scholarly journals A Prognostic Model Based on Immune-Related Long Non-Coding RNAs for Patients With Epithelial Ovarian Cancer

2021 ◽  
Author(s):  
Yao Peng ◽  
Hui Wang ◽  
Qi Huang ◽  
Jingjing Wu ◽  
Mingjun Zhang
Keyword(s):  
High Risk ◽  
Immune Cell ◽  
Cox Regression ◽  
Principal Component ◽  
Risk Groups ◽  
Low Risk ◽  
Training Set ◽  
Cox Regression Analysis ◽  
Validation Set ◽  
Differential Genes

Abstract Background: Long non-coding RNA (lncRNA), as an important regulator of gene expression, can affect a variety of physiological processes. Recent studies have shown that immune-related lncRNA play an important role in the tumor immune microenvironment and may have potential application value in the treatment and prognosis prediction of tumor patients. Epithelial ovarian cancer (EOC) is characterized by high incidence and poor prognosis. However, there are few studies on immune-related lncRNAs in EOC. In this study, we focused on the immune-related lncRNAs associated with survival in EOC. Methods: We downloaded mRNA data for EOC patients from The Cancer Genome Atlas (TCGA) database, and mRNA data for normal ovarian tissue from the Genotype-Tissue Expression (GTEx) database, and identified differential genes through differential Expression analysis. Immune-related lncRNAs were obtained through taking intersection and co-expression analysis of differential genes and immune-related genes from the Immunology Database and Analysis Portal (ImmPort). Samples in the TCGA EOC cohort were randomly divided into training set, validation set and combination set. In the training set, Cox regression analysis and LASSO regression were used to construct an immune-related lncRNA signature. Kaplan-Meier survival analysis, time-dependent ROC curve analysis, Cox regression analysis and principal component analysis were applied to verification in the training set, training set, validation set and combination set. Further studies of pathways and immune cell infiltration were conducted through Gene Set Enrichment Analysis (GESA) and the Timer data portal.Results: An immune-related lncRNA signature was identified in EOC, which was composed of six immune-related lncRNAs (KRT7-AS, USP30-AS1, AC011445.1, AP005205.2, DNM3OS and AC027348.1). The signature divided patients into high-risk and low-risk groups. The overall survival of the high-risk group was lower than that of the low-risk group, and was verified to be robust in both the training set and the combination set. This signature was identified as an independent prognostic biomarker. The signature was confirmed to be an independent prognostic biomarker. Principal component analysis showed the different distribution patterns of high-risk and low-risk groups. This signature may be related to immune cell infiltration (mainly macrophages) and differential expression of immune checkpoint-related molecules (PD-1, PDL1, etc.). Conclusions: we identified and established a prognostic signature of immune-related lncRNA in EOC, which is of great value in predicting the prognosis of clinical patients and may provide a new perspective for the immunological research and individualized treatment in EOC.

scholarly journals A prognostic model based on immune-related long noncoding RNAs for patients with epithelial ovarian cancer

2022 ◽  
Vol 15 (1) ◽  
Author(s):  
Yao Peng ◽  
Hui Wang ◽  
Qi Huang ◽  
Jingjing Wu ◽  
Mingjun Zhang
Keyword(s):  
High Risk ◽  
Immune Cell ◽  
Cox Regression ◽  
Risk Group ◽  
Principal Component ◽  
Risk Groups ◽  
Low Risk ◽  
Training Set ◽  
Cox Regression Analysis ◽  
Validation Set

