Construction of immune-related risk model for prognosis of hepatocellular carcinoma.

e16683 Background: Hepatocellular carcinoma is the most common malignant tumor. Although the treatment of HCC has significantly improved, the 5-year survival rate is still only 18%. There is increasing evidence that tumor immune microenvironment (TIM) plays critical roles during cancer initiation and progression. Based on the comprehensive exploration of the immunogenomic, an immune-related risk model was constructed to predict hepatocellular carcinoma prognosis. Methods: Transcriptomic data of HCC patients were downloaded from the TCGA database, and the differentially expressed immune-related genes (IRGs) (FDR < 0.01, |log2fold change| > 2) were identified. Functional enrichment analysis was performed to explore potential molecular mechanisms of the differentially expressed IRGs. By univariate and multivariate Cox regression analysis, we identified eight prognosis-related IRGs. Based on the expression levels of IRGs, we constructed the immune-related risk model. The Kaplan‐Meier (K‐M) survival curves, ROC curves, univariate and multivariate analysis were used to evaluate the immune-related risk model. According to the risk score, HCC patients were stratified into low and high-risk groups. CIBERSORT was applied to analyze the profiling difference of infiltrating immune cells between the two groups. Results: A total of 113 differentially expressed IRGs were identified, of which nine IRGs were correlated with the prognosis of HCC patients. Functional enrichment analysis showed that these genes were involved in immune response and immune signal pathway. The immune-related risk model consisted of eight IRGs (FABP6, RBP2, MAPT, BIRC5, PLXNA3, CSPG5, IL17D and STC2). The immune risk score was an independent prognostic factor (HR, 2.63 [1.93−3.58]; P = 8.16E−10) and the patients with a high-risk score tended to have a shorter OS than those with a low-risk score. In the TCGA cohort, high-risk patients tended to have an advanced stage. Moreover, we found that the patients in the high-risk group had higher fractions of T follicular cells helper and macrophages M0. The patients with low-risk scores had higher fractions of CD8+ T cells and CD4+ T cells. Conclusions: We have identified the immune-related risk model of hepatocellular carcinoma based on the expression profiles of eight immune-related genes. This model could predict prognosis and reflect the tumor immune microenvironment of HCC patients, which can provide new insights in the individualized treatment of HCC and potential novel targets for immunotherapy.

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Immunogenomic Landscape Analysis of Prognostic Immune-Related Genes in Hepatocellular Carcinoma

Journal of Healthcare Engineering ◽

10.1155/2021/3761858 ◽

2021 ◽

Vol 2021 ◽

pp. 1-13

Author(s):

Peng Qin ◽

Mengyu Zhang ◽

Xue Liu ◽

Ziming Dong

Keyword(s):

Hepatocellular Carcinoma ◽

Overall Survival ◽

Expression Profiles ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Functional Enrichment ◽

Differentially Expressed ◽

Receptor Interaction ◽

Immune Related Genes

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related death. HBV infection is an important risk factor for the tumorigenesis of HCC, given that the inflammatory environment is closely related to morbidity and prognosis. Consequently, it is of urgent importance to explore the immunogenomic landscape to supplement the prognosis of HCC. The expression profiles of immune‐related genes (IRGs) were integrated with 377 HCC patients to generate differentially expressed IRGs based on the Cancer Genome Atlas (TCGA) dataset. These IRGs were evaluated and assessed in terms of their diagnostic and prognostic values. A total of 32 differentially expressed immune‐related genes resulted as significantly correlated with the overall survival of HCC patients. The Gene Ontology functional enrichment analysis revealed that these genes were actively involved in cytokine‐cytokine receptor interaction. A prognostic signature based on IRGs (HSPA4, PSME3, PSMD14, FABP6, ISG20L2, TRAF3, NDRG1, NRAS, CSPG5, and IL17D) stratified patients into high-risk versus low-risk groups in terms of overall survival and remained as an independent prognostic factor in multivariate analyses after adjusting for clinical and pathologic factors. Several IRGs (HSPA4, PSME3, PSMD14, FABP6, ISG20L2, TRAF3, NDRG1, NRAS, CSPG5, and IL17D) of clinical significance were screened in the present study, revealing that the proposed clinical-immune signature is a promising risk score for predicting the prognosis of HCC.

