scholarly journals Development of Prognostic Indicator Based on Autophagy-Related lncRNA Analysis in Colon Adenocarcinoma

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Weige Zhou ◽  
Shijing Zhang ◽  
Hui-biao Li ◽  
Zheyou Cai ◽  
Shuting Tang ◽  
...  
Keyword(s):  
Risk Score ◽  
Noncoding Rna ◽  
Cox Regression ◽  
Risk Group ◽  
Expression Profiles ◽  
Colon Adenocarcinoma ◽  
High Risk Group ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Lasso Regression

There were no systematic researches about autophagy-related long noncoding RNA (lncRNA) signatures to predict the survival of patients with colon adenocarcinoma. It was necessary to set up corresponding autophagy-related lncRNA signatures. The expression profiles of lncRNAs which contained 480 colon adenocarcinoma samples were obtained from The Cancer Genome Atlas (TCGA) database. The coexpression network of lncRNAs and autophagy-related genes was utilized to select autophagy-related lncRNAs. The lncRNAs were further screened using univariate Cox regression. In addition, Lasso regression and multivariate Cox regression were used to develop an autophagy-related lncRNA signature. A risk score based on the signature was established, and Cox regression was used to test whether it was an independent prognostic factor. The functional enrichment of autophagy-related lncRNAs was visualized using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes. Ten prognostic autophagy-related lncRNAs (AC027307.2, AC068580.3, AL138756.1, CD27-AS1, EIF3J-DT, LINC01011, LINC01063, LINC02381, AC073896.3, and SNHG16) were identified to be significantly different, which made up an autophagy-related lncRNA signature. The signature divided patients with colon adenocarcinoma into the low-risk group and the high-risk group. A risk score based on the signature was a significantly independent factor for the patients with colon adenocarcinoma (HR=1.088, 95%CI=1.057−1.120; P<0.001). Additionally, the ten lncRNAs were significantly enriched in autophagy process, metabolism, and tumor classical pathways. In conclusion, the ten autophagy-related lncRNAs and their signature might be molecular biomarkers and therapeutic targets for the patients with colon adenocarcinoma.

scholarly journals Construction and Validation of a Novel Ferroptosis-Related lncRNA Signature to Predict Prognosis in Colorectal Cancer Patients

2021 ◽  
Vol 12 ◽  
Author(s):  
Wenqi Zhang ◽  
Daoquan Fang ◽  
Shuhan Li ◽  
Xiaodong Bao ◽  
Lei Jiang ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Survival Probability ◽  
Risk Model ◽  
Cox Regression ◽  
Risk Group ◽  
Quantitative Polymerase Chain Reaction ◽  
Colon Adenocarcinoma ◽  
High Risk Group ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment

Background: Colorectal cancer (CRC) ranks as the third most common malignancy worldwide but a reliable prognostic biomarker of CRC is still lack. Thus, the purpose of our study was to explore whether ferroptosis - related lncRNAs could predict the prognosis of CRC.Methods: The mRNA expression profiling of colon adenocarcinoma (COAD) and rectum adenocarcinoma (READ) patients in The Cancer Genome Atlas (TCGA) database were downloaded. Univariate Cox and multivariate Cox regression analyses was used to obtain prognostic differently expressed ferroptosis-related lncRNAs (DE-FLs) and a risk signature was developed. Quantitative polymerase chain reaction (q-PCR) was used to validated the different expressions of DE-FLs. The calibration curves, C-index and the receiver operating characteristic (ROC) curves were applied to evaluate the accuracy of nomogram. Gene set enrichment analyses (GSEA) were carried out to explore the biological mechanism between high- and low-risk group and the potential regulated pathway of prognostic DE-FLs in CRC.Results: Forty-nine DE-FLs were identified between CRC and normal tissue. Then, a 4-DE-FLs (AC016027.1, AC099850.3, ELFN1-AS1, and VPS9D1-AS1) prognostic signature model was generated. AC016027.1 was downregulated in CRC tissue; VPS9D1-AS1 and ELFN1-AS1 were upregulated by q-PCR. The model had a better accuracy presenting by 1-, 3-, and 5-years ROC curve (AUC ≥0.6), and identified survival probability (p &lt; 0.05) well. Moreover, the risk signature could play as an independent factor of CRC (p &lt; 0.05). Further, a nomogram including age, pathologic stage, T stage, and risk score with good prognostic capability (C-index = 0.789) was constructed. In addition, we found biological pathways mainly related to metabolism and apoptosis were down-regulated in high-risk group who with poor outcome. Finally, the functional enrichment showed prognostic DE-FLs may significantly impact bile secretion in CRC.Conclusion: A risk model and nomogram based on ferroptosis-related lncRNAs were created to predict 1-, 3-, and 5-years survival probability of CRC patients. Our data suggested that the prognostic lncRNAs could serve as valuable prognostic marker.

