scholarly journals A novel tp53-associated nomogram to predict the overall survival in patients with pancreatic cancer

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Xun Liu ◽  
Bobo Chen ◽  
Jiahui Chen ◽  
Shaolong Sun
Keyword(s):  
Pancreatic Cancer ◽  
Overall Survival ◽  
Regression Analysis ◽  
Risk Score ◽  
Roc Curves ◽  
Gene Signature ◽  
Survival Prediction ◽  
Training Set ◽  
Kaplan Meier ◽  
Validation Set

Abstract Background Gene mutations play critical roles in tumorigenesis and cancer development. Our study aimed to screen survival-related mutations and explore a novel gene signature to predict the overall survival in pancreatic cancer. Methods Somatic mutation data from three cohorts were used to identify the common survival-related gene mutation with Kaplan-Meier curves. RNA-sequencing data were used to explore the signature for survival prediction. First, Weighted Gene Co-expression Network Analysis was conducted to identify candidate genes. Then, the ICGC-PACA-CA cohort was applied as the training set and the TCGA-PAAD cohort was used as the external validation set. A TP53-associated signature calculating the risk score of every patient was developed with univariate Cox, least absolute shrinkage and selection operator, and stepwise regression analysis. Kaplan-Meier and receiver operating characteristic curves were plotted to verify the accuracy. The independence of the signature was confirmed by the multivariate Cox regression analysis. Finally, a prognostic nomogram including 359 patients was constructed based on the combined expression data and the risk scores. Results TP53 mutation was screened to be the robust and survival-related mutation type, and was associated with immune cell infiltration. Two thousand, four hundred fifty-five genes included in the six modules generated in the WGCNA were screened as candidate survival related TP53-associated genes. A seven-gene signature was constructed: Risk score = (0.1254 × ERRFI1) - (0.1365 × IL6R) - (0.4400 × PPP1R10) - (0.3397 × PTOV1-AS2) + (0.1544 × SCEL) - (0.4412 × SSX2IP) – (0.2231 × TXNL4A). Area Under Curves of 1-, 3-, and 5-year ROC curves were 0.731, 0.808, and 0.873 in the training set and 0.703, 0.677, and 0.737 in the validation set. A prognostic nomogram including 359 patients was constructed and well-calibrated, with the Area Under Curves of 1-, 3-, and 5-year ROC curves as 0.713, 0.753, and 0.823. Conclusions The TP53-associated signature exhibited good prognostic efficacy in predicting the overall survival of PC patients.

scholarly journals The Construction of a Prognostic Model Based on a Peptidyl Prolyl Cis–Trans Isomerase Gene Signature in Hepatocellular Carcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Huadi Shi ◽  
Fulan Zhong ◽  
Xiaoqiong Yi ◽  
Zhenyi Shi ◽  
Feiyan Ou ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Risk Score ◽  
Prognostic Value ◽  
Prognostic Model ◽  
Stepwise Regression ◽  
Gene Signature ◽  
Training Set ◽  
Model Based ◽  
Kaplan Meier ◽  
Validation Set

Objective: The aim of the present study was to construct a prognostic model based on the peptidyl prolyl cis–trans isomerase gene signature and explore the prognostic value of this model in patients with hepatocellular carcinoma.Methods: The transcriptome and clinical data of hepatocellular carcinoma patients were downloaded from The Cancer Genome Atlas and the International Cancer Genome Consortium database as the training set and validation set, respectively. Peptidyl prolyl cis–trans isomerase gene sets were obtained from the Molecular Signatures Database. The differential expression of peptidyl prolyl cis–trans isomerase genes was analyzed by R software. A prognostic model based on the peptidyl prolyl cis–trans isomerase signature was established by Cox, Lasso, and stepwise regression methods. Kaplan–Meier survival analysis was used to evaluate the prognostic value of the model and validate it with an independent external data. Finally, nomogram and calibration curves were developed in combination with clinical staging and risk score.Results: Differential gene expression analysis of hepatocellular carcinoma and adjacent tissues showed that there were 16 upregulated genes. A prognostic model of hepatocellular carcinoma was constructed based on three gene signatures by Cox, Lasso, and stepwise regression analysis. The Kaplan–Meier curve showed that hepatocellular carcinoma patients in high-risk score group had a worse prognosis (p < 0.05). The receiver operating characteristic curve revealed that the area under curve values of predicting the survival rate at 1, 2, 3, 4, and 5 years were 0.725, 0.680, 0.644, 0.630, and 0.639, respectively. In addition, the evaluation results of the model by the validation set were basically consistent with those of the training set. A nomogram incorporating clinical stage and risk score was established, and the calibration curve matched well with the diagonal.Conclusion: A prognostic model based on 3 peptidyl prolyl cis–trans isomerase gene signatures is expected to provide reference for prognostic risk stratification in patients with hepatocellular carcinoma.

