scholarly journals Prognostic Value of Autophagy-related Genes Correlated With Metastasis in Uveal Melanoma Patients

2021 ◽  
Author(s):  
Yan Sun ◽  
Xiaoran Wang ◽  
Baoxin Chen ◽  
Lang Xiong ◽  
Jingqi Huang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Survival Analysis ◽  
Uveal Melanoma ◽  
Risk Score ◽  
Protein Interactions ◽  
Cox Regression ◽  
Early Stage ◽  
Expression Profiles ◽  
High Expression ◽  
Melanoma Metastasis

Abstract Half of the patients with primary uveal melanoma will develop progressive metastasis, leading to high mortality rate. Autophagy has been demonstrated to engage in metastasis in multiple tumors. Detection, diagnosis and treatment at the early-stage of uveal melanoma may help prevent potential tumor progression and optimize the prognosis. The purpose of our study was to discover autophagy-related genes (ARGs) correlated with uveal melanoma metastasis and determine their prognostic values. We analyzed the gene expression profiles and the clinical data from the Gene Expression Omnibus (GEO) database in uveal melanoma. A total of 14 and 16 differentially expressed ARGs were identified to be related to uveal melanoma metastasis from GSE22138 and GSE27831 sets. The two datasets shared three common genes including RAF1, CDKN1A and WIPI1 that occupied the core positions in the Protein-Protein Interactions (PPI) Network of ARGs. Following that, TCGA was introduced for survival analysis of the three genes. The survival analysis showed that high expression of RAF1 was related to favorable prognosis of uveal melanoma, whereas high expression of CDKN1A and WIPI1 suggested poor prognosis. Then a three-ARG based prognostic risk score model was constructed to predict survival outcomes. Univariate and multivariate Cox regression analyses indicated that the risk score can be considered as an independent prognostic factor for uveal melanoma, exhibiting good accuracy and sensitivity. In summary, we established an autophagy-related prognostic model based on uveal melanoma metastasis, which may contribute to the detection of early metastasis and prediction of prognosis, thereby prolonging survival through early personalized intervention.

scholarly journals Integrated analysis of autophagy-related genes(ATGs) reveals the prognostic value of oral squamous cell carcinoma

2020 ◽  
Author(s):  
Guangzhao Huang ◽  
Zhi-yun Li ◽  
Yu Rao ◽  
Xiao-zhi Lv
Keyword(s):  
Survival Analysis ◽  
Signaling Pathway ◽  
Risk Score ◽  
Prognostic Value ◽  
Risk Model ◽  
Cox Regression ◽  
Expression Profiles ◽  
Differentially Expressed ◽  
Integrated Analysis ◽  
Score Model

Abstract Background: Increasing evidence demonstrated that autophagy paly a crucial role in initiation and progression of OSCC. The aim of this study was to explore the prognostic value of autophagy-related genes(ATGs) in patients with OSCC. RNA-seq and clinical data were downloaded from TCGA database following extrating ATGs expression profiles. Then, differentially expressed analysis was performed in R software EdgeR package, and the potential biological function of differentially expressed ATGs were explored by GO and KEGG enrichment analysis. Furthermore, a risk score model based on ATGs was constructed to predict the overall survival. Moreover, univariate, multivariate cox regression and survival analysis were used to select autophagy related biomarkers which were identified by RT-qPCR in OSCC cell lines, OSCC tissues and matched normal mucosal tissues. Results: Total of 232 ATGs were extrated and 37 genes were differentially expressed in OSCC. GO and KEGG analysis indicated that these differentially expressed genes were mainly located in autophagosome membrane, and associated with apoptosis, platinum drug resistance, ErbB signaling pathway and TNF signaling pathway. Furthermore, a risk score model including 9 variables was constructed and subsequently identified with univariate, multivariate cox regression, survival analysis and Receiver Operating Characteristic curve(ROC). Moreover, ATG12 and BID were identified as potential autophagy related biomakers. Conclusion: This study successfully constructed a risk model to predict the prognosis of patients with OSCC, and the risk score may be as a independent prognostic biomarker in OSCC. ATG12 and BID were identified as potential biomarkers in tumor diagnosis and treatment of OSCC.