Abstract Background Long noncoding RNAs (lncRNAs) are important regulators of gene expression and can affect a variety of physiological processes. Recent studies have shown that immune-related lncRNAs play an important role in the tumour immune microenvironment and may have potential application value in the treatment and prognosis prediction of tumour patients. Epithelial ovarian cancer (EOC) is characterized by a high incidence and poor prognosis. However, there are few studies on immune-related lncRNAs in EOC. In this study, we focused on immune-related lncRNAs associated with survival in EOC. Methods We downloaded mRNA data for EOC patients from The Cancer Genome Atlas (TCGA) database and mRNA data for normal ovarian tissue from the Genotype-Tissue Expression (GTEx) database and identified differentially expressed genes through differential expression analysis. Immune-related lncRNAs were obtained through intersection and coexpression analysis of differential genes and immune-related genes from the Immunology Database and Analysis Portal (ImmPort). Samples in the TCGA EOC cohort were randomly divided into a training set, validation set and combination set. In the training set, Cox regression analysis and LASSO regression were performed to construct an immune-related lncRNA signature. Kaplan–Meier survival analysis, time-dependent ROC curve analysis, Cox regression analysis and principal component analysis were performed for verification in the training set, validation set and combination set. Further studies of pathways and immune cell infiltration were conducted through Gene Set Enrichment Analysis (GSEA) and the Timer data portal. Results An immune-related lncRNA signature was identified in EOC, which was composed of six immune-related lncRNAs (KRT7-AS, USP30-AS1, AC011445.1, AP005205.2, DNM3OS and AC027348.1). The signature was used to divide patients into high-risk and low-risk groups. The overall survival of the high-risk group was lower than that of the low-risk group and was verified to be robust in both the validation set and the combination set. The signature was confirmed to be an independent prognostic biomarker. Principal component analysis showed the different distribution patterns of high-risk and low-risk groups. This signature may be related to immune cell infiltration (mainly macrophages) and differential expression of immune checkpoint-related molecules (PD-1, PDL1, etc.). Conclusions We identified and established a prognostic signature of immune-related lncRNAs in EOC, which will be of great value in predicting the prognosis of clinical patients and may provide a new perspective for immunological research and individualized treatment in EOC.

scholarly journals Immune‐related Long Noncoding RNA Signature for Patients with Cervical Cancer

2020 ◽  
Author(s):  
Jianfeng Zheng ◽  
Jinyi Tong ◽  
Benben Cao ◽  
Xia Zhang ◽  
Zheng Niu
Keyword(s):  
Cervical Cancer ◽  
High Risk ◽  
Risk Score ◽  
Immune Cell ◽  
Risk Group ◽  
Risk Groups ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Training Set ◽  
Score Model

Abstract Background: Cervical cancer (CC) is a common gynecological malignancy for which prognostic and therapeutic biomarkers are urgently needed. The signature based on immune‐related lncRNAs(IRLs) of CC has never been reported. This study aimed to establish an IRL signature for patients with CC.Methods: The RNA-seq dataset was obtained from the TCGA, GEO, and GTEx database. The immune scores（IS）based on single-sample gene set enrichment analysis (ssGSEA) were calculated to identify the IRLs, which were then analyzed using univariate Cox regression analysis to identify significant prognostic IRLs. A risk score model was established to divide patients into low-risk and high-risk groups based on the median risk score of these IRLs. This was then validated by splitting TCGA dataset(n=304) into a training-set(n=152) and a valid-set(n=152). The fraction of 22 immune cell subpopulations was evaluated in each sample to identify the differences between low-risk and high-risk groups. Additionally, a ceRNA network associated with the IRLs was constructed.Results: A cohort of 326 CC and 21 normal tissue samples with corresponding clinical information was included in this study. Twenty-eight IRLs were collected according to the Pearson’s correlation analysis between immune score and lncRNA expression (P < 0.01). Four IRLs (BZRAP1-AS1, EMX2OS, ZNF667-AS1, and CTC-429P9.1) with the most significant prognostic values (P < 0.05) were identified which demonstrated an ability to stratify patients into low-risk and high-risk groups by developing a risk score model. It was observed that patients in the low‐risk group showed longer overall survival (OS) than those in the high‐risk group in the training-set, valid-set, and total-set. The area under the curve (AUC) of the receiver operating characteristic curve (ROC curve) for the four IRLs signature in predicting the one-, two-, and three-year survival rates were larger than 0.65. In addition, the low-risk and high-risk groups displayed different immune statuses in GSEA. These IRLs were also significantly correlated with immune cell infiltration. Conclusions: Our results showed that the IRL signature had a prognostic value for CC. Meanwhile, the specific mechanisms of the four-IRLs in the development of CC were ascertained preliminarily.

