Pancreatic Cancer Gene Research Articles

Application of integrin subunit genes in pancreatic cancer and the construction of a prognosis model

Application of integrin subunit genes in pancreatic cancer and the construction of a prognosis model

The role of KRT7 in metastasis and prognosis of pancreatic cancer

PurposeThe aim of this study is to delve into the value of N6-Methyladenosine (m6A)-associated genes (MAGs) in pancreatic cancer (PC) prognosis.MethodsPC sequencing data and corresponding clinicopathological information were retrieved from GEO and TCGA databases. We filtered 19 MAGs in PC specimens and implemented functional annotation in biology. Later, the m6A modification pattern was stratified into m6Acluster A-B according to MAG expression levels, and further categorized into genecluster A-C based on differentially expressed genes between m6Acluster A and B. Next, a MAG-based prognostic prediction model was established by the least absolute shrinkage and selection operator (LASSO) regression analysis and multivariate Cox regression analysis. At last, the role of KRT7 in PC were explored.ResultsWe found m6Acluster A pattern presented enrichment pathways associated with cell apoptosis, proliferation, migration, and cancer pathways. Additionally, high-risk group displayed more dismal prognosis and a higher programmed death-ligand 1 expression. The survival prediction ability of the model was verified in three independent PC GEO datasets. KRT7 is the most momentous risk gene in the established prognostic model. Among 18 clinical samples, the KRT7 protein in the surviving patient samples is lower than that in the deceased patient samples. We also identified elevated expression of KRT7 in PC tumor tissues compared to normal tissues using GEPIA 2. Then, the metastasis of PC cells was promoted by overexpressed KRT7 in vitro and in vivo. And IGF2BP3 upregulated KRT7 by increasing the mRNA stability of KRT7.ConclusionsThe PPM built based on CXCL5, LY6K and KRT7 is an encouraging biomarker to define the prognosis. Additionally, IGF2BP3 promoted KRT7 by stabilizing mRNA of KRT7. And KRT7 promoted the metastasis of PC cells by promoting EMT.

Development of a Diagnostic Model for Pancreatic Ductal Adenocarcinoma Using Machine Learning and Blood-Based miRNAs

Introduction: Pancreatic ductal adenocarcinoma (PDAC) has the lowest survival rate among all major cancers due to a lack of symptoms in early stages, early detection tools, and optimal therapies for late-stage patients. Thus, effective and non-invasive diagnostic tests are greatly needed. Recently, circulating miRNAs have been reported to be altered in PDAC. They are promising biomarkers because of stability in the blood, ease of non-invasive detection, and convenient screening methods. This study aimed to use blood-based miRNA biomarkers and various analysis methods in the development of a machine-learning (ML) model for PDAC. Methods: Blood-based miRNAs associated with PDAC were collected from open sources. miRNA sequences, targeted genes, and involved pathways were used to construct a set of descriptors for an ML model. Results: Bioinformatics analysis revealed that most genes in pancreatic cancer and insulin signaling pathways were targeted by the PDAC-related miRNAs. The best-performing ML model with the Random Forest classifier was able to achieve an accuracy of 88.4%. Model evaluations of an independent PDAC-associated miRNAs test set had 100% accuracy while non-cancer miRNAs had 52.4% accuracy, indicating specificity to PDAC. Conclusions: Our results suggest an ML model developed using blood-based miRNA biomarkers’ target gene, pathway, and sequence features could be potentially implicated in PDAC diagnostics.

Anoikis-related genes linked with patient outcome in pancreatic cancer

Anoikis is programmed cell death occurring upon cell detachment from the extracellular matrix. Cancer cells need to evade anoikis to be able to metastasize to distant sites. However, the molecular features and prognostic value of anoikis-related genes (ARGs) in pancreatic cancer remain unclear. In this study, we utilized transcriptome data from the TCGA and GSE102238 databases to identify 64 ARGs significantly associated with prognosis. We used the “ConsensusClusterPlus” R package to stratify patients into high and low-risk prognostic subgroups. The KEGG and GSEA analyses revealed that the clusters with poor prognosis were enriched for the ECM receptor interaction pathway, the TP53 signaling pathway, and the galactose metabolism pathway, and that the cell cycle pathway was upregulated. A prognostic model consisting of seven ARGs (SERPINE1, EGF, E2F1, MSLN, RAB27B, ETV7, MST1) was constructed using LASSO regression and when combined with clinicopathological parameters using Cox regression, a prognostic Nomogram was created, which demonstrated high prognostic utility. Among the biomarker candidates, we report ETV7 as a novel, independent prognostic marker in pancreatic cancer. ETV7 was highly expressed in KRAS and TP53 co-occurrent mutant TCGA patients, indicating that it may be regulated by the two major driver genes of pancreatic cancer.Therefore, targeting ETV7 could be a potential focus for future therapeutic studies.

