scholarly journals An Inflammation-Related Nine-Gene Signature to Improve Prognosis Prediction of Lung Adenocarcinoma

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Ze-jing Liu ◽  
Peng-xiao Hou ◽  
Xi-xing Wang
Keyword(s):  
Overall Survival ◽  
Lung Adenocarcinoma ◽  
Prognostic Value ◽  
Risk Group ◽  
Gene Signature ◽  
High Risk Group ◽  
Risk Scores ◽  
Lasso Regression ◽  
Prognostic Signature ◽  
Prognostic Prediction

Background. A novel predictive model was rarely reported based on inflammation-related genes to explore clinical outcomes of lung adenocarcinoma (LUAD) patients. Methods. Using TCGA database, we screened nine inflammation-related genes with a prognostic value, and LASSO regression was applied for model construction. The predictive value of the prognostic signature developed from inflammation-related genes was assessed by survival assays and multivariate assays. PCA and t-SNE analysis were performed to demonstrate clustering abilities of risk scores. Results. Thirteen inflammation-related genes (BTG2, CCL20, CD69, DCBLD2, GPC3, IL7R, LAMP3, MMP14, NMUR1, PCDH7, PIK3R5, RNF144B, and TPBG) with prognostic values were finally identified. LASSO regression further screened nine candidates (BTG2, CCL20, CD69, IL7R, MMP14, NMUR1, PCDH7, RNF144B, and TPBG). Then, a prognostic prediction model using the above nine genes was constructed. A reliable clustering ability of risk score was demonstrated by PCA and t-SNE assays in 500 LUAD patients. The survival assays revealed that the overall survivals of the high-risk group were distinctly poorer than those of the low-risk group with 1-, 3-, and 5-year AUC values of 0.695, 0.666, and 0.694, respectively. Finally, multivariate assays demonstrated the scoring system as an independent prognostic factor for overall survival. Conclusions. Our study shows that the signature of nine inflammation-related genes can be used as a prognostic marker for LUAD.

scholarly journals A four-gene prognostic signature for predicting the overall survival of patients with lung adenocarcinoma

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11911
Author(s):  
Lei Liu ◽  
Huayu He ◽  
Yue Peng ◽  
Zhenlin Yang ◽  
Shugeng Gao
Keyword(s):  
Overall Survival ◽  
High Risk ◽  
Lung Adenocarcinoma ◽  
Prognostic Model ◽  
Risk Group ◽  
Gene Signature ◽  
Tnm Staging ◽  
High Risk Group ◽  
Prognostic Signature ◽  
Risk Patients

Background The prognosis of patients for lung adenocarcinoma (LUAD) is known to vary widely; the 5-year overall survival rate is just 63% even for the pathological IA stage. Thus, in order to identify high-risk patients and facilitate clinical decision making, it is vital that we identify new prognostic markers that can be used alongside TNM staging to facilitate risk stratification. Methods We used mRNA expression from The Cancer Genome Atlas (TCGA) cohort to identify a prognostic gene signature and combined this with clinical data to develop a predictive model for the prognosis of patients for lung adenocarcinoma. Kaplan-Meier curves, Lasso regression, and Cox regression, were used to identify specific prognostic genes. The model was assessed via the area under the receiver operating characteristic curve (AUC-ROC) and validated in an independent dataset (GSE50081) from the Gene Expression Omnibus (GEO). Results Our analyses identified a four-gene prognostic signature (CENPH, MYLIP, PITX3, and TRAF3IP3) that was associated with the overall survival of patients with T1-4N0-2M0 in the TCGA dataset. Multivariate regression suggested that the total risk score for the four genes represented an independent prognostic factor for the TCGA and GEO cohorts; the hazard ratio (HR) (high risk group vs low risk group) were 2.34 (p < 0.001) and 2.10 (p = 0.017). Immune infiltration estimations, as determined by an online tool (TIMER2.0) showed that CD4+ T cells were in relative abundance in the high risk group compared to the low risk group in both of the two cohorts (both p < 0.001). We established a composite prognostic model for predicting OS, combined with risk-grouping and clinical factors. The AUCs for 1-, 3-, 5- year OS in the training set were 0.750, 0.737, and 0.719; and were 0.645, 0.766, and 0.725 in the validation set. The calibration curves showed a good match between the predicted probabilities and the actual probabilities. Conclusions We identified a four-gene predictive signature which represents an independent prognostic factor and can be used to identify high-risk patients from different TNM stages of LUAD. A new prognostic model that combines a prognostic gene signature with clinical features exhibited better discriminatory ability for OS than traditional TNM staging.

