Search Results (20)

The immune landscape of tumor-draining lymph nodes (TDLNs) plays a critical role in shaping antitumor responses and influencing prognosis in oral squamous cell carcinoma (OSCC). Among patients with lymph node (LN) metastasis, clinical outcomes vary widely, yet reliable biomarkers for prognostic stratification remain limited. This study aimed to identify immune features in tumors and LNs that differentiate between favorable and poor outcomes in OSCC patients with nodal metastasis. We analyzed T cell receptor (TCR) CDR3 repertoires and the expression of immune-related genes in primary tumors and paired sentinel LNs from OSCC patients who underwent tumor resection and lymphadenectomy. Patients were divided into three groups: Group A (no nodal metastasis), Group B1 (metastasis without recurrence), and Group B2 (metastasis with recurrence). TCR diversity was assessed using the Shannon index. The expression of immune-related genes (e.g., CD3E, CD4, CD8B, FOXP3, CTLA4, IL2, IL4) was measured by quantitative PCR and normalized to GAPDH. TCR diversity was lower in tumors than in non-metastatic LNs, reflecting clonal expansion. Metastatic LNs exhibited tumor-like diversity, suggesting infiltration by tumor-reactive clones. Tumor gene expression did not differ across groups, but LNs from metastatic cases showed the reduced expression of several immune genes. Notably, CD3E, CD8B, CTLA4, IL2, and IL4 distinguished B1 from B2. The immune profiling of LNs offers superior prognostic value over tumor analysis in OSCC patients with LN metastasis. LN-based evaluation may aid in postoperative risk stratification and personalized postoperative management and could inform decisions regarding adjuvant therapy and follow-up strategies. Full article

Background: Breast cancer is a complex and heterogeneous disease characterized by distinct molecular subtypes with varying prognoses and treatment responses. Multiple factors influence breast cancer outcomes including tumor biology, patient characteristics, and treatment modalities. Demographic factors such as age, race/ethnicity, menopausal status, and body mass index have been correlated with variations in incidence, mortality, and survival rates. Over the past decade, comprehensive genomic profiling has been widely used to identify molecular biomarkers and signatures to develop novel therapeutic strategies for patients. For instance, the FLEX registry (NCT03053193) enrolled stage I–III breast cancer patients across 90 institutions in the United States and stratified risk groups based on a 70-gene signature (MammaPrint^®-MP) and molecular subtype based on an 80-gene signature (BluePrint^®-BP). This study aimed to identify the gene expression patterns and biomarkers associated with breast cancer risk and progression by integrating transcriptomic and clinical data. Methods: Targeted 111 unique gene expression and clinical data points from 978 breast cancer samples, representing each BP subtype (26% Luminal A, 26% Luminal B, 25% Basal, 23% HER2), obtained from Agendia Inc. These genes were selected based on their involvement in the mercapturic acid pathway, white and brown adipose tissue markers, inflammation markers, tumor-associated genes, apoptosis, autophagy, and ER stress markers. All statistical analyses, including principal component analysis (PCA), were performed using R version [4.4.0]. Prognostic values and genetic alterations were investigated using various web-based programs as described in the Methods section. Results: PCA of gene expression data revealed distinct clustering patterns associated with risk categories and molecular subtypes, particularly with principal component 4 (PC4). Genes related to oxidative stress, autophagy, apoptosis, and histone modification showed altered expression across risk categories and molecular subtypes. Key differentially expressed genes included SOD2, KLK5, KLK7, IL8, GSTM1/2, GLI1, CBS, and IGF1. Pathway analysis highlighted the enrichment of processes related to autophagy, cellular stress response, apoptosis, glutathione metabolism, deacetylation, and oxidative stress in high-risk and basal-like tumors compared with Ultralow and Luminal A tumors, respectively. Conclusions: This study identified gene expression signatures associated with breast cancer risk and molecular subtypes. These findings provide insights into the biological processes that may drive breast cancer progression and could inform the development of prognostic biomarkers and personalized therapeutic strategies. Full article

