Biomarker For Pancreatic Cancer Research Articles

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.

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.

Collard-like Bi2S3@Au nanocomposites-based label free electrochemical immunosensor for quantitative detection of CA19-9

Here, a label-free immunosensor was conducted using rGO/Collard-like Bi2S3@Au nanocomposite system to detect carbohydrate antigen 19-9 (CA19-9) as a diagnostic biomarker for Pancreatic cancer. The unique morphology of Collard-like Bi2S3 increases its specific surface area, which allows it to immobilize more materials and biomolecules on its surface. To realize the covalent bonding of bismuth-based materials with biomolecules, bismuth-based materials were functionalized by Au NPs. Besides, the superior electrochemical activity and large specific surface area of Bi2S3@Au improved the stability of the sensor, which made it an ideal candidate for regulating the sensing interface in this study. Meanwhile, reduced graphene oxide (rGO) with remarkable electrical conductivity to improve the current response of the sensing interface, thus broadening the linear range of the immunosensor. Based on the above materials, a label free electrochemical immunosensor was constructed for detecting CA19-9. Under optimal conditions, it displays good linear relationship within the range of 0.01 U mL-1 to 100 U mL-1, the detection limit being 0.0012 U mL-1. Additionally, the immunosensor prepared in this work is characterized by superior interference immunity, reproducibility and storage stability, and has yielded satisfactory recovery rate in testing the serum samples of patients.

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.

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.

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.

Abstract A009: Mutant KRAS in Circulating Tumor DNA Assessed by Digit Droplet PCR as a Biomarker in Pancreatic Cancer in Patients Treated with Neoadjuvant Chemotherapy

Abstract Introduction: The detectability and prognostic significance of circulating tumor DNA (ctDNA) in patients with pancreatic ductal adenocarcinoma (PDAC) after neoadjuvant chemotherapy (NAC) is unclear. The objective of this study was to investigate the rate of detection and prognostic value of KRAS mutant ctDNA in PDAC patients after treatment with NAC as assessed by digital droplet polymerase chain reaction (ddPCR). Methods: Patients with newly diagnosed, localized, PDAC were enrolled in a prospective cohort study. Peripheral blood samples were collected at diagnosis, after NAC, and after resection. DNA was extracted from patient plasma samples using the QIAamp Circulating Nucleic Acid Kit #55114 (Qiagen N.V., Venlo Netherlands). Each sample was probed (catalog number 10049550) for KRAS codon 12 exon 2 glycine substitutions for aspartic acid (G12D), valine (G12V), and arginine (G12R). Kaplan-Meier, log-rank, and Cox regression survival analyses were performed. Results: 84 patients were included in the analysis. Patients were most commonly male (53.6%) and had tumors that were radiographically resectable (57.1%). After diagnosis, 79.8% of patients received NAC and 57.1% of patients received curative-intent surgical resection. Samples were collected for analysis from 59 (70.2%), 56 (83.6%), and 45 (93.8%) patients at diagnosis, after NAC, and after resection, respectively. Mutant KRAS ctDNA was detected in 49.3% of patients at diagnosis, 69.6% of patients after NAC, and 54.1% of patients after resection, respectively. The median OS of the cohort was 25.1 months (12.0-not reached [NR]). Detection (hazard ratio [HR] 36.8, 95% confidence interval [CI] 2.9-461.4), copy number (HR 4.0 95% CI 1.6-10.2), and percentage mutation (HR 2.9 95% CI 1.2-6.8) of KRAS G12V ctDNA after resection were all independently associated with shorter OS. Mutant KRAS ctDNA detection was not associated with and OS at other study timepoints. There were 15 (17.9%) patients that cleared mutational ctDNA over the course of treatment. Clearance of ctDNA during NAC was associated with improved overall survival (OS) (18.4 mo. vs NR). Conclusions: In patients with PDAC treated with NAC, KRAS mutations are detectable in ctDNA by ddPCR in the majority of patients over the course of treatment. Detection, copy number, and percentage mutation of KRAS G12V after NAC and resection were each independently associated with shorter OS. Clearance of ctDNA is associated with improved OS. The results demonstrate that KRAS mutant ctDNA predicts prognosis after NAC and resection. Citation Format: Dominic Vitello, Dhavan Shah, Amy Wells, Larissa Masnyk, Madison Cox, Lauren Janczewski, John Abad, Kevin Dawravoo, Arlene D'Souza, Grace Suh, Robert Bayer, Massimo Cristofanilli, David Bentrem, Yingzhe Liu, Hui Zhang, Lucas Santana-Santos, Lawrence Jennings, Qiang Zhang, Akhil Chawla. Mutant KRAS in Circulating Tumor DNA Assessed by Digit Droplet PCR as a Biomarker in Pancreatic Cancer in Patients Treated with Neoadjuvant Chemotherapy [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research; 2024 Sep 15-18; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl_2):Abstract nr A009.

