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 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 Identification and validation of prognostic signature for breast cancer based on genes potentially involved in autophagy

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e9621
Author(s):  
Shanliang Zhong ◽  
Huanwen Chen ◽  
Sujin Yang ◽  
Jifeng Feng ◽  
Siying Zhou
Keyword(s):  
Breast Cancer ◽  
Cancer Survival ◽  
Cox Regression ◽  
Risk Group ◽  
Breast Cancer Survival ◽  
Low Risk ◽  
The Cancer Genome Atlas ◽  
Lasso Regression ◽  
Breast Cancer Patients ◽  
Prognostic Signature

We aimed to identify prognostic signature based on autophagy-related genes (ARGs) for breast cancer patients. The datasets of breast cancer were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Least absolute shrinkage and selection operator (LASSO) Cox regression was conducted to construct multiple-ARG risk signature. In total, 32 ARGs were identified as differentially expressed between tumors and adjacent normal tissues based on TCGA. Six ARGs (IFNG, TP63, PPP1R15A, PTK6, EIF4EBP1 and NKX2-3) with non-zero coefficient were selected from the 32 ARGs using LASSO regression. The 6-ARG signature divided patients into high-and low-risk group. Survival analysis indicated that low-risk group had longer survival time than high-risk group. We further validated the 6-ARG signature using dataset from GEO and found similar results. We analyzed the associations between ARGs and breast cancer survival in TCGA and nine GEO datasets, and obtained 170 ARGs with significant associations. EIF4EBP1, FOS and FAS were the top three ARGs with highest numbers of significant associations. EIF4EBP1 may be a key ARG which had a higher expression level in patients with more malignant molecular subtypes and higher grade breast cancer. In conclusion, our 6-ARG signature was of significance in predicting of overall survival of patients with breast cancer. EIF4EBP1 may be a key ARG associated with breast cancer survival.

scholarly journals Development of a prognostic signature for esophageal cancer based on nine immune related genes

2020 ◽  
Author(s):  
zhi Zhang ◽  
cheng chen ◽  
ying fang ◽  
sheng Li ◽  
xiaohua Wang ◽  
...  
Keyword(s):  
T Cells ◽  
Esophageal Cancer ◽  
Prognostic Model ◽  
Cox Regression ◽  
Risk Group ◽  
The Cancer Genome Atlas ◽  
Receptor Interaction ◽  
Prognostic Signature ◽  
Cox Regression Analysis ◽  
Immune Related Genes

Abstract Background: Function of the immune system is correlated with the prognosis of the tumor. The effect of immune microenvironment on esophageal cancer (EC) development has not been fully investigated. Methods: This study aimed to explore a prognostic model based on immune-related genes (IRGs) for EC. We obtained the RNA-seq dataset and clinical information of EC from the Cancer Genome Atlas (TCGA). Results: We identified 247 upregulated IRGs and 56 downregulated IRGs. Pathway analysis revealed that the most differentially expressed IRGs were enriched in Cytokine-cytokine receptor interaction. We further screened 13 survival-related IRGs and constructed regulatory networks involving related transcription factors (TFs). Finally, a prognostic model was constructed with 9 IRGs (HSPA6, S100A12, CACYBP, NOS2, DKK1, OSM ,STC2 ,NGPTL3 and NR2F2) by multivariate Cox regression analysis. The patients were classified into two subgroups with different outcomes. When adjusted with clinical factors, this model was verified as an independent predictor, which performed accurately in prognostic prediction. Next, M0 and M2 macrophages and activated mast cells were significantly enriched in high-risk group, while CD8 T cells and regulatory T cells (Tregs) were significantly enriched in low-risk group. Conclusions: Prognosis related IRGs were identified and a prognostic signature for esophageal cancer based on nine IRGs was developed.

scholarly journals Construction of a Prognostic Signature in Ewing’s Sarcoma: Based On Metabolism-Related Genes

