Genetic abnormalities in patients with multiple myeloma (MM) are important risk factors in terms of prognosis. In the present study, the prognostic value of several common MM genetic abnormalities was investigated. Interphase fluorescence in situ hybridization (iFISH) was used to detect genetic abnormalities, including 1q21 gain, t(4;14), t(11;14), t(14;16) and 17p13 deletion in 131 patients. A total of 46.6% patients were detected with one or more abnormalities using iFISH analysis. The 1q21 gain, t(4;14), t(11;14), t(14;16) and 17p13 deletion abnormalities were detected in 42.5, 6.9, 17.5, 0.8 and 10.7% of patients, respectively. Patients with t(4;14) commonly exhibited lower levels of albumin and hemoglobin. The progression-free survival (PFS) and overall survival times of iFISH-positive patients (particularly patients with two or more iFISH abnormalities) were significantly shorter than those of the patients without detectable abnormalities. The 1q21 gain and 17p13 deletion were also adverse prognostic factors for MM. Bortezomib-based therapies improved the PFS times in the patients with unfavorable iFISH abnormalities. These findings demonstrate that patients with two or more iFISH abnormalities, a gain of the 1q21 region or a 17p13 deletion were more likely to have a poor prognosis; however, bortezomib treatment improved the outcome for MM patients with unfavorable iFISH abnormalities.

The aim of this study was to establish nomograms, based on significant clinicopathologic parameters, for predicting the overall survival (OS) and the cancer-specific survival (CSS) of patients with classical Hodgkin lymphoma (CHL). The data of 43,330 CHL patients, diagnosed between 1983 and 2014, were obtainedfrom the database of the Surveillance, Epidemiology, and End Results (SEER) program. These patients were randomly divided into training (n = 30,339) and validation (n = 12,991) cohorts. The Kaplan-Meier method and Cox proportional hazards regression model were used to evaluate the prognostic effects of multiple clinicopathologic parameters on survival. Significant prognostic factors were combined to build nomograms. The predictive performance of nomograms was evaluated using the index of concordance (C-index) and calibration curves. In the training cohort, on univariate and multivariate analyses, age at diagnosis, gender, race, Ann Arbor stage, and histological type significantly correlated with the survival outcomes. These characteristics were used to establish nomograms. The nomograms showed good accuracy in predicting 1-, 5-, and 10-year OS and CSS, with a C-index of 0.794 (95% confidence interval [CI], 0.789-0.799) for OS and 0.760 (95% CI, 0.753-0.767) for CSS. In the validation cohort, the C-index for nomogram-based predictions was 0.787 (95% CI, 0.779-0.795) for OS and 0.769 (95% CI, 0.758-0.780) for CSS. All calibration curves revealed excellent consistency between predicted and actual survival. In summary, novel nomograms were established and validated to predict OS and CSS for patients with CHL. These new prognostic models could aid in improved prediction of survival outcomes leading to reasonable treatment recommendations.

To establish a nomogram for predicting the overall survival (OS) of patients with newly diagnosed multiple myeloma (MM), 304 patients with newly diagnosed MM were recruited between June 1, 2010, and June 30, 2015, from the Beijing Chaoyang Hospital, Capital Medical University, and randomly divided into training (n=214) and validation (n=90) cohorts. The Kaplan-Meier method and the Cox proportional hazards regression model were used to evaluate the prognostic effects of multiple clinical and laboratory parameters on survival. Significant prognostic factors were combined to build a nomogram. The discriminative ability and predictive accuracy of the nomogram were evaluated using the index of concordance (C-index) and calibration curves and compared with the five staging systems currently used for MM. Multivariate analysis of the training cohort revealed that the age at diagnosis, clonal bone marrow plasma cells, serum lactate dehydrogenase, serum β2-microglobulin, and del (17p) were independent risk factors for OS and were used to establish the nomogram. The C-index value of the nomogram for predicting OS was 0.749, which was significantly higher than the C-indices of the five most common staging systems currently used for MM. In the validation cohort, the C-index for nomogram-based predictions was 0.711 for OS, and the nomogram discrimination was better than the above mentioned five staging systems (P<0.001). All calibration curves revealed good consistency between predicted and actual survivals. The proposed nomogram is more accurate in predicting the prognoses of patients with newly diagnosed MM.

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