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Therefore, we built two networks, one from resistant datasets and the other from parental datasets.
We searched GEO Datasets using key term "EGFR Resistance" and, limiting our choices to human cell lines, selected studies that directly compare transcriptional profiles of matched EGFR inhibitor-sensitive vs. resistant datasets (Table 1).
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In agreement with this interpretation, the resistant dataset is also enriched in strains with a "Small size" morphology.
Specifically, among the genes with a mitochondrial function, ATP10, CYC7, AAC1, NDI1, and YSP2 were found in the resistant dataset, and ATP2 and POR1 in the sensitive dataset [ 8, 18, 24– 26].
Also present in our resistant dataset were the genes coding for histone Hta1p, yeast neutral sphingomyelinase Isc1p, protease Kex1p, yeast metacaspase Yca1p, ribosome-associated protein Stm1p, rapamycin-sensitive kinase Tor1p, and mitochondrial fission protein Mdv1p, all previously shown to increase apoptotic cell death [ 9, 27– 327.
The "Metabolic process" term was the second most significantly enriched in the resistant strain dataset, showing the importance of metabolic control over cell death regulation.
The "Mitochondrial function" class had the highest number of genes in both the resistant and sensitive datasets, reflecting the broadly recognized importance of mitochondrial control for yeast apoptosis.
Similar ratios were calculated for each gene for the sensitive relative to resistant training (NSCLC) dataset.
Sixty-three geneshoweded significantly higher expression levels in 'Carignan' when compared to the Central Asian accessions, including the lysine histidine transporter 2 (VIT_01s0010g02640) previously identified in this study to have a premature stop codon in all PM-partially resistant accessions (Supplementary Dataset S3).
However, apoptosis emerged as a distinct process only enriched in dosage resistant genes in both datasets (Additional file 5: Table S4, Table S5).
Gene annotations were used to characterize the transcriptome in yam and also perform a differential gene expression analysis between the resistant and susceptible EST datasets.
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