Tumor‐associated macrophages in liver cancer: From mechanisms to therapy

Kun, Cheng; Cai, Ning; Zhu, Jinghan; Yang, Xing; Liang, Huifang; zhang, wanguang

doi:10.1002/cac2.12345

Cited by 104 publications

(79 citation statements)

References 195 publications

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“…Furthermore, we linked the single cell transcriptomic and clinical phenotypes and verified the clinical importance of Macrophage. At present, macrophage-related therapy in HCC mainly divided into three approaches, including cutting off the source and eliminating the production of M2 macrophages, remodeling M2 macrophages to M1 macrophages, and blocking communication between M2 macrophages and liver cancer cells [ 38 ]. Blocking the interaction of macrophage and naïve CD4 + T cell may be the novel and interesting therapeutic strategy in HCC immunotherapy.…”

Section: Discussionmentioning

confidence: 99%

Single-cell transcriptomics reveals the role of Macrophage-Naïve CD4 + T cell interaction in the immunosuppressive microenvironment of primary liver carcinoma

Liu

Chen

et al. 2022

J Transl Med

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Background Liver carcinoma generally presents as an immunosuppressive microenvironment that promotes tumor evasion. The intercellular crosstalk of immune cells significantly influences the construction of an immunosuppressive microenvironment. This study aimed to investigate the important interactions between immune cells and their targeting drugs in liver carcinoma, by using single-cell and bulk transcriptomic data. Methods Single-cell and bulk transcriptomic data were retrieved from Gene Expression Omnibus (GSE159977, GSE136103, and GSE125449) and The Cancer Genome Atlas (TGCA-LIHC), respectively. Quality control, dimension reduction, clustering, and annotation were performed according to the Scanpy workflow based on Python. Cell–cell interactions were explored using the CellPhone database and CellChat. Trajectory analysis was executed using a partition-based graph abstraction method. The transcriptomic factors (TFs) were predicted using single-cell regulatory network inference and clustering (SCENIC). The target genes from TFs were used to establish a related score based on the TCGA cohort; this score was subsequently validated by survival, gene set enrichment, and immune cell infiltration analyses. Drug prediction was performed based on the Cancer Therapeutics Response Portal and PRISM Repurposing datasets. Results Thirty-one patients at four different states, including health, hepatitis, cirrhosis, and cancer, were enrolled in this study. After dimension reduction and clustering, twenty-two clusters were identified. Cell–cell interaction analyses indicated that macrophage-naive CD4 + T cell interaction significantly affect cancerous state. In brief, macrophages interact with naive CD4 + T cells via different pathways in different states. The results of SCENIC indicated that macrophages present in cancer cells were similar to those present during cirrhosis. A macrophage-naive CD4 + T cell (MNT) score was generated by the SCENIC-derived target genes. Based on the MNT score, five relevant drugs (inhibitor of polo-like kinase 1, inhibitor of kinesin family member 11, dabrafenib, ispinesib, and epothilone-b) were predicted. Conclusions This study reveals the crucial role of macrophage-naive CD4 + T cell interaction in the immunosuppressive microenvironment of liver carcinoma. Tumor-associated macrophages may be derived from cirrhosis and can initiate liver carcinoma. Predictive drugs that target the macrophage-naive CD4 + T cell interaction may help to improve the immunosuppressive microenvironment and prevent immune evasion. The relevant mechanisms need to be further validated in experiments and cohort studies. Graphical Abstract

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Section: Discussionmentioning

confidence: 99%

Single-cell transcriptomics reveals the role of Macrophage-Naïve CD4 + T cell interaction in the immunosuppressive microenvironment of primary liver carcinoma

Liu

Chen

et al. 2022

J Transl Med

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“…The composition of immune cells in the tumor microenvironment is an important predictor of bene cial survival and therapeutic outcome [41][42][43]. We found that TFPI2 expression was positively correlated with a variety of immune cells, such as CD8+T cells, B cells, cytotoxic cells and NK cells.…”

Section: Discussionmentioning

confidence: 89%

Low TFPI2 expression is associated with poor prognosis and defective immune cell infiltration in breast cancer disease

