Pan-cancer analysis of necroptosis-related gene signature for the identification of prognosis and immune significance

Integrated bioinformatic analysis identifies GADD45B as an immune-related prognostic biomarker in skin cutaneous melanoma

Skin cutaneous melanoma (SKCM) arises from melanocytes and is an aggressive form of skin cancer. If left untreated, most melanomas will metastasize, posing a major health risk. GADD45B, a member of the GADD45 family, is known to be involved in DNA damage repair; however, its specific role in SKCM remains largely unclear. In this study, we comprehensively investigated the function of GADD45B in SKCM. By integrating 26 SKCM-related datasets from The Cancer Genome Atlas (TCGA), Cancer Cell Line Encyclopedia (CCLE), cBioPortal for Cancer Genomics (cBioPortal), Gene Expression Omnibus (GEO), and other databases, we conducted functional enrichment, immune infiltration, and single-cell analyses using R. Additionally, transcriptome sequencing of 30 human SKCM cell lines, phenotype characterization of 29 SKCM lines in vitro, and macrophage polarization analysis were performed. We found that GADD45B expression was significantly downregulated in SKCM patients compared to normal controls (p < 0.001), and higher GADD45B levels correlated with better prognosis (p < 0.05). GADD45B also showed high diagnostic accuracy, with an area under the curve (AUC) of 0.986. GO and KEGG analyses revealed a strong association between GADD45B and immune-related pathways. Gene Set Variation Analysis (GSVA) and single-cell sequencing suggested that GADD45B may serve as a novel immune checkpoint, predominantly expressed in macrophages and promoting M1 polarization. In vitro, overexpression of GADD45B significantly inhibited SKCM cell proliferation, potentially via suppression of the PI3K/Akt signaling pathway, and also reduced chemotherapy resistance. Furthermore, in vivo experiments using a xenograft mouse model demonstrated that GADD45B overexpression significantly suppressed tumor growth. Mice injected with GADD45B-overexpressing tumor cells exhibited smaller tumor volumes from day 15 onwards compared to controls, with markedly reduced tumor volume and weight at the endpoint. These results underscore the potential of GADD45B as an effective tumor suppressor in SKCM. In conclusion, our findings highlight GADD45B as a key regulator in SKCM progression, capable of restraining tumor cell proliferation and enhancing apoptosis. GADD45B holds promise as a novel diagnostic and prognostic biomarker and a potential target for SKCM immunotherapy.

Integrating bioinformatics and experimental validation to Investigate IRF1 as a novel biomarker for nucleus pulposus cells necroptosis in intervertebral disc degeneration

Intervertebral disc degeneration (IDD) is a prevalent spinal disorder and the principal cause of lower back pain (LBP). Diverse forms of programmed cell death (PCD) have been identified as the key phenotypes of the disease and have the potential to serve as new indicators for the diagnosis and prognosis of IDD. However, the mechanism underlying necroptosis in IDD remains unclear. This study aimed to identify novel biomarkers that promote nucleus pulposus cell necroptosis in IDD using bioinformatic analysis and experimental validation. We analyzed multiple datasets of IDD from the Gene Expression Omnibus (GEO) database to identify necroptosis-related IDD differential genes (NRDEGs). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed, followed by logistic least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive (SVM) algorithms to identify key genes. Gene set enrichment analysis (GSEA) and logistic regression analysis were used to ascertain the potential functions of these genes and to identify key genes, respectively. We then constructed mRNA-miRNA, mRNA-TF, mRNA-drug, and functional similarity gene interaction networks for the seven key genes identified. We used IDD clinical samples and necroptotic cell model to validate our findings. Immunohistochemical staining, RT-qPCR, and western blotting results indicated that IRF1 may be a hub necroptosis-related gene. To further elucidate the function of IRF1, we constructed IRF1 knockdown and overexpression models, which revealed that IRF1 promotes necroptosis in rat nucleus pulposus cells, increases mitochondrial ROS levels, and decreases ATP levels. These findings provide new insights into the development of necroptosis in IDD and, for the first time, validate the role of IRF1 as a novel biomarker for the diagnosis and treatment of IDD.

