Assessment of the targeted effect of Sijunzi decoction on the colorectal cancer microenvironment via the ESTIMATE algorithm

Abstract

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.

Cancer-associated fibroblast-related gene signatures predict survival and drug response in patients with colorectal cancer

“…ADAM8 has been found to cleave and remodel the ECM components of the tumor stroma (Zack et al, 2009;Schlomann et al, 2015), and thus could directly contribute to tumor invasiveness and metastasis. IGFBP3 and SUSD5 promote epithelial-mesenchymal transition (EMT) through the upregulation of a major cell surface receptor of hyaluronic acid (CD44H) (Vicent et al, 2008;Du et al, 2022). SIGLEC1 is a sialic binding receptor mainly expressed by macrophages; the infiltration of SIGLEC1 + macrophages in CRC was associated with tumor progression (Cassetta et al, 2019).…”

An exosomes-related lncRNA prognostic model correlates with the immune microenvironment and therapy response in lung adenocarcinoma

“…The activity of immune cell, function, and pathway of each LUAD sample were calculated with a single sample gene set enrichment analysis (ssGSEA) algorithm using “GSVA” R package [ 26 ]. The “estimate” R package was used to assess the immune score, stromal score of each LUAD sample [ 27 ].…”

Metabolism‐related gene vaccines and immune infiltration in ovarian cancer: A novel risk score model of machine learning

“…ESTIMATE employs expression data to estimate stromal and immune cells within malignant tumor tissue 13 . Yoshihara et al 14 calculated matrix and immune scores based on single‐sample GSEA, which forms the basis of the ESTIMATE score that detects tumor purity in tumor tissue.…”

“…ESTIMATE employs expression data to estimate stromal and immune cells within malignant tumor tissue. 13 Yoshihara et al 14 A number of classic immune-related subgenic sets were chosen, namely, primary lymphocyte-specific kinase (LCK), histocompatibility complex class II (MHC-II), immunoglobulin G (IgG), hematopoietic cell kinase (HCK), co-stimulating molecules (B7-CD28), signal transduction and activation transcription 1 (STAT1), interferons, and TNF gene sets. The gene set genes are provided in the Supporting information (Table S1).…”