{"id":67290,"date":"2025-09-30T11:18:54","date_gmt":"2025-09-30T11:18:54","guid":{"rendered":"https:\/\/biomedpharmajournal.org\/?p=67290"},"modified":"2025-10-03T18:21:40","modified_gmt":"2025-10-03T18:21:40","slug":"functional-analysis-of-rhou-and-padi6-genes-in-basal-cell-carcinoma-insights-from-genome-wide-association-studies","status":"publish","type":"post","link":"https:\/\/biomedpharmajournal.org\/staging\/vol18no3\/functional-analysis-of-rhou-and-padi6-genes-in-basal-cell-carcinoma-insights-from-genome-wide-association-studies\/","title":{"rendered":"Functional Analysis of RHOU and PADI6 Genes in Basal Cell Carcinoma: Insights from Genome-Wide Association Studies"},"content":{"rendered":"

Introduction<\/strong><\/p>\n

Basal cell carcinoma (BCC) remains the most prevalent form of skin cancer worldwide, accounting for approximately 80% of all non-melanoma skin cancers.1<\/sup> The incidence of BCC continues to rise globally, creating a substantial burden on healthcare systems.2<\/sup> While environmental factors, particularly ultraviolet radiation exposure, represent the primary risk factor for BCC development, growing evidence suggests a significant genetic component in disease susceptibility and progression.3<\/sup> Recent genome-wide association studies (GWAS) have identified several genetic loci associated with BCC, yet the functional implications of these genetic variations remain incompletely understood.4<\/sup><\/p>\n

RHOU is an atypical Rho GTPase involved in regulating cytoskeletal dynamics, cell polarity, adhesion, and migration. RHOU influences keratinocyte proliferation and differentiation, processes critical to maintaining epidermal homeostasis. In BCC, where aberrant epithelial proliferation and local tissue invasion are hallmark features, dysregulation of RHOU may promote tumor growth, invasiveness, and changes in tumor architecture. Evidence from other cancers suggests RHOU plays roles in activating signaling pathways that could similarly drive basal cell carcinoma progression.<\/p>\n

PADI6 belongs to a family of enzymes that convert arginine residues into citrulline, a process known as citrullination. While PADI6 is primarily known for its role in early embryogenesis and oocyte development, emerging data suggest that PADI enzymes contribute to epigenetic regulation and inflammatory responses, both of which are relevant to skin carcinogenesis. In BCC, altered PADI6 expression could influence chromatin structure, gene expression, or immune evasion, potentially contributing to tumor initiation or progression. Its association through genome-wide studies (GWAS) points to a possible, yet unexplored, functional role in BCC susceptibility.<\/p>\n

The pathogenesis of BCC involves complex molecular mechanisms, including dysregulation of the Hedgehog signaling pathway through mutations in PTCH1 and SMO genes.5<\/sup> However, the contributions of other signaling networks and cellular processes to BCC development are still being elucidated. Emerging evidence suggests that alterations in GTPase signaling, cytoskeletal organization, and epigenetic regulation may play crucial roles in BCC pathogenesis.6,7<\/sup> Understanding these molecular mechanisms is essential for developing targeted therapies and improving clinical outcomes for BCC patients.<\/p>\n

Recent GWAS studies have identified novel genetic associations with BCC susceptibility, including variations in RHOU and PADI6 genes.8<\/sup> RHOU, a member of the Rho family of GTPases, functions as a molecular switch that regulates various cellular processes, including cytoskeletal dynamics, cell migration, and vesicle trafficking.9<\/sup> The Rho GTPase family has been implicated in cancer development and progression through their effects on cell morphology, polarity, and invasion.10<\/sup> PADI6, encoding protein-arginine deiminase type-6, has been primarily studied in the context of early embryonic development and epigenetic regulation.11<\/sup> Its potential role in cancer pathogenesis, particularly in BCC, remains largely unexplored.<\/p>\n