IAPP blocks anti-breast cancer function of CD8+T cells via targeting cuproptosis

Background

Breast cancer (BRCA) is the most prevalent type of cancer worldwide. As a highly heterogeneous cancer, it has a high recurrence rate. Since its biological behavior can be regulated by immunity and cuprotosis, so exploring potential therapeutic target to mediate immunity and cuprotosis is of great significance for BRCA therapy.

Methods

The immune-related genes and immune-cuprotosis-related deferentially expressed genes (ICR-DEGs) were identified by mining the TCGA database. Prognostic analysis, differential expression analysis, univariate and lasso regression analyses were used to determine their independent prognostic values. To evaluate the relationship between ICR-DEGs and immune scores, we constructed a prognostic risk model to evaluate immune checkpoints, and then the role of tumor immune microenvironment in BRCA was explored. Furthermore, anti-BRCA function and mechanism of islet amyloid poly-peptide (IAPP) mediated CD8+T cells were verified by means of flow cytometry, ELISA, and subcutaneous transplantation tumor model.

Results

All results suggested that immune-cuprotosis-related genes were a potential predictor of BRCA's response to immune checkpoint inhibitors and immunotherapy biomarkers. Thereby downregulation of IAPP reduced cuprotosis of CD8+T or Her2-CAR-T cells to promote the anti-BRCA function both in vitro and in vivo.

Conclusion

Our research had clarified the function and mechanism of IAPP in CD8+T cells, providing new ideas for improving the diagnosis and treatment of BRCA.

A Novel Eight-Gene Signature for Lipid Metabolism Predicts the Progression of Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma

Recently, studies on the mechanisms underlying lipid metabolic reprogramming in cancer have increased. However, its significance in cervical cancer remains unclear. In the present study, a prognostic signature was constructed for patients with cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) using the expression profiles of lipid metabolism-related genes (LMRGs). Furthermore, using various bioinformatics methods, a prognostic gene signature was developed for progression-free survival (PFS). This signature was externally validated using a cervical cancer dataset (GSE44001). The characteristics of the molecular subgroups of LMRGs were analyzed, and target LMRGs were identified via differential gene analysis of the expression profiles and weighted gene correlation network analysis. Thereafter, the identified target genes were used to develop the prognostic gene signature using univariate, least absolute shrinkage and selection operator, and multivariate Cox regression analyses. The performance of the LMRG signature was evaluated using Kaplan-Meier curves, time-dependent receiver operating characteristic curves, decision curve analysis, mutation landscapes, gene set enrichment analysis, and immune score calculation. As a result, a novel eight-LMRG signature comprising ALDH3B2, CERS3, FA2H, GLTP, NR1H3, PLIN3, SLC44A3, and SQLE was constructed. Using this gene signature, patients with CESC and significantly distinguished PFS were divided. This eight-LMRG signature exhibited independent prognostic potential and superior predictive performance compared with a previously developed 12-gene signature. Our findings suggest that our novel eight-LMRG signature contributes to the implementation of precision medicine strategies for managing patients with cervical cancer by facilitating CESC prognosis.

TGF-β Score based on Silico Analysis can Robustly Predict Prognosis and Immunological Characteristics in Lower-grade Glioma: The Evidence from Multicenter Studies

INTRODUCTION

Nowadays, mounting evidence shows that variations in TGF-β signaling pathway-related components influence tumor development. Current research has patents describing the use of anti-TGF-β antibodies and checkpoint inhibitors for the treatment of proliferative diseases. Importantly, TGF-β signaling pathway is significant for lower-grade glioma (LGG) to evade host immunity. Loss of particular tumor antigens and shutdown of professional antigenpresenting cell activity may render the anti-tumor response ineffective in LGG patients. However, the prognostic significance of TGF-β related genes in LGG is still unknown.

METHODS

We collected RNA-seq data from the GTEx database (normal cortical tissues), the Cancer Genome Atlas database (TCGA-LGG), and the Chinese Glioma Genome Atlas database (CGGA-693 and CGGA-325) for conducting our investigation.

