Immune and stromal related genes in colon cancer: Analysis of tumour microenvironment based on the cancer genome atlas (TCGA) and gene expression omnibus (GEO) databases

Wu, Yue; Jia, Haowei; Zhou, Hangyuan; Liu, Xinyu; Sun, Junfeng; Zhou, Xiaoyan; Zhao, Hongchao

doi:10.1111/sji.13119

Cited by 9 publications

(7 citation statements)

References 31 publications

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“…These data are consistent with previous TME analysis using multiple CRC cohorts, which demonstrated significant negative correlations of tumor purity with immune and stromal scores. 30 , 31 Finally, we analyzed our identified signature in the context of an immune and stromal gene signature related to CRC metastasis. A well-validated 292 gene expression signature associated with immune and stromal components of CRC tumors was previously described using data from TCGA.…”

Section: Resultsmentioning

confidence: 99%

An epigenetic signature of advanced colorectal cancer metastasis

et al. 2023

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Section: Resultsmentioning

confidence: 99%

An epigenetic signature of advanced colorectal cancer metastasis

et al. 2023

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“…Wu et al. reported that LPAR1 can mediate various biological functions of tumors ( 34 ) and participate in the activation, proliferation differentiation, and migration of immune cells ( 32 ). Our correlation analysis between the ssGSEA scores of the differentially abundant immune cells and biomarkers in this study showed that LPAR1 was positively correlated with activated CD4 T cells and effector memory CD4 T cells.…”

Section: Discussionmentioning

confidence: 99%

Identification of inflammation-related biomarkers in keloids

Wang,

Liu

et al. 2024

Front. Immunol.

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BackgroundThe relationship between inflammation-related genes (IRGs) and keloid disease (KD) is currently unclear. The aim of this study was to identify a new set of inflammation-related biomarkers in KD.MethodsGSE145725 and GSE7890 datasets were used in this study. A list of 3026 IRGs was obtained from the Molecular Signatures Database. Differentially expressed inflammation-related genes (DEGs) were obtained by taking the intersection of DEGs between KD and control samples and the list of IRGs. Candidate genes were selected using least absolute shrinkage and selection operator (LASSO) regression analysis. Candidate genes with consistent expression differences between KD and control in both GSE145725 and GSE7890 datasets were screened as biomarkers. An alignment diagram was constructed and validated, and in silico immune infiltration analysis and drug prediction were performed. Finally, RT-qPCR was performed on KD samples to analyze the expression of the identified biomarkers.ResultsA total of 889 DEGs were identified from the GSE145725 dataset, 169 of which were IRGs. Three candidate genes (TRIM32, LPAR1 and FOXF1) were identified by the LASSO regression analysis, and expression validation analysis suggested that FOXF1 and LPAR1 were down-regulated in KD samples and TRIM32 was up-regulated. All three candidate genes had consistent changes in expression in both the GSE145725 and GSE7890 datasets. An alignment diagram was constructed to predict KD. Effector memory CD4 T cells, T follicular helper cell, Myeloid derived suppressor cell, activated dendritic cell, Immature dendritic cell and Monocyte were differentially expressed between the KD and control group. Sixty-seven compounds that may act on FOXF1, 108 compounds that may act on LPAR1 and 56 compounds that may act on TRIM32 were predicted. Finally, RT-qPCR showed that the expression of LPAR1 was significantly lower in KD samples compared to normal samples whereas TRIM32 was significantly higher, while there was no difference in the expression of FOXF1.ConclusionThis study provides a new perspective to study the relationship between IRGs and KD.

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“…Studies have found that all these genes could contribute to the transition of in ammation and immune response and further affect response to anti-CTLA-4 ICIs [25], and these three genes were also identi ed as potential prognostic biomarkers for melanoma [26][27][28][29]. The other genes including FGFR4, GNAI1, CD36 and FLT1 could alter the tumor microenvironment and further impact ICIs' clinical e cacy, combination their inhibitors and ICIs might be better for melanoma [30][31][32][33]. Khunger et al [34] found that COL1A2 was associated with cancer development and progression, and patients with melanoma were more likely to be bene t from anti-CTLA-4 inhibitors when COL1A2 was highly expressed.…”

Section: Discussionmentioning

confidence: 99%

Biologically interpretable deep learning to predict response to immunotherapy in advanced melanoma using mutations and copy number variations

Zhang

Cao

et al. 2022

Preprint

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Only 30–40% of advanced melanoma patients respond effectively to immunotherapy in clinical practice, so it’s necessary to accurately identify the response of melanoma patients to immune therapy pre-clinically. Here we developed the KP-NET, a deep learning model whose structure is sparse by the KEGG pathways, which can accurately predict melanoma patients’ response to immunotherapy using information at the pathway level that is enriched from gene mutations and copy number variations data prior to immune therapy. The KP-NET demonstrated the best performance on predictive melanoma response to anti-CTLA-4 and anti-PD-1 treatment with corresponding AUROC values of 0.889 and 0.867, and selected some relevant biomarkers, genes such as PLCB2, FGFR4, RHEB and CCNB2, pathways such as Inflammatory mediator regulation of TRP channels, Notch signaling pathway, mTOR signaling pathway and TNF signaling pathway, etc. In conclusion, deep learning model guided by biological information enables accurate prediction of the response of melanoma patients to immunotherapy and relevant biomarkers before clinical treatment, which may be helpful in melanoma precision medicine.

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Immune and stromal related genes in colon cancer: Analysis of tumour microenvironment based on the cancer genome atlas (TCGA) and gene expression omnibus (GEO) databases

Cited by 9 publications

References 31 publications

An epigenetic signature of advanced colorectal cancer metastasis

An epigenetic signature of advanced colorectal cancer metastasis

Identification of inflammation-related biomarkers in keloids

Biologically interpretable deep learning to predict response to immunotherapy in advanced melanoma using mutations and copy number variations

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