GBP2 as a potential prognostic biomarker in pancreatic adenocarcinoma

Transcriptomics-based exploration of shared M1-type macrophage-related biomarker in acute kidney injury after kidney transplantation and acute rejection after kidney transplantation

BACKGROUND

Macrophage type 1 (M1) cells are associated with both acute kidney injury (AKI) during kidney transplantation and acute rejection (AR) after kidney transplantation. Our study explored M1-related biomarkers involved in both AKI and AR and their potential biological functions.

METHODS

Based on the Gene Expression Omnibus (GEO) database, the immune cell infiltration levels and differentially expressed genes were examined in AKI and AR in the kidney transplantation; M1-related genes shared in AKI and AR were identified using weighted gene co-expression analysis (WGCNA) system. Subsequently, protein-protein interaction (PPI) networks and machine learning methods to identify Hub genes and construct diagnostic models. Both AKI model and AR rat models were built to validate the expressions of Hub genes and test the injury phenotype, oxidative stress markers, and inflammatory factors. Finally, the transcription factor (TF)-Hub gene and micro-RNA (miRNA)-Hub gene regulatory networks were constructed based on identified Hub genes.

RESULTS

Out of 2167 differential expression genes (DEGs) in AKI and 2100 DEGs in AR, four M1-related Hub genes were obtained by PPI networks and machine learning methods, namely GBP2, TYROBP, CCR5, and TLR8. The calibration curves in the nomogram diagnostic model for these four Hub genes suggested the same predictive probability as an ideal model for AKI and AR after kidney transplantation (AUC values of the area under the ROC curve were all >0.7). The same observations were confirmed in ischemia reperfusion injury (IRI) and AR rat models by identifying common four Hub genes (GBP2, TYROBP, TLR8, and CCR5). Western blots showed that these four Hub genes were significantly different in rat models of IRI and AR (all p<0.05). Compared with the control group, IRI and AR groups showed aggravated histopathological damage and increased secretion of oxidative stress markers and inflammatory factors in rat kidneys (all p<0.05). Finally, TF-Hub and miRNA-Hub gene regulatory networks were constructed to provide a theoretical basis for the regulation of Hub genes.

CONCLUSION

We identified four macrophage M1-related Hub genes shared among AKI and AR after kidney transplantation. These genes may be considered for diagnosis of AKI and AR after kidney transplantation.

Identification of hub genes related to metastasis and prognosis of osteosarcoma and establishment of a prognostic model with bioinformatic methods

Osteosarcoma (OS) is the most common primary malignant bone tumor occurring in children and adolescents. Improvements in our understanding of the OS pathogenesis and metastatic mechanism on the molecular level might lead to notable advances in the treatment and prognosis of OS. Biomarkers related to OS metastasis and prognosis were analyzed and identified, and a prognostic model was established through the integration of bioinformatics tools and datasets in multiple databases. 2 OS datasets were downloaded from the Gene Expression Omnibus database for data consolidation, standardization, batch effect correction, and identification of differentially expressed genes (DEGs); following that, gene ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed on the DEGs; the STRING database was subsequently used for protein-protein interaction (PPI) network construction and identification of hub genes; hub gene expression was validated, and survival analysis was conducted through the employment of the TARGET database; finally, a prognostic model was established and evaluated subsequent to the screening of survival-related genes. A total of 701 DEGs were identified; by gene ontology and KEGG pathway enrichment analyses, the overlapping DEGs were enriched for 249 biological process terms, 13 cellular component terms, 35 molecular function terms, and 4 KEGG pathways; 13 hub genes were selected from the PPI network; 6 survival-related genes were identified by the survival analysis; the prognostic model suggested that 4 genes were strongly associated with the prognosis of OS. DEGs related to OS metastasis and survival were identified through bioinformatics analysis, and hub genes were further selected to establish an ideal prognostic model for OS patients. On this basis, 4 protective genes including TPM1, TPM2, TPM3, and TPM4 were yielded by the prognostic model.