RESULTS

In addition, previous publications were explored for the 223 regulators of the TGF-β signaling pathway, and 30 regulators with abnormal expression in TCGA and GTEx database were identified. In order to identify hub prognostic regulators, least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox regression analysis were used to screen from differentially expressed genes (DEGs). On the basis of 11 genes from LASSO-Cox regression analysis (NEDD8, CHRD, TGFBR1, TP53, BMP2, LRRC32, THBS2, ID1, NOG, TNF, and SERPINE1), TGF-β score was calculated. Multiple statistical approaches verified the predictive value of the TGF-β score for the training cohort and two external validation cohorts. Considering the importance of the TGF-β signaling pathway in immune regulation, we evaluated the prediction of the TGF-β score for immunological characteristics and the possible application of the immunotherapeutic response using six algorithms (TIMER, CIBERSORT, QUANTISEQ, MCP-counter, XCELL and EPIC) and three immunotherapy cohorts (GSE78820, Imvigor-210 and PRJEB23709). Notably, we compared our risk signature with the signature in ten publications in the meta-cohort (TCGA-LGG, CGGA-693 and CGGA-325), and the TGF-β score had the best predictive efficiency (C-index =0.812).

CONCLUSION

In conclusion, our findings suggest that TGF-β signaling pathway-related signatures are prognostic biomarkers in LGG and provide a novel tool for tumor microenvironment (TME) assessment.

A novel necroptosis-related gene signature in acute myeloid leukemia

OBJECTIVE

Necroptosis has been reported to play an important role in different cancers, including leukemia. However, biomarkers of necroptosis-related genes (NRGs) that help predict the prognosis of AML are still lacking. Our research aims to build a novel signature of NRGs that could enhance our understanding of the molecular heterogeneity in leukemia.

METHOD

Gene expression profiles as well as clinical features were downloaded from TCGA and GEO databases. Data analysis were executed using R software version 4.2.1 and GraphPad Prism version 9.0.0.

RESULT

Univariate cox regression and lasso regression were applied to identify survival-specific genes. Four genes including FADD, PLA2G4A, PYCARD and ZBP1 were considered as independent risk factors that affect the prognosis of patients. Risk scores were calculated according to the coefficient of four genes. Then clinical characteristics and risk scores were enrolled to construct a nomogram. CellMiner was also used to screen potential drugs and analyze the correlations between genes and drug sensitivity.

CONCLUSION

In general, we established a signature of four genes related to necroptosis that could be helpful for future risk stratification in patients with AML.

Identification and validation of necroptosis-related gene signatures to predict clinical outcomes and therapeutic responses in acute myeloid leukemia

BACKGROUND

Necroptosis is a tightly regulated form of necrotic cell death that promotes inflammation and contributes to disease development. However, the potential roles of necroptosis-related genes (NRGs) in acute myeloid leukemia (AML) have not been elucidated fully.

METHODS

We conducted a study to identify a robust biomarker signature for predicting the prognosis and immunotherapy efficacy based on NRGs in AML. We analyzed the genetic and transcriptional alterations of NRGs in 151 patients with AML. Then, we identified three necroptosis clusters. Moreover, a necroptosis score was constructed and assessed based on the differentially expressed genes (DEGs) between the three necroptosis clusters.

RESULTS

Three necroptosis clusters were correlated with clinical characteristics, prognosis, the tumor microenvironment, and infiltration of immune cells. A high necroptosis score was positively associated with a poor prognosis, immune-cell infiltration, expression of programmed cell death 1/programmed cell death ligand 1 (PD-1/PD-L1), immune score, stromal score, interferon-gamma (IFNG), merck18, T-cell dysfunction-score signatures, and cluster of differentiation-86, but negatively correlated with tumor immune dysfunction and exclusion score, myeloid-derived suppressor cells, and M2-type tumor-associated macrophages. Our observations indicated that a high necroptosis score might contribute to immune evasion. More interestingly, AML patients with a high necroptosis score may benefit from treatment based on immune checkpoint blockade.

CONCLUSIONS

Consequently, our findings may contribute to deeper understanding of NRGs in AML, and facilitate assessment of the prognosis and treatment strategies.

PLEKHA4 is a novel prognostic biomarker that reshapes the tumor microenvironment in lower-grade glioma

Background

Lower-grade glioma (LGG) is a primary intracranial tumor that carry a high risk of malignant transformation and limited therapeutic options. Emerging evidence indicates that the tumor microenvironment (TME) is a superior predictor for tumor progression and therapy response. PLEKHA4 has been demonstrated to be a biomarker for LGG that correlate with immune infiltration. However, the fundamental mechanism by which PLEKHA4 contributes to LGG is still poorly understood.

