Biomarker For Pancreatic Cancer Research Articles

Identifying Complex High-Risk Individuals (HRI-C) for Early Detection of Occult Pancreatic Cancers Using Risk Models and Biomarkers.

Early detection and diagnosis of pancreatic cancer (pancreatic ductal adenocarcinoma, PDAC) offers the best option for effective treatment and prolonged survival. Image-based screening programs are useful in early detection of pancreatic cancers in high-risk individuals (HRI) defined by strong family history and/or pathogenic variants (PV) in hereditary cancer syndrome (HCS) genes associated with an increased risk for PDAC development. However, this only applies in about 10% of people who eventually develop pancreatic cancer. An additional 15% of PDACs are associated with a pancreatic cystic neoplasms. Thus, the majority (~75%) of patients diagnosed with PDAC are not candidates for surveillance under an existing framework. Based on growing evidence we believe that early detection is also possible in what we call Complex High-Risk Individuals (HRI-C) through frequent, time-limited image-based screening. These HRI-C are identified by combining multiple clinical and genetic risk factors alongside early markers of pancreatic cancer (e.g. family history, past medical history, social history, and multiple genetic factors in individuals with worrisome individual or combinations of biomarkers of occult pancreatic cancer in older individuals (>50 years) such as new onset diabetes (NOD), new idiopathic acute or chronic pancreatitis, maldigestion with exocrine pancreatic insufficiency (EPI), abnormal liver injury test (especially biliary), unexplained weight loss and typical cancer-associated pain patterns). Here we propose a potentially cost-effective clinical decision pathway aimed at improving early detection, diagnosis and outcomes of this large group of individuals at risk for developing PDAC.

COL5A3: A prognostic biomarker and potential therapeutic target in pancreatic cancer

Pancreatic cancer is a malignant tumor of the digestive system with a high mortality rate and a poor prognosis. While type V collagen 3 (COL5A3) is extensively expressed in many tumor tissues, its prognostic significance and immune infiltration in pancreatic cancer remain unknown. As a result, we investigated COL5A3’s predictive function in pancreatic cancer and its relationship to immune infiltration. COL5A3 is significantly expressed in pancreatic cancer tissues compared to normal tissues. High COL5A3 expression is associated with poor clinicopathological characteristics and a worse prognosis of pancreatic cancer. The Kaplan-Meier survival analysis revealed that pancreatic cancer patients with high COL5A3 expression had a poorer prognosis than those with low COL5A3 expression. According to the ROC curve, COL5A3 has high sensitivity and specificity in the detection of pancreatic cancer. Correlation studies revealed that COL5A3 mRNA expression is associated with immune cell infiltration. This work indicates for the first time that COL5A3 may be a novel predictive biomarker linked to immune infiltration, providing a new target for pancreatic cancer detection and therapy.

Exploring shared genetic factors and prognostic biomarkers in pancreatic cancer and non-alcoholic fatty liver disease: Focus on hsa-miR-29c-3p and COL11A1 axis

Exploring shared genetic factors and prognostic biomarkers in pancreatic cancer and non-alcoholic fatty liver disease: Focus on hsa-miR-29c-3p and COL11A1 axis

MiR-766-3p Inhibit the Proliferation, Stemness, and Cell Cycle of Pancreatic Cancer Cells Through the MAPK/ERK Signaling Pathway.

As a commonly identified cancer in clinics, pancreatic cancer (PC) has poor prognostic outcomes. This work focused on clarifying the association between MIR-766-3P expression and PC development and progression, as well as the possible role as a biomarker in PC. MIR-766-3P expression within the human PC cells and samples was measured through miRNA RT-PCR. The gene levels regulated by MIR-766-3P were analyzed through western blot (WB) and qRT-PCR. To analyze whether MIR-766-3P was of certain significance in invitro and invivo PC cell proliferation, stemness, and cell cycle progression, the gain/loss-of-function assays were performed. Bioinformatics, RNA sequencing (RNA-seq), and luciferase reporter assay were conducted for exploring regulatory role of MIR-766-3P/MAPK1/MAPK/ERK signal axis in PC. In comparison with the normal controls, MIR-766-3P expression markedly decreased the tissues and cells of PC. Furthermore, MIR-766-3P could remarkably inhibit the proliferation, stemness, cell cycle progression, and development of PC. The analyses using RNA-seq, and dual-luciferase examination showed that MIR-766-3P could directly target mitogen-activated protein kinase 1 (MAPK1). According to Gene Ontology (GO) as well as Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, MIR-766-3P could affect PC malignant phenotype by MAPK1 and the regulation of the MAPK/ERK-related pathway. MIR-766-3P has a certain impact on PC malignant phenotype through combining with MAPK1 while regulating MAPK/ERK-related pathway invitro and invivo.

