Identifying General Tumor and Specific Lung Cancer Biomarkers by Transcriptomic Analysis

Abstract

The bioinformatic pipeline previously developed in our research laboratory is used to identify potential general and specific deregulated tumor genes and transcription factors related to the establishment and progression of tumoral diseases, now comparing lung cancer with other two types of cancer. Twenty microarray datasets were selected and analyzed separately to identify hub differentiated expressed genes and compared to identify all the deregulated genes and transcription factors in common between the three types of cancer and those unique to lung cancer. The winning DEGs analysis allowed to identify an important number of TFs deregulated in the majority of microarray datasets, which can become key biomarkers of general tumors and specific to lung cancer. A coexpression network was constructed for every dataset with all deregulated genes associated with lung cancer, according to DAVID’s tool enrichment analysis, and transcription factors capable of regulating them, according to oPOSSUM´s tool. Several genes and transcription factors are coexpressed in the networks, suggesting that they could be related to the establishment or progression of the tumoral pathology in any tissue and specifically in the lung. The comparison of the coexpression networks of lung cancer and other types of cancer allowed the identification of common connectivity patterns with deregulated genes and transcription factors correlated to important tumoral processes and signaling pathways that have not been studied yet to experimentally validate their role in lung cancer. The Kaplan–Meier estimator determined the association of thirteen deregulated top winning transcription factors with the survival of lung cancer patients. The coregulatory analysis identified two top winning transcription factors networks related to the regulatory control of gene expression in lung and breast cancer. Our transcriptomic analysis suggests that cancer has an important coregulatory network of transcription factors related to the acquisition of the hallmarks of cancer. Moreover, lung cancer has a group of genes and transcription factors unique to pulmonary tissue that are coexpressed during tumorigenesis and must be studied experimentally to fully understand their role in the pathogenesis within its very complex transcriptomic scenario. Therefore, the downstream bioinformatic analysis developed was able to identify a coregulatory metafirm of cancer in general and specific to lung cancer taking into account the great heterogeneity of the tumoral process at cellular and population levels.

Identification of the Transcriptional Regulatory Role of RUNX2 by Network Analysis in Lung Cancer Cells

“…Important regulatory mechanisms for the establishment and progression of lung cancer have been associated with the 26 TFs identified in our previous coexpression network analysis [ 12 , 19 , 31 ] and with 6 TFs identified in the analysis of NSCLC lung cancer regulatory networks conducted in this study ( Figure 1 ). The CoRegNet library generated a lung cancer coregulatory network with the gene expression levels of the GSE19804 dataset to assess the importance of the six transcriptional regulators found in the NSCLC-GRN in lung cancer establishment and progression and to identify a transcriptional regulator capable of participating as a coregulator of the tumor process via functional association with the most critical TFs identified in the gene coexpression network analysis [ 12 ].…”

“…The coregulatory network analyzed the cooperative function of the TFs of the NSCLC regulatory network ( Figure 2 ), along with the transcription factors identified in our previous coexpression network studies ( Table 1 ) [ 12 , 31 ]. The first coregulatory network suggests a cooperative function between ETS1 and KAT6B with seventeen transcription factors of our previous coexpression analysis.…”

“…The first coregulatory network suggests a cooperative function between ETS1 and KAT6B with seventeen transcription factors of our previous coexpression analysis. The second suggests a cooperative function between RUNX2 and three transcription factors (BRCA1, FOXM1, and RUNX1) important for the specific regulation of lung cancer establishment and progression [ 12 , 31 ], along with three transcription factors (E2F3, FHL2, and TWIST1) of the NSCLC-GRN ( Figure 1 ). Two of the six transcriptional regulators (RUNX2 and TWIST1) are deregulated in other lung diseases (LD) (i.e., idiopathic pulmonary fibrosis (IPF) and pulmonary arterial hypertension (PAH)).…”

“…RUNX2, but not TWIST1, can function as a coregulator of BRCA1, a TF associated with the regulation of common connectivity patterns between LC-PAHs [ 12 ]. RUNX2 also acts as a coregulator of FOXM1, the TF that regulates lung cancer CCPs, and RUNX1, a TF that is coexpressed in the networks of unique lung cancer genes that are not deregulated in other lung diseases (LCI) or other types of cancer (LCII) [ 31 ]. RUNX1 is a TF identified in LCI and LCII networks, which were selected to focus the analysis on lung cancer [ 12 , 31 ].…”

“…RUNX2 also acts as a coregulator of FOXM1, the TF that regulates lung cancer CCPs, and RUNX1, a TF that is coexpressed in the networks of unique lung cancer genes that are not deregulated in other lung diseases (LCI) or other types of cancer (LCII) [ 31 ]. RUNX1 is a TF identified in LCI and LCII networks, which were selected to focus the analysis on lung cancer [ 12 , 31 ]. Similarly, RUNX2 is a coregulator of three TFs (E2F3, FHL2, and TWIST1) that also appear in the NSCLC lung cancer regulatory network ( Figure 2 ).…”