scholarly journals Inflammation-Related Long Non-Coding RNA Signature Predicts the Prognosis of Gastric Carcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
ShuQiao Zhang ◽  
XinYu Li ◽  
ChunZhi Tang ◽  
WeiHong Kuang
Keyword(s):  
High Risk ◽  
Gastric Carcinoma ◽  
Risk Groups ◽  
Cytolytic Activity ◽  
Immune Checkpoints ◽  
Type I ◽  
Kaplan Meier ◽  
Non Coding Rna ◽  
Prognostic Prediction ◽  
Long Non Coding Rna

Background: Gastric carcinoma (GC) is a molecularly and phenotypically highly heterogeneous disease, making the prognostic prediction challenging. On the other hand, Inflammation as part of the active cross-talk between the tumor and the host in the tumor or its microenvironment could affect prognosis.Method: We established a prognostic multi lncRNAs signature that could better predict the prognosis of GC patients based on inflammation-related differentially expressed lncRNAs in GC.Results: We identified 10 differently expressed lncRNAs related to inflammation associated with GC prognosis. Kaplan-Meier survival analysis demonstrated that high-risk inflammation-related lncRNAs signature was related to poor prognosis of GC. Moreover, the inflammation-related lncRNAs signature had an AUC of 0.788, proving their utility in predicting GC prognosis. Indeed, our risk signature is more precise in predicting the prognosis of GC patients than traditional clinicopathological manifestations. Immune and tumor-related pathways for individuals in the low and high-risk groups were further revealed by GSEA. Moreover, TCGA based analysis revealed significant differences in HLA, MHC class-I, cytolytic activity, parainflammation, co-stimulation of APC, type II INF response, and type I INF response between the two risk groups. Immune checkpoints revealed CD86, TNFSF18, CD200, and LAIR1 were differently expressed between lowand high-risk groups.Conclusion: A novel inflammation-related lncRNAs (AC015660.1, LINC01094, AL512506.1, AC124067.2, AC016737.1, AL136115.1, AP000695.1, AC104695.3, LINC00449, AC090772.1) signature may provide insight into the new therapies and prognosis prediction for GC patients.

scholarly journals Prognostic value of an immune long non-coding RNA signature in liver hepatocellular carcinoma

2019 ◽  
Author(s):  
rui kong ◽  
Nan Wang ◽  
Wei Han ◽  
Yuejuan Zheng ◽  
Jie Lu
Keyword(s):  
Hepatocellular Carcinoma ◽  
Principal Component ◽  
Enrichment Analysis ◽  
Risk Groups ◽  
Gene Set Enrichment Analysis ◽  
Kaplan Meier ◽  
Non Coding Rna ◽  
Liver Hepatocellular Carcinoma ◽  
Long Non Coding Rna

Abstract Background: In recent years, long non-coding RNAs (lncRNAs) are emerging as crucial regulators in the immunological process of liver hepatocellular carcinoma (LIHC). Increasing studies have found that some lncRNAs could be used as a diagnostic or therapeutic target for clinical management, but little research has investigated the role of immune-related lncRNA in tumor prognosis. In this study, we aimed to develop an immune lncRNA signature for the precise diagnosis and prognosis of liver hepatocellular carcinoma. Methods: Gene expression profiles of LIHC samples obtained from TCGA were screened for immune-related genes using two reference gene sets. The optimal immune-related lncRNA signature was built via correlational analysis, univariate and multivariate cox analysis. Then the Kaplan-Meier plot, ROC curve, clinical analysis, gene set enrichment analysis, and principal component analysis were carried out to evaluate the capability of immune lncRNA signature as a prognostic indicator. Results: Six long non-coding RNA MSC−AS1, AC009005.1, AL117336.3, AL031985.3, AL365203.2, AC099850.3 were identified via correlation analysis and cox regression analysis considering their interactions with immune genes. Next, tumor samples were separated into two risk groups by the signature with different clinical outcomes. Stratification analysis showed the prognostic ability of this signature acted as an independent factor. The AUC value of ROC curve was 0.779. The Kaplan-Meier method was used in survival analysis and results showed a statistical difference between the two risk groups. The predictive performance of this signature was validated by principal component analysis (PCA). Data from gene set enrichment analysis (GSEA) further unveiled several potential biological processes of these biomarkers may involve in. Conclusion: In summary, the study demonstrated the potential role of the six-lncRNA signature served as an independent prognostic factor for LIHC patients.