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Construction of circRNA-Based ceRNA Network to Reveal the Role of circRNAs in the Progression and Prognosis of Hepatocellular Carcinoma

Frontiers in Genetics ◽

10.3389/fgene.2021.626764 ◽

2021 ◽

Vol 12 ◽

Author(s):

Rong Deng ◽

Xiaohan Cui ◽

Yuxiang Dong ◽

Yanqiu Tang ◽

Xuewen Tao ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Liver Cancer ◽

Differential Expression Analysis ◽

Reduction Process ◽

Enrichment Analysis ◽

Clinical Information ◽

Functional Enrichment Analysis ◽

Functional Enrichment ◽

Differentially Expressed ◽

Mirna Sequence

BackgroundCircular RNAs (circRNAs) are now under hot discussion as novel promising biomarkers for patients with hepatocellular carcinoma (HCC). The purpose of our study is to identify several competing endogenous RNA (ceRNA) networks related to the prognosis and progression of HCC and to further investigate the mechanism of their influence on tumor progression.MethodsFirst, we obtained gene expression data related to liver cancer from The Cancer Genome Atlas (TCGA) database (http://www.portal.gdc.cancer.gov/), including microRNA (miRNA) sequence, RNA sequence, and clinical information. A co-expression network was constructed through the Weighted Correlation Network Analysis (WGCNA) software package in R software. The differentially expressed messenger RNAs (DEmRNAs) in the key module were analyzed with the Database for Annotation Visualization and Integrated Discovery (DAVID) (https://david.ncifcrf.gov/summary.jsp) to perform functional enrichment analysis including Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO). The data of miRNA expression and clinical information downloaded from TCGA were utilized for survival analysis to detach the prognostic value of the DEmiRNAs of the key module.ResultsThe 201 differentially expressed miRNAs (DEmiRNAs) and 3,783 DEmRNAs were preliminarily identified through differential expression analysis. The co-expression networks of DEmiRNAs and DEmRNAs were constructed with WGCNA. Further analysis confirmed four miRNAs in the most significant module (blue module) were associated with the overall survival (OS) of patients with liver cancer, including hsa-miR-92b-3p, hsa-miR-122-3p, hsa-miR-139-5p, and hsa-miR-7850-5p. DAVID was used for functional enrichment analysis of 286 co-expressed mRNAs. The GO analysis results showed that the top enriched GO terms were oxidation–reduction process, extracellular exosome, and iron ion binding. In KEGG pathway analysis, the top three enriched terms included metabolic pathways, fatty acid degradation, and valine, leucine, and isoleucine degradation. In addition, we intersected the miRNA–mRNA interaction prediction results with the differentially expressed and prognostic mRNAs. We found that hsa-miR-92b-3p can be related to CPEB3 and ACADL. By overlapping the data of predicted circRNAs by circBank and differentially expressed circRNAs of GSE94508, we screened has_circ_0077210 as the upstream regulatory molecule of hsa-miR-92b-3p. Hsa_circ_0077210/hsa-miR-92b-3p/cytoplasmic polyadenylation element binding protein-3 (CPEB3) and acyl-Coenzyme A dehydrogenase, long chain (ACADL) were validated in HCC tissue.ConclusionOur research provides a mechanistic elucidation of the unknown ceRNA regulatory network in HCC. Hsa_circ_0077210 might serve a momentous therapeutic role to restrain the occurrence and development of HCC.

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Comprehensive Analysis of Ferroptosis Regulators in Lung Adenocarcinomas Identifies Prognostic and Immunotherapy-Related Biomarkers

Frontiers in Molecular Biosciences ◽

10.3389/fmolb.2021.587436 ◽

2021 ◽

Vol 8 ◽

Author(s):

Sijin Sun ◽

Wei Guo ◽

Fang Lv ◽

Guochao Zhang ◽

Juhong Wang ◽

...

Keyword(s):

Risk Score ◽

Potential Role ◽

Principal Component ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Functional Enrichment ◽

