TGF-β signaling promotes tube-structure-forming growth in pancreatic duct adenocarcinoma

In-silico analysis of TMEM2 as a pancreatic adenocarcinoma and cancer-associated fibroblast biomarker, and functional characterization of NSC777201, for targeted drug development

Pancreatic adenocarcinoma (PAAD), known as one of the deadliest cancers, is characterized by a complex tumor microenvironment, primarily comprised of cancer-associated fibroblasts (CAFs) in the extracellular matrix. These CAFs significantly alter the matrix by interacting with hyaluronic acid (HA) and the enzyme hyaluronidase, which degrades HA - an essential process for cancer progression and spread. Despite the critical role of this interaction, the specific functions of CAFs and hyaluronidase in PAAD development are not fully understood. Our study investigates this interaction and assesses NSC777201, a new anti-cancer compound targeting hyaluronidase. This research utilized computational methods to analyze gene expression data from the Gene Expression Omnibus (GEO) database, specifically GSE172096, comparing gene expression profiles of cancer-associated and normal fibroblasts. We conducted in-house sequencing of pancreatic cancer cells treated with NSC777201 to identify differentially expressed genes (DEGs) and performed functional enrichment and pathway analysis. The identified DEGs were further validated using the TCGA-PAAD and Human Protein Atlas (HPA) databases for their diagnostic, prognostic, and survival implications, accompanied by Ingenuity Pathway Analysis (IPA) and molecular docking of NSC777201, in-vitro, and preclinical in-vivo validations. The result revealed 416 DEGs associated with CAFs and 570 DEGs related to NSC777201 treatment, with nine overlapping DEGs. A key finding was the transmembrane protein TMEM2, which strongly correlated with FAP, a CAF marker, and was associated with higher-risk groups in PAAD. NSC777201 treatment showed inhibition of TMEM2, validated by rescue assay, indicating the importance of targeting TMEM2. Further analyses, including IPA, demonstrated that NSC777201 regulates CAF cell senescence, enhancing its therapeutic potential. Both in-vitro and in-vivo studies confirmed the inhibitory effect of NSC777201 on TMEM2 expression, reinforcing its role in targeting PAAD. Therefore, TMEM2 has been identified as a theragnostic biomarker in PAAD, influenced by CAF activity and HA accumulation. NSC777201 exhibits significant potential in targeting and potentially reversing critical processes in PAAD progression, demonstrating its efficacy as a promising therapeutic agent.

5-FU-miR-15a Inhibits Activation of Pancreatic Stellate Cells by Reducing YAP1 and BCL-2 Levels In Vitro

Chronic pancreatitis is characterized by chronic inflammation and fibrosis, processes heightened by activated pancreatic stellate cells (PSCs). Recent publications have demonstrated that miR-15a, which targets YAP1 and BCL-2, is significantly downregulated in patients with chronic pancreatitis compared to healthy controls. We have utilized a miRNA modification strategy to enhance the therapeutic efficacy of miR-15a by replacing uracil with 5-fluorouracil (5-FU). We demonstrated increased levels of YAP1 and BCL-2 (both targets of miR-15a) in pancreatic tissues obtained from Ptf1aCreERTM and Ptf1aCreERTM;LSL-KrasG12D mice after chronic pancreatitis induction as compared to controls. In vitro studies showed that delivery of 5-FU-miR-15a significantly decreased viability, proliferation, and migration of PSCs over six days compared to 5-FU, TGFβ1, control miR, and miR-15a. In addition, treatment of PSCs with 5-FU-miR-15a in the context of TGFβ1 treatment exerted a more substantial effect than TGFβ1 alone or when combined with other miRs. Conditioned medium obtained from PSC cells treated with 5-FU-miR-15a significantly inhibits the invasion of pancreatic cancer cells compared to controls. Importantly, we demonstrated that treatment with 5-FU-miR-15a reduced the levels of YAP1 and BCL-2 observed in PSCs. Our results strongly suggest that ectopic delivery of miR mimetics is a promising therapeutic approach for pancreatic fibrosis and that 5-FU-miR-15a shows specific promise.