2021 ◽  
Author(s):  
Bo Wu ◽  
Dong Zhu ◽  
Bo Yu ◽  
Yuanyuan Hou ◽  
Hongyu Wang ◽  
...  
Keyword(s):  
Overall Survival ◽  
Ewing’S Sarcoma ◽  
Cox Regression ◽  
Ewing's Sarcoma ◽  
Risk Group ◽  
Expression Profiles ◽  
High Risk Group ◽  
Prognostic Signature ◽  
Cox Regression Analysis ◽  
Cox Analysis

Abstract Objective: By combining the expression profiles of metabolism-related genes (MRGS) with clinical information, the expression quantities of MRGS and the influence on development and prognosis were systematically analyzed, so as to provide a theoretical basis for the clinical study on the prognosis of Ewing's sarcoma.Methods: MRGs expression profiles of 64 patients with Ewing's sarcoma were obtained from the GEO dataset. Univariate Cox regression analysis was used to identify metabolization-related differentially expressed genes (DEGs) related with prognosis in Ewing's sarcoma patients. Then, multivariate Cox analysis was used to calculate novel prognostic markers based on metabolism-related DEGs. Finally, the new prognostic index was verified on the basis of the prognostic models.Results: Univariate Cox regression analysis identified 20 metabolization-related DEGs, 9 of which were significantly associated with Ewing's sarcoma patients' overall survival. Subsequently, we used nine metabolism-related DEGs to construct metabolism-related prognostic signature for patients with Ewing's sarcoma. Based on the 9 DEGs regression coefficient, we put forward the formula of each patient's risk score, and then divided the patients into high-risk group and low-risk group. The results indicated that the survival rate and survival time were higher in the low-risk group and lower in the high-risk group. Multivariate Cox analysis showed that risk score index was indeed an independent prognostic factor for Ewing's sarcoma. In addition, the area under the receiver operating characteristic (ROC) curve for overall survival was 0.985. And a nomogram model was established.Conclusion: The experimental results suggest that the 9 metabolism-related DEGs marker may be effective in predicting the prognosis of Ewing's sarcoma to some extent, helping to individualize treatment of patients at different risks.

scholarly journals Development and Validation of a Six-lncRNA Prognostic Signature in Gastric Cancer

2021 ◽  
Author(s):  
Huihui Zeng ◽  
Ai tao Nai ◽  
Feng Ma ◽  
SHOAIB BASHIR ◽  
Yin Li ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Target Genes ◽  
Cox Regression ◽  
Expression Profiles ◽  
Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Expression Level ◽  
Training Dataset ◽  
Lasso Regression ◽  
Prognostic Signature

Abstract Background: Gastric cancer (GC) has been a leading cause of cancer-related mortality for many years. It is thought that long noncoding RNAs (lncRNAs) can play a significant role in GC. This study aimed to construct a powerful six-lncRNA signature as a prognostic biomarker for GC patients.Methods: Based on The Cancer Genome Atlas (TCGA), the expression profiles of lncRNAs and the corresponding clinical data of GC patients were obtained. Cox regression and the least absolute shrinkage and selection operator (LASSO) regression model were used to identify the prognostic lncRNA signature. A total of 337 patients were included in the combined dataset (N = 337), which was divided into a training dataset (N= 169) and a test dataset (N = 168). The reliability of the lncRNA prognostic signature was validated in three datasets.Results: A six-lncRNA prognostic signature was constructed to predict the overall survival (OS) of GC patients. The signature had better discriminability than clinical characteristics. The prognostic risk score was as follows: (expression level of RP11-284F21.7×-0.243981) + (expression level of RP11-432J22.2×-0.502378) + (expression level of RP4-584D14.5×-0.447878) + (expression level of AC093850.2×0.261822) + (expression level of AP000695.6 ×0.654318) + (expression level of AC098973.2× 0.406603). In addition, the signature was confirmed to be a significant predictor for predicting the OS. The nomogram model precisely predicted the OS of GC. Enrichment analysis indicated that the signature was mainly enriched for extracellular matrix-related functions and tumor signaling pathways. The target genes IGFBP7, VCAN, and COL1A1 had prognostic value in GC. AC098973.2 and RP11-284F21.7 was verified for the first time in GC tissues and cell lines.Conclusions: The six-lncRNA prognostic signature could predict the OS and has high clinical application value in GC.

