|
 
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| ORIGINAL ARTICLE |
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| Year : 2023 | Volume
: 7
| Issue : 1 | Page : 111-117 |
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Erb-b2 receptor tyrosine kinase 2 interaction with growth factor receptor bound protein 7 acts as a molecular switch to activate non-small cell lung cancer: An in silico prediction
Anita Chauhan, Seema Kalra
Department of Biochemistry, School of Sciences, Indira Gandhi National Open University, New Delhi, India
| Date of Submission | 12-Nov-2022 |
| Date of Decision | 18-Feb-2023 |
| Date of Acceptance | 28-Feb-2023 |
| Date of Web Publication | 14-Mar-2023 |
Correspondence Address:
Seema Kalra
School of Sciences, Indira Gandhi National Open University, Maidan Garhi, New Delhi - 110 068
India
 Source of Support: None, Conflict of Interest: None
DOI: 10.4103/bbrj.bbrj_26_23
Background: The progression and metastasis of non small cell lung cancer (NSCLC) are considered a very complex process as it involves aberrations of multiple genes and cellular pathways. Genes which are differentially expressed in NSCLC have multi interactions with other genes, which can promote the carcinogenesis. To improve diagnosis and treatment of NSCLC, it is vitally important to study these interactions and understand their roles in the molecular mechanism of NSCLC. As the need to find more potential targets for NSCLC is very paramount we have predicted very important interactions for NSCLC. Methods: In our study, some NSCLC specific genes were differentially identified from microarray datasets and text mining of SCLC and NSCLC abstracts. The expression of these genes has been seen in 8 different cancer types and NSCLC stages. A network of genes specific to NSCLC has been identified and interactions of these NSCLC specific genes have been studied. Results: We found two network modules joined through erb b2 receptor tyrosine kinase 2 (ERBB2) in NSCLC i.e. network of genes growth factor receptor bound protein 7 (GRB7), StAR related lipid transfer domain containing 3, post GPI attachment to proteins 3 and migration and invasion enhancer 1 ERBB2 interacting with GRB7 and PAK1 using GIANT. In normal lungs, ERBB2 is strongly interacting with PAK1 and in NSCLC it has strong interaction with GRB7. Conclusion: We have found that ERBB2 and GRB7 interaction is a transforming connection between normal lung and NSCLC.
Keywords: Function enrichment analysis, molecular modeling, non small cell lung carcinoma, text mining, tissue specificity
How to cite this article:
Chauhan A, Kalra S. Erb-b2 receptor tyrosine kinase 2 interaction with growth factor receptor bound protein 7 acts as a molecular switch to activate non-small cell lung cancer: An in silico prediction. Biomed Biotechnol Res J 2023;7:111-7 |
How to cite this URL:
Chauhan A, Kalra S. Erb-b2 receptor tyrosine kinase 2 interaction with growth factor receptor bound protein 7 acts as a molecular switch to activate non-small cell lung cancer: An in silico prediction. Biomed Biotechnol Res J [serial online] 2023 [cited 2023 Jun 10];7:111-7. Available from: https://www.bmbtrj.org/text.asp?2023/7/1/111/371690 |
| Introduction | |  |
Lung cancer is one of the most common cancers and it ranks fourth among the various types of cancers after breast, cervical, and oral cavity cancer.[1] Approximately 1.6 million people die of lung cancer each year globally. Non-small cell lung cancer (NSCLC) is the most common lung cancer subtype, accounting for 80%–85% of lung cancers. This is the most unsymptomatic cancer and is difficult to detect in earlier stages so more than 50% of patients have stage IV disease at the time of diagnosis.[2]
Research in the molecular basis of NSCLC has revealed many targets out of which erb-b2 receptor tyrosine kinase 2 (ERBB2), a receptor tyrosine kinase that drives oncogenesis when overexpressed.[3] ERBB2 was shown to be overexpressed in 13% to 20% of NSCLC.[4] ERBB2 has a structure with an extracellular ligand binding domain, a transmembrane domain, and an intracellular domain interacting with a multitude of signaling molecules, which can be activated in diverse manners, such as by phosphorylation, gene expression, and mutations.[5],[6],[7] ERBB2 has also been used as therapeutic target for numerous cancers, including lung cancer, colorectal cancer, breast cancer, and glioblastoma.[8],[9]
The progression and metastasis of NSCLC are considered a very complex process as it involves aberrations of multiple genes and cellular pathways.[10] Even after remarkable successes in identifying different targets for NSCLC, the precise molecular mechanism is still far from being fully understood. Genes which are differentially expressed in NSCLC have multi-interactions with other genes, which can promote the carcinogenesis.[11],[12] To improve the diagnosis and treatment of NSCLC, it is vitally important to study these interactions and understand their roles in the molecular mechanism of NSCLC.
