarker associated with the tumor immune microenvironment in colorectal carcinoma

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Chao Yang ◽  
Shuoyang Huang ◽  
Fengyu Cao ◽  
Yongbin Zheng
Keyword(s):  
Lipid Metabolism ◽  
Cox Regression ◽  
Malignant Tumors ◽  
Expression Profiles ◽  
Risk Groups ◽  
Clinical Parameter ◽  
Gene Expression Omnibus ◽  
Metabolic Reprogramming ◽  
The Cancer Genome Atlas ◽  
Immune Microenvironment

Abstract Background and aim Lipid metabolic reprogramming is considered to be a new hallmark of malignant tumors. The purpose of this study was to explore the expression profiles of lipid metabolism-related genes (LMRG) in colorectal cancer (CRC). Methods The lipid metabolism statuses of 500 CRC patients from the Cancer Genome Atlas (TCGA) and 523 from the Gene Expression Omnibus (GEO GSE39582) database were analyzed. The risk signature was constructed by univariate Cox regression and least absolute shrinkage and selection operator (LASSO) Cox regression. Results A novel four-LMRG signature (PROCA1, CCKBR, CPT2, and FDFT1) was constructed to predict clinical outcomes in CRC patients. The risk signature was shown to be an independent prognostic factor for CRC and was associated with tumour malignancy. Principal components analysis demonstrated that the risk signature could distinguish between low- and high-risk patients. There were significantly differences in abundances of tumor-infiltrating immune cells and mutational landscape between the two risk groups. Patients in the low-risk group were more likely to have higher tumor mutational burden, stem cell characteristics, and higher PD-L1 expression levels. Furthermore, a genomic-clinicopathologic nomogram was established and shown to be a more effective risk stratification tool than any clinical parameter alone. Conclusions This study demonstrated the prognostic value of LMRG and showed that they may be partially involved in the suppressive immune microenvironment formation.

scholarly journals Investigation of Genetic Determinants of Glioma Immune Phenotype by Integrative Immunogenomic Scale Analysis

2021 ◽  
Vol 12 ◽  
Author(s):  
Binghao Zhao ◽  
Yuekun Wang ◽  
Yaning Wang ◽  
Congxin Dai ◽  
Yu Wang ◽  
...  
Keyword(s):  
Immune Cell ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Clinicopathological Characteristics ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Immune Phenotype ◽  
Immune Phenotypes ◽  
Glioma Microenvironment ◽  
Genome Atlas

The immunosuppressive mechanisms of the surrounding microenvironment and distinct immunogenomic features in glioblastoma (GBM) have not been elucidated to date. To fill this gap, useful data were extracted from The Cancer Genome Atlas (TCGA), the Chinese Glioma Genome Atlas (CGGA), GSE16011, GSE43378, GSE23806, and GSE12907. With the ssGSEA method and the ESTIMATE and CIBERSORT algorithms, four microenvironmental signatures were used to identify glioma microenvironment genes, and the samples were reasonably classified into three immune phenotypes. The molecular and clinical features of these phenotypes were characterized via key gene set expression, tumor mutation burden, fraction of immune cell infiltration, and functional enrichment. Exhausted CD8+ T cell (GET) signature construction with the predictive response to commonly used antitumor drugs and peritumoral edema assisted in further characterizing the immune phenotype features. A total of 2,466 glioma samples with gene expression profiles were enrolled. Tumor purity, ESTIMATE, and immune and stromal scores served as the 4 microenvironment signatures used to classify gliomas into immune-high, immune-middle and immune-low groups, which had distinct immune heterogeneity and clinicopathological characteristics. The immune-H phenotype had higher expression of four immune signatures; however, most checkpoint molecules exhibited poor survival. Enriched pathways among the subtypes were related to immunity. The GET score was similar among the three phenotypes, while immune-L was more sensitive to bortezomib, cisplatin, docetaxel, lapatinib, and rapamycin prescriptions and displayed mild peritumor edema. The three novel immune phenotypes with distinct immunogenetic features could have utility for understanding glioma microenvironment regulation and determining prognosis. These results contribute to classifying glioma subtypes, remodeling the immunosuppressive microenvironment and informing novel cancer immunotherapy in the era of precision immuno-oncology.