scholarly journals A Nine-Immune-Related Gene Prognostic Signature for Predicting Outcomes in High-Grade Serous Ovarian Cancer

2021 ◽  
Author(s):  
Yahui Jiang ◽  
Tianjiao Lyu ◽  
Tianyu Zhou ◽  
Yiwen Shi ◽  
Weiwei Feng
Keyword(s):  
Ovarian Cancer ◽  
Overall Survival ◽  
Cox Regression ◽  
Risk Group ◽  
High Risk Group ◽  
Low Risk ◽  
Risk Scores ◽  
High Grade ◽  
Lasso Regression ◽  
Prognostic Signature

Abstract Background: Recently, immune system has been shown to be indispensable for ovarian cancer progression. The key immune-related genes (IRGs) related to the overall survival of ovarian cancer patients should be taken seriously. Here, we screened 9 survival-related IRGs in high-grade serous ovarian cancer (HGSOC) and build a prognostic signature to predict the outcome of HGSOC patients.Methods: We downloaded RNA-sequence profiles from The Cancer Genome Atlas (TCGA) and Genome Tissue Expression (GTEx) databases to identify differentially expressed genes between normal fallopian tube and HGSOC. Among these genes, IRGs were filtered based on the Immunology Database and Analysis Portal (ImmPort). Using univariate Cox regression, Lasso regression and multivariate Cox regression, we selected 9 survival-related IRGs and established a prognostic signature to compute the risk score. Patients were divided into a low-risk group and a high-risk group, and the immunological feature differences between them were analysed with the ESTIMATE R package, TIMER and GSEA software. Moreover, the prognostic signature was validated by data from Gene Expression Omnibus (GEO) datasets.Results: We obtained 1544 differentially expressed genes in HGSOC compared with normal fallopian tube, among which 99 genes were related to immunology. After univariate Cox regression, Lasso regression and multivariate Cox regression, nine IRGs (HLA-F, PSMC1, PI3, CXCL10, CXCL9, CXCL11, LRP1, STAT1 and OGN) were identified as optimal survival-related IRGs and used to establish a prognostic signature for calculating the risk scores of HGSOC patients. The prognostic signature showed its efficiency in predicting the overall survival of HGSOC patients in TCGA training cohort (p=1.018e-8) and GEO test cohort (p=2.632e-2). Age and risk scores were independent risk factors for overall survival. As the risk scores increased, the proportions of neutrophil, dendritic cells, CD8+ T cells, CD4+ T cells and B cells decreased (p values were 0.026, 1.909e-4, 9.165e-10, 0.003 and 2.658e-4, respectively). In addition, 21 out of 24 HLA-related genes were highly expressed in the low-risk group than in the high-risk group. The above might prompt a stronger immune response in the low-risk group.Conclusions: Our study constructed a nine-IRG-based prognostic signature that could effectively predict the overall survival of HGSOC patients and become a promising therapeutic target for HGSOC treatments.

scholarly journals A Seven-Gene Signature to Predict Prognosis of Patients With Hepatocellular Carcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Junli Wang ◽  
Qi Zhang ◽  
Fukang Shi ◽  
Dipesh Kumar Yadav ◽  
Zhengtao Hong ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
High Risk ◽  
Risk Group ◽  
Radical Resection ◽  
Tissue Microarrays ◽  
Gene Signature ◽  
High Risk Group ◽  
Prognostic Models ◽  
Risk Scores