scholarly journals Integrative Analysis of Identifying Methylation-Driven Genes Signature Predicts Prognosis in Colorectal Carcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Hao Huang ◽  
Jinming Fu ◽  
Lei Zhang ◽  
Jing Xu ◽  
Dapeng Li ◽  
...  
Keyword(s):  
Gene Expression ◽  
Risk Assessment ◽  
Risk Group ◽  
Integrative Analysis ◽  
Assessment Model ◽  
The Cancer Genome Atlas ◽  
Risk Assessment Model ◽  
Lasso Regression ◽  
Prognostic Signature ◽  
Training Set

BackgroundAberrant DNA methylation is a critical regulator of gene expression and plays a crucial role in the occurrence, progression, and prognosis of colorectal cancer (CRC). We aimed to identify methylation-driven genes by integrative epigenetic and transcriptomic analysis to predict the prognosis of CRC patients.MethodsMethylation-driven genes were selected for CRC using a MethylMix algorithm and LASSO regression screening strategy, and were further used to construct a prognostic risk-assessment model. The Cancer Genome Atlas (TCGA) database was obtained as the training set for both the screening of methylation-driven genes and the effect of genes signature on CRC prognosis. Then, the prognostic genes signature was validated in three independent expression arrays of CRC data from Gene Expression Omnibus (GEO).ResultsWe identified 143 methylation-driven genes, of which the combination of BATF, PHYHIPL, RBP1, and PNPLA4 expression levels was screened as a better prognostic model with the best area under the curve (AUC) (AUC = 0.876). Compared with patients in the low-risk group, CRC patients in the high-risk group had significantly poorer overall survival in the training set (HR = 2.184, 95% CI: 1.404–3.396, P &lt; 0.001). Similar results were observed in the validation set. Moreover, VanderWeele’s mediation analysis indicated that the effect of methylation on prognosis was mediated by the levels of their expression (HRindirect = 1.473, P = 0.001, Proportion mediated, 69.10%).ConclusionsWe identified a four-gene prognostic signature by integrative analysis and developed a risk-assessment model that is significantly associated with patients’ survival. Methylation-driven genes might be a potential prognostic signature for CRC patients.

scholarly journals A New RBPs-Related Signature Predicts the Prognosis of Colon Adenocarcinoma Patients

2021 ◽  
Vol 11 ◽  
Author(s):  
Kaili Chang ◽  
Chong Yuan ◽  
Xueguang Liu
Keyword(s):  
Rna Binding ◽  
Rna Binding Proteins ◽  
Risk Group ◽  
Colon Adenocarcinoma ◽  
Test Group ◽  
The Cancer Genome Atlas ◽  
Risk Scores ◽  
Good Prediction ◽  
Training Set ◽  
Cox Analysis

The dysregulation of RNA binding proteins (RBPs) is closely related to tumorigenesis and development. However, the role of RBPs in Colon adenocarcinoma (COAD) is still poorly understood. We downloaded COAD’s RNASeq data from the Cancer Genome Atlas (TCGA) database, screened the differently expressed RBPs in normal tissues and tumor, and constructed a protein interaction network. COAD patients were randomly divided into a training set (N = 315) and a testing set (N = 132). In the training set, univariate Cox analysis identified 12 RBPs significantly related to the prognosis of COAD. By multivariate COX analysis, we constructed a prognostic model composed of five RBPs (CELF4, LRRFIP2, NOP14, PPARGC1A, ZNF385A) based on the lowest Akaike information criterion. Each COAD patient was scored according to the model formula. Further analysis showed that compared with the low-risk group, the overall survival rate (OS) of patients in the high-risk group was significantly lower. The area under the curve of the time-dependent receiver operator characteristic (ROC) curve was 0.722 in the training group and 0.738 in the test group, which confirmed a good prediction feature. In addition, a nomogram was constructed based on clinicopathological characteristics and risk scores. C-index and calibration curve proved the accuracy in predicting the 1-, 3-, and 5-year survival rates of COAD patients. In short, we constructed a superior prognostic and diagnostic signature composed of five RBPs, which indicates new possibilities for individualized treatment of COAD patients.

