A four prognosis-associated lncRNAs (PALnc) based risk score system reflects immune cell infiltration and predicts patient survival in pancreatic cancer

Abstract Background Pancreatic cancer (PC) is one of the most common cancers and the leading cause of cancer-related death worldwide. Exploring novel predictive biomarkers for PC patients’ prognosis is in urgent need. Methods In the present study, we conducted Cox proportional hazards regression to identify critical prognosis-associated lncRNAs (PALncs) in TCGA PC dataset. Based on the results of multivariate analysis, a PALnc-based risk score system was established, and validated in GSE62452 dataset. The validity and reliability of the risk score system for prognosis of PC were evaluated through ROC analysis. And function enrichment analyses for the PALncs were also performed. Result In the multivariate analysis, four PALncs (LINC00476, C9orf163, LINC00346 and DSCR9) were screened out to develop a risk score system, which showed a high AUC at 3 and 5 years overall survival (0.785 at 3 year OS, 0.863 at 5 year OS) in TCGA datasets. And the ROC analysis of the risk score system for RFS in TCGA dataset revealed that AUC for RFS was 0.799 at 3 years and 0.909 at 5 years. Further, the AUC for OS in the validation cohort was 0.705 at 3 years and 0.959 at 5 years. Furthermore, the functional enrichment analysis revealed that these PALncs may be involved in various pathways related to cancer, including Ras family activation, autophagy in cancer, MAPK signaling pathway, HIF-1 signaling pathway, PI3K-Akt signaling pathway, etc. And correlation analysis of these tumor infiltrating immune cells and risk score system revealed that the infiltration level of B cell naïve, plasma cells, and CD8+ T cells are negatively correlated to the risk score system, while macrophages M2 positively correlated to the risk score system. Conclusion Our study established a four PALncs based risk score system, which reflects immune cell infiltration and predicts patient survival for PC.

Download Full-text

Identification of Autophagy-Related Prognostic Signature and Analysis of Immune Cell Infiltration in Low-Grade Gliomas

BioMed Research International ◽

10.1155/2021/7918693 ◽

2021 ◽

Vol 2021 ◽

pp. 1-24

Author(s):

Qingli Quan ◽

Xinxin Xiong ◽

Shanyun Wu ◽

Meixing Yu

Keyword(s):

Risk Score ◽

Immune Cells ◽

Roc Analysis ◽

Immune Cell ◽

Time Dependent ◽

Low Grade ◽

Immune Cell Infiltration ◽

Prognostic Signature ◽

Low Grade Gliomas ◽

Key Genes

Autophagy plays an important role in cancer. Many studies have demonstrated that autophagy-related genes (ARGs) can act as a prognostic signature for some cancers, but little has been known in low-grade gliomas (LGG). In our study, we aimed to establish a prognostical model based on ARGs and find prognostic risk-related key genes in LGG. In the present study, a prognostic signature was constructed based on a total of 8 ARGs (MAPK8IP1, EEF2, GRID2, BIRC5, DLC1, NAMPT, GRID1, and TP73). It was revealed that the higher the risk score, the worse was the prognosis. Time-dependent ROC analysis showed that the risk score could precisely predict the prognosis of LGG patients. Additionally, four key genes (TGFβ2, SERPING1, SERPINE1, and TIMP1) were identified and found significantly associated with OS of LGG patients. Besides, they were also discovered to be strongly related to six types of immune cells which infiltrated in LGG tumor. Taken together, the present study demonstrated the promising potential of the ARG risk score formula as an independent factor for LGG prediction. It also provided the autophagy-related signature of prognosis and potential therapeutic targets for the treatment of LGG.

Download Full-text

Development and validation of a ferroptosis-related prognostic model in pancreatic cancer

10.21203/rs.3.rs-139597/v2 ◽

2021 ◽

Author(s):

Chen-jie Qiu ◽

Xue-bing Wang ◽

Zi-ruo Zheng ◽

Chao-zhi Yang ◽

Kai Lin ◽

...

