Objective. The aim of this study was to explore the potential biological mechanisms of coix seed in the treatment of colorectal cancer (CRC) based on network pharmacology analysis. Methods. The active components of coix seed and their potential action targets were retrieved from Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform(TCMSP). The disease targets related to CRC were obtained from the DisGeNET database. The intersection targets of the drug targets and disease targets were selected, and a component-target-disease network was built using Cytoscape 3.8.0 tool. A global network of the core target protein interactions was constructed using String database. Biological function analysis and pathway enrichment analysis of core targets were conducted to explore the potential. Results. A total of nine active components were obtained from the TCMSP database corresponding to 37 targets. Further analysis showed that 18 overlapping targets were associated with CRC. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis was conducted based on the 18 targets and 11 significantly enriched signaling pathways implicated in CRC were identified. Conclusion. The multicomponent and multitarget characteristics of coix seed are preliminarily verified, and the potential biological mechanisms of coix seed in the treatment of CRC are predicted, which provides a theoretical basis for the experimental research.
The aim of this study was to explore the clinical value of a radiomics prediction model based on T2-weighted imaging (T2WI) and clinical indexes in predicting lateral lymph node (LLN) metastasis in rectal cancer patients. This was a retrospective analysis of 106 rectal cancer patients who had undergone LLN dissection. The clinical risk factors for LLN metastasis were selected by multivariable logistic regression analysis of the clinical indicators of the patients. The LLN radiomics features were extracted from the pelvic T2WI of the patients. The least absolute shrinkage and selection operator algorithm and backward stepwise regression method were adopted for feature selection. Three LLN metastasis prediction models were established through logistic regression analysis based on the clinical risk factors and radiomics features. Model performance was assessed in terms of discriminability and decision curve analysis in the training, verification and test sets. The model based on the combined T2WI radiomics features and clinical risk factors demonstrated the highest accuracy, surpassing the models based solely on either T2WI radiomics features or clinical risk factors. Specifically, the model achieved an AUC value of 0.836 in the test set. Decision curve analysis revealed that this model had the greatest clinical utility for the vast majority of the threshold probability range from 0.4 to 1.0. Combining T2WI radiomics features with clinical risk factors holds promise for the noninvasive assessment of the biological characteristics of the LLNs in rectal cancer, potentially aiding in therapeutic decision-making and optimizing patient outcomes.
This study aimed to explore the genes regulating lymph node metastasis in colorectal cancer (CRC) and to clarify their relationship with tumor immune cell infiltration and patient prognoses.The data sets of CRC patients were collected through the Cancer Gene Atlas database; the differentially expressed genes (DEGs) associated with CRC lymph node metastasis were screened; a protein-protein interaction (PPI) network was constructed; the top 20 hub genes were selected; the Gene Ontology functions and the Kyoto Encyclopedia of Genes and Genomes pathways were enriched and analyzed. The Least Absolute Shrinkage and Selection Operator (LASSO) regression method was employed to further screen the characteristic genes associated with CRC lymph node metastasis in 20 hub genes, exploring the correlation between the characteristic genes and immune cell infiltration, conducting a univariate COX analysis on the characteristic genes, obtaining survival-related genes, constructing a risk score formula, conducting a Kaplan-Meier analysis based on the risk score formula, and performing a multivariate COX regression analysis on the clinical factors and risk scores.A total of 62 DEGs associated with CRC lymph node metastasis were obtained. Among the 20 hub genes identified via PPI, only calcium-activated chloride channel regulator 1 (CLCA1) expression was down-regulated in lymph node metastasis, and the rest were up-regulated. A total of nine characteristic genes associated with CRC lymph node metastasis (KIF1A, TMEM59L, CLCA1, COL9A3, GDF5, TUBB2B, STMN2, FOXN1, and SCN5A) were screened using the LASSO regression method. The nine characteristic genes were significantly related to different kinds of immune cell infiltration, from which three survival-related genes (TMEM59L, CLCA1, and TUBB2B) were screened. A multi-factor COX regression showed that the risk scores obtained from TMEM59L, CLCA1, and TUBB2B were independent prognostic factors. Immunohistochemical validation was performed in tissue samples from patients with rectal and colon cancer.TMEM59L, CLCA1, and TUBB2B were independent prognostic factors associated with lymphatic metastasis of CRC.
Introduction Lateral lymph node (LLN) metastasis in rectal cancer significantly affects patient treatment and prognosis. This study aimed to comprehensively compare the performance of various predictive models in predicting LLN metastasis. Methods In this retrospective study, data from 152 rectal cancer patients who underwent lateral lymph node (LLN) dissection were collected. The cohort was divided into a training set (n=86) from Tianjin Union Medical Center (TUMC), and two testing cohorts: testing cohort (TUMC) (n=37) and testing cohort from Gansu Provincial Hospital (GSPH) (n=29). A clinical model was established using clinical data; deep transfer learning models and radiomics models were developed using MRI images of the primary tumor (PT) and largest short-axis LLN (LLLN), visible LLN (VLLN) areas, along with a fusion model that integrates features from both deep transfer learning and radiomics. The diagnostic value of these models for LLN metastasis was analyzed based on postoperative LLN pathology. Results Models based on LLLN image information generally outperformed those based on PT image information. Rradiomics models based on LLLN demonstrated improved robustness on external testing cohorts compared to those based on VLLN. Specifically, the radiomics model based on LLLN imaging achieved an AUC of 0.741 in the testing cohort (TUMC) and 0.713 in the testing cohort (GSPH) with the extra trees algorithm. Conclusion Data from LLLN is a more reliable basis for predicting LLN metastasis in rectal cancer patients with suspicious LLN metastasis than data from PT. Among models performing adequately on the internal test set, all showed declines on the external test set, with LLLN_Rad_Models being less affected by scanning parameters and data sources.
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