Mutation Burden ◽

Related Risk ◽

Lung Adenocarcinomas

Ferroptosis is a newly discovered type of programmed cell death that differs from canonical apoptosis. However, the potential role of ferroptosis in lung adenocarcinoma (LUAD) has not been elaborated. In total, 1,328 samples from databases and 36 ferroptosis regulators were included in this study. By combining random survival forest and principal component analysis algorithms, a robust prognostic ferroptosis-related risk score (FRRS) was constructed, and the performance was validated in three independent datasets. Based on the median risk score, two subgroups were identified. Then, comparisons, including of mutational profiles, functional enrichment analyses and immune components, were conducted between subgroups. An immunotherapy cohort was applied to explore potential therapeutic-related biomarkers. Finally, the clinical utility of FRRS was validated in a proteomic cohort. In the TCGA-LUAD cohort, FRRS was calculated using the expression of 11 selected genes, and patients with high FRRS had a significantly (p < 0.001) worse prognosis than those with low FRRS. Multivariate regression suggested that FRRS was an independent prognostic factor. Functional enrichment analysis indicated that FRRS was mainly involved in cell cycle, metabolic and immune-related pathways. Furthermore, FRRS was shown to be significantly (p < 0.001) associated with the abundance of CD8 T cells and tumor mutation burden (TMB). The combination of TMB and FANCD2 expression, the main contributor to FRRS, substantially increased the precision of predicting the therapeutic response. In conclusion, the present study revealed the potential role of ferroptosis regulators in LUAD and identified ferroptosis-related biomarkers for prognostic and immunotherapeutic predictions.

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Construction of circRNA-based ceRNA Network to Reveal the Role of circRNAs in the Progression and Prognosis of Hepatocellular Carcinoma

10.21203/rs.3.rs-97331/v1 ◽

2020 ◽

Author(s):

Rong Deng ◽

XiaoHan Cui ◽

Yuxiang Dong ◽

Yanqiu Tang ◽

Xuewen Tao ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Gene Ontology ◽

Liver Cancer ◽

Differential Expression Analysis ◽

Reduction Process ◽

Enrichment Analysis ◽

Clinical Information ◽

Functional Enrichment Analysis ◽

Functional Enrichment ◽

Differentially Expressed

Abstract Background: Circular RNAs (circRNAs) are now under hot discussion as novel promising bio-markers for patients with hepatocellular carcinoma. The purpose of our study is to identify several competing endogenous RNAs (ceRNAs) networks related to the prognosis and progression of hepatocellular carcinoma, and to further investigate the mechanism of their influence on tumor progression.Methods: First, we obtained gene expression data related to liver cancer from the TCGA database (http://www.portal.gdc.cancer.gov/), including miRNA-seq, RNA-seq and clinical information. A co-expression network was constructed through the WGCNA software package in R software, with the purpose of identifying important microRNAs (miRNAs) and messenger RNAs (mRNAs) related to liver cancer. The DEmRNAs in the key module were analyzed with DAVID (https://david.ncifcrf.gov/summary.jsp) to perform functional enrichment analysis including Kyoto Encyclopedia of Genes and Genomes (KEGG) and gene ontology (GO). The data of miRNA expression and clinical information downloaded from TCGA was utilized for survival analysis to detach the prognostic value of the DEmiRNAs of the key module. Results：201 DEmiRNAs and 3783 DEmRNAs were finally identified through differential expression analysis. The co-expression networks of DEmiRNA and DEmRNA were constructed by using WGCNA. Further analysis confirmed 4 miRNAs in the most significant module (blue module) were associated with the OS of patients with liver cancer, including hsa-miR-92b-3p, hsa-miR-122-3p, hsa-miR-139-5p and hsa-miR-7850-5p. DAVID was used for functional enrichment analysis of 286 co-expressed mRNAs. The Gene Ontology (GO) analysis results showed that the top enriched GO terms were oxidation-reduction process, extracellular exosome and iron ion binding. In Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, the top 3 enriched terms included metabolic pathways, fatty acid degradation and valine, leucine and isoleucine degradation. In addition, we corssed the miRNA-mRNA interactions prediction results with the differentially expressed and prognostic mRNAs, and found that hsa-miR-92b-3p can be related to cytoplasmic polyadenylation element binding protein 3 (CPEB3) and Acyl-CoA Dehydrogenase Long Chain (ACADL). By overlapping the data of predicted circRNAs by circBank and differentially expressed circRNAs of GSE94508, we screened has_circ_0077210 as the upstream regulatory molecule of hsa-miR-92b-3p. Hsa_circ_0077210/hsa-miR-92b-3p/CPEB3&ACADL were validated in hepatic cell carcinoma (HCC) tissues and human protein atlas (HPA) database.Conclusion: Our research provides a mechanistic elucidation of the unknown ceRNA regulatory network in HCC. Hsa_circ_0077210 might serve as an important biomarker to promote the occurrence and development of HCC.