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 Development and Validation of a Robust Pyroptosis-Related Signature for Predicting Prognosis and Immune Status in Patients with Colon Cancer

2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Zhicheng Zhuang ◽  
Huajun Cai ◽  
Hexin Lin ◽  
Bingjie Guan ◽  
Yong Wu ◽  
...  
Keyword(s):  
High Risk ◽  
Immune Cells ◽  
Risk Group ◽  
Risk Groups ◽  
High Risk Group ◽  
Low Risk ◽  
Immune Checkpoints ◽  
Prognostic Signature ◽  
Tumor Purity ◽  
Kaplan Meier

Background. Pyroptosis has been confirmed as a type of inflammatory programmed cell death in recent years. However, the prognostic role of pyroptosis in colon cancer (CC) remains unclear. Methods. Dataset TCGA-COAD which came from the TCGA portal was taken as the training cohort. GSE17538 from the GEO database was treated as validation cohorts. Differential expression genes (DEGs) between normal and tumor tissues were confirmed. Patients were classified into two subgroups according to the expression characteristics of pyroptosis-related DEGs. The LASSO regression analysis was used to build the best prognostic signature, and its reliability was validated using Kaplan–Meier, ROC, PCA, and t-SNE analyses. And a nomogram based on the multivariate Cox analysis was developed. The enrichment analysis was performed in the GO and KEGG to investigate the potential mechanism. In addition, we explored the difference in the abundance of infiltrating immune cells and immune microenvironment between high- and low-risk groups. And we also predicted the association of common immune checkpoints with risk scores. Finally, we verified the expression of the pyroptosis-related hub gene at the protein level by immunohistochemistry. Results. A total of 23 pyroptosis-related DEGs were identified in the TCGA cohort. Patients were classified into two molecular clusters (MC) based on DEGs. Kaplan–Meier survival analysis indicated that patients with MC1 represented significantly poorer OS than patients with MC2. 13 overall survival- (OS-) related DEGs in MCs were used to construct the prognostic signature. Patients in the high-risk group exhibited poorer OS compared to those in the low-risk group. Combined with the clinical features, the risk score was found to be an independent prognostic factor of CC patients. The above results are verified in the external dataset GSE17538. A nomogram was established and showed excellent performance. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses indicated that the varied prognostic performance between high- and low-risk groups may be related to the immune response mediated by local inflammation. Further analysis showed that the high-risk group has stronger immune cell infiltration and lower tumor purity than the low-risk group. Through the correlation between risk score and immune checkpoint expression, T-cell immunoglobulin and mucin domain-containing protein 3 (TIM-3) was predicted as a potential therapeutic target for the high-risk group. Conclusion. The 13-gene signature was associated with OS, immune cells, tumor purity, and immune checkpoints in CC patients, and it could provide the basis for immunotherapy and predicting prognosis and help clinicians make decisions for individualized treatment.

scholarly journals Prognostic Value of a Pyroptosis-Related Long Noncoding RNA Signature Associated with Osteosarcoma Microenvironment

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Xinxin Bu ◽  
Jiuxiang Liu ◽  
Rong Ding ◽  
Zhi Li
Keyword(s):  
High Risk ◽  
Noncoding Rna ◽  
Cox Regression ◽  
Bone Cancer ◽  
Predictive Biomarkers ◽  
Clinical Information ◽  
Risk Groups ◽  
High Risk Group ◽  
Immune Checkpoints ◽  
Kaplan Meier