Purpose: Hepatocellular carcinoma (HCC) is one of the most prevalent malignant diseases worldwide and has a poor prognosis. Gene-based prognostic models have been reported to predict the overall survival of patients with HCC. Unfortunately, most of the genes used in earlier prognostic models lack prospective validation and, thus, cannot be used in clinical practice.Methods: Candidate genes were selected from GEPIA (Gene Expression Profiling Interactive Analysis), and their associations with patients’ survival were confirmed by RT-PCR using cDNA tissue microarrays established from patients with HCC after radical resection. A multivariate Cox proportion model was used to calculate the coefficient of corresponding gene. The expression of seven genes of interest (MKI67, AR, PLG, DNASE1L3, PTTG1, PPP1R1A, and TTR) with two reference genes was defined to calculate a risk score which determined groups of different risks.Results: Our risk scoring efficiently classified patients (n = 129) with HCC into a low-, intermediate-, and high-risk group. The three groups showed meaningful distinction of 3-year overall survival rate, i.e., 88.9, 74.5, and 20.6% for the low-, intermediate-, and high-risk group, respectively. The prognostic prediction model of risk scores was subsequently verified using an independent prospective cohort (n = 77) and showed high accuracy.Conclusion: Our seven-gene signature model performed excellent long-term prediction power and provided crucially guiding therapy for patients who are not a candidate for surgery.

scholarly journals Establishment and Validation of a Ferroptosis-Related Gene Signature to Predict Overall Survival in Lung Adenocarcinoma

2022 ◽  
Vol 12 ◽  
Author(s):  
Su Wang ◽  
Zhen Xie ◽  
Zenghong Wu
Keyword(s):  
Overall Survival ◽  
High Risk ◽  
Lung Adenocarcinoma ◽  
Risk Group ◽  
Risk Groups ◽  
Gene Signature ◽  
Low Risk ◽  
Functional Enrichment ◽  
Related Gene ◽  
Prognostic Signature

Background: Lung adenocarcinoma (LUAD) is the most common and lethal subtype of lung cancer. Ferroptosis, an iron-dependent form of regulated cell death, has emerged as a target in cancer therapy. However, the prognostic value of ferroptosis-related genes (FRGs)x in LUAD remains to be explored.Methods: In this study, we used RNA sequencing data and relevant clinical data from The Cancer Genome Atlas (TCGA) dataset and Gene Expression Omnibus (GEO) dataset to construct and validate a prognostic FRG signature for overall survival (OS) in LUAD patients and defined potential biomarkers for ferroptosis-related tumor therapy.Results: A total of 86 differentially expressed FRGs were identified from LUAD tumor tissues versus normal tissues, of which 15 FRGs were significantly associated with OS in the survival analysis. Through the LASSO Cox regression analysis, a prognostic signature including 11 FRGs was established to predict OS in the TCGA tumor cohort. Based on the median value of risk scores calculated according to the signature, patients were divided into high-risk and low-risk groups. Kaplan–Meier analysis indicated that the high-risk group had a poorer OS than the low-risk group. The area under the curve of this signature was 0.74 in the TCGA tumor set, showing good discrimination. In the GEO validation set, the prognostic signature also had good predictive performance. Functional enrichment analysis showed that some immune-associated gene sets were significantly differently enriched in two risk groups.Conclusion: Our study unearthed a novel ferroptosis-related gene signature for predicting the prognosis of LUAD, and the signature may provide useful prognostic biomarkers and potential treatment targets.

scholarly journals A Novel Ferroptosis-Related Gene Signature Robustly Predicts Clinical Prognosis And Tumor Immunity in Patients With Kidney Renal Clear Cell Carcinoma

2021 ◽  
Author(s):  
Wei Song ◽  
Weiting Kang ◽  
Qi Zhang
Keyword(s):  
High Risk ◽  
Clear Cell ◽  
Risk Group ◽  
Gene Signature ◽  
Renal Clear Cell Carcinoma ◽  
High Risk Group ◽  
Risk Scores ◽  
Related Gene ◽  
Clinical Prognosis ◽  
Validation Set

