Epidermal Growth Factor
Epidermal growth factor (EGF) is a vital protein that plays a fundamental role in regulating cellular processes such as growth, proliferation, and differentiation. As a member of the growth factor family, it is critical for the development, repair, and maintenance of various tissues throughout the body, particularly the epidermis. [1]
The biological actions of EGF are mediated through its binding to the epidermal growth factor receptor (EGFR), a transmembrane protein found on the surface of many cell types. This binding event triggers a complex cascade of intracellular signaling pathways, including the activation of tyrosine kinases, which ultimately leads to changes in gene expression that promote cell division, survival, and migration. This intricate signaling network is essential for physiological processes like wound healing and tissue regeneration. [1]
Clinically, the EGF signaling pathway is highly significant due to its involvement in numerous pathological conditions. Dysregulation of EGFRactivity, such as overexpression or activating mutations, is a common feature in a wide array of human cancers, including non-small cell lung cancer, colorectal cancer, and breast cancer. Consequently,EGFRhas become a prime target for the development of targeted cancer therapies, such as monoclonal antibodies and small-molecule inhibitors, which aim to block its activity and inhibit tumor growth. Conversely, impairedEGF signaling can contribute to conditions characterized by poor tissue repair or developmental defects. [2]
The profound impact of EGF and EGFRresearch extends beyond basic science into significant social importance. The understanding of this pathway has revolutionized cancer treatment, leading to the development of personalized medicine approaches that have improved outcomes for many cancer patients. Ongoing research continues to explore the potential ofEGF in regenerative medicine, aiming to harness its proliferative and healing properties for tissue engineering and repair strategies, thereby holding promise for future medical innovations. [2]
Limitations
Section titled “Limitations”Generalizability and Phenotypic Characterization
Section titled “Generalizability and Phenotypic Characterization”The findings from genetic association studies of epidermal growth factor are primarily derived from cohorts of European descent, specifically Caucasians, which limits the generalizability of these results to other ethnic and racial groups.[3] Many studies explicitly excluded individuals who did not cluster with Caucasian populations, emphasizing this narrow focus. [4] Additionally, some cohorts largely comprised middle-aged to elderly individuals, potentially introducing a survival bias due to DNA collection at later examination cycles, and thus may not accurately represent younger populations. [5] Methodological approaches, such as performing only sex-pooled analyses to manage multiple testing burdens, might also obscure sex-specific genetic associations that could exist for certain phenotypes. [3] Furthermore, the practice of averaging phenotype measurements across multiple observations or from monozygotic twins necessitates careful scaling of estimated effect sizes and the proportion of variance explained to accurately reflect population-level effects. [6]
Statistical Power and Replication Challenges
Section titled “Statistical Power and Replication Challenges”Many investigations face limitations related to sample size, leading to insufficient statistical power to detect genetic variants with small or modest effects.[3] This often results in associations not achieving genome-wide significance, rendering them hypothesis-generating and requiring independent replication. [7] The inability to replicate previously reported associations is a common challenge, potentially stemming from false positive findings in initial studies, true differences in cohort characteristics that modify gene-phenotype relationships, or inadequate power in replication efforts. [5] Moreover, replication can be complicated by the distinction between SNP-level and gene-region-level associations; different studies might identify distinct SNPs within the same gene region that are in strong linkage disequilibrium with an unknown causal variant but not with each other, or reflect multiple causal variants within the same gene. [8]
Genomic Coverage and Confounding Influences
Section titled “Genomic Coverage and Confounding Influences”The scope of genome-wide association studies is inherently limited by the coverage of the SNP arrays used, meaning that studies utilizing a subset of all known SNPs may miss genes or specific variants due to incomplete genomic representation. [3] Such partial coverage can also hinder comprehensive study of candidate genes, as the available data may not be sufficient to fully elucidate their genetic architecture. [3] A significant knowledge gap remains in the investigation of gene-environment interactions, as environmental factors are known to modulate genetic influences on phenotypes, yet these interactions are often not systematically explored. [7]The effects of some identified loci might also be mediated through covariates included in multivariable adjustments, highlighting the complexity of disentangling direct genetic effects from those influenced by other biological or lifestyle factors.[3]