Background. Osteosarcoma is the most prevalent bone cancer that affects young adults and adolescents. It is the most frequent malignancy of the bone. In spite of the fact that complete surgical resection and chemotherapy have increased the overall survival of osteosarcoma patients considerably, the prognosis remains dismal in patients with recurring and/or metastasized osteosarcoma. Thus, finding predictive biomarkers representing osteosarcoma's biological variability may result in more effective treatment for osteosarcoma patients. Methods. In this research, RNA data and clinical information were obtained from TARGET database. The risk score was calculated using a technique that incorporated both univariate and multivariate Cox regression. A variety of statistical methods were employed to assess the risk score's accuracy. These included ROC curves, nomograms, and Kaplan-Meier curves. Following that, bioinformatics studies were carried out in order to investigate the possible biological processes that influence the prognosis of osteosarcoma patients. GSEA was used to investigate the variations in pathway enrichment among the different groups of genes. To examine the disparities in the immune microenvironment, the analytical methods CIBERSORT and ssGSEA were employed. Results. We discovered three differentially expressed lncRNAs (RPARP-AS1, AC009159.3, and AC124312.3) that are linked to osteosarcoma prognosis. Kaplan-Meier analysis showed the presence of a signature of high-risk lncRNAs linked with a poor prognosis for osteosarcoma. Furthermore, the AUC of the lncRNAs signature was 0.773, indicating that they are useful in predicting osteosarcoma prognosis in certain cases. In predicting osteosarcoma prognosis, our risk assessment approach outperformed conventional clinicopathological characteristics. In the high-risk group of people, GSEA showed the presence of tumor-related pathways as well as immune-related pathways. Furthermore, TARGET revealed that immune-related functions such as checkpoint, T-cell coinhibition, and costimulation were significantly different between the high-risk and low-risk groups. LAIR1, LAG3, CD44, and CD22, as well as other immune checkpoints, were shown to be expressed differentially across the two risk groups. Conclusion. This study established that pyroptosis-derived lncRNAs had a significant predictive value for osteosarcoma patients' survival, indicating that they may be a viable target for future therapy.

The Variant rs145204276 of GAS5 is Associated with the Development and Prognosis of Gastric Cancer

2018 ◽  
Vol 27 (1) ◽  
pp. 19-24 ◽  
Author(s):  
Qianjun Li ◽  
Gang Ma ◽  
Huimin Guo ◽  
Suhua Sun ◽  
Ying Xu ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Survival Rate ◽  
Cancer Progression ◽  
Regulatory Mechanism ◽  
Tumor Stage ◽  
Kaplan Meier ◽  
Non Coding Rna ◽  
Early Tumor ◽  
Long Non Coding Rna ◽  
Clinicopathological Variables

Background & Aims: Down-regulation of the growth arrest specific transcript 5 (GAS5) (long non-coding RNA) is associated with cell proliferation of gastric cancer (GC) and a poor prognosis. We aimed to investigate whether the variant rs145204276 of GAS5 is associated with the prognosis of GC in the Chinese population, and to unveil the regulatory mechanism underlying the GAS5 expression in GC tissues.Method: 1,253 GC patients and 1,354 healthy controls were included. The frequency of the genotype del/del and the allele del of rs145204276 were compared between the patients and the controls and between different subgroups of patients classified by clinicopathological variables. The overall survival rate was analyzed according to the Kaplan-Meier method using the log-rank test.Results: The frequency of genotype del/del was significantly lower in patients than in the controls (7.0% vs. 9.1%, p = 0.001). Kaplan-Meier analysis showed that genotype del/del was significantly associated with a higher survival rate (p = 0.01). Patients with late tumor stage were found to have a significantly lower rate of genotype del/del than those with an early tumor stage (4.9% vs. 8.8%, p = 0.01). Patients with UICC III and IV were found to have a significantly lower rate of genotype del/del than those with UICC I and II (5.3% vs. 8.1%, p = 0.02).Conclusion: The variant rs145204276 of GAS5 is associated with the development and prognosis of GC. The allele del of rs145204276 is associated with a remarkably lower incidence of cancer progression and metastasis.

scholarly journals U-survival for prognostic prediction of disease progression and mortality of patients with COVID-19

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Janne J. Näppi ◽  
Tomoki Uemura ◽  
Chinatsu Watari ◽  
Toru Hironaka ◽  
Tohru Kamiya ◽  
...  
Keyword(s):  
Risk Groups ◽  
Healthcare Services ◽  
Chest Ct ◽  
Prediction Performance ◽  
Survival Prediction ◽  
Concordance Index ◽  
Survival Curves ◽  
Analysis Methodology ◽  
Kaplan Meier ◽  
Prognostic Prediction

AbstractThe rapid increase of patients with coronavirus disease 2019 (COVID-19) has introduced major challenges to healthcare services worldwide. Therefore, fast and accurate clinical assessment of COVID-19 progression and mortality is vital for the management of COVID-19 patients. We developed an automated image-based survival prediction model, called U-survival, which combines deep learning of chest CT images with the established survival analysis methodology of an elastic-net Cox survival model. In an evaluation of 383 COVID-19 positive patients from two hospitals, the prognostic bootstrap prediction performance of U-survival was significantly higher (P < 0.0001) than those of existing laboratory and image-based reference predictors both for COVID-19 progression (maximum concordance index: 91.6% [95% confidence interval 91.5, 91.7]) and for mortality (88.7% [88.6, 88.9]), and the separation between the Kaplan–Meier survival curves of patients stratified into low- and high-risk groups was largest for U-survival (P < 3 × 10–14). The results indicate that U-survival can be used to provide automated and objective prognostic predictions for the management of COVID-19 patients.