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.

scholarly journals Development and validation of Pyroptosis‑related lncRNAs prediction model for bladder cancer

2022 ◽  
Author(s):  
Thongher Lia ◽  
Yanxiang Shao ◽  
Parbatraj Regmi ◽  
Xiang Li
Keyword(s):  
Bladder Cancer ◽  
Regression Analysis ◽  
Cancer Patients ◽  
Risk Score ◽  
Cox Regression ◽  
Risk Group ◽  
Expression Profiles ◽  
Low Risk ◽  
The Cancer Genome Atlas ◽  
Cox Regression Analysis

Bladder cancer is one of the highly heterogeneous disorders accompanied by a poor prognosis. This study aimed to construct a model based on pyroptosis‑related lncRNA to evaluate the potential prognostic application in bladder cancer. The mRNA expression profiles of bladder cancer patients and corresponding clinical data were downloaded from the public database from The Cancer Genome Atlas (TCGA). Pyroptosis‑related lncRNAs were identified by utilizing a co-expression network of Pyroptosis‑related genes and lncRNAs. The lncRNA was further screened by univariate Cox regression analysis. Finally, 8 pyroptosis-related lncRNA markers were established using Lasso regression and multivariate Cox regression analysis. Patients were separated into high and low-risk groups based on the performance value of the median risk score. Patients in the high-risk group had significantly poorer overall survival (OS) than those in the low-risk group (p &lt; 0.001), and In multivariate Cox regression analysis, the risk score was an independent predictive factor of OS ( HR&gt;1, P&lt;0.01). The area under the curve (AUC) of the 3- and 5-year OS in the receiver operating characteristic (ROC) curve were 0.742 and 0.739 respectively. In conclusion, these 8 pyroptosis-related lncRNA and their markers may be potential molecular markers and therapeutic targets for bladder cancer patients.

scholarly journals Identification of prognostic immune-related lncRNA signature predicting the overall survival for colorectal cancer

2021 ◽  
Author(s):  
Jianxin Li ◽  
Ting Han ◽  
Xin Wang ◽  
Yinchun Wang ◽  
Qingqiang Yang
Keyword(s):  
Colorectal Cancer ◽  
Overall Survival ◽  
High Risk ◽  
Cox Regression ◽  
Risk Group ◽  
Enrichment Analysis ◽  
High Risk Group ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Regression Analyses

Abstract Background Long non-coding RNA (lncRNA) is an important regulator of gene expression and serves fundamental role in immune regulation. The present study aimed to develop a novel immune-related lncRNA signature to accurately assess the prognosis of patients with colorectal cancer (CRC). Methods Transcriptome data and clinical information of patients with CRC were downloaded from The Cancer Genome Atlas (TCGA), and the immune-related mRNAs were extracted from immunomodulatory gene datasets IMMUNE RESPONSE and IMMUNE SYSTEM PROCESS based on the Molecular Signatures Database (MSigDB). Then, the immune-related lncRNAs were identified by a correlation analysis between immune-related mRNAs and lncRNAs. Subsequently, univariate, lasso and multivariate Cox regression were used to identify an immune-related lncRNA signature in training cohort, and the predict ability of the signature was further confirmed in the testing cohort and the entire TCGA cohort. Finally, the lncRNA-mRNA co-expression network was established to explore the biological role of the immune-related lncRNA signature. Results In total, 272 Immune-related lncRNAs were identified, five of which were applied to construct an immune-related lncRNA signature based on univariate, lasso and multivariate Cox regression analyses. The signature divided patients with CRC into low- and high-risk groups, and patients with CRC in high-risk group had poorer overall survival than those in low-risk group. Univariate and multivariate Cox regression analyses confirmed that the signature could be an independent prognostic factor in human CRC. Furthermore, functional enrichment analysis revealed that the immune-related lncRNA signature was significantly enriched in immune process and tumor classical pathways. Conclusions The present study revealed that the novel immune-related lncRNA signature could be exploited as underlying molecular biomarkers and therapeutic targets for the patients with CRC.