3D bioprinting of multicellular tumor spheroids in photocrosslinkable hyaluronan-gelatin for engineering pancreatic cancer microenvironment

3D bioprinting can be utilized to fabricate cancer-like tissue that models complex interactions within the cancer microenvironment. In human pancreatic ductal adenocarcinoma (PDAC), these interactions involve the extracellular matrix (ECM), cancer cells, and pancreatic stellate cells. Hyaluronan (HA) is a major component of ECM supporting tumor progression and chemoresistance in PDAC. In the current study, an in vitro PDAC-like tissue platform was developed by embedding multicellular pancreatic tumor-like spheroids within a novel 3D bioprinting HA-gelatin photocrosslinked hydrogel (GHP). This optimized GHP bioink (7 wt% gelatin and 0.2 wt% phenolic HA) achieved a modulus (∼5.46 kPa) closely resembling that of clinical PDAC tissue, with a dense and uniform structure superior to gelatin-only hydrogel (GN). The bioprinted 3D tumor-like spheroids within GHP exhibited distinct invasive and metastatic behavior, along with up-regulated expression of epithelial-mesenchymal transition (EMT) markers. Furthermore, gene expression analysis also revealed a ∼290-fold increase in CD44 gene and a 7.3-fold rise in S100A9 (a novel pancreatic cancer biomarker for early diagnosis). These tumor-like spheroids within 3D-bioprinted GHP constructs further demonstrated substantial chemoresistance, maintaining remarkable 98.5% viability after 48 h of exposure to a Gemcitabine and Abraxane combination, in contrast to significantly lower resistance observed in spheroids alone or co-cultured monolayers. An in-depth investigation of HA distribution within the 3D-bioprinted PDAC-like construct revealed a pattern consistent with clinical PDAC, indicating enhanced malignancy and potential tumor reprogramming. This 3D-bioprinted PDAC model holds significant potential for advancing pancreatic cancer research and preclinical drug testing.

Peritoneal Tumor DNA as a Prognostic Biomarker in Pancreatic Cancer: A Systematic Review and Meta-Analysis.

Peritoneal Tumor DNA as a Prognostic Biomarker in Pancreatic Cancer: A Systematic Review and Meta-Analysis.

Development of a Direct Electron Transfer-Type Enzymatic Sensor for the Spermine in Saliva, a Marker of Pancreatic Cancer