scholarly journals Development and Verification of the Hypoxia- and Immune-Associated Prognostic Signature for Pancreatic Ductal Adenocarcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Dongjie Chen ◽  
Hui Huang ◽  
Longjun Zang ◽  
Wenzhe Gao ◽  
Hongwei Zhu ◽  
...  
Keyword(s):  
High Risk ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Risk Score ◽  
Cox Regression ◽  
Pearson Correlation ◽  
Risk Groups ◽  
Low Risk ◽  
Ductal Adenocarcinoma ◽  
Prognostic Signature ◽  
Score Model

We aim to construct a hypoxia- and immune-associated risk score model to predict the prognosis of patients with pancreatic ductal adenocarcinoma (PDAC). By unsupervised consensus clustering algorithms, we generate two different hypoxia clusters. Then, we screened out 682 hypoxia-associated and 528 immune-associated PDAC differentially expressed genes (DEGs) of PDAC using Pearson correlation analysis based on the Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression project (GTEx) dataset. Seven hypoxia and immune-associated signature genes (S100A16, PPP3CA, SEMA3C, PLAU, IL18, GDF11, and NR0B1) were identified to construct a risk score model using the Univariate Cox regression and the Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression, which stratified patients into high- and low-risk groups and were further validated in the GEO and ICGC cohort. Patients in the low-risk group showed superior overall survival (OS) to their high-risk counterparts (p &lt; 0.05). Moreover, it was suggested by multivariate Cox regression that our constructed hypoxia-associated and immune-associated prognosis signature might be used as the independent factor for prognosis prediction (p &lt; 0.001). By CIBERSORT and ESTIMATE algorithms, we discovered that patients in high-risk groups had lower immune score, stromal score, and immune checkpoint expression such as PD-L1, and different immunocyte infiltration states compared with those low-risk patients. The mutation spectrum also differs between high- and low-risk groups. To sum up, our hypoxia- and immune-associated prognostic signature can be used as an approach to stratify the risk of PDAC.

scholarly journals A Prognostic Autophagy-Related Long Non-coding RNA (ARlncRNA) Signature in Acute Myeloid Leukemia (AML)

2021 ◽  
Vol 12 ◽  
Author(s):  
Chunxia Zhao ◽  
Yulu Wang ◽  
Famei Tu ◽  
Shuai Zhao ◽  
Xiaoying Ye ◽  
...  
Keyword(s):  
Cox Regression ◽  
Survival Curve ◽  
Pearson Correlation ◽  
Enrichment Analysis ◽  
Risk Groups ◽  
Gene Set Enrichment Analysis ◽  
Kaplan Meier ◽  
Non Coding Rna ◽  
Novel Biomarkers ◽  
Meier Survival Curve

BackgroundSome studies have proven that autophagy and lncRNA play important roles in AML. Several autophagy related lncRNA signatures have been shown to affect the survival of patients in some other cancers. However, the role of autophagy related lncRNA in AML has not been explored yet. Hence, this study aims to find an autophagy related lncRNA signature that can affect survival for AML patients.MethodA Pearson correlation analysis, a Kaplan–Meier survival curve, a univariate cox regression, and a multivariate cox regression were performed to establish an autophagy related lncRNA signature. A univariate cox regression, a multivariate cox regression, a Kaplan–Meier survival curve, and a ROC curve were applied to confirm if the signature is an independent prognosis for AML patients. The relationship between the signature and the clinical features was explored by using a T test. Gene Set Enrichment Analysis (GSEA) was used to investigate the potential tumor related pathways.ResultsA four-autophagy related lncRNA (MIR133A1HG, AL359715.1, MIRLET7BHG, and AL356752.1) signature was established. The high risk score based on signature was related to the short survival time of AML patients. The signature was an independent factor for the prognosis for AML patients (HR = 1.684, 95% CI = 1.324–2.142, P &lt; 0.001). The signature was correlated with age, leukocyte numbers, and FAB (M3 or non-M3). The P53, IL6/JAK/STAT3, TNF-α, INF-γ, and IL2/STAT5 pathways might contribute to the differences between the risk groups based on signature in AML.ConclusionThe four autophagy related lncRNAs and their signature might be novel biomarkers for predicting the survival of AML patients. Some biological pathways might be the potential mechanisms of the signature for the survival of AML patients.

