Biomarker Panel for the Diagnosis of Pancreatic Ductal Adenocarcinoma

An integrated computational biology approach defines the crucial role of TRIP13 in pancreatic cancer

Pancreatic cancer (PanCa) is one of the most aggressive forms of cancer and its incidence rate is continuously increasing every year. It is expected that by 2030, PanCa will become the 2nd leading cause of cancer-related deaths in the United States due to the lack of early diagnosis and extremely poor survival. Despite great advancements in biomedical research, there are very limited early diagnostic modalities available for the early detection of PanCa. Thus, understanding of disease biology and identification of newer diagnostic and therapeutic modalities are high priority. Herein, we have utilized high dimensional omics data along with some wet laboratory experiments to decipher the expression level of hormone receptor interactor 13 (TRIP13) in various pathological staging including functional enrichment analysis. The functional enrichment analyses specifically suggest that TRIP13 and its related oncogenic network genes are involved in very important patho-physiological pathways. These analyses are supported by qPCR, immunoblotting and IHC analysis. Based on our study we proposed TRIP13 as a novel molecular target for PanCa diagnosis and therapeutic interventions. Overall, we have demonstrated a crucial role of TRIP13 in pathogenic events and progression of PanCa through applied integrated computational biology approaches.

Identifying Effective Biomarkers for Accurate Pancreatic Cancer Prognosis Using Statistical Machine Learning

Pancreatic cancer (PC) has one of the lowest survival rates among all major types of cancer. Consequently, it is one of the leading causes of mortality worldwide. Serum biomarkers historically correlate well with the early prognosis of post-surgical complications of PC. However, attempts to identify an effective biomarker panel for the successful prognosis of PC were almost non-existent in the current literature. The current study investigated the roles of various serum biomarkers including carbohydrate antigen 19-9 (CA19-9), chemokine (C-X-C motif) ligand 8 (CXCL-8), procalcitonin (PCT), and other relevant clinical data for identifying PC progression, classified into sepsis, recurrence, and other post-surgical complications, among PC patients. The most relevant biochemical and clinical markers for PC prognosis were identified using a random-forest-powered feature elimination method. Using this informative biomarker panel, the selected machine-learning (ML) classification models demonstrated highly accurate results for classifying PC patients into three complication groups on independent test data. The superiority of the combined biomarker panel (Max AUC-ROC = 100%) was further established over using CA19-9 features exclusively (Max AUC-ROC = 75%) for the task of classifying PC progression. This novel study demonstrates the effectiveness of the combined biomarker panel in successfully diagnosing PC progression and other relevant complications among Egyptian PC survivors.

Clinical-radiomics nomogram using contrast-enhanced CT to predict histological grade and survival in pancreatic ductal adenocarcinoma

Objectives

Tumor grading is important for prognosis of pancreatic ductal adenocarcinoma (PDAC). In this study, we developed preoperative clinical-radiomics nomograms using features from contrast-enhanced CT (CECT), to discriminate high-grade and low-grade PDAC and predict overall survival (OS).

Methods

In this single-center, retrospective study conducted from February 2014 to April 2021, consecutive PDAC patients who underwent CECT and had pathologically identified grading were randomized to training (n=200) and test (n=84) cohorts for development of model to predict histological grade based on radiomics scores from CECT (HGrad). Another 42 patients were used as external validation cohort of HGrad. A nomogram (HGnom) was constructed using radiomics score, CA12-5 and smoking to predict histological grade. A second nomogram (Pnom) was constructed using radiomics score, CA12-5, TNM, adjuvant treatment, resection margin and microvascular invasion to predict OS in radical resection patients (217 of 284).

Results

Among 326 patients, 122 were high-grade (120 poorly differentiated and 2 undifferentiated). The HGrad yielded AUCs of 0.75 (95% CI: 0.64, 0.85) and 0.76 (95% CI: 0.60, 0.91) in test and validation cohorts. The HGnom achieved AUCs of 0.77 (95% CI: 0.66, 0.87), and the predicted grades calibrated well with actual grades (P=.13). OS was different between the grades predicted by radiomics scores (P=.01). The integrated AUC of the Pnom for predicting OS was 0.80 (95% CI: 0.75, 0.88).

Conclusion

Compared with the HGrad using features from CECT, the HGnom demonstrated higher performance for predicting histological grade. The Pnom helped identify patients with high survival outcome in pancreatic ductal adenocarcinoma.