Variants
Section titled “Variants”EGF(Epidermal Growth Factor) is a critical signaling protein that promotes cell growth, proliferation, differentiation, and survival across various tissues. It exerts its effects by binding to the Epidermal Growth Factor Receptor (EGFR), initiating a complex cascade of intracellular signals vital for processes like wound healing, tissue repair, and development. [9] Variants within the EGF gene itself, such as rs182994407 , rs11568972 , and rs2237045 , can potentially influence the production, stability, or overall activity of the EGF protein. Such genetic variations may alter the strength or duration of EGF signaling, thereby impacting cellular responses to growth
Genes involved in platelet function and immune signaling also play roles in broader physiological processes that can intersect with EGF-mediated pathways. GP6(Glycoprotein VI) is a primary collagen receptor found on platelets, crucial for initiating platelet activation and aggregation during hemostasis and in response to vascular injury. Thers1654425 variant, potentially located within or near GP6 or its regulatory antisense transcript GP6-AS1, may influence the expression or function of this receptor, thereby affecting platelet reactivity. [3] Similarly, PLCG2 (Phospholipase C Gamma 2) is an enzyme central to various intracellular signaling cascades, including those activated by growth factor receptors and immune receptors. The rs12445050 variant in PLCG2 could alter its enzymatic activity or its interactions with other signaling molecules, impacting diverse cellular functions such as immune responses and inflammation, which are often interconnected with EGF’s roles in tissue repair and cellular proliferation. [10]
Cellular transporters and receptors are fundamental for maintaining cellular homeostasis and mediating interactions with the extracellular environment. CD36 (Cluster of Differentiation 36) is a versatile scavenger receptor involved in the uptake of lipids, apoptotic cells, and other ligands, contributing to lipid metabolism, angiogenesis, and immune responses. The rs6961069 variant in CD36 might affect its expression levels or ligand binding capabilities, potentially influencing lipid homeostasis and inflammatory processes that can modulate growth factor signaling in tissues. [11] SLC22A5(Solute Carrier Family 22 Member 5) is a carnitine transporter vital for fatty acid metabolism, whileSLC24A3(Solute Carrier Family 24 Member 3) is a potassium-dependent sodium-calcium exchanger involved in calcium regulation within cells. Variants such asrs2631360 in SLC22A5 and rs6081565 in SLC24A3 could impact the efficiency of nutrient transport or ion balance, respectively, indirectly influencing cellular energy status and signaling pathways crucial for EGF-mediated cell growth and differentiation. [5] Furthermore, SLC35D2(Solute Carrier Family 35 Member D2) functions as a nucleotide sugar transporter, and itsrs11794772 variant may affect glycosylation, a process essential for the proper folding and function of many cell surface receptors, including EGFR.
Other variants influence gene expression and immune modulation, which can indirectly affect EGF pathways. JMJD1C (Jumonji Domain Containing 1C) is a histone demethylase that epigenetically regulates gene expression, playing roles in cell differentiation, development, and metabolic processes. The rs7080386 variant in JMJD1C could alter its enzymatic activity or expression, potentially leading to broad changes in gene transcription that impact cellular responses to growth factors like EGF. [12] ZFPM2 (Zinc Finger Protein, FOG Family Member 2) is a transcriptional corepressor critical for organ development, and its antisense transcript, ZFPM2-AS1, can modulate its expression. The rs6993770 variant associated with ZFPM2-AS1 or ZFPM2 might affect the precise control of gene expression, influencing developmental pathways and potentially interacting with EGF signaling in tissue morphogenesis and repair. [13] Finally, BANK1 (B-cell scaffold protein with ankyrin repeats 1) is a B-cell specific adaptor protein involved in B-cell receptor signaling and immune regulation. The rs28625045 variant in BANK1could impact immune cell activation and cytokine production, which are integral to inflammatory responses and tissue remodeling, processes closely linked to EGF’s roles in wound healing and cellular growth.
There is no information about epidermal growth factor in the provided context.
