scholarly journals Identification of a Pyroptosis-Related lncRNA Risk Model for Predicting Prognosis and Immune Response in Colon Adenocarcinoma

2022 ◽  
Author(s):  
Yuying Tan ◽  
Liqing Lu ◽  
Xujun Liang ◽  
Yongheng Chen
Keyword(s):  
Regression Analysis ◽  
Risk Model ◽  
Cox Regression ◽  
Malignant Tumors ◽  
Colon Adenocarcinoma ◽  
Sequencing Data ◽  
Cox Regression Analysis ◽  
Immune Infiltration ◽  
Cerna Network ◽  
Kaplan Meier

Abstract Background: Colon adenocarcinoma (COAD) is one of the most common malignant tumors and diagnosed at an advanced stage with poor prognosis in the world. Pyroptosis is involved in the initiation and progression of tumors. This research focused on constructing a pyroptosis-related ceRNA network to generate a reliable risk model for risk prediction and immune infiltration analysis of COAD.Methods: Transcriptome data, miRNA-sequencing data and clinical information were downloaded from the TCGA database. Firstly, differentially expressed mRNAs (DEmRNAs), miRNAs (DEmiRNAs), and lncRNAs (DElncRNAs) were identified to construct a pyroptosis-related ceRNA network. Secondly, a pyroptosis-related lncRNA risk model was developed applying univariate Cox regression analysis and least absolute shrinkage and selection operator method (LASSO) regression analysis. The Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses were utilized to functionally annotate RNAs contained in the ceRNA network. In addition, Kaplan-Meier analysis, receiver operating characteristic (ROC) curves, univariate and multivariate Cox regression, and nomogram were applied to validate this risk model. Finally, the relationship of this risk model with immune cells and immune checkpoint blockade (ICB) related genes were analyzed.Results: Totally 5373 DEmRNAs, 1159 DElncRNAs and 355 DEmiRNAs were identified. A pyroptosis-related ceRNA regulatory network containing 132 lncRNAs, 7miRNAs and 5 mRNAs was constructed and a ceRNA-based pyroptosis-related risk model including 11 lncRNAs was built. Tumor tissues were classified into high- and low- risk groups according to the median risk score. Kaplan-Meier analysis showed that the high-risk group had a shorter survival time; ROC analysis, independent prognostic analysis and nomogram further indicated the risk model was a significant independent prognostic factor which had excellent ability to predict patients’ risk. Moreover, immune infiltration analysis indicated that the risk model was related to immune infiltration cells (i.e., B cells naïve, T cells follicular helper, Macrophages M1, etc.) and ICB-related genes (i.e., PD-1, CTLA4, HAVCR2, etc).Conclusions: This pyroptosis-related lncRNA risk model possessed good prognostic value and the ability to predict the outcome of ICB immunotherapy in COAD.

scholarly journals Screening Prognosis-Related lncRNAs Based on WGCNA to Establish a New Risk Score for Predicting Prognosis in Patients with Hepatocellular Carcinoma

2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Xueliang Zhou ◽  
Mengmeng Dou ◽  
Zaoqu Liu ◽  
Dechao Jiao ◽  
Zhaonan Li ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Regression Analysis ◽  
Scoring System ◽  
Normal Tissue ◽  
Cox Regression ◽  
Cox Regression Analysis ◽  
Cerna Network ◽  
Kaplan Meier ◽  
Risk Scoring ◽  
Risk Scoring System