Introduction Pancreatic cancer (PC) has one of the worst prognoses among cancers because of the scarcity of early symptoms and the delay of early diagnosis. Current most available and advanced diagnosis methods of PC are based on imaging technologies such as computed tomography, positron emission tomography, magnetic resonance imaging, and endoscopic ultrasonography. However, such imaging methods cannot diagnose PC at an early stage. Patients diagnosed with PC often have metastasized, and only less than 30% of PC patients can undergo surgical resection. Therefore, there is an urgent need for more accurate, simple, and innovative methods for PC early diagnosis. Recently, salivary spermine has attracted attention as a new biomarker for PC <Asai et.al., Cancers, 2018>, and studies aimed at detecting spermine are attempted. Thus, we focused on the electrochemical measurements of spermine using enzyme.Electrochemical enzymatic biosensors are categorized into three generation principles based on the electron acceptors used for the oxidative half reaction. The 1st generation principle uses oxygen as the electron acceptor. However, the signal is affected by the oxygen concentration and a high applied potential is required to detect hydrogen peroxide. The 2nd generation principle requires the use of synthetic electron acceptors or mediators. The 3rd generation principle utilizes enzymes which are capable of direct electron transfer (DET) to the electrode, does not require any additional electron acceptors. Notably, the 3rd generation principle-based sensor can monitor target by applying relatively lower potentials, therefore will not be influenced by redox-active interferents. Considering the salivary sample contains variety of ingredient potentially interferants for enzymatic spermine monitoring, the DET-type enzymatic sensors are ideal. However, those have never been reported. In this work, we present the development of the 3rd generation principle based enzymatic sensor for spermine by employing a unique Methods SpdH was recombinantly produced using E. coli and purified by Ni2+ affinity chromatography. An electrochemical enzymatic sensor was constructed by immobilizing SpdH on gold electrodes. The DET properties of the enzyme-immobilized electrode were evaluated by cyclic voltammetry with an Ag/AgCl reference electrode and a Pt wire counter electrode, respectively. Chronoamperometry measurement was performed with SpdH immobilized gold electrodes using artificial saliva or phosphate buffer, where spermine was spiked with different concentrations. Results and Discussions For spermine measurement, we selected SpdH which was expected to be capable of DET with electrode, which was predicted from its unique protein structure. The crystal structure of SpdH shows the presence of heme b and FAD as the co-factors of the enzyme with a distinctive character. The heme b locates on the surface of the enzyme and is expected to accept electron from FAD and can transfer electrons to the electrode. The cyclic voltammetry observation showed the spermine-concentration dependent peak current increase in the absence of any additional electron acceptor, revealing that the enzyme harbors DET ability with electrode. The spermine concentration dependent currents increase was also observed by chronoamperometry measurement. The limit of detection of the enzymatic electrode was sub-micromolar level, which covers the physiologically relevant ranges of spermine. Furthermore, the electrode did not respond to electrochemically active interferants due to its characteristic of low redox potentials of heme being measurable at low potentials.This is the first report to construct a spermine sensor with a DET-type enzyme. DET-type enzymes allow simple electrode configurations operating at lower potentials. These features are less susceptible to redox-active interferents. The current achievements promise the future realization of a simple diagnosis method for PC using a non-invasive specimen, saliva. Figure 1

Abstract A020: LncTransformer: Identifying novel prognostic long-noncoding RNA biomarkers for pancreatic cancer using deep learning

Abstract Pancreatic cancer remains a formidable challenge due to high mortality rates and limited therapeutic options due to late detection of the cancer. Recent literature has focused on investigating the potential of long noncoding RNAs (lncRNAs) as biomarkers in several cancers. To identify significantly altered expression of lncRNA in pancreatic cancer, I collected information from the Cancer Genome Atlas (TCGA) and extracted RNA- sequencing (RNA-seq) transcriptomic profiles of pancreatic carcinomas. Out of 60,660 gene transcripts shared between 151 pancreatic cancer patients, 38 lncRNAs were identified to be significantly differentially expressed. To find the lncRNAs related to metastatic progression of pancreatic cancer, different machine learning algorithms, including logistic regression (LR), support vector machine (SVM), random forest classifier (RFC), and a custom autoencoder model were trained to identify potential prognostic biomarkers. The custom deep learning model predicted metastatic prognostic biomarkers with a 92% accuracy, with the second-best accuracies coming from the SVM and RFC models, having an accuracy of 76%. Using the deep learning model, the following novel lncRNA and gene biomarkers were identified: LINC01300, SERPINB13, AC010789.1, TMPRSS15, DUSP5- DT, AL513128.3, MIR205HG, LINC00486, RF00019, LINC01115, and AC133530.1. Further investigating these results, specifically the identified lncRNA, the LINC01300 was identified to be potentially circulating in the blood. If further analysis proves this hypothesis correct then LINC01300 along with other identified genes could serve as biomarkers in a liquid biopsy. The identified biomarkers would be especially helpful in rural areas with lower accessibility to healthcare as it would allow for an easier, more efficient, and cost-effective method of pancreatic cancer detection. Based on these findings, further investigations of this gene panel in vitro and in vivo will be conducted, as they could be targeted for improved outcomes in pancreatic cancer patients and assist in the diagnosis of metastatic progression based on RNA-seq data of primary pancreatic tumors or the development of a liquid biopsy. Citation Format: Shivali Singh. LncTransformer: Identifying novel prognostic long-noncoding RNA biomarkers for pancreatic cancer using deep learning [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: RNAs as Drivers, Targets, and Therapeutics in Cancer; 2024 Nov 14-17; Bellevue, Washington. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(11_Suppl):Abstract nr A020.