scholarly journals Identification and validation of a novel ferroptosis-related gene model for predicting the prognosis of gastric cancer patients

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0254368
Author(s):  
Gang Liu ◽  
Jian-ying Ma ◽  
Gang Hu ◽  
Huan Jin
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Risk Score ◽  
Cox Regression ◽  
Predictive Accuracy ◽  
Expression Profiles ◽  
Critical Role ◽  
Clinicopathological Features ◽  
The Cancer Genome Atlas ◽  
Risk Scores

Background Ferroptosis is a novel form of regulated cell death that plays a critical role in tumorigenesis. The purpose of this study was to establish a ferroptosis-associated gene (FRG) signature and assess its clinical outcome in gastric cancer (GC). Methods Differentially expressed FRGs were identified using gene expression profiles from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. Univariate and least absolute shrinkage and selection operator (LASSO) Cox regression analyses were performed to construct a prognostic signature. The model was validated using an independent GEO dataset, and a genomic-clinicopathologic nomogram integrating risk scores and clinicopathological features was established. Results An 8-FRG signature was constructed to calculate the risk score and classify GC patients into two risk groups (high- and low-risk) according to the median value of the risk score. The signature showed a robust predictive capacity in the stratification analysis. A high-risk score was associated with advanced clinicopathological features and an unfavorable prognosis. The predictive accuracy of the signature was confirmed using an independent GSE84437 dataset. Patients in the two groups showed different enrichment of immune cells and immune-related pathways. Finally, we established a genomic-clinicopathologic nomogram (based on risk score, age, and tumor stage) to predict the overall survival (OS) of GC patients. Conclusions The novel FRG signature may be a reliable tool for assisting clinicians in predicting the OS of GC patients and may facilitate personalized treatment.

scholarly journals Prognostic Biomarkers Converge on a Germinal Centre Dark Zone Phenotype As a Determinant of Adverse Outcome in Follicular Lymphoma Patients Treated with Rituximab and Chemotherapy

Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 4095-4095
Author(s):  
Anjali Silva ◽  
Clementine Sarkozy ◽  
Tracy Lackraj ◽  
Anja Mottok ◽  
Vindi Jurinovic ◽  
...  
Keyword(s):  
Gene Expression ◽  
Follicular Lymphoma ◽  
Risk Score ◽  
Research Funding ◽  
Cox Regression ◽  
Gene Set Enrichment Analysis ◽  
High Expression ◽  
Dark Zone ◽  
Gene Predictor ◽  
Cluster 2