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Construction prognosis model based on autophagy-related gene signatures in hepatocellular carcinoma

Biomarkers in Medicine ◽

10.2217/bmm-2020-0170 ◽

2020 ◽

Vol 14 (13) ◽

pp. 1229-1242

Author(s):

Jiangtao Wang ◽

Yandong Miao ◽

Juntao Ran ◽

Yuan Yang ◽

Quanlin Guan ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Risk Score ◽

Prognostic Model ◽

Clinical Stage ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Functional Enrichment ◽

Prognosis Model ◽

Clinicopathological Significance ◽

Autophagy Pathway

Aim: To develop robust and accurate prognostic biomarkers to help clinicians optimize therapeutic strategies. Materials & methods: Differentially prognosis-related autophagy genes were identified by bioinformatics analysis method. Results: Seven prognosis-related autophagy genes were more significantly related to the prognosis of hepatocellular carcinoma (HCC). Functional enrichment analysis demonstrated that these genes were mainly enriched in the autophagy pathway. BIRC5, HSPB8 and TMEM74 exhibited significant prognostic value for HCC. Besides, the risk score and BIRC5 have significant significance with clinicopathological significance of HCC. Conclusion: The research has identified a number of prognosis-related autophagy genes that associated with the survival and clinical stage of HCC. In addition, the prognostic model can be used to calculate the patient’s risk score and these prognosis-related autophagy genes might serve as therapeutic targets.

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Tumor-Infiltrating Leukocyte Compositions and Prognostic Immune-Related Genes in Pancreatic Cancer.

10.21203/rs.3.rs-119677/v1 ◽

2020 ◽

Author(s):

Buwei Teng ◽

Yuhan Yang ◽

Zengya Guo ◽

Kundong Zhang ◽

Xiaofeng Wang ◽

...

Keyword(s):

Pancreatic Cancer ◽

T Cells ◽

Cd4 T Cells ◽

Cox Regression ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Functional Enrichment ◽

Differentially Expressed ◽

Immune Related Genes

Abstract Background:Pancreatic cancer (PC) is one of the most common cancers，which has poor prognosis.At present, abundant genetic PC samples can be obtained from The Cancer Genome Atlas (TCGA) database to finish comprehensive and reliable immunogenomic analysis. Thus, there is an urgent need to systematically explore the immunogenome of PC to obtain good prognosis.Methods: In this study, according to TCGA and The Genotype-Tissue Expression (GTEx) databases, we investigated the different compositions of leukocytes between PC and normal pancreas tissues, and analyzed the expressions of immune-related genes (IRGs) and the overall survival (OS) of 178 PC patients. Subsequently, computational difference algorithm and COX regression analyses were employed to assess the differentially expressed and OS-related IRGs in PC patients. Moreover, the underlying action mechanisms and properties of these IRGs were investigated by using computational biology. Finally, multivariable COX analysis was used to develop a novel prognostic biomarker for PC according to these IRGs.Results:The results showed that CD4+ memory T cells and M0 macrophages were more common and highly dominated in PC tissues relative to the non-tumor tissues. Functional enrichment analysis demonstrated that the differentially expressed and OS‐related IRGs were actively involved in the PI3K-Akt signaling pathway. A prognostic signature according to these differentially expressed IRGs (CD2AP, IL20RB, MYEOV, NUSAP1, PCDH1, RAB27B, TNFSF10, TOP2A, TPX2, TYK2, WNT7A and BUB1B) was moderately used for prognostic predictions. Further study indicated that RAB27B was negatively related to CD4 T cells while TYK2 was positively correlated with CD4 T cells. Conclusions: Taken together, this study screened several significant IRGs, demonstrated the drivers of immune repertoire, and indicated the importance of these PC-specific IRGs in the prognosis of PC.

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Identification of a Prognostic Six-Immune-Gene Signature and a Nomogram Model for Uveal Melanoma

10.21203/rs.3.rs-1148784/v1 ◽

2022 ◽

Author(s):

Binghua Yang ◽

Yuxia Fan ◽

Renlong Liang ◽

Yi Wu ◽

Aiping Gu

Keyword(s):

