Background Occult peritoneal metastasis (PM) in advanced gastric cancer (AGC) patients is highly possible to be missed on computed tomography (CT) images. Patients with occult PMs are subject to late detection or even improper surgical treatment. We therefore aimed to develop a radiomic nomogram to preoperatively identify occult PMs in AGC patients. Patients and methods A total of 554 AGC patients from 4 centers were divided into 1 training, 1 internal validation, and 2 external validation cohorts. All patients’ PM status was firstly diagnosed as negative by CT, but later confirmed by laparoscopy (PM-positive n = 122, PM-negative n = 432). Radiomic signatures reflecting phenotypes of the primary tumor (RS1) and peritoneum region (RS2) were built as predictors of PM from 266 quantitative image features. Individualized nomograms of PM status incorporating RS1, RS2, or clinical factors were developed and evaluated regarding prediction ability. Results RS1, RS2, and Lauren type were significant predictors of occult PM (all P < 0.05). A nomogram of these three factors demonstrated better diagnostic accuracy than the model with RS1, RS2, or clinical factors alone (all net reclassification improvement P < 0.05). The area under curve yielded was 0.958 [95% confidence interval (CI) 0.923–0.993], 0.941 (95% CI 0.904–0.977), 0.928 (95% CI 0.886–0.971), and 0.920 (95% CI 0.862–0.978) for the training, internal, and two external validation cohorts, respectively. Stratification analysis showed that this nomogram had potential generalization ability. Conclusion CT phenotypes of both primary tumor and nearby peritoneum are significantly associated with occult PM status. A nomogram of these CT phenotypes and Lauren type has an excellent prediction ability of occult PM, and may have significant clinical implications on early detection of occult PM for AGC.

Background: Preoperative evaluation of the number of lymph node metastasis (LNM) is the basis of individual treatment of locally advanced gastric cancer (LAGC). However, the routinely used preoperative determination method is not accurate enough. Patients and methods: We enrolled 730 LAGC patients from five centers in China and one center in Italy, and divided them into one primary cohort, three external validation cohorts, and one international validation cohort. A deep learning radiomic nomogram (DLRN) was built based on the images from multiphase computed tomography (CT) for preoperatively determining the number of LNM in LAGC. We comprehensively tested the DLRN and compared it with three state-of-the-art methods. Moreover, we investigated the value of the DLRN in survival analysis. Results: The DLRN showed good discrimination of the number of LNM on all cohorts [overall C-indexes (95% confidence interval): 0.821 (0.785e0.858) in the primary cohort, 0.797 (0.771e0.823) in the external validation cohorts, and 0.822 (0.756e0.887) in the international validation cohort]. The nomogram performed significantly better than the routinely used clinical N stages, tumor size, and clinical model (P < 0.05). Besides, DLRN was significantly associated with the overall survival of LAGC patients (n ¼ 271). Conclusion: A deep learning-based radiomic nomogram had good predictive value for LNM in LAGC. In staging-oriented treatment of gastric cancer, this preoperative nomogram could provide baseline information for individual treatment of LAGC.

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In previous studies, we and others have shown that bone marrow mesenchymal stem cells (MSCs) are recruited to sites of growing tumors and promote tumor growth in mouse xenograft models, suggesting that interactions between MSCs and tumor cells may play an important role in this process. However, the exact mechanism remains unclear. In the present study, we investigated whether the physical presence or the continuous presence of MSCs is required for enhanced tumor growth, and we found that pretreatment of tumor cells SGC-7901 with a single dose of human MSC-conditioned medium (hMSC-CM) in vitro is sufficient to potentiate tumor growth comparable to the effect of MSC co-injection in vivo in mouse xenograft models. We further showed that significant tumor modifying activity is present in post-ultracentrifigation soluble fraction. Biochemical analysis suggests that hMSC-CM induces the expression of VEGF of tumor cells as well as the activation of RhoA-GTPase and ERK1/2. Furthermore, hMSC-CM-enhanced tumor growth is sustainable in serial transplantation, suggesting that MSC-secreted factors have profound effects on "reprogramming" of tumor growth. Our data provide new insights into the way in which MSCs modify tumor growth and offer a new and exciting opportunity to develop effective therapeutics for intercepting tumor progression.

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Reactivation of tumor suppressor genes by nontoxic bioactive food component represents a promising strategy for cancer chemoprevention. Retinoic acid receptor β (RARβ), one member of the RAR receptor family, is considered as a tumor suppressor. Reduced expression of RARβ has been reported in lung cancer and other solid tumors. DNA hypermethylation of the promoter region of RARβ is a major mechanism for its silencing in tumors. Recently, curcumin has been considered as a potential DNA methyltransferase inhibitor. Herein, we demonstrated that curcumin significantly elevate RARβ expression at the mRNA and protein levels in tested cancer cells. Additionally, curcumin decreased RARβ promoter methylation in lung cancer A549 and H460 cells. Mechanistic study demonstrated that curcumin was able to downregulate the mRNA levels of DNMT3b. In a lung cancer xenograft node mice model, curcumin exhibited protective effect against weight loss because of tumor burden. Tumor growth was strongly repressed by curcumin treatment. As the results from in vitro, RARβ mRNA were increased and DNMT3b mRNA were decreased by curcumin treatment compared with the mice in control group. Altogether, this study reveals a novel molecular mechanism of curcumin as a chemo-preventive agent for lung cancer through reactivation of RARβ.

γ-Fe(2)O(3)-DMSA-DG NPs could be developed as a tumor-targeted probe for cervical cancer imaging and therapy.