Interpreting GWAS findings in diseases like basal cell carcinoma (BCC) is challenging due to many variants being in non-coding regions with unclear functions.12<\/sup> To address this, the study uses integrated computational approaches\u2014such as pathway enrichment, protein-protein interaction (PPI) networks, gene ontology, and miRNA target prediction\u2014to uncover biological insights from GWAS data.13,14<\/sup><\/p>\n

Prior studies show that BCC-associated genes are involved in pigmentation, immune response, and DNA repair.15,16<\/sup> However, the functions of newly identified genes like RHOU and PADI6 remain unexplored. The current study focuses on these genes, using comprehensive bioinformatics analyses to understand their roles in BCC development, molecular regulation, and therapeutic potential.17,18,19<\/sup><\/p>\n

Key aspects examined include:<\/p>\n

Functional pathways and gene ontology relevance. 20<\/sup><\/p>\n

Regulatory roles of miRNAs<\/p>\n

Protein interaction networks highlighting key hubs in BCC pathogenesis. 21,22<\/sup><\/p>\n

The study ultimately identifies novel pathogenic mechanisms, potential biomarkers, and therapeutic targets in BCC, contributing to a deeper understanding and future clinical applications. 23 <\/sup>Our findings provide novel insights into the pathogenesis of BCC and identify potential therapeutic targets and biomarkers for future investigation.<\/p>\n

Objectives<\/strong><\/p>\n

To explore the functional roles of GWAS-identified genes RHOU<\/em> and PADI6<\/em> in basal cell carcinoma using in-depth bioinformatic approaches.<\/p>\n

To uncover key biological pathways, molecular functions, and cellular components linked to RHOU<\/em> and PADI6<\/em> that may drive BCC pathogenesis.<\/p>\n

To analyze the regulatory networks associated with RHOU<\/em> and PADI6<\/em>, including microRNA interactions and protein-protein interactions, to reveal novel insights into BCC development.<\/p>\n

Materials and Methods<\/strong><\/p>\n

Data Acquisition and Preprocessing<\/strong><\/p>\n

This study utilized gene lists derived from previous genome-wide association studies (GWAS) on basal cell carcinoma.24<\/sup> The primary focus was on two genes identified through these studies: RHOU and PADI6. We extracted comprehensive functional annotations for these genes using multiple bioinformatic databases and analysis platforms. The data collection process involved systematic querying of publicly available genomic and proteomic databases.<\/p>\n

Gene Ontology Enrichment Analysis<\/strong><\/p>\n

To understand the biological roles of RHOU and PADI6, we performed Gene Ontology (GO) enrichment analysis using the 2023 GO database. The analysis encompassed three main categories: biological processes, cellular components, and molecular functions. Statistical significance was assessed using Fisher’s exact test with a p-value threshold of 0.05. For each significant GO term, we calculated the overlap (number of genes in our set associated with the term), p-value, adjusted p-value (controlling for multiple testing using the Benjamini-Hochberg procedure), odds ratio, and combined score. The combined score was computed as log(p) \u00d7 odds ratio, providing a composite measure of statistical significance and effect size.<\/p>\n

Metabolite Association Analysis<\/strong><\/p>\n

To identify potential metabolic pathways involving RHOU and PADI6, we queried the Human Metabolome Database (HMDB).25<\/sup> This analysis aimed to reveal associations between our genes of interest and specific metabolites, potentially indicating metabolic processes relevant to BCC pathogenesis. Statistical significance was determined using the same methodology as the GO enrichment analysis, with significance thresholds set at p < 0.05.<\/p>\n

MicroRNA Target Analysis<\/strong><\/p>\n

We investigated the regulatory relationships between microRNAs and our target genes using two complementary databases: miRTarBase 2017 26<\/sup> and TargetScan microRNA 2017.27<\/sup> miRTarBase contains experimentally validated microRNA-target interactions, while TargetScan provides computational predictions of microRNA binding sites. This dual approach allowed us to identify both established and potential novel regulatory relationships. Statistical significance was assessed using Fisher’s exact test with appropriate multiple testing corrections.<\/p>\n

