Yanhong Zhou

Lonicerae Japonicae Flos (LJF), a traditional Chinese herb with applications in both medicine and nutrition, has emerged as a promising functional food ingredient owing to its diverse bioactive constituents and associated health‐promoting properties. This review systematically describes its botany, phytochemistry, and pharmacological activities, with particular emphasis on the roles of its principal constituents in ulcerative colitis and colorectal cancer and their implications for dietary interventions. LJF is abundant in phenolic acids and flavonoids, which exert pronounced anti‐inflammatory, antioxidant, and immunomodulatory effects through the modulation of key signaling cascades, including the NF‐κB and MAPK pathways, thereby attenuating oxidative stress, suppressing proinflammatory mediators, and preserving intestinal barrier integrity and gut microbiota homeostasis. Moreover, these constituents exhibit multiple effects against colorectal cancer, including the induction of apoptosis, inhibition of tumor cell proliferation, and suppression of angiogenesis and metastatic progression via the regulation of oncogenic signaling networks. Nevertheless, the broader application of LJF remains constrained by chemical complexity, insufficient mechanistic elucidation, and a paucity of robust clinical evidence. Future investigations integrating multiomics strategies, advanced food processing and delivery systems, and rigorously designed clinical studies are warranted to increase its translational potential in intestinal health management.

High-throughput studies of biological systems are rapidly generating a wealth of ‘omics’-scale data. Many of these studies are temporal collecting proteomics and genomics data capturing dynamic observations. While temporal omics data are essential to unravel the mechanisms of various diseases, they often include missing (or incomplete) values due to technical and experimental reasons. Data prediction methods, i.e., imputation and forecasting, have been widely used to mitigate these issues. However, existing imputation and forecasting techniques typically address static omics data representing a single time point and perform forecasting on data with complete values. In this paper, we propose a graph-based method for temporal omics data imputation and forecasting that handle omics data containing missing values at multiple time points. The method takes advantage of topological relationships (e.g., protein-protein and gene-gene interactions) among omics data samples and incorporates a graph convolutional network to first infer the missing values at different time points. Then, we combine these inferred values with the original omics data to perform temporal imputation and forecasting using a long short-term memory network. Evaluating the proposed method on two real-world datasets demonstrated a distinct advantage over existing data imputation and forecasting methods. On the omics dataset, the average mean square error of our method improved 11.3% for imputation and 6.4% for forecasting compared to the baseline methods.

Ulcerative colitis (UC) is a chronic inflammatory disease with an increasing prevalence worldwide. Naringenin (NAR) has been proven effective in preventing UC, but its mechanism has not been fully elucidated.Network pharmacology and bioinformatics methods were used to screen the genes associated with NAR and UC. A mouse model of dextran sulfate sodium (DSS)-induced ulcerative colitis was established. After treatment with NAR, the disease activity index (DAI) was scored, and colonic histopathology was observed via hematoxylin‒eosin (HE) staining. The expression of the nuclear factor erythroid 2-related factor 2 (Nrf2) signaling pathway and inflammation-related factors in the colons of UC mice was examined via western blotting and immunohistochemistry (IHC). The results of the animal experiments revealed that the model group of UC mice presented the most severe weight loss and the highest DAI scores. After the administration of NAR, weight loss was alleviated, and DAI scores were reduced (P < 0.05). NAR improved pathological manifestations in the mouse colon, such as reducing inflammatory cell infiltration and restoring goblet cell loss (P < 0.05). NAR significantly increased the protein expression levels of Nrf2, heme oxygenase 1 (HO-1), and NAD(P)H dehydrogenase [quinone] 1 (NQO1) in the colon (P < 0.05) but decreased the protein expression levels of nuclear factor kappa-B (NF-κB), tumor necrosis factor-α (TNF-α), and interleukin-1β (IL-1β) (P < 0.05). NAR increases the protein expression of Nrf2, HO-1 and NQO1 in the colon of UC mice while inhibiting the expression of NF-κB, TNF-α, and IL-1β, thus reducing inflammation. This protective effect of NAR alleviates the inflammatory response in UC model mice.

Nuclear factor erythroid 2-related factor 2 (NRF2) is a basic leucine zipper protein that participates in a complex regulatory network in the body. The activation of NRF2 can prevent and treat colorectal cancer (CRC). A variety of natural compounds can activate NRF2 to inhibit oxidative stress and inflammation to prevent the occurrence and development of CRC, inhibit the proliferation of CRC cells and induce their apoptosis. However, some studies have also shown that it also has negative effects on CRC, such as overexpression of NRF2 can promote the growth of colorectal tumors and increase the drug resistance of chemotherapeutic drugs such as 5-fluorouracil and oxaliplatin. Therefore, inhibition of NRF2 can also be helpful in the treatment of CRC. In this study, we analyze the current research progress of NRF2 in CRC from various aspects to provide new ideas for prevention and treatment based on the NRF2 signaling pathway in CRC.