Screening of Exosome-Derived Proteins and Their Potential as Biomarkers in Diagnostic and Prognostic for Pancreatic Cancer

In the oncological area, pancreatic cancer is one of the most lethal diseases, with 5-year survival rising just 10% in high-development countries. This disease is genetically characterized by KRAS as a driven mutation followed by SMAD4, CDKN2, and TP53-associated mutations. In clinical aspects, pancreatic cancer presents unspecific clinical symptoms with the absence of screening and early plasmatic biomarker, being that CA19-9 is the unique plasmatic biomarker having specificity and sensitivity limitations. We analyzed the plasmatic exosome proteomic profile of 23 patients with pancreatic cancer and 10 healthy controls by using Nanoscale liquid chromatography coupled to tandem mass spectrometry (NanoLC-MS/MS). The pancreatic cancer patients were subdivided into IPMN and PDAC. Our findings show 33, 34, and 7 differentially expressed proteins when comparing the IPMN vs. control, PDAC-No treatment vs. control, and PDAC-No treatment vs. IPMN groups, highlighting proteins of the complement system and coagulation, such as C3, APOB, and SERPINA. Additionally, PDAC with no treatment showed 11 differentially expressed proteins when compared to Folfirinox neoadjuvant therapy or Gemcitabine adjuvant therapy. So here, we found plasmatic exosome-derived differentially expressed proteins among cancer patients (IPMN, PDAC) when comparing with healthy controls, which could represent alternative biomarkers for diagnostic and prognostic evaluation, supporting further scientific and clinical studies on pancreatic cancer.

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

Background

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.

Methods

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.

Results

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.

Conclusion

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.

PDAC, the Influencer Cancer: Cross-Talk with Tumor Microenvironment and Connected Potential Therapy Strategies

Pancreatic ductal adenocarcinoma (PDAC) is among the leading causes of death by cancer in the world. What makes this pathological condition particularly lethal is a combination of clinical and molecular heterogeneity, lack of early diagnostic indexes, and underwhelming results from current therapeutic protocols. A major cause of PDAC chemoresistance seems to lie in the ability of cancer cells to spread out and fill the pancreatic parenchyma, exchanging nutrients, substrates, and even genetic material with cells from the surrounding tumor microenvironment (TME). Several components can be found in the TME ultrastructure, including collagen fibers, cancer-associated fibroblasts, macrophages, neutrophils, mast cells, and lymphocytes. Cross-talk between PDAC and TME cells results in the latter being converted into cancer-favoring phenotypes; this behavior could be compared to an influencer guiding followers into supporting his activity. Moreover, TME could be a potential target for some of the newest therapeutic strategies; these include the use of pegvorhyaluronidase-α and CAR-T lymphocytes against HER2, FAP, CEA, MLSN, PSCA, and CD133. Other experimental therapy options are being currently studied, aiming to interfere with the KRAS pathway, DNA-repairing proteins, and apoptosis resistance in PDAC cells. Hopefully these new approaches will grant better clinical outcomes in future patients.

Diagnostic Bioliquid Markers for Pancreatic Cancer: What We Have vs. What We Need

Pancreatic ductal adenocarcinoma (PDAC), the most common type of pancreatic cancer, currently has a dismal five-year survival rate of approximately 10% due to late diagnosis and a lack of efficient treatment options such as surgery. Furthermore, the majority of PDAC patients have surgically unresectable cancer, meaning cancer cells have either reached the surrounding blood vessels or metastasized to other organs distant from the pancreas area, resulting in low survival rates as compared to other types of cancers. In contrast, the five-year survival rate of surgically resectable PDAC patients is currently 44%. The late diagnosis of PDAC is a result of little or no symptoms in its early stage of development and a lack of specific biomarkers that may be utilized in routine examinations in the clinic. Although healthcare professionals understand the importance of early detection of PDAC, the research on the subject has lagged and no significant changes in the death toll of PDAC patients has been observed. This review is focused on understanding potential biomarkers that may increase the early diagnosis of PDAC patients at its surgically resectable stage. Here, we summarize the currently available biomarkers used in the clinic as well as those being developed with the hope of providing insight into the future of liquid biomarkers to be used in routine examinations for the early diagnosis of PDAC.