Protein-Protein Interaction Analysis<\/strong><\/p>\n

To map the interactome of RHOU and PADI6, we utilized protein-protein interaction (PPI) databases. This analysis identified hub proteins that interact with our genes of interest, potentially revealing functional complexes and signaling networks relevant to BCC pathogenesis. Significance was determined based on the probability of observing the given number of interactions by chance, with p < 0.05 considered statistically significant.<\/p>\n

Pathway Enrichment Analysis<\/strong><\/p>\n

Reactome Pathways 2024 database 28<\/sup> was queried to identify biological pathways significantly associated with RHOU and PADI6. This analysis provided insights into the higher-order biological processes and signaling cascades potentially dysregulated in BCC. Significance was assessed using Fisher’s exact test with Benjamini-Hochberg correction for multiple testing.<\/p>\n

Gene Expression Analysis<\/strong><\/p>\n

To investigate the expression patterns of RHOU and PADI6 in different physiological and pathological contexts, we analyzed RNA sequencing data from the Gene Expression Omnibus (GEO) database. Specifically, we examined automatic GEO signatures for human up-regulated and down-regulated genes in various experimental conditions and disease states. This analysis helped contextualize the potential roles of our target genes beyond the specific context of BCC.<\/p>\n

Statistical Analysis<\/strong><\/p>\n

All statistical analyses were performed using standard bioinformatic methodologies. For enrichment analyses, Fisher’s exact test was employed to determine the significance of associations, with p-values adjusted for multiple testing using the Benjamini-Hochberg procedure. The significance threshold was set at adjusted p < 0.05 for all analyses. Odds ratios were calculated to quantify the strength of associations, and combined scores (incorporating both statistical significance and effect size) were computed to prioritize findings.<\/p>\n

Visualization and Interpretation<\/strong><\/p>\n

Results from the various analyses were compiled into structured tables, categorized by analysis type. For each significant association, we recorded the relevant term, statistical metrics (overlap, p-value, adjusted p-value, odds ratio, combined score), and the specific genes involved. These comprehensive tables facilitated the interpretation of results and the identification of convergent themes across different analyses. The findings were then synthesized into a cohesive narrative describing the potential functional roles of RHOU and PADI6 in BCC pathogenesis, highlighting the most significant and biologically plausible mechanisms.<\/p>\n

Results<\/strong><\/p>\n

GO Biological Process 2023<\/strong><\/p>\n

Analysis of Gene Ontology biological processes (Table 1) revealed significant enrichment of RHOU in several GTPase-related signaling pathways. Most notably, RHOU showed strong association with Cdc42 protein signal transduction (p=0.0011, adjusted p=0.0143) with an odds ratio of 1427.85. RHOU was also significantly enriched in positive regulation of protein targeting to mitochondrion (p=0.0044, adjusted p=0.0175) and establishment of protein localization to mitochondrion (p=0.0049, adjusted p=0.0175). Other significant associations included Rho protein signal transduction (p=0.0077, adjusted p=0.0186), cytoskeleton organization (p=0.0165, adjusted p=0.0281), and endocytosis (p=0.0280, adjusted p=0.0374).<\/p>\n