Comprehensive Proteomics Analysis Reveals Dynamic Phenotypes of Tumor-Associated Macrophages and Their Precursor Cells in Tumor Progression

Tumor-associated macrophages (TAMs) are key regulators in tumor progression, but the precise role of bone marrow-derived monocytes (Mons) as TAM precursors and their dynamic phenotypes regulated by the tumor microenvironment (TME) remain unclear. Here, we developed an optimized microproteomics workflow to analyze low-cell-number mouse myeloid cells. We sorted TAMs and their corresponding Mons (1 × 105 per sample) from individual melanoma mouse models at both the early and late stages. We established the protein expression profiles for these cells by mass spectrometry. Subsequently, we analyzed the dynamics phenotypes of TAMs and identified a characteristic protein expression profile characterized by upregulated cholesterol metabolism and downregulated immune responses during tumor progression. Moreover, we found the downregulation of both STAT5 and PYCARD expression not only in late-stage TAMs but also in late-stage Mons, indicating a loss of the ability to induce inflammatory responses prior to Mons infiltration into TME. Taken together, our study provides valuable insights into the progression-dependent transitions between TAMs and their precursor cells, as well as the cross-organ communications of tumor and bone marrow.

A three-gene random forest model for diagnosing idiopathic pulmonary fibrosis based on circadian rhythm-related genes in lung tissue

BACKGROUND

The disorder of circadian rhythm could be a key factor mediating fibrotic lung disease Therefore, our study aims to determine the diagnostic value of circadian rhythm-related genes (CRRGs) in IPF.

METHODS

We retrieved the data on CRRGs from previous studies and the GSE150910 dataset. The participants from the GSE150910 dataset were divided into training and internal validation sets. Next, we used several various bioinformatics methods and machine learning algorithms to screen genes. Next, we identified SEMA5A, COL7A1, and TUBB3, which were included in the random forest (RF) diagnostic model. Finally, external validation was conducted on data retrieved from the GSE184316 datasets.

RESULTS

The results revealed that the RF diagnostic model could diagnose patients with IPF in the internal validation set with the area under the ROC curve (AUC) value of 0.905 and in the external validation with the AUC value of 0.767. Furthermore, real-time quantitative PCR and western blotting results revealed a significant decrease in SEMA5A (p < 0.05) expression level and an increase in COL7A1 and TUBB3 expression levels in TGF-β1-treated normal human lung fibroblasts.

CONCLUSION

We constructed an RF diagnostic model based on SEMA5A, COL7A1, and TUBB3 expression in lung tissue for diagnosing patients with IPF.

Dietary Supplementation with Puerarin Improves Intestinal Function in Piglets Challenged with Escherichia coli K88

The objective of this study was to investigate the effect of puerarin supplementation on the growth performance and intestinal function of piglets challenged with enterotoxigenic Escherichia coli (ETEC) K88. Twenty-four ternary crossbred piglets were randomly assigned to three treatment groups: control group, ETEC group (challenged with ETEC K88 on day 8), and ETEC + Puerarin group (supplemented with 5 mg/kg puerarin and challenged with ETEC K88 on day 8). All piglets were orally administered D-xylose (0.1 g/kg body weight) on day 10, and blood samples were collected after 1 h. Subsequently, piglets were killed and intestinal samples were collected for further analysis. The results showed that puerarin supplementation significantly decreased the adverse effects of ETEC K88-challenged piglets; significantly improved growth performance; increased the number of Bifidobacterium in the colon and Lactobacillus in the jejunum, cecum and colon; decreased the number of Escherichia coli in the jejunum and cecum; reduced the hydrogen peroxide content in the jejunum and myeloperoxidase activity in the jejunum and ileum; and increased the activities of catalase and superoxide dismutase in the jejunum and ileum. In addition, puerarin supplementation alleviated ETEC K88-induced intestinal injury in piglets, significantly downregulated the mRNA level of Interleukin-1β and upregulated the mRNA levels of intercellular cell adhesion molecule-1, myxovirus resistance protein 1, myxovirus resistance protein 2, and guanylate-binding protein-1 in the small intestine of piglets. In conclusion, dietary supplementation with puerarin could attenuate ETEC K88-induced intestinal injury by increasing the antioxidant and anti-inflammatory capacity and the number of beneficial intestinal bacteria in piglets.

