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Purpose The aim of the present study was to develop a nomogram for prognostic prediction of patients with lung cancer in hospice. Methods The data was collected from 1106 lung cancer patients in hospice between January 2008 and December 2018. The data were split into a training set, which was used to identify the most important prognostic factors by the least absolute shrinkage and selection operator (LASSO) and to build the nomogram, while the testing set was used to validate the nomogram. The performance of the nomogram was assessed by c-index, calibration curve and the decision curve analysis (DCA). Results A total of 1106 patients, including 835 (75%) from the training set and 271 (25%) from testing set, were retrospectively analyzed in this study. Using the LASSO regression, 5 most important prognostic predictors that included sex, Karnofsky Performance Scale (KPS), quality-of-life (QOL), edema and anorexia, were selected out of 28 variables. Validated c-indexes of training set at 15, 30, and 90 days were .778 [.737-.818], .776 [.743-.809], and .751 [.713-.790], respectively. Similarly, the validated c-indexes of testing set at 15, 30, and 90 days were .789 [.714-.864], .748 [.685-.811], and .757 [.691-.823], respectively. The nomogram-predicted survival was well calibrated, as the predicted probabilities were close to the expected probabilities. Moreover, the DCA curve showed that nomogram received superior standardized net benefit at a broad threshold. Conclusions The study built a non-lab nomogram with important predictor to analyze the clinical parameters using LASSO. It may be a useful tool to allow clinicians to easily estimate the prognosis of the patients with lung cancer in hospice.

Abstract Background Patients suffering from gastrointestinal cancer comprise a large group receiving home hospice care in China, however, little is known about the prediction of their survival time. This study aimed to develop a gastrointestinal cancer-specific non-lab nomogram predicting survival time in home-based hospice. Methods We retrospectively studied the patients with gastrointestinal cancer from a home-based hospice between 2008 and 2018. General baseline characteristics, disease-related characteristics, and related assessment scale scores were collected from the case records. The data were randomly split into a training set (75%) for developing a predictive nomogram and a testing set (25%) for validation. A non-lab nomogram predicting the 30-day and 60-day survival probability was created using the least absolute shrinkage and selection operator (LASSO) Cox regression. We evaluated the performance of our predictive model by means of the area under receiver operating characteristic curve (AUC) and calibration curve. Results A total of 1618 patients were included and divided into two sets: 1214 patients (110 censored) as training dataset and 404 patients (33 censored) as testing dataset. The median survival time for overall included patients was 35 days (IQR, 17–66). The 5 most significant prognostic variables were identified to construct the nomogram among all 28 initial variables, including Karnofsky Performance Status (KPS), abdominal distention, edema, quality of life (QOL), and duration of pain. In training dataset validation, the AUC at 30 days and 60 days were 0.723 (95% CI, 0.694–0.753) and 0.733 (95% CI, 0.702–0.763), respectively. Similarly, the AUC value was 0.724 (0.673–0.774) at 30 days and 0.725 (0.672–0.778) at 60 days in the testing dataset validation. Further, the calibration curves revealed good agreement between the nomogram predictions and actual observations in both the training and testing dataset. Conclusion This non-lab nomogram may be a useful clinical tool. It needs prospective multicenter validation as well as testing with Chinese clinicians in charge of hospice patients with gastrointestinal cancer to assess acceptability and usability.

Esophageal squamous cell carcinomas (ESCC) have become a severe threat to health and the current treatments for ESCC are frequently not effective. Recent epidemiological studies suggest that the anti-hyperglycemic agent metformin may reduce the risk of developing cancer, including ESCC, among diabetic patients. However, the antitumor effects of metformin on ESCC and the mechanisms underlying its cell cycle regulation remain elusive. The findings reported herein show that the anti-proliferative action of metformin on ESCC cell lines is partially mediated by AMPK. Moreover, we observed that metformin induced G0/G1 phase arrest accompanied by the up-regulation of p21CIP1 and p27KIP1. In vivo experiments further showed that metformin inhibited tumor growth in a ESCC xenograft model. Most importantly, the up-regulation of AMPK, p53, p21CIP1, p27KIP1 and the down-regulation of cyclinD1 are involved in the anti-tumor action of metformin in vivo. In conclusion, metformin inhibits the growth of ESCC cells both in cell cultures and in an animal model. AMPK, p53, p21CIP1, p27KIP1 and cyclinD1 are involved in the inhibition of tumor growth that is induced by metformin and cell cycle arrest in ESCC. These findings indicate that metformin has the potential for use in the treatment of ESCC.