Identifying Dendritic Cell–Related Genes Through a Co-Expression Network to Construct a 12-Gene Risk-Scoring Model for Predicting Hepatocellular Carcinoma Prognosis

Huang, Chao-Yuan; Jiang, Xiaotao; Huang, Yuancheng; Zhao, Lina; Li, Peiwu; Liu, Fengbin

doi:10.3389/fmolb.2021.636991

Cited by 7 publications

(7 citation statements)

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“…The role of BTN3A3 is expected to become a potential therapeutic target for breast cancer (30), and the upregulation of BTN3A3 has been significantly correlated with better overall and relapse-free survival (31). Moreover, it has been noted to be a novel prognostic factor for hepatocellular carcinoma (32). In non-small cell lung cancer, knocking down BTN3A3 was shown to promote cell proliferation, migration, and invasion, and patients with a low expression of BTN3A3 have poor survival (33).…”

Section: Discussionmentioning

confidence: 99%

Use of exosome transcriptome-based analysis to identify novel biomarkers in patients with locally advanced esophageal squamous cell carcinoma undergoing neoadjuvant chemoradiotherapy

Yang¹,

Zhang²,

Liu³

et al. 2023

Ann Transl Med

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Background: The prognosis of esophageal squamous cell carcinoma (ESCC) is improved by neoadjuvant chemoradiotherapy (nCRT), especially for patients with pathologic complete response (pCR). Despite the efforts to predict treatment response using multimodality, no molecule has proven to be a strong biomarker.This study aimed to profile the expression of exosome transcriptome that could predict pCR in ESCC before and after nCRT.Methods: We collected paired blood samples of 15 patients with ESCC who received nCRT and radical surgery. They were divided into 3 groups: (A) residual tumor in the first clinical response evaluation (CRE-1), (B) no residual tumor in CRE-1 but with residual tumor in CRE-2 which was performed after 5-6 weeks, and (C) no residual tumor in CRE-1 or CRE-2. For each patient, the blood sample was collected before nCRT (time point 0); and then 6 weeks after nCRT, the clinical response was evaluated, and another blood sample was collected (time point 1).Results: Using the intersection of different sets, we found 23 progression-associated messenger RNAs (mRNAs) and 67 remission-associated mRNAs. Between remission-associated mRNAs and the targets of progression-associated (carcinogenic) microRNAs (miRNAs), the intersection was acquired, and 2 miRNA-mRNA networks (IFIT2-miR-3615-IFIT2-miR-484 and BTN3A3-miR-6803-3p) were identified. Among the intersection of progression-associated (carcinogenic) mRNAs and the targets of remission-associated miRNAs, there is a network with miR-132-3p (remission-associated miRNA) located at the core, matched with DICER1, KLHL8, ANKRD12, ASH1L, and IMP4.Conclusions: Our findings identified altered plasma exosome RNAs among the different groups and between different time points of nCRT, as well as the corresponding enrichments and regulatory networks, which may serve as potentially predictors of treatment response for patients with ESCC after nCRT.

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Section: Discussionmentioning

confidence: 99%

Use of exosome transcriptome-based analysis to identify novel biomarkers in patients with locally advanced esophageal squamous cell carcinoma undergoing neoadjuvant chemoradiotherapy

Yang¹,

Zhang²,

Liu³

et al. 2023

Ann Transl Med

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“…Additionally, SDC3 was linked to more dangerous tumors and a worse prognosis in prostate cancer ( 66 ). As a gene associated with dendritic cells, SDC3 is also important in developing a risk model for predicting the prognosis of HCC ( 67 ). The anti-apoptotic activity of TNF receptor superfamily member 11B (TNFRSF11B) can bind to and suppress TRAIL (TNF-related apoptosis-inducing ligand), which inhibits the spread of HCC and improves patient prognosis ( 68 , 69 ).…”

Section: Discussionmentioning

confidence: 99%

Regulatory T-cells-related signature for identifying a prognostic subtype of hepatocellular carcinoma with an exhausted tumor microenvironment

Zhang

2022

Front. Immunol.

