Dataset Information

PPARG Drives Molecular Networks as an Inhibitor for the Pathologic Development and Progression of Lung Adenocarcinoma.

ABSTRACT: Previous studies showed that low PPARG expression was associated with poor prognosis of lung adenocarcinoma (LA) with limited mechanisms identified. We first conducted a large-scale literature-based data mining to identify potential molecular pathways where PPARG could exert influence on the pathological development of LA. Then a mega-analysis using 13 independent LA expression datasets and a Pathway Enrichment Analysis (PEA) was conducted to study the gene expression levels and the functionalities of PPARG and the PPARG-driven triggers within the molecular pathways. Finally, a protein-protein interaction (PPI) network was established to reveal the functional connection between PPARG and its driven molecules. We identified 25 PPARG-driven molecule triggers forming multiple LA-regulatory pathways. Mega-analysis using 13 LA datasets supported these pathways and confirmed the downregulation of PPARG in the case of LA (p = 1.07e -05). Results from the PEA and PPI analysis suggested that PPARG might inhibit the development of LA through the regulation of tumor cell proliferation and transmission-related molecules, including an LA tumor cell suppressor MIR145. Our results suggested that increased expression of PPARG could drive multiple molecular triggers against the pathologic development and prognosis of LA, indicating PPARG as a valuable therapeutic target for LA treatment.

SUBMITTER: Zhao M

PROVIDER: S-EPMC7199583 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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PPARG Drives Molecular Networks as an Inhibitor for the Pathologic Development and Progression of Lung Adenocarcinoma.

Zhao Min M Li Xiaoyang X Zhang Yunxiang Y Zhu Hongming H Han Zhaoqing Z Kang Yan Y

PPAR research 20200426

Previous studies showed that low PPARG expression was associated with poor prognosis of lung adenocarcinoma (LA) with limited mechanisms identified. We first conducted a large-scale literature-based data mining to identify potential molecular pathways where PPARG could exert influence on the pathological development of LA. Then a mega-analysis using 13 independent LA expression datasets and a Pathway Enrichment Analysis (PEA) was conducted to study the gene expression levels and the functionalit ...[more]

PMID: 32395124

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Project description:Background:Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest solid tumors. The rapid progression of PDAC results in an advanced stage of patients when diagnosed. However, the dynamic molecular mechanism underlying PDAC progression remains far from clear. Methods:The microarray GSE62165 containing PDAC staging samples was obtained from Gene Expression Omnibus and the differentially expressed genes (DEGs) between normal tissue and PDAC of different stages were profiled using R software, respectively. The software program Short Time-series Expression Miner was applied to cluster, compare, and visualize gene expression differences between PDAC stages. Then, function annotation and pathway enrichment of DEGs were conducted by Database for Annotation Visualization and Integrated Discovery. Further, the Cytoscape plugin DyNetViewer was applied to construct the dynamic protein-protein interaction networks and to analyze different topological variation of nodes and clusters over time. The phosphosite markers of stage-specific protein kinases were predicted by PhosphoSitePlus database. Moreover, survival analysis of candidate genes and pathways was performed by Kaplan-Meier plotter. Finally, candidate genes were validated by immunohistochemistry in PDAC tissues. Results:Compared with normal tissues, the total DEGs number for each PDAC stage were 994 (stage I), 967 (stage IIa), 965 (stage IIb), 1027 (stage III), 925 (stage IV), respectively. The stage-course gene expression analysis showed that 30 distinct expressional models were clustered. Kyoto Encyclopedia of Genes and Genomes analysis indicated that the up-regulated DEGs were commonly enriched in five fundamental pathways throughout five stages, including pathways in cancer, small cell lung cancer, ECM-receptor interaction, amoebiasis, focal adhesion. Except for amoebiasis, these pathways were associated with poor PDAC overall survival. Meanwhile, LAMA3, LAMB3, LAMC2, COL4A1 and FN1 were commonly shared by these five pathways and were unfavorable factors for prognosis. Furthermore, by constructing the stage-course dynamic protein interaction network, 45 functional molecular modules and 19 nodes were identified as featured regulators for all PDAC stages, among which the collagen family and integrins were considered as two main regulators for facilitating aggressive progression. Additionally, the clinical relevance analysis suggested that the stage IV featured nodes MLF1IP and ITGB4 were significantly correlated with shorter overall survival. Moreover, 15 stage-specific protein kinases were identified from the dynamic network and CHEK1 was particularly activated at stage IV. Experimental validation showed that MLF1IP, LAMA3 and LAMB3 were progressively increased from tumor initiation to progression. Conclusions:Our study provided a view for a better understanding of the dynamic landscape of molecular interaction networks during PDAC progression and offered potential targets for therapeutic intervention.

