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    <identifier>10.57760/sciencedb.12250</identifier>
    <datestamp>2023-10-20T11:27:16Z</datestamp>
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<oai_dc:dc xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:date>2023-10-20</dc:date>
  <dc:title>The identification of a two-gene prognostic model based on cisplatin resistance-related ceRNA network in small cell lung cancer</dc:title>
  <dc:identifier>doi:10.57760/sciencedb.12250</dc:identifier>
  <dc:language>en</dc:language>
  <dc:description>AbstractBackground:Small cell lung cancer (SCLC) is a very malignant tumor with rapid growth and early metastasis. Platinum-based chemo-resistance is the major issue for SCLC treatment failure. Identifying a new prognostic model will&amp;nbsp;help to make an accurate treatment decision for SCLC patients.Methods: Using the Genomics of Drug Sensitivity in Cancer (GDSC) database, we identified cisplatin&amp;nbsp;resistance-related&amp;nbsp;lncRNAs&amp;nbsp;in SCLC cells. Based on the competing endogenous RNA (ceRNA) network,&amp;nbsp;we identified the mRNAs&amp;nbsp;correlated with the lncRNAs. Using Cox and LASSO regression analysis, a prognostic model was established. The survival prediction accuracy&amp;nbsp;was evaluated&amp;nbsp;by receiver operating characteristic (ROC)&amp;nbsp;curve&amp;nbsp;and Kaplan-Meier analysis. GSEA, GO, KEGG and CIBERSORT&amp;nbsp;tools were used for functional enrichment and immune cells&amp;nbsp;infiltration&amp;nbsp;analysis. Results:We first screened out 10 differentially expressed lncRNAs between cisplatin resistant and sensitive SCLC cells from GDSC database. Based on ceRNA network, 31 mRNAs were identified with a correlation with the 10 lncRNAs. Furthermore, two genes (LIMK2 and PI4K2B) were identified by Cox and LASSO regression analysis to construct a prognostic model. Kaplan-Meier analysis indicated that the high-risk group had a poor overall survival compared with the low-risk group. The predicted area under the ROC&amp;nbsp;curve (AUC) was 0.853 in&amp;nbsp;the training set, and&amp;nbsp;the AUC was 0.671 in the validation set. In the meanwhile, the low expression of LIMK2 or the high expression of PI4K2B&amp;nbsp;in SCLC tumors&amp;nbsp;was also significantly associated with poor overall survival in both training and validation sets. Functional enrichment analysis showed that the low-risk group was enriched in the apoptosis pathway and high immune infiltration of T cells. Finally, an apoptosis-related gene Cathepsin D (CTSD) was identified to be up-regulated in the low-risk group, and its higher expression correlated with better overall survival&amp;nbsp;in SCLC.Conclusion:We established a prognostic model and potential biomarkers (LIMK2, PI4K2B&amp;nbsp;and CTSD), which could help to improve the risk stratification of SCLC patients.</dc:description>
  <dc:subject>Small cell lung cancer; Cisplatin; lncRNA; ceRNA; Prognostic model</dc:subject>
  <dc:creator>Yani Zhang</dc:creator>
  <dc:creator>Qizhi Zhu</dc:creator>
  <dc:creator>Jian Qi</dc:creator>
  <dc:creator>Meng Fu</dc:creator>
  <dc:creator>Ao Xu</dc:creator>
  <dc:creator>Wei Wang</dc:creator>
  <dc:creator>Hongzhi Wang</dc:creator>
  <dc:creator>Jinfu Nie</dc:creator>
  <dc:creator>Bo Hong</dc:creator>
  <dc:rights>PUBLIC</dc:rights>
  <dc:rights>https://creativecommons.org/licenses/by-nc-sa/4.0/</dc:rights>
  <dc:type>dataset</dc:type>
  <dc:relation>http://www.doi.org/10.1186/s12920-023-01536-5</dc:relation>
  <dc:publisher>Science Data Bank</dc:publisher>
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