Background/Objectives: Sinonasal mucosal melanoma (SNMM) is a rare and aggressive melanoma subtype with a notably poor prognosis compared to cutaneous melanoma (CM). Despite advances in molecular characterization, SNMM remains underexplored, posing a clinical challenge and highlighting the need for detailed molecular profiling. This study aimed to identify the molecular features of SNMM, elucidate its clinical behavior and prognostic implications, and provide insights for improved therapeutic strategies. Methods: This retrospective study analyzed 37 primary melanoma tumors diagnosed at the Hospital Clinic of Barcelona. Gene expression was examined using 1402 immuno-oncology-related probes through next-generation sequencing. Hierarchical clustering analysis (HCA), differentially expressed genes (DEGs), gene set enrichment analysis (GSEA), and the xCell algorithm were performed. The statistical methods comprised descriptive statistics, clinical variable associations, and survival analyses. Results: HCA revealed two primary clusters. Cluster A exclusively contained CM tumors (20/24), while cluster B included all SNMMs (13/13) and some CMs (4/24). Cluster B showed a higher average age at diagnosis (p = 0.018), higher mitotic index (p = 0.0478), fewer BRAF mutations (p = 0.0017), and poorer melanoma-specific survival (p = 0.0029). Cluster B showed 602 DEGs with cell cycle pathways enriched, immune pathways diminished, lower immune scores (p < 0.0001), and higher stromal scores (p = 0.0074). Conclusions: This study revealed distinct molecular characteristics and an altered tumor microenvironment in SNMMs and certain aggressive CMs. Identifying specific genes and pathways involved in cell cycle progression and immune evasion suggests potential prognostic markers, offering new avenues for enhancing treatment strategies and improving patient survival rates. Full article

Colon adenocarcinoma (COAD) is a frequently occurring and lethal cancer. Cuproptosis is an emerging type of cell death, and the underlying pathways involved in this process in COAD remain poorly understood. Transcriptomic and clinical data for COAD patients were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. We investigated alterations in DNA and chromatin of cuproptosis-related genes (CRGs) in COAD. In order to identify predictive differentially expressed genes (DEGs) and various molecular subtypes, we used consensus cluster analysis. Through univariate, multivariate, and Lasso Cox regression analyses, four CRGs were identified. A risk prognostic model for cuproptosis characteristics was constructed based on four CRGs. This study also examined the association between the risk score and the tumor microenvironment (TME), the immune landscape, and drug sensitivity. We distinguished two unique molecular subtypes using consensus clustering analysis. We discovered that the clinical characteristics, prognosis, and TME cell infiltration characteristics of patients with multilayer CRG subtypes were all connected. The internal and external evaluations of the predicted accuracy of the prognostic model built using data derived from a cuproptosis risk score were completed at the same time. A nomogram and a clinical pathological analysis make it more useful in the field of medicine. A significant rise in immunosuppressive cells was observed in the high cuproptosis risk score group, with a correlation identified between the cuproptosis risk score and immune cell infiltration. Despite generally poor prognoses, the patients with a high cuproptosis risk but low tumor mutation burden (TMB), cancer stem cell (CSC) index, or microsatellite instability (MSI) may still benefit from immunotherapy. Furthermore, the cuproptosis risk score positively correlated with immune checkpoint gene expression. Analyzing the potential sensitivity to medications could aid in the development of clinical chemotherapy regimens and decision-making. CRGs are the subject of our in-depth study, which exposed an array of regulatory mechanisms impacting TME. In addition, we performed additional data mining into clinical features, prognosis effectiveness, and possible treatment medications. COAD’s molecular pathways will be better understood, leading to more precise treatment options. Full article

The role of Chitinase-3-like protein 1 (CHI3L1) in tumor progression has been gradually clarified in different kinds of solid tumors. Hence, we aim to elucidate its prognostic value for glioma. In this study, we analyzed RNA sequencing data combined with corresponding clinical information obtained from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA) databases. Differentially expressed genes (DEGs) were acquired based on CHI3L1 expression profiles and were used for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. Cox regression, least absolute shrinkage and selection operator (LASSO) regression methods, along with a nomogram, were employed to establish a predictive model. Compared with the corresponding non-tumor tissues, CHI3L1 expression was significantly upregulated in various types of solid tumors, correlating with poor clinical outcomes including glioma. GO analysis identified oxidative stress-related genes (ORGs) that were differentially expressed and modulated by CHI3L1, with 11 genes subsequently identified as potential predictors, using Univariate-Cox regression and LASSO regression. In addition, an index of oxidative stress-related genes (ORGI) was established, demonstrating its prognostic value in conjunction with CHI3L1 expression. The aberrant expression of CHI3L1 was proved in glioma patients through immunohistochemistry (IHC). Meanwhile, the knockdown of CHI3L1 inhibited glioma growth in vitro, and real-time Quantitative PCR (qPCR) confirmed decreased ORG expression upon CHI3L1 knockdown, suggesting the potential prognostic value of CHI3L1 as a therapeutic target for glioma. Full article