Construction of a prognostic model based on the cuproptosis-related genes in pancreatic cancer

Construction of a prognostic model based on the cuproptosis-related genes in pancreatic cancer

Comprehensive analysis and experimental verification of the mechanism of anoikis related genes in pancreatic cancer

BackgroundPancreatic cancer (PC), characterized by its aggressive nature and low patient survival rate, remains a challenging malignancy. Anoikis, a process inhibiting the spread of metastatic cancer cells, is closely linked to cancer progression and metastasis through anoikis-related genes. Nonetheless, the precise mechanism of action of these genes in PC remains unclear. MethodsStudy data were acquired from the Cancer Genome Atlas (TCGA) database, with validation data accessed at the Gene Expression Omnibus (GEO) database. Differential expression analysis and univariate Cox analysis were performed to determine prognostically relevant differentially expressed genes (DEGs) associated with anoikis. Unsupervised cluster analysis was then employed to categorize cancer samples. Subsequently, a least absolute shrinkage and selection operator (LASSO) Cox regression analysis was conducted on the identified DEGs to establish a clinical prognostic gene signature. Using risk scores derived from this signature, patients with cancer were stratified into high-risk and low-risk groups, with further assessment conducted via survival analysis, immune infiltration analysis, and mutation analysis. External validation data were employed to confirm the findings, and Western blot and immunohistochemistry were utilized to validate risk genes for the clinical prognostic gene signature. ResultsA total of 20 prognostic-related DEGs associated with anoikis were obtained. The TCGA dataset revealed two distinct subgroups: cluster 1 and cluster 2. Utilizing the 20 DEGs, a clinical prognostic gene signature comprising two risk genes (CDKN3 and LAMA3) was constructed. Patients with pancreatic adenocarcinoma (PAAD) were classified into high-risk and low-risk groups per their risk scores, with the latter exhibiting a superior survival rate. Statistically significant variation was noted across immune infiltration and mutation levels between the two groups. Validation cohort results were consistent with the initial findings. Additionally, experimental verification confirmed the high expression of CDKN3 and LAMA3 in tumor samples. ConclusionOur study addresses the gap in understanding the involvement of genes linked to anoikis in PAAD. The clinical prognostic gene signature developed herein accurately stratifies patients with PAAD, contributing to the advancement of precision medicine for these patients.

In Silico Identification of Dysregulated miRNAs Targeting KRAS Gene in Pancreatic Cancer.

Pancreatic cancer (PC) is highly lethal, with KRAS mutations in up to 95% of cases. miRNAs inversely correlate with KRAS expression, indicating potential as biomarkers. This study identified miRNAs targeting KRAS and their impact on PC characteristics using in silico methods. dbDEMC identified dysregulated miRNAs in PC; TargetScan, miRDB, and PolymiRTS 3.0 identified miRNAs specific for the KRAS gene; and OncomiR evaluated the association of miRNAs with clinical characteristics and survival in PC. The correlation between miRNAs and KRAS was analysed using ENCORI/starBase. A total of 210 deregulated miRNAs were identified in PC (116 overexpressed and 94 underexpressed). In total, 16 of them were involved in the regulation of KRAS expression and 9 of these (hsa-miR-222-3p, hsa-miR-30a-5p, hsa-miR-30b-5p, hsa-miR-30e-5p, hsa-miR-377-3p, hsa-miR-495-3p, hsa-miR-654-3p, hsa-miR-877-5p and hsa-miR-885-5p) were associated with the clinical characteristics of the PC. Specifically, the overexpression of hsa-miR-30a-5p was associated with PC mortality, and hsa-miR-30b-5p, hsa-miR-377-3p, hsa-miR-495-3p, and hsa-miR-885-5p were associated with survival. Correlation analysis revealed that the expression of 10 miRNAs is correlated with KRAS expression. The dysregulated miRNAs identified in PC may regulate KRAS and some are associated with clinically relevant features, highlighting their potential as biomarkers and therapeutic targets in PC treatment. However, experimental validation is required for confirmation.

