The combination of traditional Chinese medicine (TCM) with chemotherapy has been widely applied in the treatment of gastric cancer (GC). However, previous clinical studies have been constrained by small sample sizes and a lack of investigation into the molecular mechanisms of TCM. This study aims to assess the efficacy of TCM in treating GC by leveraging the strengths of meta-analysis and multi-omics approaches while also summarizing the underlying pharmacological mechanisms.

A systematic literature review and meta-analysis were conducted using online databases to collect data before May 2024. This was to investigate the association between TCM combined with chemotherapy and the prognosis in GC. The molecular targets between the high-frequency TCMs and GC were identified through network pharmacology. The underlying mechanisms were investigated using multi-omics.

9 studies with 2,158 patients were included. The meta-analysis results demonstrated that the combination of TCM and chemotherapy significantly improved the overall survival (OS) of GC patients (OR = 2.91; 95% CI: 2.70-3.12, p < 0.00001) and enhanced their quality of life (OR = 4.00; 95% CI: 1.99-8.03, p < 0.0001). Network pharmacology analysis identified 13 potential molecular targets of TCM in GC; additionally, multi-omics analysis highlighted the significant roles of MK, MIF, GALECTIN, and CypA signaling pathways in GC.

The combination of TCM with chemotherapy significantly improves the prognosis of GC; future research can focus on these key molecular targets and signaling pathways. This supports the application of precision medicine in cancer treatment and suggests the rational use of TCM in managing GC.

In China, traditional Chinese medicines (TCMs), as a complementary therapy combined with chemotherapy, is widely used in the treatment of gastric cancer (GC). In order to systematically evaluate and synthesize existing evidence to provide a scientific basis for the efficacy and safety of this complementary therapy, we present an overview of systematic reviews (SRs) and meta-analyses (MAs) on the topic of TCMs as a complementary therapy in combination with chemotherapy for the treatment of GC.

SRs/MAs on TCMs combined with chemotherapy for GC were comprehensively searched in 8 databases. Methodological quality, risk of bias, reporting quality, and quality of evidence were assessed using the Assessment of Multiple Systematic Reviews 2 (AMSTAR-2), the Risk of Bias in Systematic (ROBIS) scale, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 (PRISMA 2020), as well as the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) system.

Thirteen published SRs/MAs were included in our study. In terms of methodology, all SRs/MAs were considered to be of very low quality. Only 3 SRs/MAs has been assessed as low risk of bias. None of the SRs/MAs has been fully reported on the checklist. A total of 97 outcome indicators extracted from the included SRs/MAs were evaluated, and only 1 item was assessed as high quality.

TCMs may be an effective and safe complementary therapy in combination with chemotherapy for the treatment of GC. However, this conclusion must be treated with caution as the quality of the evidence provided by SRs/MAs is generally low.

Jian Yun Qing Hua Decoction (JYQHD), a traditional Chinese medicine decoction, which has been applied in the treatment of gastric cancer (GC). We attempt to confirm the anti-gastric cancer effect of JYQHD and explore the mechanism of JYQHD.

Acute toxicity test was used to understand the toxicity of JYQHD. We studied the expression and prognostic outcome of COL12A1 within GC tissues through the network databases. Using several web-based databases, we analyzed the major components and targets of JYQHD, as well as known therapeutic targets in gastric cancer. The Venn diagram was utilized to obtain the overlapped genes. Lentiviral vector, shRNAs and plasmids, were used to transfect GC cells. Cell counting kit-8 (CCK8), sphere formation, malondialdehyde (MDA), glutathione (GSH), reactive oxygen species (ROS), Fe2+, transmission electron microscopy (TEM), quantitative Real-Time Polymerase Chain Reaction (qRT-PCR), Western-Blot (WB), and immunohistochemical (IHC) assays were employed to investigate the role and mechanism of COL12A1 and JYQHD in GC.

The results showed that JYQHD was non-toxic and safe. JYQHD inhibited growth and sphere formation ability through inducing the ferroptosis of GC cells, and suppressed the GC cells induced subcutaneous xenograft tumor growth. COL12A1 was highly expressed in gastric cancer tissues, indicating poor prognosis. COL12A1 specifically enhanced GC cell progression and stemness via suppressing ferroptosis. JYQHD down-regulated COL12A1 in order to suppress the stemness of GC cells via inducing ferroptosis.

COL12A1 inhibited ferroptosis and enhanced stemness in GC cells. JYQHD inhibited the development of GC cells by inhibiting cancer cell stemness via the ferroptosis pathway mediated by COL12A1.

Traditional Chinese medicine (TCM) prescriptions have made great contributions to the treatment of diseases and health preservation. To alleviate the shortage of TCM resources and improve the professionalism of automatically generated prescriptions, this paper deeply explores the connection between symptoms and herbs through deep learning technology, and realizes the automatic generation of TCM prescriptions. Particularly, this paper considers the significance of referring to similar underlying prescriptions as herbal candidates in the TCM prescribing process. Moreover, this paper incorporates the idea of referring to the potential guidance information of corresponding prescriptions when model extracts symptoms representations. To provide a reference for inexperienced young TCM doctors when they prescribe, this paper proposes a dual-branch guidance strategy combined with candidate attention model (DGSCAM) to automatically generate TCM prescriptions based on symptoms text. The format of the data used this paper is the "symptoms-prescription" data pair. The specific method is as follows. First, DGSCAM realizes the extraction of key information of prescription-guided symptoms through a dual-branch network. Then, herbal candidates in the form of prescriptions that can treat symptoms are proposed in view of the compatibility knowledge of TCM prescriptions. To our knowledge, this is the first attempt to use prescriptions as herbal candidates in the prescription generation process. We conduct extensive experiments on a mixed public and clinical dataset, achieving 37.39% precision, 25.04% recall, and 29.99% F1 score, with an average doctor score of 7.7 out of 10. The experimental results show that our proposed model is valid and can generate more specialized TCM prescriptions than the baseline models. The DGSCAM developed by us has broad application scenarios and greatly promotes the research on intelligent TCM prescribing.