A Robust Seven-Gene Signature Associated With Tumor Microenvironment to Predict Survival Outcomes of Patients With Stage III–IV Lung Adenocarcinoma

BackgroundDue to the relatively insidious early symptoms of lung adenocarcinoma (LUAD), most LUAD patients are at an advanced stage at the time of diagnosis and lose the best chance of surgical resection. Mounting evidence suggested that the tumor microenvironment (TME) was highly correlated with tumor occurrence, progress, and prognosis. However, TME in advanced LUAD remained to be studied and reliable prognostic signatures based on TME in advanced LUAD also had not been well-established. This study aimed to understand the cell composition and function of TME and construct a gene signature associated with TME in advanced LUAD.MethodsThe immune, stromal, and ESTIMATE scores of each sample from The Cancer Genome Atlas (TCGA) database were, respectively, calculated using an ESTIMATE algorithm. The LASSO and Cox regression model were applied to select prognostic genes and to construct a gene signature associated with TME. Two independent datasets from the Gene Expression Omnibus (GEO) were used for external validation. Twenty-two subsets of tumor-infiltrating immune cells (Tiics) were analyzed using the CIBERSORT algorithm.ResultsFavorable overall survival (OS) and progression-free survival (PFS) were found in patients with high immune score (p = 0.048 and p = 0.028; respectively) and stromal score (p = 0.024 and p = 0.025; respectively). Based on the immune and stromal scores, 453 differentially expressed genes (DEGs) were identified. Using the LASSO and Cox regression model, a seven-gene signature containing AFAP1L2, CAMK1D, LOXL2, PIK3CG, PLEKHG1, RARRES2, and SPP1 was identified to construct a risk stratification model. The OS and PFS of the high-risk group were significantly worse than that of the low-risk group (p < 0.001 and p < 0.001; respectively). The receiver operating characteristic (ROC) curve analysis confirmed the good potency of the seven-gene signature. Similar findings were validated in two independent cohorts. In addition, the proportion of macrophages M2 and Tregs was higher in high-risk patients (p = 0.041 and p = 0.022, respectively).ConclusionOur study established and validated a seven-gene signature associated with TME, which might serve as a prognosis stratification tool to predict survival outcomes of advanced LUAD patients. In addition, macrophages M2 polarization may lead to worse prognosis in patients with advanced LUAD.

Download Full-text

Identification of prognostic gene signature associated with microenvironment of lung adenocarcinoma

PeerJ ◽

10.7717/peerj.8128 ◽

2019 ◽

Vol 7 ◽

pp. e8128 ◽

Cited By ~ 12

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.

Download Full-text

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

Frontiers in Genetics ◽

10.3389/fgene.2021.789296 ◽

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.

Download Full-text

Identification of an immune prognostic 11-gene signature for lung adenocarcinoma

PeerJ ◽

10.7717/peerj.10749 ◽

2021 ◽

Vol 9 ◽

pp. e10749

Author(s):

Tao Yang ◽

Lizheng Hao ◽

Renyun Cui ◽

Huanyu Liu ◽

Jian Chen ◽

...

Keyword(s):

High Risk ◽

Lung Adenocarcinoma ◽

Cox Regression ◽

Risk Group ◽

Stage Iv ◽

Gene Signature ◽

High Risk Group ◽

Stage I ◽

Local Progression ◽

Immune Score

Background The immunological tumour microenvironment (TME) has occupied a very important position in the beginning and progression of non-small cell lung cancer (NSCLC). Prognosis of lung adenocarcinoma (LUAD) remains poor for the local progression and widely metastases at the time of clinical diagnosis. Our objective is to identify a potential signature model to improve prognosis of LUAD. Methods With the aim to identify a novel immune prognostic signature associated with overall survival (OS), we analysed LUADs extracted from The Cancer Genome Atlas (TCGA). Immune scores and stromal scores of TCGA-LUAD were downloaded from Estimation of STromal and Immune cells in MAlignant Tumour tissues Expression using data (ESTIMATE). LASSO COX regression was applied to build the prediction model. Then, the prognostic gene signature was validated in the GSE68465 dataset. Results The data from TCGA datasets showed patients in stage I and stage II had higher stromal scores than patients in stage IV (P < 0.05), and for immune score patients in stage I were higher than patients in stage III and stage IV (P < 0.05). The improved overall survivals were observed in high stromal score and immune score groups. Patients in the high-risk group exhibited the inferior OS (P = 2.501e − 05). By validating the 397 LUAD patients from GSE68465, we observed a better OS in the low-risk group compared to the high-risk group, which is consistent with the results from the TCGA cohort. Nomogram results showed that practical and predicted survival coincided very well, especially for 3-year survival. Conclusion We obtained an 11 immune score related gene signature model as an independent element to effectively classify LUADs into different risk groups, which might provide a support for precision treatments. Moreover, immune score may play a potential valuable sole for estimating OS in LUADs.

