A novel medication decision gene signature predicts response to individualized therapy and prognosis outcomes in hepatocellular carcinoma patients
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA881935 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR21621859_1.fastq.gz | Fastqsanger.gz | |||
| SRR21621859_2.fastq.gz | Fastqsanger.gz | |||
| SRR21621860_1.fastq.gz | Fastqsanger.gz | |||
| SRR21621860_2.fastq.gz | Fastqsanger.gz | |||
| SRR21621861_1.fastq.gz | Fastqsanger.gz |
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Project description:Molecular targeted therapy has shown potential in hepatocellular carcinoma (HCC) patients, and immunotherapy applications are developing rapidly. However, clinical guidance for making individualized therapy decisions for HCC patients remains lacking. MDH (Medication Decision in HCC) gene signatures comprising 70 genes were screened using transcriptomic data from multikinase inhibitor (TKI)-resistant HCC cells and HCC patient-derived xenograft model (PDX) models. Four MDH subtypes with distinct biological and clinical characteristics were defined by unsupervised cluster analysis of HCC data from The Cancer Genome Atlas (TCGA) database. To facilitate individualized and reasonable clinical guidance for each HCC patient, we constructed the MDH score. Comprehensive analysis suggested high MDH scores were associated with TKI resistance, a high proportion of stromal cell infiltration and poor survival outcomes. We recommend concomitant stromal activity intervention and immunotherapy for this type of HCC. Moreover, low MDH scores indicate TKI sensitivity, and a combination of targeted and immunotherapy is recommended. The nomogram constructed by iteration least absolute shrinkage and selection operator (LASSO) Cox regression analysis successfully predicted 3- or 5-year survival outcomes and mortality risks of HCC patients. In conclusion, TKI resistance model-based MDH gene signatures provide novel insight into potential mechanisms of drug resistance and heterogeneity in HCC. Integrative analysis plus a simplified decision model may aid personalized treatment and prognostic assessment among HCC patients.
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Project description:Background - Hepatocellular carcinomas (HCCs) are heterogeneous tumors with respect to etiology, cell of origin and biology. The course of the disease is unpredictable and is in part dependent on the tumor microenvironment. One of the microenvironmental factors is hypoxia, which is known to promote aggressiveness in other malignant tumors. We hypothesized that certain regions in HCC exist with chronic hypoxia and a characteristic gene expression pattern. Moreover, during the development of HCC there is an important contribution of this chronic hypoxia on prognosis via this gene expression. Until now, most research has been performed in acute hypoxic models (< 24 hours). Methods – Human hepatoblastoma cells HepG2 were cultured in either normoxic (20% O2) or hypoxic (2% O2) conditions for 72 hrs, the time it takes to adapt to chronic hypoxia. After 3 days the cells were harvested and analyzed by microarray technology. The highly significant differentially expressed genes were selected and used to assess the clinical value of our in vitro chronic hypoxia gene signature in four published patient studies. Three of these independent microarray studies on HCC patients were used as training sets to determine a minimal prognostic gene set and one study was used for validation. Gene expression analysis and correlation with clinical outcome was assessed with the bioinfomatic method of Goeman et al (). Results – In the HepG2 cells, 3592 genes were differentially expressed in cells cultured at 2% oxygen for 72 hrs. Out of these, 265 showed a high significant change (2-fold change and p=0.0001). The level of gene expression after 72 hrs was different from the acute hypoxic response (during the first 24 hours) and represented chronicity. Using computational methods we identified 7 out of the 265 highly significant genes that showed correlation with prognosis in all three different training sets and this was independently validated in a 4th dataset. With our approach we could include the largest number of HCC patients in one single study. Conclusion – We identified a 7-gene signature, which is associated with chronic hypoxia and predicts prognosis in patients with HCC. In the future this signature could be used as a diagnostic tool. In addition, chronic hypoxia gene expression information can be used in the search for new therapeutic targets. Two conditions were compared and each sample has a biological replicate. Samples are hybridized in dye-swap, resulting in 4 hybridizations.
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Project description:Background - Hepatocellular carcinomas (HCCs) are heterogeneous tumors with respect to etiology, cell of origin and biology. The course of the disease is unpredictable and is in part dependent on the tumor microenvironment. One of the microenvironmental factors is hypoxia, which is known to promote aggressiveness in other malignant tumors. We hypothesized that certain regions in HCC exist with chronic hypoxia and a characteristic gene expression pattern. Moreover, during the development of HCC there is an important contribution of this chronic hypoxia on prognosis via this gene expression. Until now, most research has been performed in acute hypoxic models (< 24 hours). Methods – Human hepatoblastoma cells HepG2 were cultured in either normoxic (20% O2) or hypoxic (2% O2) conditions for 72 hrs, the time it takes to adapt to chronic hypoxia. After 3 days the cells were harvested and analyzed by microarray technology. The highly significant differentially expressed genes were selected and used to assess the clinical value of our in vitro chronic hypoxia gene signature in four published patient studies. Three of these independent microarray studies on HCC patients were used as training sets to determine a minimal prognostic gene set and one study was used for validation. Gene expression analysis and correlation with clinical outcome was assessed with the bioinfomatic method of Goeman et al (). Results – In the HepG2 cells, 3592 genes were differentially expressed in cells cultured at 2% oxygen for 72 hrs. Out of these, 265 showed a high significant change (2-fold change and p=0.0001). The level of gene expression after 72 hrs was different from the acute hypoxic response (during the first 24 hours) and represented chronicity. Using computational methods we identified 7 out of the 265 highly significant genes that showed correlation with prognosis in all three different training sets and this was independently validated in a 4th dataset. With our approach we could include the largest number of HCC patients in one single study. Conclusion – We identified a 7-gene signature, which is associated with chronic hypoxia and predicts prognosis in patients with HCC. In the future this signature could be used as a diagnostic tool. In addition, chronic hypoxia gene expression information can be used in the search for new therapeutic targets.
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