RESUMO
Background: Tongue images (the colour, size and shape of the tongue and the colour, thickness and moisture content of the tongue coating), reflecting the health state of the whole body according to the theory of traditional Chinese medicine (TCM), have been widely used in China for thousands of years. Herein, we investigated the value of tongue images and the tongue coating microbiome in the diagnosis of gastric cancer (GC). Methods: From May 2020 to January 2021, we simultaneously collected tongue images and tongue coating samples from 328 patients with GC (all newly diagnosed with GC) and 304 non-gastric cancer (NGC) participants in China, and 16 S rDNA was used to characterize the microbiome of the tongue coating samples. Then, artificial intelligence (AI) deep learning models were established to evaluate the value of tongue images and the tongue coating microbiome in the diagnosis of GC. Considering that tongue imaging is more convenient and economical as a diagnostic tool, we further conducted a prospective multicentre clinical study from May 2020 to March 2022 in China and recruited 937 patients with GC and 1911 participants with NGC from 10 centres across China to further evaluate the role of tongue images in the diagnosis of GC. Moreover, we verified this approach in another independent external validation cohort that included 294 patients with GC and 521 participants with NGC from 7 centres. This study is registered at ClinicalTrials.gov, NCT01090362. Findings: For the first time, we found that both tongue images and the tongue coating microbiome can be used as tools for the diagnosis of GC, and the area under the curve (AUC) value of the tongue image-based diagnostic model was 0.89. The AUC values of the tongue coating microbiome-based model reached 0.94 using genus data and 0.95 using species data. The results of the prospective multicentre clinical study showed that the AUC values of the three tongue image-based models for GCs reached 0.88-0.92 in the internal verification and 0.83-0.88 in the independent external verification, which were significantly superior to the combination of eight blood biomarkers. Interpretation: Our results suggest that tongue images can be used as a stable method for GC diagnosis and are significantly superior to conventional blood biomarkers. The three kinds of tongue image-based AI deep learning diagnostic models that we developed can be used to adequately distinguish patients with GC from participants with NGC, even early GC and precancerous lesions, such as atrophic gastritis (AG). Funding: The National Key R&D Program of China (2021YFA0910100), Program of Zhejiang Provincial TCM Sci-tech Plan (2018ZY006), Medical Science and Technology Project of Zhejiang Province (2022KY114, WKJ-ZJ-2104), Zhejiang Provincial Research Center for Upper Gastrointestinal Tract Cancer (JBZX-202006), Natural Science Foundation of Zhejiang Province (HDMY22H160008), Science and Technology Projects of Zhejiang Province (2019C03049), National Natural Science Foundation of China (82074245, 81973634, 82204828), and Chinese Postdoctoral Science Foundation (2022M713203).
RESUMO
Trametes robiniophila Murr (TRM) is a traditional Chinese medicine which has been used in clinics for enhancing immunity and improving the efficacy of chemotherapy. However, the mechanisms of action of TRM are unknown. In the previous study, we found that the Trametes robiniophila Murr n-butanol extract (TRMBE) comprises the major bioactive components of TRM. In the present study, we aimed to assess the combinational effects of TRMBE and 5-fluorouracil (5-FU) on the treatment of gastric cancer (GC) and explore its mechanism of action. It was found that TRMBE significantly potentiated the anticancer activity of 5-FU and prolonged the survival time of mice bearing Mouse Forestomach Carcinoma (MFC) xenograft tumors. We observed that the combination of TRMBE and 5-FU decreased the risk of liver metastasis in vivo. Furthermore, the combination of TRMBE and 5-FU reduced the levels of immune cytokines IL-6, IL-10, and TGF-ß and increased the level of IFN-γ in peripheral blood. This combination therapy also significantly decreased the levels of polymorphonuclear myeloid-derived suppressor cells (PMN-MDSCs) and PD-1-positive CD8+ T cells and increased the levels of NK cells in tumor microenvironment (TME). However, TRMBE treatment was unable to enhance the chemosensitivity of GC to 5-FU in vivo after the depletion of CD8+ T and NK cells. Taken together, our results demonstrate that TRMBE can reshape the TME of GC by regulating PMN-MDSCs, CD8+ T cells, and NK cells, therefore improving the therapeutic effects of 5-FU. This study suggests that the combination of TRMBE and 5-FU could enhance immunity and could be a promising approach for GC treatment.
RESUMO
Gastric cancer (GC) is a common malignant tumor worldwide and poses a serious threat to human health. As a traditional Chinese medicine, Huaier (Trametes robiniophila Murr.) has been used in the clinical treatment of GC. However, the mechanism underlying the anticancer effect of Huaier remains poorly understood. In this study, we used in vivo imaging technology to determine the anticancer effect of the Huaier n-butanol extract (HBE) on orthotopic and hepatic metastasis of GC mouse models. We found that HBE suppressed tumor growth and metastasis without causing apparent host toxicity. Proteomic analysis of GC cells before and after HBE intervention revealed syntenin to be one of the most significantly downregulated proteins after HBE intervention. We further demonstrated that HBE suppressed the growth and metastasis of GC by reducing the expression of syntenin and the phosphorylation of STAT3 at Y705 and reversing the epithelial-mesenchymal transition (EMT). In addition, we confirmed that syntenin was highly expressed in GC tissue and correlated with metastasis and poor prognosis. In conclusion, our results suggest that Huaier, a clinically used anticancer drug, may inhibit the growth and liver metastasis of GC by inhibiting the syntenin/STAT3 signaling pathway and reversing EMT.