scholarly journals Identification of a Novel Metastasis-Related miRNAs-Based Signature for Predicting the Prognosis of Hepatocellular Carcinoma

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Yuan Chen ◽  
Guifu Wang ◽  
Hao Xu ◽  
Hao Wang ◽  
Dousheng Bai
Keyword(s):  
Hepatocellular Carcinoma ◽  
High Risk ◽  
Cancer Metastasis ◽  
Cox Regression ◽  
Human Cancer ◽  
Risk Groups ◽  
High Risk Group ◽  
Low Risk ◽  
Prognostic Signature

Hepatocellular carcinoma (HCC) is one of the most common internal malignancies worldwide and is associated with a poor prognosis. Abnormal expression of miRNAs is believed to play a role in the recurrent metastasis of HCC. However, limited studies on the role of miRNAs in HCC metastasis have been carried out. Therefore, this study is aimed at exploring the potential value of metastasis-related miRNAs (MRMs) in HCC. We retrieved MRMs were from the Human Cancer Metastasis Database. Differential miRNAs were identified for tumor samples of HCC patients and normal samples based on the TCGA database. Further, univariate and multivariate Cox regression analyses were used to screen MRMs known to be independent prognostic factors in HCC. These MRMs were then used to build a prognostic signature. All patients were classified into high-risk and low-risk groups based on the median of the signature scores. Moreover, GO and KEGG pathway enrichment analyses were performed to predict the function of these MRMs. Finally, a nomogram was constructed to predict the OS of patients at 1, 2, and 3 years. In our study, a total of seven prognostic MRMs (miR-140-3p, miR-9-5p, miR-942-5p, miR-324-3p, miR-29c-5p, miR-551a, and miR-149-5p) were identified and used for constructing the prognostic signature based on the training cohort. Patients in the low-risk HCC group showed better overall survival (OS) than those in the high-risk group. The results were validated using the validation cohort. In summary, the findings of this study provide evidence that MRMs-based prognostic signature is an independent biomarker in the prognosis of HCC patients.

scholarly journals Identification of a novel autophagy signature for predicting survival in patients with lung adenocarcinoma

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11074
Author(s):  
Jin Duan ◽  
Youming Lei ◽  
Guoli Lv ◽  
Yinqiang Liu ◽  
Wei Zhao ◽  
...  
Keyword(s):  
Gene Expression ◽  
High Risk ◽  
Immune Cell ◽  
Cox Regression ◽  
Risk Group ◽  
Risk Groups ◽  
High Risk Group ◽  
Low Risk ◽  
Cell Analysis ◽  
Prognostic Signature

Background Lung adenocarcinoma (LUAD) is the most commonhistological lung cancer subtype, with an overall five-year survivalrate of only 17%. In this study, we aimed to identify autophagy-related genes (ARGs) and develop an LUAD prognostic signature. Methods In this study, we obtained ARGs from three databases and downloaded gene expression profiles from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. We used TCGA-LUAD (n = 490) for a training and testing dataset, and GSE50081 (n = 127) as the external validation dataset.The least absolute shrinkage and selection operator (LASSO) Cox and multivariate Cox regression models were used to generate an autophagy-related signature. We performed gene set enrichment analysis (GSEA) and immune cell analysis between the high- and low-risk groups. A nomogram was built to guide the individual treatment for LUAD patients. Results We identified a total of 83 differentially expressed ARGs (DEARGs) from the TCGA-LUAD dataset, including 33 upregulated DEARGs and 50 downregulated DEARGs, both with thresholds of adjusted P < 0.05 and |Fold change| > 1.5. Using LASSO and multivariate Cox regression analyses, we identified 10 ARGs that we used to build a prognostic signature with areas under the curve (AUCs) of 0.705, 0.715, and 0.778 at 1, 3, and 5 years, respectively. Using the risk score formula, the LUAD patients were divided into low- or high-risk groups. Our GSEA results suggested that the low-risk group were enriched in metabolism and immune-related pathways, while the high-risk group was involved in tumorigenesis and tumor progression pathways. Immune cell analysis revealed that, when compared to the high-risk group, the low-risk group had a lower cell fraction of M0- and M1- macrophages, and higher CD4 and PD-L1 expression levels. Conclusion Our identified robust signature may provide novel insight into underlying autophagy mechanisms as well as therapeutic strategies for LUAD treatment.

