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

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 < 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.

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Identification of Epithelial–Mesenchymal Transition-Related Prognostic lncRNAs Biomarkers Associated With Melanoma Microenvironment

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.679133 ◽

2021 ◽

Vol 9 ◽

Author(s):

Bo Xiao ◽

Liyan Liu ◽

Zhuoyuan Chen ◽

Aoyu Li ◽

Pingxiao Wang ◽

...

Keyword(s):

Cox Regression ◽

Epithelial Mesenchymal Transition ◽

Enrichment Analysis ◽

Roc Curves ◽

Risk Groups ◽

Gene Set Enrichment Analysis ◽

Gene Set Enrichment ◽

Mesenchymal Transition ◽

Cox Regression Analysis ◽

Kaplan Meier

Melanoma is the most common cancer of the skin, associated with a worse prognosis and distant metastasis. Epithelial–mesenchymal transition (EMT) is a reversible cellular biological process that plays significant roles in diverse tumor functions, and it is modulated by specific genes and transcription factors. The relevance of EMT-related lncRNAs in melanoma has not been determined. Therefore, RNA expression data and clinical features were collected from the TCGA database (N = 447). Melanoma samples were randomly assigned into the training (315) and testing sets (132). An EMT-related lncRNA signature was constructed via comprehensive analyses of lncRNA expression level and corresponding clinical data. The Kaplan-Meier analysis showed significant differences in overall survival in patients with melanoma in the low and high-risk groups in two sets. Receiver operating characteristic (ROC) curves were used to measure the performance of the model. Cox regression analysis indicated that the risk score was an independent prognostic factor in two sets. Besides, a nomogram was constructed based on the independent variables. Gene Set Enrichment Analysis (GSEA) was applied to evaluate the potential biological functions in the two risk groups. Furthermore, the melanoma microenvironment was evaluated using ESTIMATE and CIBERSORT algorithms in the risk groups. This study indicates that EMT-related lncRNAs can function as potential independent prognostic biomarkers for melanoma survival.

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

10.21203/rs.2.19313/v1 ◽

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.

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Extent of lymph node resection and effect on pancreatic cancer overall survival.

Journal of Clinical Oncology ◽

10.1200/jco.2020.38.4_suppl.682 ◽

2020 ◽

Vol 38 (4_suppl) ◽

pp. 682-682

Author(s):

Brian Cox ◽

Nicholas Manguso ◽

Humair Quadri ◽

Jessica Crystal ◽

Katelyn Mae Atkins ◽

...

Keyword(s):

Pancreatic Cancer ◽

Overall Survival ◽

Logistic Regression ◽

Lymph Node ◽

Cox Regression ◽

Survival Curve ◽

Regression Analyses ◽

Kaplan Meier ◽

The Impact ◽

Meier Survival Curve

682 Background: Lymph node (LN) metastases affect overall survival (OS) in pancreatic cancer (PC). However, a LN sampling threshold does not exist. We examined the impact of nodal sampling on overall survival (OS). Methods: Patients with Stage I-III PC ≥55 years old who underwent curative resection from 2004-2016 were identified from the National Cancer Database (NCDB). After adjusting for age, gender, grade, stage, and Charlson-Deyo score, multiple binomial logistic regression analyses assessed the impact of the LN ratio (LNR) on OS. LNR was defined as the number of positive LN over the number of LN examined. Regression analyses, a Cox-Regression, and a Kaplan-Meier survival curve assessed how many LN should be sampled. Results: A total of 13,673 patients, median age 69 years (55-90), were included. Most were Caucasian (86.6%) males with Charlson-Deyo scores ≤ 1 (90.3%) and moderately to poorly differentiated PC (90.1%). Median number of LN examined was 15 (1-75) with a median of 1 positive LN (0-35). As expected, increased number of positive LNs was associated with reduced OS, p < 0.001. After data normalization, an increasing LNR was associated with a 12-fold likelihood of death [OR: 11.9, p < 0.001 (CI 6.0, 23.7)]. Subsequent regression models established evaluation of ≥ 16 LNs as the greatest predictor of OS. A regression model evaluating < or ≥ 16 lymph nodes was performed to ascertain the effects of age, gender, ethnicity, grade, stage, and LN examined on OS. The logistic regression model correctly classified 74.5% of cases with a specificity of 99.6% (p < 0.001). Examination of < 16 LN, Caucasian race, grade, stage, and higher Charlson-Deyo scores were significantly associated with decreased OS. If ≥ 16 LNs were examined, patients had a 1.5-fold likelihood of better OS, p < 0.001 (CI 1.4, 1.6). An adjusted Cox Regression showed increased HR of 1.2, p < 0.001 (CI 1.1, 1.2) and an unadjusted Kaplan Meier survival curve predicted ≥ 16 LN examined are associated with an increase in OS of 2.8 months [log-rank: 32.0, p < 0.001]. Conclusions: Patients undergoing curative intent resection for PC should have adequate nodal sampling. Stratification of patients by LNR may provide useful information of OS. Examination of ≥ 16 LNs impacts OS in patients with Stage I-III PC.