A population-based nomogram to individualize treatment modality for pancreatic cancer patients underlying surgery

As the most aggressive tumor, TNM staging does not accurately identify patients with pancreatic cancer who are sensitive to therapy. This study aimed to identify associated risk factors and develop a nomogram to predict survival in pancreatic cancer surgery patients and to select the most appropriate comprehensive treatment regimen. First, the survival difference between radiotherapy and no radiotherapy was calculated based on propensity score matching (PSM). Cox regression was conducted to select the predictors of overall survival (OS). The model was constructed using seven variables: histologic type, grade, T stage, N stage, stage, chemotherapy and radiotherapy. All patients were classified into high- or low-risk groups based on the nomogram. The nomogram model for OS was established and showed good calibration and acceptable discrimination (C-index 0.721). Receiver operating characteristic curve (ROC) and DCA curves showed that nomograms had better predictive performance than TNM stage. Patients were divided into low-risk and high-risk groups according to nomogram scores. Radiotherapy is recommended for high-risk patients but not for low-risk patients. We have established a well-performing nomogram to effectively predict the prognosis of pancreatic cancer patients underlying surgery. The web version of the nomogram https://rockeric.shinyapps.io/DynNomapp/ may contribute to treatment optimization in clinical practice.

Can combined use of tumor markers in pancreatic cancer be a solution to short- and long-term consequences?: A retrospective study

As in other types of cancer, tumor markers are used in pancreatic ductal adenocarcinoma (PDAC) for disease follow-up, especially after surgery. There has been shown to be a significant correlation between the tumor marker levels and poor prognosis in locally or systemic advanced stage PDAC patients. However, there is no significant correlation between prognosis and marker levels in patients with early stage PDAC patients. This study aimed to examine the effect of the carbohydrate antigen 19-9 (Ca19-9)/carcinoembryogenic antigen (CEA) ratio in ductal adenocarcinoma of the pancreatic head on disease prognosis and mean survival. This retrospective study was conducted with 129 pancreatic head adenocarcinoma patients who were treated with whipple procedure at the Ankara University Surgical Oncology Clinic between 2010 and 2020. All patients' demographics, stage of the disease, CEA, CA 19-9 levels, and CEA/Ca 19-9 ratio were enrolled and compared statistically. A new cutoff value was calculated for the Ca19-9/CEA ratio. A Ca19-9/CEA ratio >29.77 showed 69.9% sensitivity and 70.9% specificity for the probability of the T3 and T4 stages. The cutoff value for the Ca19-9/CEA ratio was 27.18. This cutoff value had a sensitivity of 79.4% and a specificity of 80.3% for lymph node metastasis. Patients with a Ca19-9/CEA ratio below the cutoff value of 28.475 had a mean survival of 93.161 months and those with a value higher than the cutoff value had a mean survival of 28.541 months (P < .001). A simple combination of tumor markers was determined to have higher accuracy rates in predicting tumor prognosis and in determining mean survival, which are particularly needed in early stage cancers.

Construction and validation of an aging-related gene signature predicting the prognosis of pancreatic cancer

Background: Pancreatic cancer is a malignancy with a high mortality rate and worse prognosis. Recently, public databases and bioinformatics tools make it easy to develop the prognostic risk model of pancreatic cancer, but the aging-related risk signature has not been reported. The present study aimed to identify an aging-related risk signature with potential prognostic value for pancreatic cancer patients. Method: Gene expression profiling and human clinical information of pancreatic cancer were derived from The Cancer Genome Atlas database (TCGA). Aging-related gene sets were downloaded from The Molecular Signatures Database and aging-related genes were obtained from the Human Ageing Genomic Resources database. Firstly, Gene set enrichment analysis was carried out to investigate the role of aging process in pancreatic cancer. Secondly, differentially expressed genes and aging-related prognostic genes were screened on the basis of the overall survival information. Then, univariate COX and LASSO analysis were performed to establish an aging-related risk signature of pancreatic cancer patients. To facilitate clinical application, a nomogram was established to predict the survival rates of PCa patients. The correlations of risk score with clinical features and immune status were evaluated. Finally, potential therapeutic drugs were screened based on the connectivity map (Cmap) database and verified by molecular docking. For further validation, the protein levels of aging-related genes in normal and tumor tissues were detected in the Human Protein Atlas (HPA) database. Result: The genes of pancreatic cancer were markedly enriched in several aging-associated signaling pathways. We identified 14 key aging-related genes related to prognosis from 9,020 differentially expressed genes and establish an aging-related risk signature. This risk model indicated a strong prognostic capability both in the training set of TCGA cohort and the validation set of PACA-CA cohort and GSE62452 cohort. A nomogram combining risk score and clinical variables was built, and calibration curve and Decision curve analysis (DCA) have proved that it has a good predictive value. Additionally, the risk score was tightly linked with tumor immune microenvironment, immune checkpoints and proinflammatory factors. Moreover, a candidate drug, BRD-A47144777, was screened and verified by molecular docking, indicating this drug has the potential to treat PCa. The protein expression levels of GSK3B, SERPINE1, TOP2A, FEN1 and HIC1 were consistent with our predicted results. Conclusion: In conclusion, we identified an aging-related signature and nomogram with high prediction performance of survival and immune cell infiltration for pancreatic cancer. This signature might potentially help in providing personalized immunotherapy for patients with pancreatic cancer.