Download Full-text

Construction and validation of a robust epigenetic gene-set based signature in ovarian cancer

10.21203/rs.3.rs-123005/v1 ◽

2020 ◽

Author(s):

Wenbin Shen ◽

Wei Jiang ◽

Shuang Ye ◽

Min Sun ◽

Huijuan Yang ◽

...

Keyword(s):

Ovarian Cancer ◽

Regression Model ◽

Cox Regression ◽

Risk Group ◽

External Validation ◽

Epigenetic Factors ◽

Cox Regression Model ◽

Internal Validation ◽

Gene Set ◽

Validation Set

Abstract Background Epigenetic factors play a critical role in tumor development and progression. The aim of this study was to construct and validate a robust epigenetic gene-set based signature for predicting prognosis of ovarian cancer (OC). Methods Public microarray data of OC patients from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were identified and patients from TCGA dataset were randomized 3:1 into discovery and internal validation series. GSE14764 and GSE26712 from GEO database were combined as the external validation set. LASSO Cox regression model was performed in the discovery set to filter the most useful prognostic epigenetic factors. Results Based on LASSO Cox regression model, we built a 26 epigenetic factors based prognostic signature. In the discovery set, patients in high risk group showed significantly poorer overall survival than that patients in low risk group (HR: 2.11, 95% CI: 1.65–2.72, P < 0.001). The results were further validated in the internal validation set (HR: 1.69, 95% CI: 1.07–2.63, P = 0.020) and external validation set (HR 1.95, 95% CI 1.41–2.69; p < 0.001). Survival ROC at 5 year suggested that the epigenetic signature (AUC = 0.700) had better prognostic accuracy than any other clinicopathological factors in the entire cohort. In addition, survival decision curve analysis unveiled a considerable value of clinical utility of the epigenetic signature. Conclusions We successfully developed a robust epigenetic signature that can accurately predict prognosis in OC.

Download Full-text

Analysis of DNA damage response gene signature for prognosis prediction of esophageal squamous cell carcinoma.

Journal of Clinical Oncology ◽

10.1200/jco.2021.39.15_suppl.e16073 ◽

2021 ◽

Vol 39 (15_suppl) ◽

pp. e16073-e16073

Author(s):

Weitao Zhuang ◽

Xiao-song Ben ◽

Dan Tian ◽

Zihao Zhou ◽

Gang Chen ◽

...

Keyword(s):

High Risk ◽

Dna Damage Response ◽

Squamous Cell ◽

Cox Regression ◽

Risk Group ◽

Gene Signature ◽

Prognostic Signature ◽

Training Set ◽

Prognosis Prediction ◽

Damage Response

e16073 Background: Esophageal squamous cell cancer (ESCC) is a malignant tumor with a poor 5-year relative survival. A prognosis prediction signature associated with DNA Damage Response (DDR) genes in ESCC was explored in this study. Methods: The clinical and gene expression profiles of ESCC patients were downloaded from the GEO and TCGA database. Univariate Cox regression and 1000 iterations of 10-fold cross-validation of LASSO Cox regression with binomial deviance minimization criteria were used to identify DDR genes as potential object and a prognostic signature for ESCC survival prediction, followed by validation of the signature via TCGA cohort and identification of independent prognostic predictors. A nomogram for prognosis prediction was built and Gene Set Enrichment Analysis (GSEA) was performed to further understand the underlying molecular mechanisms. Results: A signature of 8 DDR genes were constructed as being significantly associated with overall survival (OS) among patients with esophageal squamous cell carcinoma. The pronostic signature stratified ESCC patients into low- vs high-risk groups in terms of OS in the training set, testing set and the validation cohorts, and remained as an independent prognostic factor in multivariate analyses (hazard ratio (HR) in training set, 0.17 [95% CI, 0.09-0.35; P < 0 .001], HR in testing set, 0.38 [95% CI, 0.16-0.93; P = 0.029], HR in discovery cohort, 0.171 [95% CI, 0.03-0.48; P < 0 .001]) after adjusting for clinicopathological factors. The 8-DDR gene signature achieved a higher accuracy (C-index, 0.69; AUCs for 1-, 3- and 5-year OS, 0.74, 0.77 and 0.76, respectively) than 7 previously reported multigene signatures (C-index range, 0.53 to 0.60; AUCs range, 0.55to 0.66, 0.54 to 0.64 and 0.62 to 0.66, respectively) for estimation of survival in comparable cohorts. A nomogram incorporating tumor location, grade, adjuvant therapy and signature-based risk group showed better predictive performance for 1- and 3- year survival than for 5 year survival. Moreover, GSEA revealed that the DNA repair was more prominently enriched in the high-risk group while the low-risk group had not enrichment of any process (P > 0.05 for all). Conclusions: Taken together, our study identified 8 DDR genes related to the prognosis of ESCC patients, and constructed a robust prognostic signature to effectively stratify ESCC patients with different survival rates, which may help recognize high-risk patients potentially benefiting from more aggressive treatment.