Methods

Multiple bioinformatic tools, including Tumor Immune Estimation Resource (TIMER), Gene Expression Profiling Interactive Analysis (GEPIA2), Shiny Methylation Analysis Resource Tool (SMART), etc., were incorporated to analyze the PLEKHA4. ESTIMATE, ssGSEA, CIBERSORT, TIDE and CellMiner algorithms were employed to determine the association of PLEKHA4 with TME, immunotherapy response and drug sensitivities. Immunohistochemistry (IHC)-based tissue microarrays and M2 macrophage infiltration assay were conducted to verify their associations.

Results

PLEKHA4 expression was found to be dramatically upregulated and strongly associated with unfavorable overall survival (OS) and disease-specific survival (DSS) in LGG patients, as well as their poor clinicopathological characteristics. Cox regression analysis identified that PLEKHA4 was an independent prognostic factor. Methylation analysis revealed that DNA methylation correlates with PLEKHA4 expression and indicates a better outcome in LGG. Moreover, PLEKHA4 was remarkably correlated with immune responses and TME remodeling, as evidenced by its positive correlation with particular immune marker subsets and the putative infiltration of immune cells. Surprisingly, the proportion of M2 macrophages in TME was strikingly higher than others, inferring that PLEKHA4 may regulate the infiltration and polarization of M2 macrophages. Evidence provided by IHC-based tissue microarrays and M2 macrophage infiltration assay further validated our findings. Moreover, PLEKHA4 expression was found to be significantly correlated with chemokines, interleukins, and their receptors, further supporting the critical role of PLEKHA4 in reshaping the TME. Additionally, we found that PLEKHA4 expression was closely associated with drug sensitivities and immunotherapy responses, indicating that PLEKHA4 expression also had potential clinical significance in guiding immunotherapy and chemotherapy in LGG.

Conclusion

PLEKHA4 plays a pivotal role in reshaping the TME of LGG patients, and may serve as a potential predictor for LGG prognosis and therapy.

Gene expression in organoids: an expanding horizon

Recent development of human three-dimensional organoid cultures has opened new doors and opportunities ranging from modelling human development in vitro to personalised cancer therapies. These new in vitro systems are opening new horizons to the classic understanding of human development and disease. However, the complexity and heterogeneity of these models requires cutting-edge techniques to capture and trace global changes in gene expression to enable identification of key players and uncover the underlying molecular mechanisms. Rapid development of sequencing approaches made possible global transcriptome analyses and epigenetic profiling. Despite challenges in organoid culture and handling, these techniques are now being adapted to embrace organoids derived from a wide range of human tissues. Here, we review current state-of-the-art multi-omics technologies, such as single-cell transcriptomics and chromatin accessibility assays, employed to study organoids as a model for development and a platform for precision medicine.

Serine and one-carbon metabolism sustain non-melanoma skin cancer progression

Non-melanoma skin cancer (NMSC) is a tumor that arises from human keratinocytes, showing abnormal control of cell proliferation and aberrant stratification. Cutaneous basal cell carcinoma (cBCC) and cutaneous squamous cell carcinoma (cSCC) are the most common sub-types of NMSC. From a molecular point of view, we are still far from fully understanding the molecular mechanisms behind the onset and progression of NMSC and to unravel targetable vulnerabilities to leverage for their treatment, which is still essentially based on surgery. Under this assumption, it is still not elucidated how the central cellular metabolism, a potential therapeutical target, is involved in NMSC progression. Therefore, our work is based on the characterization of the serine anabolism/catabolism and/or one-carbon metabolism (OCM) role in NMSC pathogenesis. Expression and protein analysis of normal skin and NMSC samples show the alteration of the expression of two enzymes involved in the serine metabolism and OCM, the Serine Hydroxy-Methyl Transferase 2 (SHMT2) and Methylen-ThetraHydroFolate dehydrogenase/cyclohydrolase 2 (MTHFD2). Tissues analysis shows that these two enzymes are mainly expressed in the proliferative areas of cBCC and in the poorly differentiated areas of cSCC, suggesting their role in tumor proliferation maintenance. Moreover, in vitro silencing of SHMT2 and MTHFD2 impairs the proliferation of epidermoid cancer cell line. Taken together these data allow us to link the central cellular metabolism (serine and/or OCM) and NMSC proliferation and progression, offering the opportunity to modulate pharmacologically the involved enzymes activity against this type of human cancer.