Table 1: GO Biological Process 2023<\/strong><\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Term<\/strong><\/td>\nOverlap<\/strong><\/td>\nP-value<\/strong><\/td>\nAdjusted P-value<\/strong><\/td>\nOld P-value<\/strong><\/td>\nOld Adjusted P-value<\/strong><\/td>\nOdds Ratio<\/strong><\/td>\nCombined Score<\/strong><\/td>\nGenes<\/strong><\/td>\n<\/tr>\n
Cdc42 Protein Signal Transduction (GO:0032488)<\/td>\n1\/8<\/td>\n0.0011995
\n541091196<\/td>\n		0.0143946
\n493094355<\/td>\n		0<\/td>\n0<\/td>\n1427.8571
\n42857143<\/td>\n		9603.4892
\n3469958<\/td>\n		RHOU<\/td>\n<\/tr>\n
Positive Regulation Of Protein Targeting To Mitochondrion (GO:1903955)<\/td>\n1\/30<\/td>\n0.0044934
\n147342301<\/td>\n		0.017516451
\n1383681<\/td>\n		0<\/td>\n0<\/td>\n344.275862
\n0689655<\/td>\n		1860.8600
\n40913568<\/td>\n		RHOU<\/td>\n<\/tr>\n
Positive Regulation Of Establishment Of Protein Localization To Mitochondrion (GO:1903749)<\/td>\n1\/33<\/td>\n0.0049420
\n167963792<\/td>\n		0.01751645
\n11383681<\/td>\n		0<\/td>\n0<\/td>\n311.953125<\/td>\n1656.465407
\n6932638<\/td>\n		RHOU<\/td>\n<\/tr>\n
Regulation Of Protein Targeting To Mitochondrion (GO:1903214)<\/td>\n1\/39<\/td>\n0.0058388
\n170461227<\/td>\n		0.017516
\n511383681<\/td>\n		0<\/td>\n0<\/td>\n262.61842
\n10526316<\/td>\n		1350.70617
\n04673467<\/td>\n		RHOU<\/td>\n<\/tr>\n
Rho Protein Signal Transduction (GO:0007266)<\/td>\n1\/52<\/td>\n0.0077800
\n371147531<\/td>\n		0.018672089
\n0754074<\/td>\n		0<\/td>\n0<\/td>\n195.54901
\n960784315<\/td>\n		949.62400
\n9021504<\/td>\n		RHOU<\/td>\n<\/tr>\n
Cytoskeleton Organization (GO:0007010)<\/td>\n1\/111<\/td>\n0.01655845
\n06223493<\/td>\n		0.02817295
\n13766027<\/td>\n		0<\/td>\n0<\/td>\n90.395454
\n54545454<\/td>\n		370.698985
\n83844775<\/td>\n		RHOU<\/td>\n<\/tr>\n
Regulation Of Small GTPase Mediated Signal Transduction (GO:0051056)<\/td>\n1\/118<\/td>\n0.01759650
\n93316656<\/td>\n		0.0281729
\n513766027<\/td>\n		0<\/td>\n0<\/td>\n84.95726
\n495726495<\/td>\n		343.23200
\n01415874<\/td>\n		RHOU<\/td>\n<\/tr>\n
Positive Regulation Of Intracellular Protein Transport (GO:0090316)<\/td>\n1\/126<\/td>\n0.018781967
\n5844018<\/td>\n		0.02817295
\n13766027<\/td>\n		0<\/td>\n0<\/td>\n79.488<\/td>\n315.95351
\n5922278<\/td>\n		RHOU<\/td>\n<\/tr>\n
Endocytosis (GO:0006897)<\/td>\n1\/189<\/td>\n0.02808414
\n8529689<\/td>\n		0.03744553
\n13729188<\/td>\n		0<\/td>\n0<\/td>\n52.683510
\n63829788<\/td>\n		188.214474
\n40701544<\/td>\n		RHOU<\/td>\n<\/tr>\n
Regulation Of Intracellular Signal Transduction (GO:1902531)<\/td>\n1\/297<\/td>\n0.04389364
\n89206369<\/td>\n		0.05267237
\n87047643<\/td>\n		0<\/td>\n0<\/td>\n33.278716
\n21621622<\/td>\n		104.028789
\n03876676<\/td>\n		RHOU<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

GO Cellular Component 2023<\/strong><\/p>\n

Table 2 shows that PADI6 was significantly associated with cortical granule (p=0.0007, adjusted p=0.0029) with a remarkably high odds ratio of 2499.12. RHOU showed associations with endosome membrane (p=0.0531, adjusted p=0.0763), cytoplasmic vesicle membrane (p=0.0572, adjusted p=0.0763), and bounding membrane of organelle (p=0.1178), though with less statistical significance than the PADI6 association.<\/p>\n