Background. Hepatocellular carcinoma (HCC) remains an important cause of cancer death. The molecular mechanism of hepatocarcinogenesis and prognostic factors of HCC have not been completely uncovered. Methods. In this study, we screened out differentially expressed lncRNAs (DE lncRNAs), miRNAs (DE miRNAs), and mRNAs (DE mRNAs) by comparing the gene expression of HCC and normal tissue in The Cancer Genome Atlas (TCGA) database. DE mRNAs were used to perform Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Then, the miRNA and lncRNA/mRNA modules that were most closely related to the survival time of patients with HCC were screened to construct a competitive endogenous RNA (ceRNA) network by weighted gene coexpression network analysis (WGCNA). Moreover, univariable Cox regression and Kaplan-Meier curve analyses of DE lncRNAs and DE mRNAs were conducted. Finally, the lasso-penalized Cox regression analysis and nomogram model were used to establish a new risk scoring system and predict the prognosis of patients with liver cancer. The expression of survival-related DE lncRNAs was verified by qRT-PCR. Results. A total of 1896 DEmRNAs, 330 DElncRNAs, and 76 DEmiRNAs were identified in HCC and normal tissue samples. Then, the turquoise miRNA and turquoise lncRNA/mRNA modules that were most closely related to the survival time of patients with HCC were screened to construct a ceRNA network by WGCNA. In this ceRNA network, there were 566 lncRNA-miRNA-mRNA regulatory pairs, including 30 upregulated lncRNAs, 16 downregulated miRNAs, and 75 upregulated mRNAs. Moreover, we screened out 19 lncRNAs and 14 hub mRNAs related to prognosis from this ceRNA network by univariable Cox regression and Kaplan-Meier curve analyses. Finally, a new risk scoring system was established by selecting the optimal risk lncRNAs from the 19 prognosis-related lncRNAs through lasso-penalized Cox regression analysis. In addition, we established a nomogram model consisting of independent prognostic factors to predict the survival rate of HCC patients. Finally, the correlation between the risk score and immune cell infiltration and gene set enrichment analysis were determined. Conclusions. In conclusion, the results may provide potential biomarkers or therapeutic targets for HCC and the establishment of the new risk scoring system and nomogram model provides the new perspective for predicting the prognosis of HCC.

scholarly journals An immune-associated gene prognostic index risk model for stomach adenocarcinoma

2021 ◽  
Author(s):  
Weijie Xue ◽  
Bingzi Dong ◽  
Yixiu Wang ◽  
Yuwei Xie ◽  
Qingkai Xue ◽  
...  
Keyword(s):  
T Cells ◽  
Regression Analysis ◽  
Risk Model ◽  
Cox Regression ◽  
Malignant Tumors ◽  
Prognostic Index ◽  
Low Risk ◽  
Cox Regression Analysis ◽  
Stomach Adenocarcinoma ◽  
Immune Related Genes

Abstract Background Stomach adenocarcinoma (STAD) is one of the most common malignant tumors worldwide. In this study, we attempt to construct a valid immune-associated gene prognostic index risk model which could predict the survival of HCC patients and the efficacy of immune check point inhibitors (ICIs) treatment. Methods The transcriptome, clinical and gene mutational data were obtained from the TCGA database. And immune-related genes were downloaded from the ImmPort and InnateDB databases. Functional and enrichment analysis was performed to identify the potential molecular function and mechanism of these differentially expressed immune-associated genes. And then candidates genes related to overall survival (OS) of STAD was obtained by weighted gene co-expression network analysis (WGCNA). Next, the immune prognostic risk model was constructed via multivariate Cox regression analysis and verified with GEO STAD cohort. Afterwards, the association between the risk model and the immune characteristics and was estimated. Finally, the correlation between the risk model and efficacy of ICIs therapy. Results A total of 493 immune-related genes were identified to enriched in function associated to immune response as well as in immune and tumor-related pathways. Based on the cox regression analysis, we constructed an immune-associated gene prognostic index (IAGPI) risk model based on 8 genes (RNASE2, CGB5, INHBE, PTGER3, CTLA4, DUSP1, APOA1 and CD36). Patients were divided into two subsets according to risk score. Patients in low risk set had a better OS than those in high. In the low risk set, there were more CD8 T cells, activated memory CD4 T cells, follicular helper T cells and M1 macrophages, while monocytes, M2 macrophages, eosinophils and neutrophils were more plentiful in the high. And patients in the low risk set were more sensitive to ICIs therapy. Conclusion The IAGPI risk model can precisely predict prognosis, reflect tumor immune microenvironment and predict the efficacy of ICIs therapy in STAD patients.

scholarly journals Integrated Analysis of lncRNA-Mediated ceRNA Network Reveals a Prognostic Signature for Hepatocellular Carcinoma

2020 ◽  
Vol 11 ◽  
Author(s):  
Jian-Rong Sun ◽  
Chen-Fan Kong ◽  
Kun-Min Xiao ◽  
Jia-Lu Yang ◽  
Xiang-Ke Qu ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Regression Analysis ◽  
Cox Regression ◽  
Tnm Stage ◽  
The Cancer Genome Atlas ◽  
Integrated Analysis ◽  
Cox Regression Analysis ◽  
Cerna Network ◽  
Kaplan Meier ◽  
Risk Patients