The function of guanylate binding protein 3 (GBP3) in human cancers by pan-cancer bioinformatics

As a guanylate binding protein (GBPs) member, GBP3 is immune-associated and may participate in oncogenesis and cancer therapy. Since little has been reported on GBP3 in this field, we provide pan-cancer bioinformatics to investigate the role of GBP3 in human cancers. The GBP3 expression, related clinical outcomes, immune infiltrates, potential mechanisms and mutations were conducted using tools including TIMER2.0, GEPIA2.0, SRING, DAVID and cBioPortal. Results showed an increased risk of high GBP3 in Brain Lower Grade Glioma (LGG) and Lung Squamous Cell Carcinoma (LUSC) and a decreased risk of GBP3 in Sarcoma (SARC) and Skin Cutaneous Melanoma (SKCM) (p ≤ 0.05). GBP3 was negatively correlated with CAFs in Esophageal Adenocarcinoma (ESCA) and positively correlated with CAFs in LGG, LUSC and TGCG (p ≤ 0.05). In addition, GBP3 was positively correlated with CD8+ T cells in Bladder Urothelial Carcinoma (BLCA), Cervical Squamous Cell Carcinoma (CESC), Kidney Renal Clear Cell Carcinoma (KIRC), SARC, SKCM, SKCM-Metastasis and Uveal Melanoma (UVM) (p ≤ 0.05). Potentially, GBP3 may participate in the homeostasis between immune and adaptive immunity in cancers. Moreover, the most frequent mutation sites of GBP3 in cancers are R151Q/* and K380N. This study would provide new insight into cancer prognosis and therapy.

GBP2 promotes clear cell renal cell carcinoma progression through immune infiltration and regulation of PD‑L1 expression via STAT1 signaling

Guanylate‑binding protein 2 (GBP2) has been widely studied in cancer, however, its potential role in clear cell renal cell carcinoma (ccRCC) is not fully elucidated. The present study aimed to explore the effect of GBP2 on tumor progression and its possible underlying molecular mechanisms in ccRCC. The Cancer Genome Atlas, Gene Expression Omnibus, Cancer Cell Line Encyclopedia databases, and several bioinformatics analysis tools, such as Gene Expression Profiling Interactive Analysis 2, Kaplan‑Meier plotter, UALCAN, LinkedOmics, Metascape, GeneMANIA and Tumor Immune Estimation Resource, were used to characterize the functional relationship between GBP2 and ccRCC. Focusing on the association between GBP2 and programmed death ligand 1 (PD‑L1) in vitro, the regulatory mechanism was investigated by knockdown and overexpression of GBP2 in Caki‑1 and 786‑O cells using reverse transcription‑quantitative PCR, western blotting and co‑immunoprecipitation techniques. The results indicated that GBP2 was commonly upregulated in ccRCC, correlating with worse prognosis. In addition, GBP2 expression levels were positively associated with different patterns of immune cell infiltration, suggesting that the GBP2 gene regulates PD‑L1 expression via the signal transducer and activator of transcription 1 (STAT1) pathway. The present study suggested that GBP2 regulates tumor immune infiltration and promotes tumor immune escape through PD‑L1 expression, revealing a potential immunotherapeutic target for ccRCC.

IRF2-ferroptosis related gene is associated with prognosis and EMT in gliomas

Ferroptosis is a new type of programmed cell death that has excellent anti-tumor potential in different tumors. However, the research on ferroptosis in glioma is still incomplete. In this study, we aimed to revealed the relationship between ferroptosis-related genes (FRGs) and glioma. We collected gene expression profiles of glioma patients from the TCGA and CGGA databases. All glioma samples were classified into five subtypes using the R software ConsensusClusterPlus. Subsequently, we performed single sample gene set enrichment analysis (ssGSEA) to explore the correlation between different subtypes and immune status and ferroptosis. Then co-expression modules were constructed via weighted gene co-expression network analysis (WGCNA). A Gene Ontology (GO) analysis was conducted to analyze the potential biological functions of the genes in the modules. Finally, we identified 10 hub genes using the PPI network. The in vitro experiments were used to validate our predictions. We found that the expression level of IRF2 is positively correlated with the grade of glioma. The overexpression of IRF2 could protect glioma cells from ferroptosis and enhance the invasive and migratory abilities. Silence of IRF2 had the opposite effect. In conclusion, we demonstrated a novel ferroptosis-related signature for predicting prognosis, and IRF2 could be a potential biomarker for diagnosis and treatment in glioma.

