This meta‐analysis was conducted to evaluate the efficacy and safety of the addition of Traditional Chinese Medicine (TCMs) to capecitabine‐based regimens for colorectal cancer (CRC) in term of tumor. The eight electronic databases including Cochrane Library, PubMed, Web of Science (WOS), Excerpt Medica Database (Embase), Chinese Biomedical Literature Database (CBM), China National Knowledge Infrastructure (CNKI), Chinese Science and Technology Journals (CQVIP), and Wanfang Database were systematically searched for eligible studies from their inception to March 2021. Thirty‐nine randomized controlled trials were involved in this study, and all the data were analyzed by Review Manager 5.3 (Nordic Cochran Centre, Copenhagen, Denmark) and R 4.0.5 software. The meta‐analyses suggested that TCMs in combination with capecitabine‐based regimens increased objective response rate (ORR) in the palliative treatment of CRC (risk ratio [RR], 1.35 [1.17, 1.55],I² = 0%), disease control rate (DCR) (RR, 1.22 [1.12, 1.32],I² = 3%), and quality of life (QOL) (RR, 1.71 [1.44, 2.03],I² = 0%), with decreased risks of myelosuppression, anemia, thrombocytopenia, liver/renal dysfunction, neurotoxicity, nausea/vomiting, neutropenia, diarrhea, leukopenia, improved the peripheral lymphocyte, reduced the expression of tumor markers, and related factors. Further sensitivity analysis of specific plant‐based TCMs found that dangshen, fuling, and gancao had significantly higher contributions to the results of the RR. The results show that capecitabine‐based chemotherapy combined with TCM in the treatment of CRC increases the efficiency of ORR and DCR, reduces chemotherapeutic agents‐associated adverse reactions, and improves their life quality as compared with chemotherapy alone, but further randomized and large sample of studies are needed.

Objective. This work aimed to study the compounds, targets, and pathways of turmeric and corn silk for gout and to explore the mechanism of the same disease with different treatments based on network pharmacology and molecular docking. Methods. We used the TCMSP, PubChem, and SEA databases to screen the compounds and targets of turmeric and corn silk, gout-related proteins through TTD, Drugbank, DisGeNET, GeneCards, OMIM, and PharmGkb, and used Cytoscape to construct a compound-target-disease network. Then, we constructed a protein-protein interaction network (PPI) and used Metascape to perform GO and KEGG analysis. Finally, molecular docking (SYBYL) was used to verify the degree of binding between key targets and compounds. Results. We found bisacumol, campesterol, and stigmasterol to be the main turmeric compounds that exerted a marked effect on gout treatment by targeting protein processing in the endoplasmic reticulum through the HSPA1B, HSP90AB1, and STUB1 proteins. The main corn silk compound, Mandenol, treated gout by targeting the Hippo signaling pathway through the CTNNB1, YWHAG, and YWHAZ proteins. Conclusion. Turmeric and corn silk can treat the same disease, gout, through different pathways and targets. The scientific connotation of same disease with different treatments can be preliminarily clarified by analyzing targets and pathways.

Background: Cancer cell survival, proliferation, and metabolism are all intertwined with mitochondria. However, a complete description of how the features of mitochondria relate to the tumor microenvironment (TME) and immunological landscape of colorectal cancer (CRC) has yet to be made.We performed subgroup analysis on CRC patient data obtained from the databases using non‐negative matrix factorization (NMF) clustering. Construct a prognostic model using the mitochondrial‐related gene (MRG) risk score, and then compare it to other models for accuracy. Comprehensive analyses of the risk score, in conjunction with the TME and immune landscape, were performed, and the relationship between the model and different types of cell death, radiation and chemotherapy, and drug resistance was investigated. Results from immunohistochemistry and single‐cell sequencing were utilized to verify the model genes, and a drug sensitivity analysis was conducted to evaluate possible therapeutic medicines. The pan‐cancer analysis is utilized to further investigate the role of genes in a wider range of malignancies.Methods and Results: We found that CRC patients based on MRG were divided into two groups with significant differences in survival outcomes and TME between groups. The predictive power of the risk score was further shown by building a prognostic model and testing it extensively in both internal and external cohorts. Multiple immune therapeutic responses and the expression of immunological checkpoints demonstrate that the risk score is connected to immunotherapy success. The correlation analysis of the risk score provide more ideas and guidance for prognostic models in clinical treatment.Conclusion: The TME, immune cell infiltration, and responsiveness to immunotherapy in CRC were all thoroughly evaluated on the basis of MRG features. The comparative validation of multiple queues and models combined with clinical data ensures the effectiveness and clinical practicality of MRG features. Our studies help clinicians create individualized treatment programs for individuals with cancer.