Bioinformatical and experimental validation on EFNA5 biomarker for pancreatic cancer

Pancreatic cancer is one of the most aggressive malignancies in the digestive system, characterized by vague early symptoms and rapid disease progression. Currently, treatment options for pancreatic cancer are limited, with unsatisfactory outcomes and poor prognosis, as the overall 5-year survival rate is less than 10%. Therefore, it is crucial to identify new biomarkers for pancreatic cancer.We analyzed the differential expression of EFNA5 at the mRNA level using data from the TCGA, GTEX, and GEO databases, followed by validation of EFNA5 protein expression differences through immunohistochemistry. Kaplan-Meier survival analysis and Cox regression analysis were used to explore the prognostic value of EFNA5. KEGG, GO enrichment analyses, and GSEA were employed to predict the biological functions of EFNA5. Bioinformatics algorithms were utilized to analyze the impact of EFNA5 on drug sensitivity, immune cell infiltration, and tumor mutational burden. Finally, we conducted cell phenotype experiments to investigate the effects of EFNA5 on the proliferation, migration, and invasion of pancreatic cancer cells. Bioinformatics data showed that the mRNA level of EFNA5 was higher in pancreatic cancer tissues compared to normal tissues. Immunohistochemistry experiments confirmed that the protein expression level of EFNA5 was significantly higher in pancreatic cancer tissues (n = 19) than in adjacent normal tissues (n = 19). Survival analysis indicated that the overall survival (OS) and progression-free survival (PFS) were worse in the high EFNA5 expression group compared to the low expression group. Univariate and multivariate Cox regression analyses demonstrated that EFNA5 is a risk factor affecting the prognosis of pancreatic cancer patients. KEGG pathway enrichment analysis revealed that differentially expressed genes (DEGs) between high and low EFNA5 expression groups were primarily enriched in pathways such as PI3K/Akt, axon guidance, and focal adhesion. GO enrichment analysis showed that DEGs were mainly associated with functions like epidermis development, cell adhesion, and intercellular junctions. Immune cell infiltration analysis found that EFNA5 expression was correlated with the infiltration levels of various immune cells. The tumor mutational burden was higher in the high EFNA5 expression group compared to the low expression group. Drug sensitivity analysis indicated that the high EFNA5 expression group had lower sensitivity to several commonly used chemotherapy drugs for pancreatic cancer. Cellular experiments demonstrated that knocking down EFNA5 inhibited the proliferation, invasion, and migration of pancreatic cancer cells. EFNA5 is overexpressed in pancreatic cancer tissues, and its high expression is associated with poor prognosis in pancreatic cancer patients. Downregulation of EFNA5 reduces the proliferation, migration, and invasion capabilities of the pancreatic cancer cell line BxPC-3. EFNA5 is closely related to the occurrence, development, prognosis, immune infiltration, and drug sensitivity of pancreatic cancer. Our study suggests that EFNA5 is a potential biomarker and therapeutic target.

Serum PCSK9 is a novel serological biomarker for the diagnosis and prognosis of pancreatic cancer.