GBP2 serves as a novel prognostic biomarker and potential immune microenvironment indicator in renal cell carcinoma

Since the application of immune checkpoint therapy (ICT) has gradually become a new strategy for clear cell renal cell carcinoma (ccRCC) treatment, biomarkers that predict the individual response to ICT is needed. This study aimed to identify a new clinical indicator for postoperative surveillance of ccRCC and prediction of ICT response. We investigated the GBP2 expression and its relation with immune cell infiltration in tumor microenvironment using public databases, clinical specimens and ccRCC cell lines. Bioinformatic analysis using public database revealed that GBP2 expression is higher in cancer tissues than in adherent normal tissues among different cancer types including ccRCC, and the same results were acquired from clinical tissue samples tested by Western Blot and PCR. In ccRCC cell lines, CCk-8 proliferation assay and apoptosis assessment suggested GBP2 facilitates the malignancy of ccRCC. 286 ccRCC patients were randomly divided into a training or validation cohort, and immunohistochemistry (IHC) and Kaplan-Meier analysis revealed that higher GBP2 expression is related to worse prognosis. C-index analysis implied that integrating GBP2 expression with TNM stage improved the accuracy in predicting prognosis of ccRCC patients compared to the solitary use of either. Bioinformatic analysis implied a relation between GBP2 and immunity, and GBP2 expression is positively related with suppressive immune markers in ccRCC microenvironment. Taken together, our study demonstrated the potential of GBP2 to sever as a prognostic predictor of ccRCC, and an association between GBP2 and tumor-infiltrating lymphocytes in ccRCC was observed, making it a promising indicator of ICT response.

GBP2 as a potential prognostic predictor with immune-related characteristics in glioma

Guanylate binding protein 2 (GBP2) is a member of the guanine binding protein family, and its relationship with prognostic outcomes and tumor immune microenvironments in glioma remains elusive. We found GBP2 were increased in glioma tissues at both mRNA and protein levels. Kaplan-Meier curves revealed that high GBP2 expression was linked with worse survival of glioma patients, and multivariate Cox regression analysis indicated that high GBP2 expression was an independent prognostic factor for glioma. Combined analysis in immune database revealed that the expression of GBP2 was significantly related to the level of immune infiltration and immunomodulators. Single-cell analysis illustrated the high expression of GBP2 in malignant glioma cells showed the high antigen presentation capability, which were confirmed by real-time polymerase chain reaction (qRT-PCR) data. Additionally, the hsa-mir-26b-5p and hsa-mir-335-5p were predicted as GBP2 regulators and were validated in U87 and U251 cells. Our results first decipher immune-related characteristics and noncoding regulators of GBP2 in glioma, which may provide insights into associated immunotherapies and prognostic predictor.

Lower Expression of GBP2 Associated With Less Immune Cell Infiltration and Poor Prognosis in Skin Cutaneous Melanoma (SKCM)

Guanylate binding protein 2 (GBP2) could bind to guanine nucleotides (GMP, GDP, and GTP) and exhibits antiviral activity against influenza virus through the innate immune response. Some researchers have demonstrated that the value of GBP2 in predicting the prognosis of multiple cancers and the complex correlation with immune response. However, the correlation of GBP2 to prognosis and immune cell infiltration level were unknown in skin cutaneous melanoma (SKCM). The GBP2 expression in multiple cancers were evaluated through Tumor Immune Estimation Resource (TIMER) and Oncomine. We also evaluated the influence of GBP2 on overall survival in multiple caners through GEPIA, TIMER, and tissue microarray. The correlation between GBP2 expression level and immune cell or gene markers of immune infiltration level was explored on TIMER and GEPIA. Gene set enrichment analysis was performed using the TCGA dataset. The GBP2 expression level represented a significant reduction and the GBP2 expression was lower compared with the SKCM-Metastasis with P<0.01. Lower GBP2 expression was significantly correlated with the poor overall survival of SKCM patients. Simultaneously, higher GBP2 expression predicted the better SKCM-free survival with P=0.019. GBP2 expression was positively correlated with the infiltration cells of B-cell, CD8+ T-cell, CD4+ T-cell, macrophage, neutrophil, and dendritic cell in SKCM. And there was a significant negative correlation between the expression of GBP2 and DNA methylation in the cBioPortal database (P=3.39e-42). Gene set enrichment analysis revealed that GBP2 was closely correlated with multiple pathways of immune response in cancer. In conclusion, Lower expression of GBP2 associated with less immune cell infiltration and poor prognosis in SKCM and the high promoter methylation of GBP2 represented a promising biomarker for poor prognostication in SKCM.

