The normal lung tissue specific genes were collected from TiSGeD (Tissue-specific gene database; http://bioinf.xmu.edu.cn/databases/TiSGeD/index.html). Human adult lung tissue related genes with tissue specificity measure score (SPM) ≥ 0.9 (represents high tissue specificity) were considered. The lung tissue specific “Mouse” and “developmental” genes were omitted.

DDD comparisons were made at various tissue stages to elucidate the selective differential expression levels of human lung tissue specific genes for normal (Case 1) and cancerous (Case 2) conditions. In Case 1, the Normal lung tissues (11 tissue libraries) were considered as a ‘Reference’ samples and the remaining normal human tissues (251 tissue libraries) were ‘Query’ samples. In Case 2, the Normal lung tissues (11 tissue libraries) were considered as ‘Reference’ samples and the cancerous human lung tissues (8 tissue libraries) were ‘Query’ samples. These comparisons were designed systematically so as to identify altered Gene expression of varying treatment conditions of ‘Reference’ and ‘Query’ samples. These pair wise comparisons resulted in a relative abundance of ESTs among the contrasting cDNA libraries of digitally ‘pooled’ contracts from Unigene Database.

Org.Hs.eg.db package in R-program was used for the computation of Semantic similarity score while the GO-based similarity score was computed based on the three orthogonal gene ontologies generated for Molecular Function (MF), Cellular Component (CC) and Biological process (BP). GOSemSim of R-program was used to calculate semantic similarity between the GO terms and the gene products. In this study, GO terms derived from human annotations were used for calculations. The estimation of between-term similarity was based on the Wang semantic similarity measure 12 . Aggregation of between-term similarities was done with the highest between-term similarity approach, which selectively aggregates maximum between-gene similarity values 9 . Given a pair of gene products, gi and gj, annotated to a set of GO terms, the GO-driven similarity, SIM (gi, gj), is calculated by aggregating the maximum interest similarity values as follows:

S i m g i , g j = ∑ 1 ≤ i ≤ m m a x S i m g i 1 g j + ∑ 1 ≤ j ≤ m m a x S i m g j 1 g i / m + n

where, two sets of GO terms gi = {gi1, gi2, …………., gim} & gj = {gj1, gj2, ……………, gjn} as query and reference sequence. Method max calculates the maximum semantic similarity score over given pairs of GO terms between these two sets, while average calculates the average semantic similarity score over a given pairs of GO terms. The hierarchical clustering of tissue specific, differentially expressed genes in relation with a normal lung tissue is shown in a Dendrogram. In the colour code of heat map, red represents a low semantic similarity below the median level, whereas, the green represents a high semantic similarity above the median level.

The clustering analysis was carried out by the program pvclust 15 . It is an add-on package for a statistical software R to perform the bootstrap analysis of clustering and also to assess the uncertainty in hierarchical cluster analysis. The package calculates the approximately unbiased (AU) and bootstrap probability (BP) p-values for each cluster. Stability of the clustering was accessed at 95% probability (α = 0.95).

In order to find the lung tissue specific differentially expressed genes, two Unigene pools (A and B) were constructed (See Additional file 1). For analysis, in the DDD1, we employed the UniGene pool (A) representing 39 human normal tissues excluding normal lung tissue and UniGene pool (B) representing 11 counterpart lung normal tissues were employed for analysis (Table 1). Similarly, in DDD2, UniGene pool (A) representing 8 human lung tumours and UniGene pool (B) representing 11 counterpart lung normal tissues were employed (Table 1). The fold change of normal lung (DDD1) and lung carcinoma candidate genes (DDD2) were calculated based on transcript frequency values. The candidate genes with an expression of at least 2-fold difference were taken into analysis. In DDD1, amongst the total of 519 differentially expressed genes 268 genes were up-regulated (≥2-fold) and 234 genes were down-regulated (≥2-fold). In DDD2, amongst the total of 203 differentially expressed candidate genes, 147 genes (≥2-fold) including 33 unknown were up-regulated (≥2-fold) and 55 genes were down-regulated (≥2-fold). Comparison of DDD1 with DDD2 has revealed that in total 76 genes from DDD1 were differentially expressed in DDD2 (See Additional file 2). From the literature survey, amongst the 76 genes, 18 of them were found to be commonly expressed in all types of cancerous conditions (See Additional file 3) 16 . Excluding these 18 from the 76, the remaining 58 genes were predicted as the lung tissue specific tumour genes (See Additional file 2). The molecular functions of these 58 genes were found to be involved in broad range of cellular functions with majority of the genes playing many different roles like structural, extracellular and intracellular functions. This subtractive approach eliminated most of the commonly expressing genes; for example, housekeeping genes. This approach has also helped to eliminate genes expressing in more than 10 cancerous conditions (See Additional file 4).

