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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).
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Objective: The aim of this study was to analyze the association between the expression of chromatin assembly factor 1 subunit A (CHAF1A) in gastric cancer (GC) and clinicopathological features, disease prognosis, and expression of programmed cell death-ligand 1 (PD-L1). Material and Methods. A total of 140 GC tissue specimens were collected between January 2013 and December 2017. CHAF1A expression in GC and paracancerous tissues was determined. Then, the associations between CHAF1A expression level in the collected tissues and clinicopathological features as well as PD-L1 expression level were investigated. Cox regression analyses were carried out to determine whether CHAF1A is an independent prognostic factor for GC. Finally, the association between CHAF1A expression levels and survival of the GC patients was investigated. Results: A significantly higher level of CHAF1A expression in GC tissues was found compared to that in paracancerous tissues (p=0.042). CHAF1A expression level in GC tissues was found to be strongly associated with family history (p=0.005), smoking history (p=0.016), T stage (p=0.001), tumor marker AFP (p=0.017), tumor marker CEA (p=0.027), and PD-L1 expression (p=0.029). CHAF1A expression was also found to be positively correlated to PD-L1 expression (p=0.012). Moreover, high CHAF1A expression levels were found to lead to poor prognosis (p=0.019). Univariate and multivariate analyses all showed that CHAF1A was an independent poorer prognostic factor for gastric cancer (p=0.021, HR = 1.175, 95% CI: 1.090-2.890 for univariate analyses; p=0.014, HR = 2.191, 95% CI:1.170-4.105 for multivariate analyses). A high level of CHAF1A expression was thus found to be an independent risk factor for GC prognosis. Conclusion: High CHAF1A expression is associated with poor GC prognosis and positively correlated to PD-L1 expression. Thus, CHAF1A expression level may be used as a novel biomarker for GC diagnosis.