GBP2 facilitates the progression of glioma via regulation of KIF22/EGFR signaling

Identifying the mechanism of glioma progression is critical for diagnosis and treatment. Although studies have shown that guanylate-binding protein 2(GBP2) has critical roles in various cancers, its function in glioma is unclear. In this work, we demonstrate that GBP2 has high expression levels in glioma tissues. In glioma cells, depletion of GBP2 impairs proliferation and migration, whereas overexpression of GBP2 enhances proliferation and migration. Regarding the mechanism, we clarify that epidermal growth factor receptor (EGFR) signaling is regulated by GBP2, and also demonstrate that GBP2 interacts directly with kinesin family member 22(KIF22) and regulates glioma progression through KIF22/EGFR signaling in vitro and in vivo. Therefore, our study provides new insight into glioma progression and paves the way for advances in glioma treatment.

Adipogenic Transdifferentiation and Regulatory Factors Promote the Progression and the Immunotherapy Response of Renal Cell Carcinoma: Insights From Integrative Analysis

Background

Adipogenic transdifferentiation was an important carcinogenic factor in various tumors, while studies on its role in clear cell renal cell carcinoma (ccRCC) were still relatively few. This study aimed to investigate its prognostic value and mechanism of action in ccRCC.

Methods

Gene expression profiles and clinical data of ccRCC patients were obtained from The Cancer Genome Atlas database. Nonnegative matrix factorization was used for clustering. Gene set variation analysis (GSVA) and gene set enrichment analysis (GSEA) were used to analyze the pathways and biological process activities. single-sample GSEA (ssGSEA) was utilized to quantify the relative abundance of each immune cell. Tumor Immune Estimation Resource (TIMER) was used to evaluate the proportion of various immune infiltrating cells across diverse cancer types. Real-Time PCR was performed to examine the gene expression. R software was utilized to analyze the expression and prognostic role of genes in ccRCC.

Results

A total of 49 adipose-related genes (ARGs) were screened for differential expression between normal and ccRCC tissues. Based on differentially expressed ARGs, patients with ccRCC were divided into two adipose subtypes with different clinical, molecular, and pathway characteristics. Patients in cluster A exhibited more advanced pathological stages, higher expressions of RARRES2 and immune checkpoint genes, higher immune infiltration scores, and less nutrient metabolism pathways. Adipose differentiation index (ADI) was constructed according to the above ARGs and survival data, and its robustness and accuracy was validated in different cohorts. In addition, it was found that the expression of ARGs was associated with immune cell infiltration and immune checkpoint in ccRCC, among which GBP2 was thought to be the most relevant gene to the tumor immune microenvironment and play a potential role in carcinogenesis and invasion of tumor cells.

Conclusion

Our analysis revealed the consistency of higher adipogenic transdifferentiation of tumor cells with worse clinical outcomes in ccRCC. The 16-mRNA signature could predict the prognosis of ccRCC patients with high accuracy. ARGs such as GBP2 might shed light on the development of novel biomarkers and immunotherapies of ccRCC.

What genes are differentially expressed in individuals with schizophrenia? A systematic review

Schizophrenia is a severe, complex mental disorder characterized by a combination of positive symptoms, negative symptoms, and impaired cognitive function. Schizophrenia is highly heritable (~80%) with multifactorial etiology and complex polygenic genetic architecture. Despite the large number of genetic variants associated with schizophrenia, few causal variants have been established. Gaining insight into the mechanistic influences of these genetic variants may facilitate our ability to apply these findings to prevention and treatment. Though there have been more than 300 studies of gene expression in schizophrenia over the past 15 years, none of the studies have yielded consistent evidence for specific genes that contribute to schizophrenia risk. The aim of this work is to conduct a systematic review and synthesis of case-control studies of genome-wide gene expression in schizophrenia. Comprehensive literature searches were completed in PubMed, EmBase, and Web of Science, and after a systematic review of the studies, data were extracted from those that met the following inclusion criteria: human case-control studies comparing the genome-wide transcriptome of individuals diagnosed with schizophrenia to healthy controls published between January 1, 2000 and June 30, 2020 in the English language. Genes differentially expressed in cases were extracted from these studies, and overlapping genes were compared to previous research findings from the genome-wide association, structural variation, and tissue-expression studies. The transcriptome-wide analysis identified different genes than those previously reported in genome-wide association, exome sequencing, and structural variation studies of schizophrenia. Only one gene, GBP2, was replicated in five studies. Previous work has shown that this gene may play a role in immune function in the etiology of schizophrenia, which in turn could have implications for risk profiling, prevention, and treatment. This review highlights the methodological inconsistencies that impede valid meta-analyses and synthesis across studies. Standardization of the use of covariates, gene nomenclature, and methods for reporting results could enhance our understanding of the potential mechanisms through which genes exert their influence on the etiology of schizophrenia. Although these results are promising, collaborative efforts with harmonization of methodology will facilitate the identification of the role of genes underlying schizophrenia.

