Pathogenesis Of Pancreatic Cancer Research Articles

Pancreatic cancer: from early detection to personalized treatment approaches.

Pancreatic cancer is notorious for its persistently poor prognosis and health outcomes, so some of the questions that may be begged are "Why is it mostly diagnosed at end stage?", "What could we possibly do with the advancing technology in today's world to detect early pancreatic cancer and intervene?", and "Are there any implementation of the existing novel imaging technologies?". Well, to start with, this is in part because the majority of patients presented would already have reached a locally advanced or metastatic stage at the time of diagnosis due to its highly aggressive characteristics and lack of symptoms. Due to this striking disparity in survival, advancements in early detection and intervention are likely to significantly increase patients' survival. Presently, screening is frequently used in high-risk individuals in order to obtain an early pancreatic cancer diagnosis. Having a thorough understanding of the pathogenesis and risk factors of pancreatic cancer may enable us to identify individuals at high risk, diagnose the disease early, and begin treatment promptly. In this review, the authors outline the clinical hurdles to early pancreatic cancer detection, describe high-risk populations, and discuss current screening initiatives for high-risk individuals. The ultimate goal of this current review is to study the roles of both traditional and novel imaging modalities for early pancreatic cancer detection. A lot of the novel imaging techniques mentioned seem promising, but they need to be put to the test on a large scale and may need to be combined with other non-invasive biomarkers before they can be widely used.

The Molecular Characteristics and Therapeutic Implications of O-Glycan Synthesis in Pancreatic Cancer by Integrating Transcriptome and Single-Cell Data.

Glycans constitute the primary components of proteins that regulate key carcinogenic processes in cancer progression. This study investigated the significance of O-glycan synthesis in the pathogenesis, outcome, and therapy of pancreatic cancer (PC). Transcriptomic data and clinical prognostic information of PC were acquired via TCGA and GEO databases. CSA database was used to obtain single-cell data of PC. The O-glycan biosynthesis signaling pathway and its related genes were acquired via the MSigDB platform. The nonnegative matrix factorization (NMF) clustering was utilized to construct the O-glycan biosynthesis-associated molecular subtypes in PC. The LASSO and Cox regression were utilized to build the prognostic prediction model. We utilized real-time quantitative PCR (qRT-PCR) to verify the expressed levels of model genes. Single-cell analysis was utilized to investigate the levels of target genes and O-glycan biosynthesis signaling pathway in the PC tumour microenvironment. We obtained 30 genes related to O-glycan biosynthesis, among which 15 were associated with the prognosis of PC. All PC samples were grouped into two distinct molecular subtypes associated with O-glycan biosynthesis: OGRGcluster C1 and OGRGcluster C2, and compared to OGRGcluster C1. PCs in OGRGcluster C2 had a more advanced clinical stage and pathological grade, worse prognosis, and more active O-glycan biosynthesis function. Immune analysis indicated that naïve B cell, CD8+ T cell, memory-activated CD4+ T cell, and monocytes displayed remarkably higher infiltration levels in OGRGcluster C1 while resting NK cell, macrophages M0, resting dendritic cell, activated dendritic cell, and neutrophils exhibited markedly higher infiltration levels in OGRGcluster C2. OGRGcluster C1 exhibited higher sensitivities to drugs, such as cisplatin, irinotecan, KRAS(G12C) inhibitor-12, oxaliplatin, paclitaxel, and sorafenib. Besides, we built the O-glycan biosynthesis-related prognostic model (including SPRR1B, COL17A1, and ECT2) with a good prediction performance. SPRR1B, COL17A1, and ECT2 were remarkably highly expressed in PC tissues and linked to a poor outcome. Single-cell analysis revealed that Oglycan biosynthesis was observed only in PC, and consistent with this, the target genes were significantly enriched in PC. We first constructed molecular subtypes and prognostic models related to O-glycan biosynthesis in PC. It is clear that O-glycan biosynthesis is related to the development, prognosis, immune microenvironment, and treatment of PC. This provides new strategies for stratification, diagnosis, and treatment of PC patients.

New avenues for the treatment of immunotherapy-resistant pancreatic cancer.

