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Identification of potential target genes and crucial pathways in small cell lung cancer based on bioinformatic strategy and human samples.

ABSTRACT: Small cell lung cancer (SCLC) is a carcinoma of the lungs with strong invasion, poor prognosis and resistant to multiple chemotherapeutic drugs. It has posed severe challenges for the effective treatment of lung cancer. Therefore, searching for genes related to the development and prognosis of SCLC and uncovering their underlying molecular mechanisms are urgent problems to be resolved. This study is aimed at exploring the potential pathogenic and prognostic crucial genes and key pathways of SCLC via bioinformatic analysis of public datasets. Firstly, 117 SCLC samples and 51 normal lung samples were collected and analyzed from three gene expression datasets. Then, 102 up-regulated and 106 down-regulated differentially expressed genes (DEGs) were observed. And then, functional annotation and pathway enrichment analyzes of DEGs was performed utilizing the FunRich. The protein-protein interaction (PPI) network of the DEGs was constructed through the STRING website, visualized by Cytoscape. Finally, the expression levels of eight hub genes were confirmed in Oncomine database and human samples from SCLC patients. It showed that CDC20, BUB1, TOP2A, RRM2, CCNA2, UBE2C, MAD2L1, and BUB1B were upregulated in SCLC tissues compared to paired adjacent non-cancerous tissues. These suggested that eight hub genes might be viewed as new biomarkers for prognosis of SCLC or to guide individualized medication for the therapy of SCLC.

SUBMITTER: Chen X

PROVIDER: S-EPMC7665632 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Identification of potential target genes and crucial pathways in small cell lung cancer based on bioinformatic strategy and human samples.

Chen Xiuwen X Wang Li L Su Xiaomin X Luo Sen-Yuan SY Tang Xianbin X Huang Yugang Y

PloS one 20201113 11

Small cell lung cancer (SCLC) is a carcinoma of the lungs with strong invasion, poor prognosis and resistant to multiple chemotherapeutic drugs. It has posed severe challenges for the effective treatment of lung cancer. Therefore, searching for genes related to the development and prognosis of SCLC and uncovering their underlying molecular mechanisms are urgent problems to be resolved. This study is aimed at exploring the potential pathogenic and prognostic crucial genes and key pathways of SCLC ...[more]

PMID: 33186389

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Project description:Background: The molecular mechanism of tumorigenesis remains to be fully understood in breast cancer. It is urgently required to identify genes that are associated with breast cancer development and prognosis and to elucidate the underlying molecular mechanisms. In the present study, we aimed to identify potential pathogenic and prognostic differentially expressed genes (DEGs) in breast adenocarcinoma through bioinformatic analysis of public datasets. Methods: Four datasets (GSE21422, GSE29431, GSE42568, and GSE61304) from Gene Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA) dataset were used for the bioinformatic analysis. DEGs were identified using LIMMA Package of R. The GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) analyses were conducted through FunRich. The protein-protein interaction (PPI) network of the DEGs was established through STRING (Search Tool for the Retrieval of Interacting Genes database) website, visualized by Cytoscape and further analyzed by Molecular Complex Detection (MCODE). UALCAN and Kaplan-Meier (KM) plotter were employed to analyze the expression levels and prognostic values of hub genes. The expression levels of the hub genes were also validated in clinical samples from breast cancer patients. In addition, the gene-drug interaction network was constructed using Comparative Toxicogenomics Database (CTD). Results: In total, 203 up-regulated and 118 down-regulated DEGs were identified. Mitotic cell cycle and epithelial-to-mesenchymal transition pathway were the major enriched pathways for the up-regulated and down-regulated genes, respectively. The PPI network was constructed with 314 nodes and 1,810 interactions, and two significant modules are selected. The most significant enriched pathway in module 1 was the mitotic cell cycle. Moreover, six hub genes were selected and validated in clinical sample for further analysis owing to the high degree of connectivity, including CDK1, CCNA2, TOP2A, CCNB1, KIF11, and MELK, and they were all correlated to worse overall survival (OS) in breast cancer. Conclusion: These results revealed that mitotic cell cycle and epithelial-to-mesenchymal transition pathway could be potential pathways accounting for the progression in breast cancer, and CDK1, CCNA2, TOP2A, CCNB1, KIF11, and MELK may be potential crucial genes. Further, it could be utilized as new biomarkers for prognosis and potential new targets for drug synthesis of breast cancer.