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Decreased DHRS1 expression is a novel predictor of poor survival in patients with hepatocellular carcinoma

Biomarkers in Medicine ◽

10.2217/bmm-2021-0041 ◽

2021 ◽

Vol 15 (15) ◽

pp. 1319-1331

Author(s):

Li Li ◽

Hui-Jing Situ ◽

Wen-Cheng Ma ◽

Xuan Liu ◽

Lu-Lu Wang

Keyword(s):

Hepatocellular Carcinoma ◽

Cox Regression ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Aberrant Expression ◽

Normal Tissues ◽

Multiple Data ◽

Kaplan Meier ◽

Survival Gene ◽

Mrna And Protein Expression

Aim: To investigate the effect of aberrant expression of DHRS1 on hepatocellular carcinoma (HCC). Materials & methods: Kaplan–Meier and Cox regression analyses were performed to evaluate the correlation between DHRS1 and overall survival. Gene set enrichment analysis was performed to explore the potential function of DHRS1 in HCC. Results: Multiple data analysis revealed that DHRS1 mRNA and protein expression level were remarkably lower in HCC than that in normal tissues. In survival analysis, patients with low DHRS1 expression presented a poorer prognosis, and was an independent risk factor for HCC. Conclusion: Decreased DHRS1 expression may be a potential predictor of poor prognosis in HCC.

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Establishment of an Autophagy-Related Clinical Prognosis Model for Predicting the Overall Survival of Osteosarcoma

BioMed Research International ◽

10.1155/2021/5428425 ◽

2021 ◽

Vol 2021 ◽

pp. 1-17

Author(s):

Jianyi Li ◽

Xiaojie Tang ◽

Yukun Du ◽

Jun Dong ◽

Zheng Zhao ◽

...

Keyword(s):

Gene Expression ◽

Overall Survival ◽

Molecular Mechanisms ◽

Cox Regression ◽

Enrichment Analysis ◽

Risk Groups ◽

Gene Set Enrichment Analysis ◽

Data Set ◽

Primary Tumor Site ◽

Clinical Prognosis

Purpose. Osteosarcoma is the most common primary and highly invasive bone tumor in children and adolescents. The purpose of this study is to construct a multi-gene expression feature related to autophagy, which can be used to predict the prognosis of patients with osteosarcoma. Materials and methods. The clinical and gene expression data of patients with osteosarcoma were obtained from the target database. Enrichment analysis of autophagy-related genes related to overall survival (OS-related ARGs) screened by univariate Cox regression was used to determine OS-related ARGs function and signal pathway. In addition, the selected OS-related ARGs were incorporated into multivariate Cox regression to construct prognostic signature for the overall survival (OS) of osteosarcoma. Use the dataset obtained from the GEO database to verify the signature. Besides, gene set enrichment analysis (GSEA) were applied to further elucidate the molecular mechanisms. Finally, the nomogram is established by combining the risk signature with the clinical characteristics. Results. Our study eventually included 85 patients. Survival analysis showed that patients with low riskScore had better OS. In addition, 16 genes were included in OS-related ARGs. We also generate a prognosis signature based on two OS-related ARGs. The signature can significantly divide patients into low-risk groups and high-risk groups, and has been verified in the data set of GEO. Subsequently, the riskScore, primary tumor site and metastasis status were identified as independent prognostic factors for OS and a nomogram were generated. The C-index of nomogram is 0.789 (95% CI: 0.703~0.875), ROC curve and calibration chart shows that nomogram has a good consistency between prediction and observation of patients. Conclusions. ARGs was related to the prognosis of osteosarcoma and can be used as a biomarker of prognosis in patients with osteosarcoma. Nomogram can be used to predict OS of patients and improve treatment strategies.

