Prognostic Biomarker For Pancreatic Cancer Research Articles

The EFNA4 gene is a potential prognostic biomarker in pancreatic cancer: a bioinformatics analysis.

Pancreatic cancer is a highly aggressive malignancy with poor prognosis, and there is an urgent need to understand its molecular mechanisms for early diagnosis and treatment. Despite surgical resection being the only effective treatment, most patients are diagnosed at an advanced stage, missing the optimal window for therapy. Identifying novel biomarkers is crucial for prognostic assessment, treatment planning, and early intervention. Ephrin A4 (EFNA4), a member of the receptor tyrosine kinase family, is involved in vascular and epithelial development via regulation of cell migration and rejection. However, the role of EFNA4 in pancreatic cancer has not been reported. Therefore, our study aimed to clarify the role of EFNA4 in pancreatic cancer through bioinformatics analysis and vitro experiments. The expression of EFNA4 and its potential value as a diagnostic and prognostic biomarker in pancreatic cancer was analyzed using data from The Cancer Genome Atlas (TCGA) and the Gene Expression Profiling Interactive Analysis (GEPIA) database. According to the expression level of EFNA4, patients were divided into high expression group and low expression group, and the correlation between overall survival (OS) and disease-free survival (DFS) with different expression levels of EFNA4 and clinical parameters were analyzed. Subsequently, reverse-transcription quantitative polymerase chain reaction (RT-qPCR) was performed to detect EFNA4 expression. The proliferation, invasion, and cloning ability of the cells were detected via Cell Counting Kit 8 (CCK8), Transwell, and plate cloning assays, respectively. EFNA4 is highly expressed in pancreatic cancer, and upregulation of EFNA4 is associated with poor prognosis. In this study, EFNA4 expression was correlated with T stage and TNM (tumor-node-metastasis) stage of pancreatic cancer, and the median survival time and progression-free survival (PFS) were worse in those with high EFNA4 expression (394 days) than in those with low expression (525 days) [hazard ratio (HR): 1.47, 95% confidence interval (CI): 1.00-2.16, P=0.047]. In addition, EFNA4 was also found to be involved in the regulation of signal pathways such as cell adhesion, cyclic AMP, insulin secretion, pancreatic secretion, and protein digestion and absorption. In vitro experiments demonstrated that EFNA4 knockdown significantly inhibited the proliferation, cloning ability, and invasiveness of the PANC-1 and SW1990 pancreatic cancer cell lines. The abnormal expression of EFNA4 in pancreatic cancer is associated with poor prognosis. Knockout of EFNA4 gene could significantly inhibit the proliferation and invasion of pancreatic cancer cells. Therefore, EFNA4 may be one of the molecular targets for poor prognosis of patients with pancreatic cancer.

Interleukin-1α as a Potential Prognostic Biomarker in Pancreatic Cancer.

We assessed the prognostic role of pro-inflammatory cytokines of the IL-1 superfamily in patients with pancreatic cancer. This retrospective study was performed using two independent cohorts of patients with pancreatic cancer: the International Cancer Genome Consortium (ICGC, N = 267) cohort and The Cancer Genome Atlas (TCGA, N = 178) cohort. Univariate Cox regressions were used to identify prognosis-related pro-inflammatory cytokines of the IL-1 superfamily. Cytokines associated with outcome were included in a multivariate Cox model with relevant clinicopathological variables to identify prognostic biomarkers. IL-1α was the only pro-inflammatory cytokine of the IL-1 superfamily that was significantly associated with prognosis in both cohorts. In the training cohort (ICGC), the decile of patients with the lowest IL1A expression had better overall survival (HR = 1.99 [1.01-3.93], p = 0.05) and better relapse-free survival (HR = 1.85 [1.02-3.34], p = 0.04) than the group with the highest IL1A expression. The validation cohort (TCGA) confirmed these results: the decile with the lowest IL1A expression had better overall survival (HR = 3.00 [1.14-7.90], p = 0.03) and a lower risk of progression (HR = 3.11 [1.24-7.80], p = 0.01). IL1A is an independent prognostic marker and could be considered a potential therapeutic target in pancreatic cancer patients.