Abstract Objective: This study aimed to construct a ferroptosis-related gene signature to predict clinical prognosis and tumor immunity in patients with kidney renal clear cell carcinoma (KIRC).Methods: The mRNA expression profiles and corresponding clinical data of KIRC patients were downloaded from The Cancer Genome Atlas (TCGA), which were randomly divided into training (398 patients) and validation set (132 patients). The iron death related (IDR) prediction model was constructed based on training set and 60 ferroptosis-related genes from previous literatures, followed by prognostic performance evaluation and verification using the validation set. Moreover, functional enrichment, immune cell infiltration, metagene clusters correlation, and TIDE scoring analyses were performed. Results: In total, 23 ferroptosis-related genes were significantly associated with overall survival (OS). The IDR prediction model (a 10-gene signature) was then constructed to stratify patients into two risk groups. The OS of KIRC patients with high-risk scores was significantly shorter than those with low-risk scores. Moreover, the risk score was confirmed as an independent prognostic predictor for OS. The positive and negative correlated genes with this model were significantly enriched in p53 signaling pathway, and cGMP-PKG signaling pathway. The patients in the high-risk group had higher ratios of plasma cells, T cells CD8, and T cells regulatory Tregs. Furthermore, IgG, HCK, LCK, and Interferson metagenes were significantly correlated with risk score. By TIDE score analysis, patients in the high-risk group could benefit from immunotherapy.Conclusions: The identified ferroptosis-related gene signature is significantly correlated with clinical prognosis and immune immunity in KIRC patients.

scholarly journals Identification of prognostic gene signature associated with microenvironment of lung adenocarcinoma

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e8128 ◽  
Author(s):  
Cheng Yue ◽  
Hongtao Ma ◽  
Yubai Zhou
Keyword(s):  
High Risk ◽  
Lung Adenocarcinoma ◽  
Prognostic Model ◽  
Cox Regression ◽  
Risk Group ◽  
Expression Profiles ◽  
Gene Signature ◽  
Low Risk ◽  
Differentially Expressed ◽  
Lasso Regression

Background Lung cancer has the highest morbidity and mortality worldwide, and lung adenocarcinoma (LADC) is the most common pathological subtype. Accumulating evidence suggests the tumor microenvironment (TME) is correlated with the tumor progress and the patient’s outcome. As the major components of TME, the tumor-infiltrated immune cells and stromal cells have attracted more and more attention. In this study, differentially expressed immune and stromal signature genes were used to construct a TME-related prognostic model for predicting the outcomes of LADC patients. Methods The expression profiles of LADC samples with clinical information were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). The differentially expressed genes (DEGs) related to the TME of LADC were identified using TCGA dataset by Wilcoxon rank sum test. The prognostic effects of TME-related DEGs were analyzed using univariate Cox regression. Then, the least absolute shrinkage and selection operator (LASSO) regression was performed to reduce the overfit and the number of genes for further analysis. Next, the prognostic model was constructed by step multivariate Cox regression and risk score of each sample was calculated. Then, survival and Receiver Operating Characteristic (ROC) analyses were conducted to validate the model using TCGA and GEO datasets, respectively. The Kyoto Encyclopedia of Genes and Genomes analysis of gene signature was performed using Gene Set Enrichment Analysis (GSEA). Finally, the overall immune status, tumor purity and the expression profiles of HLA genes of high- and low-risk samples was further analyzed to reveal the potential mechanisms of prognostic effects of the model. Results A total of 93 TME-related DEGs were identified, of which 23 DEGs were up-regulated and 70 DEGs were down-regulated. The univariate cox analysis indicated that 23 DEGs has the prognostic effects, the hazard ratio ranged from 0.65 to 1.25 (p < 0.05). Then, seven genes were screened out from the 23 DEGs by LASSO regression method and were further analyzed by step multivariate Cox regression. Finally, a three-gene (ADAM12, Bruton Tyrosine Kinase (BTK), ERG) signature was constructed, and ADAM12, BTK can be used as independent prognostic factors. The three-gene signature well stratified the LADC patients in both training (TCGA) and testing (GEO) datasets as high-risk and low-risk groups, the 3-year area under curve (AUC) of ROC curves of three GEO sets were 0.718 (GSE3141), 0.646 (GSE30219) and 0.643 (GSE50081). The GSEA analysis indicated that highly expressed ADAM12, BTK, ERG mainly correlated with the activation of pathways involving in focal adhesion, immune regulation. The immune analysis indicated that the low-risk group has more immune activities and higher expression of HLA genes than that of the high-risk group. In sum, we identified and constructed a three TME-related DEGs signature, which could be used to predict the prognosis of LADC patients.

