Background: Lung carcinoid is a rare malignant tumor with poor survival. The current study established a nomogram model for predicting cancer-specific survival (CSS) in patients with lung carcinoid tumors. Methods: A total of 1956 patients diagnosed with primary lung carcinoid tumors were extracted from the Surveillance, Epidemiology, and End Results database. The specific predictors of CSS for lung carcinoid tumors were identified and integrated to build a nomogram. Validation of the nomogram was conducted using parameters concordance index (C-index), calibration plots, decision curve analyses (DCAs), and the receiver operating characteristic (ROC) curve. Results: Age at diagnosis, grade, histological type, N stage, M stage, surgery of the primary site, radiation of the primary site, and tumor size were independent prognostic factors of CSS. High discriminative accuracy of the nomogram model was shown in the training cohort (C-index = 0.873), which was also testified in the internal validation cohort (C-index = 0.861). In both cohorts, the calibration plots showed good concordance between the predicted and observed CSS at 3, 5, and 10 years. The DCA showed great potential for clinical application. The ROC curve showed superior survival predictive ability of the nomogram model (area under the curve = 0.868). Conclusions: We developed a practical nomogram that provided independent predictions of CSS for patients with lung carcinoid tumors. This nomogram may have the potential to assist clinicians in prognostic evaluations or developing individualized therapies for patients with this neoplasm.

Large cell neuroendocrine carcinoma (LCNEC) is a rare and typically aggressive malignancy with poor prognosis. This study developed a nomogram model to predict the overall survival (OS) of patients with LCNEC. LCNEC patients were identified from the Surveillance, Epidemiology, and End Results database between 2004–2014. Univariate and multivariate Cox regression models were used to determine demographic and clinicopathological features associated with OS. A nomogram model was generated to predict OS and its performance was assessed by Harrell’s concordance index (C-index), calibration plots, and subgroup analysis by risk scores. Of 3048 eligible patients with LCNEC, 2138 were randomly grouped into the training set and 910 into the validation set. Age at diagnosis, gender, tumor stage, N stage, tumor size, and surgery of primary site were independent prognostic factors of OS. C-index values of the nomogram were 0.75 (95% CI, 0.74–0.76) and 0.76 (95% CI, 0.74–0.77) in the training and validation sets, respectively. In both cohorts, the calibration plots showed good concordance between the predicted and observed OS at 3 and 5 years. Kaplan-Meier curves revealed significant differences in OS in patients stratified by nomogram-based risk score, and patients with a higher-than-median risk score had poorer OS. This is the first nomogram developed and validated in a large population-based cohort for predicting OS in patients with LCNEC, and it shows favorable discrimination and calibration abilities. Use of this proposed nomogram has the potential to improve prediction of survival risk, and lead to individualized clinical decisions for LCNEC.

The prognostic factors for survival among patients with secondary osteosarcoma remain unclear. The aim of this study was to develop a practical nomogram for predicting cancer-specific survival (CSS) in patients with osteosarcoma as a secondary malignancy. The surveillance, epidemiology, and end results database was used for the identification of osteosarcoma cases. The total sample comprised 5860 cases of primary osteosarcoma and 268 cases of secondary osteosarcoma during the period from 1973 to 2015. The CSS and overall survival (OS) of primary and secondary osteosarcomas were analyzed. The predictors of CSS for secondary osteosarcoma were identified and integrated to build a nomogram. Validation of the nomogram was performed using concordance index (C-index) and calibration plots. The results indicated that patients with secondary osteosarcoma had poorer CSS and OS than patients with primary osteosarcoma. The nomogram model exhibited high discriminative accuracy in the training cohort (C-index = 0.826), which was confirmed in the internal validation cohort (C-index = 0.791). In addition, the calibration plots confirmed good concordance for prediction of CSS at 3, 5, and 10 years. In conclusion, we developed a practical nomogram that provided individual predictions of CSS for patients with secondary osteosarcoma. This nomogram may help clinicians with prognostic evaluations and with the development of individualized therapies for this aggressive disease.

Genetic alterations could be responsible lung cancer, the leading cause of worldwide cancer death. This study investigated gene mutations in a Han Chinese family of lung cancer using the whole genome exome sequencing and subsequent Sanger sequencing validation and then confirmed alteration of prominin 1(PROM1) and cyclic AMP-response element binding protein-regulated transcription co-activator2 (CRTC2) in blood samples of 343 sporadic lung cancer patients vs. 280 healthy controls as well as in 200 pairs of lung cancer and the corresponding normal tissues using PCR-restriction fragment length polymorphism and directed DNA sequencing of PCR products. The data showed PROM1 (p. S281R) and CRTC2 (p. R379C) mutations, in 5 and 2 cases of these 343 sporadic lung cancer patients, respectively. Notably, these mutations were absent in the healthy controls. Furthermore, in the 200 lung cancer and the matched normal tissues, PROM1 mutation occurred in 3 patients (i.e., one squamous cell carcinoma and two adenocarcinomas) with a mutation frequency of 1.5%, while CRTC2 mutation occurred in 5 patients (two squamous cell carcinomas and three adenocarcinomas) with a mutation frequency of 2.5%. The data from the current study demonstrated novel PROM1 and CRTC2 mutations, which could promote lung cancer development.

Mesenchymal stem cells (MSCs) are considered the best candidate in stem cells therapy due to their multipotent differentiation ability, low expression of co-stimulatory molecules (CD80, CD86, CD34 and HLA-II) and immunosuppression effects on in vivo immune responses. MSCs were now widely used in clinical trials but received no encourage results. The major problem was the fate of engrafted MSCs in vivo could not be defined. Some studies indicated that MSCs could induce immune response and result in the damage and rejection of MSCs. As toll like receptors (TLRs) are important in inducing of immune responses, in this study we study the role of TLR7 in mediating the immune status of MSCs isolated from umbilical cord.

Mucosal-associated invariant T (MAIT) cells are an atypical subset of T lymphocytes, which have a highly conserved semi-constant αβ chain of T-cell receptor (TCR) and recognize microbe-derived vitamin B metabolites via major histocompatibility complex class I related-1 molecule (MR1). MAIT cells get activated mainly through unique TCR-dependent and TCR-independent pathways, and express multiple functional and phenotypic traits, including innate-like functionality, T helper (Th) 1 cell immunity, Th 17 cell immunity, and tissue homing. Given the functions, MAIT cells are extensively reported to play a key role in mucosal homeostasis and infectious diseases. In the current work, we review the basic characteristics of MAIT cells and their roles in mucosal homeostasis and development of respiratory infectious diseases as well as their potential therapeutic targets.