Table 2: GO Cellular Component 2023<\/strong><\/p>\n\n\n\n\n\n\n\n
Term<\/strong><\/td>\nOverlap<\/strong><\/td>\nP-value<\/strong><\/td>\nAdjusted P-value<\/strong><\/td>\nOld P-value<\/strong><\/td>\nOld Adjusted P-value<\/strong><\/td>\nOdds Ratio<\/strong><\/td>\nCombined Score<\/strong><\/td>\nGenes<\/strong><\/td>\n<\/tr>\n
Cortical Granule (GO:0060473)<\/td>\n1\/5<\/td>\n0.000749831
\n4718703743<\/td>\n		0.0029993
\n258874814<\/td>\n		0<\/td>\n0<\/td>\n2499.125<\/td>\n17982.8589
\n97820545<\/td>\n		PADI6<\/td>\n<\/tr>\n
Endosome Membrane (GO:0010008)<\/td>\n1\/361<\/td>\n0.05318084
\n46011125<\/td>\n		0.0763000
\n118067423<\/td>\n		0<\/td>\n0<\/td>\n27.2736111
\n11111112<\/td>\n		80.0223299
\n0571518<\/td>\n		RHOU<\/td>\n<\/tr>\n
Cytoplasmic Vesicle Membrane (GO:0030659)<\/td>\n1\/389<\/td>\n0.0572250
\n088550567<\/td>\n		0.076300011
\n8067423<\/td>\n		0<\/td>\n0<\/td>\n25.269329
\n896907216<\/td>\n		72.289595
\n80285303<\/td>\n		RHOU<\/td>\n<\/tr>\n
Bounding Membrane Of Organelle (GO:0098588)<\/td>\n1\/819<\/td>\n0.11789328
\n72819009<\/td>\n		0.11789328
\n72819009<\/td>\n		0<\/td>\n0<\/td>\n11.723105
\n134474327<\/td>\n		25.063710
\n49171824<\/td>\n		RHOU<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

GO Molecular Function 2023<\/strong><\/p>\n

As shown in Table 3, PADI6 was significantly associated with hydrolase activity acting on carbon-nitrogen bonds in linear amidines (p=0.0013, adjusted p=0.0107) with an odds ratio of 1249.31. RHOU showed associations with GTP binding (p=0.0298, adjusted p=0.0532), guanyl-nucleotide exchange factor activity (p=0.0301, adjusted p=0.0532), and GTPase activity (p=0.0392, adjusted p=0.0532), consistent with its role in GTPase signaling.<\/p>\n