Abstract A067: lncTransformer: Identifying novel prognostic biomarkers for pancreatic cancer using deep learning models

Abstract Pancreatic cancer remains a formidable challenge due to high mortality rates and limited therapeutic options due to late detection of the cancer. Recent literature has focused on investigating the potential of long noncoding RNAs (lncRNAs) as biomarkers in several cancers. To identify significantly altered expression of lncRNA in pancreatic cancer, I collected information from the Cancer Genome Atlas (TCGA) and extracted RNA-sequencing (RNA-seq) transcriptomic profiles of pancreatic carcinomas. Out of 60,660 gene transcripts shared between 151 pancreatic cancer patients, 38 lncRNAs were identified to be significantly differentially expressed. To find the lncRNAs related to metastatic progression of pancreatic cancer, different machine learning algorithms, including logistic regression (LR), support vector machine (SVM), random forest classifier (RFC), and a custom autoencoder model were trained to identify potential prognostic biomarkers. The custom deep learning model predicted metastatic prognostic biomarkers with a 92% accuracy, with the second-best accuracies coming from the SVM and RFC models, having an accuracy of 76%. Using the deep learning model, the following novel lncRNA and gene biomarkers were identified: LINC01300, SERPINB13, AC010789.1, TMPRSS15, DUSP5-DT, AL513128.3, MIR205HG, LINC00486, RF00019, LINC01115, and AC133530.1. Further investigating these results, specifically the identified lncRNA, the LINC01300 was identified to be potentially circulating in the blood. If further analysis proves this hypothesis correct then LINC01300 along with other identified genes could serve as a biomarker panel for a liquid biopsy. The identified biomarkers would be especially helpful in rural areas with lower accessibility to healthcare as it would allow for an easier, more efficient, and cost-effective method of pancreatic cancer detection. Based on these findings, further investigations of this gene panel are being conducted, in vitro and in vivo, to create a novel liquid biopsy to assist in the prognostic diagnosis of metastatic progression of pancreatic cancer. Citation Format: Shivali Singh. lncTransformer: Identifying novel prognostic biomarkers for pancreatic cancer using deep learning models [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr A067.

Diagnostic Utility of MicroRNAs in Pancreatic Cancers.

Pancreatic cancer (PC) is a serious malignant tumor with a high mortality rate, mainly due to late diagnosis and a lack of effective therapeutic interventions. The possibility of recognizing this cancer with reliable biomarkers using minimally invasive methods is of great importance for improving early detection, prognostic assessment, and targeted treatment methods. In recent years, small non-coding RNAs, especially microRNAs, have emerged as promising candidates for biomarkers of pancreatic cancer. Despite the methodological and analytical limitations of microRNA determination and a lack of available automated and standardized tests, a prospective systematic assessment of this new parameter's use in the detection of pancreatic cancer is warranted. This review provides general information on the diagnostic and prognostic utility of microRNAs, which appear to be well-established in many studies. In summary, microRNAs are promising non-invasive biomarkers of pancreatic cancer, offering potential opportunities for early detection, prognosis, and treatment monitoring. As research in this field progresses, microRNAs should become valuable tools in the fight against pancreatic cancer.

Pan-Immune-Inflammation Value as a Novel Prognostic Biomarker for Advanced Pancreatic Cancer.

This study investigated the prognostic value of the pan-immune-inflammationvalue (PIV) in patients with advanced-stage pancreatic cancer (PC). The cohort comprised 71 patients, with a median age of 65 years (range: 37-83). The majority (69%) of patients received the FOLFIRINOX regimen as first-line therapy. Using ROC curve analysis, PIV demonstrated high diagnostic accuracy in predicting mortality, with an area under the curve (AUC) of 0.84 (95% CI: 0.72-0.97) and an optimal cut-off point of 276.5. Elevated PIV was significantly associated with mortality (p = 0.014), and patients with high PIV exhibited significantly poorer overall survival (OS) and progression-free survival (PFS) than those with low PIV (OS: 9.0 months vs. 26.0 months, p < 0.001; PFS: 7.0 months vs. 15.0 months, p < 0.001). Univariate and multivariate analyses identified PIV and the selected chemotherapy regimens as independent prognostic factors for OS and PFS. High PIV values are associated with worse clinical outcomes, reinforcing its role as a reliable prognostic biomarker in advanced-stage PC. These findings underscore the importance of PIV in guiding therapeutic strategies and warrant further investigation in larger cohorts.

POLQ immunostaining behaves as a prognostic factor for pancreatic carcinoma.