A necroptosis related prognostic model of pancreatic cancer based on single cell sequencing analysis and transcriptome analysis

Background

As a tumor type with high mortality and poor therapeutic effect, the pathogenesis of pancreatic cancer is still unclear. It is necessary to explore the significance of necroptosis in pancreatic cancer.

Methods

Pancreatic cancer transcriptome data were obtained from the TCGA database, ICGC database, and GSE85916 in the GEO database. The TCGA cohort was set as a training cohort, while the ICGC and GSE85916 cohort were set as the validation cohorts. Single-cell sequencing data of pancreatic cancer were obtained from GSE154778 in the GEO database. The genes most associated with necroptosis were identified by weighted co-expression network analysis and single-cell sequencing analysis. COX regression and Lasso regression were performed for these genes, and the prognostic model was established. By calculating risk scores, pancreatic cancer patients could be divided into NCPTS_high and NCPTS_low groups, and survival analysis, immune infiltration analysis, and mutation analysis between groups were performed. Cell experiments including gene knockdown, CCK-8 assay, clone formation assay, transwell assay and wound healing assay were conducted to explore the role of the key gene EPS8 in pancreatic cancer. PCR assays on clinical samples were further used to verify EPS8 expression.

Results

We constructed the necroptosis-related signature in pancreatic cancer using single-cell sequencing analysis and transcriptome analysis. The calculation formula of risk score was as follows: NCPTS = POLR3GL * (-0.404) + COL17A1 * (0.092) + DDIT4 * (0.007) + PDE4C * (0.057) + CLDN1 * 0.075 + HMGA2 * 0.056 + CENPF * 0.198 +EPS8 * 0.219. Through this signature, pancreatic cancer patients with different cohorts can be divided into NCPTS_high and NCPTS_low group, and the NCPTS_high group has a significantly poorer prognosis. Moreover, there were significant differences in immune infiltration level and mutation level between the two groups. Cell assays showed that in CAPAN-1 and PANC-1 cell lines, EPS8 knockdown significantly reduced the viability, clonogenesis, migration and invasion of pancreatic cancer cells. Clinical PCR assay of EPS8 expression showed that EPS8 expression was significantly up-regulated in pancreatic cancer (*P<0.05).

Conclusion

Our study can provide a reference for the diagnosis, treatment and prognosis assessment of pancreatic cancer.

A Novel-Defined Necroptosis-Related miRNA Signature for Forecasting the Prognosis of Low-Grade Glioma

Background

Increasing evidence has shown that necroptosis has enormous significance in the generation and deterioration of cancer, and miRNA molecular markers involved in necroptosis in low-grade gliomas (LGGs) have not been thoroughly reported.

Methods

Using the miRNA data of 512 samples from The Cancer Genome Atlas (TCGA), 689 miRNAs from LGG samples were split into high immunity score and low immunity score groups for analysis. The differential miRNAs related to necroptosis were analyzed by univariate Cox regression analysis. On the basis of the outcome of univariate Cox regression analysis, miRNAs with significant differences were selected to construct a multivariate Cox regression model and calculate the risk score. Then, we evaluated whether the risk score could be used as an unaided prognostic factor.

Results

Overall, six differential miRNAs were identified (hsa-miR-148a-3p, hsa-miR-141-3p, hsa-miR-223-3p, hsa-miR-7-5p, hsa-miR-500a-3p, and hsa-miR-200a-5p). Univariate and multivariate Cox regression analyses were performed, and the c index was 0.71. Then, by mixing the risk score with clinicopathological factors, univariate Cox regression (HR: 2.7146, 95% CI: 1.8402−4.0044, P < 0.0001) and multivariate Cox regression analyses (HR: 2.3280, 95% CI: 1.5692−3.4536, P < 0.001) were performed. The data suggested that the risk score is an unaided prognostic indicator, which is markedly related with the overall survival time of LGG sufferers. Thus, a lower risk score is correlated with better prediction of LGG.

Conclusion

In order to achieve the ultimate goal of improving the living conditions of patients, we established prognostic risk model using 6 miRNAs related to necroptosis, which has the ability to predict the prognosis of LGG. It is possible to further enrich the therapeutic targets for LGG and provide clinical guidance for the treatment of LGG in the future.