Download Full-text

Identification of a 5-Gene Metabolic Signature for Predicting Prognosis Based on an Integrated Analysis of Tumor Microenvironment in Lung Adenocarcinoma

Journal of Oncology ◽

10.1155/2020/5310793 ◽

2020 ◽

Vol 2020 ◽

pp. 1-12 ◽

Cited By ~ 1

Author(s):

Xiaolin Yu ◽

Xiaomei Zhang ◽

Yanxia Zhang

Keyword(s):

Tumor Microenvironment ◽

Confidence Interval ◽

Lung Adenocarcinoma ◽

Cox Regression ◽

External Validation ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Integrated Analysis ◽

Lasso Regression ◽

The Status

Lung adenocarcinoma (LUAD) is a common subtype of lung cancer with a depressing survival rate. The reprogramming of tumor metabolism was identified as a new hallmark of cancer in tumor microenvironment (TME), and we made a comprehensive exploration to reveal the prognostic role of the metabolic-related genes. Transcriptome profiling data of LUAD were, respectively, downloaded from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. Based on the extracted metabolic-related genes, a novel 5-gene metabolic prognostic signature (including GNPNAT1, LPGAT1, TYMS, LDHA, and PTGES) was constructed by univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression. This signature confirmed its robustness and accuracy by external validation in multiple databases. It could be an independent risk factor for LUAD, and the nomograms possessed moderately accurate performance with the C-index of 0.755 (95% confidence interval: 0.706–0.804) and 0.691 (95% confidence interval: 0.636–0.746) in training set and testing set. This signature could reveal the metabolic features according to the results of gene set enrichment analysis (GSEA) and meanwhile monitor the status of TME through ESTIMATE scores and the infiltration levels of immune cells. In conclusion, this gene signature is a cost-effective tool which could indicate the status of TME to provide more clues in the exploration of new diagnostic and therapeutic strategy.

Download Full-text

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

PeerJ ◽

10.7717/peerj.11911 ◽

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.

Download Full-text

A Novel Platelet-Related Gene Signature for Predicting the Prognosis of Triple-Negative Breast Cancer

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.795600 ◽

2022 ◽

Vol 9 ◽

Author(s):

Jindong Xie ◽

Yutian Zou ◽

Feng Ye ◽

Wanzhen Zhao ◽

Xinhua Xie ◽

...

Keyword(s):

Breast Cancer ◽

High Risk ◽

Triple Negative Breast Cancer ◽

Triple Negative ◽

Cox Regression ◽

Risk Group ◽

Gene Signature ◽

The Cancer Genome Atlas ◽

Functional Enrichment ◽

Clinical Prognosis

Regarded as the most invasive subtype, triple-negative breast cancer (TNBC) lacks the expression of estrogen receptors (ERs), progesterone receptors (PRs), and human epidermal growth factor receptor 2 (HER2) proteins. Platelets have recently been shown to be associated with metastasis of malignant tumors. Nevertheless, the status of platelet-related genes in TNBC and their correlation with patient prognosis remain unknown. In this study, the expression and variation levels of platelet-related genes were identified and patients with TNBC were divided into three subtypes. We collected cohorts from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. By applying the least absolute shrinkage and selection operator (LASSO) Cox regression method, we constructed a seven-gene signature which classified the two cohorts of patients with TNBC into low- or high-risk groups. Patients in the high-risk group were more likely to have lower survival rates than those in the low-risk group. The risk score, incorporated with the clinical features, was confirmed as an independent factor for predicting the overall survival (OS) time. Functional enrichment analyses revealed the involvement of a variety of vital biological processes and classical cancer-related pathways that could be important to the ultimate prognosis of TNBC. We then built a nomogram that performed well. Moreover, we tested the model in other cohorts and obtained positive outcomes. In conclusion, platelet-related genes were closely related to TNBC, and this novel signature could serve as a tool for the assessment of clinical prognosis.

Download Full-text

A three-gene signature might predict prognosis in patients with acute myeloid leukemia

Bioscience Reports ◽

10.1042/bsr20193808 ◽

2020 ◽

Vol 40 (6) ◽

Author(s):

Xin Zhu ◽

Qian Zhao ◽

Xiaoyu Su ◽

Jinming Ke ◽

Yunyun Yi ◽

...