scholarly journals Development of a prognostic signature for esophageal cancer based on nine immune related genes

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Zhi Zhang ◽  
Cheng Chen ◽  
Ying Fang ◽  
Sheng Li ◽  
Xiaohua Wang ◽  
...  
Keyword(s):  
T Cells ◽  
Esophageal Cancer ◽  
Prognostic Model ◽  
Cox Regression ◽  
Risk Group ◽  
The Cancer Genome Atlas ◽  
Receptor Interaction ◽  
Prognostic Signature ◽  
Cox Regression Analysis ◽  
Immune Related Genes

Abstract Background Function of the immune system is correlated with the prognosis of the tumor. The effect of immune microenvironment on esophageal cancer (EC) development has not been fully investigated. Methods This study aimed to explore a prognostic model based on immune-related genes (IRGs) for EC. We obtained the RNA-seq dataset and clinical information of EC from the Cancer Genome Atlas (TCGA). Results We identified 247 upregulated IRGs and 56 downregulated IRGs. Pathway analysis revealed that the most differentially expressed IRGs were enriched in Cytokine-cytokine receptor interaction. We further screened 13 survival-related IRGs and constructed regulatory networks involving related transcription factors (TFs). Finally, a prognostic model was constructed with 9 IRGs (HSPA6, S100A12, CACYBP, NOS2, DKK1, OSM, STC2, NGPTL3 and NR2F2) by multivariate Cox regression analysis. The patients were classified into two subgroups with different outcomes. When adjusted with clinical factors, this model was verified as an independent predictor, which performed accurately in prognostic prediction. Next, M0 and M2 macrophages and activated mast cells were significantly enriched in high-risk group, while CD8 T cells and regulatory T cells (Tregs) were significantly enriched in low-risk group. Conclusions Prognosis related IRGs were identified and a prognostic signature for esophageal cancer based on nine IRGs was developed.

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 Iron-Bound Lipocalin-2 from Tumor-Associated Macrophages Drives Breast Cancer Progression Independent of Ferroportin

Metabolites ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 180
Author(s):  
Christina Mertens ◽  
Matthias Schnetz ◽  
Claudia Rehwald ◽  
Stephan Grein ◽  
Eiman Elwakeel ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Tumor Progression ◽  
Tumor Cells ◽  
Cancer Progression ◽  
Lung Metastases ◽  
Expression Profiles ◽  
The Cancer Genome Atlas ◽  
Lipocalin 2 ◽  
Tumor Associated Macrophages

Macrophages supply iron to the breast tumor microenvironment by enforced secretion of lipocalin-2 (Lcn-2)-bound iron as well as the increased expression of the iron exporter ferroportin (FPN). We aimed at identifying the contribution of each pathway in supplying iron for the growing tumor, thereby fostering tumor progression. Analyzing the expression profiles of Lcn-2 and FPN using the spontaneous polyoma-middle-T oncogene (PyMT) breast cancer model as well as mining publicly available TCGA (The Cancer Genome Atlas) and GEO Series(GSE) datasets from the Gene Expression Omnibus database (GEO), we found no association between tumor parameters and Lcn-2 or FPN. However, stromal/macrophage-expression of Lcn-2 correlated with tumor onset, lung metastases, and recurrence, whereas FPN did not. While the total iron amount in wildtype and Lcn-2−/− PyMT tumors showed no difference, we observed that tumor-associated macrophages from Lcn-2−/− compared to wildtype tumors stored more iron. In contrast, Lcn-2−/− tumor cells accumulated less iron than their wildtype counterparts, translating into a low migratory and proliferative capacity of Lcn-2−/− tumor cells in a 3D tumor spheroid model in vitro. Our data suggest a pivotal role of Lcn-2 in tumor iron-management, affecting tumor growth. This study underscores the role of iron for tumor progression and the need for a better understanding of iron-targeted therapy approaches.

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