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Diagnostic marker for lung cancer through microarray analysis

ABSTRACT: Dr. Kate Olson's Lab is interested to expand these analysis to comparisons between human normal lung tissue and human lung tumor tissue. In hopes that this will help in finding a diagnostic marker for lung cancer. Since there will be more variability between these samples, we would like to get preliminary data on ten normal lung tissue and ten lung tumor tissues from the same patient. We have already analyzed 4 cell lines and we saw gene expression variability related to how metastatic the cell lines were. This has been written and submitted for publication. We would like to expand these analysis to comparisons between human normal lung tissue and human lung tumor tissue. We hope that this will help in finding a diagnostic marker for lung cancer. Since there will be more variability between these samples, we would like to get preliminary data on ten normal lung tissue and ten lung tumor tissues from the same patient. The samples were dissected by a pathologist prior to extraction to maximize the amount of normal tissue included in the tumor samples. RNA samples from non tumor and tumor lung cancer patients were received by Core E. The RNA was amplified, labeled, and hybridized to the GLYCOv3 microarrays.

ORGANISM(S): Homo sapiens

SUBMITTER: Head Steven

PROVIDER: S-ECPF-GEOD-29927 | biostudies-other |

REPOSITORIES: biostudies-other

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Project description:BackgroundHepatocellular carcinoma (HCC) is a tumor with a high recurrence rate, poor prognosis, and rapid progression. Therefore, it is necessary to find a novel biomarker for HCC. Coiled-coil domain containing 25 (CCDC25) has been identified as a target molecule that mediates liver metastasis in colon cancer. However, the molecular mechanisms of CCDC25 in HCC are unknown. This study aimed to explore the role of CCDC25 in HCC.MethodsThe expression of CCDC25 in HCC was identified through The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Receiver operating characteristic curve (ROC) curves were drawn to evaluate the diagnostic value of CCDC25 for HCC. The effect of CCDC25 on the prognosis of HCC was analyzed by using the Kaplan-Meier plotter. Co-expressed genes and Gene Set Enrichment Analysis (GSEA) were used to explore the related functions and regulatory signaling pathways of CCDC25. Moreover, we employed the Tumor Immune Estimation Resource (TIMER) database and CIBERSORT algorithm to investigate the relationship between CCDC25 and the tumor immune microenvironment (TME) in HCC. Meanwhile, the effect of CCDC25 on the sensitivity of HCC patients to chemotherapy drugs was evaluated. Finally, we explored the prognostic methylation sites of CCDC25 using the MethSurv database.ResultsCCDC25 expression was low in HCC. Low CCDC25 expression was significantly associated with poor overall survival of HCC and may be comparable to the ability of AFP to diagnose HCC. Dysregulation of glucose metabolism, fatty acid metabolism, amino acid metabolism, ubiquitination modification, and apoptosis inhibition caused by CCDC25 downregulation may be the causes and results of HCC. In addition, CCDC25 was positively correlated with the infiltration level of various adaptive antitumor immune cells. The levels of immune cell infiltration and immune checkpoint expression were lower in the samples with high CCDC25 expression. What is more, we found that downregulated CCDC25 may increase the sensitivity or resistance of HCC patients to multiple drugs, including sorafenib. We also identified a methylation site for CCDC25, which may be responsible for poor prognosis and low CCDC25 expression in HCC patients. Finally, CCDC25 may be associated with HCC ferroptosis.ConclusionsCCDC25 may be a potential diagnostic and prognostic marker for HCC and is associated with immune infiltration and ferroptosis.

Project description:BackgroundDue to the high cost and low reproducibility of many microarray experiments, it is not surprising to find a limited number of patient samples in each study, and very few common identified marker genes among different studies involving patients with the same disease. Therefore, it is of great interest and challenge to merge data sets from multiple studies to increase the sample size, which may in turn increase the power of statistical inferences. In this study, we combined two lung cancer studies using microarray GeneChip, employed two gene shaving methods and a two-step survival test to identify genes with expression patterns that can distinguish diseased from normal samples, and to indicate patient survival, respectively.ResultsIn addition to common data transformation and normalization procedures, we applied a distribution transformation method to integrate the two data sets. Gene shaving (GS) methods based on Random Forests (RF) and Fisher's Linear Discrimination (FLD) were then applied separately to the joint data set for cancer gene selection. The two methods discovered 13 and 10 marker genes (5 in common), respectively, with expression patterns differentiating diseased from normal samples. Among these marker genes, 8 and 7 were found to be cancer-related in other published reports. Furthermore, based on these marker genes, the classifiers we built from one data set predicted the other data set with more than 98% accuracy. Using the univariate Cox proportional hazard regression model, the expression patterns of 36 genes were found to be significantly correlated with patient survival (p < 0.05). Twenty-six of these 36 genes were reported as survival-related genes from the literature, including 7 known tumor-suppressor genes and 9 oncogenes. Additional principal component regression analysis further reduced the gene list from 36 to 16.ConclusionThis study provided a valuable method of integrating microarray data sets with different origins, and new methods of selecting a minimum number of marker genes to aid in cancer diagnosis. After careful data integration, the classification method developed from one data set can be applied to the other with high prediction accuracy.

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Diagnostic marker for lung cancer through microarray analysis

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