Pancreatic ductal adenocarcinoma (PDAC) is often diagnosed at late stages, limiting treatment options and survival rates. Pyroptosis-related gene signatures hold promise as PDAC prognostic markers, but limited gene pools and small sample sizes hinder their utility. We aimed to enhance PDAC prognosis with a comprehensive multi-algorithm analysis. Using R, we employed natural language processing and latent Dirichlet allocation on PubMed publications to identify pyroptosis-related genes. We collected PDAC transcriptome data (n = 1273) from various databases, conducted a meta-analysis, and performed differential gene expression analysis on tumour and non-cancerous tissues. Cox and LASSO algorithms were used for survival modelling, resulting in a pyroptosis-related gene expression-based prognostic index. Laboratory and external validations were conducted. Bibliometric analysis revealed that pyroptosis publications focus on signalling pathways, disease correlation, and prognosis. We identified 357 pyroptosis-related genes, validating the significance of BHLHE40, IL18, BIRC3, and APOL1. Elevated expression of these genes strongly correlated with poor PDAC prognosis and guided treatment strategies. Our accessible nomogram model aids in PDAC prognosis and treatment decisions. We established an improved gene signature for pyroptosis-related genes, offering a novel model and nomogram for enhanced PDAC prognosis. Full article

The objective of this study was to identify a kind of prognostic signature based on oxidative stress- and anoikis-related genes (OARGs) for predicting the prognosis and immune landscape of NSCLC. Initially, We identified 47 differentially expressed OARGs that primarily regulate oxidative stress and epithelial cell infiltration through the PI3K-Akt pathway. Subsequently, 10 OARGs related to prognosis determined two potential clusters. A cluster was associated with a shorter survival level, lower immune infiltration, higher stemness index and tumor mutation burden. Next, The best risk score model constructed by prognostic OARGs was the Random Survival Forest model, and it included SLC2A1, LDHA and PLAU. The high-risk group was associated with cluster A and poor prognosis, with a higher tumor mutation burden, stemness index and proportion of M0-type macrophages, and a lower immune checkpoint expression level, immune function score and IPS score. The calibration curve and decision-making curve showed that the risk score combined with clinical pathological characteristics could be used to construct a nomogram for guiding the clinical treatment strategies. Finally, We found that all three hub genes were highly expressed in tumor tissues, and LDHA expression was mainly regulated by has-miR-338-3p, has-miR-330-5p and has-miR-34c-5p. Altogether, We constructed an OARG-related prognostic signature to reveal potential relationships between the signature and clinical characteristics, TME, stemness, tumor mutational burden, drug sensitivity and immune landscape in NSCLC patients. Full article

Amino acid metabolism has been implicated in tumorigenesis and tumor progression. Alterations in intracellular and extracellular metabolites associated with metabolic reprogramming in cancer have profound effects on gene expression, cell differentiation, and tumor immune microenvironment. However, the prognostic significance of amino acid metabolism in head and neck cancer remains to be further investigated. In this study, we identified 98 differentially expressed genes related to amino acid metabolism in head and neck cancer in The Cancer Genome Atlas. Using batch univariate Cox regression and Lasso regression, we extracted nine amino acid metabolism-related genes. Based on that, we developed the amino acid metabolism index. The prognostic value of this index was validated in two Gene Expression Omnibus cohorts. The results show that this model can help predict tumor recurrence and prognosis. The infiltration of immune cells in the tumor microenvironment was analyzed, and it was discovered that the high index is associated with an immunosuppressive microenvironment. In addition, this study demonstrated the impact of the amino acid metabolism index on clinical indicators, survival of patients with head and neck cancer, and the prediction of treatment response to immune checkpoint inhibitors. We conducted several cell experiments and demonstrated that epigenetic drugs could affect the index and enhance tumor immunity. In conclusion, our study demonstrates that the index not only has important prognostic value in head and neck cancer patients but also facilitates patient stratification for immunotherapy. Full article

