Effects of ambient temperature, relative humidity and absolute humidity on risk of nasopharyngeal carcinoma in China.

Nasopharyngeal carcinoma (NPC) has a unique geographic distribution. It is unknown whether meteorological factors are related to the incidence of NPC. To investigate the effect of ambient temperature, relative humidity (RH), and absolute humidity (AH) on the incidence of NPC, we collected the incidence rate of NPC in 2016 and meteorological data from 2006 to 2016 from 484 cities and counties across 31 provinces in China. Generalized additive models with quasi-Poisson regression and generalized linear models with natural cubic splines were employed respectively to elucidate the nonlinear relationships and specify the partial linear relationships. Subgroup and interactive analysis were also conducted. Temperature (R2 = 0.68, p < .001), RH (R2 = 0.47, p < .001), and AH (R2 = 0.70, p < .001) exhibited nonlinear correlations with NPC incidence rate. The risk of NPC incidence increased by 20.3% (95% confidence intervals [CI]: [18.9%, 21.7%]) per 1°C increase in temperature, by 6.3% (95% CI: [5.3%, 7.2%]) per 1% increase in RH, and by 32.2% (95% CI: [30.7%, 33.7%]) per 1 g/m3 increase in AH, between their the 25th and the 99th percentiles. In addition, the combination of low temperature and low RH was also related to increased risk (relative risk: 1.60, 95% CI: [1.18, 2.17]). Males and eastern or rural populations tended to be more vulnerable. In summary, this study suggests that ambient temperature, RH, and particularly AH are associated with the risk of NPC incidence.

Machine learning-based radiomics nomograms to predict number of fields in postoperative IMRT for breast cancer.

BACKGROUND: Breast cancer is now the most commonly diagnosed cancer in women worldwide. Radiotherapy is an important part of the treatment for breast cancer, while setting proper number of fields dramatically affects the benefits one can receive. Machine learning and radiomics have been widely investigated in the management of breast cancer. This study aims to provide models to predict the best number of fields based on machine learning and improve the prediction performance by adding clinical factors. METHODS: Two-hundred forty-two breast cancer patients were retrospectively enrolled for this study, all of whom received postoperative intensity modulated radiation therapy. The patients were randomized into a training set and a validation set at a ratio of 7:3. Radiomics shape features were extracted for eight machine learning algorithms to predict the number of fields. Univariate and multivariable logistic regression were implemented to screen clinical factors. A combined model of rad-score and clinical factors were finally constructed. The area under receiver operating characteristic curve, precision, recall, F1 measure and accuracy were used to evaluate the model. RESULTS: Random Forest outperformed from eight machine learning algorithms while predicting the number of fields. Prediction performance of the radiomics model was better than the clinical model, while the predictive nomogram combining the rad-score and clinical factors performed the best. CONCLUSIONS: The model combining rad-score and clinical factors performed the best. Nomograms constructed from the combined models can be of reliable references for medical dosimetrists.

LINC01376 promotes nasopharyngeal carcinoma tumorigenesis by competitively binding to the SP1/miR-4757/IGF1 axis.

The long non-coding RNA (lncRNA)-microRNA (miRNA) interaction network plays a crucial part in the pathogenesis of nasopharyngeal carcinoma (NPC). Here, we discovered a relationship between LINC01376 and miR-4757 in NPC tumor development. First, LINC01376 was abnormally overexpressed in NPC tissues and cells, and its elevated expression was associated with advanced clinical stage and shorter distant metastasis-free survival time. Moreover, biological experiments showed that LINC01376 facilitated the proliferative, invasive, and migratory abilities of NPC cells in vitro and in vivo. Mechanistically, bioinformatics and RT-qPCR assays revealed that LINC01376 knockdown upregulated the expression level of downstream miR-4757, including miR-4757 primary transcript (pri-miR-4757) and mature miR-4757. Furthermore, LINC01376 competitively sponged the transcription factor SP1 and reduced its enrichment in the upstream promoter region of miR-4757 to repress miR-4757 expression. Finally, insulin-like growth factor 1(IGF1) was identified as the target of miR-4757. Rescue experiments indicated that LINC01376 accelerated NPC cell proliferation, migration, and invasion through the miR-4757-5p/IGF1 axis. In conclusion, the SP1/miR-4757/IGF1 axis, which is regulated by LINC01376 in NPC deterioration and metastasis, is expected to provide new insights into the molecular mechanism of NPC carcinogenesis.