High Risk ◽

Uveal Melanoma ◽

Risk Score ◽

Risk Group ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Gene Signature ◽

Functional Enrichment ◽

Immune Gene ◽

Prognostic Signature

Abstract Background: To identify an immune-related prognostic signature and find potential therapeutic targets for uveal melanoma. Methods: The RNA-sequencing data obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets. The prognostic six-immune-gene signature was constructed through least absolute shrinkage and selection operator and multi-variate Cox regression analyses. Functional enrichment analysis and single sample GSEA were carried out. In addition, a nomogram model established by integrating clinical variables and this signature risk score was also constructed and evaluated.Results: We obtained 130 prognostic immune genes, and six of them were selected to construct a prognostic signature in the TCGA uveal melanoma dataset. Patients were classified into high-risk and low-risk groups according to a median risk score of this signature. High-risk group patients had poorer overall survival in comparison to the patients in the low-risk group (p < 0.001). These findings were further validated in two external GEO datasets. A nomogram model proved to be a good classifier for uveal melanoma by combining this signature. Both functional enrichment analysis and single sample GSEA analysis verified that this signature was truly correlated with immune system. In addition, in vitro cell experiments results demonstrated the consistent trend of our computational findings.Conclusion: Our newly identified six-immune-gene signature and a nomogram model could be used as meaningful prognostic biomarkers, which might provide uveal melanoma patients with individualized clinical prognosis prediction and potential novel treatment targets.

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Identification of Ten-LncRNA Signatures Predict Disease-Free and Overall Survival for Hepatocellular Carcinoma

10.21203/rs.3.rs-613111/v1 ◽

2021 ◽

Author(s):

Jiong Lu ◽

Sishu Yang ◽

xianze xiong

Keyword(s):

Hepatocellular Carcinoma ◽

Overall Survival ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Mapk Signaling ◽

Functional Enrichment ◽

Differentially Expressed ◽

Pathway Enrichment Analysis ◽

Operating Characteristics ◽

Disease Free

Abstract Background: The prognosis of hepatocellular carcinoma (HCC) is bleak though it has been improved over recent years. Early diagnosis could improve the survival. Plenty of researches indicate that long non-coding RNAs (lncRNAs) could play an important role in prognostic prediction of cancer as a kind of biomarker. Methods: We downloaded clinicopathological characteristics and lncRNA expression data of HCC patients from The Cancer Genome Atlas (TCGA) database. The ratio of training sets to validation sets was 2:1. Significant differentially expressed lncRNAs were identified by log-rank test and cox regression. All the significant lncRNAs were selected into the least absolute shrinkage and selection operator regression (LASSO) analysis and constructed risk-score formula by linear combination. Performance of the signatures were validated by receiver operating characteristics (ROC) curves and Kaplan-Meier survival curves. The correlated messenger RNAs (mRNA) were evaluated by functional enrichment analysis. Results: We identified and validated ten-lncRNAs based signatures to predict disease-free survival (DFS) and overall survival (OS) of HCC respectively. Stratified survival analysis showed that the performance of lncRNAs related signatures was better than tumor, node, metastasis(TNM) staging system. Functional enrichment analysis showed that organelle fission and regulation of mRNA metabolic process were significantly enriched in differentially expressed lncRNAs (DElncRNAs). Transcriptional misregulation in cancer and mitogen-activated protein kinase (MAPK) signaling pathway were significantly enriched pathways in the pathway enrichment analysis. Conclusion: we constructed two lncRNAs based signatures which could predict prognosis of HCC more accurate than the traditional ways.

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Systems Biology Approaches Reveal a Multi-stress Responsive WRKY Transcription Factor and Stress Associated Gene Co-expression Networks in Chickpea

Current Bioinformatics ◽

10.2174/1574893614666190204152500 ◽

2019 ◽

Vol 14 (7) ◽

pp. 591-601 ◽

Cited By ~ 1

Author(s):

Aravind K. Konda ◽

Parasappa R. Sabale ◽

Khela R. Soren ◽

Shanmugavadivel P. Subramaniam ◽

Pallavi Singh ◽

...