Abstract Introduction : Follicular lymphoma (FL) is a clinically and genetically heterogeneous disease with highly variable patient outcomes. Recently, Huet et al. proposed a 23-gene expression-based risk score for predicting progression-free survival (PFS) in FL patients treated with rituximab and chemotherapy (Huet et al. Lancet Oncology 2018). The m7-FLIPI risk score has also been described as a clinico-genetic model predicting patient outcomes (Pastore et al. Lancet Oncology 2015). Moreover, EZH2 wild-type status and high expression of the FOXP1 transcription factor are associated with increased risk of lymphoma progression (Mottok et al. Blood 2018). This multitude of prognostic tools in FL raises the question whether they identify common biology. The aims of this study were to assess whether the 23-gene predictor score identifies a poor risk group of patients in our own gene expression dataset, and whether commonality exists between the 23-gene score, the m7-FLIPI, EZH2 mutation status and FOXP1 expression. Methods: In our previous work, we generated Illumina DASL microarray expression profiles for 137 FL patients who were treated with rituximab and CVP chemotherapy (cyclophosphamide, vincristine and prednisone). Using genes from the 23-gene linear risk predictor, we determined each patient's risk score by setting coefficients at -1 and +1 for genes associated with favorable and unfavorable PFS, respectively. We dichotomized the distribution of scores using the maximally selected log-rank statistic. We also performed unsupervised, hierarchical clustering to identify underlying subgroups in an unbiased fashion. Survival analyses were performed using the log-rank test and Cox regression analyses. We used gene set enrichment analysis to identify concordant differences of relevant gene signatures between specimens with either low or high expression of FOXP1. Results : Twenty genes from the 23-gene predictor (87%) were identified in the DASL gene expression dataset. The coefficients from univariate Cox regression analysis from our data were correlated with coefficients from Huet et al. (Pearson r = .7, P < .001; Spearman r = .44, P = .051). All poor-risk genes from the 23-gene predictor were associated with poor PFS in our data, and vice versa. Concordantly, calculated risk scores were significantly associated with PFS in the univariate Cox regression analysis (P = .007). Dichotomizing the distribution of risk scores identified 68% of cases with high risk score who had inferior PFS and OS compared to 32% of cases with low risk score (5-year PFS 54% vs. 77%, P = .004; 5-year OS 73% vs. 86%, P = .04). Hence, the risk score stratified patients into groups with diverging outcomes. This association was found to be independent of the Follicular Lymphoma Prognostic Index (FLIPI). In addition, the mean risk score was significantly higher in cases with high expression of FOXP1 (P < .001) and in cases with high m7-FLIPI risk score (P = .023). Unsupervised hierarchical clustering identified two main clusters ("cluster 1" and "cluster 2") that were characterized by low and high expression of genes associated with poor outcome, respectively. Patients from "cluster 2" experienced worse PFS compared to patients in "cluster 1" (P = .046; 5-year PFS 54% vs. 68%). The 5-year OS was 72% for patients in "cluster 2", vs. 81% in "cluster 1" (P = .13). We have previously reported that a germinal centre dark zone signature is enriched in cases with high FOXP1 expression, and the ICA13 signature reported by Huet et al. has been described as being highly expressed in centroblasts. Using gene set enrichment analysis, we found that genes with positive weight and coefficients in the ICA13 and the 23-gene predictor score, respectively, were enriched in the FOXP1-high phenotype (adjusted P = .009 and .005, respectively). GeneMANIA illustrated co-expression interconnectivity among ORAI2, TCF4, AFF3, FOXO1, CXCR4 and FOXP1, suggesting that genes with prognostic significance operate in tightly regulated networks. Conclusions: Our results exemplify the robustness of the predictor model by Huet et al. Further, we demonstrate biomarker convergence on a common phenotype: FOXP1 expression, EZH2 wild-type status and expression of dark zone-related genes, which characterize a subset of FL cases with adverse outcome following rituximab and chemotherapy. Disclosures Sarkozy: Roche/Genentech: Consultancy. Sehn:Roche/Genentech: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Karyopharm: Consultancy, Honoraria; Lundbeck: Consultancy, Honoraria; Seattle Genetics: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; Abbvie: Consultancy, Honoraria; Celgene: Consultancy, Honoraria; TG Therapeutics: Consultancy, Honoraria; Merck: Consultancy, Honoraria; Morphosys: Consultancy, Honoraria. Weigert:Novartis: Research Funding; Roche: Research Funding. Steidl:Juno Therapeutics: Consultancy; Tioma: Research Funding; Bristol-Myers Squibb: Research Funding; Roche: Consultancy; Seattle Genetics: Consultancy; Nanostring: Patents & Royalties: patent holding.

scholarly journals Identification of a Seven-lncRNA Immune Risk Signature and Construction of a Predictive Nomogram for Lung Adenocarcinoma

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Donghui Jin ◽  
Yuxuan Song ◽  
Yuan Chen ◽  
Peng Zhang
Keyword(s):  
Gene Expression ◽  
Lung Cancer ◽  
Survival Analysis ◽  
High Risk ◽  
Lung Adenocarcinoma ◽  
Prognostic Value ◽  
Cox Regression ◽  
Risk Group ◽  
Expression Profiles ◽  
Clinicopathological Features