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Identification of Seven Novel Ferroptosis-Related Long Non - Coding RNA Signatures as a Diagnostic Biomarker for Acute Myeloid Leukemia

10.21203/rs.3.rs-502041/v1 ◽

2021 ◽

Author(s):

Zhiyuan Zheng ◽

Wei Wu ◽

Zehang Lin ◽

Shuhan Liu ◽

Qiaoqian Chen ◽

...

Keyword(s):

Acute Myeloid Leukemia ◽

Risk Score ◽

Myeloid Leukemia ◽

Cox Regression ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Gene Set Enrichment ◽

Gene Set ◽

Kaplan Meier ◽

Acute Myeloid

Abstract Background: Ferroptosis is a newly discovered type of programmed cell death that participates in the biological processes of various cancers. However, the mechanism by which ferroptosis modulates acute myeloid leukemia (AML) remains unclear. This study aimed to investigate the role of ferroptosis-related long non-coding RNAs (lncRNAs) in AML and establish a corresponding prognostic model.Methods: RNA-sequencing data and clinicopathological characteristics were obtained from The Cancer Genome Atlas database, and ferroptosis-related genes were obtained from the FerrDb database. The “limma” R package, Cox regression, and the least absolute shrinkage and selection operator were used to determine the ferroptosis-related lncRNA signature with the lowest Akaike information criteria (AIC). The risk score of ferroptosis-related lncRNAs was calculated and patients with AML were divided into high- and low-risk groups based on the median risk score. The Kaplan-Meier curve and Cox regression were used to evaluate the prognostic value of the risk score. Finally, gene set enrichment analysis (GSEA) and single-sample gene set enrichment analysis (ssGSEA) were performed to explore the biological functions of the ferroptosis-related lncRNAs.Results: Seven ferroptosis-related lncRNA signatures were identified in the training group, and Kaplan-Meier and Cox regression analyses confirmed that risk scores were independent prognostic predictors of AML in both the training and validation groups (All P < 0.05). In addition, the area under the curve (AUC) analysis confirmed that the signatures had a good predictive ability for the prognosis of AML. GSEA and ssGSEA showed that the seven ferroptosis-related lncRNAs were related to glutathione metabolism and tumor immunity.Conclusions: In this study, seven novel ferroptosis-related lncRNA signatures (AP001266.2, AC133961.1, AF064858.3, AC007383.2, AC008906.1, AC026771.1, and KIF26B-AS1) were established. These signatures were shown to accurately predict the prognosis of AML, which would provide new insights into strategies for the development of new AML therapies.

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The right atrial diameter as a risk factor predicting recurrence of atrial fibrillation after catheter ablation at mid-term follow-up

10.21203/rs.3.rs-23323/v1 ◽

2020 ◽

Author(s):

Wang Xiaofei ◽

Wang Wenli ◽

Zou Cao

Keyword(s):