scholarly journals Weighted Gene Co-Expression Network Analysis Reveals a New Survival Model for Prognostic Prediction in Ewing Sarcoma

2021 ◽  
Author(s):  
Debao Li ◽  
Lei Wang ◽  
Guanghui Wang ◽  
Yaowen Yang ◽  
Weiyu Yang ◽  
...  
Keyword(s):  
Overall Survival ◽  
Network Analysis ◽  
Ewing Sarcoma ◽  
Prognostic Model ◽  
Risk Group ◽  
Expression Profiles ◽  
High Risk Group ◽  
Low Risk ◽  
Lasso Regression ◽  
Survival Status

Abstract Background: Ewing sarcoma (ES) is a malignant bone or soft-tissue cancer that mainly arises in children and young adults. However, the prognosis of Ewing sarcoma remains very poor, and there is no effective prediction method. The aim of our study was to identify a prognostic model for ES patients based on prognosis-associated mRNA expression profiles. Methods: The GSE17679 dataset was downloaded from the Gene Expression Omnibus (GEO) database. Differently expressed genes (DEGs) between ES and normal control were identified using R package “limma”. A weighted gene co-expression network analysis (WGCNA) was used to screen gene modules associated with recurrence/metastasis and survival status based on DEGs. Results: The prognostic model was constructed based on genes in MEbrown module, which was most associated with recurrence/metastasis and survival status, using Kaplan-Meier survival and lasso regression analysis. Sixteen genes were screened to construct the prognostic model. ES patients were grouped into high- and low-risk groups based on the median of risk score calculated for each of them. ES patients in high-risk group have worse survival than patients in low-risk group. The AUCs (Area under the ROC curve) for 1-year, 3-year, and 6-year overall survival were 0.903, 0.995, 0.953. Conclusions: Taken together, our research constructed a prognostic model which has excellent prediction performance for overall survival of ES patients.

scholarly journals Identification of a Pyroptosis-Related Gene Signature for Predicting Overall Survival and Response to Immunotherapy in Hepatocellular Carcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Susu Zheng ◽  
Xiaoying Xie ◽  
Xinkun Guo ◽  
Yanfang Wu ◽  
Guobin Chen ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
High Risk ◽  
Regression Model ◽  
Risk Score ◽  
Cox Regression ◽  
Risk Group ◽  
Gene Signature ◽  
High Risk Group ◽  
Related Gene

Pyroptosis is a novel kind of cellular necrosis and shown to be involved in cancer progression. However, the diverse expression, prognosis and associations with immune status of pyroptosis-related genes in Hepatocellular carcinoma (HCC) have yet to be analyzed. Herein, the expression profiles and corresponding clinical characteristics of HCC samples were collected from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Then a pyroptosis-related gene signature was built by applying the least absolute shrinkage and selection operator (LASSO) Cox regression model from the TCGA cohort, while the GEO datasets were applied for verification. Twenty-four pyroptosis-related genes were found to be differentially expressed between HCC and normal samples. A five pyroptosis-related gene signature (GSDME, CASP8, SCAF11, NOD2, CASP6) was constructed according to LASSO Cox regression model. Patients in the low-risk group had better survival rates than those in the high-risk group. The risk score was proved to be an independent prognostic factor for overall survival (OS). The risk score correlated with immune infiltrations and immunotherapy responses. GSEA indicated that endocytosis, ubiquitin mediated proteolysis and regulation of autophagy were enriched in the high-risk group, while drug metabolism cytochrome P450 and tryptophan metabolism were enriched in the low-risk group. In conclusion, our pyroptosis-related gene signature can be used for survival prediction and may also predict the response of immunotherapy.

scholarly journals A Transcription Factor Signature Predicts Prognosis of Patients with Adrenocortical Carcinoma

2021 ◽  
Author(s):  
Jianyu Zhao ◽  
Bo Liu ◽  
Xiaoping Li
Keyword(s):  
Overall Survival ◽  
High Risk ◽  
Risk Score ◽  
Adrenocortical Carcinoma ◽  
Cox Regression ◽  
Risk Group ◽  
High Risk Group ◽  
Prognostic Signature ◽  
Abdominal Masses ◽  
Cox Analysis