Multiomics integration reveals NETosis heterogeneity and TLR2 as a prognostic biomarker in pancreatic cancer

Pancreatic ductal adenocarcinoma (PDAC) is a highly malignant neoplasm characterized by a poor prognosis and limited therapeutic strategy. The PDAC tumor microenvironment presents a complex heterogeneity, where neutrophils emerge as the predominant constituents of the innate immune cell population. Leveraging the power of single-cell RNA-seq, spatial RNA-seq, and multi-omics approaches, we included both published datasets and our in-house patient cohorts, elucidating the inherent heterogeneity in the formation of neutrophil extracellular traps (NETs) and revealed the correlation between NETs and immune suppression. Meanwhile, we constructed a multi-omics prognostic model that suggested the patients exhibiting downregulated expression of NETs may have an unfavorable outcome. We also confirmed TLR2 as a potent prognosis factor and patients with low TLR2 expression had more effective T cells and an overall survival extension for 6 months. Targeting TLR2 might be a promising strategy to reverse immunosuppression and control tumor progression for an improved prognosis.

Integration of single-nucleus and exosome RNA sequencing dissected inter-cellular communication and biomarkers in pancreatic ductal adenocarcinoma

BackgroundMounting evidence underscores the importance of cell communication within the tumor microenvironment, which is pivotal in tumor proliferation, invasion, and metastasis. Exosomes play a crucial role in cell-to-cell communication. Although single-cell RNA sequencing (scRNA-seq) provides insights into individual cell transcriptional characteristics, it falls short of comprehensively capturing exosome-mediated intercellular communication. MethodWe analyzed Pancreatic Ductal Adenocarcinoma (PDAC) tissues, separating supernatant and precipitate for exosome purification and single-cell nucleus suspension. We then constructed Single-nucleus RNA sequencing (snRNA-seq) and small RNA-seq libraries from these components. Our bioinformatic analysis integrated these sequences with ligand-receptor analysis and public miRNA data to map the cell communication network. ResultsWe established intercellular communication networks using bioinformatic analysis to track exosome miRNA effects and ligand-receptor pairs. Significantly, hsa-miR-1293 emerged as a prognostic biomarker for pancreatic cancer, linked to immune evasion, increased myeloid-derived suppressor cells, and poorer prognosis. Targeting this miRNA may enhance anti-tumor immunity and improve outcomes. ConclusionOur study offers a novel approach to constructing intercellular communication networks using snRNA-seq and exosome-small RNA sequencing. By integrating miRNA tracing with ligand-receptor analysis, we illuminate the complex interactions in the pancreatic cancer microenvironment, highlighting the pivotal role of miRNAs and identifying potential biomarkers and therapeutic targets.

Diagnostic and prognostic value of circulating exosomal glypican-1 in pancreatic cancer: a meta-analysis.

Pancreatic cancer (PC) is usually detected in the advanced stages. Liquid biopsy has become a revolutionary strategy for cancer diagnosis and prognosis prediction. This study aims to investigate the diagnostic and prognostic value of circulating exosomal glypican-1 (GPC-1) in PC. We systematically searched relevant studies. For diagnostic accuracy, pooled sensitivity and specificity and the area under the summary receiver operating characteristic curve (AUC) were calculated. Regarding prognostic value, hazard ratios (HRs) and 95% CIs for overall survival (OS) were summarized by using a random-effects model. We found 8 studies that examined the diagnostic value of circulating exosomal GPC-1 in PC, and 3 studies that investigated its prognostic value. Pooled sensitivity and specificity were 0.88 (95% CI, 0.65-0.97) and 0.86 (95% CI, 0.72-0.94). The AUC was 0.93 (95% CI, 0.90-0.95). Prognostic analysis showed that higher levels of circulating exosomal GPC-1 were associated with poorer OS in PC patients, and the combined HR for OS was 4.59 (random-effects model, 95% CI = 1.17-18.03, P = .022). The results of both studies were robust and neither had publication bias. Circulating exosomal GPC-1 may be used as a diagnostic and prognostic biomarker for PC. However, this result needs to be validated by further research using a larger sample size.

Immune-related signature identifies IL1R2 as an immunological and prognostic biomarker in pancreatic cancer.

