Early diagnosis of pancreatic cancer: What strategies to avoid a foretold catastrophe

Early detection of pancreatic cancer in patients with recurrent pancreatitis: A case report

BACKGROUND

Pancreatic cancer presents a challenge with its low early diagnosis and treatment rates, leading to high metastasis and mortality rates. The median survival time for advanced pancreatic cancer is a mere 3 months. However, there's hope: small pancreatic cancers diagnosed at an early stage (T1) or those less than or equal to 1 cm in diameter boast an impressive 5-year survival rate of nearly 100%. This underscores the critical importance of early pancreatic cancer detection for significantly improving prognosis.

CASE SUMMARY

Pancreatic cancer, a malignant tumor of the digestive tract, poses challenges in both diagnosis and treatment due to its occult and atypical clinical symptoms. Clinically, patients with recurrent pancreatitis should be vigilant, as it may be indicative of pancreatic cancer, particularly in middle-aged and elderly patients. Here, we presented the case of a patient who experienced recurrent acute pancreatitis within a span of 2 months. During the initial episode of pancreatitis, routine imaging failed to identify the cause of pancreatic cancer. However, upon recurrence of acute pancreatitis, endoscopic ultrasonography (EUS) revealed a space-occupying lesion approximately 1 cm in size in the pancreatic body. Subsequent EUS coupled with fine-needle aspiration examination demonstrated atypical pancreatic gland epithelium. Ultimately, the patient underwent surgery and was diagnosed with an intraductal papillary mucinous tumor of the pancreas (severe epithelial dysplasia, focal cancer).

CONCLUSION

We recommend EUS for patients with recurrent pancreatitis of unknown etiology to exclude early pancreatic cancer.

Evaluation of the diagnostic utility of NCOA3, Maspin and VHL protein expression in pancreatic ductal adenocarcinoma: An immunohistochemical study

Pancreatic ductal adenocarcinoma (PDAC) is a lethal tumor with a high mortality rate. The distinction between PDAC and chronic pancreatitis is sometimes challenging on routine histopathological examination, highlighting the need to identify biomarkers that can facilitate this distinction. This retrospective study was conducted to evaluate the diagnostic utility of nuclear receptor co-activator 3 (NCOA3), Maspin and Von Hippel-Lindau protein (VHL) immunostaining in PDAC. Eighty cases of PDAC, 46 cases of chronic pancreatitis and 53 normal pancreatic tissue were immunohistochemically assessed using NCOA3, Maspin and VHL antibodies on sections from a tissue microarray. NCOA3, Maspin and VHL were positive in 90 %, 100 % and 35 %, of PDAC cases respectively, whereas NCOA3, Maspin and VHL expressions were positive in 3.8 %, 0 and 100 % of normal pancreatic tissue and in 15.2 %, 21.7 % and 97.8 % of chronic pancreatitis cases respectively. Significant differences were observed between PDAC and other groups regarding NCOA3, Maspin and VHL expression (p < 0.001). The H scores of NCOA3, Maspin and VHL could significantly distinguish between PDAC and normal cases with high sensitivity (90 %, 100 % and 98.75 % respectively) and specificity (100 %, 100 % and 96.23 % respectively). Similar findings were found in the distinction between PDAC and chronic pancreatitis (Sensitivity: 90 %, 95.25 % and 98.75 %; specificity: 100 %, 100 % and 93.48 % for NCOA3, Maspin and VHL respectively). In conclusion, NCOA3 and Maspin were found to be significantly expressed in PDAC compared to non-tumorous tissue while VHL was significantly expressed in non-tumorous tissue. A panel of NCOA3, Maspin and VHL could potentially distinguish PDAC from non-tumorous pancreatic tissue.

Detection of uridine diphosphate glucuronosyltransferase 1A1 for pancreatic cancer imaging and treatment via a "turn-on" fluorescent probe

Uridine diphosphate glucuronosyltransferase 1A1 (UGT1A1) is expressed ubiquitously in cancer cells and can metabolize exogenous substances. Studies show higher UGT1A1 levels in pancreatic cancer cells than normal cells. Therefore, we need a method to monitor the activity level of UGT1A1 in pancreatic cancer cells and in vivo. Here, we report a fluorescent probe, BCy-panc, for UGT1A1 imaging in cells and in vivo. Compared with other molecular probes, this probe is readily prepared, with high selectivity and sensitivity for the detection of UGT1A1. Our results show that BCy-panc rapidly detects UGT1A1 in pancreatic cancer. In addition, there is an urgent need for evidence to clarify the relationship between UGT1A1 and pancreatic cancer development. The present investigation found that the increase of UGT1A1 by chrysin was effective in inducing apoptosis in pancreatic cancer cells. These results indicate that the synergistic effect of chrysin and cisplatin at the cellular level is superior to that of cisplatin alone. The UGT1A1 level may be a biomarker for early diagnosis of cancer. Meanwhile, UGT1A1 plays a crucial role in pancreatic cancer, and the combination of chrysin and cisplatin may provide effective ideas for pancreatic cancer treatment.