Identifying and Validating an Acidosis-Related Signature Associated with Prognosis and Tumor Immune Infiltration Characteristics in Pancreatic Carcinoma

BACKGROUND

Acidosis in the tumor microenvironment (TME) is involved in tumor immune dysfunction and tumor progression. We attempted to develop an acidosis-related index (ARI) signature to improve the prognostic prediction of pancreatic carcinoma (PC).

METHODS

Differential gene expression analyses of two public datasets (GSE152345 and GSE62452) from the Gene Expression Omnibus database were performed to identify the acidosis-related genes. The Cancer Genome Atlas-pancreatic carcinoma (TCGA-PAAD) cohort in the TCGA database was set as the discovery dataset. Univariate Cox regression and the Kaplan-Meier method were applied to screen for prognostic genes. The least absolute shrinkage and selection operator (LASSO) Cox regression was used to establish the optimal model. The tumor immune infiltrating pattern was characterized by the single-sample gene set enrichment analysis (ssGSEA) method, and the prediction of immunotherapy responsiveness was conducted using the tumor immune dysfunction and exclusion (TIDE) algorithm.

RESULTS

We identified 133 acidosis-related genes, of which 37 were identified as prognostic genes by univariate Cox analysis in combination with the Kaplan-Meier method (p values of both methods < 0.05). An acidosis-related signature involving seven genes (ARNTL2, DKK1, CEP55, CTSV, MYEOV, DSG2, and GBP2) was developed in TCGA-PAAD and further validated in GSE62452. Patients in the acidosis-related high-risk group consistently showed poorer survival outcomes than those in the low-risk group. The 5-year AUCs (areas under the curve) for survival prediction were 0.738 for TCGA-PAAD and 0.889 for GSE62452, suggesting excellent performance. The low-risk group in TCGA-PAAD showed a higher abundance of CD8+ T cells and activated natural killer cells and was predicted to possess an elevated proportion of immunotherapeutic responders compared with the high-risk counterpart.

CONCLUSIONS

We developed a reliable acidosis-related signature that showed excellent performance in prognostic prediction and correlated with tumor immune infiltration, providing a new direction for prognostic evaluation and immunotherapy management in PC.

Construction of a five-gene prognostic model based on immune-related genes for the prediction of survival in pancreatic cancer

Purpose: To identify differentially expressed immune-related genes (DEIRGs) and construct a model with survival-related DEIRGs for evaluating the prognosis of patients with pancreatic cancer (PC).

Methods: Six microarray gene expression datasets of PC from the Gene Expression Omnibus (GEO) and Immunology Database and Analysis Portal (ImmPort) were used to identify DEIRGs. RNA sequencing and clinical data from The Cancer Genome Atlas Program-Pancreatic Adenocarcinoma (TCGA-PAAD) database were used to establish the prognostic model. Univariate, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analyses were applied to determine the final variables of the prognostic model. The median risk score was used as the cut-off value to classify samples into low- and high-risk groups. The prognostic model was further validated using an internal validation set of TCGA and an external validation set of GSE62452.

Results: In total, 142 DEIRGs were identified from six GEO datasets, 47 were survival-related DEIRGs. A prognostic model comprising five genes (i.e., ERAP2, CXCL9, AREG, DKK1, and IL20RB) was established. High-risk patients had poor survival compared with low-risk patients. The 1-, 2-, 3-year area under the receiver operating characteristic (ROC) curve of the model reached 0.85, 0.87, and 0.93, respectively. Additionally, the prognostic model reflected the infiltration of neutrophils and dendritic cells. The expression of most characteristic immune checkpoints was significantly higher in the high-risk group versus the low-risk group.

Conclusions: The five-gene prognostic model showed reliably predictive accuracy. This model may provide useful information for immunotherapy and facilitate personalized monitoring for patients with PC.