Keyword(s):

Pancreatic Cancer ◽

T Cell ◽

High Risk ◽

Risk Score ◽

Prognostic Model ◽

Immune Cell ◽

Risk Group ◽

High Risk Group ◽

Low Risk ◽

Immune Cell Infiltration

Abstract Background: The purpose of this study was to identify ferroptosis-related genes (FRGs) associated with the prognosis of pancreatic cancer and to construct a prognostic model based on FRGs. Methods: Based on pancreatic cancer data obtained from The Cancer Genome Atlas database, we established the prognostic model from 232 FRGs. A nomogram was constructed by combining the prognostic model and clinicopathological features. Gene Expression Omnibus datasets and tissue samples obtained from our center were utilized to validate the model. Relationship between risk score and immune cell infiltration was explored by CIBERSORT and TIMER.Results: The prognostic model was established based on four FRGs (ENPP2, ATG4D, SLC2A1 and MAP3K5) and can be an independent risk factor in pancreatic cancer (HR 1.648, 95% CI 1.335-2.035, p < 0.001). Based on the median risk score, patients were divided into a high-risk group and a low-risk group. The prognosis of the low-risk group was significantly better than that of the high-risk group. In the high-risk group, patients treated with chemotherapy had a better prognosis. The nomogram showed that the model was the most important element. Gene set enrichment analysis identified three key pathways, namely, TGFβ signaling, HIF signaling pathway and adherens junction. The prognostic model can also affect the immune cell infiltration, such as macrophages M0, M1, CD4+T cell and CD8+T cell. Conclusion: A ferroptosis-related prognostic model can be employed to predict the prognosis of pancreatic cancer. Ferroptosis can be an important marker and immunotherapy can be a potential therapeutic target for pancreatic cancer.

Download Full-text

Development and Validation of a Ferroptosis-related Prognostic Model in Pancreatic Cancer

10.21203/rs.3.rs-139597/v1 ◽

2021 ◽

Author(s):

Chen-jie Qiu ◽

Xue-bing Wang ◽

Zi-ruo Zheng ◽

Chao-zhi Yang ◽

Kai Lin ◽

...

Keyword(s):

Pancreatic Cancer ◽

T Cell ◽

High Risk ◽

Risk Score ◽

Prognostic Model ◽

Immune Cell ◽

Risk Group ◽

High Risk Group ◽

Immune Cell Infiltration ◽

Cox Analysis

Abstract Background: With the development of genomics, ferroptosis has been determined to be highly important in cancer. The purpose of this study was to identify ferroptosis-related genes (FRGs) associated with the prognosis of pancreatic cancer and to construct a prognostic model based on FRGs. Methods: Based on pancreatic cancer data obtained from The Cancer Genome Atlas (TCGA) database, we employed univariate Cox analysis, least absolute shrinkage and selection operator (LASSO) analysis and multivariate Cox analysis to establish the prognostic model from 232 FRGs. A nomogram was constructed by combining the prognostic model and clinicopathological features. Gene Expression Omnibus (GEO) datasets and tissue samples obtained from our center were utilized to validate the prognostic model. Relationship between risk score and immune cell infiltration was explored by CIBERSORT and TIMER.Results: The prognostic model was established based on four FRGs (ENPP2, ATG4D, SLC2A1 and MAP3K5) and can be an independent risk factor in pancreatic cancer (HR 1.648, 95% CI 1.335-2.035, p < 0.001). Based on the median risk score, patients were divided into a high-risk group and a low-risk group. The KM curve indicated that the overall survival (OS) of the low-risk group was significantly better than that of the high-risk group. The nomogram showed that the prognostic model was the most important element. Gene set enrichment analysis (GSEA) identified three key pathways, namely, TGFβ signaling, HIF signaling pathway and adherens junction. GSE57495, GSE62452 and 88 pancreatic cancer tissues from our center were utilized to validate the prognostic model. The prognostic model can also affect the immune cell infiltration, such as macrophages M0, M1, CD4+T cell and CD8+T cell. Conclusion: A ferroptosis-related prognostic model can be employed to predict the prognosis of pancreatic cancer. Ferroptosis can be an important marker and immunotherapy can be a potential therapeutic target for pancreatic cancer.

Download Full-text

Establishment of a 7-miRNA–Based Risk Score System for Predicting Prognosis of Pancreatic Cancer

Pancreas ◽

10.1097/mpa.0000000000001542 ◽

2020 ◽

Vol 49 (5) ◽

pp. 655-662

Author(s):

Hongkai Zhuang ◽

Zuyi Ma ◽

Kaijun Huang ◽

Zixuan Zhou ◽

Bowen Huang ◽

...

