Open AccessAssessment of the targeted effect of Sijunzi decoction on the colorectal cancer microenvironment via the ESTIMATE algorithm
Author(s) -
Jiaxin Du,
Quyuan Tao,
Ying Li,
Zhan-Ming Huang,
Jin He,
Wenjia Lin,
Xin Huang,
Jingyan Zeng,
Yi Zhao,
Lingyu Liu,
Qian Xu,
Xue Han,
Lixia Chen,
Xinlin Chen,
Yi Wen
Publication year - 2022
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0264720
Subject(s) - stromal cell , kegg , colorectal cancer , tumor microenvironment , immune system , downregulation and upregulation , adjuvant , cancer research , oncology , medicine , biology , cancer , immunology , gene , gene expression , transcriptome , genetics
Objective Sijunzi decoction (SJZD) was used to treat patients with colorectal cancer (CRC) as an adjuvant method. The aim of the study was to investigate the therapeutic targets and pathways of SJZD towards the tumor microenvironment of CRC via network pharmacology and the ESTIMATE algorithm. Methods The ESTIMATE algorithm was used to calculate immune and stromal scores to predict the level of infiltrating immune and stromal cells. The active targets of SJZD were searched in the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) and UniProt database. The core targets were obtained by matching the differentially expressed genes in CRC tissues and the targets of SJZD. Then, GO, KEGG and validation in TCGA were carried out. Results According to the ESTIMATE algorithm and survival analysis, the median survival time of the low stromal score group was significantly higher than that of the high stromal score group ( P = 0.018), while the patients showed no significant difference of OS between different immune groups ( P = 0.19). A total of 929 genes were upregulated and 115 genes were downregulated between the stromal score groups (|logFC| > 2, adjusted P < 0.05); 357 genes were upregulated and 472 genes were downregulated between the immune score groups. The component-target network included 139 active components and 52 related targets. The core targets were HSPB1 , SPP1 , IGFBP3 , and TGFB1 , which were significantly associated with poor prognosis in TCGA validation. GO terms included the response to hypoxia, the extracellular space, protein binding and the TNF signaling pathway. Immunoreaction was the main enriched pathway identified by KEGG analysis. Conclusion The core genes ( HSPB1 , SPP1 , IGFBP3 and TGFB1 ) affected CRC development and prognosis by regulating hypoxia, protein binding and epithelial-mesenchymal transition in the extracellular matrix.