Andrographolide suppresses the malignancy of pancreatic cancer via alleviating DNMT3B-dependent repression of tumor suppressor gene ZNF382

BackgroundPancreatic ductal adenocarcinoma (PDAC) is a highly aggressive cancer type that urgently requires effective therapeutic strategies. Andrographolide, a labdane diterpenoid compound abundant in Andrographis paniculata, has anticancer effects against various cancer types, but its anticancer activity and mechanism against PDAC remain largely uncharacterized. PurposeThis study explores novel drug target(s) and underlying molecular mechanism of andrographolide against PDAC. Study design and methodsThe malignant phenotypes of PDAC cells, PANC-1 and MIA PaCa-2 cells, were measured using MTT, clonogenic assays, and Transwell migration assays. A PDAC xenograft animal model was used to evaluate tumor growth in vivo. Western blot, immunofluorescence and immunohistochemistry were used for measuring protein expression. The TCGA database was analyzed to evaluate promoter methylation status, gene expression, and their relationship with patient survival rates. RT-qPCR was used for detecting mRNA expression. Reporter assays were used for detecting signal transduction pathways. Promoter DNA methylation was determined by sodium bisulfite treatment and methylation-specific PCR (MSP). The biological function and role of specific genes involved in drug effects were measured through gene overexpression. ResultsAndrographolide treatment suppressed the proliferation and migration of PDAC cells and impaired tumor growth in vivo. Furthermore, andrographolide induced the mRNA and protein expression of zinc finger protein 382 (ZNF382) in PDAC cells. Overexpression of ZNF382 inhibited malignant phenotypes and cancer-associated signaling pathways (AP-1, NF-κB and β-catenin) and oncogenes (ZEB-1, STAT-3, STAT-5, and HIF-1α). Overexpression of ZNF382 delayed growth of PANC-1 cells in vivo. ZNF382 mRNA and protein expression was lower in tumor tissues than in adjacent normal tissues of pancreatic cancer patients. Analysis of the TCGA database found the ZNF382 promoter is hypermethylated in primary pancreatic tumors which correlates with its low expression. Furthermore, andrographolide inhibited the expression of DNA methyltransferase 3 beta (DNMT3B) and increased the demethylation of the ZNF382 promoter in PDAC cells. Overexpression of DNMT3B attenuated the andrographolide-suppressed proliferation and migration of PDAC cells. ConclusionOur finding revealed that ZNF382 acts as a tumor suppressor gene in pancreatic cancer and andrographolide restores ZNF382 expression to suppress pancreatic cancer, providing a novel molecular target and a promising therapeutic approach for treating pancreatic cancer.

A prognostic model for anoikis-related genes in pancreatic cancer

Anoikis, a distinct form of programmed cell death, is crucial for both organismal development and maintaining tissue equilibrium. Its role extends to the proliferation and progression of cancer cells. This study aimed to establish an anoikis-related prognostic model to predict the prognosis of pancreatic cancer (PC) patients. Gene expression data and patient clinical profiles were sourced from The Cancer Genome Atlas (TCGA-PAAD: Pancreatic Adenocarcinoma) and the International Cancer Genome Consortium (ICGC-PACA: Pancreatic Ductal Adenocarcinoma). Non-cancerous pancreatic tissue gene expression data were obtained from the Genotype-Tissue Expression (GTEx) project. The R package was used to construct anoikis-related PC prognostic models, which were later validated with the ICGC-PACA database. Survival analyses demonstrated a poorer prognosis for patients in the high-risk group, consistent across both TCGA-PAAD and ICGC-PACA datasets. A nomogram was designed as a predictive tool to estimate patient mortality. The study also analyzed tumor mutations and immune infiltration across various risk groups, uncovering notable differences in tumor mutation patterns and immune landscapes between high- and low-risk groups. In conclusion, this research successfully developed a prognostic model centered on anoikis-related genes, offering a novel tool for predicting the clinical trajectory of PC patients.