Pancreatic cancer (PC) is characterized by its extremely aggressive nature and ranks 14th in the number of new cancer cases worldwide. However, due to its complexity, it ranks 7th in the list of the most lethal cancers worldwide. The pathogenesis of PC involves several complex processes, including familial genetic factors associated with risk factors such as obesity, diabetes mellitus, chronic pancreatitis, and smoking. Mutations in genes such as KRAS, TP53, and SMAD4 are linked to the appearance of malignant cells that generate pancreatic lesions and, consequently, cancer. In this context, some therapies are used for PC, one of which is immunotherapy, which is extremely promising in various other types of cancer but has shown little response in the treatment of PC due to various resistance mechanisms that contribute to a drop in immunotherapy efficiency. It is therefore clear that the tumor microenvironment (TME) has a huge impact on the resistance process, since cellular and non-cellular elements create an immunosuppressive environment, characterized by a dense desmoplastic stroma with cancer-associated fibroblasts, pancreatic stellate cells, extracellular matrix, and immunosuppressive cells. Linked to this are genetic mutations in TP53 and immunosuppressive factors that act on T cells, resulting in a shortage of CD8+ T cells and limited expression of activation markers such as interferon-gamma. In this way, finding new strategies that make it possible to manipulate resistance mechanisms is necessary. Thus, techniques such as the use of TME modulators that block receptors and stromal molecules that generate resistance, the use of genetic manipulation in specific regions, such as microRNAs, the modulation of extrinsic and intrinsic factors associated with T cells, and, above all, therapeutic models that combine these modulation techniques constitute the promising future of PC therapy. Thus, this study aims to elucidate the main mechanisms of resistance to immunotherapy in PC and new ways of manipulating this process, resulting in a more efficient therapy for cancer patients and, consequently, a reduction in the lethality of this aggressive cancer.

MicroRNA-130b Suppresses Malignant Behaviours and Inhibits the Activation of the PI3K/Akt Signaling Pathway by Targeting MET in Pancreatic Cancer.

There has been interested in the microRNAs' roles in pancreatic cancer (PC) cell biology, particularly in regulating pathways related to tumorigenesis. The study aimed to explore the hub miRNAs in PC and underlying mechanisms by bioinformatics and fundamental experiments. RNA datasets collected from the Gene Expression Omnibus were analysed to find out differentially expressed RNAs (DERNAs). The miRNA-mRNA and protein-protein interaction (PPI) networks were built. The clinicopathological features and expressions of hub miRNAs and hub mRNAs were explored. Dual-luciferase reporter gene assay was performed to assess the interaction between microRNA and target gene. RT-qPCR and western blot were employed to explore RNA expression. The roles of RNA were detected by CCK-8 test, wound healing, transwell, and flow cytometry experiment. We verified 40 DEmiRNAs and 1613 DEmRNAs, then detected a total of 69 final functional mRNAs (FmRNAs) and 23 DEmiRNAs. In the miRNA-mRNA networks, microRNA-130b (miR-130b) was the hub RNA with highest degrees. Clinical analysis revealed that miR-130b was considerably lower expressed in cancerous tissues than in healthy ones, and patients with higher-expressed miR-130b had a better prognosis. Mechanically, miR-130b directly targeted MET in PC cells. Cell functional experiments verified that miR-130b suppressed cell proliferation, migration, promoted apoptosis, and inhibited the PI3K/Akt pathway by targeting MET in PC cells. Our findings illustrated the specific molecular mechanism of miR-130b regulating PC progress. The miR-130b/MET axis may be an alternative target in the therapeutic intervention of PC and provide an opportunity to deepen our understanding of the pathogenesis of PC.

The Expression Analysis of Long Non-coding RNAs Related to Wnt/β-Catenin Signaling in Pancreatic Cancer Patients.

Background The oncogenic Wnt/β-catenin signaling plays a critical role in carcinogenesis, prognosis, and resistance to therapy. Pancreatic cancer (PC) has high mortality because of its poor prognosis. Several studies have suggested that lncRNAs are directly involved in the development and progression of PC as well as in Wnt/β-catenin signaling. In this study, we investigated and compared the expression of Wnt/β-catenin signaling-related ZFAS1 and HCG11 lncRNAs, and their targets, CTNNB1 and IGF2BP1 genes in the blood of patients with PC and healthy individuals. A total of 47 PC patients and 50 healthy individuals participated in this study. RNA was extracted from the peripheral blood samples of participants, and cDNA was synthesized. The expression level of the selected genes was quantified by real-time PCR. The expression of HCG11 lncRNA and CTNNB1 genes in patients with PC was significantly upregulated compared to healthy individuals, and the expression of the ZFAS1 lncRNA was significantly downregulated. According to the analysis of the ROC curve, the diagnostic powers of ZFAS1 and CTNNB1 in PC were 0.67 and 0.69, respectively. Altogether, the present study suggests a role for ZFAS1 and HCG11 lncRNAs and CTNNB1 and IGF2BP1 in the pathogenesis of pancreatic cancer. Moreover, the peripheral expression of these lncRNAs may be useful as potential biomarkers for PC.