The crucial role of LncRNA MIR210HG involved in the regulation of human cancer and other disease

Long noncoding RNAs (lncRNAs) have evoked considerable interest in recent years due to their critical functions in the regulation of disease processes. Abnormal expression of lncRNAs is found in multiple diseases, and lncRNAs have been exploited for diverse medical applications. The lncRNA MIR210HG is a recently discovered lncRNA that is widely dysregulated in human disease. MIR210HG was described to have biological functions with potential roles in disease development, including cell proliferation, invasion, migration, and energy metabolism. And MIR210HG dysregulation was confirmed to have promising clinical values in disease diagnosis, treatment, and prognosis. In this review, we systematically summarize the expression profiles, roles, underlying mechanisms, and clinical applications of MIR210HG in human disease.

Carbohydrate antigen 125 supplements carbohydrate antigen 19-9 for the prediction of invasive intraductal papillary mucinous neoplasms of the pancreas

BACKGROUND

Intraductal papillary mucinous neoplasms (IPMNs) are characterized by their abundant mucin production and malignant potential. IPMNs of the pancreas are mainly managed according to their radiographic indications, but this approach lacks accuracy with regard to IPMN grading. Therefore, serological biomarkers such as CA19-9 and CA125 (MUC16) should be employed to assist in predicting the invasiveness of IPMNs.

METHODS

We investigated the preoperative serum levels of CA19-9, CA125 and CEA in 381 surgical patients with a definite pathological diagnosis of IPMN from July 2010 to December 2019 at the Shanghai Cancer Center. We calculated the Youden indices of each point on the receiver operating characteristic (ROC) curves to identify the most appropriate cut-off values of CA19-9, CA125 and CEA for recognizing malignant IPMNs. Serological biomarker differences were correlated with clinicopathological features of IPMNs, and diagnostic indices of different scenarios were calculated to find the optimum strategy.

RESULTS

The malignant group had higher serum levels of CA19-9, CA125 and CEA. According to the ROC curves, the cut-off values of CA19-9, CA125 and CEA were readjusted to 38.3 U/ml, 13.4 U/ml and 5.3 μg/L. CA19-9 elevation was significantly associated with vascular invasion and perineural infiltration. CA125 showed good efficacy in predicting invasive IPMN in the CA19-9-negative subgroup.

CONCLUSIONS

Serological biomarkers are useful and sensitive indicators for recognizing invasive IPMNs. CA19-9 is the most important diagnostic index among all routinely measured serum biomarkers for differentiating malignant from benign IPMNs. CA19-9 should be combined with CA125 to enable more accurate predictions of IPMN malignancy.

Identification of novel early pancreatic cancer biomarkers KIF5B and SFRP2 from “first contact” interactions in the tumor microenvironment

Background

Pancreatic cancer is one of the most difficult cancers to detect early and most patients die from complications arising due to distant organ metastases. The lack of bona fide early biomarkers is one of the primary reasons for late diagnosis of pancreatic cancer. It is a multifactorial disease and warrants a novel approach to identify early biomarkers.

Methods

In order to characterize the proteome, Extracellular vesicles (EVs) isolated from different in vitro conditions mimicking tumor-microenvironment interactions between pancreatic cancer epithelial and stromal cells were analyzed using high throughput mass spectrometry. The biological activity of the secreted EVome was analyzed by investigating changes in distant organ metastases and associated early changes in the microbiome. Candidate biomarkers (KIF5B, SFRP2, LOXL2, and MMP3) were selected and validated on a mouse-human hybrid Tissue Microarray (TMA) that was specifically generated for this study. Additionally, a human TMA was used to analyze the expression of KIF5B and SFRP2 in progressive stages of pancreatic cancer.