DNA polymerase theta (POLQ) is a translesion synthesis polymerase essential for the repair of double strand breaks by the error-prone TMEJ (Theta Mediated End Joining) pathway. Although POLQ participates in maintaining genome stability, several studies have shown that its overexpression correlates with cancer progression and poor prognosis. Due to the fact that its role as a biomarker in pancreatic cancer remains unexplored, we aimed to study the usefulness of POLQ H-score as a prognostic factor in a pancreatic cancer patient cohort. We evaluated POLQ gene expression using a web-based tool to deliver gene expression profiling and interactive analyses based on TCGA and GTEx (GEPIA) and we examined the POLQ immunostaining in 152 biliopancreatic cancer surgical specimens using tissue microarrays. Association with survival was evaluated by Kaplan Meier curves and uni-multivariate Cox regression. GEPIA analysis showed statistical differences according to POLQ mRNA levels in Disease Free Survival (DFS) (log rank 0.023, HR 2.8, p=0.029) and Overall Survival (OS) (log rank 0.011, HR 3.1, p=0.016). For immunohistochemistry (IHC) evaluation, POLQ H-score was calculated, and showed statistical differences for OS in Kaplan Meier curves (log rank 0.001) and uni-multivariate analysis (HR 2.27; 95% CI 1.24-4.15, p=0.008). Our results indicate that POLQ is an independent prognostic factor in pancreatic cancer when analyzed by immunostaining, which is in agreement with the results shown by the POLQ gene expression analysis (GEPIA).

The complement C3a/C3aR pathway is associated with treatment resistance to gemcitabine-based neoadjuvant therapy in pancreatic cancer

Gemcitabine is a standard first-line drug for pancreatic cancer chemotherapy. Nevertheless, gemcitabine resistance is common and significantly limits its therapeutic efficacy, impeding advancements in pancreatic cancer treatment. In this study, through a comprehensive analysis of gemcitabine-resistant cell lines and patient samples, 39 gemcitabine resistance-associated risk genes were identified, and two distinct gemcitabine response-related phenotypes were delineated. Through a combination of bioinformatics analysis and in vivo and in vitro experiments, we identified the C3a/C3aR signaling pathway as a pivotal player in the development of gemcitabine resistance in pancreatic cancer. We found that activation of the C3a/C3aR signaling pathway promoted the proliferation, migration and gemcitabine resistance of pancreatic cancer cells, while the C3aR antagonist SB290157 effectively counteracted these effects by impeding the activation of the C3a/C3aR pathway. Our study reveals the fundamental role of complement C3a in the progression of pancreatic cancer, suggesting that complement C3a may serve as a promising biomarker in pancreatic cancer.

A Controllability Reinforcement Learning Method for Pancreatic Cancer Biomarker Identification.

Pancreatic cancer is one of the most malignant cancers with rapid progression and poor prognosis. The use of transcriptional data can be effective in finding new biomarkers for pancreatic cancer. Many network-based methods used to identify cancer biomarkers are proposed, among which the combination of network controllability appears. However, most of the existing methods do not study RNA, rely on priori and mutations information, or can only achieve classification tasks. In this study, we propose a method combined Relational Graph Convolutional Network and Deep Q-Network called RDDriver to identify pancreatic cancer biomarkers based on multi-layer heterogeneous transcriptional regulation network. Firstly, we construct a regulation network containing long non-coding RNA, microRNA, and messenger RNA. Secondly, Relational Graph Convolutional Network is used to learn the node representation. Finally, we use the idea of Deep Q-Network to build a model, which score and prioritize each RNA with the Popov-Belevitch-Hautus criterion. We train RDDriver on three small simulated networks, and calculate the average score after applying the model parameters to the regulation networks separately. To demonstrate the effectiveness of the method, we perform experiments for comparison between RDDriver and other eight methods based on the approximate benchmark of three types cancer drivers RNAs.

CA19-9 as a Diagnostic Biomarker for Pancreatic Cancer

CA19-9 is a biomarker often used in pancreatic cancer diagnosis; it measures serum CA19-9 levels in the blood which corresponds to cancer status and even staging. It is used clinically but not as a screening biomarker as it doesn’t have the specificity needed to make it cost-effective in a screening programme. It can be used before and after treatment as it can help in choosing a treatment plan. It is used to see if this treatment is working, so it works as a predictive and prognostic biomarker as well. It’s recently been used alongside tumour marker genes to improve its sensitivity and specificity.