Pancreatic cancer is one of the most aggressive malignancies, a robust prognostic signature and novel biomarkers are urgently needed for accurate stratification of the patients and optimization of clinical decision-making. A list of bioinformatic analysis were applied in public dataset to construct an immune-related signature. Furthermore, the most pivotal gene in the signature was identified. The potential mechanism of the core gene function was revealed through GSEA, CIBERSORT, ESTIMATE, immunophenoscore (IPS) algorithm, single-cell analysis, and functional experiment. An immune-related prognostic signature and associated nomogram were constructed and validated. Among the genes constituting the signature, interleukin 1 receptor type II (IL1R2) was identified as the gene occupying the most paramount position in the risk signature. Meanwhile, knockdown of IL1R2 significantly inhibited the proliferation, invasion, and migration ability of pancreatic cancer cells. Additionally, high IL1R2 expression was associated with reduced CD8+ T cell infiltration in pancreatic cancer microenvironment, which may be due to high programmed cell death-ligand-1 (PD-L1) expression in cancer cells. Finally, the IPS algorithm proved that patients with high IL1R2 expression possessed a higher tumor mutation burden and a higher probability of benefiting from immunotherapy. In conclusion, our study constructed an efficient immune-related prognostic signature and identified the key role of IL1R2 in the development of pancreatic cancer, as well as its potential to serve as a biomarker for immunotherapy efficacy prediction for pancreatic cancer.

MICAL2 implies immunosuppressive features and acts as an independent and adverse prognostic biomarker in pancreatic cancer

At present, clinical outcomes of pancreatic cancer patients are still poor. New therapeutic targets for pancreatic cancer are urgently needed. Previous studies have indicated that Microtubule Associated Monooxygenase, Calponin and LIM Domain Containing 2 (MICAL2) is highly expressed in many tumors and promotes tumor progression. However, the role played by MICAL2 in pancreatic cancer remains unclear. Based on gene expression and clinical information from multiple datasets, we used comprehensive bioinformatics analysis in combination with tissue microarray to explore the function and clinical value of MICAL2. The results showed that MICAL2 was highly expressed in pancreatic cancer tissue and exhibited potential diagnostic capability. High expression of MICAL2 was also associated with poor prognosis and acted as an independent prognostic factor. MICAL2, mainly expressed in fibroblasts of pancreatic cancer, was closely related to metastasis and immune-related features, such as epithelial-mesenchymal transformation, extracellular cell matrix degradation, and inflammatory response. Furthermore, higher MICAL2 expression in pancreatic cancer was also associated with an increase in cancer-associated fibroblasts as well as M2 macrophage infiltration, and a reduction in CD8 + T cell infiltration, thereby facilitating the formation of an immunosuppressive microenvironment. Our results helped elucidate the clinical value and function in metastasis and immunity of MICAL2 in pancreatic cancer. These findings provided potential clinical strategies for diagnosis, targeted therapy combination immunotherapy, and prognosis in patients with pancreatic cancer.

EPYC functions as a novel prognostic biomarker for pancreatic cancer

Pancreatic cancer (PC) has become a worldwide challenge attributed to its difficult early diagnosis and rapid progression. Treatments continue to be limited besides surgical resection. Hence, we aimed to discover novel biological signatures as clinically effective therapeutic targets for PC via the mining of public tumor databases. We found that epiphycan (EPYC) could function as an independent risk factor to predict the poor prognosis in PC based on integrated bioinformatics analysis. We downloaded associated PC data profiles from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) online websites, then applied the software Rstudio to filter out genes under the strict criteria. After the batch survival analysis using Log-rank test and univariate cox regression, we obtained 39 candidate genes. Subsequently, we narrowed the scope to 8 genes by establishing a Lasso regression model. Eventually, we focused on 2 genes (EPYC and MET) by further building a multivariate cox regression model. Given that the role of EPYC in PC remains obscure, we then performed a series of molecular functional experiments, including RT-qPCR, CCK8, EdU, colony formation, Transwell, western blot, cell live-dead staining, subcutaneous tumor formation, to enhance our insight into its underlying molecular mechanisms. The above results demonstrated that EPYC was highly expressed in PC cell lines and could promote the proliferation of PCs via PI3K-AKT signaling pathway in vivo and in vitro. We arrived at a conclusion that EPYC was expected to be a biological neo-biomarker for PC followed by being a potential therapeutic target.