scholarly journals A Novel Hypoxic-Angiogenesis-Immune-Related Gene Model for Prognostic and Therapeutic Effect Prediction in Hepatocellular Carcinoma Patients

2022 ◽  
Vol 2022 ◽  
pp. 1-27
Author(s):  
Wen Lv ◽  
Qi Yao
Keyword(s):  
Hepatocellular Carcinoma ◽  
High Risk ◽  
Liver Cancer ◽  
Cox Regression ◽  
Risk Group ◽  
Immune Dysfunction ◽  
Risk Groups ◽  
High Risk Group ◽  
Low Risk ◽  
Prognostic Prediction

Background. Hepatocellular carcinoma (HCC) is one of the most heterogeneous malignant tumors that have been discovered so far, which makes the prognostic prediction difficult. The hypoxia, angiogenesis, and immunity-related genes (HAIRGs) are closely related to the development of liver cancer. However, the prognostic and treatment effect of hypoxia, angiogenesis, and immunity-related genes in HCC continues to be further clarified. Methods. The gene expression quantification data and clinical information in patients with liver cancer were downloaded from the TCGA database, and HAIRG signature was built by using the least absolute shrinkage and selection operator (LASSO) technique. Patient from the ICGC database validated the model. Then, tumor immune dysfunction and exclusion (TIDE) algorithm was applied to estimate the clinical response to immunotherapy and the sensitivity of drugs was evaluated by the half-maximal inhibitory concentration (IC50). Result. The HAIRGs were identified between the HCC patients and normal patients in the TCGA database. In univariate Cox regression analysis, seventeen differentially expressed genes (DEGs) were associated with overall survival (OS). An eight HAIRG signature model was constructed and was used to divide the patients into two groups according to the median value of the risk score base on the TCGA dataset. Patients in the high-risk group had a significant reduction in OS compared to those in the low-risk group ( P < 0.001 in the TCGA, P < 0.001 in the ICGC). For TCGA and ICGC databases of univariate Cox regression analyses, the risk score was used as an independent predictor of OS ( HR > 1 , P < 0.001 ). Functional analysis showed that the relevant immune pathways and immune responses were enriched, cellular component analysis showed that the immunoglobulin complex and other related substances were enriched, and immune status existed a difference in the high- and low-risk groups. Then, the tumor immune dysfunction and exclusion (TIDE) algorithm presented differences in immune response in the high- and low-risk groups ( P < 0.05 ), and based on drug sensitivity prediction, patients in the high-risk group were more sensitive to cisplatin compared to those in the low-risk group in both the TCGA and ICGC cohorts ( P < 0.05 ). Conclusions. HAIRG signature can be utilized for prognostic prediction in HCC, while it can be considered a prediction model for clinical evaluation of immunotherapy response and chemotherapy sensitivity in HCC.

scholarly journals Development and Verification of the Hypoxia- and Immune-Associated Prognostic Signature for Pancreatic Ductal Adenocarcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Dongjie Chen ◽  
Hui Huang ◽  
Longjun Zang ◽  
Wenzhe Gao ◽  
Hongwei Zhu ◽  
...  
Keyword(s):  
High Risk ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Risk Score ◽  
Cox Regression ◽  
Pearson Correlation ◽  
Risk Groups ◽  
Low Risk ◽  
Ductal Adenocarcinoma ◽  
Prognostic Signature ◽  
Score Model

We aim to construct a hypoxia- and immune-associated risk score model to predict the prognosis of patients with pancreatic ductal adenocarcinoma (PDAC). By unsupervised consensus clustering algorithms, we generate two different hypoxia clusters. Then, we screened out 682 hypoxia-associated and 528 immune-associated PDAC differentially expressed genes (DEGs) of PDAC using Pearson correlation analysis based on the Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression project (GTEx) dataset. Seven hypoxia and immune-associated signature genes (S100A16, PPP3CA, SEMA3C, PLAU, IL18, GDF11, and NR0B1) were identified to construct a risk score model using the Univariate Cox regression and the Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression, which stratified patients into high- and low-risk groups and were further validated in the GEO and ICGC cohort. Patients in the low-risk group showed superior overall survival (OS) to their high-risk counterparts (p &lt; 0.05). Moreover, it was suggested by multivariate Cox regression that our constructed hypoxia-associated and immune-associated prognosis signature might be used as the independent factor for prognosis prediction (p &lt; 0.001). By CIBERSORT and ESTIMATE algorithms, we discovered that patients in high-risk groups had lower immune score, stromal score, and immune checkpoint expression such as PD-L1, and different immunocyte infiltration states compared with those low-risk patients. The mutation spectrum also differs between high- and low-risk groups. To sum up, our hypoxia- and immune-associated prognostic signature can be used as an approach to stratify the risk of PDAC.

