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Gene expression profiling of tumor-adjacent histologically normal lung tissues from lung SCC patients

ABSTRACT: Tumor-adjacent noncancerous tissues often exhibited abnormalities on molecular levels, which is described as field effect of cancerization. Accumulated evidence demonstrated that filed effect may also play important role in cancer progression. In the present study, we found that the gene expression profile in noncancerous lung tissues adjacent to lung squamous cell carcinoma (SCC) was significantly associated with regional lymph node status of patients. Significance Analysis of Microarrays (SAM) showed that 121 genes were significantly associated with lymph node metastasis (delta=0.75, FDR=0.069). Interestingly, all of the significant genes were up-regulated in the lymph node positive samples. For these genes, the most significant biological GO terms were extracellular structure organization, cell adhesion, regulation of cell motion/migration, and vessel development, etc, which were also involved in EMT process supported by another experiment in vitro. Tumor-adjacent histologically normal lung tissues were collected from 60 primary lung SCC patients, of whom 34 (56.7%) suffered regional lymph node metastasis. Gene expression profiling analysis of these samples was performed using Agilent 4x44K human whole genome gene expression microarray (G4112F).

ORGANISM(S): Homo sapiens

SUBMITTER: Cao Bangrong

PROVIDER: S-ECPF-GEOD-40588 | biostudies-other |

REPOSITORIES: biostudies-other

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Project description:BackgroundAdenocarcinoma (ADC) and squamous cell carcinoma (SCC) are the most prevalent histological types among lung cancers. Distinguishing between these subtypes is critically important because they have different implications for prognosis and treatment. Normally, histopathological analyses are used to distinguish between the two, where the tissue samples are collected based on small endoscopic samples or needle aspirations. However, the lack of cell architecture in these small tissue samples hampers the process of distinguishing between the two subtypes. Molecular profiling can also be used to discriminate between the two lung cancer subtypes, on condition that the biopsy is composed of at least 50 % of tumor cells. However, for some cases, the tissue composition of a biopsy might be a mix of tumor and tumor-adjacent histologically normal tissue (TAHN). When this happens, a new biopsy is required, with associated cost, risks and discomfort to the patient. To avoid this problem, we hypothesize that a computational method can distinguish between lung cancer subtypes given tumor and TAHN tissue.MethodsUsing publicly available datasets for gene expression and DNA methylation, we applied four classification tasks, depending on the possible combinations of tumor and TAHN tissue. First, we used a feature selector (ReliefF/Limma) to select relevant variables, which were then used to build a simple naïve Bayes classification model. Then, we evaluated the classification performance of our models by measuring the area under the receiver operating characteristic curve (AUC). Finally, we analyzed the relevance of the selected genes using hierarchical clustering and IPA® software for gene functional analysis.ResultsAll Bayesian models achieved high classification performance (AUC > 0.94), which were confirmed by hierarchical cluster analysis. From the genes selected, 25 (93 %) were found to be related to cancer (19 were associated with ADC or SCC), confirming the biological relevance of our method.ConclusionsThe results from this study confirm that computational methods using tumor and TAHN tissue can serve as a prognostic tool for lung cancer subtype classification. Our study complements results from other studies where TAHN tissue has been used as prognostic tool for prostate cancer. The clinical implications of this finding could greatly benefit lung cancer patients.

Project description:BackgroundXenobiotic-metabolizing enzymes (XME) play a critical role in the activation and detoxification of several carcinogens. However, the role of XMEs in colorectal carcinogenesis is unclear.MethodsWe investigated the expression of XMEs in human colorectal tissues among patients with stage I-IV colorectal cancer (n = 71) from the ColoCare Study. Transcriptomic profiling using paired colorectal tumor and adjacent normal mucosa tissues of XMEs (GSTM1, GSTA1, UGT1A8, UGT1A10, CYP3A4, CYP2C9, GSTP1, and CYP2W1) by RNA microarray was compared using Wilcoxon rank-sum tests. We assessed associations between clinicopathologic, dietary, and lifestyle factors and XME expression with linear regression models.ResultsGSTM1, GSTA1, UGT1A8, UGT1A10, and CYP3A4 were all statistically significantly downregulated in colorectal tumor relative to normal mucosa tissues (all P ≤ 0.03). Women had significantly higher expression of GSTM1 in normal tissues compared with men (β = 0.37, P = 0.02). By tumor site, CYP2C9 expression was lower in normal mucosa among patients with rectal cancer versus colon cancer cases (β = -0.21, P = 0.0005). Smokers demonstrated higher CYP2C9 expression levels in normal mucosa (β = 0.17, P = 0.02) when compared with nonsmokers. Individuals who used NSAIDs had higher GSTP1 tumor expression compared with non-NSAID users (β = 0.17, P = 0.03). Higher consumption of cooked vegetables (>1×/week) was associated with higher CYP3A4 expression in colorectal tumor tissues (β = 0.14, P = 0.007).ConclusionsXMEs have lower expression in colorectal tumor relative to normal mucosa tissues and may modify colorectal carcinogenesis via associations with clinicopathologic, lifestyle, and dietary factors.ImpactBetter understanding into the role of drug-metabolizing enzymes in colorectal cancer may reveal biological differences that contribute to cancer development, as well as treatment response, leading to clinical implications in colorectal cancer prevention and management.

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Gene expression profiling of tumor-adjacent histologically normal lung tissues from lung SCC patients

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