Keyword(s):

Pancreatic Cancer ◽

Risk Score ◽

Score System ◽

Risk Score System

Download Full-text

A novel risk score system for prognostic evaluation in adenocarcinoma of the oesophagogastric junction: a large population study from the SEER database and our center

BMC Cancer ◽

10.1186/s12885-021-08558-1 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Jun Wang ◽

Le Shi ◽

Jing Chen ◽

Beidi Wang ◽

Jia Qi ◽

...

Keyword(s):

Risk Score ◽

Patient Survival ◽

Risk Scores ◽

Rank Test ◽

Seer Database ◽

Score System ◽

Prognostic Evaluation ◽

Log Rank Test ◽

Oesophagogastric Junction ◽

Risk Score System

Abstract Background The incidence rate of adenocarcinoma of the oesophagogastric junction (AEG) has significantly increased over the past decades, with a steady increase in morbidity. The aim of this study was to explore a variety of clinical factors to judge the survival outcomes of AEG patients. Methods We first obtained the clinical data of AEG patients from the Surveillance, Epidemiology, and End Results Program (SEER) database. Univariate and least absolute shrinkage and selection operator (LASSO) regression models were used to build a risk score system. Patient survival was analysed using the Kaplan-Meier method and the log-rank test. The specificity and sensitivity of the risk score were determined by receiver operating characteristic (ROC) curves. Finally, the internal validation set from the SEER database and external validation sets from our center were used to validate the prognostic power of this model. Results We identified a risk score system consisting of six clinical features that can be a good predictor of AEG patient survival. Patients with high risk scores had a significantly worse prognosis than those with low risk scores (log-rank test, P-value < 0.0001). Furthermore, the areas under ROC for 3-year and 5-year survival were 0.74 and 0.75, respectively. We also found that the benefits of chemotherapy and radiotherapy were limited to stage III/IV AEG patients in the high-risk group. Using the validation sets, our novel risk score system was proven to have strong prognostic value for AEG patients. Conclusions Our results may provide new insights into the prognostic evaluation of AEG.

Download Full-text

Comprehensive Analysis of Autophagy-Associated lncRNAs Reveal Potential Prognostic Prediction in Pancreatic Cancer

Frontiers in Oncology ◽

10.3389/fonc.2021.596573 ◽

2021 ◽

Vol 11 ◽

Author(s):

Guangyu Chen ◽

Gang Yang ◽

Junyu Long ◽

Jinshou Yang ◽

Cheng Qin ◽

...

Keyword(s):

Pancreatic Cancer ◽

Risk Score ◽

Expression Profile ◽

Cox Regression ◽

Risk Groups ◽

Cancer Genome ◽

Low Risk ◽

Score System ◽

Prognostic Prediction ◽

Risk Score System

Pancreatic cancer (PC) is a highly malignant tumor in the digestive system. Both long noncoding RNAs (lncRNAs) and autophagy play vital roles in the development and progress of PC. Here, we constructed a prognostic risk score system based on the expression profile of autophagy-associated lncRNAs for prognostic prediction in PC patients. Firstly, we extracted the expression profile of lncRNA and clinical information from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases. The autophagy-associated genes were from The Human Autophagy Database. Through Cox regression and survival analysis, we screened out seven autophagy-associated lncRNAs and built the risk score system in which the patients with PC were distinguished into high- and low-risk groups in both training and validation datasets. PCA plot displayed distinct discrimination, and risk score system displayed independently predictive value for PC patient survival time by multivariate Cox regression. Then, we built a lncRNA and mRNA co-expression network via Cytoscape and Sankey diagram. Finally, we analyzed the function of lncRNAs in high- and low-risk groups by gene set enrichment analysis (GSEA). The results showed that autophagy and metabolism might make significant effects on PC patients of low-risk groups. Taken together, our study provides a new insight to understand the role of autophagy-associated lncRNAs and finds novel therapeutic and prognostic targets in PC.

Download Full-text

Five‑long non‑coding RNA risk score system for the effective prediction of gastric cancer patient survival

Oncology Letters ◽

10.3892/ol.2019.10124 ◽

2019 ◽

Cited By ~ 3

Author(s):

Zunqi Hu ◽

Dejun Yang ◽

Yuan Tang ◽

Xin Zhang ◽

Ziran Wei ◽

...