Bioinformatics Analysis of LINC00917 Targets miR-3690/DDX39A Axis to Exacerbate Pancreatic Cancer Cell Growth Using Real-Time Quantitative Reverse Transcription PCR

This study aims to reveal the effect of long intergenic non-protein coding RNA 917 (LINC00917) on pancreatic cancer (PC). Specifically, high expression of LINC00917 was validated in five PC cell lines. Colony formation, TUNEL, transwell, immunofluorescence staining and sphere formation assays were conducted in this research, and the results illustrated that LINC00917 accelerated PC cell proliferation, migration, EMT, but impaired the cell apoptosis. Moreover, a panel of mechanism assays were carried out to probe the involvement of LINC00917ceRNA mechanism. LINC00917 was corroborated to sequester miR-3690 to elevate DExD-box helicase 39A (DDX39A) expression. Additionally, the inhibition of miR-3690 on PC cell malignant phenotype was also demonstrated. We illustrated that LINC00917 promoted PC cell proliferation and inhibited the cell apoptosis. Furthermore, LINC00917 contributed to PC cell migration, EMT and stemness. The high enrichment of LINC00917 was detected in biotin-labeled wild-type miR-3690, indicating that LINC00917 bound miR-3690 at the predicted sites. We found that miR-3690 was sponged by LINC00917 and involved in LINC00917-mediated cell proliferation, apoptosis and migration. miR-3690 targeted DDX39A in PC cells, and DDX39A could completely rescue the functional effects of miR-3690. On the whole, DDX39A was required in LINC00917-mediated cell malignant behavior in PC cells. LINC00917 led to DDX39A increment and mediated PC cell biological functions through serving the miR-3690 sponge, indicating LINC00917 can be as a promising biomarker for PC.

OSBPL10-CNBP axis mediates hypoxia-induced pancreatic cancer development.

Pancreatic ductal adenocarcinoma (PDAC) is one of malignancies with worst outcomes among digestive system tumors. Identification of novel biomarkers is of great significance for treatment researchesand prognosis prediction of pancreatic cancer patients. Due to OSBPL10 known involvement in oncogenic activity in other tumors, we elucidated the mechanism underlying its contribution to pancreatic cancer progression. We employed data from the Gene Expression Omnibus database to detect the expression of OSBPL10 in normal and pancreatic cancer tissues. A series of assays were conducted to assess the impact of OSBPL10 on the proliferation and metastatic capacities of pancreatic cancer cells and the influence of OSBPL10 on macrophages were evaluated by Flow cytometry. In addition, Co-immunoprecipitation, mass spectrometry, and western blot assays were utilized to investigate the potential mechanisms of OSBPL10 activity. From our study, OSBPL10 is revealed to be upregulated in pancreatic cancer, with poor prognosis. The overexpression promotes malignant behaviors of pancreatic cancer cells and has an impact on tumor immune microenvironment by stimulating the transformation M1 macrophages into M2 macrophages. Mechanistically, hypoxia induces the expression of OSBPL10 through interaction between hypoxia-inducible factor 1-α and the promoter region of OSBPL10. Additionally, OSBPL10 directly bound to CNBP, mediating CNBP expression and ultimately regulating the proliferation and metastasis capacity of pancreatic cancer cells, as well as influencing macrophage polarization. The research emphasized the oncogenic role of OSBPL10 in pancreatic cancer, uncovering key mechanisms involving hypoxia, HIF-1α, and CNBP. The finding suggests that OSBPL10 is a novel biomarker in pancreatic cancer, making it a potential therapeutic target for intervention in this malignancy.

HATZFS predicts pancreatic cancer driver biomarkers by hierarchical reinforcement learning and zero-forcing set