The Role of Endocrine Disruption Chemical-Regulated Aryl Hydrocarbon Receptor Activity in the Pathogenesis of Pancreatic Diseases and Cancer.

The aryl hydrocarbon receptor (AHR) serves as a ligand-activated transcription factor crucial for regulating fundamental cellular and molecular processes, such as xenobiotic metabolism, immune responses, and cancer development. Notably, a spectrum of endocrine-disrupting chemicals (EDCs) act as agonists or antagonists of AHR, leading to the dysregulation of pivotal cellular and molecular processes and endocrine system disruption. Accumulating evidence suggests a correlation between EDC exposure and the onset of diverse pancreatic diseases, including diabetes, pancreatitis, and pancreatic cancer. Despite this association, the mechanistic role of AHR as a linchpin molecule in EDC exposure-related pathogenesis of pancreatic diseases and cancer remains unexplored. This review comprehensively examines the involvement of AHR in EDC exposure-mediated regulation of pancreatic pathogenesis, emphasizing AHR as a potential therapeutic target for the pathogenesis of pancreatic diseases and cancer.

The MUC1–HIF-1α signaling axis regulates pancreatic cancer pathogenesis through polyamine metabolism remodeling

Dysregulation of polyamine metabolism has been implicated in cancer initiation and progression; however, the mechanism of polyamine dysregulation in cancer is not fully understood. In this study, we investigated the role of MUC1, a mucin protein overexpressed in pancreatic cancer, in regulating polyamine metabolism. Utilizing pancreatic cancer patient data, we noted a positive correlation between MUC1 expression and the expression of key polyamine metabolism pathway genes. Functional studies revealed that knockdown of spermidine/spermine N1-acetyltransferase 1 (SAT1), a key enzyme involved in polyamine catabolism, attenuated the oncogenic functions of MUC1, including cell survival and proliferation. We further identified a regulatory axis whereby MUC1 stabilized hypoxia-inducible factor (HIF-1α), leading to increased SAT1 expression, which in turn induced carbon flux into the tricarboxylic acid cycle. MUC1-mediated stabilization of HIF-1α enhanced the promoter occupancy of the latter on SAT1 promoter and corresponding transcriptional activation of SAT1, which could be abrogated by pharmacological inhibition of HIF-1α or CRISPR/Cas9-mediated knockout of HIF1A. MUC1 knockdown caused a significant reduction in the levels of SAT1-generated metabolites, N1-acetylspermidine and N8-acetylspermidine. Given the known role of MUC1 in therapy resistance, we also investigated whether inhibiting SAT1 would enhance the efficacy of FOLFIRINOX chemotherapy. By utilizing organoid and orthotopic pancreatic cancer mouse models, we observed that targeting SAT1 with pentamidine improved the efficacy of FOLFIRINOX, suggesting that the combination may represent a promising therapeutic strategy against pancreatic cancer. This study provides insights into the interplay between MUC1 and polyamine metabolism, offering potential avenues for the development of treatments against pancreatic cancer.

Analytical Validation of Loss of Heterozygosity and Mutation Detection in Pancreatic Fine-Needle Aspirates by Capillary Electrophoresis and Sanger Sequencing.

