The current study examined risk factors for sporadic pancreatic neuroendocrine tumors (PNETs), including smoking, alcohol use, first-degree family history of any cancer (FHC), and diabetes in the Han Chinese ethnic group. In this clinic-based case-control analysis on 385 patients with sporadic PNETs and 614 age- and sex-matched controls, we interviewed subjects using a specific questionnaire on demographics and potential risk factors. An unconditional multivariable logistic regression analysis was used to estimate adjusted odds ratios (AORs). No significant differences were found between patients and controls in terms of demographic variables. Most of the patients with PNETs had well-differentiated PNETs (G1, 62.9%) and non-advanced European Neuroendocrine Tumor Society (ENETS) stage (stage I or II, 83.9%). Ever/heavy smoking, a history of diabetes and a first-degree FHC were independent risk factors for non-functional PNETs. Only heavy drinking was found to be an independent risk factor for functional PNETs (AOR = 1.87; 95% confidence interval [CI], 1.01–3.51). Ever/heavy smoking was also associated with advanced ENETS staging (stage III or IV) at the time of diagnosis. This study identified first-degree FHC, ever/heavy smoking, and diabetes as risk factors for non-functional PNETs, while heavy drinking as a risk factor for functional PNETs.
BACKGROUND Additional prostate cancer (PCa) risk-associated single nucleotide polymorphisms (SNPs) continue to be identified. It is unclear whether addition of newly identified SNPs improves the discriminative performance of biopsy outcomes over previously established SNPs. METHODS A total of 667 consecutive patients that underwent prostate biopsy for detection of PCa at Huashan Hospital and Changhai Hospital, Shanghai, China were recruited. Genetic scores were calculated for each patient using various combinations of 29 PCa risk-associated SNPs. Performance of these genetic scores for discriminating prostate biopsy outcomes were compared using the area under a receiver operating characteristic curve (AUC). RESULTS The discriminative performance of genetic score derived from a panel of all 29 SNPs (24 previous and 5 new) was similar to that derived from the 24 previously established SNPs, the AUC of which were 0.60 and 0.61, respectively (P = 0.72). When SNPs with the strongest effect on PCa risk (ranked based on contribution to the total genetic variance from an external study) were sequentially added to the models for calculating genetic score, the AUC gradually increased and peaked at 0.62 with the top 13 strongest SNPs. Under the 13-SNP model, the PCa detection rate was 21.52%, 36.74%, and 51.98%, respectively for men with low (<0.5), intermediate (0.5–1.5), and high (>1.5) genetic score, P-trend = 9.91 × 10−6. CONCLUSION Genetic score based on PCa risk-associated SNPs implicated to date is a significant predictor of biopsy outcome. Additional small-effect PCa risk-associated SNPs to be discovered in the future are unlikely to further improve predictive performance.
Background: Colon cancer is a common and highly malignant cancer. Its morbidity is rapidly increasing, and its prognosis is poor. Currently, immunotherapy is a rapidly developing therapeutic modality of colon cancer. This study aimed to construct a prognostic risk model based on immune genes for the early diagnosis and accurate prognostic prediction of colon cancer. Methods: Transcriptomic data and clinical data were downloaded from The Cancer Genome Atlas database. Immune genes were obtained from the ImmPort database. Differentially expressed (DE) immune genes between 473 colon cancer and 41 adjacent normal tissues were identified. The entire cohort was randomly divided into the training and testing cohort. The training cohort was used to construct the prognostic model. The testing and entire cohorts were used to validate the model. The clinical utility of the model and its correlation with immune cell infiltration were analyzed. Results: A total of 333 DE immune genes (176 up-regulated and 157 down-regulated) were detected. We developed and validated a five-immune gene model of colon cancer, including LBP, TFR2, UCN, UTS2, and MC1R. This model was approved to be an independent prognostic variable, which was more accurate than age and the pathological stage for predicting overall survival at five years. Besides, as the risk score increased, the content of CD8+ T cells in colon cancer was decreased. Conclusions: We developed and validated a five-immune gene model of colon cancer, including LBP, TFR2, UCN, UTS2, and MC1R. This model could be used as an instrumental variable in the prognosis prediction of colon cancer.
We demonstrate for the first time that IGFBP7 is downregulated in pancreatic cancer, and low expression of IGFBP7 is correlated with increased proliferation and poor postoperative survival. IGFBP7 may be a tumor suppressor in PDAC.
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