As the need to find more potential targets for NSCLC is very paramount we have predicted very important interactions for NSCLC. In our study, we have differentially identified some NSCLC-specific genes from microarray datasets and text mining of SCLC and NSCLC abstracts. The expression of these genes has been seen in 8 different cancer types and NSCLC stages. A network of genes specific to NSCLC has been identified and interactions of these NSCLC-specific genes have been studied. The flowchart of the methodology is shown in [Figure 1].
| Methods | | |
Text-mining
PMIDs were downloaded from PubMed for NSCLC using keywords “NSCLC targets,” “NSCLC specific genes” and “gene targets for NSCLC.” Similarly, abstracts for SCLC were downloaded using keywords “SCLC targets,” “SCLC specific genes” and “gene targets for SCLC.” Literature-based text mining tool Genie.[13] was used to retrieve two lists; one NSCLC-specific genes and another SCLC-specific genes. The common genes and duplicates from both the lists were removed to identify differentially expressed genes (DEGs) specific to NSCLC.
Dataset collection
Gene expression dataset (GSE10245) was downloaded from GEO database.[14] 181 DEGs were identified using MeV 4.9 viewer.[15] 336 NSCLC genes from text mining were authenticated by analyzing DEGs from the microarray dataset to draw experimental evidence. Eighty-five genes which were common and verified as NSCLC-specific were selected for further study.
Lung-specific gene network selection
Gene interaction network of the selected genes was studied in 9 different tissue types, i.e., lung, brain, blood, liver, kidney, blood plasma, heart, stomach, pancreas, and intestine. A network of 5 genes specific to the lung was identified and selected for further study. Expression of these genes was seen in different stages of NSCLC for which processed files of two NSCLC datasets (GSE5008) and (GSE21933) were downloaded from ArrayExpress database.[16]
Tissue and cancer-specific co-operation biological network (TCSBN)
Coexpression of these 5 genes, ERBB2, PAK1 (p21 (RAC1) activated kinase 1), KRT7 (keratin 7), ING2 (inhibitor of growth family member 2), and GJA1 (gap junction protein alpha 1) was analyzed using TCSBN database.[17] Expression of these genes in 9 different cancers including NSCLC and respective 8 normal tissues was analyzed. Significant co-expression of these genes was only seen in breast and lung so stage- and tissue-based gene clustering was done.
Clustering
The clustering of these genes was performed using ClustVis.[18] based on expression in tissues and expression in different stages of NSCLC. In tissue-based clustering, the breast and lung formed the same cluster. Further, the network of genes was also observed in normal as well as cancer tissues of the lung and breast. Using these analyses from Tissue and Cancer-Specific Biological Network database (TSCBN), we could identify a network of genes which was specific to the lung only using.
Function enrichment analysis
The network of genes was further analyzed for functional enrichment using STRING.[19] The cascading network of genes was enriched with the regulation of cell growth and cell differentiation. The identified network has a prominent role in Erb signaling pathway and MEPK signaling pathways.