Hepatocellular carcinoma (HCC) is one of the most common types of malignancy and is associated with high mortality. Prior research suggests that long non-coding RNAs (lncRNAs) play a crucial role in the development of HCC. Therefore, it is necessary to identify lncRNA-associated therapeutic biomarkers to improve the accuracy of HCC prognosis. Transcriptomic data of HCC obtained from The Cancer Genome Atlas (TCGA) database were used in the present study. Differentially expressed RNAs (DERNAs), including 74 lncRNAs, 16 miRNAs, and 35 mRNAs, were identified using bioinformatics analysis. The DERNAs were subsequently used to reconstruct a competing endogenous RNA (ceRNA) network. A lncRNA signature was revealed using Cox regression analysis, including LINC00200, MIR137HG, LINC00462, AP002478.1, and HTR2A-AS1. Kaplan-Meier plot demonstrated that the lncRNA signature is highly accurate in discriminating high- and low-risk patients (P < 0.05). The area under curve (AUC) value exceeded 0.7 in both training and validation cohort, suggesting a high prognostic potential of the signature. Furthermore, multivariate Cox regression analysis indicated that both the TNM stage and the lncRNA signature could serve as independent prognostic factors for HCC (P < 0.05). Then, a nomogram comprising the TNM stage and the lncRNA signature was determined to raise the accuracy in predicting the survival of HCC patients. In the present study, we have introduced a ceRNA network that could contribute to provide a new insight into the identification of potential regulation mechanisms for the development of HCC. The five-lncRNA signature could serve as a reliable biosignature for HCC prognosis, while the nomogram possesses strong potential in clinical applications.

scholarly journals The relationship between miR-219-5p Gene Polymorphisms and the prognosis of colorectal cancer

2020 ◽  
Author(s):  
Yang Yan ◽  
Xiaohui Du ◽  
Shaoyou Xia ◽  
Songyan Li ◽  
Da Teng ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Regression Analysis ◽  
Prognostic Value ◽  
Cox Regression ◽  
Malignant Tumors ◽  
Survival Rates ◽  
Tnm Staging ◽  
Cox Regression Analysis ◽  
Normal Tissues ◽  
Kaplan Meier

Abstract Background Colorectal cancer (CRC) is one of the most common malignant tumors, its morbidity and mortality are increasing year by year, it is a serious threat to people's health. Some studies have reported that miR-219-5p acts as a tumor suppressor in some malignant tumors. So the purpose of this study was to investigate the prognostic value of miR-219-5p expression in CRC patients. Methods QRT-PCR was used to detect the expression levels of miR-219-5p in CRC tissues and corresponding normal tissues (P < 0.001). The prognostic value of miR-219-5p in CRC was analyzed by Kaplan-Meier and Cox regression analysis. Results The results indicated that the expression of miR-219-5p was significantly lower in CRC tissues, and its expression was closely correlated with tumor differentiation, TNM staging and lymph node metastasis (all P < 0.05). Moreover, Kaplan Meier survival analysis showed that the patients with low expression of miR-219-5p had worse overall survival rates (P < 0.05). Cox regression analysis further demonstrated that miR-219-5p expression was an independent prognostic factor for survival time in CRC patients (P = 0.018, HR = 2.026 and 95%CI: 1.127–3.643). Conclusions All the results suggest that miR-219-5p expression can be used as a potential prognostic biomarker for CRC patients.

scholarly journals Construction of a Pseudogene‐associated ceRNA Network and Identification of Prognostic Pseudogene Biomarkers for Colorectal Cancer

2021 ◽  
Author(s):  
Zhuoqi Li ◽  
Jing Zhou ◽  
Liankun Gu ◽  
Baozhen Zhang
Keyword(s):  
Gene Expression ◽  
Colorectal Cancer ◽  
Cox Regression ◽  
Expression Profiles ◽  
Colon Adenocarcinoma ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Cox Regression Analysis ◽  
Cerna Network ◽  
Kaplan Meier