scholarly journals A novel signature of two long non-coding RNAs in BRCA mutant ovarian cancer to predict prognosis and efficiency of chemotherapy

2020 ◽  
Vol 13 (1) ◽  
Author(s):  
Yinglian Pan ◽  
Li Ping Jia ◽  
Yuzhu Liu ◽  
Yiyu Han ◽  
Qian Li ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
High Risk ◽  
Cox Regression ◽  
Risk Group ◽  
Gynecologic Cancer ◽  
High Risk Group ◽  
Low Risk ◽  
The Cancer Genome Atlas ◽  
Chemotherapy Treatment ◽  
Prognostic Signature

Abstract Background In this study we aimed to identify a prognostic signature in BRCA1/2 mutations to predict disease progression and the efficiency of chemotherapy ovarian cancer (OV), the second most common cause of death from gynecologic cancer in women worldwide. Methods Univariate Cox proportional-hazards and multivariate Cox regression analyses were used to identifying prognostic factors from data obtained from The Cancer Genome Atlas (TCGA) database. The area under the curve of the receiver operating characteristic curve was assessed, and the sensitivity and specificity of the prediction model were determined. Results A signature consisting of two long noncoding RNAs(lncRNAs), Z98885.2 and AC011601.1, was selected as the basis for classifying patients into high and low-risk groups (median survival: 7.2 years vs. 2.3 years). The three-year overall survival (OS) rates for the high- and low-risk group were approximately 38 and 100%, respectively. Chemotherapy treatment survival rates indicated that the high-risk group had significantly lower OS rates with adjuvant chemotherapy than the low-risk group. The one-, three-, and five-year OS were 100, 40, and 15% respectively in the high-risk group. The survival rate of the high-risk group declined rapidly after 2 years of OV chemotherapy treatment. Multivariate Cox regression associated with other traditional clinical factors showed that the 2-lncRNA model could be used as an independent OV prognostic factor. Analyses of data from the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) indicated that these signatures are pivotal to cancer development. Conclusion In conclusion, Z98885.2 and AC011601.1 comprise a novel prognostic signature for OV patients with BRCA1/2 mutations, and can be used to predict prognosis and the efficiency of chemotherapy.

scholarly journals Discovery and validation of immune- related long non-coding RNA biomarkers associated with prognosis in hepatocellular carcinoma

2020 ◽  
Author(s):  
Li Liu ◽  
She Tian ◽  
Zhu Li ◽  
Yongjun Gong ◽  
Hao Zhang
Keyword(s):  
Hepatocellular Carcinoma ◽  
High Risk ◽  
Cox Regression ◽  
Risk Group ◽  
Survival Curve ◽  
Principal Component ◽  
High Risk Group ◽  
Low Risk ◽  
Cox Regression Analysis ◽  
Immune Related Genes

Abstract Background : Hepatocellular carcinoma (HCC) is one of the most common clinical malignant tumors, resulting in high mortality and poor prognosis. Studies have found that LncRNA plays an important role in the onset, metastasis and recurrence of hepatocellular carcinoma. The immune system plays a vital role in the development, progression, metastasis and recurrence of cancer. Therefore, immune-related lncRNA can be used as a novel biomarker to predict the prognosis of hepatocellular carcinoma. Methods : The transcriptome data and clinical data of HCC patients were obtained by using The Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA‑LIHC), and immune-related genes were extracted from the Molecular Signatures Database (IMMUNE RESPONSE M19817 and IMMUNE SYSTEM PROCESS M13664). By constructing the co-expression network and Cox regression analysis, 13 immune-lncRNAs was identified to predict the prognosis of HCC patients. Patients were divided into high risk group and low risk group by using the risk score formula, and the difference in overall survival (OS) between the two groups was reflected by Kaplan-Meier survival curve. The time - dependent receiver operating characteristics (ROC) analysis and principal component analysis (PCA) were used to evaluate 13 immune -lncRNAs signature. Results : Through TCGA - LIHC extracted from 343 cases of patients with hepatocellular carcinoma RNA - Seq data and clinical data, 331 immune-related genes were extracted from the Molecular Signatures Database , co-expression networks and Cox regression analysis were constructed, 13 immune-lncRNAs signature was identified as biomarkers to predict the prognosis of patients. At the same time using the risk score median divided the patients into high risk and low risk groups, and through the Kaplan-Meier survival curve analysis found that high-risk group of patients' overall survival (OS) less low risk group of patients. The AUC value of the ROC curve is 0.828, and principal component analysis (PCA) results showed that patients could be clearly divided into two parts by immune-lncRNAs, which provided evidence for the use of 13 immune-lncRNAs signature as prognostic markers. Conclusion : Our study identified 13 immune-lncRNAs signature that can effectively predict the prognosis of HCC patients, which may be a new prognostic indicator for predicting clinical outcomes.

