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A multi-gene expression profile panel for predicting liver metastasis: An algorithmic approach.
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- Author(s): Shah K;Shah K; Patel S; Patel S; Mirza S; Mirza S; Rawal RM; Rawal RM
- Source:
PloS one [PLoS One] 2018 Nov 01; Vol. 13 (11), pp. e0206400. Date of Electronic Publication: 2018 Nov 01 (Print Publication: 2018).- Publication Type:
Journal Article; Research Support, Non-U.S. Gov't- Language:
English - Source:
- Additional Information
- Source: Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet ISSN: 1932-6203 (Electronic) Linking ISSN: 19326203 NLM ISO Abbreviation: PLoS One Subsets: MEDLINE
- Publication Information: Original Publication: San Francisco, CA : Public Library of Science
- Subject Terms: Algorithms* ; Gene Expression Profiling*; Biomarkers, Tumor/*genetics ; Liver Neoplasms/*diagnosis ; Liver Neoplasms/*secondary ; Lung Neoplasms/*genetics ; Lung Neoplasms/*pathology; Adult ; Aged ; Aged, 80 and over ; Cell Line, Tumor ; Early Detection of Cancer/methods ; Female ; Gene Expression Regulation, Neoplastic ; Humans ; Liver Neoplasms/genetics ; Liver Neoplasms/mortality ; Lung Neoplasms/diagnosis ; Lung Neoplasms/mortality ; Male ; Middle Aged ; Oligonucleotide Array Sequence Analysis ; Predictive Value of Tests ; Prognosis ; Sensitivity and Specificity ; Survival Analysis
- Abstract: Background & Aim: Liver metastasis has been found to affect outcome in prostate, pancreatic and colorectal cancers, but its role in lung cancer is unclear. The 5 year survival rate remains extensively low owing to intrinsic resistance to conventional therapy which can be attributed to the genetic modulators involved in the pathogenesis of the disease. Thus, this study aims to generate a model for early diagnosis and timely treatment of liver metastasis in lung cancer patients.
Methods: mRNA expression of 15 genes was quantified by real time PCR on lung cancer specimens with (n = 32) and without (n = 30) liver metastasis and their normal counterparts. Principal Component analysis, linear discriminant analysis and hierarchical clustering were conducted to obtain a predictive model. The accuracy of the models was tested by performing Receiver Operating Curve analysis.
Results: The expression profile of all the 15 genes were subjected to PCA and LDA analysis and 5 models were generated. ROC curve analysis was performed for all the models and the individual genes. It was observed that out of the 15 genes only 8 genes showed significant sensitivity and specificity. Another model consisting of the selected eight genes was generated showing a specificity and sensitivity of 90.0 and 96.87 respectively (p <0.0001). Moreover, hierarchical clustering showed that tumors with a greater fold change lead to poor prognosis.
Conclusion: Our study led to the generation of a concise, biologically relevant multi-gene panel that significantly and non-invasively predicts liver metastasis in lung cancer patients.
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- Publication Date: Date Created: 20181102 Date Completed: 20190419 Latest Revision: 20190419
- Publication Date: 20240104
- Accession Number: PMC6211708
- Accession Number: 10.1371/journal.pone.0206400
- Accession Number: 30383826
- Source:
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