Project description:Lung cancer is the world's most common malignancies and ranks first among all cancer-related deaths. Lung adenocarcinoma (LUAD) is the most frequent histological type in lung cancer. Its pathogenesis has not yet been fully elucidated, so it is of great significance to explore related genes for elucidating the molecular mechanism involved in occurrence and development of LUAD.To explore the crucial genes associated with LUAD development and progression, microarray datasets GSE7670, GSE10072, and GSE31547 were acquired from the Gene Expression Omnibus (GEO) database. R language Limma package was adopted to screen the differentially expressed genes (DEGs). The clusterProfiler package was used for enrichment analysis and annotation of the Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG) pathways for DEGs. The Search Tool for the Retrieval of Interacting Genes database (STRING) was used to construct the protein interaction network for DEGs, while Cytoscape was adopted to visualize it. The functional module was screened with Cytoscape's MCODE (The Molecular Complex Detection) plugin. The crucial genes associated with LUAD were identified by cytoHubba plugin. Kaplan-Meier plotter online tool was used to perform survival analysis of the hub gene.Three hundred twenty-one DEGs in total were screened, of which 105 were upregulated and 216 were downregulated. It was found that some GO terms and pathways (e.g., collagen trimer, extracellular structure organization, heparin binding, complement and coagulation cascades, malaria, protein digestion and absorption, and PPAR signaling pathway) were considerably enriched in DEGs. UBE2C, TOP2A, RRM2, CDC20, CCNB2, KIAA0101, BUB1B, TPX2, PRC1, and CDK1 were identified as crucial genes. Survival analysis showed that the overexpression of UBE2C, TOP2A, RRM2, CDC20, CCNB2, KIAA0101, BUB1B, TPX2, and PRC1 significantly reduced the overall survival of LUAD patients. One of the crucial genes: UBE2C was validated by immunohistochemistry to be upregulated in LUAD tissues.This study screened out potential biomarkers of LUAD, providing a theoretical basis for elucidating the pathogenesis and evaluating the prognosis of LUAD.

Project description:BackgroundIt has been proven that the treatment window of small cell lung cancer (SCLC) is short, so it is vital to find other possible therapeutic targets. CD39 inhibits natural killer (NK) cells and promotes the occurrence and metastasis of tumors. There has been little research about the role of CD39 in SCLC, so we explored the correlation between CD39 and other surface antigens, and its association with survival in SCLC.MethodsThis study included 75 patients with SCLC from Shanghai Pulmonary Hospital. After paraffin embedding and sectioning, immunohistochemistry (IHC) was applied. Then we identify cutoff value for CD39 and other surface antigens based on the analysis of ROC curve in RFS by SPSS. All statistical analyses were based on SPSS and Graphpad Prism8. Chi-square test, Kendall's tau-b correlation analysis, Logistic regression analysis, Kaplan-Meier method, univariate and multivariate Cox regression analysis were conducted. In all analyses, P = 0.05 distinguished whether they had statistical significance.ResultsOf the 75 SCLC patients enrolled in this study, 61.33% positively expressed CD39. A correlation between CD39 and programmed cell death-ligand 1 (PD-L1) (P=0.007), CD3 (P<0.001), CD4 (P<0.001), CD8 (P<0.001), and forkhead box P3 (FOXP3) (P<0.001) on tumor-infiltrating lymphocytes (TILs) was identified by correlation analysis and logistic regression analysis. Based on Kaplan-Meier survival analysis, we found that CD39 affected relapse-free survival (RFS) [negative vs. positive, 95% confidence interval (CI): 0.2765-0.9862, P=0.0390]. SCLC patients with high-expressed CD39 and low-expressed PD-L1 had poor prognosis (P<0.001). Positive expression of CD39 and negative expression of CD3, CD4, CD8, and FOXP3 also indicated shorter RFS (P=0.0409). Univariate and multivariate Cox regression analysis was performed to confirm the factors that influenced RFS.ConclusionsCD39, programmed cell death-1 (PD-1), and PD-L1 expressed on TILs but not on tumor cells. CD39 has a significant association with PD-L1, CD3, CD4, CD8, and FOXP3 on TILs. The positive expression of CD39 predicts poor prognosis. SCLC patients with low expression of CD39 combined with high expression of PD-L1 or CD3, CD4, CD8, and FOXP3 have a more favorable prognosis.

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Identification of potential target genes and crucial pathways in small cell lung cancer based on bioinformatic strategy and human samples.

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Identification of potential target genes and crucial pathways in small cell lung cancer based on bioinformatic strategy and human samples.

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