scholarly journals Identification of a Five-Autophagy-Related-lncRNA Signature as a Novel Prognostic Biomarker for Hepatocellular Carcinoma

2021 ◽  
Vol 7 ◽  
Author(s):  
Xiaoyu Deng ◽  
Qinghua Bi ◽  
Shihan Chen ◽  
Xianhua Chen ◽  
Shuhui Li ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
High Risk ◽  
Cox Regression ◽  
Risk Group ◽  
High Risk Group ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Prognostic Signature ◽  
Prediction Ability ◽  
Cox Regression Analysis

Although great progresses have been made in the diagnosis and treatment of hepatocellular carcinoma (HCC), its prognostic marker remains controversial. In this current study, weighted correlation network analysis and Cox regression analysis showed significant prognostic value of five autophagy-related long non-coding RNAs (AR-lncRNAs) (including TMCC1-AS1, PLBD1-AS1, MKLN1-AS, LINC01063, and CYTOR) for HCC patients from data in The Cancer Genome Atlas. By using them, we constructed a five-AR-lncRNA prognostic signature, which accurately distinguished the high- and low-risk groups of HCC patients. All of the five AR lncRNAs were highly expressed in the high-risk group of HCC patients. This five-AR-lncRNA prognostic signature showed good area under the curve (AUC) value (AUC = 0.751) for the overall survival (OS) prediction in either all HCC patients or HCC patients stratified according to several clinical traits. A prognostic nomogram with this five-AR-lncRNA signature predicted the 3- and 5-year OS outcomes of HCC patients intuitively and accurately (concordance index = 0.745). By parallel comparison, this five-AR-lncRNA signature has better prognosis accuracy than the other three recently published signatures. Furthermore, we discovered the prediction ability of the signature on therapeutic outcomes of HCC patients, including chemotherapy and immunotherapeutic responses. Gene set enrichment analysis and gene mutation analysis revealed that dysregulated cell cycle pathway, purine metabolism, and TP53 mutation may play an important role in determining the OS outcomes of HCC patients in the high-risk group. Collectively, our study suggests a new five-AR-lncRNA prognostic signature for HCC patients.

Identification of Novel Tumor Microenvironment-Related Long Non-coding RNAs to Predict the Prognosis for Hepatocellular Carcinoma

2021 ◽  
Author(s):  
Shenglan Huang ◽  
Jian Zhang ◽  
Dan Li ◽  
Xiaolan Lai ◽  
Lingling Zhuang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
High Risk ◽  
Tumor Microenvironment ◽  
Cox Regression ◽  
Risk Group ◽  
High Risk Group ◽  
Low Risk ◽  
Clinicopathological Parameters ◽  
Kaplan Meier ◽  
The Impact