Fast cancer imaging in pancreatic biopsies using infrared imaging

Pancreatic cancer, particularly Pancreatic ductal adenocarcinoma, remains a highly lethal form of cancer with limited early diagnosis and treatment options. Infrared (IR) spectroscopy, combined with machine learning, has demonstrated great potential in detecting various cancers. This study explores the translation of a diagnostic model from Fourier Transform Infrared to Quantum Cascade Laser (QCL) microscopy for pancreatic cancer classification. Furthermore, QCL microscopy offers faster measurements with selected frequencies, improving clinical feasibility. Thus, the goals of the study include establishing a QCL-based model for pancreatic cancer classification and creating a fast surgical margin detection model using reduced spectral information. The research involves preprocessing QCL data, training Random Forest (RF) classifiers, and optimizing the selection of spectral features for the models. Results demonstrate successful translation of the diagnostic model to QCL microscopy, achieving high predictive power (AUC = 98%) in detecting cancerous tissues. Moreover, a model for rapid surgical margin recognition, based on only a few spectral frequencies, is developed with promising differentiation between benign and cancerous regions. The findings highlight the potential of QCL microscopy for efficient pancreatic cancer diagnosis and surgical margin detection within clinical timeframes of minutes per surgical resection tissue.

[Early detection of sporadic pancreatic cancer]

The incidence of pancreatic cancer is rising. At present, pancreatic cancer is the third most common cancer-causing death in Germany, but it is expected to become the second in 2030 and finally the leading cause of cancer death in 2050. Pancreatic ductal adenocarcinoma (PC) is generally diagnosed at advanced stages, and 5-year-survival has remained poor. Early detection of sporadic PC at stage IA, however, can yield a 5-year-survival rate of about 80%. Early detection initiatives aim at identifying persons at high risk. People with new-onset diabetes at age 50 or older have attracted much interest. Novel strategies regarding how to detect sporadic PC at an early stage are being discussed.

Construction and validation of a nomogram model for predicting early death in patients with metastatic pancreatic adenocarcinoma based on SEER database

BACKGROUND

Pancreatic adenocarcinoma is a highly aggressive malignancy that presents a considerable risk of early death (survival time ≤ 3 mo). As such, it is of great significance to develop an effective nomogram for predicting the likelihood of early death in patients with metastatic pancreatic adenocarcinoma.

AIM

To construct and validate a predictive nomogram model for early death in patients with metastatic pancreatic adenocar-cinoma.

METHODS

We extracted data from the SEER database of 18603 eligible patients with metastatic pancreatic adenocarcinoma from 2010 to 2015, and randomly divided them into training and validation cohorts in a 7:3 ratio. Univariate and multivariate logistic regression analyses were performed on the training cohort to identify the risk factors for early death, based on which a nomogram was constructed. The performance of the nomogram was verified by receiver operating characteristic (ROC) curve and calibration curve analyses in both the training and validation cohorts. The clinical practicability of the nomogram was evaluated by decision curve analysis (DCA).

RESULTS

Age, sex, primary site, grade, T stage, N stage, brain metastasis, bone metastasis, liver metastasis, lung metastasis, surgery, radiotherapy, and chemotherapy were identified as independent risk factors for early death in patients with metastatic pancreatic adenocarcinoma. Based on these variables, a nomogram was constructed. The areas under the ROC curves of the nomogram in the training and validation cohorts were 0.810 (95% confidence interval [CI]: 0.802-0.811) and 0.802 (95%CI: 0.790-0.813), respectively, indicating good discrimination. The calibration curves showed good calibration degrees in both cohorts, and the DCA results demonstrated that the nomogram had better clinical net benefit in predicting early mortality compared with TNM stage.

CONCLUSION

The constructed nomogram has good predictive ability for early death in patients with metastatic pancreatic adeno-carcinoma. This will help clinicians develop individualized treatment plans for these patients.