scholarly journals Identification and validation of a pyroptosis-related prognostic signature for thyroid cancer

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Pu Wu ◽  
Jinyuan Shi ◽  
Wei Sun ◽  
Hao Zhang
Keyword(s):  
Thyroid Cancer ◽  
Risk Score ◽  
Prognostic Model ◽  
Immune Cell ◽  
Cox Regression ◽  
Checkpoint Inhibitors ◽  
Risk Groups ◽  
Low Risk ◽  
Prognostic Signature ◽  
Cox Regression Analysis

Abstract Background Pyroptosis is a form of programmed cell death triggered by inflammasomes. However, the roles of pyroptosis-related genes in thyroid cancer (THCA) remain still unclear. Objective This study aimed to construct a pyroptosis-related signature that could effectively predict THCA prognosis and survival. Methods A LASSO Cox regression analysis was performed to build a prognostic model based on the expression profile of each pyroptosis-related gene. The predictive value of the prognostic model was validated in the internal cohort. Results A pyroptosis-related signature consisting of four genes was constructed to predict THCA prognosis and all patients were classified into high- and low-risk groups. Patients with a high-risk score had a poorer overall survival (OS) than those in the low-risk group. The area under the curve (AUC) of the receiver operator characteristic (ROC) curves assessed and verified the predictive performance of this signature. Multivariate analysis showed the risk score was an independent prognostic factor. Tumor immune cell infiltration and immune status were significantly higher in low-risk groups, which indicated a better response to immune checkpoint inhibitors (ICIs). Of the four pyroptosis-related genes in the prognostic signature, qRT-PCR detected three of them with significantly differential expression in THCA tissues. Conclusion In summary, our pyroptosis-related risk signature may have an effective predictive and prognostic capability in THCA. Our results provide a potential foundation for future studies of the relationship between pyroptosis and the immunotherapy response.

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 A Ferroptosis-Related Signature Robustly Predicts Clinical Outcomes and Associates With Immune Microenvironment for Thyroid Cancer

2021 ◽  
Vol 8 ◽  
Author(s):  
Mingqin Ge ◽  
Jie Niu ◽  
Ping Hu ◽  
Aihua Tong ◽  
Yan Dai ◽  
...  
Keyword(s):  
Thyroid Cancer ◽  
High Risk ◽  
Risk Score ◽  
Immune Cell ◽  
Cox Regression ◽  
Low Risk ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Immune Microenvironment ◽  
Cox Regression Analysis

Objective: This study aimed to construct a prognostic ferroptosis-related signature for thyroid cancer and probe into the association with tumor immune microenvironment.Methods: Based on the expression profiles of ferroptosis-related genes, a LASSO cox regression model was established for thyroid cancer. Kaplan-Meier survival analysis was presented between high and low risk groups. The predictive performance was assessed by ROC. The predictive independency was validated via multivariate cox regression analysis and stratified analysis. A nomogram was established and verified by calibration curves. The enriched signaling pathways were predicted via GSEA. The association between the signature and immune cell infiltration was analyzed by CIBERSORT. The ferroptosis-related genes were validated in thyroid cancer tissues by immunohistochemistry and RT-qPCR.Results: A ferroptosis-related eight gene model was established for predicting the prognosis of thyroid cancer. Patients with high risk score indicated a poorer prognosis than those with low risk score (p = 1.186e-03). The AUCs for 1-, 2-, and 3-year survival were 0.887, 0.890, and 0.840, respectively. Following adjusting other prognostic factors, the model could independently predict the prognosis (p = 0.015, HR: 1.870, 95%CI: 1.132–3.090). A nomogram combining the signature and age was constructed. The nomogram-predicted probability of 1-, 3-, and 5-year survival approached the actual survival time. Several ferroptosis-related pathways were enriched in the high-risk group. The signature was distinctly associated with the immune cell infiltration. After validation, the eight genes were abnormally expressed between thyroid cancer and control tissues.Conclusion: Our findings established a prognostic ferroptosis-related signature that was associated with the immune microenvironment for thyroid cancer.