Abstract Colorectal cancer (CRC) is one of the most common and deadly malignant carcinomas. Many long noncoding RNAs (lncRNA) have been reported to play an important role in the tumorigenesis of CRC by interacting with miRNAs and influencing the expression of some mRNAs through a competing endogenous RNA (ceRNA) network. Pseudogenes are one kind of lncRNA and can act as RNA sponges for miRNAs and regulate gene expression via ceRNA networks, but there are few studies about pseudogenes in CRC. In this study, total of 31 differentially expressed (DE) pseudogenes, 17 DE miRNAs and 152 DE mRNAs were identified by analyzing the expression profiles of colon adenocarcinoma (COAD) obtained from The Cancer Genome Atlas (TCGA). And a ceRNA network was constructed based on these RNAs. Kaplan–Meier analysis showed that 7 pseudogenes, 4 miRNAs and 30 mRNAs were significantly associated with overall survival. Then multivariate Cox regression analysis on the ceRNA-related DE pseudogenes was performed and a 5-pseudogene signature with the greatest prognostic value for CRC was identified. What’s more, the results were validated by the Gene Expression Omnibus (GEO) database, and quantitative real‐time PCR (qRT‐PCR) in 113 pairs of CRC tissues. In conclusion, this study provides a pseudogene-associated ceRNA network and 7 prognostic pseudogene biomarkers, and a 5-pseudogene prognostic risk signature that may be useful to predict the survival of CRC patients.

scholarly journals Screening of miRNAs as Prognostic Biomarkers for Colon Adenocarcinoma and Biological Function Analysis of Their Target Genes

2021 ◽  
Vol 11 ◽  
Author(s):  
Guoliang Zheng ◽  
GuoJun Zhang ◽  
Yan Zhao ◽  
Zhichao Zheng
Keyword(s):  
Regression Analysis ◽  
Survival Analysis ◽  
High Risk ◽  
Prognostic Model ◽  
Target Genes ◽  
Risk Model ◽  
Cox Regression ◽  
Colon Adenocarcinoma ◽  
Training Set ◽  
Cox Regression Analysis

We constructed a prognostic risk model for colon adenocarcinoma (COAD) using microRNAs (miRNAs) as biomarkers. Clinical data of patients with COADs and miRNA-seq data were from TCGA, and the differential expression of miRNAs (carcinoma vs. para-carcinoma tissues) was assessed using R software. COAD data were randomly divided into Training and Testing Sets. A linear prognostic risk model was constructed using Cox regression analysis based on the Training Set. Patients were classified as high-risk or low-risk according to the score of the prognostic model. Survival analysis and receiver operating characteristic (ROC) curves were used to evaluate model performance. The gene targets in the prognostic model were identified and their biological functions were analyzed. Analysis of COAD and normal cell lines using qPCR was used to verify the model. There were 134 up-regulated and 140 down-regulated miRNAs. We used the Training Set to develop a prognostic model based on the expression of seven miRNAs. ROC analysis indicated this model had acceptable prediction accuracy (area under the curve=0.784). Kaplan-Meier survival analysis showed that overall survival was worse in the high-risk group. Cox regression analysis showed that the 7-miRNA Risk Score was an independent prognostic factor. The 2,863 predicted target genes were mainly enriched in the MAPK, PI3K-AKT, proteoglycans in cancer, and mTOR signaling pathways. For unknown reasons, expression of these miRNAs in cancerous and normal cells differed somewhat from model predictions. Regardless, the 7-miRNA Risk Score can be used to predict COAD prognosis and may help to guide clinical treatment.

scholarly journals Identification of crucial genes of pyrimidine metabolism as biomarkers for gastric cancer prognosis

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Zhengxin Wu ◽  
Jinshui Tan ◽  
Yifan Zhuang ◽  
Mengya Zhong ◽  
Yubo Xiong ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Regression Analysis ◽  
Survival Analysis ◽  
Risk Model ◽  
Cox Regression ◽  
Clinical Samples ◽  
Pyrimidine Metabolism ◽  
Cox Regression Analysis ◽  
Kaplan Meier ◽  
Cox Analysis