Analysis and validation of the potential of the MYO1E gene in pancreatic adenocarcinoma based on a bioinformatics approach

Pancreatic adenocarcinoma (PAAD) is a common digestive cancer, and its prognosis is poor. Myosin 1E (MYO1E) is a class I myosin family member whose expression and function have not been reported in PAAD. In the present study, bioinformatics analysis was used to explore the expression levels of MYO1E in PAAD and its prognostic value, and the immunological role of MYO1E in PAAD was analyzed. The study revealed that a variety of malignancies have substantially increased MYO1E expression. Further investigation demonstrated that PAAD tissues exhibited greater levels of MYO1E mRNA and protein expression than normal tissues. High MYO1E expression is associated with poor prognosis in patients with PAAD. MYO1E expression was also associated with pathological stage in patients with PAAD. Functional enrichment analysis demonstrated that MYO1E was linked to multiple tumor-related mechanisms in PAAD. The pancreatic adenocarcinoma tumor microenvironment (TME) was analyzed and it was revealed that MYO1E expression was positively associated with tumor immune cell infiltration. In addition, MYO1E was closely associated with some tumor chemokines/receptors and immune checkpoints. In vitro experiments revealed that the suppression of MYO1E expression could inhibit pancreatic adenocarcinoma cell proliferation, invasion and migration. Through preliminary analysis, the present study evaluated the potential function of MYO1E in PAAD and its function in TME, and MYO1E may become a potential biomarker for PAAD.

The role of BHLHE40 in clinical features and prognosis value of PDAC by comprehensive analysis and in vitro validation

Pancreatic ductal adenocarcinoma (PDAC) is the leading cause of cancer-related mortality, primarily due to the abundance of cancer-associated fibroblasts (CAFs), depleted effector T cells, and increased tumor cell stemness; hence, there is an urgent need for efficient biomarkers with prognostic and therapeutic potential. Here, we identified BHLHE40 as a promising target for PDAC through comprehensive analysis and weighted gene coexpression network analysis of RNA sequencing data and public databases, taking into account the unique characteristics of PDAC such as cancer-associated fibroblasts, infiltration of effector T cells, and tumor cell stemness. Additionally, we developed a prognostic risk model based on BHLHE40 and three other candidate genes (ITGA2, ITGA3, and ADAM9) to predict outcomes in PDAC patients. Furthermore, we found that the overexpression of BHLHE40 was significantly associated with T stage, lymph node metastasis, and American Joint Committee on Cancer (AJCC) stage in a cohort of 61 PDAC patients. Moreover, elevated expression levels of BHLHE40 were validated to promote epithelial-mesenchymal transition (EMT) and stemness-related proteins in BXPC3 cell lines. Compared to the parent cells, BXPC3 cells with BHLHE40 overexpression showed resistance to anti-tumor immunity when co-cultured with CD8⁺ T cells. In summary, these findings suggest that BHLHE40 is a highly effective biomarker for predicting prognosis in PDAC and holds great promise as a target for cancer therapy.

Early Diagnosis of Pancreatic Cancer: Clinical Premonitions, Timely Precursor Detection and Increased Curative-Intent Surgery

BACKGROUND

The overall poor prognosis in pancreatic cancer is related to late clinical detection. Early diagnosis remains a considerable challenge in pancreatic cancer. Unfortunately, the onset of clinical symptoms in patients usually indicate advanced disease or presence of metastasis.

ANALYSIS AND RESULTS

Currently, there are no designated diagnostic or screening tests for pancreatic cancer in clinical use. Thus, identifying risk groups, preclinical risk factors or surveillance strategies to facilitate early detection is a target for ongoing research. Hereditary genetic syndromes are a obvious, but small group at risk, and warrants close surveillance as suggested by society guidelines. Screening for pancreatic cancer in asymptomatic individuals is currently associated with the risk of false positive tests and, thus, risk of harms that outweigh benefits. The promise of cancer biomarkers and use of 'omics' technology (genomic, transcriptomics, metabolomics etc.) has yet to see a clinical breakthrough. Several proposed biomarker studies for early cancer detection lack external validation or, when externally validated, have shown considerably lower accuracy than in the original data. Biopsies or tissues are often taken at the time of diagnosis in research studies, hence invalidating the value of a time-dependent lag of the biomarker to detect a pre-clinical, asymptomatic yet operable cancer. New technologies will be essential for early diagnosis, with emerging data from image-based radiomics approaches, artificial intelligence and machine learning suggesting avenues for improved detection.

CONCLUSIONS

Early detection may come from analytics of various body fluids (eg 'liquid biopsies' from blood or urine). In this review we present some the technological platforms that are explored for their ability to detect pancreatic cancer, some of which may eventually change the prospects and outcomes of patients with pancreatic cancer.