Lung adenocarcinoma represents a significant global health challenge. Despite advances in diagnosis and treatment, the prognosis remains poor for many patients. In this study, we aimed to identify cuproptosis-related genes and to develop a deep neural network model to predict the prognosis of lung adenocarcinoma. We screened differentially expressed genes from The Cancer Genome Atlas data through differential analysis of cuproptosis-related genes. We then used this information to establish a prognostic model using a deep neural network, which we validated using data from the Gene Expression Omnibus. Our deep neural network model incorporated nine cuproptosis-related genes and achieved an area under the curve of 0.732 in the training set and 0.646 in the validation set. The model effectively distinguished between distinct risk groups, as evidenced by significant differences in survival curves (p < 0.001), and demonstrated significant independence as a standalone prognostic predictor (p < 0.001). Functional analysis revealed differences in cellular pathways, the immune microenvironment, and tumor mutation burden between the risk groups. Furthermore, our model provided personalized survival probability predictions with a concordance index of 0.795 and identified the drug candidate BMS-754807 as a potentially sensitive treatment option for lung adenocarcinoma. In summary, we presented a deep neural network prognostic model for lung adenocarcinoma, based on nine cuproptosis-related genes, which offers independent prognostic capabilities. This model can be used for personalized predictions of patient survival and the identification of potential therapeutic agents for lung adenocarcinoma, which may ultimately improve patient outcomes. Full article

Background: Epigenetics studies heritable or inheritable mechanisms that regulate gene expression rather than altering the DNA sequence. However, no research has investigated the link between TME-related genes (TRGs) and epigenetic-related genes (ERGs) in GC. Methods: A complete review of genomic data was performed to investigate the relationship between the epigenesis tumor microenvironment (TME) and machine learning algorithms in GC. Results: Firstly, TME-related differential expression of genes (DEGs) performed non-negative matrix factorization (NMF) clustering analysis and determined two clusters (C1 and C2). Then, Kaplan–Meier curves for overall survival (OS) and progression-free survival (PFS) rates suggested that cluster C1 predicted a poorer prognosis. The Cox–LASSO regression analysis identified eight hub genes (SRMS, MET, OLFML2B, KIF24, CLDN9, RNF43, NETO2, and PRSS21) to build the TRG prognostic model and nine hub genes (TMPO, SLC25A15, SCRG1, ISL1, SOD3, GAD1, LOXL4, AKR1C2, and MAGEA3) to build the ERG prognostic model. Additionally, the signature’s area under curve (AUC) values, survival rates, C-index scores, and mean squared error (RMS) curves were evaluated against those of previously published signatures, which revealed that the signature identified in this study performed comparably. Meanwhile, based on the IMvigor210 cohort, a statistically significant difference in OS between immunotherapy and risk scores was observed. It was followed by LASSO regression analysis which identified 17 key DEGs and a support vector machine (SVM) model identified 40 significant DEGs, and based on the Venn diagram, eight co-expression genes (ENPP6, VMP1, LY6E, SHISA6, TMEM158, SYT4, IL11, and KLK8) were discovered. Conclusion: The study identified some hub genes that could be useful in predicting prognosis and management in GC. Full article