Effect of SIRT7 on inhibiting the epithelial-mesenchymal transformation in pancreatic cancer cells and related mechanism

Objective: To investigate the effect and mechanism of SIRT7 in epithelial mesenchymal transformation (EMT) of pancreatic cancer cells. Methods: The pancreatic cancer cells were divided into siControl, siSIRT7, over-expression SIRT7, siSIRT7+siCOL4A1, and siSIRT7+siSLUG groups using siRNA or plasmid transfection. The proliferation, migration and invasion of pancreatic cancer cells were detected by EdU, wound healing assay and Transwell experiments, respectively. The expression of EMT and cancer stem cell (CSC) markers were detected by quantitative real-time reverse transcription polymerase chain reaction assay (qRT-PCR) and western blot. RNA sequencing (RNA-seq) in SIRT7 knockdown PANC-1 cells was performed to explore the signaling pathways and target genes regulated by SIRT7. Then the target genes directly regulated by SIRT7 were identified with quantitative chromatin immunoprecipitation experiment (q-ChIP) and chromatin immunoprecipitation polymerase chain reaction (ChIP-PCR). The expressions of SIRT7 and target genes were detected by immunohistochemical (IHC) in pancreatic cancer tissues, and the correlation between SIRT7 and target gene expression was analyzed using TCGA dataset. The correlation between expression of SIRT7 or target genes and survival was analyzed on KM-plotter website. Finally, GeneMANIA, STRING and ENCORI were used to predict SIRT7-related proteins and miRNAs. Results: EdU assay showed that the cell proliferation rates in SIRT7-overexpressed PANC-1 [(19.33±0.35)%] and BxPC-3 cells [(17.00±1.89)%] were lower than those in the control group [(31.60±1.37)% and (24.33±0.78)%, respectively, P＜0.05]. The proliferation rates of SIRT7-knockdown PANC-1 [(23.94±1.00)% and (27.08±0.97)%] and BxPC-3 cells [(22.00±1.86)% and (25.96±1.61)%] were higher than those of the siControl group [(11.80±1.86)% and (13.42±1.39)%, respectively, P＜0.05]. In PANC-1 cells, the wound healing assay showed that the relative migration rate of SIRT7-overexpression cells [(76.67±2.74)%] was lower than that of control cells [(100.00±2.13)%, P＜0.05]; the relative migration rate of cells with SIRT7 knockdown [(134.22±4.08)% and (199.82±9.20)%, respectively] was higher than that of siControl group [(102.24±3.13)%, P＜0.05]. Compared with the control group, SIRT7 overexpression decreased the number of migrated BxPC-3 cells (45.66±1.69 vs 28.33±2.62, P＜0.05); while SIRT7 knockdown increased these numbers (65.66±2.86 and 82.00±2.94 versus 33.00±0.81, P＜0.01). Transwell experiment revealed that the number of invaded cells in SIRT7 overexpression groups (16.33±2.05 and 34.66±1.69) was lower than that control groups (54.33±4.64 and 58.66±5.90, P＜0.05); with SIRT7 knockdown, the numbers of invaded PANC-1 (63.66±2.49 and 69.33±3.29) and BxPC-3 cells (134.33±3.09 and 181.66±4.02) were higher than those in control groups (35.33±2.49 and 42.00±0.81, P˂0.05). Also, SIRT7 knockdown decreased the expressions of epithelial markers and increased the expressions of mesenchymal and CSC markers. RNA-seq analysis showed that SIRT7 was involved in regulating a variety of cancer-related signaling pathways, including the pancreatic cancer pathway and the EMT pathway. Furthermore, SIRT7 could directly bind to the promoter regions of target genes, such as COL4A1 and SLUG. SIRT7 was negatively correlated with the expression and function of COL4A1 and SLUG in pancreatic cancer cells. The expressions of SIRT7, COL4A1, SLUG and SOX2 were verified in pancreatic cancer tissues by IHC. Finally, SIRT7 was predicted to be associated with many proteins and miRNAs based on GeneMANIA, STRING, and ENCORI online tools. Conclusions: SIRT7 can inhibit the EMT of pancreatic cancer cells through transcriptionally inhibiting the expression of target genes, such as COL4A1 and SLUG. Thus, SIRT7 may serve as a potential tumor suppressor gene in pancreatic cancer.