Project description:ImportanceIt is currently unclear whether high-resolution computed tomography can preoperatively identify pathologic tumor invasion for ground-glass opacity lung adenocarcinoma.ObjectivesTo evaluate the diagnostic value of high-resolution computed tomography for identifying pathologic tumor invasion for ground-glass opacity featured lung tumors.Design, setting, and participantsThis prospective, multicenter diagnostic study enrolled patients with suspicious malignant ground-glass opacity nodules less than or equal to 30 mm from November 2019 to July 2021. Thoracic high-resolution computed tomography was performed, and pathologic tumor invasion (invasive adenocarcinoma vs adenocarcinoma in situ or minimally invasive adenocarcinoma) was estimated before surgery. Pathologic nonadenocarcinoma, benign diseases, or those without surgery were excluded from analyses; 673 patients were recruited, and 620 patients were included in the analysis. Statistical analysis was performed from October 2021 to January 2022.ExposurePatients were grouped according to pathologic tumor invasion.Main outcomes and measuresPrimary end point was diagnostic yield for pathologic tumor invasion. Secondary end point was diagnostic value of radiologic parameters.ResultsAmong 620 patients (442 [71.3%] female; mean [SD] age, 53.5 [12.0] years) with 622 nodules, 287 (46.1%) pure ground-glass opacity nodules and 335 (53.9%) part-solid nodules were analyzed. The median (range) size of nodules was 12.1 (3.8-30.0) mm; 47 adenocarcinomas in situ, 342 minimally invasive adenocarcinomas, and 233 invasive adenocarcinomas were confirmed. Overall, diagnostic accuracy was 83.0% (516 of 622; 95% CI, 79.8%-85.8%), diagnostic sensitivity was 82.4% (192 of 233; 95% CI, 76.9%-87.1%), and diagnostic specificity was 83.3% (324 of 389; 95% CI, 79.2%-86.9%). For tumors less than or equal to 10 mm, 3.6% (8 of 224) were diagnosed as invasive adenocarcinomas. The diagnostic accuracy was 96.0% (215 of 224; 95% CI, 92.5%-98.1%), diagnostic specificity was 97.2% (210 of 216; 95% CI, 94.1%-99.0%); for tumors greater than 20 mm, 6.9% (6 of 87) were diagnosed as adenocarcinomas in situ or minimally invasive adenocarcinomas. The diagnostic accuracy was 93.1% (81 of 87; 95% CI, 85.6%-97.4%) and diagnostic sensitivity was 97.5% (79 of 81; 95% CI, 91.4%-99.7%). For tumors between 10 to 20 mm, the diagnostic accuracy was 70.7% (220 of 311; 95% CI, 65.3%-75.7%), diagnostic sensitivity was 75.0% (108 of 144; 95% CI, 67.1%-81.8%), and diagnostic specificity was 67.1% (112 of 167; 95% CI, 59.4%-74.1%). Tumor size (odds ratio, 1.28; 95% CI, 1.18-1.39) and solid component size (odds ratio, 1.31; 95% CI, 1.22-1.42) could each independently serve as identifiers of pathologic invasive adenocarcinoma. When the cutoff value of solid component size was 6 mm, the diagnostic sensitivity was 84.6% (95% CI, 78.8%-89.4%) and specificity was 82.9% (95% CI, 75.6%-88.7%).Conclusions and relevanceIn this diagnostic study, radiologic analysis showed good performance in identifying pathologic tumor invasion for ground-glass opacity-featured lung adenocarcinoma, especially for tumors less than or equal to 10 mm and greater than 20 mm; these results suggest that a solid component size of 6 mm could be clinically applied to distinguish pathologic tumor invasion.

Dataset Information

PPARG Drives Molecular Networks as an Inhibitor for the Pathologic Development and Progression of Lung Adenocarcinoma.

Publications

PPARG Drives Molecular Networks as an Inhibitor for the Pathologic Development and Progression of Lung Adenocarcinoma.

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