Motif identification
Using STRING database a network of ERBB2, PAK1, and KRT7 genes were identified as a motif [Figure 2]. In this network, ERBB2 was also interacting with four more genes growth factor receptor bound protein 7 (GRB7), StAR-related lipid transfer domain containing 3 (STARD3), post-GPI attachment to proteins 3 (PGAP3), and migration and invasion enhancer 1 (MIEN1). These four genes are co-expressed with ERBB2 in breast and prostate cancers.[20]
Interaction and structure prediction
Interestingly, STRING analysis showed ERBB2 interacting with PAK1 in normal lung cells and GRB7 in NSCLC. This interesting finding was validated from literature.[21] Regions of ERBB2 binding to GRB7 and PAK1 were predicted and an interacting structure for PAK1 and ERBB2 was modeled using SWISS MODEL tool[22] of SPDBV.[23] The energy of PAK1 and the model was minimized to repair distorted geometries by moving atoms to release internal constraints. Hex was used for docking. It is an interactive molecular graphics program for calculating and displaying feasible docking modes of pairs of protein and DNA molecules.[24] Hex can also calculate Protein-Ligand Docking, assuming the ligand is rigid, and it can superpose pairs of molecules using only knowledge of their three-dimensional (3D) shapes. The parameters used in HEX for the docking process were; Correlation type – Shape only, FFT Mode –3D fast lite, Grid Dimension –0.6, Receptorrange –180, Ligand Range –180, Twist range –360, Distance Range –40.
| Results | | |
Text mining of PubMed abstracts
2000 NSCLC and 2007 SCLC abstracts were downloaded from PubMed. Retrieved list of PMIDs of all abstracts was given as input to literature-based text mining tool Genie. Two lists of each 1605 NSCLC and 1540 SCLC-specific genes were retrieved. Common genes in both the lists were removed to identify 336 DEGs which were specific to NSCLC. The identified gene list was authenticated from an experimental microarray dataset from GEO database. One hundred and eighty-one DEGs were retrieved from dataset. We shortlisted 85 genes which were common in both lists [Figure 3]. The gene interaction network of the selected genes was studied in 9 different tissue types, i.e., lung, brain, blood, liver, kidney, blood plasma, heart, stomach, pancreas, and intestine. A network of 5 genes specific to the lung was identified [Figure 4]. | Figure 3: (a) Volcano plot of DEGs. (b) Venn diagram showing 85 NSCLC genes common between 336 NSCLC genes from text mining and 181 NSCLC genes from the Microarray dataset. NSCLC: Non-small cell lung cancer, DEGs: differentially expressed genes
Click here to view |
Data processing
We downloaded NSCLC datasets [GSE50081] and [GSE21933] from ArrayExpress database and from the processed file we retrieved the expression values of PAK1, GJA1, KRT7, ERBB2, ING2 in different stages of NSCLC including 1A, 1B, 2A, 2B, 3A, 3B, and IV. Up and down expression of genes from healthy to different stages of cancer was seen. All 5 genes are highly upregulated in advanced stages of NSCLC. To identify a network of these genes as a target, we went for the tissue and cancer-specific coexpression of these genes using TCSBN. Mean co-expression of all 5 genes in 8 cancer types (skin, breast, prostate, brain, liver, colon, ovary, and lung) and their respective normal tissues were studied [Figure 5]. Significant coexpression of genes was only seen in breast and lung and their respective cancers. | Figure 5: Mean expression of genes in cancers and their respective normal tissues using TCSBN
Click here to view |
Clustering of genes
Clustering of these genes using ClustVis was done based on expression in tissues and expression in different stages of NSCLC [Figure 6]c. In stage-specific clustering, we could see that ING2 and ERBB2 are forming one cluster whereas PAK1 and KRT7 are forming separate cluster [Figure 6]b. In tissue-based clustering breast and lung were forming a cluster [Figure 6]a. The network of genes in tissues of the lung and breast was further studied using TSCBN to be more specific. We could identify a network of genes which was specific to the lung only [Figure 7]. | Figure 6: (a) In tissue-based clustering breast and lung were forming a cluster. (b) In stage-specific clustering we could see that ING2 and ERBB2 are forming one cluster whereas PAK1, KRT7, and GJA2 are forming cascading clusters. (c) Expression of genes in different stages of NSCLC. NSCLC: Non-small cell lung cancer, ERBB2: Erb-b2 receptor tyrosine kinase 2
Click here to view |
Gene interaction and function enrichment
The interactions and enriched functions of PAK1, GJA1, KRT7, ERBB2, and ING2 were further analyzed using STRING. Four genes PAK1, GJA1, KRT7, and ERBB2 were forming a cascading network and ING2 was not interacting with this network. So, we moved on with four cascading network genes [Figure 2]. The cascading network of genes is enriched with the regulation of cell growth and cell differentiation. Erb signaling pathway and MEPK signaling pathways are also enriched in this network. Hence, we found this network of genes important for NSCLC.