Pancreatic cystic disease, including duct dilation, represents precursor states towards the development of pancreatic cancer, a form of malignancy with relatively low incidence but high mortality. While most of these cysts (>85%) are benign, the remainder can progress over time, leading to malignant transformation, invasion, and metastasis. Cytologic diagnosis is challenging, limited by the paucity or complete absence of cells representative of cystic lesions and fibrosis. Molecular analysis of fluids collected from endoscopic-guided fine-needle aspiration of pancreatic cysts and dilated duct lesions can be used to evaluate the risk of progression to malignancy. The basis for the enhanced diagnostic utility of molecular approaches is the ability to interrogate cell-free nucleic acid of the cyst/duct and/or extracellular fluid. The allelic imbalances at tumor suppressor loci and the selective oncogenic drivers are used clinically to help differentiate benign stable pancreatic cysts from those progressing toward high-grade dysplasia. Methods are discussed and used to determine the efficacy for diagnostic implementation. Here, we report the analytical validation of methods to detect causally associated molecular changes integral to the pathogenesis of pancreatic cancer from pancreatic cyst fluids.

The Role of Local Angiotensin II/Angiotensin Type 1-receptor Mechanisms in Adipose Tissue Dysfunction to Promote Pancreatic Cancer.

Obesity and adipose tissue dysfunction are important risk factors for pancreatic cancer. Pancreatic cancer is one of the most lethal cancers globally. The renin-angiotensin system (RAS) is expressed in many tissues, including adipose tissue. Dysregulation of angiotensin II and angiotensin II receptors in adipose tissue through the activation of different signaling pathways leads to adipose tissue dysfunction, including insulin resistance, adipose tissue inflammation, adipocytokines secretion, and metabolic alterations. The pathogenesis of pancreatic cancer remains uncertain. However, there is evidence that dysregulation of local angiotensin II in adipose tissue that occurs in association with obesity is, in part, responsible for the initiation and progression of pancreatic cancer. Due to the role of local angiotensin II in the dysfunction of adipose tissue, angiotensin receptor blockers may be considered a new therapeutic strategy in the amelioration of the complications related to adipose tissue dysfunction and prevention of pancreatic cancer. This review aims to consider the biological roles of local angiotensin II and angiotensin II receptors in adipose tissue dysfunction to promote pancreatic cancer progression with a focus on adipose tissue inflammation and metabolic reprogramming.

Citric acid promotes SPARC release in pancreatic cancer cells and inhibits the progression of pancreatic tumors in mice on a high-fat diet.

Over the years, pancreatic cancer has experienced a global surge in incidence and mortality rates, largely attributed to the influence of obesity and diabetes mellitus on disease initiation and progression. In this study, we investigated the pathogenesis of pancreatic cancer in mice subjected to a high-fat diet (HFD) and observed an increase in citric acid expenditure. Notably, citrate treatment demonstrates significant efficacy in promoting tumor cell apoptosis, suppressing cell proliferation, and inhibiting tumor growth in vivo. Our investigations revealed that citrate achieved these effects by releasing secreted protein acidic and rich in cysteine (SPARC) proteins, repolarizing M2 macrophages into M1 macrophages, and facilitating tumor cell apoptosis. Overall, our research highlights the critical role of citric acid as a pivotal metabolite in the intricate relationship between obesity and pancreatic cancer. Furthermore, we uncovered the significant metabolic and immune checkpoint function of SPARC in pancreatic cancer, suggesting its potential as both a biomarker and therapeutic target in treating this patient population.

Illuminating the immunological landscape: mitochondrial gene defects in pancreatic cancer through a multiomics lens.

This comprehensive review delves into the complex interplay between mitochondrial gene defects and pancreatic cancer pathogenesis through a multiomics approach. By amalgamating data from genomic, transcriptomic, proteomic, and metabolomic studies, we dissected the mechanisms by which mitochondrial genetic variations dictate cancer progression. Emphasis has been placed on the roles of these genes in altering cellular metabolic processes, signal transduction pathways, and immune system interactions. We further explored how these findings could refine therapeutic interventions, with a particular focus on precision medicine applications. This analysis not only fills pivotal knowledge gaps about mitochondrial anomalies in pancreatic cancer but also paves the way for future investigations into personalized therapy options. This finding underscores the crucial nexus between mitochondrial genetics and oncological immunology, opening new avenues for targeted cancer treatment strategies.