scholarly journals A novel ferroptosis-related genes model for prognosis prediction of lung adenocarcinoma

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Fei Li ◽  
Dongcen Ge ◽  
Shu-lan Sun
Keyword(s):  
Regression Analysis ◽  
High Risk ◽  
Prediction Model ◽  
Cox Regression ◽  
Risk Group ◽  
Training Set ◽  
Cox Regression Analysis ◽  
Key Genes ◽  
Validation Set ◽  
Prognostic Prediction

Abstract Background Ferroptosis is a newly discovered form of cell death characterized by iron-dependent lipid peroxidation. This study aims to investigate the potential correlation between ferroptosis and the prognosis of lung adenocarcinoma (LUAD). Methods RNA-seq data were collected from the LUAD dataset of The Cancer Genome Atlas (TCGA) database. Based on ferroptosis-related genes, differentially expressed genes (DEGs) between LUAD and paracancerous specimens were identified. The univariate Cox regression analysis was performed to screen key genes associated with the prognosis of LUAD. LUAD patients were divided into the training set and validation set. Then, we screened out key genes and built a prognostic prediction model involving 5 genes using the least absolute shrinkage and selection operator (LASSO) regression with tenfold cross-validation and the multivariate Cox regression analysis. After dividing LUAD patients based on the median level of risk score as cut-off value, the generated prognostic prediction model was validated in the validation set. Moreover, we analyzed the somatic mutations, and estimated the scores of immune infiltration in the high-risk and low-risk groups. Functional enrichment analysis of DEGs was performed as well. Results High-risk scores indicated the worse prognosis of LUAD. The maximum area under curve (AUC) of the training set and the validation set in this study was 0.7 and 0.69, respectively. Moreover, we integrated the age, gender, and tumor stage to construct the composite nomogram. The charts indicated that the AUC of LUAD cases with the survival time of 1, 3 and 5 years was 0.698, 0.71 and 0.73, respectively. In addition, the mutation frequency of LUAD patients in the high-risk group was significantly higher than that in the low-risk group. Simultaneously, DEGs were mainly enriched in ferroptosis-related pathways by analyzing the functional results. Conclusions This study constructs a novel LUAD prognosis prediction model involving 5 ferroptosis-related genes, which can be used as a promising tool for decision-making of clinical therapeutic strategies of LUAD.

scholarly journals Development and Validation of an Mesenchymal-Related Long Non-Coding RNA Prognostic Model in Glioma

2021 ◽  
Vol 11 ◽  
Author(s):  
Kebing Huang ◽  
Xiaoyu Yue ◽  
Yinfei Zheng ◽  
Zhengwei Zhang ◽  
Meng Cheng ◽  
...  
Keyword(s):  
High Risk ◽  
Prognostic Model ◽  
Cox Regression ◽  
Risk Groups ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Migration And Invasion ◽  
Aberrant Expression ◽  
Cox Regression Analysis ◽  
Genome Atlas

