Objective:Based on network pharmacology to study the mechanism of Shaoyao-Gancao Decoction in treating tension-type headache. Methods:Searched for the active ingredients and potential targets of Shaoyao-Gancao Decoction from TCMSP database, and adopted the targets of tension-type headache from GeneCards, DisGeNET, Drugbank and OMIM databases. Then obtained all the intersections of Shaoyao-Gancao Decoction and tension-type headache, and uploaded them to the STRING databases to construct a PPI network and conduct topological properties analysis. Finally, established a Chinese medicine regulatory network of Chinese medicine-components-target genes-disease by Cytoscape 3.6.1 software. To perform the GO function enrichment analysis and KEGG analysis on the core targets. Results:There were 51 intersections of Shaoyao-Gancao Decoction and tension-type headache. The topological properties analysis suggested that CASP3, JUN, HSP90AA1, MAPK1, STAT3, CCND1, ESR1, RELA, PTGS2, MAPK14 may be the potential targets for the treatment of tension-type headache in Shaoyao-Gancao Decoction. Gene ontology enrichment analysis showed 876 biological processes, 101 molecular functions and 62 cellular components. KEGG pathway enrichment analysis showed 25 related signaling pathways, including TNF signaling pathway, serotonergic synapse, NOD-like receptor signaling pathway, Dopaminergic synapse and Sphingolipid signaling pathway. Conclusion:The treatment of tension-type headache by Shaoyao-Gancao Decoction verified the characteristics of multi-component, multi-target and multi-pathway, which provided reference for the clinical medication.

Objective:To investigate the effects of different chemotherapy dose intensity on the short-term efficacy and adverse reactions of patients with advanced colon cancer.Methods:A real-world database of patients with advanced colon cancer in Wangjing Hospital of China Academy of Chinese Medical Sciences and China-Japan Friendship Hospital from January 2017 to December 2020 was established, including 105 patients treated with the same chemotherapy regimen for two consecutive cycles. The patients were grouped according to the average relative dose intensity (ARDI) of chemotherapy, and the population differences, treatment regimens, short-term efficacy and adverse reactions of different chemotherapy dose intensities were evaluated. The receiver operating characteristic (ROC) curve was used to analyze the predictive value of ARDI for short-term efficacy.Results:There were 31 patients in the high dose intensity group (ARDI≥80%) , 34 patients in the medium dose intensity group (80%0.05) . ROC curve analysis found that the area under the curve predicting objective response was 0.70, the sensitivity was 92.30%, and the specificity was 52.10% when the chemotherapy ARDI threshold was 64%. Conclusion:In the real world, colon cancer patients receiving high dose intensive chemotherapy have better objective response rate, and tumor markers decreased significantly. The baseline status of colon cancer patients receiving low dose intensive chemotherapy is poor, but there is no statistically significant difference in disease control rate between patients receiving low dose intensive chemotherapy and patients receiving high dose intensive chemotherapy, and the adverse reactions are controllable.

Objective To explore the predictive value of immune inflammation combined with liver function hematological indicators for the metastasis of colorectal cancer. Methods A retrospective analysis of clinical data of 133 patients with colorectal cancer was conducted. The patients were divided into three groups based on disease progression after 24 months of postoperative follow-up: non-metastasis group (n=38), liver metastasis group (n=43), and non-liver distant metastasis group (n=52). The immune inflammatory markers and liver function hematological indicators of progression-free survival were analyzed. Nomogram prediction models were constructed using univariate and multivariate logistic regression analyses to identify risk factors for metastasis of colorectal cancer. The accuracy of the nomogram was validated using receiver operating characteristic (ROC) curve and calibration curve, and the clinical predictive efficacy was evaluated through decision curve and clinical impact curve. Results Univariate and multivariate logistic regression analyses showed that pan-immune-inflammatory value (PIV), prognostic nutritional index (PNI), and bile acid (BA) were independent predictors of colorectal cancer metastasis. The area under the ROC curve of the combined prediction of metastasis was 0.84; neutrophil/lymphocyte ratio (NLR) and BA were independent predictors of liver metastasis from colorectal cancer. The area under the ROC curve of the combined prediction of liver metastasis was 0.83; PIV and PNI were independent predictive factors for the occurrence of non-liver distant metastasis from colorectal cancer. The area under the ROC curve for the combined prediction of non-liver distant metastasis was 0.83. The calibration curve, decision curve, and clinical impact curve showed that the three models had good accuracy and net benefit value. Conclusion The nomogram constructed based on immune inflammation and liver function hematological indicators can predict the metastasis of patients with colorectal cancer and has high predictive efficacy and clinical application prospects.

[Abstract] Objective: To explore the key genes and molecular mechanisms of liver metastasis in colorectal cancer (CRC), and to provide potential targets and biomarkers for the treatment of CRC with liver metastasis. Methods: Based on the bioinformatics method, the gene data sets of CRC liver metastasis were downloaded from the GEO database to screen the differentially expressed genes (DEGs); the GO and KEGG enrichment analyses of DEGs were performed by using DAVID online tool, and the protein-protein interaction (PPI) network was constructed to screen out the key genes, and subsequently the prognosis was analyzed. Results: A total of 321 DEGs were selected from 183 CRC specimens and 39 liver metastasis specimens, including 153 up-regulated genes and 168 downregulated genes. The results of enrichment analysis of GO and KEGG showed that the functions of DEGs were mainly related to protein activation cascade, inflammatory response, extracellular matrix, platelet degranulation, complement and coagulation cascade reaction etc. 8 key CRC genes (ALB, APOB, FGA, F2, APOA1, SERPINC1, FGG and AHSG) were screened by PPI network. Survival analysis showed that patients with high expressions of SERPINC1 and FGG had poor prognosis(all P<0.05). Conclusion: The biological functions and signaling pathways of DEGs are related to the occurrence and development of liver metastasis. The 8 key genes may be the potential therapeutic targets of CRC liver metastasis, and SERPINC1 and FGG may be new prognostic markers.