Non-invasive biomarkers for early diagnosis of pancreatic cancer risk: metabolite genomewide association study based on the KCPS-II cohort

BackgroundPancreatic cancer is a lethal disease with a high mortality rate. The difficulty of early diagnosis is one of its primary causes. Therefore, we aimed to discover non-invasive biomarkers that facilitate the early diagnosis of pancreatic cancer risk.MethodsThe study subjects were randomly selected from the Korean Cancer Prevention Study-II and matched by age, sex, and blood collection point [pancreatic cancer incidence (n = 128) vs. control (n = 256)]. The baseline serum samples were analyzed by non-targeted metabolomics, and XGBoost was used to select significant metabolites related to pancreatic cancer incidence. Genomewide association study for the selected metabolites discovered valuable single nucleotide polymorphisms (SNPs). Moderation and mediation analysis were conducted to explore the variables related to pancreatic cancer risk.ResultsEleven discriminant metabolites were selected by applying a cut-off of 4.0 in XGBoost. Five SNP presented significance in metabolite-GWAS (p ≤ 5 × 10–6) and logistic regression analysis. Among them, the pair metabolite of rs2370981, rs55870181, and rs72805402 displayed a different network pattern with clinical/biochemical indicators on comparison with allelic carrier and non-carrier. In addition, we demonstrated the indirect effect of rs59519100 on pancreatic cancer risk mediated by γ-glutamyl tyrosine, which affects the smoking status. The predictive ability for pancreatic cancer on the model using five SNPs and four pair metabolites with the conventional risk factors was the highest (AUC: 0.738 [0.661–0.815]).ConclusionsSignatures involving metabolites and SNPs discovered in the present research may be closely associated with the pathogenesis of pancreatic cancer and for use as predictive biomarkers allowing early pancreatic cancer diagnosis and therapy.

Inverse correlation of abundance of sphingomyelins with length of associated N-acyl fatty acid chain contributes to pathogenesis of pancreatic cancer

Inverse correlation of abundance of sphingomyelins with length of associated N-acyl fatty acid chain contributes to pathogenesis of pancreatic cancer

Precision oncology: Artificial intelligence, circulating cell-free DNA, and the minimally invasive detection of pancreatic cancer-A pilot study.

Pancreatic cancer (PC) is among the most lethal cancers. The lack of effective tools for early detection results in late tumor detection and, consequently, high mortality rate. Precision oncology aims to develop targeted individual treatments based on advanced computational approaches of omics data. Biomarkers, such as global alteration of cytosine (CpG) methylation, can be pivotal for these objectives. In this study, we performed DNA methylation profiling of pancreatic cancer patients using circulating cell-free DNA (cfDNA) and artificial intelligence (AI) including Deep Learning (DL) for minimally invasive detection to elucidate the epigenetic pathogenesis of PC. The Illumina Infinium HD Assay was used for genome-wide DNA methylation profiling of cfDNA in treatment-naïve patients. Six AI algorithms were used to determine PC detection accuracy based on cytosine (CpG) methylation markers. Additional strategies for minimizing overfitting were employed. The molecular pathogenesis was interrogated using enrichment analysis. In total, we identified 4556 significantly differentially methylated CpGs (q-value < 0.05; Bonferroni correction) in PC versus controls. Highly accurate PC detection was achieved with all 6 AI platforms (Area under the receiver operator characteristics curve [0.90-1.00]). For example, DL achieved AUC (95% CI): 1.00 (0.95-1.00), with a sensitivity and specificity of 100%. A separate modeling approach based on logistic regression-based yielded an AUC (95% CI) 1.0 (1.0-1.0) with a sensitivity and specificity of 100% for PC detection. The top four biological pathways that were epigenetically altered in PC and are known to be linked with cancer are discussed. Using a minimally invasive approach, AI, and epigenetic analysis of circulating cfDNA, high predictive accuracy for PC was achieved. From a clinical perspective, our findings suggest that that early detection leading to improved overall survival may be achievable in the future.

The Expanding Roles of Long Non‐Coding RNAs in Pancreatic Cancer

AbstractPancreatic cancer is a clinically challenging disease with a poor survival rate and is one of the leading causes of cancer‐related morbidity. It is highly resistant to treatment and effective diagnostic tools during the early stages of the disease are lacking. Long non‐coding RNAs (lncRNAs) participate extensively in the physiological processes of various diseases, especially multiple types of cancers. lncRNAs have been shown to perform essential functions and are gaining increasing attention in pancreatic cancer research. This review summarizes the roles of lncRNAs in the occurrence, development, and therapeutic resistance of pancreatic cancer by categorizing their regulation of epigenetic inheritance, transcription, post‐transcription, and related signaling pathways. Potential clinical implications, including diagnostic and prognostic biomarkers, as well as the therapeutic prospects of these lncRNAs in pancreatic cancer, are also discussed. Novel insights into the role of lncRNAs in the pathogenesis of pancreatic cancer may help researchers develop novel therapeutic strategies.