In vivo CRISPR screens reveal SCAF1 and USP15 as drivers of pancreatic cancer

Functionally characterizing the genetic alterations that drive pancreatic cancer is a prerequisite for precision medicine. Here, we perform somatic CRISPR/Cas9 mutagenesis screens to assess the transforming potential of 125 recurrently mutated pancreatic cancer genes, which revealed USP15 and SCAF1 as pancreatic tumor suppressors. Mechanistically, we find that USP15 functions in a haploinsufficient manner and that loss of USP15 or SCAF1 leads to reduced inflammatory TNFα, TGF-β and IL6 responses and increased sensitivity to PARP inhibition and Gemcitabine. Furthermore, we find that loss of SCAF1 leads to the formation of a truncated, inactive USP15 isoform at the expense of full-length USP15, functionally coupling SCAF1 and USP15. Notably, USP15 and SCAF1 alterations are observed in 31% of pancreatic cancer patients. Our results highlight the utility of in vivo CRISPR screens to integrate human cancer genomics and mouse modeling for the discovery of cancer driver genes with potential prognostic and therapeutic implications.

Unlocking Clinical Insights: Lymphocyte-Specific Protein Tyrosine Kinase Candidates as Promising Therapeutic Targets for Pancreatic Cancer Risk Stratification.

Background: Uncover the pivotal link between lymphocyte-specific protein tyrosine kinase (Lck)-related genes and clinical risk stratification in pancreatic cancer. Methods: This study identifies shared genes between differentially expressed genes (DEGs) and Lck-related genes in pancreatic cancer using a methodological framework rooted in The Cancer Genome Atlas database. Feature gene selection is accomplished and a signature model is constructed. Statistical significant clinical endpoints such as overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI) were defined. Results: After performing random survival forest, Lasso regression, and multivariate Cox regression model, 7 trait genes out of 272 Lck-associated DEGs are selected to create a signature model that is independent of other clinical factors and can predict OS and DSS. It appears that high-risk patients have activated the TP53 signaling pathway and the cell cycle signaling pathway. LAMA3 turned out to be the hub gene of the signature with high expression in pancreatic cancer. Patients with increased expression of LAMA3 had a short OS, DSS, and PFI in comparison. The candidate competing endogenous RNA network of LAMA3 turned out to be OPI5-AS1/hsa-miR-186-5p/LAMA3 axis. Conclusions: A characteristic signature of seven Lck-related genes, especially LAMA3, has been shown to be a key factor in clinical risk stratification for pancreatic cancer.

A prognostic binary classifier comprised of five critical mRNAs stratified pancreatic cancer patients following resection

BackgroundPancreatic cancer is characterized by an extremely poor prognosis, even following potentially curative resection. Classical prognostic markers such as histopathological or clinical parameters have limited predictive power. The present study aimed to establish a prognostic model combining mRNA expression data with histopathological and clinical data to better predict survival and stratify pancreatic cancer patients following resection. We pioneered three models in one study and systematically evaluated the clinical benefits of all three models. MethodsTo identify differentially expressed genes in pancreatic cancer, mRNA data from normal (GTEx database) and pancreatic cancer (TCGA database) tissues were used. Survival analysis was carried out to identify prognosis-relevant genes from the identified differentially expressed genes and LASSO regression was used to filter out hub genes. The risk score of several hub genes was calculated according to gene expression and coefficients. Validation was carried out using an independent set of GEO microarray data. Multivariate COX regression was used for identifying independent clinical and pathological risk factors related to patient's survival in the TCGA database and a prognostic model combining mRNA expression data with histopathological and clinical data was established. Another prognostic model using clinicopathological factors from the SEER database was conceived based on multivariate COX regression. NRI (net reclassification improvement) and IDI (integrated discrimination index) were used to compare the predictive capabilities of the different models. ResultsWe identified 1589 differentially expressed genes (DEGs) through the comparison of normal and pancreatic cancer tissues, of whom 317 were associated with prognosis(p < 0.05). LASSO regression identified five hub genes, MYEOV, ANXA2P2, MET, CEP55, and KRT7, that were used for the five-mRNA-classifier prognostic model. The classifier could stratify patients into a short and long survival group: 5-year overall survival in the training set (TCGA, 6 % vs 52 %, p < 0.001), test set (TCGA, 18 % vs 55 %,p < 0.01) and external validation set (GEO, 0 % vs 25 %, p < 0.05). Sensitivity analysis showed that the mRNA model (model 1) was better than the clinicopathological no-mRNA model (model 2) in predicting 5-year survival in the TCGA database (AUC: 0.877 vs 0.718, z = 3.165, p < 0.01) and better than the multi-factor prognostic model (model 3) from the SEER database (AUC: 0.754, z = 2.637, p < 0.01). On predictive performance, model 1 improved model 2 (NRI = 0.084, z = 1.288, p = 0.198; IDI = 0.055, z = 1.041,p = 0.298) and model 3 (NRI = 0.167,z = 1.961,p = 0.05; IDI = 0.086, z = 1.427, p = 0.154). ConclusionThe five-mRNA-classifier is a reliable and feasible instrument to predict the prognosis of pancreatic cancer patients following resection. It might help in patiens counseling and assist clinicians in providing individualized treatment for patients in different risk groups.