Differentially expressed normal lung tissue specific genes (DDD1) were identified from UniGene libraries representing 39 human normal tissues (251 libraries, 1903301 ESTs, S. No. 1–21, 23–40) and counterpart normal lung tissue (11 libraries, 125630 ESTs, S. No. 22 only). Differentially expressed lung tissue specific genes (DDD2) were identified from UniGene libraries representing lung cancer libraries (8 libraries, 22694 ESTs, S. No. 41 only) and counterpart normal lung tissue (11 libraries, 125630 ESTs, S. No. 22 only).

To identify lung tissue specific clusters from the 202 genes from DDD2 cancerous condition, firstly they were subjected to similarity clustering analysis using the 47 lung tissue specific genes from TiSGeD (See Additional file 5). Before the semantic similarity clustering analysis, the Unigene ID were converted into Entrez ID. During this process, the 202 genes of DDD2 reduced to 145 and the 47 lung tissue specific genes of TiSGeD were reduced to 28 due to gene duplication. Using GOSemSim package, the similarity correlation matrix was constructed between the 145 predicted lung specific differentially expressed cancer genes from DDD2 and 28 genes from TiSGeD. The differential expression levels of these clustered genes were depicted in the form of a Heat Map (Figure 1). The similarity correlation matrix produced seven gene clusters at 95% confidence level, using the pvclust program (Figure 2). The clusters 1–4 have 14 genes and the clusters 5, 6 and 7 have 36, 74 and 14 genes respectively.

In the ID conversions from Unigene to Entrez, the 58 lung tissue specific tumour genes were reduced to 38 genes (Table 2). These 38 genes were matched with the 7 clusters. This38 genes formed four panels with the corresponding cluster 4, 5, 6 and 7 respectively. The panels 1–4 contained 2, 9, 21 and 6 genes respectively. This leads to identification of the lung tissue specific clusters of the normal lung tissue specific genes differentially regulated in lung cancer condition.

Note:

We then analysed the functional significance of each panel as given below.

The cluster 4 had two-lung cancer related genes ubiquitin thiolesterase (UCHL1) and Lactotransferrin (LTF). In the normal lung (DDD1 data), UCHL1 was down-regulated and LTF was up-regulated (Table 2). This was reversed during the lung cancer condition where UCHL1 up-regulated and the LTF highly down-regulated (Table 2). These two proteins were found to be important in the cancer progression. UCH-L1 up-regulation promoted prostate cancer metastasis through epithelial-to-mesenchymal transition (EMT) induction and LTF expression decreased in lung prostate cancer progression 17 18 . Both of them were co-expressed in almost six different lung adenocarcinoma cell lines, as evident by mSigDB. This suggested that UCH-L1 and LTF could be novel diagnostic and therapeutic targets for lung cancer metastasis diagnostic markers.

The cluster 5 was playing the common functional role of immune response and complement activation. The down-regulated RPSA, RPL9, TMSB4X and TUBA1B in normal lung (DDD1) were significantly up-regulated in lung cancer (DDD2) (Table 1). The analysis resulted that all these up regulated genes played the role of tumour cell resistance to the anti-cancer agents. In gastric cancers, the up-regulation of RPSA/LRP contributed to drug resistance via hypoxia-inducible-factor dependent mechanism 19 . Similarly, there was a link between the TMSB4X and TUBA1B and the anti-cancer drug resistance to the drug Paclitaxel (PTX) observed in the cervical and breast/ovarian cancers respectively 20 21 .