scholarly journals A Prognostic Model for Hepatocellular Carcinoma Patients Based on Signature Ferroptosis-related Genes

2021 ◽  
Author(s):  
Sizhe Wan ◽  
Yiming Lei ◽  
Mingkai Li ◽  
Bin Wu
Keyword(s):  
Overall Survival ◽  
T Cells ◽  
High Risk ◽  
Risk Score ◽  
Cox Regression ◽  
Risk Group ◽  
High Risk Group ◽  
Low Risk ◽  
Tnm Stage ◽  
Functional Enrichment

Abstract BackgroundWith the increasing number of HCC patients, it is necessary to accurately predicting the prognosis of these patients. Ferroptosis has been confirmed to be closely related to HCC progression. However, there is still a challenge in predicting the survival of HCC patients through ferroptosis-related genes.MethodThe RNA-seq data and corresponding clinical data of HCC from TCGA database were downloaded to establish a prognosis model, and data of ICGC and GSE14520 as the validation set. The risk score was constructed with 5 genes identified by univariate and LASSO Cox regression analysis. Then, risk score, TNM stage and cirrhosis were included to construct a nomogram, through univariate and multivariate Cox regression analysis.Results5 genes were identified from 70 ferroptosis-related DEGs to construct a gene signature to predict HCC patient survival from TCGA cohort. PCA and heatmap results show that there are obvious differences in patients with different score groups. Then, we included risk score, TNM stage and cirrhosis to construct a nomogram to further predict the overall survival of the patients. Survival analysis indicates that overall survival of the low- risk group is significantly higher than that of the high-risk group. Similarly, the data in the GSE14520 cohort also confirmed good performance for the nomogram. Furthermore, KEGG and GO functional enrichment analyses indicates the difference in overall survival between groups is closely related to immune-related pathways. Finally, through analyzing the immune status of all patients, we found that compared with patients in the low-risk group, “Macrophages M0”, “T cells CD8”, and “T cells regulatory” of the high-risk group were significantly higher.ConclusionThe nomogram based on ferroptosis-related genes has a good performance for the prognosis of HCC patients. The model may provide a reference for evaluation of HCC patients by targeting ferroptosis.

scholarly journals A Novel Signature Based on Pyroptosis-Related Genes for Predicting Breast Cancer

2021 ◽  
Author(s):  
Shuang Shen ◽  
Xin Chen ◽  
Rui Qu ◽  
Youming Guo ◽  
Yingying Su ◽  
...  
Keyword(s):  
Breast Cancer ◽  
High Risk ◽  
Risk Score ◽  
Cox Regression ◽  
Risk Group ◽  
High Risk Group ◽  
Low Risk ◽  
The Cancer Genome Atlas ◽  
Independent Predictive Factor ◽  
Tumour Immunity

Abstract Background: Breast cancer (BC) surpassed lung cancer as the most frequent malignant tumour in women. In recent years, pyroptosis has revealed itself as an inflammatory form of programmed cell death. However, it is unclear as to the expression of genes associated with pyroptosis in BC and its relationship to prognosis. Results: In this study, we identified 31 pyroptosis regulators that are differentially expressed between BC and normal breast. The differently expressed genes (DEG) allow BC patients to be divided into three subtypes. Through single-factor and multi-factor COX regression and the application of least absolute contraction and selection operator (LASSO) Cox regression method, the survival prognostic value of each gene related to pyroptosis in The Cancer Genome Atlas (TCGA) cohort was evaluated, and a 4-gene signature was constructed. BC patients of the TCGA cohort are divided into low-risk or high-risk groups by risk score. The survival of the low-risk group was significantly higher than the high-risk group (P <0.001). Using the median risk score from the TCGA cohort, BC patients from the Gene Expression Omnibus (GEO) cohort were divided into two risk sub-groups and similar conclusions were drawn. In combination with clinicopathological characteristics, the risk score is an independent predictive factor of OS in BC patients. Gene ontology (GO) and Kyoto Encylopedia of Genes and Genomes (KEGG) indicated that the high-risk group's immune genes were enriched and immune status was reduced. Conclusions: In conclusion, pyroptosis-related genes are important for tumour immunity and can be used to predict the prognosis of BC.

scholarly journals Comprehensive Analysis of the Immune-Oncology Targets and Immune Infiltrates of N6-Methyladenosine-Related Long Noncoding RNA Regulators in Breast Cancer