Abstract Introduction: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors with poor prognosis. Tumor microenvironment (TME) plays a vital role in the tumor progression of HCC. Thus, we aimed to analyze the association of TME with HCC prognosis, and construct an TME-related lncRNAs signature for predicting the prognosis of HCC patients.Methods: We firstly assessed the stromal/immune /Estimate scores within the HCC microenvironment using the ESTIMATE algorithm based on TCGA database, and its associations with survival and clinicopathological parameters were also analyzed. Then, different expression lncRNAs were filtered out according to immune/stromal scores. Cox regression was performed to built an TME-related lncRNAs risk signature. Kaplan–Meier analysis was carried out to explored the prognostic values of the risk signature. Furthermore, we explored the biological functions and immune microenvironment feathers in high- and low risk groups. Lastly, we probed the association of the risk signature with the treatment responses to immune checkpoint inhibitors (ICIs) in HCC by comparing the immunophenoscore (IPS).Results: Stromal/immune /Estimate scores of HCC patients were obtained based on the ESTIMATE algorithm. The Kaplan-Meier curve analysis showed the high stromal/immune/ Estimate scores were significantly associated with better prognosis of the HCC patients. Then, six TME-related lncRNAs were screened for constructing the prognosis model. Kaplan-Meier survival curves suggested that HCC patients in high-risk group had worse prognosis than those with low-risk. ROC curve and Cox regression analyses demonstrated the signature could predict HCC survival exactly and independently. Function enrichment analysis revealed that some tumor- and immune-related pathways associated with HCC tumorigenesis and progression might be activated in high-risk group. We also discovered that some immune cells, which were beneficial to enhance immune responses towards cancer, were remarkably upregulated in low-risk group. Besides, there was closely correlation of immune checkmate inhibitors (ICIs) with the risk signature and the signature can be used to predict treatment response of ICIs.Conclusions: We analyzed the impact of the tumor microenvironment scores on the prognosis of patients with HCC. A novel TME-related prognostic risk signature was established, which may improve prognostic predictive accuracy and guide individualized immunotherapy for HCC patients.

scholarly journals Utility of radiomics for predicting patient survival in hepatocellular carcinoma with portal vein tumor thrombosis treated with stereotactic body radiotherapy

2020 ◽  
Author(s):  
Kui Wu ◽  
Yongjie Shui ◽  
Wenzheng Sun ◽  
Sheng Lin ◽  
Haowen Pang
Keyword(s):  
High Risk ◽  
Clinical Characteristics ◽  
Cox Regression ◽  
Patient Survival ◽  
Risk Group ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Risk Groups ◽  
High Risk Group ◽  
Low Risk

Abstract Objective This study aimed to develop and validate the combination of radiomic features and clinical characteristics that can predict patient survival in HCC with PVTT treated with SBRT. Materials and Methods The prediction model was developed in a primary cohort of 70 patients with HCC and PVTT treated with SBRT, using data acquired between December 2015 and June 2017. The radiomic features were extracted from computed tomography (CT) scans. A least absolute shrinkage and selection operator regression model was used to build the radiomic feature. Multivariate Cox-regression hazard models were created for analyzing survival outcomes and the radiomic features and clinical characteristics were presented with a nomogram. The area under the curve (AUC) of the receiver operating characteristic curve was used to evaluate the model. Participants were divided into a high-risk group and a low-risk group based on the radiomic features. Results A total of seven radiomic features and five clinical characteristics were extracted for survival analysis. A combination of the radiomic features and clinical characteristics resulted in better performance for the estimation of overall survival (OS) [AUC = 0.859 (CI: 0.770–0.948)] than that with clinical characteristics alone [AUC = 0.761 (CI: 0.641–0.881)]. These patients were divided into high-risk and low-risk groups according to the radiomic features. Conclusion This study demonstrated that a nomogram of combined radiomic features and clinical characteristics can be conveniently used to facilitate individualized preoperative prediction of OS in patients with HCC with PVTT before SBRT.

scholarly journals Development and Validation of a Robust Pyroptosis-Related Signature for Predicting Prognosis and Immune Status in Patients with Colon Cancer

2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Zhicheng Zhuang ◽  
Huajun Cai ◽  
Hexin Lin ◽  
Bingjie Guan ◽  
Yong Wu ◽  
...  
Keyword(s):  
High Risk ◽  
Immune Cells ◽  
Risk Group ◽  
Risk Groups ◽  
High Risk Group ◽  
Low Risk ◽  
Immune Checkpoints ◽  
Prognostic Signature ◽  
Tumor Purity ◽  
Kaplan Meier