High-throughput studies of biological systems are rapidly generating a wealth of ’omics’-scale data. Many of these studies are temporal collecting proteomics and genomics data capturing dynamic observations. While temporal omics data are essential to unravel the mechanisms of various diseases, they often include missing (or incomplete) values due to technical and experimental reasons. Data prediction methods, i.e ., imputation and forecasting, have been widely used to mitigate these issues. However, existing imputation and forecasting techniques typically address static omics data representing a single time point and perform forecasting on data with complete values. In this paper, we propose a graph-based method for temporal omics data imputation and forecasting that handle omics data containing missing values at multiple time points. The method takes advantage of topological relationships (e.g., protein-protein and gene-gene interactions) among omics data samples and incorporates a graph convolutional network to first infer the missing values at different time points. Then, we combine these inferred values with the original omics data to perform temporal imputation and forecasting using a long short-term memory network. Evaluating the proposed method on two real-world datasets demonstrated a distinct advantage over existing data imputation and forecasting methods. On the omics dataset, the average mean square error of our method improved 11.3% for imputation and 6.4% for forecasting compared to the baseline methods.

Introduction: Nod-like receptor C4 (NLRC4) is a member of the Nod-like receptor (NLR) family, and its expression mediates the activation of caspase-1 (CASP1). Abnormal expression of NLRC4 and CASP1 is associated with multiple tumors. However, the expression differences, prognostic value and immune correlation of NLRC4 and CASP1 in colorectal cancer (CRC) remain to be determined. Methods: In this study, TCGA, CCLE, HPA, PrognoScan, STRING and GeneMANIA databases were used to analyze differences in expression, prognostic value, genetic alterations and immune cell infiltration of NLRC4 and CASP1 in CRC patients. Then, we further validated the expression of NLRC4 and CASP1 in CRC using immunohistochemistry (IHC). Results: NLRC4 and CASP1 were expressed low in CRC tissues and CRC cell lines. The expression of NLRC4 was significantly related to the patient’s gender and lymph node metastasis. NLRC4 and CASP1 down-regulated expression was observably correlated with poor survival and diverse immune cells infiltration in CRC patients. NLRC4 and CASP1 have a gene mutation alteration. NLRC4 and CASP1 had a significant positive correlation in CRC. Conclusion: This study will provide new ideas for the prognosis and treatment in CRC. NLRC4 and CASP1 are expected to be novel biomarkers and potential immunotherapy targets in CRC patients.

Background: Colorectal cancer (CRC) is one of the most common gastrointestinal tumors, ranking in the top 5 of all common tumors in terms of incidence and mortality. However, the mechanisms driving the evolution of colorectal cancer remain unclear. Therefore, we investigated the association between Mapk14 expression and clinicopathological and tumor-infiltrating immune cells.Methods: In this study, we collected CRC patient data from The Cancer Genome Atlas (TCGA), compared the expression level in CRC and normal colorectal tissue using the Wilcoxon rank sum test and assessed the relationship between Mapk14 and clinicopathological features using the Welch one-way ANOVA test. Kaplan-Meier and timeROC GSE17537 datasets were obtained from the Gene Expression Omnibus (GEO) dataset to assess the prognostic impact of the Mapk14 gene on colorectal cancer. Second, we further explored the methylation level of Mapk14 and its influencing factors. Single-cell sequencing of Mapk14 in the tumor immune microenvironment (TIME) was analyzed using the GSE108989 dataset. Further analyses based on the TIMER method were performed to assess the correlation between Mapk14 and tumor immune infiltration, immune checkpoints, tumor mutational load and microsatellite instability. Finally, the results of the bioinformatics analysis were verified by an immunohistochemical analysis.Results: The results showed that the expression of Mapk14 was upregulated in CRC tumor tissues compared with normal colorectal tissues and the high expression of Mapk14 was associated with poor clinicopathological features and poor prognoses in the CRC array. In addition, cg05798012 and cg25375420 of Mapk14 are the main DNA methylation sites affecting OS. Single-cell sequencing of the tumor immune microenvironment showed that the abundance and cell state of dysfunctional T cells changed greatly. Importantly, the abnormal overexpression of Mapk14 in colorectal cancer is related to the level of immune infiltration of immune cells (including CD8⁺ T cells, neutrophils, dendritic cells, B cells, CD4⁺ T cells, and macrophages). The high expression of Mapk14 was significantly correlated with immune checkpoints (including SIGLEC15, TIGIT, LAG3, CTLA4 and PDCDILG2), while the high expression of Mapk14 was negatively correlated with TMB and MSI but mostly positively correlated with drug sensitivity. Finally, the immunohistochemical results confirmed that the clinical stage (Ⅰ, Ⅱ, Ⅲ and Ⅳ) and M stage (M0 and M1) affected the abnormally high expression of Mapk14.Conclusion: A comprehensive bioinformatics study and experimental validation revealed that Mapk14 could serve as a novel prognostic biomarker associated with immune infiltration and pharmacotherapy and may represent a potential therapeutic target for the treatment of CRC.