High N6-methyladenosine-activated TCEAL8 mRNA is a novel pancreatic cancer marker.

N6-methyladenosine (m6A) is an RNA modification involved in RNA processing and widely found in transcripts. In cancer cells, m6A is upregulated, contributing to their malignant transformation. In this study, we analyzed gene expression and m6A modification in cancer tissues, ducts, and acinar cells derived from pancreatic cancer patients using MeRIP-seq. We found that dozens of RNAs highly modified by m6A were detected in cancer tissues compared with ducts and acinar cells. Among them, the m6A-activated mRNA TCEAL8 was observed, for the first time, as a potential marker gene in pancreatic cancer. Spatially resolved transcriptomic analysis showed that TCEAL8 was highly expressed in specific cells, and activation of cancer-related signaling pathways was observed relative to TCEAL8-negative cells. Furthermore, among TCEAL8-positive cells, the cells expressing the m6A-modifying enzyme gene METTL3 showed co-activation of Notch and mTOR signaling, also known to be involved in cancer metastasis. Overall, these results suggest that m6A-activated TCEAL8 is a novel marker gene involved in the malignant transformation of pancreatic cancer.

Investigating the anti-cancer compounds from Calliandra harrisii for precision medicine in pancreatic cancer via in-silico drug design and GC-MS analysis.

Pancreatic cancer is a fatal illness caused by mutations in multiple genes. Pancreatic cancer damages the organ that helps in digestion, resulting in symptoms including fatigue, bloating, and nausea. The use of medicinal plants has been crucial in the treatment of numerous disorders. The medicinal plant Calliandra Harrisi has been widely exploited for its possibilities in biology and medicine. The current study aimed to assess the biopotential of biologically active substances against pancreatic cancer. The GC-MS data of these phytochemicals from Calliandra Harrisi were further subjected tocomputational approaches with pancreatic cancer genes toevaluate their potential as therapeutic candidates. Molecular docking analysis revealed that N-[Carboxymethyl] maleamic acid is the leading molecule responsible for protein denaturation inhibition, having the highest binding affinity of 6.8 kJ/mol among all other compounds with KRAS inflammatory proteins. Furthermore, ADMET analysis and Lipinski's rule validation were also performed revealing its higher absorption in the gastrointestinal tract. The results of the hepatotoxicity test demonstrated that phytochemicals are non-toxic, safe to use, and do not cause necrosis, fibrosis, or vacuolar degeneration even at excessive levels. Calliandra Harrisi has phytoconstituents that have a variety of pharmacological uses in consideration.

The GFPT2-O-GlcNAcylation-YBX1 axis promotes IL-18 secretion to regulate the tumor immune microenvironment in pancreatic cancer

The immunosuppressive microenvironment caused by several intrinsic and extrinsic mechanism has brought great challenges to the immunotherapy of pancreatic cancer. We identified GFPT2, the key enzyme in hexosamine biosynthesis pathway (HBP), as an immune-related prognostic gene in pancreatic cancer using transcriptome sequencing and further confirmed that GFPT2 promoted macrophage M2 polarization and malignant phenotype of pancreatic cancer. HBP is a glucose metabolism pathway leading to the generation of uridine diphosphate N-acetylglucosamine (UDP-GlcNAc), which is further utilized for protein O-GlcNAcylation. We confirmed GFPT2-mediated O-GlcNAcylation played an important role in regulating immune microenvironment. Through cellular proteomics, we identified IL-18 as a key downstream of GFPT2 in regulating the immune microenvironment. Through CO-IP and protein mass spectrum, we confirmed that YBX1 was O-GlcNAcylated and nuclear translocated by GFPT2-mediated O-GlcNAcylation. Then, YBX1 functioned as a transcription factor to promote IL-18 transcription. Our study elucidated the relationship between the metabolic pathway of HBP in cancer cells and the immune microenvironment, which might provide some insights into the combination therapy of HBP vulnerability and immunotherapy in pancreatic cancer.

Integrated analysis of scRNA-seq and bulk RNA-seq reveals that GPRC5A is an important prognostic gene in pancreatic cancer and is associated with B-cell Infiltration in pancreatic cancer.