Glioma is well known as the most aggressive and prevalent primary malignant tumor in the central nervous system. Molecular subtypes and prognosis biomarkers remain a promising research area of gliomas. Notably, the aberrant expression of mesenchymal (MES) subtype related long non-coding RNAs (lncRNAs) is significantly associated with the prognosis of glioma patients. In this study, MES-related genes were obtained from The Cancer Genome Atlas (TCGA) and the Ivy Glioblastoma Atlas Project (Ivy GAP) data sets of glioma, and MES-related lncRNAs were acquired by performing co-expression analysis of these genes. Next, Cox regression analysis was used to establish a prognostic model, that integrated ten MES-related lncRNAs. Glioma patients in TCGA were divided into high-risk and low-risk groups based on the median risk score; compared with the low-risk groups, patients in the high-risk group had shorter survival times. Additionally, we measured the specificity and sensitivity of our model with the ROC curve. Univariate and multivariate Cox analyses showed that the prognostic model was an independent prognostic factor for glioma. To verify the predictive power of these candidate lncRNAs, the corresponding RNA-seq data were downloaded from the Chinese Glioma Genome Atlas (CGGA), and similar results were obtained. Next, we performed the immune cell infiltration profile of patients between two risk groups, and gene set enrichment analysis (GSEA) was performed to detect functional annotation. Finally, the protective factors DGCR10 and HAR1B, and risk factor SNHG18 were selected for functional verification. Knockdown of DGCR10 and HAR1B promoted, whereas knockdown of SNHG18 inhibited the migration and invasion of gliomas. Collectively, we successfully constructed a prognostic model based on a ten MES-related lncRNAs signature, which provides a novel target for predicting the prognosis for glioma patients.

scholarly journals A Novel Ferroptosis-Related Genes Model for Prognosis Prediction of Lung Adenocarcinoma

2021 ◽  
Author(s):  
Fei Li ◽  
Dongcen Ge ◽  
Shu-lan Sun
Keyword(s):  
Regression Analysis ◽  
High Risk ◽  
Prediction Model ◽  
Lung Adenocarcinoma ◽  
Risk Score ◽  
Cox Regression ◽  
Risk Group ◽  
Low Risk ◽  
Cox Regression Analysis ◽  
Validation Set

Abstract Background. Ferroptosis is a newly discovered form of cell death characterized by iron-dependent lipid peroxidation. The aim of this study is to investigate the relationship between ferroptosis and the prognosis of lung adenocarcinoma (LUAD).Methods. RNA-seq data was collected from the LUAD dataset of The Cancer Genome Altas (TCGA) database. We used ferroptosis-related genes as the basis, and identify the differential expression genes (DEGs) between cancer and paracancer. The univariate Cox regression analysis were used to screen the prognostic-related genes. We divided the patients into training and validation sets. Then, we screened out key genes and built a 5 genes prognostic prediction model by the applications of the least absolute shrinkage and selection operator (LASSO) 10-fold cross-validation and the multi-variate Cox regression analysis. We divided the cases by the median value of risk score and validated this model in the validation set. Meanwhile, we analyzed the somatic mutations, and estimated the score of immune infiltration in the high- and low-risk groups, as well as performed functional enrichment analysis of DEGs.Results. The result revealed that the high-risk score triggered the worse prognosis. The maximum area under curve (AUC) of the training set and the validation set of in this study was 0.7 and 0.69. Moreover, we integrated the age, gender, and tumor stage to construct the composite nomogram. The charts indicated that the AUC of cases with survival time of 1, 3 and 5 years are 0.698, 0.71 and 0.73. In addition, the mutation frequency of patients in the high-risk group was higher than that in the low-risk group. Simultaneously, DEGs were mainly enriched in ferroptosis-related pathways by analyzing the functional results.Conclusion. This study constructed a novel LUAD prognosis prediction model base on 5 ferroptosis-related genes, which can provide a prognostic evaluation tool for the clinical therapeutic decision.

scholarly journals Construction of a prognostic model for lung squamous cell carcinoma based on seven N6-methylandenosine-related autophagy genes

2021 ◽  
Vol 18 (5) ◽  
pp. 6709-6723
Author(s):  
Xin Yu ◽  
◽  
Jun Liu ◽  
Ruiwen Xie ◽  
Mengling Chang ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Prognostic Model ◽  
Immune Cell ◽  
Cox Regression ◽  
Risk Group ◽  
Lung Squamous Cell Carcinoma ◽  
Risk Groups ◽  
Low Risk ◽  
Immune Cell Infiltration ◽  
Cox Regression Analysis