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 Identifying and Validating an Acidosis-Related Signature Associated with Prognosis and Tumor Immune Infiltration Characteristics in Pancreatic Carcinoma

2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Pingfei Tang ◽  
Weiming Qu ◽  
Dajun Wu ◽  
Shihua Chen ◽  
Minji Liu ◽  
...  
Keyword(s):  
Gene Expression ◽  
High Risk ◽  
Pancreatic Carcinoma ◽  
Cox Regression ◽  
Risk Group ◽  
Immune Dysfunction ◽  
Low Risk ◽  
Excellent Performance ◽  
Kaplan Meier ◽  
Prognostic Prediction

Background. Acidosis in the tumor microenvironment (TME) is involved in tumor immune dysfunction and tumor progression. We attempted to develop an acidosis-related index (ARI) signature to improve the prognostic prediction of pancreatic carcinoma (PC). Methods. Differential gene expression analyses of two public datasets (GSE152345 and GSE62452) from the Gene Expression Omnibus database were performed to identify the acidosis-related genes. The Cancer Genome Atlas–pancreatic carcinoma (TCGA-PAAD) cohort in the TCGA database was set as the discovery dataset. Univariate Cox regression and the Kaplan–Meier method were applied to screen for prognostic genes. The least absolute shrinkage and selection operator (LASSO) Cox regression was used to establish the optimal model. The tumor immune infiltrating pattern was characterized by the single-sample gene set enrichment analysis (ssGSEA) method, and the prediction of immunotherapy responsiveness was conducted using the tumor immune dysfunction and exclusion (TIDE) algorithm. Results. We identified 133 acidosis-related genes, of which 37 were identified as prognostic genes by univariate Cox analysis in combination with the Kaplan–Meier method ( p values of both methods < 0.05). An acidosis-related signature involving seven genes (ARNTL2, DKK1, CEP55, CTSV, MYEOV, DSG2, and GBP2) was developed in TCGA-PAAD and further validated in GSE62452. Patients in the acidosis-related high-risk group consistently showed poorer survival outcomes than those in the low-risk group. The 5-year AUCs (areas under the curve) for survival prediction were 0.738 for TCGA-PAAD and 0.889 for GSE62452, suggesting excellent performance. The low-risk group in TCGA-PAAD showed a higher abundance of CD8+ T cells and activated natural killer cells and was predicted to possess an elevated proportion of immunotherapeutic responders compared with the high-risk counterpart. Conclusions. We developed a reliable acidosis-related signature that showed excellent performance in prognostic prediction and correlated with tumor immune infiltration, providing a new direction for prognostic evaluation and immunotherapy management in PC.

scholarly journals The long non-coding RNA LUCAT1 is a negative feedback regulator of interferon responses in humans

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Shiuli Agarwal ◽  
Tim Vierbuchen ◽  
Sreya Ghosh ◽  
Jennie Chan ◽  
Zhaozhao Jiang ◽  
...  
Keyword(s):  
Immune Responses ◽  
Myeloid Cells ◽  
Type I ◽  
Targeted Deletion ◽  
Interferon Stimulated Genes ◽  
Non Coding Rna ◽  
Interferon Responses ◽  
Long Non Coding Rna ◽  
Feedback Regulator

AbstractLong non-coding RNAs are important regulators of biological processes including immune responses. The immunoregulatory functions of lncRNAs have been revealed primarily in murine models with limited understanding of lncRNAs in human immune responses. Here, we identify lncRNA LUCAT1 which is upregulated in human myeloid cells stimulated with lipopolysaccharide and other innate immune stimuli. Targeted deletion of LUCAT1 in myeloid cells increases expression of type I interferon stimulated genes in response to LPS. By contrast, increased LUCAT1 expression results in a reduction of the inducible ISG response. In activated cells, LUCAT1 is enriched in the nucleus where it associates with chromatin. Further, LUCAT1 limits transcription of interferon stimulated genes by interacting with STAT1 in the nucleus. Together, our study highlights the role of the lncRNA LUCAT1 as a post-induction feedback regulator which functions to restrain the immune response in human cells.

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