Background. The incidence of lung cancer is the highest of all cancers, and it has the highest death rate. Lung adenocarcinoma (LUAD) is a major type of lung cancer. This study is aimed at identifying the prognostic value of immune-related long noncoding RNAs (lncRNAs) in LUAD. Materials and Methods. Gene expression profiles and the corresponding clinicopathological features of LUAD patients were obtained from The Cancer Genome Atlas (TCGA). The least absolute shrinkage and selection operator (LASSO) Cox regression algorithm was performed on the prognostic immune-related lncRNAs to calculate the risk scores, and a risk signature was constructed. Survival analysis was performed to assess the prognostic value of the risk signature. A nomogram was also constructed based on the clinicopathological features and risk signature. Results. A total of 437 LUAD patients with gene expression data and clinicopathological features were obtained in this study, which was considered the combination set. They were randomly and equally divided into a training set and a validation set. Seven immune-related lncRNAs (AC092794.1, AL034397.3, AC069023.1, AP000695.1, AC091057.1, HLA-DQB1-AS1, and HSPC324) were identified and used to construct a risk signature. The patients were divided into the low- and high-risk groups based on the median risk score of -0.04074. Survival analysis suggested that patients in the low-risk group had a longer overall survival (OS) than those in the high-risk group (p=1.478e−02). A nomogram was built that could predict the 1-, 3-, and 5-year survival rates of LUAD patients (C-index of the nomogram was 0.755, and the AUCs for the 1-, 3-, and 5-year survivals were 0.826, 0.719, and 0.724, respectively). The validation and combination sets confirmed these results. Conclusion. Our study identified seven novel immune-related lncRNAs and generated a risk signature, as well as a nomogram, that could predict the prognosis of LUAD patients.

KIAA0101 in Malignant Pleural Mesothelioma: a Potential Diagnostic and Prognostic Marker

2021 ◽  
Vol 24 ◽  
Author(s):  
Ping Lin ◽  
Yuean Zhao ◽  
Xiaoqian Li ◽  
Zongan Liang
Keyword(s):  
Gene Expression ◽  
Regression Analysis ◽  
Malignant Pleural Mesothelioma ◽  
Cox Regression ◽  
Expression Profiles ◽  
Gene Set Enrichment Analysis ◽  
High Expression ◽  
Pleural Mesothelioma ◽  
Hub Genes ◽  
Cox Regression Analysis

Background: Currently, there are no reliable diagnostic and prognostic markers for malignant pleural mesothelioma (MPM). The objective of this study was to identify hub genes that could be helpful for diagnosis and prognosis in MPM by using bioinformatics analysis. Materials and Methods: The gene expression profiles were downloaded from the Gene Expression Omnibus (GEO) database and The Cancer Genome Atlas (TCGA). Weighted gene co-expression network analysis (WGCNA), LASSO regression analysis, Cox regression analysis, and Gene Set Enrichment Analysis (GSEA) were performed to identify hub genes and their functions. Results: A total of 430 up-regulated and 867 downregulated genes in MPM were identified based on the GSE51024 dataset. According to the WGCNA analysis, differentially expressed genes were classified into 8 modules. Among them, the pink module was most closely associated with MPM. According to genes with GS > 0.8 and MM > 0.8, six genes were selected as candidate hub genes (NUSAP1, TOP2A, PLOD2, BUB1B, UHRF1, KIAA0101) in the pink module. In the LASSO model, three genes (NUSAP1, PLOD2, and KIAA0101) were identified with non-zero regression coefficients and were considered hub genes among the 6 candidates. The hub gene-based LASSO model can accurately distinguish MPM from controls (AUC = 0.98). Moreover, the high expression level of KIAA0101, PLOD2, and NUSAP1 were all associated with poor prognosis compared to the low level in Kaplan–Meier survival analyses. After further multivariate Cox analysis, only KIAA0101 (HR = 1.55, 95% CI = 1.05-2.29) was identified as an independent prognostic factor among these hub genes. Finally, GSEA revealed that high expression of KIAA0101 was closely associated with 10 signaling pathways. Conclusion: Our study identified several hub genes relevant to MPM, including NUSAP1, PLOD2, and KIAA0101. Among these genes, KIAA0101 appears to be a useful diagnostic and prognostic biomarker for MPM, which may provide new clues for MPM diagnosis and therapy.

scholarly journals Identification of an Immunologic Signature of Lung Adenocarcinomas Based on Genome-Wide Immune Expression Profiles

2021 ◽  
Vol 7 ◽  
Author(s):  
Bo Ling ◽  
Guangbin Ye ◽  
Qiuhua Zhao ◽  
Yan Jiang ◽  
Lingling Liang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Lung Cancer ◽  
Risk Score ◽  
Cox Regression ◽  
Expression Profiles ◽  
External Validation ◽  
Immune Gene ◽  
Clinical Prognosis ◽  
Resistance Pathway ◽  
Lung Adenocarcinomas