Results

The EVome of co-cultured epithelial and stromal cells is different from individual cells with distinct protein compositions. EVs secreted from stromal and cancer cells cultures could not induce significant changes in Pre-Metastatic Niche (PMN) modulation, which was assessed by changes in the distant organ metastases. However, they did induce significant changes in the early microbiome, as indicated by differences in α and β-diversities. KIF5B and SFRP2 show promise for early detection and investigation in progressive pancreatic cancer. These markers are expressed in all stages of pancreatic cancer such as low grade PanINs, advanced cancer, and in liver and soft tissue metastases.

Conclusions

Proteomic characterization of EVs derived from mimicking conditions of epithelial and stromal cells in the tumor-microenvironment resulted in the identification of several proteins, some for the first time in EVs. These secreted EVs cannot induce changes in distant organ metastases in in vivo models of EV education, but modulate changes in the early murine microbiome. Among all the proteins that were analyzed (MMP3, KIF5B, SFRP2, and LOXL2), KIF5B and SFRP2 show promise as bona fide early pancreatic cancer biomarkers expressed in progressive stages of pancreatic cancer.

Application of Proteomics in Pancreatic Ductal Adenocarcinoma Biomarker Investigations: A Review

Pancreatic ductal adenocarcinoma (PDAC), a highly aggressive malignancy with a poor prognosis is usually detected at the advanced stage of the disease. The only US Food and Drug Administration-approved biomarker that is available for PDAC, CA 19-9, is most useful in monitoring treatment response among PDAC patients rather than for early detection. Moreover, when CA 19-9 is solely used for diagnostic purposes, it has only a recorded sensitivity of 79% and specificity of 82% in symptomatic individuals. Therefore, there is an urgent need to identify reliable biomarkers for diagnosis (specifically for the early diagnosis), ascertain prognosis as well as to monitor treatment response and tumour recurrence of PDAC. In recent years, proteomic technologies are growing exponentially at an accelerated rate for a wide range of applications in cancer research. In this review, we discussed the current status of biomarker research for PDAC using various proteomic technologies. This review will explore the potential perspective for understanding and identifying the unique alterations in protein expressions that could prove beneficial in discovering new robust biomarkers to detect PDAC at an early stage, ascertain prognosis of patients with the disease in addition to monitoring treatment response and tumour recurrence of patients.

The Urokinase Plasminogen Activation System in Pancreatic Cancer: Prospective Diagnostic and Therapeutic Targets

Pancreatic cancer is a highly aggressive malignancy that features high recurrence rates and the poorest prognosis of all solid cancers. The urokinase plasminogen activation system (uPAS) is strongly implicated in the pathophysiology and clinical outcomes of patients with pancreatic ductal adenocarcinoma (PDAC), which accounts for more than 90% of all pancreatic cancers. Overexpression of the urokinase-type plasminogen activator (uPA) or its cell surface receptor uPAR is a key step in the acquisition of a metastatic phenotype via multiple mechanisms, including the increased activation of cell surface localised plasminogen which generates the serine protease plasmin. This triggers multiple downstream processes that promote tumour cell migration and invasion. Increasing clinical evidence shows that the overexpression of uPA, uPAR, or of both is strongly associated with worse clinicopathological features and poor prognosis in PDAC patients. This review provides an overview of the current understanding of the uPAS in the pathogenesis and progression of pancreatic cancer, with a focus on PDAC, and summarises the substantial body of evidence that supports the role of uPAS components, including plasminogen receptors, in this disease. The review further outlines the clinical utility of uPAS components as prospective diagnostic and prognostic biomarkers for PDAC, as well as a rationale for the development of novel uPAS-targeted therapeutics.

Low APOA-1 Expression in Hepatocellular Carcinoma Patients Is Associated With DNA Methylation and Poor Overall Survival

Background: Hepatocellular carcinoma (HCC) is the most frequent fatal malignancy, and it has a poor prognosis. Apolipoprotein 1 (APOA-1), the main protein component of high-density lipoproteins, is involved in numerous biological processes. Thus, this study was performed to detect the clinical significance of APOA-1 mRNA, APOA-1 expression, and APOA-1DNA methylation in patients with HCC.