scholarly journals Five gene signatures were identified in the prediction of overall survival in resectable pancreatic cancer

BMC Surgery ◽  
2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Chao Wu ◽  
Zuowei Wu ◽  
Bole Tian
Keyword(s):  
Pancreatic Cancer ◽  
Overall Survival ◽  
Regression Analysis ◽  
Risk Score ◽  
Cox Regression ◽  
Risk Group ◽  
Gene Set Enrichment Analysis ◽  
Resectable Pancreatic Cancer ◽  
Cox Regression Analysis ◽  
Validation Set

Abstract Background Although genes have been previously detected in pancreatic cancer (PC), aberrant genes that play roles in resectable pancreatic cancer should be further assessed. Methods Messenger RNA samples and clinicopathological data corrected with PC were downloaded from The Cancer Genome Atlas (TCGA). Resectable PC patients were randomly divided into a primary set and a validation set. Univariable Cox regression analysis, lasso-penalized Cox regression analysis, and multivariable Cox analysis were implemented to distinguish survival-related genes (SRGs). A risk score based on the SRGs was calculated by univariable Cox regression analysis. A genomic-clinical nomogram was established by integrating the risk score and clinicopathological data to predict overall survival (OS) in resectable PC. Results Five survival-related genes (AADAC, DEF8, HIST1H1C, MET, and CHFR) were significantly correlated with OS in resectable PC. The resectable PC patients, based on risk score, were sorted into a high-risk group that showed considerably unfavorable OS (p < 0.001) than the low-risk group, in both the primary set and the validation set. The concordance index (C-index) was calculated to evaluate the predictive performance of the nomogram were respectively in the primary set [0.696 (0.608–0.784)] and the validation set [0.682 (0.606–0.758)]. Additionally, gene set enrichment Analysis discovered several meaningful enriched pathways. Conclusion Our study identified five prognostic gene biomarkers for OS prediction and which facilitate postoperative molecular target therapy for the resectable PC, especially the nomic-clinical nomogram which may be used as an effective model for the postoperative OS evaluation and also an optimal therapeutic tool for the resectable PC.

scholarly journals Prognostic Value of an Immune-Related Gene Signature in Oral Squamous Cell Carcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Chao Zhu ◽  
Liqun Gu ◽  
Mianfeng Yao ◽  
Jiang Li ◽  
Changyun Fang
Keyword(s):  
Overall Survival ◽  
Squamous Cell Carcinoma ◽  
Regression Analysis ◽  
Oral Squamous Cell Carcinoma ◽  
Risk Score ◽  
Cox Regression ◽  
Gene Signature ◽  
Related Gene ◽  
Cox Regression Analysis ◽  
Immune Related Gene

The prognosis and immunotherapy response rates are unfavorable in patients with oral squamous cell carcinoma (OSCC). The tumor microenvironment is associated with tumor prognosis and progression, and the underlying mechanisms remain unclear. We obtained differentially expressed immune-related genes from OSCC mRNA data in The Cancer Genome Atlas (TCGA) database. Overall survival-related risk signature was constructed by univariate Cox regression analysis and LASSO Cox regression analysis. The prognostic performance was validated with receiver operating characteristic (ROC) analysis and Kaplan–Meier survival curves in the TCGA and Gene Expression Omnibus (GEO) datasets. The risk score was confirmed to be an independent prognostic factor and a nomogram was built to quantify the risk of outcome for each patient. Furthermore, a negative correlation was observed between the risk score and the infiltration rate of immune cells, as well as the expression of immunostimulatory and immunosuppressive molecules. Functional enrichment analysis between different risk score subtypes detected multiple immune-related biological processes, metabolic pathways, and cancer-related pathways. Thus, the immune-related gene signature can predict overall survival and contribute to the personalized management of OSCC patients.

scholarly journals FAM225B Is a Prognostic lncRNA for Patients with Recurrent Glioblastoma