CTSK and PLAU as Prognostic Biomarker and Related to Immune Infiltration in Pancreatic Cancer: Evidence from Bioinformatics Analysis and qPCR

Pancreatic adenocarcinoma (PAAD) is a malignancy with the highest mortality rate worldwide. There is a pressing need for novel biomarkers that can facilitate early detection and serve as targets for therapeutic interventions beyond the commonly utilized CA199 marker. This study utilized microarray datasets (GSE15471, GSE62165, and GSE28735) from the Gene Expression Omnibus (GEO) to identify differentially expressed genes (DEGs) and construct a protein-protein interaction network using STRING and Cytoscape. Hub genes were selected using BiNGO. Expression profiles and clinical data from the Cancer Genome Atlas (TCGA) were then used to compare the expression levels of CTSK and PLAU in pancreatic cancer and healthy pancreatic tissues via the Wilcoxon rank-sum test, with further validation using qPCR. Functional enrichment analysis was conducted to explore potential signaling pathways and biological functions. Prognostic values were assessed by the Kaplan-Meier and Cox regression analyses, and an overall survival (OS) nomogram was created to predict 1-, 2-, and 3-year survival after cancer diagnosis. The infiltration of immune cells was evaluated by single-sample gene set enrichment analysis. The methylation status of both genes was analyzed using the UALCAN and MethSurv databases. The results demonstrated that CTSK and PLAU were overexpressed in pancreatic cancer and that the hypomethylation status of both genes was associated with a poor prognosis. The overexpression of both genes was positively correlated with various immune cells, and functional enrichment analysis revealed that they were associated with immune cell infiltration. Besides, the effects of PLAU on the migration and invasion of pancreatic cancer cells were also verified by scratch and transwell experiments. Consequently, CTSK and PLAU have potential as prognostic biomarkers for pancreatic cancer.

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.

NSD3, a member of nuclear receptor-binding SET domain family, is a potential prognostic biomarker for pancreatic cancer.

Members of the nuclear receptor-binding SET domain (NSD) family of histone H3 lysine 36 methyltransferases comprise NSD1, NSD2 (MMSET/WHSC1), and NSD3 (Wolf-Hirschhorn syndrome candidate 1-like 1, WHSC1L1). While the expression of NSD genes is essential to normal biological processes and cancer, knowledge of their expression levels to prognosticate in cancer remains unclear. We analyzed the expression patterns for NSD family genes across multiple cancer types and examined their association with clinical features and patient survival profiles. Next, we explored the association between NSD3 expression and described features of the tumor microenvironment (TME) in PAAD, a severe type of pancreatic cancer. In particular, we correlated promoter methylation levels for NSD3 with patient outcomes in PAAD. Finally, we explored the putative oncogenic roles for NSD3 using a series of experiments with pancreatic cancer cells. We report that the expression of NSD family members is correlated with clinical prognosis across multiple types of cancers. Also, we demonstrate that NSD3 variants are most prevalent among NSD genes across cancers we analyzed. Notably, when compared with NSD1 and NSD2, we find that NSD3 is prominently expressed, and its expression is significantly linked with clinical outcome in pancreatic cancer. Furthermore, NSD3 is frequently amplified, exhibits low promoter methylation, and is correlated with immune cell infiltration and enhanced proliferation of pancreatic cancer. Finally, we demonstrate that knockdown of NSD3 alters H3K36me2 methylation, downstream gene expression and EGFR/ERK signaling in pancreatic cancer cells. We find that expression levels, the presence of genetic variants of NSD family genes, as well as their promoter methylation are correlated with clinical outcomes in cancer, including pancreatic cancer. Our in vitro experiments suggest that NSD3 may be relevant to gene expression regulation and growth factor signaling in pancreatic cancer.

Immunocyte Infiltration Analysis and Immunohistochemistry Identify EVL as a Potential Prognostic Biomarker for Pancreatic Cancer

Ena-VASP-like (EVL), a member of the Enabled/vasodilator stimulated phosphoprotein family, is functionally expressed in various cancers. This study explored the prognostic value and potential mechanism of EVL in pancreatic cancer (PC). RNA-seq obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were used to evaluate EVL expression differences, and clinical samples were collected for validation. The prognostic value of EVL was evaluated by survival data obtained from TCGA and clinical samples. The biological pathways involved in EVL were evaluated by functional enrichment analysis such as GO, KEGG, and GSEA. We used immune infiltration analysis to estimate the correlation between EVL and tumor-infiltrating immune cells (TICs). The expression of EVL is down-regulated in PC tissues, which is an independent factor affecting survival time. Survival analysis suggested EVL-high expression was associated with good prognosis in PC patients. The results of the enrichment analysis suggested that the biological function of EVL was closely related to the immune mechanism. Tumor immune infiltration analysis showed that high expression of EVL was accompanied by high levels of immune infiltration. Furthermore, EVL was strongly correlated with the content of immune cells such as CD8+ T cells, B cells, regulatory T cells, CD4+ Tem cells, and follicular Th cells. EVL is a potential independent prognostic marker and immunotherapy target for PC. Mechanistically, EVL may affect the prognosis by extensively promoting immune cell infiltration, including strengthening the anti-tumor immune response of CD8+ T cells.

