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pmid:33153411

CSF1R and HCST: Novel Candidate Biomarkers Predicting the Response to Immunotherapy in Non-Small Cell Lung Cancer.

Xiaoguang Qi · Chunyan Qi · Tao Wu · Yi Hu

Technology in cancer research & treatment · 2020 doi:10.1177/1533033820970663

Paper

complete

Experiment

complete

Data

partial

Analysis

complete

Execution

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title
CSF1R and HCST: Novel Candidate Biomarkers Predicting the Response to Immunotherapy in Non-Small Cell Lung Cancer.
doi
10.1177/1533033820970663
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Xiaoguang Qi
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Chunyan Qi
[2]
Tao Wu
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Yi Hu
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Technology in cancer research & treatment
year
2020
abstract
OBJECTIVE: Precision immunotherapy in non-small cell lung cancer (NSCLC) have been the focus of tumor immunity research. The aim of this study is to identify novel candidate biomarkers predicting the response to immunotherapy in NSCLC. METHODS: GSE126044 was obtained from Gene Expression Omnibus (GEO). According to the response to anti-PD-1 antibody, 2 groups were divided: response group and non-response group. Differentially expressed genes (DEGs) were screened using R. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed. ROC curves and possible pathways of the seed genes were further analyzed. RESULTS: In total, 588 DEGs (487 upregulated DEGs and 101 downregulated) were identified. GO and KEGG analyses showed that upregulated DEGs were mainly enriched in immune response and cell adhesion pathways, while VEGF signaling pathway and metabolic pathways were mainly enriched in downregulated DEGs. In addition, CSF1 R and HCST showed more powerful predictive ability than PDL1. More importantly, these candidate genes were not only positively correlated with the expression of PDL1 and the infiltration of CD8+ T cells in the immune microenvironment, but also might improve the prognosis in lung squamous cell carcinoma. CONCLUSIONS: CSF1 R and HCST might be novel predictive markers for immunotherapy in NSCLC.
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The aim of this study is to identify novel candidate biomarkers predicting the response to immunotherapy in NSCLC.\nMETHODS: GSE126044 was obtained from Gene Expression Omnibus (GEO). According to the response to anti-PD-1 antibody, 2 groups were divided: response group and non-response group. Differentially expressed genes (DEGs) were screened using R. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed. ROC curves and possible pathways of the seed genes were further analyzed.\nRESULTS: In total, 588 DEGs (487 upregulated DEGs and 101 downregulated) were identified. GO and KEGG analyses showed that upregulated DEGs were mainly enriched in immune response and cell adhesion pathways, while VEGF signaling pathway and metabolic pathways were mainly enriched in downregulated DEGs. In addition, CSF1 R and HCST showed more powerful predictive ability than PDL1. More importantly, these candidate genes were not only positively correlated with the expression of PDL1 and the infiltration of CD8+ T cells in the immune microenvironment, but also might improve the prognosis in lung squamous cell carcinoma.\nCONCLUSIONS: CSF1 R and HCST might be novel predictive markers for immunotherapy in NSCLC.", "claims": [{"id": "claim-method", "statement": "The screening of DEGs and the generation of volcano plots were performed using the limma package.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33153411", "locator": "sections[4] \"Screening of Differentially Expressed Genes (DEGs)\" paragraph 2", "quote": "The screening of DEGs and the generation of volcano plots were performed using the limma package."}], "state": "complete"}, {"id": "claim-data-access", "statement": "GSE126044, GSE110390 and GSE136961 were obtained from the GEO database by searching for the keywords checkpoint inhibitor or immunotherapy or PD-1 and lung cancer. 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