Risk Factor ◽

Independent Risk Factor ◽

Cox Regression ◽

Survival Curve ◽

Right Atrial ◽

Cutoff Value ◽

Kaplan Meier ◽

Significant Difference ◽

Meier Survival Curve

Abstract Background Left atrial diameter (LAD) has been confirmed to predict recurrence of atrial fibrillation (AF) after catheter ablation (CA). The influence of right atrium (RA) size on the prognosis after CA was relatively unclear and lack of research. The objective of the present study was to investigate the relationship between right atrial diameter (RAD) and the mid-term outcome of AF after CA. Methods This study retrospectively examined 121 patients who underwent initial CA for symptomatic AF. Cox regression model was used to find risk factors of recurrence. Receiver operating characteristic (ROC) curve was used to evaluate predictive power and determine clinic cutoff value. Kaplan-Meier survival curve and log-rank test were used to analyze success rate. Results There were 94 (77.7%) patients of freedom from AF after 24.2 ± 4.5 months’ follow-up. Multivariate Cox regression analysis showed both hypertension and RAD were independent risk factors of arrhythmia recurrence after ablation regardless of AF type (HR: 4.915; 95% CI: 1.370-17.635; P = 0.015 and HR: 1.059; 95% CI: 1.001–1.120; P = 0.045, respectively). However, in patients with paroxysmal AF (par-AF), Multivariate analysis showed RAD become the only independent risk factor (HR: 1.031; 95% CI: 1.016–1.340; P = 0.029). ROC curve demonstrated the cutoff value of RAD was 35.5 mm with an area under the curve (AUC) of 0.715 (95% CI: 0.586–0.843, P = 0.009), sensitivity of 81.3% and specificity of 54.2%. Kaplan-Meier survival curve showed significant difference of freedom from par-AF (67.5 vs. 91.4%, log-rank, P = 0.015) between patients with RAD ≥ 35.5 mm and < 35.5 mm in this subgroup. Nevertheless, in patients with persistent AF (per-AF), no risk factor of arrhythmia recurrence was found. In addition, Kaplan-Meier survival curve showed no significant difference of freedom from per-AF (69.7 vs. 87.5%, log-rank, P = 0.31) between patients with RAD ≥ 35.5 mm and < 35.5 mm. Conclusions RAD was the independent risk factor predicting recurrence of AF after CA only in patients with par-AF. In patients with RAD < 35.5 mm, there was a significantly higher freedom from par-AF recurrence compared with RAD ≥ 35.5 mm after a mid-term follow-up.

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Development and Validation of a Hypoxia-Related Signature for Predicting Survival Outcomes in Patients With Bladder Cancer

Frontiers in Genetics ◽

10.3389/fgene.2021.670384 ◽

2021 ◽

Vol 12 ◽

Author(s):

Facai Zhang ◽

Xiaoming Wang ◽

Yunjin Bai ◽

Huan Hu ◽

Yubo Yang ◽

...

Keyword(s):

Bladder Cancer ◽

Cox Regression ◽

Risk Group ◽

Enrichment Analysis ◽

Risk Groups ◽

Gene Set Enrichment Analysis ◽

Risk Scores ◽

Survival Outcomes ◽

Gene Set Enrichment ◽

Hypoxia Signature

ObjectivesThis study aimed to develop and validate a hypoxia signature for predicting survival outcomes in patients with bladder cancer.MethodsWe downloaded the RNA sequence and the clinicopathologic data of the patients with bladder cancer from The Cancer Genome Atlas (TCGA) (https://portal.gdc.cancer.gov/repository?facetTab=files) and the Gene Expression Omnibus (GEO) (https://www.ncbi.nlm.nih.gov/geo/) databases. Hypoxia genes were retrieved from the Molecular Signatures Database (https://www.gsea-msigdb.org/gsea/msigdb/index.jsp). Differentially expressed hypoxia-related genes were screened by univariate Cox regression analysis and Lasso regression analysis. Then, the selected genes constituted the hypoxia signature and were included in multivariate Cox regression to generate the risk scores. After that, we evaluate the predictive performance of this signature by multiple receiver operating characteristic (ROC) curves. The CIBERSORT tool was applied to investigate the relationship between the hypoxia signature and the immune cell infiltration, and the maftool was used to summarize and analyze the mutational data. Gene-set enrichment analysis (GSEA) was used to investigate the related signaling pathways of differentially expressed genes in both risk groups. Furthermore, we developed a model and presented it with a nomogram to predict survival outcomes in patients with bladder cancer.ResultsEight genes (AKAP12, ALDOB, CASP6, DTNA, HS3ST1, JUN, KDELR3, and STC1) were included in the hypoxia signature. The patients with higher risk scores showed worse overall survival time than the ones with lower risk scores in the training set (TCGA) and two external validation sets (GSE13507 and GSE32548). Immune infiltration analysis showed that two types of immune cells (M0 and M1 macrophages) had a significant infiltration in the high-risk group. Tumor mutation burden (TMB) analysis showed that the risk scores between the wild types and the mutation types of TP53, MUC16, RB1, and FGFR3 were significantly different. Gene-Set Enrichment Analysis (GSEA) showed that immune or cancer-associated pathways belonged to the high-risk groups and metabolism-related signal pathways were enriched into the low-risk group. Finally, we constructed a predictive model with risk score, age, and stage and validated its performance in GEO datasets.ConclusionWe successfully constructed and validated a novel hypoxia signature in bladder cancer, which could accurately predict patients’ prognosis.