Pancreatic cancer (PC) is a malignancy with poor prognosis. This investigation aimed to determine the relevant genes that affect the prognosis of PC and investigate their relationship with immune infiltration. : First, we acquired PC single-cell chip data from the GEO database to scrutinize dissimilarities in immune cell infiltration and differential genes between cancerous and adjacent tissues. Subsequently, we combined clinical data from TCGA to identify genes relevant to PC prognosis. Employing Cox and Lasso regression analyses, we constructed a multifactorial Cox prognostic model, which we subsequently confirmed. The prognostic gene expression in PC was authenticated using RT-PCR. Moreover, we employed the TIMER online database to examine the relationship between the expression of prognostic genes and T and B cell infiltration. Additionally, the expression of GPRC5A and its correlation with B cells infiltration and patient prognosis were ascertained in tissue chips using multiple immune fluorescence staining. The single-cell analysis unveiled dissimilarities in B-cell infiltration between cancerous and neighboring tissues. We developed a prognostic model utilizing three genes, indicating that patients with high-risk scores experienced a more unfavorable prognosis. Immune infiltration analysis revealed a significant correlation among YWHAZ, GPRC5A, and B cell immune infiltration. In tissue samples, GPRC5A exhibited substantial overexpression and a robust association with an adverse prognosis, demonstrating a positive correlation with B cell infiltration. GPRC5A is an independent risk factor in PC and correlated with B cell immune infiltration in PC. These outcomes indicated that GPRC5A is a viable target for treating PC.

Integrated bioinformatics analysis reveals upregulated extracellular matrix hub genes in pancreatic cancer: Implications for diagnosis, prognosis, immune infiltration, and therapeutic strategies.

Pancreatic cancer (PC) stands out as one of the most formidable malignancies and exhibits an exceptionally unfavorable clinical prognosis due to the absence of well-defined diagnostic indicators and its tendency to develop resistance to therapeutic interventions. The primary objective of this present study was to identify extracellular matrix (ECM)-related hub genes (HGs) and their corresponding molecular signatures, with the intent of potentially utilizing them as biomarkers for diagnostic, prognostic, and therapeutic applications. Three microarray datasets were sourced from the NCBI database to acquire upregulated differentially expressed genes (DEGs), while MatrisomeDB was employed for filtering ECM-related genes. Subsequently, a protein-protein interaction (PPI) network was established using the STRING database. The created network was visually inspected through Cytoscape, and HGs were identified using the CytoHubba plugin tool. Furthermore, enrichment analysis, expression pattern analysis, clinicopathological correlation, survival analysis, immune cell infiltration analysis, and examination of chemical compounds were carried out using Enrichr, GEPIA2, ULCAN, Kaplan Meier plotter, TIMER2.0, and CTD web platforms, respectively. The diagnostic and prognostic significance of HGs was evaluated through the ROC curve analysis. Ten genes associated with ECM functions were identified as HGs among 131 DEGs obtained from microarray datasets. Notably, the expression of these HGs exhibited significantly (p < 0.05) higher in PC, demonstrating a clear association with tumor advancement. Remarkably, higher expression levels of these HGs were inversely correlated with the likelihood of patient survival. Moreover, ROC curve analysis revealed that identified HGs are promising biomarkers for both diagnostic (AUC > 0.75) and prognostic (AUC > 0.64) purposes. Furthermore, we observed a positive correlation between immune cell infiltration and the expression of most HGs. Lastly, our study identified nine compounds with significant interaction profiles that could potentially act as effective chemical agents targeting the identified HGs. Taken together, our findings suggest that COL1A1, KRT19, MMP1, COL11A1, SDC1, ITGA2, COL1A2, POSTN, FN1, and COL5A1 hold promise as innovative biomarkers for both the diagnosis and prognosis of PC, and they present as prospective targets for therapeutic interventions aimed at impeding the progression PC.