2021 ◽  
Vol 9 ◽  
Author(s):  
Xiaoqiang Zhang ◽  
Li Shen ◽  
Ruyu Cai ◽  
Xiafei Yu ◽  
Junzhe Yang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
High Risk ◽  
Risk Score ◽  
Noncoding Rna ◽  
Immune Cell ◽  
Cox Regression ◽  
High Risk Group ◽  
The Cancer Genome Atlas ◽  
Risk Scores ◽  
Immune Oncology

Breast cancer (BRCA) has become the highest incidence of cancer due to its heterogeneity. To predict the prognosis of BRCA patients, sensitive biomarkers deserve intensive investigation. Herein, we explored the role of N6-methyladenosine-related long non-coding RNAs (m6A-related lncRNAs) as prognostic biomarkers in BRCA patients acquired from The Cancer Genome Atlas (TCGA; n = 1,089) dataset and RNA sequencing (RNA-seq) data (n = 196). Pearson’s correlation analysis, and univariate and multivariate Cox regression were performed to select m6A-related lncRNAs associated with prognosis. Twelve lncRNAs were identified to construct an m6A-related lncRNA prognostic signature (m6A-LPS) in TCGA training (n = 545) and validation (n = 544) cohorts. Based on the 12 lncRNAs, risk scores were calculated. Then, patients were classified into low- and high-risk groups according to the median value of risk scores. Distinct immune cell infiltration was observed between the two groups. Patients with low-risk score had higher immune score and upregulated expressions of four immune-oncology targets (CTLA4, PDCD1, CD274, and CD19) than patients with high-risk score. On the contrary, the high-risk group was more correlated with overall gene mutations, Wnt/β-catenin signaling, and JAK-STAT signaling pathways. In addition, the stratification analysis verified the ability of m6A-LPS to predict prognosis. Moreover, a nomogram (based on risk score, age, gender, stage, PAM50, T, M, and N stage) was established to evaluate the overall survival (OS) of BRCA patients. Thus, m6A-LPS could serve as a sensitive biomarker in predicting the prognosis of BRCA patients and could exert positive influence in personalized immunotherapy.

scholarly journals A 7-lncRNA signature associated with the prognosis of colon adenocarcinoma

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e8877
Author(s):  
Xiaorui Fu ◽  
Jinzhong Duanmu ◽  
Taiyuan Li ◽  
Qunguang Jiang
Keyword(s):  
Cancer Progression ◽  
Risk Group ◽  
Expression Profiles ◽  
Colon Adenocarcinoma ◽  
The Cancer Genome Atlas ◽  
Receptor Interaction ◽  
Lasso Regression ◽  
Prognostic Signature ◽  
Link Type ◽  
Cox Analysis

Background Colon adenocarcinoma (COAD) is the most common colon cancer exhibiting high mortality. Due to their association with cancer progression, long noncoding RNAs (lncRNAs) are now being used as prognostic biomarkers. In the present study, we used relevant clinical information and expression profiles of lncRNAs originating from The Cancer Genome Atlas database, aiming to construct a prognostic lncRNA signature to estimate the prognosis of patients. Methods The samples were randomly spilt into training and validation cohorts. In the training cohort, prognosis-related lncRNAs were selected from differentially expressed lncRNAs using the univariate Cox analysis. Furthermore, the least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox analysis were employed for identifying prognostic lncRNAs. The prognostic signature was constructed by these lncRNAs. Results The prognostic model was able to calculate each COAD patient’s risk score and split the patients into groups of low and high risks. Compared to the low-risk group, the high-risk group had significant poor prognosis. Next, the prognostic signature was validated in the validation, as well as all cohorts. The receiver operating characteristic (ROC) curve and c-index were determined in all cohorts. Moreover, these prognostic lncRNA signatures were combined with clinicopathological risk factors to construct a nomogram for predicting the prognosis of COAD in the clinic. Finally, seven lncRNAs (CTC-273B12.10, AC009404.2, AC073283.7, RP11-167H9.4, AC007879.7, RP4-816N1.7, and RP11-400N13.2) were identified and validated by different cohorts. The Kyoto Encyclopedia of Genes and Genomes analysis of the mRNAs co-expressed with the seven prognostic lncRNAs suggested four significantly upregulated pathways, which were AGE-RAGE, focal adhesion, ECM-receptor interaction, and PI3K/Akt signaling pathways. Conclusion Thus, our study verified that the seven lncRNAs mentioned can be used as biomarkers to predict the prognosis of COAD patients and design personalized treatments.