The search for cancer biomarkers is of great significance for the early diagnosis and clinical treatment planning of pancreatic cancer. RNA transcription networks can effectively represent the interactions between RNA molecules, and the irregular perturbations in these interactions can lead to pathological states, making them commonly used for identifying cancer biomarkers. However, traditional network-based methods for identifying cancer biomarkers often rely on prior information and mutation data. Furthermore, most network controllability-based methods face the limitation of high computational complexity, preventing their integration with deep learning methods. In this paper, we propose a novel deep learning framework, called HATZFS, for identifying driver biomarkers in pancreatic cancer using RNA differential expression data. The proposed model combines hierarchical deep Q-network (HDQN), graph attention network (GAT), and zero-forcing set (ZFS). Firstly, a pancreatic cancer RNA transcription regulatory network is constructed to embed the complex relationship among Long non-coding RNA (lncRNA), microRNA (miRNA) and messenger RNA (mRNA). Then, we transform it into multiple subgraphs using graph sampling method, and apply GAT to learn the node features of the subgraphs. Finally, the HATZFS performs HDQN to select subgraphs and identify important RNA as driver nodes, and determining the controllability of the subgraphs based on ZFS. More important, the paper theoretically proves that when all subgraphs are controllable, the original graph is also controllable, and the union of the driver node sets of all subgraphs is equal to the driver node set of the original graph. To confirm the effectiveness of the proposed model, comprehensive experiments are conducted on the benchmark dataset consisting of 14 databases, comparing it with eight other algorithms. The experimental results demonstrate that the proposed model outperforms other methods. Overall, HATZFS not only provides the importance of all RNA molecules in the pancreatic cancer RNA regulatory network, but also identifies the driver node set of the network as driver biomarkers. Source code can be accessed at https://github.com/HongJieTongXue/HATZFS.

Polymorphisms within autophagy-related genes as susceptibility biomarkers for pancreatic cancer: A meta-analysis of three large European cohorts and functional characterization.

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers with patients having unresectable or metastatic disease at diagnosis, with poor prognosis and very short survival. Given that genetic variation within autophagy-related genes influences autophagic flux and susceptibility to solid cancers, we decided to investigate whether 55,583 single nucleotide polymorphisms (SNPs) within 234 autophagy-related genes could influence the risk of developing PDAC in three large independent cohorts of European ancestry including 12,754 PDAC cases and 324,926 controls. The meta-analysis of these populations identified, for the first time, the association of the BIDrs9604789 variant with an increased risk of developing the disease (ORMeta = 1.31, p = 9.67 × 10-6). We also confirmed the association of TP63rs1515496 and TP63rs35389543 variants with PDAC risk (OR = 0.89, p = 6.27 × 10-8 and OR = 1.16, p = 2.74 × 10-5). Although it is known that BID induces autophagy and TP63 promotes cell growth, cell motility and invasion, we also found that carriers of the TP63rs1515496G allele had increased numbers of FOXP3+ Helios+ T regulatory cells and CD45RA+ T regulatory cells (p = 7.67 × 10-4 and p = 1.56 × 10-3), but also decreased levels of CD4+ T regulatory cells (p = 7.86 × 10-4). These results were in agreement with research suggesting that the TP63rs1515496 variant alters binding sites for FOXA1 and CTCF, which are transcription factors involved in modulating specific subsets of regulatory T cells. In conclusion, this study identifies BID as new susceptibility locus for PDAC and confirms previous studies suggesting that the TP63 gene is involved in the development of PDAC. This study also suggests new pathogenic mechanisms of the TP63 locus in PDAC.

Single hollow-core microstructure fiber biosensor for pancreatic cancer diagnosis in terahertz regime

Abstract Pancreatic cancer is a kind of malignant tumor that is difficult to detect in its early stages, developing rapidly and with a 5-year survival rate of only 5% to 10%. Therefore early diagnosis and discovery of pancreatic cancer are very important for the successful treatment of the disease. Here, we report a single hollow-core microstructural fiber (SHC-MSF) biosensor based on a ZEONEX substrate, which has been optimized for the early detection of pancreatic cancer biomarkers. The proposed SHC-MSF biosensor adopts a single-aperture structure to increase the contact range with assay analytes to improve the detection sensitivity. Its biosensing performance was numerically analyzed using a finite element method with a perfect matching layer. Numerical results demonstrated that the proposed MSF-biosensor presented ultra-high sensitivity (bilirubin: 105.55%, glucose: 105.34%, creatinine: 105.67%) and negligible confinement loss (bilirubin: 5.52×10-14 cm-1, glucose: 1.65×10-14 cm-1, creatinine: 5.57×10-14 cm-1) in the range of 0.3~2.0 THz. Moreover, the SHC-MSF biosensor could selectively detect and distinguish cancer markers of different concentrations in the blood to achieve a more accurate diagnosis of pancreatic cancer. Finally, fabrication tolerance analysis of the proposed MSF-biosensor is provided to ensure the feasibility of rapid preparation.