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 ... -  《-》

Positive feedback regulation between glycolysis and histone lactylation drives oncogenesis in pancreatic ductal adenocarcinoma.

Metabolic reprogramming and epigenetic alterations contribute to the aggressiveness of pancreatic ductal adenocarcinoma (PDAC). Lactate-dependent histone modification is a new type of histone mark, which links glycolysis metabolite to the epigenetic process of lactylation. However, the role of histone lactylation in PDAC remains unclear. The level of histone lactylation in PDAC was identified by western blot and immunohistochemistry, and its relationship with the overall survival was evaluated using a Kaplan-Meier survival plot. The participation of histone lactylation in the growth and progression of PDAC was confirmed through inhibition of histone lactylation by glycolysis inhibitors or lactate dehydrogenase A (LDHA) knockdown both in vitro and in vivo. The potential writers and erasers of histone lactylation in PDAC were identified by western blot and functional experiments. The potential target genes of H3K18 lactylation (H3K18la) were screened by CUT&Tag and RNA-seq analyses. The candidate target genes TTK protein kinase (TTK) and BUB1 mitotic checkpoint serine/threonine kinase B (BUB1B) were validated through ChIP-qPCR, RT-qPCR and western blot analyses. Next, the effects of these two genes in PDAC were confirmed by knockdown or overexpression. The interaction between TTK and LDHA was identified by Co-IP assay. Histone lactylation, especially H3K18la level was elevated in PDAC, and the high level of H3K18la was associated with poor prognosis. The suppression of glycolytic activity by different kinds of inhibitors or LDHA knockdown contributed to the anti-tumor effects of PDAC in vitro and in vivo. E1A binding protein p300 (P300) and histone deacetylase 2 were the potential writer and eraser of histone lactylation in PDAC cells, respectively. H3K18la was enriched at the promoters and activated the transcription of mitotic checkpoint regulators TTK and BUB1B. Interestingly, TTK and BUB1B could elevate the expression of P300 which in turn increased glycolysis. Moreover, TTK phosphorylated LDHA at tyrosine 239 (Y239) and activated LDHA, and subsequently upregulated lactate and H3K18la levels. The glycolysis-H3K18la-TTK/BUB1B positive feedback loop exacerbates dysfunction in PDAC. These findings delivered a new exploration and significant inter-relationship between lactate metabolic reprogramming and epigenetic regulation, which might pave the way toward novel lactylation treatment strategies in PDAC therapy.

Li F ，Si W ，Xia L ，Yin D ，Wei T ，Tao M ，Cui X ，Yang J ，Hong T ，Wei R ... -  《Molecular Cancer》

Development and Verification of the Hypoxia- and Immune-Associated Prognostic Signature for Pancreatic Ductal Adenocarcinoma.

We aim to construct a hypoxia- and immune-associated risk score model to predict the prognosis of patients with pancreatic ductal adenocarcinoma (PDAC). By unsupervised consensus clustering algorithms, we generate two different hypoxia clusters. Then, we screened out 682 hypoxia-associated and 528 immune-associated PDAC differentially expressed genes (DEGs) of PDAC using Pearson correlation analysis based on the Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression project (GTEx) dataset. Seven hypoxia and immune-associated signature genes (S100A16, PPP3CA, SEMA3C, PLAU, IL18, GDF11, and NR0B1) were identified to construct a risk score model using the Univariate Cox regression and the Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression, which stratified patients into high- and low-risk groups and were further validated in the GEO and ICGC cohort. Patients in the low-risk group showed superior overall survival (OS) to their high-risk counterparts (p < 0.05). Moreover, it was suggested by multivariate Cox regression that our constructed hypoxia-associated and immune-associated prognosis signature might be used as the independent factor for prognosis prediction (p < 0.001). By CIBERSORT and ESTIMATE algorithms, we discovered that patients in high-risk groups had lower immune score, stromal score, and immune checkpoint expression such as PD-L1, and different immunocyte infiltration states compared with those low-risk patients. The mutation spectrum also differs between high- and low-risk groups. To sum up, our hypoxia- and immune-associated prognostic signature can be used as an approach to stratify the risk of PDAC.