<abstract> <sec><title>Objective</title><p>We aimed to construct a novel prognostic model based on N6-methyladenosine (m6A)-related autophagy genes for predicting the prognosis of lung squamous cell carcinoma (LUSC).</p> </sec> <sec><title>Methods</title><p>Gene expression profiles and clinical information of Patients with LUSC were downloaded from The Cancer Genome Atlas (TCGA) database. In addition, m6A- and autophagy-related gene profiles were obtained from TCGA and Human Autophagy Database, respectively. Pearson correlation analysis was performed to identify the m6A-related autophagy genes, and univariate Cox regression analysis was conducted to screen for genes associated with prognosis. Based on these genes, LASSO Cox regression analysis was used to construct a prognostic model. The corresponding prognostic score (PS) was calculated, and patients with LUSC were assigned to low- and high-risk groups according to the median PS value. An independent dataset (GSE37745) was used to validate the prognostic ability of the model. CIBERSORT was used to calculate the differences in immune cell infiltration between the high- and low-risk groups.</p> </sec> <sec><title>Results</title><p>Seven m6A-related autophagy genes were screened to construct a prognostic model: <italic>CASP4</italic>, <italic>CDKN1A</italic>, <italic>DLC1</italic>, <italic>ITGB1</italic>, <italic>PINK1</italic>, <italic>TP63</italic>, and <italic>EIF4EBP1</italic>. In the training and validation sets, patients in the high-risk group had worse survival times than those in the low-risk group; the areas under the receiver operating characteristic curves were 0.958 and 0.759, respectively. There were differences in m6A levels and immune cell infiltration between the high- and low-risk groups.</p> </sec> <sec><title>Conclusions</title><p>Our prognostic model of the seven m6A-related autophagy genes had significant predictive value for LUSC; thus, these genes may serve as autophagy-related therapeutic targets in clinical practice.</p> </sec> </abstract>

scholarly journals Identification of a five-miRNA signature as a novel potential prognostic biomarker in patients with nasopharyngeal carcinoma

Hereditas ◽  
2022 ◽  
Vol 159 (1) ◽  
Author(s):  
Bo Tu ◽  
Ling Ye ◽  
Qingsong Cao ◽  
Sisi Gong ◽  
Miaohua Jiang ◽  
...  
Keyword(s):  
High Risk ◽  
Nasopharyngeal Carcinoma ◽  
Prognostic Model ◽  
Target Genes ◽  
Cox Regression ◽  
External Validation ◽  
Risk Groups ◽  
Low Risk ◽  
Mirna Signature ◽  
Cox Regression Analysis

Abstract Background MicroRNAs (miRNAs) are involved in the prognosis of nasopharyngeal carcinoma (NPC). This study used clinical data and expression data of miRNAs to develop a prognostic survival signature for NPC patients to detect high-risk subject. Results We identified 160 differentially expressed miRNAs using RNA-Seq data from the GEO database. Cox regression model consisting of hsa-miR-26a, hsa-let-7e, hsa-miR-647, hsa-miR-30e, and hsa-miR-93 was constructed by the least absolute contraction and selection operator (LASSO) in the training set. All the patients were classified into high-risk or low-risk groups by the optimal cutoff value of the 5-miRNA signature risk score, and the two risk groups demonstrated significant different survival. The 5-miRNA signature showed high predictive and prognostic accuracies. The results were further confirmed in validation and external validation set. Results from multivariate Cox regression analysis validated 5-miRNA signature as an independent prognostic factor. A total of 13 target genes were predicted to be the target genes of miRNA target genes. Both PPI analysis and KEGG analysis networks were closely related to tumor signaling pathways. The prognostic model of mRNAs constructed using data from the dataset GSE102349 had higher AUCs of the target genes and higher immune infiltration scores of the low-risk groups. The mRNA prognostic model also performed well on the independent immunotherapy dataset Imvigor210. Conclusions This study constructed a novel 5-miRNA signature for prognostic prediction of the survival of NPC patients and may be useful for individualized treatment of NPC patients.

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