Keyword(s):

Expression Pattern ◽

Gene Networks ◽

Molecular Mechanisms ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Wrky Transcription Factor ◽

Functional Enrichment ◽

Differentially Expressed ◽

Callose Deposition ◽

Significant Probability

Background: Chickpea is a nutritional rich premier pulse crop but its production encounters setbacks due to various stresses and understanding of molecular mechanisms can be ascribed foremost importance. Objective: The investigation was carried out to identify the differentially expressed WRKY TFs in chickpea in response to herbicide stress and decipher their interacting partners. Methods: For this purpose, transcriptome wide identification of WRKY TFs in chickpea was done. Behavior of the differentially expressed TFs was compared between other stress conditions. Orthology based cofunctional gene networks were derived from Arabidopsis. Gene ontology and functional enrichment analysis was performed using Blast2GO and STRING software. Gene Coexpression Network (GCN) was constructed in chickpea using publicly available transcriptome data. Expression pattern of the identified gene network was studied in chickpea-Fusarium interactions. Results: A unique WRKY TF (Ca_08086) was found to be significantly (q value = 0.02) upregulated not only under herbicide stress but also in other stresses. Co-functional network of 14 genes, namely Ca_08086, Ca_19657, Ca_01317, Ca_20172, Ca_12226, Ca_15326, Ca_04218, Ca_07256, Ca_14620, Ca_12474, Ca_11595, Ca_15291, Ca_11762 and Ca_03543 were identified. GCN revealed 95 hub genes based on the significant probability scores. Functional annotation indicated role in callose deposition and response to chitin. Interestingly, contrasting expression pattern of the 14 network genes was observed in wilt resistant and susceptible chickpea genotypes, infected with Fusarium. Conclusion: This is the first report of identification of a multi-stress responsive WRKY TF and its associated GCN in chickpea.

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A prognostic model based on seven immune-related genes predicts the overall survival of patients with hepatocellular carcinoma

BioData Mining ◽

10.1186/s13040-021-00261-y ◽

2021 ◽

Vol 14 (1) ◽

Author(s):

Qian Yan ◽

Wenjiang Zheng ◽

Boqing Wang ◽

Baoqian Ye ◽

Huiyan Luo ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Overall Survival ◽

High Risk ◽

Risk Score ◽

Immune Cells ◽

Prognostic Model ◽

Risk Groups ◽

Low Risk ◽

Prognostic Performance ◽

Immune Related Genes

Abstract Background Hepatocellular carcinoma (HCC) is a disease with a high incidence and a poor prognosis. Growing amounts of evidence have shown that the immune system plays a critical role in the biological processes of HCC such as progression, recurrence, and metastasis, and some have discussed using it as a weapon against a variety of cancers. However, the impact of immune-related genes (IRGs) on the prognosis of HCC remains unclear. Methods Based on The Cancer Gene Atlas (TCGA) and Immunology Database and Analysis Portal (ImmPort) datasets, we integrated the ribonucleic acid (RNA) sequencing profiles of 424 HCC patients with IRGs to calculate immune-related differentially expressed genes (DEGs). Survival analysis was used to establish a prognostic model of survival- and immune-related DEGs. Based on genomic and clinicopathological data, we constructed a nomogram to predict the prognosis of HCC patients. Gene set enrichment analysis further clarified the signalling pathways of the high-risk and low-risk groups constructed based on the IRGs in HCC. Next, we evaluated the correlation between the risk score and the infiltration of immune cells, and finally, we validated the prognostic performance of this model in the GSE14520 dataset. Results A total of 100 immune-related DEGs were significantly associated with the clinical outcomes of patients with HCC. We performed univariate and multivariate least absolute shrinkage and selection operator (Lasso) regression analyses on these genes to construct a prognostic model of seven IRGs (Fatty Acid Binding Protein 6 (FABP6), Microtubule-Associated Protein Tau (MAPT), Baculoviral IAP Repeat Containing 5 (BIRC5), Plexin-A1 (PLXNA1), Secreted Phosphoprotein 1 (SPP1), Stanniocalcin 2 (STC2) and Chondroitin Sulfate Proteoglycan 5 (CSPG5)), which showed better prognostic performance than the tumour/node/metastasis (TNM) staging system. Moreover, we constructed a regulatory network related to transcription factors (TFs) that further unravelled the regulatory mechanisms of these genes. According to the median value of the risk score, the entire TCGA cohort was divided into high-risk and low-risk groups, and the low-risk group had a better overall survival (OS) rate. To predict the OS rate of HCC, we established a gene- and clinical factor-related nomogram. The receiver operating characteristic (ROC) curve, concordance index (C-index) and calibration curve showed that this model had moderate accuracy. The correlation analysis between the risk score and the infiltration of six common types of immune cells showed that the model could reflect the state of the immune microenvironment in HCC tumours. Conclusion Our IRG prognostic model was shown to have value in the monitoring, treatment, and prognostic assessment of HCC patients and could be used as a survival prediction tool in the near future.

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