Breast cancer (BC) is among the most universal malignant tumors in women worldwide. Aging is a complex phenomenon, caused by a variety of factors, that plays a significant role in tumor development. Consequently, it is crucial to screen for prognostic aging-related long non-coding RNAs (lncRNAs) in BC. The BC samples from the breast-invasive carcinoma cohort were downloaded from The Cancer Genome Atlas (TCGA) database. The differential expression of aging-related lncRNAs (DEarlncRNAs) was screened by Pearson correlation analysis. Univariate Cox regression, LASSO–Cox analysis, and multivariate Cox analysis were performed to construct an aging-related lncRNA signature. The signature was validated in the GSE20685 dataset from the Gene Expression Omnibus (GEO) database. Subsequently, a nomogram was constructed to predict survival in BC patients. The accuracy of prediction performance was assessed through the time-dependent receiver operating characteristic (ROC) curves, Kaplan–Meier analysis, principal component analyses, decision curve analysis, calibration curve, and concordance index. Finally, differences in tumor mutational burden, tumor-infiltrating immune cells, and patients’ response to chemotherapy and immunotherapy between the high- and low-risk score groups were explored. Analysis of the TCGA cohort revealed a six aging-related lncRNA signature consisting of MCF2L-AS1, USP30-AS1, OTUD6B-AS1, MAPT-AS1, PRR34-AS1, and DLGAP1-AS1. The time-dependent ROC curve proved the optimal predictability for prognosis in BC patients with areas under curves (AUCs) of 0.753, 0.772, and 0.722 in 1, 3, and 5 years, respectively. Patients in the low-risk group had better overall survival and significantly lower total tumor mutational burden. Meanwhile, the high-risk group had a lower proportion of tumor-killing immune cells. The low-risk group could benefit more from immunotherapy and some chemotherapeutics than the high-risk group. The aging-related lncRNA signature can provide new perspectives and methods for early BC diagnosis and therapeutic targets, especially tumor immunotherapy. Full article

Background: Among cancer-related deaths, hepatocellular carcinoma (HCC) ranks fourth, and traditional Chinese medicine (TCM) treatment is an important complementary alternative therapy for HCC. Curcumin is a natural ingredient extracted from Curcuma longa with anti-HCC activity, while the therapeutic mechanisms of curcumin remain unclear, especially on ferroptosis and cuproptosis. Methods: Differentially expressed genes (DEGs) of curcumin treatment in PLC, KMCH, and Huh7 cells were identified, respectively. The common genes among them were then obtained to perform functional enrichment analysis and prognostic analysis. Moreover, weighted gene co-expression network analysis (WGCNA) was carried out for the construction of the co-expression network. The ferroptosis potential index (FPI) and the cuproptosis potential index (CPI) were subsequently used to quantitatively analyze the levels of ferroptosis and cuproptosis. Finally, single-cell transcriptome analysis of liver cancer was conducted. Results: We first identified 702, 515, and 721 DEGs from curcumin-treated PLC, KMCH, and Huh7 cells, respectively. Among them, HMOX1, CYP1A1, HMGCS2, LCN2, and MTTP may play an essential role in metal ion homeostasis. By WGCNA, grey60 co-expression module was associated with curcumin treatment and involved in the regulation of ion homeostasis. Furthermore, FPI and CPI assessment showed that curcumin had cell-specific effects on ferroptosis and cuproptosis in different HCC cells. In addition, there are also significant differences in ferroptosis and cuproptosis levels among 16 HCC cell subtypes according to single-cell transcriptome data analysis. Conclusions: We developed CPI and combined it with FPI to quantitatively analyze curcumin-treated HCC cells. It was found that ferroptosis and cuproptosis, two known metal ion-mediated forms of programmed cell death, may have a vital effect in treating HCC with curcumin, and there are significant differences in various liver cancer cell types and curcumin treatment which should be considered in the clinical application of curcumin. Full article

Objective: Glioma is the most common primary malignancy of the adult central nervous system (CNS), with a poor prognosis and no effective prognostic signature. Since late 2019, the world has been affected by the rapid spread of SARS-CoV-2 infection. Research on SARS-CoV-2 is flourishing; however, its potential mechanistic association with glioma has rarely been reported. The aim of this study was to investigate the potential correlation of SARS-CoV-2-related genes with the occurrence, progression, prognosis, and immunotherapy of gliomas. Methods: SARS-CoV-2-related genes were obtained from the human protein atlas (HPA), while transcriptional data and clinicopathological data were obtained from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) databases. Glioma samples were collected from surgeries with the knowledge of patients. Differentially expressed genes were then identified and screened, and seven SARS-CoV-2 related genes were generated by LASSO regression analysis and uni/multi-variate COX analysis. A prognostic SARS-CoV-2-related gene signature (SCRGS) was then constructed based on these seven genes and validated in the TCGA validation cohort and CGGA cohort. Next, a nomogram was established by combining critical clinicopathological data. The correlation between SCRGS and glioma related biological processes was clarified by Gene set enrichment analysis (GSEA). In addition, immune infiltration and immune score, as well as immune checkpoint expression and immune escape, were further analyzed to assess the role of SCRGS in glioma-associated immune landscape and the responsiveness of immunotherapy. Finally, the reliability of SCRGS was verified by quantitative real-time polymerase chain reaction (qRT-PCR) on glioma samples. Results: The prognostic SCRGS contained seven genes, REEP6, CEP112, LARP4B, CWC27, GOLGA2, ATP6AP1, and ERO1B. Patients were divided into high- and low-risk groups according to the median SARS-CoV-2 Index. Overall survival was significantly worse in the high-risk group than in the low-risk group. COX analysis and receiver operating characteristic (ROC) curves demonstrated excellent predictive power for SCRGS for glioma prognosis. In addition, GSEA, immune infiltration, and immune scores indicated that SCRGS could potentially predict the tumor microenvironment, immune infiltration, and immune response in glioma patients. Conclusions: The SCRGS established here can effectively predict the prognosis of glioma patients and provide a potential direction for immunotherapy. Full article

