Data mining and manual curation of published microarray datasets to establish a multi-gene panel for prediction of liver metastasis

A multi-gene expression profile panel for predicting liver metastasis: An algorithmic approach

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.

Unravelling the link between embryogenesis and cancer metastasis

PURPOSE

Cancer as opposed to embryonic development is characterized by dysregulated, uncontrolled and clonal growth of cells. Inspite of that they share certain commonality in gene expression patterns and a number of cellular & molecular features. Consequently, in the present study we aimed to evaluate the role of a definite set of genes in fetal liver, primary liver cancers and metastatic liver tissue.

METHODS

The relative expression of fourteen candidate genes obtained by data mining and manual curation of published data (CXCL12, CXCR4, CK7, CDH1, CTNNB1, CLDN4, VEGFA, HIF1A, MMP9, p53, OPN, CDKN2A, TGFBR2, MUC16, β-actin) were performed on 62 tissues (32 liver metastasis tissues and 30 primary Liver cancer tissues), Fetal liver tissues (below and above 20weeks of gestation) and 2 sets of control samples by real-time quantitative reverse transcription PCR (qRT-PCR).

RESULTS

Results showed significant down-regulation of MMP9 and TP53 in Fetal liver above 20weeks of gestation whereas it was up-regulated in fetal liver below 20weeks of gestation, primary liver cancers and liver metastasis. Contradictory to that OPN and CDKN2A were significantly up-regulated in primary liver cancer, liver metastasis; down-regulated in fetal liver above 20weeks of gestation but were not expressed during early embryo development (below 20weeks of gestation). Moreover, MMP9 and TP53 demonstrated a strong correlation with MUC16 whereas CDKN2A and OPN showed correlation with CXCL12/CXCR4 signifying that MUC16, CXCL12/CXCR4 might be involved in the complex process of cancer metastasis.

CONCLUSION

MMP9, OPN, TP53 and CDKN2A were the identified markers that were expressed in a similar pattern in early embryonic development and cancer development & invasion suggesting that these genes are activated during embryogenesis and might be re-expressed in cancer metastasis. Moreover, these genes govern a pathway that might be activated during cancer metastasis. Thus, targeting these molecules may provide better treatment for metastatic liver cancers.