Establishment of a Novel Prognostic Prediction Model for Gastric Cancer Based on Necroptosis-Related Genes

Background: Necroptosis plays a crucial role in the progression of multiple types of cancer. However, the role of necroptosis in gastric cancer (GC) remains unclear. The aim of this study is to establish a necroptosis-related prediction model, which could provide information for treatment monitoring.

Methods: The TCGA-STAD cohort was employed to establish a prognostic prediction signature and the GEO dataset was employed for external validation. The correlation between the risk score and the immune landscape, tumor mutational burden (TMB), microsatellite instability (MSI), as well as therapeutic responses of different therapies were analyzed.

Results: We constructed a prognostic model based on necroptosis-associated genes (NAGs), and its favorable predictive ability was confirmed in an external cohort. The risk score was confirmed as an independent determinant, and a nomogram was further established for prognosis. A high score implies higher tumor immune microenvironment (TIME) scores and more significant TIME cell infiltration. High-risk patients presented with lower TMB, and low-TMB patients had worse overall survival (OS). Meanwhile, Low-risk scores are characterized by MSI-high (MSI-H), lower Tumor Immune Dysfunction and Exclusion (TIDE) score, and higher immunogenicity in immunophenoscore (IPS) analysis.

Conclusion: The developed NAG score provides a novel and effective method for predicting the outcome of GC as well as potential targets for further research.

Deconvolution of tumor composition using partially available DNA methylation data

Background

Deciphering proportions of constitutional cell types in tumor tissues is a crucial step for the analysis of tumor heterogeneity and the prediction of response to immunotherapy. In the process of measuring cell population proportions, traditional experimental methods have been greatly hampered by the cost and extensive dropout events. At present, the public availability of large amounts of DNA methylation data makes it possible to use computational methods to predict proportions.

Results

In this paper, we proposed PRMeth, a method to deconvolve tumor mixtures using partially available DNA methylation data. By adopting an iteratively optimized non-negative matrix factorization framework, PRMeth took DNA methylation profiles of a portion of the cell types in the tissue mixtures (including blood and solid tumors) as input to estimate the proportions of all cell types as well as the methylation profiles of unknown cell types simultaneously. We compared PRMeth with five different methods through three benchmark datasets and the results show that PRMeth could infer the proportions of all cell types and recover the methylation profiles of unknown cell types effectively. Then, applying PRMeth to four types of tumors from The Cancer Genome Atlas (TCGA) database, we found that the immune cell proportions estimated by PRMeth were largely consistent with previous studies and met biological significance.

Conclusions

Our method can circumvent the difficulty of obtaining complete DNA methylation reference data and obtain satisfactory deconvolution accuracy, which will be conducive to exploring the new directions of cancer immunotherapy. PRMeth is implemented in R and is freely available from GitHub (https://github.com/hedingqin/PRMeth).

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04893-7.

A panel of necroptosis-related genes predicts the prognosis of pancreatic adenocarcinoma

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The 5-NRGs signature can predict the prognosis of pancreatic adenocarcinoma.

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The 5-NRGs signature reflects the immune landscape of pancreatic adenocarcinoma.

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The 5-NRGs can be detected in exosomes of serum using RT-qPCR method.

Pancreatic adenocarcinoma (PAAD) has become one of the deadliest malignancies in the world. Since necroptosis plays a crucial role in regulating the immune system, it is necessary to develop novel prognostic biomarkers associated with necroptosis and explore its potential role in PAAD. The transcriptome RNA-seq data of PAAD were downloaded from the TCGA and GTEx databases. A prognostic signature was constructed by the least absolute shrinkage and selection operator (LASSO) Cox regression, and its prognostic value was evaluated by nomogram and validated in an independent GEO cohort. We identified a total of 24 differentially expressed NRGs in PAAD, and constructed a prognostic signature with 5 NRGs, which showed good performance in predicting the prognosis of PAAD patients. The ROC curves for 1-, 3-, and 5-year survival rate were 0.652, 0.778, and 0.817, respectively. This prognostic signature showed consistent prognosis prediction in an independent patient cohort. Furthermore, the correlations between 5-NRGs signature and TMB, MSI, histopathological classification, immune infiltration, immune types, and immunomodulators were all significant. Notably, the expression profiles of the five NRGs in exosomes of serum were consistent with their expression in tumor tissues. These data suggested that the 5-NRGs signature is a promising biomarker for predicting the prognosis of PAAD.