Although CA19-9 is an essential blood biomarker of pancreatic cancer (PC), its sensitivity and specificity are limited for early detection. We analyzed the serum proprotein convertase subtilisin/kexin type 9 (sPCSK9) in PC patients, benign disease groups (BDG), and healthy controls (HC) by ELISA. Consistently, sPCSK9 was considerably lower in PC patients than in HC (Z = -2.546, P < 0.05), and sPCSK9 in PC patients was statistically significantly higher than in BDG (Z = -5.457, P < 0.001). sPCSK9 was linked to the invasion of lymph nodes (χ2 = 6.846, P < 0.01). According to ROC curves, combining sPCSK9 with CA19-9 could potentially enhance the diagnostic capability of CA19-9 in early-stage PC patients. Furthermore, the low sPCSK9 group (n = 41) exhibited statistically significantly prolonged overall survival compared to the high sPCSK9 group (n = 15), with median survival times of 27 months (95% CI [17.59-36.41]) and 11 months (95% CI [7.21-14.79]), respectively (P = 0.022). The diagnostic performance of CA19-9 for early-stage PC patients could be improved by combining sPCSK9 with CA19-9. Moreover, the higher sPCSK9 group has a significantly shorter overall survival rate.

Liquid-liquid phase separation-related features of PYGB/ACTR3/CCNA2/ITGB1/ATP8A1/RAP1GAP2 predict the prognosis of pancreatic cancer.

The growth and metastasis of pancreatic cancer (PC) has been found to be closely associated with liquid-liquid phase separation (LLPS). This study sought to identify LLPS-related biomarkers in PC to construct a robust prognostic model. Transcriptomic data and clinical information related to PC were retrieved from publicly accessible databases. The PC-related data set was subjected to differential expression, Mendelian randomization (MR), univariate Cox, and least absolute selection and shrinkage operator analyses to identify biomarkers. Using the biomarkers, we subsequently constructed a risk model, identified the independent prognostic factors of PC, established a nomogram, and conducted an immune analysis. The study identified four genes linked with an increased risk of PC; that is, PYGB, ACTR3, CCNA2, and ITGB1. Conversely, ATP8A1, and RAP1GAP2 were found to provide protection against PC. These findings contributed significantly to the development of a highly precise risk model in which risk, age, and pathology N stage were categorized as independent factors in predicting the prognosis of PC patients. Using these factors, a nomogram was established to predict survival outcomes accurately. An immune analysis revealed varying levels of eosinophils, gamma delta T cells, and other immune cells between the distinct risk groups. The high-risk patients exhibited increased potential for immune escape, while the low-risk patients showed a higher response to immunotherapy. Six genes were identified as having potential causal relationships with PC. These genes were integrated into a prognostic risk model, thereby serving as unique prognostic signatures. Our findings provide novel insights into predicting the prognosis of PC patients.

Single-cell analysis of chemotherapy-resistant microenvironment identifies a chemo-response biomarker for pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDAC) is a highly fatal, aggressive cancer due to limited response chemotherapy. The tumor microenvironment (TME) has emerged as a key player in the development of chemoresistance and in malignant progression. In this study, we hypothesized that chemotherapy response is predictable by chemotherapy-related cell types and their differentially expressed genes (DEGs). DEGs of chemoresistance cell types were identified via single cell analysis and Wilcoxon test. A chemotherapy response signature was established using a random forest model and validated with public datasets. Bulk cell fraction was analyzed using BayesPrism algorithms. Log-rank test was used to analyze survival of PDAC patients. We found that natural killer (NK) cells, myeloid cells, and erythroid cells were highly infiltrated in chemo-resistant TME. A total of 36 chemoresistance-related DEGs of chemo-resistant cells were identified in chemo-resistant PDAC. Functional enrichment analysis showed that chemoresistance upregulated various inflammation-related pathways, including TGF-β signaling. Based on these features, we constructed a random forest model to predict the response and survival for PDAC patients, which accurately distinguished high-risk and chemoresistant patients with significantly poorer prognosis in both the training and independent validation datasets. Cox regression analysis indicated that predicted labels were an independent prognostic factor in PDAC. Moreover, deconvolution of TME confirmed higher infiltration levels of M2 macrophage and NK cells in predicted chemoresistance. When combined with chemotherapy response related tumor mutations, the model showed excellent ability in predicting chemotherapy response and survival. The TME was closely associated with the chemotherapy response and prognosis of PDAC. Our TME-based random forest model predicted chemotherapy response with complementary knowledge to the response-related genetic mutations.