Abstract Background Metabolic reprogramming has been reported in various kinds of cancers and is related to clinical prognosis, but the prognostic role of pyrimidine metabolism in gastric cancer (GC) remains unclear. Methods Here, we employed DEG analysis to detect the differentially expressed genes (DEGs) in pyrimidine metabolic signaling pathway and used univariate Cox analysis, Lasso-penalizes Cox regression analysis, Kaplan–Meier survival analysis, univariate and multivariate Cox regression analysis to explore their prognostic roles in GC. The DEGs were experimentally validated in GC cells and clinical samples by quantitative real-time PCR. Results Through DEG analysis, we found NT5E, DPYS and UPP1 these three genes are highly expressed in GC. This conclusion has also been verified in GC cells and clinical samples. A prognostic risk model was established according to these three DEGs by Univariate Cox analysis and Lasso-penalizes Cox regression analysis. Kaplan–Meier survival analysis suggested that patient cohorts with high risk score undertook a lower overall survival rate than those with low risk score. Stratified survival analysis, Univariate and multivariate Cox regression analysis of this model confirmed that it is a reliable and independent clinical factor. Therefore, we made nomograms to visually depict the survival rate of GC patients according to some important clinical factors including our risk model. Conclusion In a word, our research found that pyrimidine metabolism is dysregulated in GC and established a prognostic model of GC based on genes differentially expressed in pyrimidine metabolism.

scholarly journals Screening prognosis-related lncRNAs based on WGCNA to establish a new risk score for predicting prognosis in patients with hepatocellular carcinoma

2020 ◽  
Author(s):  
Xueliang Zhou ◽  
Mengmeng Dou ◽  
Zaoqu Liu ◽  
Dechao Jiao ◽  
Zhaonan Li ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Regression Analysis ◽  
Scoring System ◽  
Normal Tissue ◽  
Cox Regression ◽  
Cox Regression Analysis ◽  
Cerna Network ◽  
Kaplan Meier ◽  
Risk Scoring ◽  
Risk Scoring System

Abstract Background: Hepatocellular carcinoma (HCC) remains an important cause of cancer death. The molecular mechanism of hepatocarcinogenesis and prognostic factors of HCC have not been completely uncovered. Methods: In this study, we screened out differentially expressed lncRNAs (DE lncRNAs), miRNAs (DE miRNAs), and mRNAs (DE mRNAs) by comparing the gene expression of HCC and normal tissue in the The Cancer Genome Atlas (TCGA) database. DE mRNAs were used to perform gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Then, the miRNA and lncRNA/mRNA modules that were most closely related to the survival time of patients with HCC were screened to construct a competitive endogenous RNA (ceRNA) network by weighted gene coexpression network analysis (WGCNA). Moreover, univariable Cox regression and Kaplan-Meier curve analyses of DE lncRNAs and DE mRNAs were conducted. Finally, the lasso‐penalized Cox regression analysis and nomogram model were used to establish new risk scoring system and predict the prognosis of patients with liver cancer. Results: A total of 1896 DEmRNAs, 330 DElncRNAs, and 76 DEmiRNAs were identified in HCC and normal tissue samples. Then, the turquoise miRNA and turquoise lncRNA/mRNA modules that were most closely related to the survival time of patients with HCC were screened to construct a ceRNA network by WGCNA. In this ceRNA network, there were 566 lncRNA-miRNA-mRNA regulatory pairs, including 30 upregulated lncRNAs, 16 downregulated miRNAs and 75 upregulated mRNAs. Moreover, we screened out 19 lncRNAs and 14 hub mRNAs related to prognosis from this ceRNA network by univariable Cox regression and Kaplan-Meier curve analyses. Finally, a new risk scoring system was established by selecting the optimal risk lncRNAs from the 19 prognosis-related lncRNAs through lasso‐penalized Cox regression analysis. In addition, we established a nomogram model consisting of independent prognostic factors to predict the survival rate of HCC patients. Conclusions: In conclusion, the results may provide potential biomarkers or therapeutic targets for HCC, and the establishment of new risk scoring system and nomogram model provide the new perspective for predicting the prognosis of HCC.

scholarly journals Determination of a six-gene prognostic model for cervical cancer based on WGCNA combined with LASSO and Cox-PH analysis

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Shiyan Li ◽  
Fengjuan Han ◽  
Na Qi ◽  
Liyang Wen ◽  
Jia Li ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Regression Analysis ◽  
Risk Model ◽  
Cox Regression ◽  
Hub Genes ◽  
Cox Regression Analysis ◽  
Kaplan Meier ◽  
Risk Curve ◽  
Modular Analysis ◽  
Upregulated Genes

