Nomogram model for predicting early recurrence for resectable pancreatic cancer: A multicenter study.

Pancreatic cancer is a highly aggressive malignancy that is characterized by early metastasis, high recurrence, and therapy resistance. Early recurrence after surgery is one of the important reasons affecting the prognosis of pancreatic cancer. This study aimed to establish an accurate preoperative nomogram model for predicting early recurrence (ER) for resectable pancreatic adenocarcinoma. We retrospectively analyzed patients who underwent pancreatectomy for pancreatic ductal adenocarcinoma between January 2011 and December 2020. The training set consisted of 604 patients, while the validation set included 222 patients. Survival was estimated using Kaplan-Meier curves. The factors influencing early recurrence of resectable pancreatic cancer after surgery were investigated, then the predictive model for early recurrence was established, and subsequently the predictive model was validated based on the data of the validation group. The preoperative risk factors for ER included a Charlson age-comorbidity index ≥ 4 (odds ratio [OR]: 0.628), tumor size > 3.0 cm on computed tomography (OR: 0.628), presence of clinical symptoms (OR: 0.515), carbohydrate antigen 19-9 > 181.3 U/mL (OR 0.396), and carcinoembryonic antigen > 6.01 (OR: 0.440). The area under the curve (AUC) of the predictive model in the training group was 0.711 (95% confidence interval: 0.669-0.752), while it reached 0.730 (95% CI: 0.663-0.797) in the validation group. The predictive model may enable the prediction of the risk of postoperative ER in patients with resectable pancreatic ductal adenocarcinoma, thereby optimizing preoperative decision-making for effective treatment.

Crosstalk Between Peripheral Innervation and Pancreatic Ductal Adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive lethal malignancy, characterized by late diagnosis, aggressive growth, and therapy resistance, leading to a poor overall prognosis. Emerging evidence shows that the peripheral nerve is an important non-tumor component in the tumor microenvironment that regulates tumor growth and immune escape. The crosstalk between the neuronal system and PDAC has become a hot research topic that may provide novel mechanisms underlying tumor progression and further uncover promising therapeutic targets. In this review, we highlight the mechanisms of perineural invasion and the role of various types of tumor innervation in the progression of PDAC, summarize the potential signaling pathways modulating the neuronal-cancer interaction, and discuss the current and future therapeutic possibilities for this condition.

Preoperative ultrasound combined with routine blood tests in predicting the malignant risk of pancreatic cystic neoplasms.

OBJECTIVE: Accurate preoperative identification of benign or malignant pancreatic cystic neoplasms (PCN) may help clinicians make better intervention choices and will be essential for individualized treatment. METHODS: Preoperative ultrasound and laboratory examination findings, and demographic characteristics were collected from patients. Multiple logistic regression was used to identify independent risk factors associated with malignant PCN, which were then included in the nomogram and validated with an external cohort. The Net Reclassification Index (NRI) and Integrated Discrimination Improvement (IDI) were calculated to evaluate the improvement in the predictive power of the new model with respect to that of a combined imaging and tumor marker prediction model. RESULTS: Malignant PCN were found in 83 (40.7%) and 33 (38.7%) of the model and validation cohorts, respectively. Multivariate analysis identified age, tumor location, imaging of tumor boundary, blood type, mean hemoglobin concentration, neutrophil-to-lymphocyte ratio, carbohydrate antigen 19-9, and carcinoembryonic antigen as independent risk factors for malignant PCN. The calibration curve indicated that the predictions based on the nomogram were in excellent agreement with the actual observations. A nomogram score cutoff of 192.5 classified patients as having low vs. high risk of malignant PCN. The model achieved good C-statistics of 0.929 (95% CI 0.890-0.968, P < 0.05) and 0.951 (95% CI 0.903-0.998, P < 0.05) in predicting malignancy in the development and validation cohorts, respectively. NRI = 0.268; IDI = 0.271 (P < 0.001 for improvement). The DCA curve indicated that our model yielded greater clinical benefits than the comparator model. CONCLUSIONS: The nomogram showed excellent performance in predicting malignant PCN and may help surgeons select patients for detailed examination and surgery. The nomogram is freely available at https://wangjunjinnomogram.shinyapps.io/DynNomapp/.

Optimal subsidy policies of the Chinese cruise market under the impact of COVID-19.

The spread of the COVID-19 pandemic has caused severe damage to the Chinese cruise market since 2020. It is crucial for the local government to reformulate the subsidy policy to respond to the changing environment. We propose a cruise supply chain system to investigate the choice of subsidy recipients and the setting of optimal subsidy levels with a budget-constrained government during the access restriction period and post-epidemic period. We find that in both periods, as long as the subsidy achieves the optimal level, either the cruise lines, the travel agency, or the passengers as recipients of the subsidy policy can maximize the market demand and recover the cruise market after the COVID-19 outbreak. However, as the budget increases, subsidizing passengers can improve the "low price dilemma" of the Chinese cruise market. Compared with the access restriction period, the local government should adjust the subsidy level in the post-epidemic period. Interestingly, the subsidy policy does not always positively impact the international cruise line's profit in the post-epidemic period.

An Integrative Pan-Cancer Analysis Revealing MLN4924 (Pevonedistat) as a Potential Therapeutic Agent Targeting Skp2 in YAP-Driven Cancers.

Background: YAP, coded by YAP1 gene, is critical in the Hippo pathway. It has been reported to be involved in the tumorigenesis and progression of several cancers. However, its roles on tumor cell proliferation in diverse cancers remain to be elucidated. And there is currently no clinically feasible drug that can directly target YAP in cancers. This research aimed to explore the regulatory mechanism of YAP in promoting tumor proliferation of multiple cancers, in order to find new strategies for inhibiting the overgrowth of YAP-driven cancers. Methods: We investigated the expression pattern of YAP1 in pan-cancer across numerous databases and our cohorts. First, univariate Cox regression analysis and survival analysis were used to evaluate the effect of YAP1 on the prognosis of cancer patients. Second, TIMER was used to explore the relationship between YAP1 expression and tumor cell proliferation. Third, functional and pathway enrichment was performed to search for targets of YAP involved in cell cycle in cancers. At last, GDSC and CCLE datasets were used to assess the correlation between SKP2 expression and MLN4924 IC50 values. Results: Differential expression analysis of multiple databases and qPCR validation showed that YAP1 was generally overexpressed in pan-cancers. Survival analysis revealed that YAP1 over-expression was significantly related to poor prognosis of patients with PAAD. The expression level of YAP1 was positively correlated with the proliferation in varieties of tumors. Further, SKP2 was confirmed as a target of YAP in promoting tumor cell proliferation. In addition, SKP2 expression was negatively correlated with MLN4924 IC50 values in almost all cancer types. Conclusion: YAP1 is frequently overexpressed in human cancers. YAP promoted tumor cell proliferation by up-regulating SKP2 expression in multiple cancers. The comprehensive pan-cancer analysis suggested that inhibition of Skp2 with MLN4924 might be an effective therapeutic strategy for attenuating tumor cell proliferation in YAP-driven cancers.