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Junsheng Li ◽  
Qian Zhang ◽  
Peicong Ge ◽  
Chaofan Zeng ◽  
Fa Lin ◽  
...  
Keyword(s):  
Overall Survival ◽  
Prognostic Value ◽  
Cox Regression ◽  
Recurrent Glioblastoma ◽  
Regulation Of Transcription ◽  
Training Set ◽  
Cox Regression Analysis ◽  
Kaplan Meier ◽  
Validation Set ◽  
Pathway Analyses

Objective. The overall survival of patients with recurrent glioblastoma (rGBM) is quite different, so clinical outcome prediction is necessary to guide personalized clinical treatment for patients with rGBM. The expression level of lncRNA FAM225B was analyzed to determine its prognostic value in rGBMs. Methods. We collected 109 samples of Chinese Glioma Genome Atlas (CGGA) RNA sequencing dataset and divided into training set and validation set. Then, we analyzed the expression of FAM225B, clinical characteristics, and overall survival (OS) information. Kaplan-Meier survival analysis was used to estimate the OS distributions. The prognostic value of FAM225B in rGBMs was tested by univariate and multivariate Cox regression analyses. Moreover, we analyzed the biological processes and signaling pathways of FAM225B. Results. We found that FAM225B was upregulated in rGBMs ( P = 0.0009 ). The expression of FAM225B increased with the grades of gliomas ( P < 0.0001 ). The OS of rGBMs in the low-expression group was significantly longer than that in the high-expression group ( P = 0.0041 ). Similar result was found in the training set ( P = 0.0340 ) and verified in the validation set ( P = 0.0292 ). In multivariate Cox regression analysis, FAM225B was identified to be an independent prognostic factor for rGBMs ( P = 0.003 ). Biological process and KEGG pathway analyses implied FAM225B mainly played a functional role on transcription, regulation of transcription, cell migration, focal adhesion, etc. Conclusions. FAM225B is expected to be as a new prognostic biomarker for the identification of rGBM patients with poor outcome. And our study provided a potential therapeutic target for rGBMs.

scholarly journals Risk Score Generated from CT-Based Radiomics Signatures for Overall Survival Prediction in Non-Small Cell Lung Cancer

Cancers ◽  
2021 ◽  
Vol 13 (14) ◽  
pp. 3616
Author(s):  
Viet-Huan Le ◽  
Quang-Hien Kha ◽  
Truong Nguyen Khanh Hung ◽  
Nguyen Quoc Khanh Le
Keyword(s):  
Overall Survival ◽  
High Risk ◽  
Risk Score ◽  
Low Risk ◽  
Survival Prediction ◽  
Clinical Parameters ◽  
Training Set ◽  
Prediction Ability ◽  
Nsclc Patients ◽  
Overall Survival Prediction

This study aimed to create a risk score generated from CT-based radiomics signatures that could be used to predict overall survival in patients with non-small cell lung cancer (NSCLC). We retrospectively enrolled three sets of NSCLC patients (including 336, 84, and 157 patients for training, testing, and validation set, respectively). A total of 851 radiomics features for each patient from CT images were extracted for further analyses. The most important features (strongly linked with overall survival) were chosen by pairwise correlation analysis, Least Absolute Shrinkage and Selection Operator (LASSO) regression model, and univariate Cox proportional hazard regression. Multivariate Cox proportional hazard model survival analysis was used to create risk scores for each patient, and Kaplan–Meier was used to separate patients into two groups: high-risk and low-risk, respectively. ROC curve assessed the prediction ability of the risk score model for overall survival compared to clinical parameters. The risk score, which developed from ten radiomics signatures model, was found to be independent of age, gender, and stage for predicting overall survival in NSCLC patients (HR, 2.99; 95% CI, 2.27–3.93; p < 0.001) and overall survival prediction ability was 0.696 (95% CI, 0.635–0.758), 0.705 (95% CI, 0.649–0.762), 0.657 (95% CI, 0.589–0.726) (AUC) for 1, 3, and 5 years, respectively, in the training set. The risk score is more likely to have a better accuracy in predicting survival at 1, 3, and 5 years than clinical parameters, such as age 0.57 (95% CI, 0.499–0.64), 0.552 (95% CI, 0.489–0.616), 0.621 (95% CI, 0.544–0.689) (AUC); gender 0.554, 0.546, 0.566 (AUC); stage 0.527, 0.501, 0.459 (AUC), respectively, in 1, 3 and 5 years in the training set. In the training set, the Kaplan–Meier curve revealed that NSCLC patients in the high-risk group had a lower overall survival time than the low-risk group (p < 0.001). We also had similar results that were statistically significant in the testing and validation set. In conclusion, risk scores developed from ten radiomics signatures models have great potential to predict overall survival in NSCLC patients compared to the clinical parameters. This model was able to stratify NSCLC patients into high-risk and low-risk groups regarding the overall survival prediction.

