Evaluation of the roles and regulatory mechanisms of PD-1 target molecules in NSCLC progression

Zhang, Yunqiang; Yao, Yuan; Zhang, Jun; Lin, Cheng-Yi; Guo, Jialong; Liu, Huasong; Guo, Qiang

doi:10.21037/atm-21-2963

Cited by 18 publications

(21 citation statements)

References 24 publications

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“…In the present study, GEO2R analysis was used to investigate the DEGs in the MSCs of normal tissues and of lung tissues. As described in a previous study, the absolute value of logFold change (FC) > 0.585 and p < 0.05 were defined as the criteria for significant difference ( Zhang et al, 2021 ).…”

Section: Methodsmentioning

confidence: 99%

“…GSEA was used to explore the possible functions and mechanisms associated with each gene. The risk scores of NSCLC patients were ranked, and the gene expression data of NSCLC patients obtained from TCGA were grouped according to the median risk value to explore the signaling pathways enriched in the high-risk and low-risk groups ( Zhang et al, 2021 ). Signaling pathways with nominal (NOM) p < 0.05 was regarded as significantly enriched in the two risk groups.…”

Section: Methodsmentioning

confidence: 99%

“…In recent years, risk models, nomograms, and immunological assays have become tools for evaluating the prognosis of cancer patients and for discovering new mechanisms that could delay cancer progression ( Hu et al, 2020 ; Guo et al, 2021 ; Chao et al, 2021 ; Wang L. et al, 2021 ; Zhang et al, 2021 ). Melatonin reduces the viability and induces apoptosis of mutant -KRAS NSCLC cells.…”

Section: Introductionmentioning

confidence: 99%

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Clinical Roles of Risk Model Based on Differentially Expressed Genes in Mesenchymal Stem Cells in Prognosis and Immunity of Non-small Cell Lung Cancer

Guo

Xiao

et al. 2022

Front. Genet.

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The tumor microenvironment (TME) plays an important regulatory role in the progression of non-small cell lung cancer (NSCLC). Mesenchymal stem cells (MSCs) in the TME might contribute to the occurrence and development of cancer. This study evaluates the role of differentially expressed genes (DEGs) of MSCs and the development of NSCLC and develops a prognostic risk model to assess the therapeutic responses. The DEGs in MSCs from lung tissues and from normal tissues were analyzed using GEO2R. The functions and mechanisms of the DEGs were analyzed using the Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Additionally, the Cancer Genome Atlas (TCGA) database was used to determine the expression levels of the DEGs of MSCs in the NSCLC tissues. The prognostic factors of NSCLC related to MSCs were screened by survival analysis, meta-analysis, Cox regression analysis, and a prognostic risk model and nomogram was developed. The signaling mechanisms and immune roles that risk model participate in NSCLC development were determined via Gene Set Enrichment Analysis and CIBERSORT analysis. Compared to the normal tissues, 161 DEGs were identified in the MSCs of the lung tissues. These DEGs were associated with mechanisms, such as DNA replication, nuclear division, and homologous recombination. The overexpression of DDIT4, IL6, ITGA11, MME, MSX2, POSTN, and TRPA1 were associated with dismal prognosis of NSCLC patients. A high-risk score based on the prognostic risk model indicated the dismal prognosis of NSCLC patients. The nomogram showed that the age, clinical stage, and risk score affected the prognosis of NSCLC patients. Further, the high-risk model was associated with signaling mechanisms, such as the ECM-receptor interaction pathways, cytokine-cytokine receptor interaction, and MAPK pathways, involved in the progression of NSCLC and was also related to the components of the immune system, such as macrophages M0, T follicular helper cells, regulatory T cells. Therefore, the risk model and nomogram that was constructed on the basis of MSC-related factors such as POSTN, TRPA1, and DDIT4 could facilitate the discovery of target molecules that participate in the progression of NSCLC, which might also serve as new candidate markers for evaluating the prognosis of NSCLC patients.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Clinical Roles of Risk Model Based on Differentially Expressed Genes in Mesenchymal Stem Cells in Prognosis and Immunity of Non-small Cell Lung Cancer

Guo

Xiao

et al. 2022

Front. Genet.