Keyword(s):

Gastric Cancer ◽

Cancer Patient ◽

Risk Score ◽

Gastric Cancer Patient ◽

Patient Survival ◽

Score System ◽

Non Coding Rna ◽

Risk Score System ◽

Long Non Coding Rna

Download Full-text

An 8 miRNA-Based Risk Score System for Predicting the Prognosis of Patients With Papillary Thyroid Cancer

Technology in Cancer Research & Treatment ◽

10.1177/1533033820965594 ◽

2020 ◽

Vol 19 ◽

pp. 153303382096559

Author(s):

Wanwan Yi ◽

Jin Liu ◽

Shuping Qu ◽

Hengwei Fan ◽

Zhongwei Lv

Keyword(s):

Signaling Pathway ◽

Risk Score ◽

Hedgehog Signaling ◽

Mirna Gene ◽

Enrichment Analysis ◽

Training Set ◽

Score System ◽

Gene Regulatory ◽

Validation Set ◽

Risk Score System

Background: Dysregulation of microRNAs (miRNAs) in papillary thyroid cancer (PTC) might influence prognosis of PTC. This study is aimed to develop a risk score system for predicting prognosis of PTC. Methods: The miRNA and gene expression profiles of PTC were obtained from The Cancer Genome Atlas database. PTC samples were randomly separated into training set (n = 248) and validation set (n = 248). The differentially expressed miRNAs (DE-miRNAs) in the training set were screened using limma package. The independent prognosis-associated DE-miRNAs were identified for building a risk score system. Risk score of PTC samples in the training set was calculated and samples were divided into high risk group and low risk group. Kaplan-Meier curves and receiver operating characteristic (ROC) curve were used to assess the accuracy of the risk score system in the training set, validation set and entire set. Finally, a miRNA-gene regulatory network was visualized by Cytoscape software, followed by enrichment analysis. Results: Totally, 162 DE-miRNAs between tumor and control groups in the training set were identified. An 8 independent prognosis-associated DE-miRNAs, (including miR-1179, miR-133b, miR-3194, miR-3912, miR-548j, miR-6720, miR-6734, and miR-6843) based risk score system was developed. The area under ROC curve in the training set, validation set and entire set was all above 0.93. A miRNA-gene regulatory network involving the 8 DE-miRNAs were built and functional enrichment analysis suggested the genes in the network were significantly enriched into 13 pathways, including calcium signaling pathway and hedgehog signaling pathway. Conclusion: The risk score system developed this study might be used for predicting the prognosis of PTC. Besides, the 8 miRNAs might affect the prognosis of PTC via hedgehog signaling pathway and calcium signaling pathway.

Download Full-text

Establishment of a Prognostic Prediction System Based on Tumor Microenvironment of Pancreatic Cancer

10.21203/rs.3.rs-955286/v1 ◽

2021 ◽

Author(s):

Bo Liu ◽

Tingting Fu ◽

Ping He ◽

Ke Xu

Keyword(s):

Pancreatic Cancer ◽

Regression Analysis ◽

Risk Score ◽

Cox Regression ◽

External Validation ◽

Prediction System ◽

Score System ◽

Cox Regression Analysis ◽

Prognostic Prediction ◽

Risk Score System

Abstract Purpose: Pancreatic cancer (PC) is an inflammatory tumor. Tumor microenvironment (TME) plays an important role in the development of PC. This study aims to explore hub genes of TME and establish a prognostic prediction system for PC.Methods: High throughput RNA-sequencing and clinical data of PC were downloaded from TCGA and ICGC database, respectively. PC Patients were divided into High- and low-score group by using stromal, immune scores system based on ESTIMATE. Differentially expressed genes (DEGs) between High- and low-score patients were screened and survival related DEGs were identified as candidate genes by univariate COX regression analysis. Final variables for establishment of the prognostic prediction system were determined by LASSO analysis and multivariate COX regression analysis. The predictive power of the prognostic system was evaluated by internal and external validation. Results: A total of 210 candidate genes were identified by stromal, immune scores system and survival analyses. Finally, the prognostic risk score system was constructed by the following genes: FAM57B, HTRA3, CXCL10, GABRP, SPRR1B, FAM83A, LY6D. In process of internal validation, Harrell's C-index of this prognostic risk score system was 0.73, and the area under the receiver operating characteristic curve (AUC) value of 1-year, 2-year and 3-year OS period was 0.67, 0.76 and 0.86, respectively. In the external validation set, the survival prediction C-index was 0.71, and the AUC was 0.81, 0.72, 0.78 at 1-year, 2-year and 3-year, respectively.Conclusion: This prognostic risk score system based on TME demonstrated a good predictive capacity to the prognosis of PC.

Download Full-text