Abstract Aim This study aimed to establish a risk model of hub genes to evaluate the prognosis of patients with cervical cancer. Methods Based on TCGA and GTEx databases, the differentially expressed genes (DEGs) were screened and then analyzed using GO and KEGG analyses. The weighted gene co-expression network (WGCNA) was then used to perform modular analysis of DEGs. Univariate Cox regression analysis combined with LASSO and Cox-pH was used to select the prognostic genes. Then, multivariate Cox regression analysis was used to screen the hub genes. The risk model was established based on hub genes and evaluated by risk curve, survival state, Kaplan-Meier curve, and receiver operating characteristic (ROC) curve. Results We screened 1265 DEGs between cervical cancer and normal samples, of which 620 were downregulated and 645 were upregulated. GO and KEGG analyses revealed that most of the upregulated genes were related to the metastasis of cancer cells, while the downregulated genes mostly acted on the cell cycle. Then, WGCNA mined six modules (red, blue, green, brown, yellow, and gray), and the brown module with the most DEGs and related to multiple cancers was selected for the follow-up study. Eight genes were identified by univariate Cox regression analysis combined with the LASSO Cox-pH model. Then, six hub genes (SLC25A5, ENO1, ANLN, RIBC2, PTTG1, and MCM5) were screened by multivariate Cox regression analysis, and SLC25A5, ANLN, RIBC2, and PTTG1 could be used as independent prognostic factors. Finally, we determined that the risk model established by the six hub genes was effective and stable. Conclusions This study supplies the prognostic value of the risk model and the new promising targets for the cervical cancer treatment, and their biological functions need to be further explored.

scholarly journals Exploration of the Immune-Related Signatures and Immune Infiltration Analysis in Melanoma

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Ai-lan Li ◽  
Yong-mei Zhu ◽  
Lai-qiang Gao ◽  
Shu-yue Wei ◽  
Ming-tao Wang ◽  
...  
Keyword(s):  
Regression Analysis ◽  
High Risk ◽  
Immune Cell ◽  
Risk Model ◽  
Cox Regression ◽  
Cell Distribution ◽  
Cox Regression Analysis ◽  
Immune Infiltration ◽  
Immune Signatures ◽  
Wilcoxon Rank Sum Test

In the present study, we aimed to investigate immune-related signatures and immune infiltration in melanoma. The transcriptome profiling and clinical data of melanoma were downloaded from The Cancer Genome Atlas database, and their matched normal samples were obtained from the Genotype-Tissue Expression database. After merging the genome expression data using Perl, the limma package was used for data normalization. We screened the differentially expressed genes (DEGs) and obtained immune signatures associated with melanoma by an immune-related signature list from the InnateDB database. Univariate Cox regression analysis was used to identify potential prognostic immune genes, and LASSO analysis was used to identify the hub genes. Next, based on the results of multivariate Cox regression analysis, we constructed a risk model for melanoma. We investigated the correlation between risk score and clinical characteristics and overall survival (OS) of patients. Based on the TIMER database, the association between selected immune signatures and immune cell distribution was evaluated. Next, the Wilcoxon rank-sum test was performed using CIBERSORT, which confirmed the differential distribution of immune-infiltrating cells between different risk groups. We obtained a list of 91 differentially expressed immune-related signatures. Functional enrichment analysis indicated that these immune-related DEGs participated in several areas of immune-related crosstalk, including cytokine-cytokine receptor interactions, JAK–STAT signaling pathway, chemokine signaling pathway, and Th17 cell differentiation pathway. A risk model was established based on multivariate Cox analysis results, and Kaplan-Meier analysis was performed. The Kruskal-Wallis test suggested that a high risk score indicated a poorer OS and correlated with higher American Joint Committee on Cancer-TNM (AJCC-TNM) stages and advanced pathological stages ( P < 0.01 ). Furthermore, the association between hub immune signatures and immune cell distribution was evaluated in specific tumor samples. The Wilcoxon rank-sum test was used to estimate immune infiltration density in the two groups, and results showed that the high-risk group exhibited a lower infiltration density, and the dominant immune cells included M0 macrophages ( P = 0.023 ) and activated mast cells ( P = 0.005 ).

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