Construction of a prognostic model with exosome biogenesis- and release-related genes and identification of RAB27B in immune infiltration of pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive and fatal disease. Exosomes are extracellular vesicles that plays a vital rule in the progression and metastasis of PDAC. However, the specific mechanism of exosome biogenesis and release in the tumorigenesis and development of pancreatic cancer remains elusive. The aim of this study is to develop novel biomarkers and construct a reliable prognostic signature to accurately stratify patients and optimize clinical decision-making. Gene expression and clinical data were acquired from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Univariate Cox regression analysis, random forest analysis, least absolute shrinkage and selection operator (LASSO) regression analysis, and multivariate Cox regression analysis were used to construct the risk signature. The effectiveness of the model was validated by survival point plot, Kaplan-Meier survival analysis, and receiver operating characteristic (ROC) curve in training, testing and entire cohorts. Meanwhile, single sample gene set enrichment analysis (ssGSEA), ESTIMATE and CIBERSORT algorithm were utilized to assess the association of the risk signature with the immune status in the PDAC tumor microenvironment. We also performed functional enrichment, tumor mutation analysis, and DNA methylation analyses based on the risk signature. The function of the core gene was further verified by polymerase chain reaction (PCR), western blot, bicinchoninic acid (BCA), immunohistochemistry (IHC) and in vitro experiments including cell proliferation, migration, and apoptosis experiments. We constructed an exosome biogenesis- and release-related risk model which could serve as an effective and independent prognosis predictor for PDAC patients. The immune infiltration analysis revealed that our signature was related to the PDAC immune microenvironment, mainly associated with a lower proportion of natural killer (NK) cells and CD8+ T cells. Tissue microarray IHC confirmed the association of RAB27B with poor prognosis in PDAC. Knockdown of RAB27B expression promoted PDAC cells' apoptosis, while decreased cellular proliferation and migration. Also, knockdown of RAB27B expression led to reduced exosome secretion, while RAB27B overexpression promoted exosome secretion. The predictive signature can predict overall survival, help elucidate the mechanism of exosome biogenesis and release, and provide immunotherapy guidance for PDAC patients.

Li TY ，Qin C ，Zhao BB ，Li ZR ，Wang YY ，Zhao YT ，Wang WB ... -  《-》

A novel immunogenomic signature to predict prognosis and reveal immune infiltration characteristics in pancreatic ductal adenocarcinoma.

The immune response in the tumor microenvironment (TME) plays a crucial role in cancer progression and recurrence. We aimed to develop an immune-related gene (IRG) signature to improve prognostic predictive power and reveal the immune infiltration characteristics of pancreatic ductal adenocarcinoma (PDAC). The Cancer Genome Atlas (TCGA) PDAC was used to construct a prognostic model as a training cohort. The International Cancer Genome Consortium (ICGC) and the Gene Expression Omnibus (GEO) databases were set as validation datasets. Prognostic genes were screened by using univariate Cox regression. Then, a novel optimal prognostic model was developed by using least absolute shrinkage and selection operator (LASSO) Cox regression. Cell type identification by estimating the relative subsets of RNA transcripts (CIBERSORT) and estimation of stromal and immune cells in malignant tumors using expression data (ESTIMATE) algorithms were used to characterize tumor immune infiltrating patterns. The tumor immune dysfunction and exclusion (TIDE) algorithm was used to predict immunotherapy responsiveness. A prognostic signature based on five IRGs (MET, ERAP2, IL20RB, EREG, and SHC2) was constructed in TCGA-PDAC and comprehensively validated in ICGC and GEO cohorts. Multivariate Cox regression analysis demonstrated that this signature had an independent prognostic value. The area under the curve (AUC) values of the receiver operating characteristic (ROC) curve at 1, 3, and 5 years of survival were 0.724, 0.702, and 0.776, respectively. We further demonstrated that our signature has better prognostic performance than recently published ones and is superior to traditional clinical factors such as grade and tumor node metastasis classification (TNM) stage in predicting survival. Moreover, we found higher abundance of CD8+ T cells and lower M2-like macrophages in the low-risk group of TCGA-PDAC, and predicted a higher proportion of immunotherapeutic responders in the low-risk group. We constructed an optimal prognostic model which had independent prognostic value and was comprehensively validated in external PDAC databases. Additionally, this five-genes signature could predict immune infiltration characteristics. Moreover, the signature helped stratify PDAC patients who might be more responsive to immunotherapy.

Li A ，Ye B ，Lin F ，Wang Y ，Miao X ，Jiang Y ... -  《-》

Construction of a prognostic model with histone modification-related genes and identification of potential drugs in pancreatic cancer.

Chen Y ，Xu R ，Ruze R ，Yang J ，Wang H ，Song J ，You L ，Wang C ，Zhao Y ... -  《Cancer Cell International》

Novel lactylation-related signature to predict prognosis for pancreatic adenocarcinoma.