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“…The 160 ESCA cases were assigned to high-SPDL1 and low-SPDL1 expression groups according to the median value of SDPL1 expression. The effect of SPDL1 expression level on each gene was investigated using GSEA, and each analysis was performed 1,000 times ( Guo et al, 2021 ; Zhang et al, 2021 ).…”

Section: Methodsmentioning

confidence: 99%

SPDL1 Overexpression Is Associated With the 18F-FDG PET/CT Metabolic Parameters, Prognosis, and Progression of Esophageal Cancer

et al. 2022

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Esophageal cancer (ESCA) is one of the common malignant tumors. The roles and signaling mechanisms of spindle apparatus coiled-coil protein 1 (SPDL1) in ESCA progression have not been reported previously. Therefore, the expression levels and potential clinical roles of SPDL1 were investigated using data from multiple databases and tissue samples of 53 ESCA patients who underwent 18F-FDG positron emission tomography (PET)/computed tomography (CT) before therapy. The signaling mechanisms of SPDL1 involved in ESCA progression were investigated via bioinformatics analysis. The effects of SPDL1 on the growth and migration of ESCA cells were investigated using CCK-8, Edu, and transwell assays. SPDL1 was upregulated in ESCA tissues. Increased SPDL1 expression was associated with age, grade, drinking history, cancer stage, lymph node metastasis, TP53 mutation, and poor prognosis in patients with ESCA. SPDL1 overexpression was significantly correlated with SUVmax, SUVmean, and TLG of PET/CT. SPDL1 silencing inhibited cell proliferation, migration, and invasion. SPDL1 was significantly enriched in cell cycle, spliceosome, DNA replication, and other processes. The hub genes of a constructed protein–protein interaction network included CDK1, BUB1, CCNB1, BUB1B, CCNA2, CDC20, MAD2L1, AURKB, NDC80, and PLK1, which were related to SPDL1 expression. The findings of this study suggest that SPDL1 may serve as a biomarker of ESCA prognosis.

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“…A computational approach based on protein–protein interaction (PPI) network analysis was used in a similar study, and Stratifin had an important role in NSCLC 6 development. Furthermore, Yun-Qiang Zhang et al 7 proposed HIST1H2BH and PLK1 as prognostic biomarkers for NSCLC patients.…”

Section: Introductionmentioning

confidence: 99%

Drug repositioning in non-small cell lung cancer (NSCLC) using gene co-expression and drug–gene interaction networks analysis

MotieGhader

Tabrizi-Nezhadi

Paskeh

et al. 2022

Sci Rep

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Lung cancer is the most common cancer in men and women. This cancer is divided into two main types, namely non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC). Around 85 to 90 percent of lung cancers are NSCLC. Repositioning potent candidate drugs in NSCLC treatment is one of the important topics in cancer studies. Drug repositioning (DR) or drug repurposing is a method for identifying new therapeutic uses of existing drugs. The current study applies a computational drug repositioning method to identify candidate drugs to treat NSCLC patients. To this end, at first, the transcriptomics profile of NSCLC and healthy (control) samples was obtained from the GEO database with the accession number GSE21933. Then, the gene co-expression network was reconstructed for NSCLC samples using the WGCNA, and two significant purple and magenta gene modules were extracted. Next, a list of transcription factor genes that regulate purple and magenta modules' genes was extracted from the TRRUST V2.0 online database, and the TF–TG (transcription factors–target genes) network was drawn. Afterward, a list of drugs targeting TF–TG genes was obtained from the DGIdb V4.0 database, and two drug–gene interaction networks, including drug-TG and drug-TF, were drawn. After analyzing gene co-expression TF–TG, and drug–gene interaction networks, 16 drugs were selected as potent candidates for NSCLC treatment. Out of 16 selected drugs, nine drugs, namely Methotrexate, Olanzapine, Haloperidol, Fluorouracil, Nifedipine, Paclitaxel, Verapamil, Dexamethasone, and Docetaxel, were chosen from the drug-TG sub-network. In addition, nine drugs, including Cisplatin, Daunorubicin, Dexamethasone, Methotrexate, Hydrocortisone, Doxorubicin, Azacitidine, Vorinostat, and Doxorubicin Hydrochloride, were selected from the drug-TF sub-network. Methotrexate and Dexamethasone are common in drug-TG and drug-TF sub-networks. In conclusion, this study proposed 16 drugs as potent candidates for NSCLC treatment through analyzing gene co-expression, TF–TG, and drug–gene interaction networks.

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Evaluation of the roles and regulatory mechanisms of PD-1 target molecules in NSCLC progression

Cited by 18 publications

References 24 publications

Clinical Roles of Risk Model Based on Differentially Expressed Genes in Mesenchymal Stem Cells in Prognosis and Immunity of Non-small Cell Lung Cancer

Clinical Roles of Risk Model Based on Differentially Expressed Genes in Mesenchymal Stem Cells in Prognosis and Immunity of Non-small Cell Lung Cancer

SPDL1 Overexpression Is Associated With the 18F-FDG PET/CT Metabolic Parameters, Prognosis, and Progression of Esophageal Cancer

Drug repositioning in non-small cell lung cancer (NSCLC) using gene co-expression and drug–gene interaction networks analysis

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