Background. Pyroptosis has been confirmed as a type of inflammatory programmed cell death in recent years. However, the prognostic role of pyroptosis in colon cancer (CC) remains unclear. Methods. Dataset TCGA-COAD which came from the TCGA portal was taken as the training cohort. GSE17538 from the GEO database was treated as validation cohorts. Differential expression genes (DEGs) between normal and tumor tissues were confirmed. Patients were classified into two subgroups according to the expression characteristics of pyroptosis-related DEGs. The LASSO regression analysis was used to build the best prognostic signature, and its reliability was validated using Kaplan–Meier, ROC, PCA, and t-SNE analyses. And a nomogram based on the multivariate Cox analysis was developed. The enrichment analysis was performed in the GO and KEGG to investigate the potential mechanism. In addition, we explored the difference in the abundance of infiltrating immune cells and immune microenvironment between high- and low-risk groups. And we also predicted the association of common immune checkpoints with risk scores. Finally, we verified the expression of the pyroptosis-related hub gene at the protein level by immunohistochemistry. Results. A total of 23 pyroptosis-related DEGs were identified in the TCGA cohort. Patients were classified into two molecular clusters (MC) based on DEGs. Kaplan–Meier survival analysis indicated that patients with MC1 represented significantly poorer OS than patients with MC2. 13 overall survival- (OS-) related DEGs in MCs were used to construct the prognostic signature. Patients in the high-risk group exhibited poorer OS compared to those in the low-risk group. Combined with the clinical features, the risk score was found to be an independent prognostic factor of CC patients. The above results are verified in the external dataset GSE17538. A nomogram was established and showed excellent performance. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses indicated that the varied prognostic performance between high- and low-risk groups may be related to the immune response mediated by local inflammation. Further analysis showed that the high-risk group has stronger immune cell infiltration and lower tumor purity than the low-risk group. Through the correlation between risk score and immune checkpoint expression, T-cell immunoglobulin and mucin domain-containing protein 3 (TIM-3) was predicted as a potential therapeutic target for the high-risk group. Conclusion. The 13-gene signature was associated with OS, immune cells, tumor purity, and immune checkpoints in CC patients, and it could provide the basis for immunotherapy and predicting prognosis and help clinicians make decisions for individualized treatment.

Construction of a Prognostic Model Related to Ferroptosis and Iron-Metabolism and the Relationship with the Immune Microenvironment of Gastric Cancer

2021 ◽  
Author(s):  
Fang Wen ◽  
Xiaoxue Chen ◽  
Wenjie Huang ◽  
Shuai Ruan ◽  
Suping Gu ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
High Risk ◽  
Iron Metabolism ◽  
Risk Model ◽  
Cox Regression ◽  
Risk Group ◽  
Risk Groups ◽  
High Risk Group ◽  
Low Risk ◽  
Immune Microenvironment

Abstract Background: The diagnosis rate and mortality of gastric cancer (GC) are among the highest in the global, so it is of great significance to predict the survival time of GC patients. Ferroptosis and iron-metabolism make a critical impact on tumor development and are closely linked to the treatment of cancer and the prognosis of patients. However, the predictive value of the genes involved in ferroptosis and iron-metabolism in GC and their effects on immune microenvironment remain to be further clarified.Methods: In this study, the RNA sequence information and general clinical indicators of GC patients were acquired from the public databases. We first systematically screen out 134 DEGs and 13 PRGs related to ferroptosis and iron-metabolism. Then, we identified six PRDEGs (GLS2, MTF1, SLC1A5, SP1, NOX4, and ZFP36) based on the LASSO-penalized Cox regression analysis. The 6-gene prognostic risk model was established in the TCGA cohort and the GC patients were separated into the high- and the low-risk groups through the risk score median value. GEO cohort was used for verification. The expression of PRDEGs was verified by quantitative QPCR.Results: Our study demonstrated that patients in the low-risk group had a higher survival probability compared with those in high-risk group. In addition, univariate and multivariate Cox regression analyses confirmed that the risk score was an independent prediction parameter. The ROC curve analysis and nomogram manifested that the risk model had the high predictive ability and was more sensitive than general clinical features. Furthermore, compared with the high-risk group, the low-risk group had higher TMB and a longer 5-year survival period. In the immune microenvironment of GC, there were also differences in immune function and highly infiltrated immune cells between the two risk groups.Conclusions: The prognostic risk model based on the six genes associated with ferroptosis and iron-metabolism has a good performance for predicting the prognosis of patients with GC. The treatment of cancer by inducing tumor ferroptosis or mediating tumor iron-metabolism, especially combined with immunotherapy, provides a new possibility for individualized treatment of GC patients.

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