Evidence from the population study and mouse model strongly indicates that SR-A may function as a tumor modulator to inhibit lung cancer growth through affecting the tumor microenvironment.

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This study aimed to investigate the risk factors of second primary cancer among female breast cancer (BC) survivors, with emphasis on the prediction of the individual risk conditioned on the patient's characteristics. We identified 208,474 BC patients diagnosed between 2004 and 2010 from the Surveillance, Epidemiology and End Results (SEER) database. Subdistribution proportional hazard model and competing-risk nomogram were used to explore the risk factors of second primary BC and non-BC, and to predict the 5-and 10-year probabilities of second primary BC. Model performance was evaluated via calibration curves and decision curve analysis. The overall 3-, 5-, and 10-year cumulative incidences for second primary BC were 0.9%, 1.6% and 4.4%, and for second primary non-BC were 2.3%, 3.9%, and 7.8%, respectively. Age over 70 years at diagnosis, black race, tumor size over 2 cm, negative hormone receptor, mixed histology, localized tumor, lumpectomy alone, and surgeries plus radiotherapy were significantly associated with increased risk of second BC. The risk of second non-BC was only related to age, race and tumor size. The proposed risk model as well as its nomogram was clinically beneficial to identify patients at high risk of developing second primary breast cancer.

Background: Previous studies have reported an increased risk for second primary malignancies (SPMs) after cervical cancer (CC). This study aims to quantify and assess the risk of developing SPMs in long-term survivors of CC.Methods: A population-based cohort of CC patients aged 20-79 years was obtained from the Surveillance, Epidemiology, and End Results (SEER) database. A competing risk model and corresponding nomogram were constructed to predict the 3-, 5-, and 10-year cumulative risks of SPMs. A Fine-Gray plot was created to validate the model. Finally, we performed decision curve analysis (DCA) to evaluate the clinical usefulness of the model by calculating the net benefit.Results: A total of 34,295 patients were identified, and approximately 6.3% of the study participants developed SPMs. According to the multivariable competing-risk model, older black CC survivors with localized disease who were treated with radiation therapy were more susceptible to SPMs. The 3-, 5-, and 10-year cumulative incidences of SPMs were 2.5%, 3.6%, and 6.2%, respectively. Calibration curves showed good agreement between the predicted and observed models. The DCA yielded a wide range of risk thresholds at which the net benefits could be obtained from our proposed model. Conclusions:This study provides physicians with a practical, individualized prognostic estimate to assess the risk of SPMs among CC survivors. CC survivors remain at a high risk of developing SPMs, and further surveillance should focus especially on the patients with black race, older age, localized disease, or those having received radiation therapy.

Background: Lung cancer (LC) patients are at high risk of developing second primary cancer (SPC). This study aimed to explore the risk factors associated with SPC and provide an individualized risk prediction model for LC patients. Methods: Initial primary lung cancer (IPLC) patients diagnosed between 1998 and 2011 were identified from the Surveillance, Epidemiology, and End Results (SEER) database. A Fine-Gray multivariate competing-risk model was used to estimate the risk of SPC, and the model was assessed regarding discrimination and calibration. A nomogram was designed for clinical convenience to predict the 3-, 5-, and 10-year probabilities of developing SPCs. Results: A total of 142,491 IPLC patients were considered in this study and 14,374(10.01%) developed SPC within a maximum study period of approximately 19 years. Seven independent prognostic factors were identified according to the competing-risk model, and the SEER summary stage and surgery were the strongest predictors. The model was well calibrated and had good discrimination ability(C-index = 0.746). Conclusions: LC survivors had an increased risk of SPC and factors associated with good prognosis often predicted SPC. Consideration should be given to increasing the duration of routine follow-up even after 10 years of initial diagnosis for those at the highest risk and site-specific follow-up strategy is also required.