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Regulatory T-Cells (Tregs) are important in the progression of hepatocellular cancer (HCC). The goal of this work was to look into Tregs-related genes and develop a Tregs-related prognostic model. We used the weighted gene coexpression network analysis (WGCNA) to look for Tregs-related genes in the TCGA, ICGC, and GSE14520 cohorts and then used the non-negative matrix factorization (NMF) algorithm to find Tregs-related subpopulations. The LASSO-Cox regression approach was used to determine Tregs-related genes, which were then condensed into a risk score. A total of 153 overlapping genes among the three cohorts were considered Tregs-related genes. Based on these genes, two Tregs-associated clusters that varied in both prognostic and biological characteristics were identified. When compared with Cluster 1, Cluster 2 was a TME-exhausted HCC subpopulation with substantial immune cell infiltration but a poor prognosis. Five Tregs-related genes including HMOX1, MMP9, CTSC, SDC3, and TNFRSF11B were finally used to construct a prognostic model, which could accurately predict the prognosis of HCC patients in the three datasets. Patients in the high-risk scores group with bad survival outcomes were replete with immune/inflammatory responses, but exhausted T cells and elevated PD-1 and PD-L1 expression. The results of qRT-PCR and immunohistochemical staining (IHC) analysis in clinical tissue samples confirmed the above findings. Moreover, the signature also accurately predicted anti-PD-L1 antibody responses in the IMvigor210 dataset. Finally, HMOX1, MMP9, and TNFRSF11B were expressed differently in Hep3B and Huh7 cells after being treated with a PD1/PD-L1 inhibitor. In conclusion, our study uncovered a Tregs-related prognostic model that could identify TMEexhausted subpopulations and revealed that PD1/PD-L1 inhibitors could alter the expression levels of HMOX1, MMP9, and TNFRSF11B in Hep3B and Huh7 cells, which might help us better understand Tregs infiltration and develop personalized immunotherapy treatments for HCC patients.

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“…The TIMER2 database contains information on 32 cancers and 10,897 tissues from TCGA database, which allows systematic analysis of the correlation between one or more tumours and immune cell infiltration as well as the correlation between the expression of relevant genes in tumour tissues and the prognosis, mutation and copy number of patients ( Ju et al, 2020 ). ImmuCellAI ( http://bioinfo.life.hust.edu.cn/web/ImmuCellAI/ ) is a powerful and unique method for accurately screening tumour immune function using 24 different types of immune cells, including T cells ( Huang et al, 2021 ). Furthermore, the XCell algorithm was used to examine several features of tumours, including the composition of infiltrating immune cells, based on the gene expression data ( Schulze et al, 2020 ).…”

Section: Methodsmentioning

confidence: 99%

OAS3 is a Co-Immune Biomarker Associated With Tumour Microenvironment, Disease Staging, Prognosis, and Treatment Response in Multiple Cancer Types

Hou

Zhang

et al. 2022

Front. Cell Dev. Biol.

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2′,5′-oligoadenylate synthase (OAS) is a class of enzymes induced by interferons and mainly encoded by the OAS1, OAS2, and OAS3 genes, which activate the potential RNA enzymes to degrade viral mRNA, inhibit viral protein synthesis and promote apoptosis in virus-infected cells. OAS3 is associated with breast cancer prognosis. However, the expression and prognosis of OAS3 and tumour-infiltrating lymphocytes in pan-cancer remain unknown. In the present study, we have systematically investigated and confirmed the role of OAS3 in tumour immune infiltration, immune escape, tumour progression, response to treatment, and prognosis of different cancer types using various bioinformatics methods. The findings suggest that OAS3 is aberrantly expressed in almost all TCGA cancer types and subtypes and is associated with tumour staging, metastasis, and prognostic deterioration in different tumours. In addition, OAS3 expression is associated with the prognosis and chemotherapeutic outcomes of various cancers. In terms of immune-infiltrating levels, OAS3 expression is positively associated with the infiltration of immunosuppressive cells. These findings suggest that OAS3 is correlated with prognosis and immune-infiltrating levels.

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Identifying Dendritic Cell–Related Genes Through a Co-Expression Network to Construct a 12-Gene Risk-Scoring Model for Predicting Hepatocellular Carcinoma Prognosis

Cited by 7 publications

References 58 publications

Use of exosome transcriptome-based analysis to identify novel biomarkers in patients with locally advanced esophageal squamous cell carcinoma undergoing neoadjuvant chemoradiotherapy

Use of exosome transcriptome-based analysis to identify novel biomarkers in patients with locally advanced esophageal squamous cell carcinoma undergoing neoadjuvant chemoradiotherapy

Regulatory T-cells-related signature for identifying a prognostic subtype of hepatocellular carcinoma with an exhausted tumor microenvironment

OAS3 is a Co-Immune Biomarker Associated With Tumour Microenvironment, Disease Staging, Prognosis, and Treatment Response in Multiple Cancer Types

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