Metabolic reprogramming has recently been recognized as related to immune disorders in ulcerative colitis (UC), but the specific metabolic pathways and genes involved remain unclear. Here, Mendelian randomization confirmed that mannose and mannonate exhibited a negative causal relationship with UC, and that the immune cell phenotype HLA DR on CD33dim HLA DR+ CD11b− mediated the effect of mannonate on UC. Bulk RNA sequencing data revealed that mannose metabolism abnormity is critical for driving the innate and acquired immune response. A well-performing diagnostic model related to mannose metabolism was constructed using SVM analysis, achieving an AUC-ROC value of 0.987 in the training set and an AUC-ROC value of 0.899 in the validation set. Single-cell analysis revealed that epithelial cells in which the mannose metabolism pathway was inactivated demonstrated increased intercell communication with myeloid cells, T cells, and B cells. In vitro experiments confirmed that KHK and AKR1B10 were suppressed under inflammatory stimulation, which may hinder mannose-related metabolism. This study elucidates the protective role of mannose metabolism in UC and provides a novel gene signature for diagnosis and treatment.

Gastric precancerous lesions (GPL) represent a crucial stage in the complex process of gastric carcinogenesis that leads to gastric cancer (GC), one of the most prevalent cancers and a major source of cancer mortality worldwide. Many studies have identified the gastrointestinal microbiota, or gut microbiota, as an important contributor to both the pathogenesis and treatment of GPL and GC, thus understanding its role in this transition is crucial. The purpose of this literature review is to introduce the current landscape of microbiota research associated with GPL and GC, with an emphasis on Helicobacter pylori (H. pylori ) driven microbial dysbiosis and its modulation through Western medicine and traditional Chinese medicine (TCM) approaches. By elucidating the underlying mechanisms of H. pylori colonization, patterns, and interactions among microbiota, as well as the influence of microbial metabolites, this review highlights crucial driving factors of gastric carcinogenesis. The role of microbiota in conventional interventions, including H. pylori eradication, immunotherapy, as well as TCM herbal decoctions, is also discussed to provide a detailed understanding of the complex interactions between therapy and microbiota and how it could be potentially targeted for effective management of GPL and GC. Ultimately, microbiota-targeting therapeutics may represent a new path toward early detection, targeted treatment, improved prognosis, and potentially reduced incidence of GPL and GC.

In this letter, we have commented on the study by Zhang et al , which utilized bioinformatics and immunohistochemistry to assess the value of fatty acid-binding protein 4 (FABP4) as a biomarker for colon adenocarcinoma (COAD). Their findings improve our understanding of FABP4 in cancer cell adhesion and immune cell infiltration. However, differential expression analysis was insufficient to demonstrate a direct association between FABP4 expression and the occurrence and progression of COAD. Using Mendelian randomization for causal inferences can provide a solid biological foundation for model construction. Furthermore, integrating machine and deep learning approaches may yield more robust and precise prognostic outcomes than using a single Cox regression model. In addition, integrating genome-wide association study data to identify additional pathogenic genes involved in the regulation of fatty acid metabolism may facilitate the development of a multi-target strategy. This approach could potentially mitigate the compensatory effects associated with targeting FABP4 alone, and enhance therapeutic efficacy. Enhancing experimental validation would further improve the reliability of the results. With the continuous advancement of machine learning, multi-omics technologies, and experimental techniques, future studies may systematically integrate diverse sequencing datasets to offer novel insights into the early diagnosis, individualized treatment, and prognostic evaluation of COAD.