Nomogram to predict survival of patients with advanced and metastatic pancreatic Cancer.

BACKGROUND: Nomograms are rarely employed to estimate the survival of patients with advanced and metastatic pancreatic cancer (PC). Herein, we developed a comprehensive approach to using a nomogram to predict survival probability in patients with advanced and metastatic PC. METHODS: A total of 323 patients with advanced and metastatic PC were identified from the Chinese People's Liberation Army (PLA) General Hospital. A baseline nomogram was constructed using baseline variables of 323 patients. Additionally, 233 patients, whose tumors showed initial responses to first-line chemotherapy, were enrolled in the chemotherapy response-based model. 128 patients and 108 patients with advanced and metastatic PC from January 2019 to April 2021 were selected for external validating baseline model and chemotherapy response-based model. The 1-year and 2-year survival probability was evaluated using multivariate COX regression models. The discrimination and calibration capacity of the nomograms were assessed using C-statistic and calibration plots. The predictive accuracy and net benefit of the nomograms were evaluated using ROC curve and DCA, respectively. RESULTS: In the baseline model, six variables (gender, KPS, baseline TB, baseline N, baseline WBC and baseline CA19-9) were used in the final model. In the chemotherapy response-based model, nine variables (KPS, gender, ascites, baseline N, baseline CA 19-9, baseline CEA, change in CA 19-9 level at week, change in CEA level at week and initial response to chemotherapy) were included in the final model. The C-statistics of the baseline nomogram and the chemotherapy response-based nomogram were 0.67 (95% CI, 0.62-0.71) and 0.74 (95% CI, 0.69-0.77), respectively. CONCLUSION: These nomograms were constructed to predict the survival probability of patients of advanced and metastatic PC. The baseline model and chemotherapy response-based model performed well in survival prediction.

Identification of DNA methylation-driven genes and construction of a nomogram to predict overall survival in pancreatic cancer.

BACKGROUND: The incidence and mortality of pancreatic cancer (PC) has gradually increased. The aim of this study was to identify survival-related DNA methylation (DNAm)-driven genes and establish a nomogram to predict outcomes in patients with PC. METHODS: The gene expression, DNA methylation database, and PC clinical samples were downloaded from TCGA. DNAm-driven genes were identified by integrating analyses of gene expression and DNA methylation data. Survival-related DNAm-driven genes were screened via univariate, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses to develop a risk score model for prognosis. Based on analyses of clinical parameters and risk score, a nomogram was built and validated. The independent cohort from GEO database were used for external validation. RESULTS: A total of 16 differentially expressed methylation-driven genes were identified. Based on LASSO Cox regression and multivariate Cox regression analysis, six genes (FERMT1, LIPH, LAMA3, PPP1R14D, NQO1, VSIG2) were chosen to develop the risk score model. In the Kaplan-Meier analysis, age, T stage, N stage, AJCC stage, radiation therapy history, tumor size, surgery type performed, pathological type, chemotherapy history, and risk score were potential prognostic factors in PC (P < 0.1). In the multivariate analysis, stage, chemotherapy, and risk score were significantly correlated to overall survival (P < 0.05). The nomogram was constructed with the three variables (stage, chemotherapy, and risk score) for predicting the 1-year, 2-year, and 3-year survival rates of PC patients. Nomogram performance was assessed by receiver operating characteristic (ROC) curves and calibration curves. 1-year, 2-year and 3-year AUC of nomogram model was 0.899, 0.765 and 0.776, respectively. CONCLUSIONS: In our study, we successfully identified the six DNAm-driven genes (FERMT1, LIPH, LAMA3, PPP1R14D, NQO1, VSIG2) with a relationship to the outcomes of PC patients. The nomogram including stage, chemotherapy, and risk score could be used to predict survival in PC patients.

CA19-9 decrease and survival according to platelet level in patients with advanced pancreatic cancer.

BACKGROUND: CA19-9 decrease during treatment has been associated with superior survival of pancreatic cancer in several studies. The evidence to show the correlation of high platelet level with inferior survival is insufficient in pancreatic cancer. It also remains unclear whether the association between CA19-9 decrease and survival was corresponded to different levels of platelet in metastatic pancreatic cancer. METHODS: We measured CA19-9 serum concentration and platelet level at baseline and after the second cycle of chemotherapy for 200 advanced pancreatic cancer patients. A Cox proportional hazards model was used to compute mortality hazard ratios (HRs) for CA19-9 decrease, adjusting for potential confounders, including age, sex, KPS, prediagnosis body mass index, Diabetes Mellitus, tumor location, first-line chemotherapy regimen, and radiotherapy. RESULTS: We found that the association of CA19-9 decrease with superior overall survival was stronger in advanced pancreatic cancer with a low level of platelet (Pinteraction <  0.001) compared with intermediate and high level of platelet. Multivariable-adjusted hazard ratios per unit decrease of CA19-9 change was 0.45 [95% confidence interval (CI), 0.33 to 0.62] in cases with low platelet level, 0.74 (95% CI, 0.50 to 1.09) in cases with intermediate platelet level, and 0.94 (95% CI, 0.74 to 1.10) in cases with high platelet level. A similar differential association was found between CA19-9 decrease and progression-free survival in strata of platelet level (Pinteraction = 0.034). CONCLUSION: The association of CA19-9 decrease with superior pancreatic cancer survival appeared to be pronounced in patients with a low platelet level. This finding could provide supports for the underlying mechanisms of CA19-9 involved in platelet / tumor cell interaction.

Nectar yeasts enhance the interaction between Clematis akebioides and its bumblebee pollinator.

It has been hypothesised that intense metabolism of nectar-inhabiting yeasts (NIY) may change nectar chemistry, including volatile profile, which may affect pollinator foraging behaviours and consequently plant fitness. However, empirical evidence for the plant-microbe-pollinator interactions remains little known. To test this hypothesis, we use a bumblebee-pollinated vine Clematis akebioides endemic to southwest China as an experimental model plant. To quantify the incidence and density of Metschnikowia reukaufii, a cosmopolitan NIY in floral nectar, a combination of yeast cultivation and microscopic cell-counting method was used. To examine the effects of NIY on plant-pollinator interactions, we used real flowers filled with artificial nectar with or without yeast cells. Then the volatile metabolites produced in the yeast-inoculated nectar were analysed with coupled gas chromatography and mass spectrometry (GC-MS). On average 79.3% of the C. akebioides flowers harboured M. reukaufii, and cell density of NIY was high to 7.4 × 104 cells mm-3 . In the field population, the presence of NIY in flowers of C. akebioides increased bumblebee (Bombus friseanus) pollinator visitation rate and consequently seed set per flower. A variety of fatty acid derivatives produced by M. reukaufii may be responsible for the above beneficial interactions. The volatiles produced by the metabolism of M. reukaufii may serve as an honest signal to attract bumblebee pollinators and indirectly promote the female reproductive fitness of C. akebioides, forming a potentially tripartite plant-microbe-pollinator mutualism.