Chen D ，Huang H ，Zang L ，Gao W ，Zhu H ，Yu X ... -  《Frontiers in Immunology》

Unraveling pancreatic ductal adenocarcinoma immune prognostic signature through a naive B cell gene set.

Pancreatic ductal adenocarcinoma (PDAC), a leading cause of cancer mortality, has a complex pathogenesis involving various immune cells, including B cells and their subpopulations. Despite emerging research on the role of these cells within the tumor microenvironment (TME), the detailed molecular interactions with tumor-infiltrating immune cells (TIICs) are not fully understood. We applied CIBERSORT to quantify TIICs and naive B cells, which are prognostic for PDAC. Marker genes from scRNA-seq and modular genes from weighted gene co-expression network analysis (WGCNA) were integrated to identify naive B cell-related genes. A prognostic signature was constructed utilizing ten machine-learning algorithms, with validation in external cohorts. We further assessed the immune cell diversity, ESTIMATE scores, and immune checkpoint genes (ICGs) between patient groups stratified by risk to clarify the immune landscape in PDAC. Our analysis identified 994 naive B cell-related genes across single-cell and bulk transcriptomes, with 247 linked to overall survival. We developed a 12-gene prognostic signature using Lasso and plsRcox algorithms, which was confirmed by 10-fold cross-validation and showed robust predictive power in training and real-world cohorts. Notably, we observed substantial differences in immune infiltration between patients with high and low risk. Our study presents a robust prognostic signature that effectively maps the complex immune interactions in PDAC, emphasizing the critical function of naive B cells and suggesting new avenues for immunotherapeutic interventions. This signature has potential clinical applications in personalizing PDAC treatment, enhancing the understanding of immune dynamics, and guiding immunotherapy strategies.

Zhang S ，Ta N ，Zhang S ，Li S ，Zhu X ，Kong L ，Gong X ，Guo M ，Liu Y ... -  《-》

Prognostic Stratification Based on HIF-1 Signaling for Evaluating Hypoxic Status and Immune Infiltration in Pancreatic Ductal Adenocarcinomas.

Pancreatic ductal adenocarcinoma (PDAC) has a hypoxic and desmoplastic tumor microenvironment (TME), leading to treatment failure. We aimed to develop a prognostic classifier to evaluate hypoxia status and hypoxia-related molecular characteristics of PDAC. In this study, we classified PDAC into three clusters based on 16 known hypoxia-inducible factor 1 (HIF-1)-related genes. Nine differentially expressed genes were identified to construct an HIF-1 score system, whose predictive efficacy was evaluated. Furthermore, we investigated oncogenic pathways and immune-cell infiltration status of PDAC with different scores. The C-index of the HIF-1score system for OS prediction in the meta-PDAC cohort and the other two validation cohorts were 0.67, 0.63, and 0.65, respectively, indicating that it had a good predictive value for patient survival. Furthermore, the area under the curve (AUC) of the receiver operating characteristic (ROC) curve of the HIF-1α score system for predicting 1-, 3-, and 4-year OS indicated the HIF-1α score system had an optimal discrimination of prognostic prediction for PDAC. Importantly, our model showed superior predictive ability compared to previous hypoxia signatures. We also classified PDAC into HIF-1 scores of low, medium, and high groups. Then, we found high enrichment of glycolysis, mTORC1 signaling, and MYC signaling in the HIF-1 score high group, whereas the cGMP metabolic process was activated in the low score group. Of note, analysis of public datasets and our own dataset showed a high HIF-1 score was associated with high immunosuppressive TME, evidenced by fewer infiltrated CD8+ T cells, B cells, and type 1 T-helper cells and reduced cytolytic activity of CD8+ T cells. In summary, we established a specific HIF-1 score system to discriminate PDAC with various hypoxia statuses and immune microenvironments. For highly hypoxic and immunosuppressive tumors, a combination treatment strategy should be considered in the future.

Zhuang H ，Wang S ，Chen B ，Zhang Z ，Ma Z ，Li Z ，Liu C ，Zhou Z ，Gong Y ，Huang S ，Hou B ，Chen Y ，Zhang C ... -  《Frontiers in Immunology》