scholarly journals Immune-Related Four-lncRNA Signature for Patients with Cervical Cancer

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Jianfeng Zheng ◽  
Benben Cao ◽  
Xia Zhang ◽  
Zheng Niu ◽  
Jinyi Tong
Keyword(s):  
Cervical Cancer ◽  
High Risk ◽  
Immune Cell ◽  
Risk Group ◽  
Pearson Correlation ◽  
Characteristic Curve ◽  
Risk Groups ◽  
High Risk Group ◽  
Low Risk ◽  
Gynecological Malignancy

Cervical cancer (CC) is a common gynecological malignancy for which prognostic and therapeutic biomarkers are urgently needed. The signature based on immune-related lncRNAs (IRLs) of CC has never been reported. This study is aimed at establishing an IRL signature for patients with CC. A cohort of 326 CC and 21 normal tissue samples with corresponding clinical information was included in this study. Twenty-eight IRLs were collected according to the Pearson correlation analysis between the immune score and lncRNA expression ( p < 0.01 ). Four IRLs (BZRAP1-AS1, EMX2OS, ZNF667-AS1, and CTC-429P9.1) with the most significant prognostic values ( p < 0.05 ) were identified which demonstrated an ability to stratify patients into the low-risk and high-risk groups by developing a risk score model. It was observed that patients in the low-risk group showed longer overall survival (OS) than those in the high-risk group in the training set, valid set, and total set. The area under the curve (AUC) of the receiver operating characteristic curve (ROC curve) for the four-IRL signature in predicting the one-, two-, and three-year survival rates was larger than 0.65. In addition, the low-risk and high-risk groups displayed different immune statuses in GSEA. These IRLs were also significantly correlated with immune cell infiltration. Our results showed that the IRL signature had a prognostic value for CC. Meanwhile, the specific mechanisms of the four IRLs in the development of CC were ascertained preliminarily.

Integrated Transcriptomic Analysis of Necrosis-related Gene in Diffuse Gliomas

2019 ◽  
Vol 80 (04) ◽  
pp. 240-249
Author(s):  
Jiajia Wang ◽  
Jie Ma
Keyword(s):  
Gene Expression ◽  
High Risk ◽  
Differentially Expressed Genes ◽  
Expression Profiles ◽  
Prognostic Index ◽  
Gene Expression Profiles ◽  
Risk Groups ◽  
Low Risk ◽  
Differentially Expressed ◽  
Low Grade

Glioblastoma multiforme (GBM), an aggressive brain tumor, is characterized histologically by the presence of a necrotic center surrounded by so-called pseudopalisading cells. Pseudopalisading necrosis has long been used as a prognostic feature. However, the underlying molecular mechanism regulating the progression of GBMs remains unclear. We hypothesized that the gene expression profiles of individual cancers, specifically necrosis-related genes, would provide objective information that would allow for the creation of a prognostic index. Gene expression profiles of necrotic and nonnecrotic areas were obtained from the Ivy Glioblastoma Atlas Project (IVY GAP) database to explore the differentially expressed genes.A robust signature of seven genes was identified as a predictor for glioblastoma and low-grade glioma (GBM/LGG) in patients from The Cancer Genome Atlas (TCGA) cohort. This set of genes was able to stratify GBM/LGG and GBM patients into high-risk and low-risk groups in the training set as well as the validation set. The TCGA, Repository for Molecular Brain Neoplasia Data (Rembrandt), and GSE16011 databases were then used to validate the expression level of these seven genes in GBMs and LGGs. Finally, the differentially expressed genes (DEGs) in the high-risk and low-risk groups were subjected to gene ontology enrichment, Kyoto Encyclopedia of Genes and Genomes pathway, and gene set enrichment analyses, and they revealed that these DEGs were associated with immune and inflammatory responses. In conclusion, our study identified a novel seven-gene signature that may guide the prognostic prediction and development of therapeutic applications.

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