Methods: Data mining was performed using clinical and survival data from the Cancer Genome Atlas (TCGA) and Oncomine databases. The serum concentration of APOA-1 was measured in 316 patients with HCC and 100 healthy individuals at Renmin Hospital of Wuhan University, and the intact clinical information was reviewed and determined using univariate and multivariate Cox hazard models.

Results: Bioinformatic analysis revealed that APOA-1 mRNA was present at lower levels in the serum of patients with HCC than in that of healthy individuals, and there was a strong negative correlation between levels of APOA-1 mRNA and APOA-1 DNA methylation. High expression of APOA-1 transcription correlated with better overall survival (p = 0.003), and APOA-1 hypermethylation correlated with progress-free survival (p = 0.045) in HCC sufferers. Next, the clinical data analysis demonstrated that APOA-1 protein levels in the serum were significantly lower in patients with HCC than in healthy controls. Furthermore, the expression of APOA-1 was significantly associated with some significant clinical indexes, and elevated APOA-1 expression was significantly associated with favorable (OS; HR:1.693, 95% CI: 1.194–2.401, p = 0.003) and better progression-free survival (PFS; HR = 1.33, 95% CI = 1.194–2.401, p = 0.045). Finally, enrichment analysis suggested that co-expressed genes of APOA-1 were involved in lipoprotein metabolism and FOXA2/3 transcription factor networks.

Conclusion: APOA-1 mRNA expression is negatively regulated by DNA methylation in HCC. Low expression of APOA-1 might be a potential risk biomarker to predict survival in patients with HCC.

Towards an updated view on the clinical management of pancreatic adenocarcinoma: Current and future perspectives

Pancreatic cancer has a dire prognosis and will represent the second leading cause of cancer death in the next 10 years. The multifactorial approach represents one of the main issues in controlling the extension of this neoplasm. In recent years, the characteristics of the tumor microenvironment, metastasis mechanisms and the relationship between immune system and neoplastic cells have been described, which has made it possible to understand the pathophysiology of pancreatic adenocarcinoma. Currently, there is a failure to provide an effective preventive method or early detection, so patients present with an advanced stage at the time of diagnosis. Despite numerous efforts, little progress has been made in clinical outcome and in improving survival in long term. Therefore, in the recent years, diverse diagnostic tests, treatments and possible approaches have been developed in the fields of radiotherapy, chemotherapy and surgery to find a combination of them that improves life expectancy in patients diagnosed with pancreatic cancer. At the moment, numerous clinical trials are being conducted to evaluate preventive diagnostic procedures such as serological markers or perfecting available imaging tests. On the other hand, implementation of immunotherapy is being studied in a neoplasm that has lagged in the application of this procedure since present possible treatments do not substantially improve quality of life. Therefore, the purpose of our study is to summarize the main progresses that have been made in the diagnosis, treatment and screening of this disease, explaining the limitations that have been observed and analyzing future prospects in the management of this illness.

Primary pancreatic cystadenocarcinoma with ovarian metastases in a 38-year-old female: case report

Pancreatic cystadenocarcinoma with ovarian metastases is rare and easily misdiagnosed as primary ovarian cancers. Here we report 38-year-old female manifested tumors in pancreas and ovary concurrently, which was difficult to distinguish the primary site. She was admitted to hospital because of abdominal distension and a palpable mass in the lower abdomen. Abdominal ultrasound showed a lesion in pancreas and two masses in bilateral ovaries. Computed tomography (CT) revealed the hypo-enhancing pancreatic mass and the large pelvic lesion simultaneously. The largest cross-sectional of the right mass was 12×15.1 cm and 15.4×18.3 cm for the left side, probably malignant lesions. In addition, the level of the serum CA19-9 and CA125 were higher than the normal level. Positron emission tomography CT (PET-CT) revealed there might be the cystadenocarcinoma in the pancreatic tail with multiple metastatic lesions implanted in the pelvic. After comprehensive examination, she received bilateral salpingo-oophorectomy and biopsy of the pancreatic tumor. The pathological finding revealed that it was pancreatic cystadenocarcinoma with ovarian metastases. Postoperatively, she received the chemotherapy and the follow-up continued for 26 months until she died. This case reminded doctors that pancreatic primaries should be paid attention when dealing with metastatic ovarian malignancies although it was rare. Choosing effective diagnostic method and timely surgical intervention are essential to improve prognosis.