Background: Lung cancer is one of the most common types of cancer, and it has a poor prognosis. It is urgent to identify prognostic biomarkers to guide therapy.Methods: The immune gene expression profiles for patients with lung adenocarcinomas (LUADs) were obtained from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO). The relationships between the expression of 45 immune checkpoint genes (ICGs) and prognosis were analyzed. Additionally, the correlations between the expression of 45 biomarkers and immunotherapy biomarkers, including tumor mutation burden (TMB), mismatch repair defects, neoantigens, and others, were identified. Ultimately, prognostic ICGs were combined to determine immune subgroups, and the prognostic differences between these subgroups were identified in LUAD.Results: A total of 11 and nine ICGs closely related to prognosis were obtained from the GEO and TCGA databases, respectively. CD200R1 expression had a significant negative correlation with TMB and neoantigens. CD200R1 showed a significant positive correlation with CD8A, CD68, and GZMB, indicating that it may cause the disordered expression of adaptive immune resistance pathway genes. Multivariable Cox regression was used to construct a signature composed of four prognostic ICGs (IDO1, CD274, CTLA4, and CD200R1): Risk Score = −0.002*IDO1+0.031*CD274−0.069*CTLA4−0.517*CD200R1. The median Risk Score was used to classify the samples for the high- and low-risk groups. We observed significant differences between groups in the training, testing, and external validation cohorts.Conclusion: Our research provides a method of integrating ICG expression profiles and clinical prognosis information to predict lung cancer prognosis, which will provide a unique reference for gene immunotherapy for LUAD.

scholarly journals A Five-Gene Risk Score Model for Predicting the Prognosis of Multiple Myeloma Patients Based on Gene Expression Profiles

2021 ◽  
Vol 12 ◽  
Author(s):  
Xiaotong Chen ◽  
Lintao Liu ◽  
Mengping Chen ◽  
Jing Xiang ◽  
Yike Wan ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
High Risk ◽  
Risk Score ◽  
Cox Regression ◽  
Risk Group ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Low Risk ◽  
Score Model

Multiple myeloma is a heterogeneous plasma cell malignancy that remains incurable because of the tendency of relapse for most patients. Survival outcomes may vary widely due to patient and disease variables; therefore, it is necessary to establish a more accurate prognostic model to improve prognostic precision and guide clinical therapy. Here, we developed a risk score model based on myeloma gene expression profiles from three independent datasets: GSE6477, GSE13591, and GSE24080. In this model, highly survival-associated five genes, including EPAS1, ERC2, PRC1, CSGALNACT1, and CCND1, are selected by using the least absolute shrinkage and selection operator (Lasso) regression and univariate and multivariate Cox regression analyses. At last, we analyzed three validation datasets (including GSE2658, GSE136337, and MMRF datasets) to examine the prognostic efficacy of this model by dividing patients into high-risk and low-risk groups based on the median risk score. The results indicated that the survival of patients in low-risk group was greatly prolonged compared with their counterparts in the high-risk group. Therefore, the five-gene risk score model could increase the accuracy of risk stratification and provide effective prediction for the prognosis of patients and instruction for individualized clinical treatment.

scholarly journals Effect of differential hypoxia-related gene expression on glioblastoma

2021 ◽  
Vol 49 (5) ◽  
pp. 030006052110137
Author(s):  
Chao-Qun Lin ◽  
Lu-Kui Chen
Keyword(s):  
Gene Expression ◽  
Risk Score ◽  
Cox Regression ◽  
Expression Profiles ◽  
The Cancer Genome Atlas ◽  
Risk Scores ◽  
Prognostic Indicators ◽  
Related Gene ◽  
Scoring Model ◽  
Risk Scoring