scholarly journals A Glycolysis-Based Long Non-coding RNA Signature Accurately Predicts Prognosis in Renal Carcinoma Patients

2021 ◽  
Vol 12 ◽  
Author(s):  
Honghao Cao ◽  
Hang Tong ◽  
Junlong Zhu ◽  
Chenchen Xie ◽  
Zijia Qin ◽  
...  
Keyword(s):  
Clinical Significance ◽  
Risk Score ◽  
Cox Regression ◽  
Risk Group ◽  
Enrichment Analysis ◽  
Roc Curves ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Survival Prediction ◽  
Kaplan Meier

BackgroundThe prognosis of renal cell carcinoma (RCC) varies greatly among different risk groups, and the traditional indicators have limited effect in the identification of risk grade in patients with RCC. The purpose of our study is to explore a glycolysis-based long non-coding RNAs (lncRNAs) signature and verify its potential clinical significance in prognostic prediction of RCC patients.MethodsIn this study, RNA data and clinical information were downloaded from The Cancer Genome Atlas (TCGA) database. Univariate and multivariate cox regression displayed six significantly related lncRNAs (AC124854.1, AC078778.1, EMX2OS, DLGAP1-AS2, AC084876.1, and AC026401.3) which were utilized in construction of risk score by a formula. The accuracy of risk score was verified by a series of statistical methods such as receiver operating characteristic (ROC) curves, nomogram and Kaplan-Meier curves. Its potential clinical significance was excavated by gene enrichment analysis.ResultsKaplan-Meier curves and ROC curves showed reliability of the risk score to predict the prognosis of RCC patients. Stratification analysis indicated that the risk score was independent predictor compare to other traditional clinical parameters. The clinical nomogram showed highly rigorous with index of 0.73 and precisely predicted 1-, 3-, and 5-year survival time of RCC patients. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene set enrichment analysis (GSEA) depicted the top ten correlated pathways in both high-risk group and low-risk group. There are 6 lncRNAs and 25 related mRNAs including 36 lncRNA-mRNA links in lncRNA-mRNA co-expression network.ConclusionThis research demonstrated that glycolysis-based lncRNAs possessed an important value in survival prediction of RCC patients, which would be a potential target for future treatment.

scholarly journals Development and Validation of a 7-Gene Prognostic Signature to Improve Survival Prediction in Pancreatic Ductal Adenocarcinoma

2021 ◽  
Vol 8 ◽  
Author(s):  
Zengyu Feng ◽  
Hao Qian ◽  
Kexian Li ◽  
Jianyao Lou ◽  
Yulian Wu ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Cox Regression ◽  
Predictive Accuracy ◽  
Disease Free Survival ◽  
Roc Curves ◽  
Gene Signature ◽  
Ductal Adenocarcinoma ◽  
Survival Prediction ◽  
Cox Regression Analysis

Background: Previous prognostic signatures of pancreatic ductal adenocarcinoma (PDAC) are mainly constructed to predict the overall survival (OS), and their predictive accuracy needs to be improved. Gene signatures that efficaciously predict both OS and disease-free survival (DFS) are of great clinical significance but are rarely reported.Methods: Univariate Cox regression analysis was adopted to screen common genes that were significantly associated with both OS and DFS in three independent cohorts. Multivariate Cox regression analysis was subsequently performed on the identified genes to determine an optimal gene signature in the MTAB-6134 training cohort. The Kaplan–Meier (K-M), calibration, and receiver operating characteristic (ROC) curves were employed to assess the predictive accuracy. Biological process and pathway enrichment analyses were conducted to elucidate the biological role of this signature.Results: Multivariate Cox regression analysis determined a 7-gene signature that contained ASPH, DDX10, NR0B2, BLOC1S3, FAM83A, SLAMF6, and PPM1H. The signature had the ability to stratify PDAC patients with different OS and DFS, both in the training and validation cohorts. ROC curves confirmed the moderate predictive accuracy of this signature. Mechanically, the signature was related to multiple cancer-related pathways.Conclusion: A novel OS and DFS prediction model was constructed in PDAC with multi-cohort and cross-platform compatibility. This signature might foster individualized therapy and appropriate management of PDAC patients.