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Establishment and Validation of a Novel Metabolism-related Gene Signature for Predicting Overall Survival of Patients with Breast Cancer

10.21203/rs.3.rs-222322/v1 ◽

2021 ◽

Author(s):

Jian Li ◽

Yang Liu ◽

Fei Liu ◽

Qiang Tian ◽

Baojiang Li ◽

...

Keyword(s):

Breast Cancer ◽

Cox Regression ◽

Treatment Strategies ◽

Enrichment Analysis ◽

Risk Groups ◽

Gene Signature ◽

Gene Set Enrichment Analysis ◽

Current Status ◽

Lasso Regression ◽

Related Gene

Abstract It is well known that Breast cancer is a heterogeneous disease.Although the current recurrence and mortality rate have been greatly improved, many people still suffer relapse and metastasis.Metabolic reprograming is currently considered to be a new hallmark of cancer.Therefore,in this study, we comprehensively analyzed the prognostic effect of metabolic-related gene signatures in breast cancer and its relationship with the immune microenvironment.We constructed a novel metabolic-related gene signature containing 6 genes to distinguish between high and low risk groups by univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression, and validated its robustness and accuracy through multiple databases.The metabolic gene signature may be an independent risk factor for BC both in the training and the testing set,the nomogram has a moderately accurate performance,and the C index was 0.757 and 0.728 respectively.The signature can reveal metabolic characteristics based on gene set enrichment analysis and at the same time monitor the status of TME.This gene signature can be used as a promising independent prognostic marker for BC patients, and can indicate the current status of TME, providing more clues for exploring new diagnostic and treatment strategies.

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The increasing expression of GPX7 related to the malignant clinical features leading to poor prognosis of glioma patients

Chinese Neurosurgical Journal ◽

10.1186/s41016-021-00235-3 ◽

2021 ◽

Vol 7 (1) ◽

Author(s):

Jiawei Yao ◽

Xin Chen ◽

Zhendong Liu ◽

Ruotian Zhang ◽

Cheng Zhang ◽

...

Keyword(s):

Cox Regression ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Multidimensional Data ◽

Normal Brain ◽

Cox Regression Analysis ◽

Diagnosis And Prognosis ◽

Kaplan Meier ◽

Potential Biomarker ◽

Therapy Target

Abstract Background Glioma is the most common malignant brain tumor in adults. The standard treatment scheme of glioma is surgical resection combined alternative radio- and chemotherapy. However, the outcome of glioma patients was unsatisfied. Here, we aimed to explore the molecular and biological function characteristics of GPX7 in glioma. Methods The multidimensional data of glioma samples were downloaded from Chinese Glioma Genome Atlas (CGGA). RT-qPCR method was used to identify the expression status of GPX7. Kaplan–Meier curves and Cox regression analysis were used to explore the prognostic value of GPX7. Gene Set Enrichment Analysis (GSEA) was applied to investigate the GPX7-related functions in glioma. Results The results indicated that the expression of GPX7 in glioma was higher compared to that in normal brain tissue. Univariate and multivariate Cox regression analyses confirmed that the expression value of GPX7 was an independent prognostic factor in glioma. The GSEA analysis showed that GPX7 was significantly enriched in the cell cycle pathway, ECM pathway, focal adhesion pathway, and toll-like receptor pathway. Conclusions The GPX7 was recommended as an independent risk factor for patients diagnosed with glioma for the first time and GPX7 could be potentially used as the therapy target in future. Furthermore, we attempted to explore a potential biomarker for improving the diagnosis and prognosis of patients with glioma.

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