Identification of non-small cell lung cancer and lung-specific motif
Using STRING database a network of ERBB2, PAK1, and KRT7 genes was identified as a motif. In this network ERBB2 was also interacting with four more genes GRB7, STARD3, PGAP3, and MIEN1 [Figure 8]a and [Figure 8]b. These four genes are co-expressed with ERBB2 in breast and prostate cancers. | Figure 8: NSCLC and normal lung-specific motif using STRING. (a) ERBB2, PAK1, KRT7 genes form a motif in normal lung. (b) ERBB2 interaction with five more genes GRB7, STARD3, PGAP3, and MIEN1 forms a separate module in NSCLC. NSCLC: Non-small cell lung cancer. ERBB2: Erb-b2 receptor tyrosine kinase 2, PGAP3: post-GPI attachment to proteins 3, MIENI: Migration and invasion enhancer 1, STARD3: StAR-related lipid transfer domain containing 3
Click here to view |
Interaction prediction
Motif GRB7, ERBB2, and PAK1 were selected for further study as both the interactions are strong and evident. ERBB2 is acting as a common switch between normal and cancer states. GRB7 is binding to ERBB2 at region 11351141 through SH2 domain and we could find a structure in PDB for the same. To predict the interaction between PAK1 and ERBB2 we downloaded a PDB structure for SH3 domain (561) of BetaPix in rats (Rho guanine nucleotide exchange factor 7 in human) interacting with the consensus motif of PAK, i.e. PXXXPR. Pairwise alignment of the binding residues of BetaPix was done with ERBB2 and a 43 residue regions “LPDLSVFQNLQVIRGRILHNGAYSLTLQGLG ISWLGLRSLREL” were showing similarity with 20 residues and 11 residues were identical. We then modeled a 3D structure for this region of ERBB2 using SWISS-MODEL [Figure 9]. Using SPDBV energy of PAK1 and model was minimized to repair distorted geometries by moving atoms to release internal constraints [Figure 10]. The binding of PAK1 to ERBB2 was studied to know the confirmation changes made to the structure to accommodate the GRB7 [Figure 11]. | Figure 9: Pairwise alignment of BetaPix with ERBB2. ERBB2: Erb-b2 receptor tyrosine kinase 2
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 | Figure 10: Energy minimized structures of PAK1 and ERBB2 model. ERBB2: Erb-b2 receptor tyrosine kinase 2
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| Discussion | | |
NSCLC being the most asymptomatic of all cancers has gained a lot of scientific attention.[25] In our study, we have identified a motif for NSCLC. In cancers, individual gene targets are mutated and cells can develop an alternative pathway to bypass those targets.[26],[27] Hence, finding a network of targets can be of immense use in cancer treatment.
We have done text mining of 2000 NSCLC and 2007 SCLC abstracts, retrieved the genes, and selected the DEGs of NSCLC. As to lower the risk or error and stringent word matching we verified the list of 336 text-mined NSCLC genes with181 NSCLC-specific genes retrieved from the microarray dataset. There were 85 genes which were common in both the lists. The gene interaction network of the selected genes was studies in 9 different tissue types, i.e., lung, brain, blood, liver, kidney, blood plasma, heart, stomach, pancreas, and intestine. A network of 5 genes, ERBB2, PAK1, GJA1, KRT7, and ING2 specific to the lung was identified.