In this cluster, NT5C2, API5, CPN, PRKAR1A and COPB1 were fully down-regulated in lung cancer (Table 1). The down regulation of NT5C3 altered the tumour cell sensitivity to cytidine based anti-cancer drugs 22 . The anti-apoptosis gene API5 down-regulation linked to increase in the survival and resistance cancer cells to chemotherapy 23 . To our knowledge, the major copper carrying protein CPN (ceruloplasmin) down regulation link to chemotherapy/drug resistance is not yet studied. But increased level of copper in lewis lung carcinoma cells were related with the development of multi drug resistance 24 . The PRKAR1A down-regulation also linked to multidrug-resistant (MDR) in colon carcinoma cells 25 . The COPB1 was an essential component for the coatomer formation 26 . These coatomers were involved in the drug trafficking pathways and endocytic drug delivery 27 . So, it was expected that the down-regulation of COPB1 might have a role in the chemotherapy which needs to be taken up and studied. We are surprised to find that all these results suggest that the cluster 5 functionally represents a panel of chemotherapy/drug resistance related lung cancer biomarkers.

In cluster 6, the upregulated FTL (65 fold in our study) and ALDOA (7 fold in our study) were regulated by hypoxia inducible factor (HIF) during lung cancer 28 29 30 31 . The COL1A1 (23 fold in our study) and GAPDH (11 fold in our study) were regulated by hypoxia 32 33 34 . IGKC (8 fold in our study) up-regulated in lung cancer patients but no literature data was available for its interaction either with HIF or hypoxia 35 . The HIF, TGM2, CSNK1A1, CSNK2A1, CTNNA1, NAMPT)/Visfatin, TNFRSF1A, ETS1 and SRC-1 were down-regulated and proposed as the biomarkers for lung cancer. We found all of them to be interacting with the HIF in cancerous condition 36 37 38 39 40 41 42 43 44 45 . The down- regulated FN1 and APLP2 showed hypoxia dependent differential regulation 46 47 48 . The DMBT1/SAG interacted HIF-1 was a kind of feedback loop in response to hypoxia. The hypoxia induced HIF-1 to transactivate SAG and the induced SAG then promoted HIF-1alpha ubiquitination and degradation 49 . The FBJ/c-Jun/AP-1 interacted with HIF during hypoxia that controlled the transcriptional regulation of the Cyr61 gene in retinal vascular endothelial cells 50 . The role of AIB1/SRC-3/NCoA during hypoxia condition were exhibited by controlling the expression levels of HIF induced erythropoietin (EPO) gene during hypoxia 42 .

However, in this cluster, the AZIN1 and TICAM2 were down-regulated and were lacking direct experimental evidence to support their regulation with HIF or hypoxia during cancer. The following literature analysis suggests their possible regulations either with HIF or hypoxia. The AZIN1 was an inhibitor for the antizyme and both were highly regulated in human cancers and antizyme induced HIF, during increased cellular redox potential 51 52 53 . The TICAM2 physically bridged toll like receptor-4 (TLR4) with TICAM1 and the TLR4 partially regulated by the HIF during adenocarcinoma 54 55 .

All these results suggest that the cluster 6 represents the panel of either HIF or Hypoxia related lung cancer biomarkers.

In the Cluster 7, there were seven lung biomarkers, mostly encoding for lung tissue specific extra cellular matrix proteins. The epigenetic analysis using Methycancer database (http://methycancer.genomics.org.cn) revealed that amongst the seven, KIAA1324, NET1, NTN3, RPL10 and TFPI2 were epigenetically regulated through DNA methylation. In the remaining two, SFTPA1 was epigenetically regulated 56 57 58 . However, the experimental evidence was lacking the epigenetic related data for CRISP3. However, the Gene card database analysis of CRISP3 showed that the CRISP3 orthlogous gene C-type lectin domain family 18 member A (CLEC18A) epigenetically regulated through DNA methylation (http://www.genecards.org). All these results show that the cluster 7 represented the panel of epigenetically regulated lung cancer specific extra cellular matrix biomarkers.