scholarly journals Development and validation of a nomogram with an autophagy-related gene risk model for predicting survival in patients with melanoma

2020 ◽  
Author(s):  
Hao Zuo ◽  
Luojun Chen ◽  
Na Li ◽  
Qibin Song
Keyword(s):  
Overall Survival ◽  
Risk Score ◽  
Risk Model ◽  
Enrichment Analysis ◽  
Functional Enrichment ◽  
Survival Prediction ◽  
High Morbidity ◽  
Clinical Variables ◽  
Kaplan Meier ◽  
Score Model

Abstract Background: Melanoma is the third most common skin malignant tumor in the clinic, with high morbidity and mortality. Autophagy plays an important role in the development and progression of melanoma. We aimed to establish an autophagy-related genes(ARGs) expression based risk model for individualized prognosis prediction in patients with melanoma.Methods: Differentially expressed autophagy-related genes (DEARGs) in melanoma and normal skin samples were screened using TCGA and GTEx database. These DEARGs were used to perform KEGG functional enrichment analysis and GO analysis. Univariate and multivariate Cox regression analyses were performed on DEARGs to identify the optimal prognosis-related genes. These prognosis-related DEARGs were used to construct a risk score model, and the predictive effect of this risk model on the prognosis of melanoma patients was tested by the Kaplan-Meier curve, log-rank test, and ROC curve. Method of univariate and multivariate analysis were used to confirmed that the risk model of independent predictive value relative to other clinical variables, and build a nomogram based on the independent prognostic factors in the univariate analysis to predict overall survival(OS) in patients with melanoma, we used internal validation and calculation of concordance index (C-index) to test prediction effect of the nomogram. We also used the t-test to analyze the relationship between risk factors (risk genes and risk score) and clinical variables in the risk model.Results: We screened and finally obtained 6 optimal DEARGs (risk gene) through univariate and multivariate Cox analysis to construct the risk model: EIF2AK2(HR=0.403, P=0.007), IFNG(HR=0.659, P=0.003), DAPK2(HR=0.441, P=0.022), PTK6(HR=1.609, P=6.04E-05), BIRC5(HR=2.479, P=0.001), and EGFR(HR=1.474, P=0.004) were selected to establish the prognostic risk score model and validated in the entire melanoma cohort. The results of GO enrichment analysis showed that the gene function of the DEARGs was concentrated in the functions of gland morphogenesis, protein insertion into membrane, and autophagy. The results of KEGG enrichment analysis showed that the function of the DEARGs was concentrated in the autophagy–animal, p53 signaling pathway, and platinum drug resistance. Kaplan-Meier survival analysis demonstrated that patients with high risk scores had significantly poorer overall survival (OS, log-rank P=6.402E−11). The model was identified as an independent prognostic factor. Finally, a prognostic nomogram including the risk model, T-stage, N-stage, and radiotherapy was constructed, and the calibration plots indicated its excellent predictive performance.Conclusion: The autophagy-related six-gene risk score model could be a prognostic biomarker and suggest therapeutic targets for melanoma. The prognostic nomogram could help individualized survival prediction and improve treatment strategies.

scholarly journals A Comprehensive Prognostic and Immunological Analysis of a Six-Gene Signature Associated With Glycolysis and Immune Response in Uveal Melanoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Jun Liu ◽  
Jianjun Lu ◽  
Wenli Li
Keyword(s):  
Immune Response ◽  
Risk Stratification ◽  
Cox Regression ◽  
Predictive Ability ◽  
Gene Signature ◽  
Risk Scores ◽  
Survival Prediction ◽  
Regression Analyses ◽  
Training Set ◽  
Kaplan Meier