Necroptosis is a newly developed cell death pathway that differs from necrosis and apoptosis; however, the potential mechanism of necroptosis-related genes in EAC and whether they are associated with the prognosis of EAC patients remain unclear. We obtained 159 NRGs from the Kyoto Encyclopedia of Genes and Genomes (KEGG) and performed differential expression analysis of the NRGs in 9 normal samples and 78 EAC tumor samples derived from The Cancer Genome Atlas (TCGA). Finally, we screened 38 differentially expressed NRGs (DE-NRGs). The results of the GO and KEGG analyses indicated that the DE-NRGs were mainly enriched in the functions and pathways associated with necroptosis. Protein interaction network (PPI) analysis revealed that TNF, CASP1, and IL-1B were the core genes of the network. A risk score model based on four DE-NRGs was constructed by Least Absolute Shrinkage and Selection Operator (LASSO) regression, and the results showed that the higher the risk score, the worse the survival. The model achieved more efficient diagnosis compared with the clinicopathological variables, with an area under the receiver operating characteristic (ROC) curve of 0.885. The prognostic value of this model was further validated using Gene Expression Omnibus (GEO) datasets. Gene set enrichment analyses (GSEA) demonstrated that several metabolism-related pathways were activated in the high-risk population. Single-sample GSEA (ssGSEA) provided further confirmation that this prognostic model was remarkably associated with the immune status of EAC patients. Finally, the nomogram map exhibited a certain prognostic prediction efficiency, with a C-index of 0.792 and good consistency. Thus, the prognostic model based on four NRGs could better predict the prognosis of EAC and help to elucidate the mechanism of necroptosis-related genes in EAC, which can provide guidance for the target prediction and clinical treatment of EAC patients. Full article

Purpose: Pancreatic adenocarcinoma (PAAD) is one of the most lethal malignancies, with less than 10% of patients surviving more than 5 years. Existing biomarkers for reliable survival rate prediction need to be enhanced. As a result, the objective of this study was to create a novel immune-related gene prognostic index (IRGPI) for estimating overall survival (OS) and to analyze the molecular subtypes based on this index. Materials and procedures: RNA sequencing and clinical data were retrieved from publicly available sources and analyzed using several R software packages. A unique IRGPI and optimum risk model were developed using a machine learning algorithm. The prediction capability of our model was then compared to that of previously proposed models. A correlation study was also conducted between the immunological tumor microenvironment, risk groups, and IRGPI genes. Furthermore, we classified PAAD into different molecular subtypes based on the expression of IRGPI genes and investigated their features in tumor immunology using the K-means clustering technique. Results: A 12-gene IRGPI (FYN, MET, LRSAM1, PSPN, ERAP2, S100A1, IL20RB, MAP3K14, SEMA6C, PRKCG, CXCL11, and GH1) was established, and verified along with a risk model. OS prediction by our model outperformed previous gene signatures. According to the findings of our correlation studies, different risk groups and IRGPI genes were found to be tightly related to tumor microenvironments, and PAAD could be further subdivided into immunologically distinct molecular subtypes based on the expression of IRGPI genes. Conclusion: The current study constructed and verified a unique IRGPI. Furthermore, our findings revealed a connection between the IRGPI and the immunological microenvironment of tumors. PAAD was differentiated into several molecular subtypes that might react differently to immunotherapy. These findings could provide new insights for precision and translational medicine for more innovative immunotherapy strategies. Full article