Abstract Background: Adrenocortical carcinoma (ACC) is a rare endocrine cancer that manifests as abdominal masses and excessive steroid hormone levels. Transcription factors (TFs) deregulation is found to be involved in adrenocortical tumorigenesis and cancer progression. This study aimed to construct a TF-based prognostic signature for prediction of survival of ACC patients.Methods: The gene expression profile for ACC patients were downloaded from TCGA and GEO datasets. The univariate Cox analysis was applied to identify survival-related TFs and the LASSO Cox regression was conducted to construct the TF signature. The multivariate analysis was used to reveal the independent prognostic factors.Results: We identified a 13-TF prognostic signature comprised of CREB3L3, NR0B1, CENPA, FOXM1, E2F2, MYBL2, HOXC11, ZIC2, ZNF282, DNMT1, TCF3, ELK4, and KLF6 using the univariate Cox analysis and LASSO Cox regression. The risk score based on the TF-signature could classify patients into low- and high-risk group. Kaplan-Meier analyses showed that patients in the high-risk group had significantly shorter overall survival compared to the low-risk patients. ROC curves showed that the prognostic signature predicted the overall survival of ACC patients with good sensitivity and specificity. Furthermore, the TF-risk score was an independent prognostic factor.Conclusion: Taken together, we identified a 13-TF prognostic marker to predict overall survival in ACC patients.

scholarly journals Ferroptosis-related gene signature predicts the prognosis of papillary thyroid carcinoma

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Jinyuan Shi ◽  
Pu Wu ◽  
Lei Sheng ◽  
Wei Sun ◽  
Hao Zhang
Keyword(s):  
High Risk ◽  
Immune Cells ◽  
Risk Group ◽  
Gene Signature ◽  
High Risk Group ◽  
Low Risk ◽  
Lasso Regression ◽  
Papillary Thyroid ◽  
Related Gene ◽  
Normal Thyroid

Abstract Background Papillary thyroid carcinoma (PTC) is the most common type of thyroid cancer (TC), accounting for more than 80% of all cases. Ferroptosis is a novel iron-dependent and Reactive oxygen species (ROS) reliant type of cell death which is distinct from the apoptosis, necroptosis and pyroptosis. Considerable studies have demonstrated that ferroptosis is involved in the biological process of various cancers. However, the role of ferroptosis in PTC remains unclear. This study aims at exploring the expression of ferroptosis-related genes (FRG) and their prognostic values in PTC. Methods A ferroptosis-related gene signature was constructed using lasso regression analysis through the PTC datasets of the Cancer Genome Atlas (TCGA). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to investigate the bioinformatics functions of significantly different genes (SDG) of ferroptosis. Additionally, the correlations of ferroptosis and immune cells were assessed through the single-sample gene set enrichment analysis (ssGSEA) and CIBERSORT database. Finally, SDG were test in clinical PTC specimens and normal thyroid tissues. Results LASSO regression model was utilized to establish a novel FRG signature with 10 genes (ANGPTL7, CDKN2A, DPP4, DRD4, ISCU, PGD, SRXN1, TF, TFRC, TXNRD1) to predicts the prognosis of PTC, and the patients were separated into high-risk and low-risk groups by the risk score. The high-risk group had poorer survival than the low-risk group (p < 0.001). Receiver operating characteristic (ROC) curve analysis confirmed the signature's predictive capacity. Multivariate regression analysis identified the prognostic signature-based risk score was an independent prognostic indicator for PTC. The functional roles of the DEGs in the TGCA PTC cohort were explored using GO enrichment and KEGG pathway analyses. Immune related analysis demonstrated that the most types of immune cells and immunological function in the high-risk group were significant different with those in the low-risk group. Quantitative Real-Time Polymerase Chain Reaction (qRT-PCR) verified the SDG have differences in expression between tumor tissue and normal thyroid tissue. In addition, cell experiments were conducted to observe the changes in cell morphology and expression of signature’s genes with the influence of ferroptosis induced by sorafenib. Conclusions We identified differently expressed FRG that may involve in PTC. A ferroptosis-related gene signature has significant values in predicting the patients’ prognoses and targeting ferroptosis may be an alternative for PTC’s therapy.

Sign in / Sign up

Export Citation Format

Share Document