Uveal melanoma (UM) is a subtype of melanoma with poor prognosis. This study aimed to construct a new prognostic gene signature that can be used for survival prediction and risk stratification of UM patients. In this work, transcriptome data from the Molecular Signatures Database were used to identify the cancer hallmarks most relevant to the prognosis of UM patients. Weighted gene co-expression network, univariate least absolute contraction and selection operator (LASSO), and multivariate Cox regression analyses were used to construct the prognostic gene characteristics. Kaplan–Meier and receiver operating characteristic (ROC) curves were used to evaluate the survival predictive ability of the gene signature. The results showed that glycolysis and immune response were the main risk factors for overall survival (OS) in UM patients. Using univariate Cox regression analysis, 238 candidates related to the prognosis of UM patients were identified (p &lt; 0.05). Using LASSO and multivariate Cox regression analyses, a six-gene signature including ARPC1B, BTBD6, GUSB, KRTCAP2, RHBDD3, and SLC39A4 was constructed. Kaplan–Meier analysis of the UM cohort in the training set showed that patients with higher risk scores had worse OS (HR = 2.61, p &lt; 0.001). The time-dependent ROC (t-ROC) curve showed that the risk score had good predictive efficiency for UM patients in the training set (AUC &gt; 0.9). Besides, t-ROC analysis showed that the predictive ability of risk scores was significantly higher than that of other clinicopathological characteristics. Univariate and multivariate Cox regression analyses showed that risk score was an independent risk factor for OS in UM patients. The prognostic value of risk scores was further verified in two external UM cohorts (GSE22138 and GSE84976). Two-factor survival analysis showed that UM patients with high hypoxia or immune response scores and high risk scores had the worst prognosis. Moreover, a nomogram based on the six-gene signature was established for clinical practice. In addition, risk scores were related to the immune infiltration profiles. Taken together, this study identified a new prognostic six-gene signature related to glycolysis and immune response. This six-gene signature can not only be used for survival prediction and risk stratification but also may be a potential therapeutic target for UM patients.

scholarly journals Identification of a Seven-Gene Signature and Establishment of a Nomogram Predicting Overall Survival in Head and Neck Squamous Cell Carcinoma

2020 ◽  
Author(s):  
Haige Zheng ◽  
Xiangkun Wu ◽  
Huixian Liu ◽  
Yumin Lu ◽  
Hengguo Li
Keyword(s):  
Overall Survival ◽  
Squamous Cell Carcinoma ◽  
Regression Analysis ◽  
High Risk ◽  
Cell Carcinoma ◽  
Cox Regression ◽  
Risk Groups ◽  
Gene Signature ◽  
Cox Regression Analysis ◽  
Kaplan Meier

Abstract Background: Head and neck squamous cell carcinoma (HNSCC) is a highly heterogeneous tumor with high incidence and poor prognosis. Therefore, effective predictive models are needed to evaluate patient outcomes and optimize treatment. Methods: Ten gene microarray datasets were obtained from the gene expression omnibus (GEO) database. Level 3 mRNA expression and clinical data were obtained in The Cancer Genome Atlas (TCGA) database. We identified highly robust differentially-expressed genes (DEGs) between HNSCC and normal tissue in nine GEO and TCGA datasets using Robust Rank Aggregation (RRA) method. Univariate Cox regression analysis and lasso Cox regression analysis were performed to identify DEGs related to the Overall-survival (OS) and to construct a prognostic gene signature. External validation was performed using GSE65858. Moreover, gene set enrichment analyses (GSEA) analysis was used to analyze significantly rich pathways in high-risk and low-risk groups, and tumor immunoassays were used to clarify immune correlation of the prognostic gene. Finally, integrate multiple forecast indicators were used to build a nomogram using the TCGA-HNSCC dataset. Kaplan–Meier analysis, receiver operating characteristic (ROC), a calibration plot, Harrell’s concordance index (C-index), and decision curve analysis (DCA) were used to test the predictive capability of the seven genetic signals and the nomogram. Results: A novel seven-gene signature (including SLURP1, SCARA5, CLDN10, MYH11, CXCL13, HLF, and ITGA3) was established to predict overall survival in HNSCC patients. ROC curve performed well in the training and validation data sets. Kaplan–Meier analysis demonstrated that low-risk groups had a longer survival time. The nomogram containing seven genetic markers and clinical prognostic factors was a good predictor of HNSCC survival and showed a certain net clinical benefit through the DCA curve. Further research demonstrated that the infiltration degree of CD8 + T cells, B cells, neutrophils, and NK cells were significantly lower in the high-risk group.Conclusion: Our analysis established a seven-gene model and nomogram to accurately predict the prognosis status of HNSCC patients, immune relevance was also described, which may provide a new possibility for individual treatment and medical decision-making.

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