Keyword(s):

Acute Myeloid Leukemia ◽

Transcription Factor ◽

High Risk ◽

Myeloid Leukemia ◽

Cox Regression ◽

Risk Group ◽

White Blood Cells ◽

Gene Signature ◽

Low Risk ◽

Acute Myeloid

Abstract The identification of effective signatures is crucial to predict the prognosis of acute myeloid leukemia (AML). The investigation aimed to identify a new signature for AML prognostic prediction by using the three-gene expression (octamer-binding transcription factor 4 (OCT4), POU domain type 5 transcription factor 1B (POU5F1B) and B-cell-specific Moloney murine leukemia virus integration site-1 pseudogene 1 (BMI1P1). The expressions of genes were obtained from our previous study. Only the specimens in which three genes were all expressed were included in this research. A three-gene signature was constructed by the multivariate Cox regression analyses to divide patients into high-risk and low-risk groups. Receiver operating characteristic (ROC) analysis of the three-gene signature (area under ROC curve (AUC) = 0.901, 95% CI: 0.821–0.981, P<0.001) indicated that it was a more valuable signature for distinguishing between patients and controls than any of the three genes. Moreover, white blood cells (WBCs, P=0.004), platelets (PLTs, P=0.017), percentage of blasts in bone marrow (BM) (P=0.011) and complete remission (CR, P=0.027) had significant differences between two groups. Furthermore, high-risk group had shorter leukemia-free survival (LFS) and overall survival (OS) than low-risk group (P=0.026; P=0.006), and the three-gene signature was a prognostic factor. Our three-gene signature for prognosis prediction in AML may serve as a prognostic biomarker.

Download Full-text

Comprehensive analysis of the significance of ferroptosis-related genes in the prognosis and immunotherapy of oral squamous cell carcinoma

10.21203/rs.3.rs-115737/v1 ◽

2020 ◽

Author(s):

Junhao Yin ◽

Xiaoli Zeng ◽

Zexin Ai ◽

Miao Yu ◽

Yang'ou Wu ◽

...

Keyword(s):

Gene Expression ◽

Overall Survival ◽

Squamous Cell Carcinoma ◽

High Risk ◽

Tumor Microenvironment ◽

Oral Squamous Cell Carcinoma ◽

Cox Regression ◽

Risk Group ◽

Low Risk ◽

Regression Analyses

Abstract Background: Oral squamous cell carcinoma (OSCC) is a life-threatening disease that emerged as a major international health concern, associated with poor prognosis and the absence of specific biomarkers. Studies have shown that the ferroptosis-related genes (FRGs) can be used as tumor prognostic markers. However, FRGs’ prognostic value in OSCC needs further exploration. Our aim was to construct a novel FRG signature for overall survival (OS) prediction in OSCC patients and explore its role in immunotherapy.Methods: In our study, gene expression profile and clinical data of OSCC patients were collected from a public domain. FRGs were available from the FerrDb database. We performed univariate and multivariate Cox regression analyses to construct a multigene signature. The Kaplan-Meier (K-M) and receiver operating characteristic (ROC) methods were utilized to test the effectiveness of the FRG signature. A differential gene expression analysis was performed by the limma R package, followed by functional enrichment analyses. CIBERSORT was applied to analyze the tumor microenvironment (TME). Finally, the expression of human leukocyte antigen (HLA) and immune checkpoint molecules were analyzed to confirm the sensitivity of immunotherapy.Results: A total of 103 FRGs, expressed in OSCC (FRGs-OSCC), were identified from the two datasets by the Venn analysis. The Cox regression analysis identified 5 FRGs-OSCC that were associated with overall survival (all P < 0.01). The FRGs-OSCC risk model was established to classify patients into high risk and low risk groups. Compared with the low risk group, the survival time of the high-risk group was significantly reduced (P < 0.001). According to the multivariate Cox regression analyses, the risk score acted as an independent predictor for OS (HR > 1, P < 0.001). The accuracy of the FRGs-OSCC risk predictive model was confirmed by ROC curve analysis. The results of the Kyoto Encyclopedia of Genes and Genomes (KEGG) showed significant enrichment of immune-related pathways, and a difference in tumor microenvironment between the two groups. The low risk group had the characteristics of higher expression of HLA and immune checkpoints (IDO1, LAG3, PDCD1 and TIGHT), a lower tumor purity and a higher infiltration of immune cells, indicating a more sensitive response to immunotherapy.Conclusions: The novel FRGs-OSCC risk score system can be used to predict OSCC prognosis. Ferroptosis targeting may be a therapeutic option for OSCC.

Download Full-text