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Four transcription profile-based models identify novel prognostic signatures in oesophageal cancer.

ABSTRACT: Oesophageal cancer (ESCA) is a clinically challenging disease with poor prognosis and health-related quality of life. Here, we investigated the transcriptome of ESCA to identify high risk-related signatures. A total of 159 ESCA patients of The Cancer Genome Atlas (TCGA) were sorted by three phases. In the discovery phase, differentially expressed transcripts were filtered; in the training phase, two adjusted Cox regressions and two machine leaning models were used to construct and estimate signatures; and in the validation phase, prognostic signatures were validated in the testing dataset and the independent external cohort. We constructed two signatures from three types of RNA markers by Akaike information criterion (AIC) and least absolute shrinkage and selection operator (LASSO) Cox regressions, respectively, and all candidate markers were further estimated by Random Forest (RFS) and Support Vector Machine (SVM) algorithms. Both signatures had good predictive performances in the independent external oesophageal squamous cell carcinoma (ESCC) cohort and performed better than common clinicopathological indicators in the TCGA dataset. Machine learning algorithms predicted prognosis with high specificities and measured the importance of markers to verify the risk weightings. Furthermore, the cell function and immunohistochemical (IHC) staining assays identified that the common risky marker FABP3 is a novel oncogene in ESCA.

SUBMITTER: Liu T

PROVIDER: S-EPMC6933393 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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Four transcription profile-based models identify novel prognostic signatures in oesophageal cancer.

Liu Tongyan T Fang Panqi P Han Chencheng C Ma Zhifei Z Xu Weizhang W Xia Wenjia W Hu Jingwen J Xu Youtao Y Xu Lin L Yin Rong R Wang Siwei S Zhang Qin Q

Journal of cellular and molecular medicine 20191119 1

Oesophageal cancer (ESCA) is a clinically challenging disease with poor prognosis and health-related quality of life. Here, we investigated the transcriptome of ESCA to identify high risk-related signatures. A total of 159 ESCA patients of The Cancer Genome Atlas (TCGA) were sorted by three phases. In the discovery phase, differentially expressed transcripts were filtered; in the training phase, two adjusted Cox regressions and two machine leaning models were used to construct and estimate signa ...[more]

PMID: 31746108

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Project description:BackgroundThe peroxisome proliferator-activated receptor alpha (PPAR?) is a ligand-activated transcription factor that regulates lipid catabolism and inflammation and is hepatocarcinogenic in rodents. It is presumed that the functions of PPAR? in liver depend on cross-talk between parenchymal (hepatocytes) and non-parenchymal (Kupffer and endothelial cells) fractions as well as inter-organ interactions. In order to determine how cellular composition and inter-organ interactions influence gene expression upon pharmacological activation of PPAR?, we performed a meta-analysis of transcriptomics data obtained from mouse hepatocytes (containing only the parenchymal fraction), mouse liver slices (containing both fractions), and mouse livers exposed to a PPAR? agonist. The aim was to obtain a comprehensive view of common and model-specific PPAR?-dependent genes and biological processes to understand the impact of cross-talk between parenchymal and non-parenchymal fractions as well as the effect of inter-organ interactions on the hepatic PPAR? transcriptome.To this end we analyzed microarray data of experiments performed in mouse primary hepatocytes treated with the PPAR? agonist Wy14643 for 6 or 24 h (in vitro), mouse precision cut liver slices treated with Wy14643 for 24 h (ex vivo), and livers of wild type and Ppara knockout mice treated with Wy14643 for 6 h or 5 days (in vivo).ResultsIn all models, activation of PPAR? significantly altered processes related to various aspects of lipid metabolism. In ex vivo and in vivo models, PPAR? activation significantly regulated processes involved in inflammation; these processes were unaffected in hepatocytes. Only in vivo models showed significant regulation of genes involved in coagulation, carcinogenesis, as well as vesicular trafficking and extracellular matrix.ConclusionsPPAR?-dependent regulation of genes/processes involved in lipid metabolism is mostly independent of the presence of non-parenchymal cells or systemic factors, as it was observed in all liver models. PPAR?-dependent regulation of inflammatory genes requires the presence of non-parenchymal cells, as it was observed only ex vivo and in vivo. However, the full spectrum of PPAR? biology at the level of lipid metabolism, immunity, carcinogenesis, as well as novel aspects of PPAR? signaling such as coagulation, vesicular trafficking and the extracellular matrix, seems to require systemic factors, as it was observed exclusively in vivo.

Project description:BackgroundDiagnostic models based on gene signatures of nasopharyngeal carcinoma (NPC) were constructed by random forest (RF) and artificial neural network (ANN) algorithms. Least absolute shrinkage and selection operator (Lasso)-Cox regression was used to select and build prognostic models based on gene signatures. This study contributes to the early diagnosis and treatment, prognosis, and molecular mechanisms associated with NPC.MethodsTwo gene expression datasets were downloaded from the Gene Expression Omnibus (GEO) database, and differentially expressed genes (DEGs) associated with NPC were identified by gene expression differential analysis. Subsequently, significant DEGs were identified by a RF algorithm. ANN were used to construct a diagnostic model for NPC. The performance of the diagnostic model was evaluated by area under the curve (AUC) values using a validation set. Lasso-Cox regression examined gene signatures associated with prognosis. Overall survival (OS) and disease-free survival (DFS) prediction models were constructed and validated from The Cancer Genome Atlas (TCGA) database and the International Cancer Genome Consortium (ICGC) database.ResultsA total of 582 DEGs associated with NPC were identified, and 14 significant genes were identified by the RF algorithm. A diagnostic model for NPC was successfully constructed using ANN, and the validity of the model was confirmed on the training set AUC =0.947 [95% confidence interval (CI): 0.911-0.969] and the validation set AUC =0.864 (95% CI: 0.828-0.901). The 24-gene signatures associated with prognosis were identified by Lasso-Cox regression, and prediction models for OS and DFS of NPC were constructed on the training set. Finally, the ability of the model was validated on the validation set.ConclusionsSeveral potential gene signatures associated with NPC were identified, and a high-performance predictive model for early diagnosis of NPC and a prognostic prediction model with robust performance were successfully developed. The results of this study provide valuable references for early diagnosis, screening, treatment and molecular mechanism research of NPC in the future.

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Four transcription profile-based models identify novel prognostic signatures in oesophageal cancer.

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Four transcription profile-based models identify novel prognostic signatures in oesophageal cancer.

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