C4orf47 contributes to the dormancy of pancreatic cancer under hypoxic conditions.

In our comprehensive analysis of pancreatic cancer pathology, we found that the C4orf47 molecule was upregulated in hypoxic environments. C4orf47 is reported to be a centrosome-associated protein, but its biological significance in cancer is completely unknown; therefore, we assessed its role in pancreatic cancer. We found that C4orf47 was a direct target of HIF-1α and is upregulated in hypoxic conditions, in which it suppressed the cell cycle and inhibits cell proliferation through up-regulation of the cell cycle repressors Fbxw-7, P27, and p57; and the down-regulation of the cell cycle promoters c-myc, cyclinD1, and cyclinC. Furthermore, C4orf47 induced epithelial-mesenchymal transition and enhanced their cell plasticity and invasiveness. In addition, the p-Erk/p-p38 ratio was significantly enhanced and down-regulated CD44 expression by C4orf47 suppression, suggesting that C4orf47 is involved in pancreatic cancer dormancy under hypoxic conditions. Furthermore, the potential of C4orf47 expression was a good prognostic biomarker for pancreatic cancer. These results contribute to the elucidation of the pathology of refractory pancreatic cancer and the development of novel therapeutic strategies.

An Efficient Novel Approach with Multi Class Label Classification through Machine Learning Models for Pancreatic Cancer

Pancreatic cancer is right now the fourth largest cause of cancer-related deaths. Early diagnosis is one good solution for pancreatic cancer patients and reduces the mortality rate. Accurate and earlier diagnosis of the pancreatic tumor is a demanding task due to several factors such as delayed diagnosis and absence of early warning symptoms. The conventional distributed machine learning techniques such as SVM and logistic regression were not efficient to minimize the error rate and improve the classification of pancreatic cancer with higher accuracy. Therefore, a novel technique called Distributed Hybrid Elitism gene Quadratic discriminant Reinforced Learning Classifier System (DHEGQDRLCS) is developed in this paper. First, the number of data samples is collected from the repository dataset. This repository contains all the necessary files for the identification of prognostic biomarkers for pancreatic cancer. After the data collection, the separation of training and testing samples is performed for the accurate classification of pancreatic cancer samples. Then the training samples are considered and applied to Distributed Hybrid Elitism gene Quadratic discriminant Reinforced Learning Classifier System. The proposed hybrid classifier system uses the Kernel Quadratic Discriminant Function to analyze the training samples. After that, the Elitism gradient gene optimization is applied for classifying the samples into multiple classes such as non-cancerous pancreas, benign hepatobiliary disease i.e., pancreatic cancer, and Pancreatic ductal adenocarcinoma. Then the Reinforced Learning technique is applied to minimize the loss function based on target classification results and predicted classification results. Finally, the hybridized approach improves pancreatic cancer diagnosing accuracy. Experimental evaluation is carried out with pancreatic cancer dataset with Hadoop distributed system and different quantitative metrics such as Accuracy, balanced accuracy, F1-score, precision, recall, specificity, TN, TP, FN, FP, ROC_AUC, PRC_AUC, and PRC_APS. The performance analysis results indicate that the DHEGQDRLCS provides better diagnosing accuracy when compared to existing methods.

Integrated bioinformatics analysis shows integrin alpha 3 is a prognostic biomarker for pancreatic cancer.

