Abstract
To precisely diagnose metastasis state is important for tailoring treatments for gastric cancer patients. However, the routinely employed radiological and pathologic tests for tumour metastasis have considerable high false negative rates, which may retard the identification of reproducible metastasis-related molecular biomarkers for gastric cancer. In this research, using three datasets, we firstly shwed that differentially expressed genes (DEGs) between metastatic tissue samples and non-metastatic tissue samples could hardly be reproducibly detected with a proper statistical control when the metastatic and non-metastatic samples were defined by TNM stage alone. Then, assuming that undetectable micrometastases are the prime cause for recurrence of early stage patients with curative resection, we reclassified all the “non-metastatic” samples as metastatic samples whenever the patients experienced tumour recurrence during follow-up after tumour resection. In this way, we were able to find distinct and reproducible DEGs between the reclassified metastatic and non-metastatic tissue samples and concordantly significant DNA methylation alterations distinguishing metastatic tissues and non-metastatic tissues of gastric cancer. Our analyses suggested that the follow-up recurrence information for patients should be employed in the research of tumour metastasis in order to decrease the confounding effects of false non-metastatic samples with undetected micrometastases.
Highlights
Because gene expression profiling has the advantage of exploring the tumour progression systematically based on the multiple gene disorders, many researches have exploited the high throughout data to study the transcriptional characteristics of metastasis and identify transcriptional biomarkers for metastasis[11,12,13]
With false discovery rate (FDR) < 20%, 124 differentially expressed genes (DEGs) were detected in the The Cancer Genome Atlas (TCGA) batch 220, of which only 3 and 15 DEGs were detected as DEGs in GSE15459 and GSE62254 and the concordance scores were as low as 0% and 33.3% (p = 0.94), respectively
The low concordance scores and small overlaps between DEGs identified from independent datasets indicated that differential gene expression signals were weak and poorly reproducible in the three datasets when the samples were grouped by the TNM stage only (Supplementary Table 2), possibly due to confounding factors such as false negatives and/or false positive samples
Summary
Because gene expression profiling has the advantage of exploring the tumour progression systematically based on the multiple gene disorders, many researches have exploited the high throughout data to study the transcriptional characteristics of metastasis and identify transcriptional biomarkers for metastasis[11,12,13]. The same irreproducibility problem may exist for the basic task of extracting differentially expressed genes (DEGs) between the metastasis and non-metastasis samples[17], which might make it unreliable to investigate the metastasis based on DEGs. In this research, using three datasets of gene expression profiles for gastric cancer, we firstly showed that DEGs between metastatic tissue samples and non-metastatic tissue samples could hardly be reproducibly detected with a proper statistical control when the metastasis and non-metastasis samples were defined by TNM stage alone. Because micrometastases not found by the routine pathology diagnosis could be the major cause for recurrence after curative surgery[18,19,20], we could hypothesize that the patients diagnosed as non-metastasis cases but subsequently suffered the recurrence should have developed micrometastases before the surgery According to this hypothesis, we reclassified all the “non-metastatic” samples of patients, defined according to TNM stage, as metastatic samples whenever the patients experienced tumour recurrence during follow-up after tumour resection. We were able to find distinct and reproducible DEGs and concordant DNA methylation alterations between the reclassified metastatic and non-metastatic tissue samples with a proper statistical control false discovery rate (FDR) of less than 20%
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