Commentary: In silico identification of potential key regulatory factors in smoking-induced lung cancer

Salem A. El-aarag1, Mahmoud ElHefnawi2,3*

1Central Administration of Pharmaceutical Affaires (CAPA), Ministry of health and population, Egypt
2Biomedical informatics and chemoinformatics group, Informatics and systems department, National Research Center, Egypt
3Center of informatics, Nile university, Egypt

Due to the complexity and heterogeneity of cancer, it is inevitable to investigate carcinogenesis from the systems perspective1,2. This was the driver of our study3. In the next sections, we will highlight some aspects regarding to the methodology of the study and the most important findings.

In our study3, we concentrated only on the drivers of lung cancer among smokers, only taking into consideration smoking as the sole factor for lung cancer development. Smoking is a major contributor to lung cancer development4. Spira et al. (2007) made an unique experiment for studying smoking-induced lung cancer. We found that Wang and Chen (2011) analyzed only Spira’s dataset, integrated microarray gene expression profiles and information on protein-protein interactions (PPIs) and developed a network-based biomarker which established a set of 40 biomarkers with potentially important roles in lung carcinogenesis2.

Our approach included network-based and enrichment analysis of differentially expressed genes (DEGs) between normal and cancerous lung. We identified differentially expressed genes (DEGs) between normal and cancerous lung using two software (GEO2R and Python script analysis tools) with different style of outputs. The former produced single list ordered by significance, whereas the last one produced two lists (up- and downregulated genes). As Enrichment analysis is sensitive to input list size, DEGs were divided into 9 different lists according to their length and source. We concentrated on the enriched items with higher overlap among these lists so that our conclusion could be more reliable5,6.

While using atBioNet (network analysis tool), we found that using the stringent options produced good repeatability provided that using large DEG lists (n => 500). We used stringent options to secure the efficiency. We set atBioNet to add only nodes which directly connected to at least two input nodes using (K2 Human Subset Database) which is a smaller and more robust database so that the added nodes represent the most accurate hidden information7.

For enrichment analysis (Enrichr), we didn’t use the standard Fisher’s exact test as usual, but we applied the z-score method which outperformed it8.

Both approaches showed that MAPK signaling is the most significant pathway related to lung cancer in smokers. Both approaches identified that MAPK, Toll-like receptor signaling pathway, and renal cell carcinoma signaling pathways as being important in smoking-induced lung cancer (Fig. 1)9-10. Targeting MET which has been associated with both sporadic and inherited forms of human papillary renal carcinomas may be therapeutic target for treatment of a gefitinib/erlotinib-resistant lung tumor cell line with acquired MET amplification11,12. Melanoma, renal cell carcinoma, and glioma have all been found to be potentially related to lung cancer. Pathway such as dentatorubropallidoluysian atrophy was predicted here for the first time as being significant pathways in smoking-induced lung cancer. There was a strong coorelation between this pathway and breast cancer prognosis13. In addition, various intracellular signaling pathways and metabolic and other cellular processes were found to be closely related to lung cancer.


Figure 1

Taken together, these new findings may contribute to the development of ficolin-2 as a novel immunotherapeutic agent that can prevent several important diseases including cancers and infectious diseases.

We found that cyclic AMP-responsive element-binding protein 1(CREB1), nuclear ubiquitous casein and cyclin-dependent kinase substrate 1A, P1 (NUCKS1), Homeobox protein Hox-B4 (HOXB4), and N-myc proto-oncogene protein (MYCN) were the most potential significant transcription factors in lung cancer among smokers (Fig. 2)14-23. Cone-rod homeobox protein (CRX), GA binding protein (GABP), and transcription factor 3 (TCF3) have not been previously implicated in smoking-induced lung cancer. CRX and TCF3 have been detected in other cancers24-26. GABP enables cells to escape apoptosis27.


Figure 2

Mitogen-activated protein kinase 1 (MAPK1), insulin-like growth factor 1 receptor (IGFIR), and ribosomal protein S6 kinase alpha-1 (RPS6KA1) were found to be highly related to smoking-induced lung cancer (Fig. 3)28-35. We predicted 5'-AMP-activated protein kinase catalytic subunit alpha-1 (PRKAA1) as a new putative kinase in lung cancer. It can potentially serve as a therapeutic target in chronic myelomonocytic leukemia36.


Figure 3

We identified Eight proteins as potential hubs in lung cancer-associated signaling—i.e., Methylphosphate capping enzyme, cyclin-dependent kinase 1, protein kinase C alpha, COP9 signalosome subunit 5, glycogen synthase kinase 3B, breast cancer 1, E1A-binding protein P300, and peptidyl-prolyl cis-trans isomerase NIMA-interacting 1 (Fig. 4)2,37-48.


Figure 4

The approach used in this study found well-known key factors which previously reported and validated as drug targets for lung cancer treatment and new potential factors which we claimed. Further study is needed to validate our new potential key regulatory fators in lung cancer among smokers.

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Article Info

Article Notes

  • Published on: December 21, 2017


  • Methodology

  • Protein-protein interactions
  • Carcinoma


Dr. Mahmoud ElHefnawi,
Biomedical informatics and chemoinformatics group, Informatics and systems department, National Research Center, Egypt