Integrin subunit alpha 3 (ITGA3) expression correlates with the development and prognosis of human cancers. This study aimed to investigate the association of ITGA3 expression with pancreatic cancer (PCa) prognosis. The ITGA3 gene expression data were extracted from The Cancer Genome Atlas (TCGA) pancreatic adenocarcinoma (PAAD) cohort and 14 Gene Expression Omnibus microarray datasets. The differences in ITGA3 expression levels between tumor and non-tumor tissues were compared using the Mann-Whitney U test. Cox regression analysis and meta-analysis were performed to detect the association of ITGA3 expression with PCa prognosis. ITGA3 expression was higher in tumors than in controls. Tumors with advanced grades (3/4) had higher ITGA3 levels compared with early-grade tumors (1/2). The meta-analysis of the TCGA PAAD cohort and seven microarray datasets (GSE28735, GSE62452, GSE79668, GSE71729, GSE57495, GSE78229, and GSE21501) showed that ITGA3 was a prognostic biomarker in PCa (hazard ratio (HR) = 1.38, 95% confidence interval (CI) 1.26-1.51, p < 0.00001). Five ITGA3-related genes, including ITGB1 (HR = 1.6), ITGB5 (HR = 1.6), ITGB6 (HR = 1.6), LAMA3 (HR = 2.1), and CD9 (HR = 2.3), correlated with PCa prognosis significantly (p < 0.05). Functional enrichment analysis showed that ITGA3 was related to "hsa04151: PI3K-Akt signaling pathway" and "hsa04510: Focal adhesion." We concluded that high ITGA3 expression was a potential prognostic biomarker in PCa.

Circulating MicroRNAs as Noninvasive Diagnostic and Prognostic Biomarkers in Pancreatic Cancer: A Review.

Pancreatic cancer (PC) is one of the most lethal human cancers. Currently, most PC cases are diagnosed at an already advanced stage. Early detection of PC is critical to improving survival rates. Therefore, there is an urgent need to identify biomarkers for the early detection of PC.Recently, circulating miRNAs in whole blood and other body fluids have been reported as promising biomarkers for the early detection of various cancers, including PC. Furthermore, due to minimal invasiveness and technical availability, circulating miRNAs hold promise for further wide usage. As a potential novel molecular marker, circulating miRNAs not only represent promising noninvasive diagnostic and prognostic tools but could also improve the evaluation of tumor classification, metastasis, and curative effect. The purpose of this review is to outline the available information regarding circulating miRNAs as biomarkers for the early detection of PC.

CircLMTK2 Silencing Attenuates Gemcitabine Resistance in Pancreatic Cancer by Sponging miR-485-5p and to Target PAK1

Pancreatic cancer (PC) has a high degree of malignancy and poor prognosis, and countless patients have distant metastasis when diagnosed. Gemcitabine (GEM) chemotherapy is one of the main ways of treatment. However, PC cells have been displayed chemoresistance to GEM during treatment. Circular RNAs (circRNAs) have been demonstrated to be the most popular diagnostic and prognostic biomarkers in PC with GEM resistance. Here, we assessed the potential of circLMTK2 in the GEM resistance of PC cells. Functional assays were implemented to measure the impacts of circLMTK2 on the proliferation, migration/invasion, and apoptosis of GEM-resistant PC cells. Bioinformatics analysis and mechanical experiments displayed the underlying mechanism of circLMTK2 in GEM-resistant PC cells. We found that circLMTK2 was upregulated in PC and GEM-resistant PC tissues and cells. CircLMTK2 knockdown suppressed proliferation, invasion, migration, and enhanced apoptosis in GEM-resistant PC cells. Moreover, circLMTK2 silencing could decrease GEM resistance-associated tumor size in vivo. In terms of mechanism, circLMTK2 served as a sponge for miR-485-5p, and miR-485-5p bound to p21 (RAC1) activated kinase 1 (PAK1), which were clarified via the dual-luciferase assay in PC cell lines. We confirmed that circLMTK2 knockdown attenuated GEM-resistant PC cells by regulating PAK1 via miR-485-5p. Our study demonstrated that circLMTK2 may be a novel diagnostic and prognostic biomarker in GEM-resistant PC cells.

Identification of Candidate Therapeutic Target Genes and Profiling of Tumor-Infiltrating Immune Cells in Pancreatic Cancer via Integrated Transcriptomic Analysis.