Abstract 3350: The pan-quadruplex drug QN-302 targets up-regulated genes in pancreatic cancer and in other cancer types and correlate with poor patient prognosis

Abstract We have previously disclosed (Ahmed et al, ACS Med Chem Lett, 2020) a novel small molecule G-quadruplex binding compound, QN-302, that is highly potent in pancreatic ductal adenocarcinoma (PDAC) cells and in several in vivo models for this disease. QN-302 was invented at University College London UK and has been subsequently developed by Qualigen Therapeutics Inc. It was cleared by the FDA in August 2023 for clinical evaluation and is currently in a Phase 1 clinical trial at select oncology centers in the USA. Genes that are significantly down-regulated by QN-302 are mostly up-regulated genes in human PDAC. We report here that some of these genes are also up regulated in other cancer types, whose expression frequently correlates with poor patient prognosis. We have used human cancer data from https://www.proteinatlas.org which contains pathology information based on mRNA and protein expression data from 17 different forms of human cancer, together with millions of in-house generated immunohistochemically stained tissue sections, images and Kaplan-Meier plots showing the correlation between mRNA expression of each human protein gene and cancer patient survival. We have revisited the RNA-seq data for QN-302 in PDAC MIA-PACA2 cells exposed to QN-302 for 24 hrs. (GSE151741 at https://www.ncbi.nlm.nih.gov/geo/) in detail and now identify a small group of eleven down-regulated genes, all with &amp;gt;3-fold expression changes. Interestingly all these genes and their gene products have been previously identified as playing a role in human PDAC (they also have varying roles in other cancer types). Some have been validated as anticancer targets. 10/11 of these genes (https://www.proteinatlas.org/) have been previously reported as being over-expressed in significant fractions of large (&amp;gt;150) groups of human cancer patients from The Cancer Genome Atlas (TCGA) project. Members of the PDAC susceptible gene panel are mostly disfavored in prostate, lung and breast cancer. However, the patient data does not discriminate between cancer sub-types nor does it show resistant data. They are especially favored in colorectal, liver and renal cancers, suggesting that QN-302 may have anticancer effects in these cancers as well as in PDAC. We also do not exclude the strong possibility that each cancer type also has its own group of G-quadruplex containing genes that are sensitive to QN-302. Citation Format: Stephen Neidle, Ahmed A. Ahmed, Tariq Arshad. The pan-quadruplex drug QN-302 targets up-regulated genes in pancreatic cancer and in other cancer types and correlate with poor patient prognosis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3350.

Abstract 1645: Telomere-related genes associated with clinical-pathological in pancreatic ductal adenocarcinoma

Abstract Pancreatic ductal adenocarcinoma (PDAC) stands as one of the leading causes of cancer deaths worldwide. Studies have suggested that telomeres shortening is linked to genetic instability, higher risk of developing pancreatic cancer and pancreatic tumorigenesis. However, previous research has predominantly focused on either telomere length or specific telomere-related genes in pancreatic cancer. Therefore, it is essential to elucidate the association between telomere-related genes and pancreatic cancer to identify novel therapeutic strategies and establish prognostic biomarkers. Telomere-relevant genes were downloaded from the TelNet database. Expression data from primary tumour of The Cancer Genome Atlas-PAAD (TCGA-PAAD) cohort, the Genome Sequence Archive CRA001160 dataset and the GTEx database were obtained for screen for differentially expressed genes (DEGs). Gene set enrichment analisis was performed using the Enrichr software. The gene essentiality queried from the gene dependency database of the Achilles-DepMap project was also assessed. Finally, data of The Cancer Genome Atlas-PanCancer CaPa cohort and healthy pancreas from the GTEx database, obtained from the UCSC Xena project, were used to correlate gene expression with stemness signatures and clinical-pathological variables. We identified 300 telomere-associated DEGs, Of these, 73 genes were common to studies and they were to be enriched in biological processes related to the cell cycle and telomere homeostasis. The differentially expresed of ASS1, S100P, IGF2BP2, MET, ABCC3 and RAC1 genes were significantly associated with overall survival and recurrence-free survival, conferring them prognostic value. Furthermore, the expression profiles of these genes are significantly associated with the histological grade of the disease, as well as with tumour T-N classification, the presence of residual tumour, the degree of response to treatment and stemness signatures. Among these, three genes of particular interest (KLF3, RAC1 and ABI1) were identified and shown to be highly essential in pancreatic cancer cell lines. In summary, we have identified a telomere gene-related signature for pancreatic cancer, highlighting the prognostic value of KLF3, RAC1, and ABI1 genes. This signature is significantly associated with clinical-pathological variables of the disease, potentially offering new insights for individualized therapy. Citation Format: Erika Curiel Gómez, Vilma Maldonado Lagunas, Jorge Meléndez Zajgla. Telomere-related genes associated with clinical-pathological in pancreatic ductal adenocarcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1645.