Key Variants
Section titled “Key Variants”| RS ID | Gene | Related Traits |
|---|---|---|
| rs1654425 | GP6-AS1, GP6 | blood protein amount platelet volume level of acrosin-binding protein in blood level of amyloid-beta precursor protein in blood C-C motif chemokine 7 level |
| rs7080386 | JMJD1C | platelet volume liver fibrosis measurement FOXO1/IRAK4 protein level ratio in blood CDKN2D/MANF protein level ratio in blood TMSB10/ZBTB16 protein level ratio in blood |
| rs6993770 | ZFPM2-AS1, ZFPM2 | platelet count platelet crit platelet component distribution width vascular endothelial growth factor A amount interleukin 12 measurement |
| rs182994407 rs11568972 rs2237045 | EGF | epidermal growth factor measurement |
| rs2631360 | SLC22A5 | amount of early activation antigen CD69 (human) in blood carbonic anhydrase 13 measurement level of transforming acidic coiled-coil-containing protein 3 in blood level of FYN-binding protein 1 in blood level of glutamine amidotransferase-like class 1 domain-containing protein 3, mitochondrial in blood |
| rs28625045 | BANK1 | trem-like transcript 1 protein measurement metalloproteinase inhibitor 3 measurement level of alpha-(1,6)-fucosyltransferase in blood level of heparanase in blood laminin subunit alpha-4 measurement |
| rs6081565 | SLC24A3 | amount of vascular endothelial growth factor C (human) in blood dickkopf‐related protein 1 measurement epidermal growth factor measurement CD40 ligand measurement |
| rs11794772 | SLC35D2 | C-C motif chemokine 13 level level of aldo-keto reductase family 1 member B1 in blood tumor necrosis factor ligand superfamily member 12 amount amount of vascular endothelial growth factor C (human) in blood level of forkhead box protein O3 in blood |
| rs6961069 | CD36 | platelet count C-C motif chemokine 13 level level of amyloid-beta precursor protein in blood amount of arylsulfatase B (human) in blood C-C motif chemokine 5 measurement |
| rs12445050 | PLCG2 | platelet component distribution width platelet volume platelet count level of amyloid-beta precursor protein in blood C-C motif chemokine 13 level |
References
Section titled “References”[1] Alberts, Bruce, et al. “Molecular Biology of the Cell.” Garland Science, 2014.
[2] Hanahan, Douglas, and Robert A. Weinberg. “Hallmarks of Cancer: The Next Generation.”Cell, vol. 144, no. 5, 2011, pp. 646–674.
[3] Yang Q et al. Genome-wide association and linkage analyses of hemostatic factors and hematological phenotypes in the Framingham Heart Study. BMC Med Genet. 2007;8 Suppl 1:S12. PMID: 17903294.
[4] Pare, G. et al. “Novel association of ABO histo-blood group antigen with soluble ICAM-1: results of a genome-wide association study of 6,578 women.” PLoS Genetics, vol. 4, no. 7, 2008, p. e1000118.
[5] Benjamin EJ. Genome-wide association with select biomarker traits in the Framingham Heart Study. BMC Med Genet. 2007;8 Suppl 1:S11. PMID: 17903293.
[6] Benyamin, B. et al. “Variants in TF and HFE explain approximately 40% of genetic variation in serum-transferrin levels.”American Journal of Human Genetics, vol. 84, no. 1, 2009, pp. 60–65.
[7] Vasan, R. S. et al. “Genome-wide association of echocardiographic dimensions, brachial artery endothelial function and treadmill exercise responses in the Framingham Heart Study.”BMC Medical Genetics, vol. 8, no. S1, 2007, p. S2.
[8] Sabatti, C. et al. “Genome-wide association analysis of metabolic traits in a birth cohort from a founder population.”Nature Genetics, vol. 41, no. 1, 2009, pp. 35–46.
[9] Melzer D et al. A genome-wide association study identifies protein quantitative trait loci (pQTLs). PLoS Genet. 2008 May 9;4(5):e1000072. PMID: 18464913.
[10] Wilk JB et al. Framingham Heart Study genome-wide association: results for pulmonary function measures. BMC Med Genet. 2007;8 Suppl 1:S8. PMID: 17903307.
[11] O’Donnell CJ et al. Genome-wide association study for subclinical atherosclerosis in major arterial territories in the NHLBI’s Framingham Heart Study. BMC Med Genet. 2007;8 Suppl 1:S7. PMID: 17903303.
[12] Kathiresan S et al. Common variants at 30 loci contribute to polygenic dyslipidemia. Nat Genet. 2008 Dec;40(12):1500-5. PMID: 19060906.
[13] Saxena R et al. Genome-wide association analysis identifies loci for type 2 diabetes and triglyceride levels. Science. 2007 Apr 27;316(5826):1001-5. PMID: 17463246.