Objective Glioblastoma (GB) is a refractory malignancy with a high rate of recurrence and treatment resistance. Hypoxia-related genes are promising prognostic indicators for GB, so we herein developed a reliable hypoxia-related gene risk scoring model to predict the prognosis of patients with GB. Method Gene expression profiles and corresponding clinicopathological features of patients with GB were obtained from the Cancer Genome Atlas (TCGA; n = 160) and Gene Expression Omnibus (GEO) GSE7696 (n = 80) databases. Univariate and multivariate Cox regression analyses of differentially expressed hypoxia-related genes were performed using R 3.5.1 software. Result Fourteen prognosis-related genes were identified and used to construct a risk signature. Patients with high-risk scores had significantly lower overall survival (OS) than those with low-risk scores. The median risk score was used as a critical value and for OS prediction in an independent external verification GSE7696 cohort. Risk score was not significantly affected by clinical-related factors. We also developed a prediction nomogram based on the TCGA training set to predict survival rates, and included six independent prognostic parameters in the TCGA prediction model. Conclusion We determined a reliable hypoxia-related gene risk scoring model for predicting the prognosis of patients with GB.

scholarly journals MNBDR: A Module Network Based Method for Drug Repositioning

Genes ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 25
Author(s):  
He-Gang Chen ◽  
Xiong-Hui Zhou
Keyword(s):  
Gene Expression ◽  
Protein Interactions ◽  
Drug Response ◽  
Drug Repositioning ◽  
Expression Profiles ◽  
Drug Repurposing ◽  
Recent Decade ◽  
Data Set ◽  
Module Network ◽  
The Cross

Drug repurposing/repositioning, which aims to find novel indications for existing drugs, contributes to reducing the time and cost for drug development. For the recent decade, gene expression profiles of drug stimulating samples have been successfully used in drug repurposing. However, most of the existing methods neglect the gene modules and the interactions among the modules, although the cross-talks among pathways are common in drug response. It is essential to develop a method that utilizes the cross-talks information to predict the reliable candidate associations. In this study, we developed MNBDR (Module Network Based Drug Repositioning), a novel method that based on module network to screen drugs. It integrated protein–protein interactions and gene expression profile of human, to predict drug candidates for diseases. Specifically, the MNBDR mined dense modules through protein–protein interaction (PPI) network and constructed a module network to reveal cross-talks among modules. Then, together with the module network, based on existing gene expression data set of drug stimulation samples and disease samples, we used random walk algorithms to capture essential modules in disease development and proposed a new indicator to screen potential drugs for a given disease. Results showed MNBDR could provide better performance than popular methods. Moreover, functional analysis of the essential modules in the network indicated our method could reveal biological mechanism in drug response.

scholarly journals Development of prognosis model for colon cancer based on autophagy-related genes

2020 ◽  
Vol 18 (1) ◽  
Author(s):  
Xu Wang ◽  
Yuanmin Xu ◽  
Ting Li ◽  
Bo Chen ◽  
Wenqi Yang
Keyword(s):  
Colon Cancer ◽  
Regression Analysis ◽  
High Risk ◽  
Cancer Patients ◽  
Risk Score ◽  
Cox Regression ◽  
Patient Survival ◽  
Expression Profiles ◽  
Cox Regression Analysis ◽  
Risk Patients

Abstract Background Autophagy is an orderly catabolic process for degrading and removing unnecessary or dysfunctional cellular components such as proteins and organelles. Although autophagy is known to play an important role in various types of cancer, the effects of autophagy-related genes (ARGs) on colon cancer have not been well studied. Methods Expression profiles from ARGs in 457 colon cancer patients were retrieved from the TCGA database (https://portal.gdc.cancer.gov). Differentially expressed ARGs and ARGs related to overall patient survival were identified. Cox proportional-hazard models were used to investigate the association between ARG expression profiles and patient prognosis. Results Twenty ARGs were significantly associated with the overall survival of colon cancer patients. Five of these ARGs had a mutation rate ≥ 3%. Patients were divided into high-risk and low-risk groups based on Cox regression analysis of 8 ARGs. Low-risk patients had a significantly longer survival time than high-risk patients (p < 0.001). Univariate and multivariate Cox regression analysis showed that the resulting risk score, which was associated with infiltration depth and metastasis, could be an independent predictor of patient survival. A nomogram was established to predict 1-, 3-, and 5-year survival of colon cancer patients based on 5 independent prognosis factors, including the risk score. The prognostic nomogram with online webserver was more effective and convenient to provide information for researchers and clinicians. Conclusion The 8 ARGs can be used to predict the prognosis of patients and provide information for their individualized treatment.

Sign in / Sign up

Export Citation Format

Share Document