Interleukin (IL)-16, a multifunctional cytokine, plays a fundamental role in inflammatory diseases, as well as in the development and progression of tumors. Genetic variation in the DNA sequence of the IL-16 gene may lead to altered cytokine production and/or activity, and this variation may modulate an individual's susceptibility to both colorectal cancer (CRC) and gastric cancer (GC). To test this hypothesis, we investigated the association of IL-16 gene polymorphisms with serum levels of IL-16 and the risk of CRC and GC in a Chinese population. We analyzed single-nucleotide polymorphisms of the IL-16 gene in 596 cancer patients (376 patients with CRC and 220 patients with GC), and also in 480 age- and sex-matched controls using polymerase chain reaction-restriction fragment length polymorphism and DNA sequencing methods. Serum IL-16 levels were measured by enzyme-linked immunosorbent assay. The rs11556218 T/G polymorphism of the IL-16 gene was significantly associated with the susceptibility to CRC and GC patients. Both male and female patients carrying the G allele had a significantly higher risk for developing CRC and GC compared with individuals carrying the T allele. Alternatively, women carrying the T allele (rs4072111 C/T) showed a decreased risk for CRC and GC compared with individuals carrying the C allele. In patients with CRC or GC, IL-16 serum levels were significantly higher than those in the healthy controls, although no significant association between IL-16 polymorphisms and serum levels of IL-16 was observed. Our data indicate that IL-16 polymorphisms may contribute to CRC and GC susceptibility.

Background and purpose: Recurrence is the main risk for high-grade serous ovarian cancer (HGSOC) and few prognostic biomarkers were reported. In this study, we proposed a novel deep learning (DL) method to extract prognostic biomarkers from preoperative computed tomography (CT) images, aiming at providing a non-invasive recurrence prediction model in HGSOC. Materials and methods: We enrolled 245 patients with HGSOC from two hospitals, which included a feature-learning cohort (n = 102), a primary cohort (n = 49) and two independent validation cohorts from two hospitals (n = 49 and n = 45). We trained a novel DL network in 8917 CT images from the featurelearning cohort to extract the prognostic biomarkers (DL feature) of HGSOC. Afterward, a DL-CPH model incorporating the DL feature and Cox proportional hazard (Cox-PH) regression was developed to predict the individual recurrence risk and 3-year recurrence probability of patients. Results: In the two validation cohorts, the concordance-index of the DL-CPH model was 0.713 and 0.694. Kaplan-Meier's analysis clearly identified two patient groups with high and low recurrence risk (p = 0.0038 and 0.0164). The 3-year recurrence prediction was also effective (AUC = 0.772 and 0.825), which was validated by the good calibration and decision curve analysis. Moreover, the DL feature demonstrated stronger prognostic value than clinical characteristics. Conclusions: The DL method extracts effective CT-based prognostic biomarkers for HGSOC, and provides a non-invasive and preoperative model for individualized recurrence prediction in HGSOC. In addition, the DL-CPH model provides a new prognostic analysis method that can utilize CT data without follow-up for prognostic biomarker extraction.

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MicroRNAs (miRNAs) function as gene regulator and they participate in diverse biological pathways. Common single nucleotide polymorphisms (SNPs) in pre-microRNAs may change their property through altering miRNAs expression and/or maturation. We conducted a pilot study to test whether SNPs in pre-microRNAs were associated with cervical squamous cell carcinoma (CSCC). Genotypes of three SNPs in pre-miRNAs (hsa-miR-196a2 rs11614913 C/T, hsa-miR-499 rs3746444 A/G, and hsa-miR-146a rs2910164 G/C) in 226 CSCC patients and 309 control subjects were determined with the use of PCR-restriction fragment length polymorphism (RFLP) assay. Significantly increased CSCC risks were found to be associated with G allele of rs3746444 and G allele of rs2910164 (P = 0.017, OR = 1.454, and P = 0.016, OR = 1.355, respectively). Increased CSCC risks were associated with them in different genetic model (P = 0.0004, OR = 1.98 for rs3746444 in an overdominant model, and P = 0.024, OR = 2.10 for rs2910164 in a codominant model, respectively). Results of stratified analyses revealed that rs2910164 is associated with tumor differentiation and lymph node status (P = 0.043, OR = 2.08, and a borderline P = 0.057, OR = 0.41, respectively). No association between rs11614913 and CSCC risk was observed. The present study provides evidence that rs3746444 and rs2910164 are associated with CSCC, indicating that common genetic polymorphisms in pre-microRNAs contribute to the pathogenesis of CSCC.

T2N1 classification is a unique subgroup with higher risk of distant metastasis. Improved outcomes of T2N1 NPC with predominantly WHO II histology after chemoradiation has not been reported.