Studying the expression of these genes in healthy tissue and NSCLC stages (1a, 1b, 2a, 2b, 3a, 3b, 4), we found that ERBB2, PAK1, GJA1, KRT7 are overexpressed in NSCLC. ERBB2, PAK1, and KRT7 are highly upregulated in the advanced stages of NSCLC. Previous studies also show that overexpression PAK1, KRT7, and ERBB2 are used for prognosis in different cancers.[28],[29],[30]
To identify a network of these genes as a target, we went for the tissue and cancer-specific coexpression of these genes using TCSBN. The mean coexpression of all 5 genes in 8 cancer types and their respective normal tissues were studied. Coexpression of these genes in NSCLC and Breast cancer was significantly higher than in other cancer types. Hence, we performed the tissue-based and stage-based clustering. In tissue-based clustering, breast, and lung formed a cluster and in stage-based clustering ERBB2 and ING1 form a cluster in stages 3a, 1b, 2a, 2b, 1a, and 3b, whereas PAK1 and KRT7 formed a different cluster. We then identified a cascading network of genes ERBB2, PAK1, GJA1, and KRT7 which was specific in normal lungs not in breast.
We found a network of genes GRB7, STARD3, PGAP3, and MIEN1 in NSCLC using GIANT. ERBB2 was interacting with GRB7 and PAK1. Two network modules are joined through ERBB2. In normal lungs, ERBB2 is strongly interacting with PAK1 and in NSCLC it has strong interaction with GRB7. Previous studies on prostate cancer have also shown that GRB7, MIEN1, STARD3, and PGAP3 are coexpressed as an amplicon.[31]
We have found that ERBB2 is a transforming connection between normal lung and NSCLC. We further wanted to see if there is any structural basis for interaction between PAK1, ERBB2, and GRB7 which is responsible for the transition of a normal state to cancer state.
We found that PAK1 and GRB7 are binding to different regions of ERBB2. GRB7 through its SH2 domain binds to 1135–1141 region on ERBB2, for which we could find a structure in PDB.[32] No structural studies on PAK1 interacting with ERBB2 is done earlier. PAK family has 6 members PAK1-6 and they interact through a consensus motif PXXXPR where arginine residue is required for its high affinity binding. We retrieved the structure showing the interaction of this region with SH3 domain of BetaPix which is Rho guanine nucleotide exchange factor7 (in human) and a signaling protein. Region 561 of BetaPix interacts with PAK1 through PXXXPR motif.[33]
Pairwise alignment of SH3 domain of BetaPix with ERBB2 was performed. A 43-residue-long region on ERBB2 “LPDLSVFQNLQVIRGRILHNGAYSLTLQGLGISWLG LRSLREL” show similarity with BetaPix SH3 domain. A 3D structure was modeled in SwissProt for this region. It formed a loop structure with a tryptophan having a deep groove for binding of any protein. PAK1 bind to ERBB2 in this region. PAK1 is a threonine/serine kinase which helps in phosphorylation. It binds to the ERBB2 through arginine residue and alter the ERBB2 pathway which enhances the binding of GRB7 to ERBB2 in cancer state. Grb7 interacts with ERBB2, participates in ERBB2 signaling, and promotes cell survival and cell migration. ERBB2 exerts a repressive control on Grb7 via the PI3K-Akt pathway.[34] ERBB2 has been a proved target in many cancers.[35],[36],[37],[38] ERBB2 acts as a regulator of transition from normal to cancer state in NSCLC.
| Conclusion | | |
Our study predicted an interaction which is a transforming connection between normal lung and NSCLC.
Limitations of study
The results obtained are theoretical predictions. For more understanding advanced research to confirm the binding of GRB7 to ERBB2 is needed.
Financial support and sponsorship
Chauhan is supported by the research fellowship from Indira Gandhi National Open University during the conduct of study. No disclosures were reported by the other authors.
Conflicts of interest
There are no conflicts of interest.
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[Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5], [Figure 6], [Figure 7], [Figure 8], [Figure 9], [Figure 10], [Figure 11]
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