Lactate, previously considered a metabolic byproduct, is pivotal in cancer progression and maintaining the immunosuppressive tumor microenvironment. Further investigations confirmed that lactate is a primary regulator, introducing recently described post-translational modifications of histone and non-histone proteins, termed lysine lactylation. Pancreatic adenocarcinomas are characterized by increased glycolysis and lactate accumulation. However, our understanding of lactylation-related genes in pancreatic adenocarcinomas remains limited. To construct a novel lactylation-related gene signature to predict the survival of patients with pancreatic cancer. RNA-seq and clinical data of pancreatic adenocarcinoma (PDAC) were obtained from the GTEx (Genotype-Tissue Expression) and TCGA (The Cancer Genome Atlas) databases via Xena Explorer, and GSE62452 datasets from GEO. Data on lactylation-related genes were obtained from publicly available sources. Differential expressed genes (DEGs) were acquired by using R package "DESeq2" in R. Univariate COX regression analysis, LASSO Cox and multivariate Cox regressions were produced to construct the lactylation-related prognostic model. Further analyses, including functional enrichment, ESTIMATE, and CIBERSORT, were performed to analyze immune status and treatment responses in patients with pancreatic cancer. PDAC and normal human cell lines were subjected to western blot analysis under lactic acid intervention; two PDAC cell lines with the most pronounced lactylation were selected. Subsequently, RT-PCR was employed to assess the expression of LRGs genes; SLC16A1, which showed the highest expression, was selected for further investigation. SLC16A1-mediated lactylation was analyzed by immunofluorescence, lactate production analysis, colony formation, transwell, and wound healing assays to investigate its role in promoting the proliferation and migration of PDAC cells. In vivo validation was performed using an established tumor model. In this study, we successfully identified 10 differentially expressed lactylation-related genes (LRGs) with prognostic value. Subsequently, a lactylation-related signature was developed based on five OS-related lactylation-related genes (SLC16A1, HLA-DRB1, KCNN4, KIF23, and HPDL) using Lasso Cox hazard regression analysis. Subsequently, we evaluated the clinical significance of the lactylation-related genes in pancreatic adenocarcinoma. A comprehensive examination of infiltrating immune cells and tumor mutation burden was conducted across different subgroups. Furthermore, we demonstrated that SLC16A1 modulates lactylation in pancreatic cancer cells through lactate transport. Both in vivo and in vitro experiments showed that decreasing SLC16A1 Level and its lactylation significantly inhibited tumor progression, indicating the potential of targeting the SLC16A1/Lactylation-associated signaling pathway as a therapeutic strategy against pancreatic adenocarcinoma. We constructed a novel lactylation-related prognostic signature to predict OS, immune status, and treatment response of patients with pancreatic adenocarcinoma, providing new strategic directions and antitumor immunotherapies.

Peng T ，Sun F ，Yang JC ，Cai MH ，Huai MX ，Pan JX ，Zhang FY ，Xu LM ... -  《-》

Development and Verification of a novel cuproptosis- and immune-associated based prognostic genetic signature for pancreatic ductal adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is a malignancy with a dismal prognosis. Cuproptosis, a novel mechanism mediated by protein lipoylation, results in acute proteotoxic stress and ultimately cell death. However, the clinical impacts of cuproptosis-associated genes and their relationship with immune status in PDAC have not been documented. In this study, we aimed at constructing a cuproptosis- and immune-associated prognostic signature to stratify and predict the prognosis for PDAC patients. The gene expression profiles of 176 PDAC patients from The Cancer Genome Atlas and 167 normal pancreas tissues from the Genotype-Tissue Expression Project were analyzed for differentially expressed genes (DEGs) between PDAC and normal tissues. Pearson correlation analyses were performed to screen out cuproptosis- and immune-associated DEGs. The risk signature of DEGs was constructed using the least absolute shrinkage and selection operator (LASSO) Cox regression analysis, which was validated in the Gene Expression Omnibus (GEO) cohort (n = 114). The immune characteristics in the two risk groups were evaluated using single-sample gene set enrichment analysis and ESTIMATE algorithms. A total of 91 cuproptosis- and immune-associated DEGs were screened out, and eight prognostic-related genes were identified using LASSO Cox regression. The prognostic-related genes were then used to construct a risk scoring model, which stratified patients into low- and high-risk groups and were further verified in the external GEO database. The patients in the high-risk group had significantly shorter overall survival than those in the low-risk group. A nomogram based on the risk signature was then constructed. Immune infiltration evaluation suggested that immune status was more activated in the low-risk group. The mutation spectrum also differed between high- and low-risk groups. Our cuproptosis- and immune-associated genetic risk signature could be a prognostic biomarker for PDAC. Cuproptosis might be a promising therapeutic target for PDAC.

Xu X ，Liang JH ，Xu QC ，Yin XY ... -  《-》