Zhu

Dai

Zhang

et al. 2022

Preprint

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Background Breast cancer is the leading cause of death in female cancer population worldwide. The mainstay treatment for breast cancer includes chemotherapy, targeted therapy, endocrine therapy and immunotherapy. Tissue factor pathway inhibitor 2 (TFPI2) plays an important role in balancing the activity of various enzymes in the human environment. However, the correlation of TFPI2 to the prognosis and the immune infiltration in breast cancer has not been reported. Methods We firstly analyzed the expression level of TFPI2 via high-throughput sequencing, clinical sample detection and cancer genome profiling (TCGA). Results TFPI2 expression was significantly downregulated in different subtypes of breast cancer samples. Downregulation of TFPI2 expression was associated with multiple clinical features and poor prognosis. In the tumor microenvironment (TME), TFPI2 is positively correlated with immune cells and immune modulators (chemokines, receptors, immunostimulants, immunosuppressants and MHC). With TFPI2 upregulation, immune and stromal components significantly increased. Conclusion This is the first comprehensive study to reveal that TFPI2 may serve as a novel prognostic biomarker associated with tumor immune infiltration and provide a potential therapeutic target in breast cancer.

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“…It also implied a connection between RM_Score and tumor immune. Nowadays, immunotherapy is gaining popularity as a promising treatment option for advanced HCC with immune escape [29,30]. In light of this, we assessed the RM_Score model's accuracy in predicting immunotherapy response.…”

Section: Discussionmentioning

confidence: 99%

Integrated investigation of the clinical implications and targeted landscape for RNA methylation modifications in hepatocellular carcinoma

Zhang

Gao

et al. 2023

Eur J Med Res

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Background RNA methylation (RM) is a crucial post-translational modification (PTM) that directs epigenetic regulation. It mostly consists of N1-methyladenosine (m1A), 5-methylcytosine (m5C), N3-methylcytidine (m3C), N6-methyladenosine (m6A), and 2′-O-methylation (Nm). The “writers” mainly act as intermediaries between these modifications and associated biological processes. However, little is known about the interactions and potential functions of these RM writers in hepatocellular carcinoma (HCC). Methods The expression properties and genetic alterations of 38 RM writers were assessed in HCC samples from five bioinformatic datasets. Two patterns associated with RM writers were identified using consensus clustering. Then, utilizing differentially expressed genes (DEGs) from different RM subtypes, we built a risk model called RM_Score. Additionally, we investigated the correlation of RM_Score with clinical characteristics, tumor microenvironment (TME) infiltration, molecular subtypes, therapeutic response, immunotherapy effectiveness, and competing endogenous RNA (ceRNA) network. Results RM writers were correlated with TME cell infiltration and prognosis. Cluster_1/2 and gene.cluster_A/B were shown to be capable of distinguishing the HCC patients with poor prognosis after consensus and unsupervised clustering of RNA methylation writers. Additionally, we constructed RNA modification pattern-specific risk model and subdivided the cases into RM_Score high and RM_Score low subgroups. In individual cohorts or merged datasets, the high RM_Score was related to a worse overall survival of HCC patients. RM_Score also exhibited correlations with immune and proliferation related pathways. In response to anti-cancer treatments, the RM_Score had a negative correlation (drug sensitive) with drugs that focused on the MAPK/ERK and metabolism signaling, and a positive correlation (drug resistant) with compounds targeting RKT and PI3K/mTOR signaling pathway. Notably, the RM_Score was connected to the therapeutic effectiveness of PD-L1 blockage, implying that RM writers may be the target of immunotherapy to optimize clinical outcomes. Additionally, a ceRNA network was generated including 2 lncRNAs, 4 miRNAs, and 7 mRNAs that was connected to RM writers. Conclusions We thoroughly investigated the potential functions of RNA methylation writers and established an RM_patterns-based risk model for HCC patients. This study emphasized the critical functions of RM modification in TME infiltration, targeted therapy, and immunotherapy, providing potential targets for HCC.

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Tumor‐associated macrophages in liver cancer: From mechanisms to therapy

Cited by 104 publications

References 195 publications

Single-cell transcriptomics reveals the role of Macrophage-Naïve CD4 + T cell interaction in the immunosuppressive microenvironment of primary liver carcinoma

Single-cell transcriptomics reveals the role of Macrophage-Naïve CD4 + T cell interaction in the immunosuppressive microenvironment of primary liver carcinoma

Low TFPI2 expression is associated with poor prognosis and defective immune cell infiltration in breast cancer disease

Integrated investigation of the clinical implications and targeted landscape for RNA methylation modifications in hepatocellular carcinoma

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