scholarly journals A prognostic five immune-related long noncoding RNA signature for patients with colon cancer

2020 ◽  
Author(s):  
Kankan Zhao ◽  
Mengchuan Wang ◽  
Houlong Kang ◽  
Aiguo Wu
Keyword(s):  
Colon Cancer ◽  
Regression Analysis ◽  
High Risk ◽  
Noncoding Rna ◽  
Cox Regression ◽  
Risk Group ◽  
High Risk Group ◽  
Low Risk ◽  
The Cancer Genome Atlas ◽  
Cox Regression Analysis

Abstract Background: Our study aimed to identify immune related long non-coding RNAs (LncRNAs) to serve as potential prognostic indicators and immune therapeutic targets in patients with colon cancer.Methods: The Cancer Genome Atlas (TCGA) and Molecular Signatures Databases (MSigDB) database were used to identify immune related lncRNAs in patients with colon cancer. The least absolute shrinkage and selection operator (LASSO) analysis, and multivariate Cox proportional hazards regression analysis were employed to screen prognostic lncRNAs and construct immune-related multi-lncRNA signature. We used time-dependent receiver operating characteristic curve to assess the performance of this signature in colon cancer by calculating the area under the curve (AUC). Univariate and multivariate Cox regression analysis were performed to verify the independence of the prognostic value of this signature in colon cancer.Results: Five immune related lncRNAs (AC025575.2, AL161729.4, ELFN1-AS1, LINC00513, MIR210HG) were found to be significantly associated with overall survival (OS) of patients with colon cancer. Then, we developed a five immune-related lncRNA signature. According to this signature, patients were ranked into a high risk group (n = 208) and a low risk group (n = 209). Kaplan-Meier curve and log-rank method showed that patients in high risk group had worse OS than patients in low risk group (P = 5.5644e-05). AUC for predicting 3 year survival and 5 year survival was 0.776 and 0.762 respectively, which indicated good performance of this signature. Finally, this five immune-related lncRNA signature was demonstrated to be independently associated with prognosis of patients with colon cancer.Conclusion: We developed a five immune-related lncRNA signature as a prognostic biomarker for patients with colon cancer.

scholarly journals Development and validation of ferroptosis-related lncRNAs signature for hepatocellular carcinoma

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11627
Author(s):  
Jiaying Liang ◽  
Yaofeng Zhi ◽  
Wenhui Deng ◽  
Weige Zhou ◽  
Xuejun Li ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
High Risk ◽  
Immune Cell ◽  
Cox Regression ◽  
Malignant Tumors ◽  
Risk Group ◽  
Expression Profiles ◽  
Immune Checkpoint Blockade ◽  
High Risk Group ◽  
The Cancer Genome Atlas

Background Hepatocellular carcinoma (HCC) with high heterogeneity is one of the most frequent malignant tumors throughout the world. However, there is no research to establish a ferroptosis-related lncRNAs (FRlncRNAs) signature for the patients with HCC. Therefore, this study was designed to establish a novel FRlncRNAs signature to predict the survival of patients with HCC. Method The expression profiles of lncRNAs were acquired from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. FRlncRNAs co-expressed with ferroptosis-related genes were utilized to establish a signature. Cox regression was used to construct a novel three FRlncRNAs signature in the TCGA cohort, which was verified in the GEO validation cohort. Results Three differently expressed FRlncRNAs significantly associated with prognosis of HCC were identified, which composed a novel FRlncRNAs signature. According to the FRlncRNAs signature, the patients with HCC could be divided into low- and high-risk groups. Patients with HCC in the high-risk group displayed shorter overall survival (OS) contrasted with those in the low-risk group (P < 0.001 in TCGA cohort and P = 0.045 in GEO cohort). This signature could serve as a significantly independent predictor in Cox regression (multivariate HR > 1, P < 0.001), which was verified to a certain extent in the GEO cohort (univariate HR > 1, P < 0.05). Meanwhile, it was also a useful tool in predicting survival among each stratum of gender, age, grade, stage, and etiology,etc. This signature was connected with immune cell infiltration (i.e., Macrophage, Myeloid dendritic cell, and Neutrophil cell, etc.) and immune checkpoint blockade targets (PD-1, CTLA-4, and TIM-3). Conclusion The three FRlncRNAs might be potential therapeutic targets for patients, and their signature could be utilized for prognostic prediction in HCC.

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