Lung Cancer Signature Biomarkers: tissue specific semantic similarity based clustering of Digital Differential Display (DDD) data

Abstract

BackgroundThe tissue-specific Unigene Sets derived from more than one million expressed sequence tags (ESTs) in the NCBI, GenBank database offers a platform for identifying significantly and differentially expressed tissue-specific genes by in-silico methods. Digital differential display (DDD) rapidly creates transcription profiles based on EST comparisons and numerically calculates, as a fraction of the pool of ESTs, the relative sequence abundance of known and novel genes. However, the process of identifying the most likely tissue for a specific disease in which to search for candidate genes from the pool of differentially expressed genes remains difficult. Therefore, we have used ‘Gene Ontology semantic similarity score’ to measure the GO similarity between gene products of lung tissue-specific candidate genes from control (normal) and disease (cancer) sets. This semantic similarity score matrix based on hierarchical clustering represents in the form of a dendrogram. The dendrogram cluster stability was assessed by multiple bootstrapping. Multiple bootstrapping also computes a p-value for each cluster and corrects the bias of the bootstrap probability.ResultsSubsequent hierarchical clustering by the multiple bootstrapping method (α = 0.95) identified seven clusters. The comparative, as well as subtractive, approach revealed a set of 38 biomarkers comprising four distinct lung cancer signature biomarker clusters (panel 1–4). Further gene enrichment analysis of the four panels revealed that each panel represents a set of lung cancer linked metastasis diagnostic biomarkers (panel 1), chemotherapy/drug resistance biomarkers (panel 2), hypoxia regulated biomarkers (panel 3) and lung extra cellular matrix biomarkers (panel 4).ConclusionsExpression analysis reveals that hypoxia induced lung cancer related biomarkers (panel 3), HIF and its modulating proteins (TGM2, CSNK1A1, CTNNA1, NAMPT/Visfatin, TNFRSF1A, ETS1, SRC-1, FN1, APLP2, DMBT1/SAG, AIB1 and AZIN1) are significantly down regulated. All down regulated genes in this panel were highly up regulated in most other types of cancers. These panels of proteins may represent signature biomarkers for lung cancer and will aid in lung cancer diagnosis and disease monitoring as well as in the prediction of responses to therapeutics.

AXIN2 Polymorphisms, the β-Catenin Destruction Complex Expression Profile and Breast Cancer Susceptibility

“…We observe down-regulation of CK1α and PP2A in no-ductal carcinoma patients, suggesting a different molecular signature in blood for each breast cancer histological type. Consistent with priors studies, CK1α showed decrease expression in lung carcinoma cells, indicating that it may be involved in carcinogenesis process (Xie et al, 2003;Srivastava et al, 2012) and PP2A was down-regulated in clear cell renal cell carcinoma and prostatic cancer. The authors found PP2A decreased expression associated with cancer risk (Westermarck and Hahn, 2008;Li et al, 2014).…”

Nicotinamide Phosphoribosyl Transferase (Nampt) Is a Target of MicroRNA-26b in Colorectal Cancer Cells

“…Studies have shown that Nampt is significantly increased in primary colorectal cancer [5], [6], [7] and other cancers [8], [9], [10], [11]. Thus, Nampt may be a good biomarker of malignant potential and stage progression [1], [8], [25], [26].…”

“…Thus, Nampt may be a good biomarker of malignant potential and stage progression [1], [8], [25], [26]. Nampt-mediated NAD biosynthesis plays a critical role in cell metabolism, survival functions, and stress resistance through sirtuins and other NAD-consuming regulators [1].…”

“…Nampt has drawn significant interest not only in the fields of metabolism and immune response, but also in the field of cancer. A number of cancers show increased expression of Nampt [5], [6], [7], [8], [9], [10], [11], and inhibitors of Nampt offer promising therapeutic applications in cancer [12], [13], [14], [15]. However, the mechanism through which Nampt becomes upregulated is unclear.…”

Towards the Prediction of Mutations in Genomic Sequences

“…Furthermore, those types of predictions were based on computational approaches not involving NGS, such as microarray analysis, qPCR, and others [6][7] [8] in various types of cancer such as colorectal [9], lung [10], prostate [11], breast cancer [12] and others.…”