Multiple cystic sphere formation from PK-8 cells in three-dimensional culture

Three-dimensional (3D) culture of cancer cells mimics the in vivo environment. Recently, we reported that pancreatic ductal adenocarcinoma (PDAC) cell lines with epithelial and mesenchymal features formed differently shaped spheres in 3D culture. However, only PK-8 cells, the epithelial PDAC cell line with the highest E-cadherin expression among the eight PDAC cell lines, formed multiple cystic spheres in 3D culture. Optical coherence tomography revealed interconnected cysts inside the spheres. A weak inter-cellular adhesion, individual cell degeneration, necrosis, and secretory granules in the cytoplasm were observed in the PK-8 spheres using electron microscopy. The expression of MUC1, MUC5AC, and amylase was increased in PK-8 cells in the 3D culture compared with that in 2D culture. These findings suggest that highly E-cadherin-expressing epithelial PK-8 cells form multiple cystic spheres, which may be promoted by enhanced mucin and amylase synthesis in 3D culture.

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PK-8 cells highly express E-cadherin in multiple cystic spheres in 3D culture.

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The cysts within the PK-8 spheres are interconnected.

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Individual cell degeneration and necrosis was observed in PK-8 spheres.

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MUC1, MUC5, and amylase levels increased in PK-8 spheres.

An integrated model of acinar to ductal metaplasia-related N7-methyladenosine regulators predicts prognosis and immunotherapy in pancreatic carcinoma based on digital spatial profiling

Acinar-to-ductal metaplasia (ADM) is a recently recognized, yet less well-studied, precursor lesion of pancreatic ductal adenocarcinoma (PDAC) developed in the setting of chronic pancreatitis. Through digital spatial mRNA profiling, we compared ADM and adjacent PDAC tissues from patient samples to unveil the bridging genes during the malignant transformation of pancreatitis. By comparing the bridging genes with the 7-methylguanosine (m7G)-seq dataset, we screened 19 m7G methylation genes for a subsequent large sample analysis. We constructed the “m7G score” model based on the RNA-seq data for pancreatic cancer in The Cancer Genome Atlas (TCGA) database and The Gene Expression Omnibus (GEO) database. Tumors with a high m7G score were characterized by increased immune cell infiltration, increased genomic instability, higher response rate to combined immune checkpoint inhibitors (ICIs), and overall poor survival. These findings indicate that the m7G score is associated with tumor invasiveness, immune cell infiltration, ICI treatment response, and overall patients’ survival. We also identified FN1 and ITGB1 as core genes in the m7Gscore model, which affect immune cell infiltration and genomic instability not only in pancreatic cancer but also in pan-cancer. FN1 and ITGB1 can inhibit immune T cell activition by upregulation of macrophages and neutrophils, thereby leading to immune escape of pancreatic cancer cells and reducing the response rate of ICI treatment.

Discovering key transcriptomic regulators in pancreatic ductal adenocarcinoma using Dirichlet process Gaussian mixture model

Pancreatic Ductal Adenocarcinoma (PDAC) is the most lethal type of pancreatic cancer, late detection leading to its therapeutic failure. This study aims to determine the key regulatory genes and their impacts on the disease’s progression, helping the disease’s etiology, which is still mostly unknown. We leverage the landmark advantages of time-series gene expression data of this disease and thereby identified the key regulators that capture the characteristics of gene activity patterns in the cancer progression. We have identified the key gene modules and predicted the functions of top genes from a reconstructed gene association network (GAN). A variation of the partial correlation method is utilized to analyze the GAN, followed by a gene function prediction task. Moreover, we have identified regulators for each target gene by gene regulatory network inference using the dynamical GENIE3 (dynGENIE3) algorithm. The Dirichlet process Gaussian process mixture model and cubic spline regression model (splineTimeR) are employed to identify the key gene modules and differentially expressed genes, respectively. Our analysis demonstrates a panel of key regulators and gene modules that are crucial for PDAC disease progression.