Pancreatic cancer (PC) has a dismal prognosis despite advancing scientific and technological knowledge. The exploration of novel genes is critical to improving current therapeutic measures. This research is aimed at selecting hub genes that can act as candidate therapeutic target genes and as prognostic biomarkers in PC. Gene expression profiles of datasets GSE101448, GSE15471, and GSE62452 were extracted from the GEO database. The “limma” package was performed to select differentially expressed genes (DEGs) between PC and normal tissue samples in each dataset. Robust rank aggregation (RRA) algorithm was conducted to integrate multiple expression profiles and identify robust DEGs. GO analysis and KEGG analysis were conducted to identify the functional correlation of the DEGs. The CIBERSORT algorithm was conducted to estimate the immune cell composition of each tissue sample. STRING and Cytoscape were used to establish the protein-protein interaction (PPI) network. The cytoHubba plugin in Cytoscape was performed to identify hub genes. Survival analysis based on hub gene expression was performed with clinical information from TCGA database. 566 robust DEGs (338 upregulated genes and 226 downregulated genes) were identified. Tumor tissue had a higher infiltration of resting dendritic cells and tumor-associated macrophages (TAM), including M0, M1, and M2 macrophages, while infiltration levels of B memory cells, plasma cells, T cells CD8, T follicular helper cells, and NK cells in normal tissue were relatively higher. GO terms and KEGG pathway analysis results revealed enrichment in tumor-associated pathways, including the extracellular matrix organization, cell−substrate adhesion cytokine−cytokine receptor interaction, calcium signaling pathway, and glycine, serine, and threonine metabolism, to name a few. Finally, FN1, MSLN, PLAU, and VCAN were selected as hub genes. High expression of FN1, MSLN, PLAU, and VCAN in PC significantly correlated with poor prognosis. Integrated transcriptomic analysis was used to provide new insights into PC pathogenesis. FN1, MSLN, PLAU, and VCAN may be considered as novel biomarkers of PC.

Clinicopathological and prognostic significance of circulating immune cells in the patients with pancreatic cancer

Pancreatic cancer is characterized by immune tolerance and immunotherapeutic resistance. Circulating cells may reflect the general immune status of the patient. However, the circulating immune status of pancreatic cancer are largely uncharacterized. Here, the subset distribution was analyzed in peripheral blood samples from 101 patients with pancreatic cancer and 142 healthy volunteers by using flow cytometry. The differences of the subpopulation distribution in the two groups and the relation between clinical parameters with the subset were determined. Moreover, the clinical application of each subset as prognosis biomarker was also assessed by Kaplan-Meier analysis. The reduced proportion of total lymphocyte and upregulated CD4/CD8 ratio were observed in pancreatic cancer than those in healthy controls. Of note, increased proportions of lymphocyte and NKT cells were noticed more frequently in patients over 60 years (P = 0.043) and patients with metastasis (P = 0.027), respectively. However, our correlation analyses revealed no correlation between the proportions of T cells, B cell and NK cells with clinicopathologic features. Furthermore, the analysis displayed that proportions of CD4+T cell, B cell and CD4/CD8 ratio significantly reduced in the cohort of post-operation, while the frequency of CD8+T cell and NKT cells elevated remarkably. Finally, the Kaplan-Meier analysis indicated that patients with high lymphocyte proportion might have prolonged overall survival (P = 0.007). The altered distribution of peripheral blood immune cell subpopulation in pancreatic cancer and its relationship with clinical outcome highlight the potential use of circulating immune subsets as prognostic biomarkers in pancreatic cancer.

Integrin β1 in Pancreatic Cancer: Expressions, Functions, and Clinical Implications

Simple SummaryPancreatic cancer (PC) is a highly aggressive malignant tumor with an extremely poor prognosis. Early diagnosis and treatment are key to improving the survival rate of PC patients. Emerging studies show that integrins might contribute to the pathogenesis of PC. This review presents the various signaling pathways that are mediated by integrins in PC and emphasizes the multiple functions of integrin β1 in malignant behaviors of PC. It also discusses the clinical significance of integrin β1 as well as integrin β1-based therapy in PC patients.Pancreatic cancer (PC) is characterized by rapid progression and a high mortality rate. The current treatment is still based on surgical treatment, supplemented by radiotherapy and chemotherapy, and new methods of combining immune and molecular biological treatments are being explored. Despite this, the survival rate of PC patients is still very disappointing. Therefore, clarifying the molecular mechanism of PC pathogenesis and developing precisely targeted drugs are key to improving PC prognosis. As the most common β subunit of the integrin family, integrin β1 has been proved to be closely related to the vascular invasion, distant metastasis, and survival of PC patients, and treatment targeting integrin β1 in PC has gained initial success in animal models. In this review, we summarize the various signaling pathways by which integrins are involved in PC, focusing on the roles of integrin β1 in the malignant behaviors of PC. Additionally, recent studies regarding the feasibility of integrin β1 as a diagnostic and prognostic biomarker in PC are also discussed. Finally, we present the progress of several integrin β1-based clinical trials to highlight the potential of integrin β1 as a target for personalized therapy in PC.