Circulating microRNAs as Potential Biomarkers in Pancreatic Cancer-Advances and Challenges.

There is an urgent unmet need for robust and reliable biomarkers for early diagnosis, prognosis, and prediction of response to specific treatments of many aggressive and deadly cancers, such as pancreatic cancer, and liquid biopsy-based miRNA profiling has the potential for this. MiRNAs are a subset of non-coding RNAs that regulate the expression of a multitude of genes post-transcriptionally and thus are potential diagnostic, prognostic, and predictive biomarkers and have also emerged as potential therapeutics. Because miRNAs are involved in the post-transcriptional regulation of their target mRNAs via repressing gene expression, defects in miRNA biogenesis pathway and miRNA expression perturb the expression of a multitude of oncogenic or tumor-suppressive genes that are involved in the pathogenesis of various cancers. As such, numerous miRNAs have been identified to be downregulated or upregulated in many cancers, functioning as either oncomes or oncosuppressor miRs. Moreover, dysregulation of miRNA biogenesis pathways can also change miRNA expression and function in cancer. Profiling of dysregulated miRNAs in pancreatic cancer has been shown to correlate with disease diagnosis, indicate optimal treatment options and predict response to a specific therapy. Specific miRNA signatures can track the stages of pancreatic cancer and hold potential as diagnostic, prognostic, and predictive markers, as well as therapeutics such as miRNA mimics and miRNA inhibitors (antagomirs). Furthermore, identified specific miRNAs and genes they regulate in pancreatic cancer along with downstream pathways can be used as potential therapeutic targets. However, a limited understanding and validation of the specific roles of miRNAs, lack of tissue specificity, methodological, technical, or analytical reproducibility, harmonization of miRNA isolation and quantification methods, the use of standard operating procedures, and the availability of automated and standardized assays to improve reproducibility between independent studies limit bench-to-bedside translation of the miRNA biomarkers for clinical applications. Here I review recent findings on miRNAs in pancreatic cancer pathogenesis and their potential as diagnostic, prognostic, and predictive markers.

Proposing new early detection indicators for pancreatic cancer: Combining machine learning and neural networks for serum miRNA-based diagnostic model.

Pancreatic cancer (PC) is a lethal malignancy that ranks seventh in terms of global cancer-related mortality. Despite advancements in treatment, the five-year survival rate remains low, emphasizing the urgent need for reliable early detection methods. MicroRNAs (miRNAs), a group of non-coding RNAs involved in critical gene regulatory mechanisms, have garnered significant attention as potential diagnostic and prognostic biomarkers for pancreatic cancer (PC). Their suitability stems from their accessibility and stability in blood, making them particularly appealing for clinical applications. In this study, we analyzed serum miRNA expression profiles from three independent PC datasets obtained from the Gene Expression Omnibus (GEO) database. To identify serum miRNAs associated with PC incidence, we employed three machine learning algorithms: Support Vector Machine-Recursive Feature Elimination (SVM-RFE), Least Absolute Shrinkage and Selection Operator (LASSO), and Random Forest. We developed an artificial neural network model to assess the accuracy of the identified PC-related serum miRNAs (PCRSMs) and create a nomogram. These findings were further validated through qPCR experiments. Additionally, patient samples with PC were classified using the consensus clustering method. Our analysis revealed three PCRSMs, namely hsa-miR-4648, hsa-miR-125b-1-3p, and hsa-miR-3201, using the three machine learning algorithms. The artificial neural network model demonstrated high accuracy in distinguishing between normal and pancreatic cancer samples, with verification and training groups exhibiting AUC values of 0.935 and 0.926, respectively. We also utilized the consensus clustering method to classify PC samples into two optimal subtypes. Furthermore, our investigation into the expression of PCRSMs unveiled a significant negative correlation between the expression of hsa-miR-125b-1-3p and age. Our study introduces a novel artificial neural network model for early diagnosis of pancreatic cancer, carrying significant clinical implications. Furthermore, our findings provide valuable insights into the pathogenesis of pancreatic cancer and offer potential avenues for drug screening, personalized treatment, and immunotherapy against this lethal disease.

