Construction of a nomogram to predict the survival of metastatic gastric cancer patients that received immunotherapy

Abstract

BackgroundImmunotherapy has shown promising results for metastatic gastric cancer (MGC) patients. Nevertheless, not all patients can benefit from anti-PD-1 treatment. Thus, this study aimed to develop and validate a prognostic nomogram for MGC patients that received immunotherapy.MethodsHerein, MGC patients treated with anti-PD-1 between 1 October 2016 and 1 June 2022 at two separate Chinese PLA General Hospital centers were enrolled and randomly divided into training and validation sets (186 and 80 patients, respectively). The nomogram was constructed based on a multivariable Cox model using baseline variables from the training cohort. Its predictive accuracy was validated by the validation set. The consistency index (C-index) and calibration plots were used to evaluate the discriminative ability and accuracy of the nomogram. The net benefit of the nomogram was evaluated using decision curve analysis (DCA). Finally, we stratified patients by median total nomogram scores and performed Kaplan–Meier survival analyses.ResultsWe developed the nomogram based on the multivariate analysis of the training cohort, including four parameters: surgery history, treatment line, lung immune prognostic index (LIPI), and platelet-to-lymphocyte ratio (PLR). The C-index of the nomogram was 0.745 in the training set. The calibration curve for 1- and 2-year survival showed good agreement between nomogram predictions and actual observations. In the validation group, the calibration curves demonstrated good performance of the nomogram, with a C-index for overall survival (OS) prediction of 0.713. The OS of patients with a score greater than the median nomogram score was significantly longer than patients with a score lower or equal to the median (p < 0.001).ConclusionWe constructed a nomogram to predict the outcomes of MGC patients that received immunotherapy. This nomogram might facilitate individualized survival predictions and be helpful during clinical decision-making for MGC patients under anti-PD-1 therapy.

SMOC2, OGN, FCN3, and SERPINA3 could be biomarkers for the evaluation of acute decompensated heart failure caused by venous congestion

“…For the construction of a diagnostic nomogram for the precompensation phase of heart failure, our analysis leveraged the ’rms’ package, which facilitated the visualization of the contributory weight of each candidate gene as individual points on the nomogram, with ’Total Points’ reflecting the cumulative score derived from all selected genes 22 . Subsequently, boxplots were generated to illustrate the expression profiles of these genes, and Receiver Operating Characteristic (ROC) curves were plotted to assess their diagnostic efficacy.…”

“…For the construction of a diagnostic nomogram for the precompensation phase of heart failure, our analysis leveraged the 'rms' package, which facilitated the visualization of the contributory weight of each candidate gene as individual points on the nomogram, with 'Total Points' reflecting the cumulative score derived from all selected genes 22 .…”

Survival benefits of gastrectomy in patients with metastatic gastric cancer: A meta-analysis

“…In the current study, patients who received gastrectomy were grouped into the gastrectomy group, gastrectomy + metastasectomy group or gastrectomy ± metastasectomy group, and patients who did not receive resection were classified into the non-resection group. In this regard, 16 studies 23,28,30,32,37,40,42,44,50,51,53,55,59,61,63,64 made comparisons between the gastrectomy group and the non-resection group, 8 studies 22,25,34,41,43,54,60,66 compared the gastrectomy + metastasectomy group with the non-resection group, and 22 studies 24,26,27,29,31,33,35,36,38,39,[45][46][47]48,49,52,[56][57][58]62,65,67 compared the gastrectomy ± metastasectomy group with the non-resection group. Of 46 cohort studies, 30 were of medium quality and 16 had high quality.…”

Identification of immune-related genes for the diagnosis of ischaemic heart failure based on bioinformatics and three machine learning models

“…We utilized the rms package to develop a nomogram for the identification of IHF diagnostic genes 26 . Points represent the scores of candidate genes and Total Points represent the sum of all the above gene scores.…”