Genetic features of pathogenesis in pancreatic adenocarcinomas

To date, the problem of pancreatic cancer is the high mortality rate. The IARC predicts that pancreatic cancer will become the second leading cause of cancer deaths by 2030. Because patients usually present with advanced disease and respond poorly to broad-spectrum chemotherapy, overall survival is only 10%. This highlights the urgent clinical need to identify new therapeutic approaches for patients with pancreatic adeno-carcinoma. Objective: To identify patterns of genetic changes in the development of pancreatic carcinoma and to consider pathogenesis of this process. Materials and methods: data from multi-genetic panels of Memorial Sloan Kettering Cancer Center, articles of the journal Cancers, and PubMed re-source articles. Results: the work has uncovered major and alternative pathways in the pathogenesis of pancreatic cancer, established hypotheses of similar pathways in tumors of other localizations and the presence of fundamental pathogenetic links in pathogenesis, indicating the universality of certain genetic changes in tumor growth.

Noncoding RNAs: an emerging modulator of drug resistance in pancreatic cancer.

Pancreatic cancer is the eighth leading cause of cancer-related deaths worldwide. Chemotherapy including gemcitabine, 5-fluorouracil, adriamycin and cisplatin, immunotherapy with immune checkpoint inhibitors and targeted therapy have been demonstrated to significantly improve prognosis of pancreatic cancer patients with advanced diseases. However, most patients developed drug resistance to these therapeutic agents, which leading to shortened patient survival. The detailed molecular mechanisms contributing to pancreatic cancer drug resistance remain largely unclear. The growing evidences have shown that noncoding RNAs (ncRNAs), including microRNAs (miRNAs), long noncoding RNAs (lncRNAs) and circular RNAs (circRNAs), are involved in pancreatic cancer pathogenesis and development of drug resistance. In the present review, we systematically summarized the new insight on of various miRNAs, lncRNAs and circRNAs on drug resistance of pancreatic cancer. These results demonstrated that targeting the tumor-specific ncRNA may provide novel options for pancreatic cancer treatments.

PANoptosis-related molecular subtype and prognostic model associated with the immune microenvironment and individualized therapy in pancreatic cancer.

PANoptosis is an inflammatory type of programmed cell death regulated by PANopotosome. Mounting evidence has shown that PANoptosis could be involved in cancer pathogenesis and the tumor immune microenvironment. Nevertheless, there have been no studies on the mechanism of PANoptosis on pancreatic cancer (PC) pathogenesis. We downloaded the data on transcriptomic and clinical features of PC patients from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus databases. Additionally, the data on copy number variation (CNV), methylation and somatic mutations of genes in 33 types of cancers were obtained from TCGA. Next, we identified the PANoptosis-related molecular subtype using the consensus clustering analysis, and constructed and validated the PANoptosis-related prognostic model using LASSO and Cox regression analyses. Moreover, RT-qPCR was performed to determine the expression of genes involved in the model. We obtained 66 PANoptosis-related genes (PANRGs) from published studies. Of these, 24 PC-specific prognosis-related genes were identified. Pan-cancer analysis revealed complex genetic changes, including CNV, methylation, and mutation in PANRGs were identified in various cancers. By consensus clustering analysis, PC patients were classified into two PANoptosis-related patterns: PANcluster A and B. In PANcluster A, the patient prognosis was significantly worse compared to PANcluster B. The CIBERSORT algorithm showed a significant increase in the infiltration of CD8+ T cells, monocytes, and naïve B cells, in patients in PANcluster B. Additionally, the infiltration of macrophages, activated mast cells, and dendritic cells were higher in patients in PANcluster A. Patients in PANcluster A were more sensitive to erlotinib, selumetinib and trametinib, whereas patients in PANcluster B were highly sensitive to irinotecan, oxaliplatin and sorafenib. Moreover, we constructed and validated the PANoptosis-related prognostic model to predict the patient's survival. Finally, the GEPIA and Human Protein Atlas databases were analyzed, and RT-qPCR was performed. Compared to normal tissues, a significant increase in CXCL10 and ITGB6 (associated with the model) expression was observed in PC tissues. We first identified the PANoptosis-related molecular subtypes and established a PANoptosis-related prognostic model for predicting the survival of patients with PC. These results would aid in exploring the mechanisms of PANoptosis in PC pathogenesis.