Table 3: GO Molecular Function 2023<\/strong><\/p>\n\n\n\n\n\n\n\n\n\n\n\n
Term<\/strong><\/td>\nOverlap<\/strong><\/td>\nP-value<\/strong><\/td>\nAdjusted P-value<\/strong><\/td>\nOld P-value<\/strong><\/td>\nOld Adjusted P-value<\/strong><\/td>\nOdds Ratio<\/strong><\/td>\nCombined Score<\/strong><\/td>\nGenes<\/strong><\/td>\n<\/tr>\n
Hydrolase Activity, Acting On Carbon-Nitrogen (But Not Peptide) Bonds, In Linear Amidines (GO:0016813)<\/td>\n1\/9<\/td>\n0.001349
\n4318923464<\/td>\n		0.01079545
\n51387712<\/td>\n		0<\/td>\n0<\/td>\n1249.3125<\/td>\n8255.546
\n4452602<\/td>\n		PADI6<\/td>\n<\/tr>\n
GTP Binding (GO:0005525)<\/td>\n1\/201<\/td>\n0.02984930
\n08283308<\/td>\n		0.053276
\n626651987<\/td>\n		0<\/td>\n0<\/td>\n49.4925<\/td>\n173.797559
\n22762263<\/td>\n		RHOU<\/td>\n<\/tr>\n
Guanyl-Nucleotide Exchange Factor Activity (GO:0005085)<\/td>\n1\/203<\/td>\n0.03014328
\n49570313<\/td>\n		0.053276
\n626651987<\/td>\n		0<\/td>\n0<\/td>\n48.997524
\n5247525<\/td>\n		171.579194
\n1822581<\/td>\n		RHOU<\/td>\n<\/tr>\n
Guanyl Ribonucleotide Binding (GO:0032561)<\/td>\n1\/226<\/td>\n0.03351983
\n52152786<\/td>\n		0.0532766
\n26651987<\/td>\n		0<\/td>\n0<\/td>\n43.937777
\n777777775<\/td>\n		149.19590
\n55604995<\/td>\n		RHOU<\/td>\n<\/tr>\n
GTPase Activity (GO:0003924)<\/td>\n1\/265<\/td>\n0.03922736
\n45668329<\/td>\n		0.05327662
\n6651987<\/td>\n		0<\/td>\n0<\/td>\n37.3731060
\n6060606<\/td>\n		121.028345
\n36731562<\/td>\n		RHOU<\/td>\n<\/tr>\n
Ribonucleoside Triphosphate Phosphatase Activity (GO:0017111)<\/td>\n1\/270<\/td>\n0.0399574
\n699889902<\/td>\n		0.0532766
\n26651987<\/td>\n		0<\/td>\n0<\/td>\n36.669144
\n98141264<\/td>\n		118.07243
\n35197098<\/td>\n		RHOU<\/td>\n<\/tr>\n
GTPase Regulator Activity (GO:0030695)<\/td>\n1\/424<\/td>\n0.06226399
\n92823089<\/td>\n		0.06971729
\n61048257<\/td>\n		0<\/td>\n0<\/td>\n23.13711
\n58392435<\/td>\n		64.237237
\n2008254<\/td>\n		RHOU<\/td>\n<\/tr>\n
Purine Ribonucleoside Triphosphate Binding (GO:0035639)<\/td>\n1\/476<\/td>\n0.06971729
\n61048257<\/td>\n		0.06971729
\n61048257<\/td>\n		0<\/td>\n0<\/td>\n20.5494736
\n84210525<\/td>\n		54.72955
\n384796592<\/td>\n		RHOU<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

HMDB Metabolites<\/strong><\/p>\n

Table 4 indicates associations between RHOU and two metabolites: guanosine triphosphate (p=0.0674, adjusted p=0.0678) and magnesium (p=0.0678, adjusted p=0.0678), both with odds ratios above 21, reflecting the gene’s involvement in GTP-dependent processes.<\/p>\n

Table 4: HMDB Metabolites<\/strong><\/p>\n\n\n\n\n\n
Term<\/strong><\/td>\nOverlap<\/strong><\/td>\nP-value<\/strong><\/td>\nAdjusted P-value<\/strong><\/td>\nOld P-value<\/strong><\/td>\nOld Adjusted P-value<\/strong><\/td>\nOdds Ratio<\/strong><\/td>\nCombined Score<\/strong><\/td>\nGenes<\/strong><\/td>\n<\/tr>\n
Guanosine triphosphate (HMDB01273)<\/td>\n1\/460<\/td>\n0.0674281
\n96372244<\/td>\n		0.06785768
\n82736759<\/td>\n		0<\/td>\n0<\/td>\n21.283224
\n40087146<\/td>\n		57.394301
\n07698986<\/td>\n		RHOU<\/td>\n<\/tr>\n
Magnesium (HMDB00547)<\/td>\n1\/463<\/td>\n0.0678576
\n882736759<\/td>\n		0.06785768
\n82736759<\/td>\n		0<\/td>\n0<\/td>\n21.1417748
\n9177489<\/td>\n		56.878617
\n34168601<\/td>\n		RHOU<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

miRTarBase 2017<\/strong><\/p>\n

MicroRNA interaction analysis (Table 5) identified several microRNAs targeting RHOU. The most significant associations were with mmu-miR-199a-5p (p=0.0047, adjusted p=0.0336, odds ratio=322.03), hsa-miR-126-3p (p=0.0086, adjusted p=0.0336, odds ratio=174.91), and hsa-miR-452-5p (p=0.0112, adjusted p=0.0336, odds ratio=134.61).<\/p>\n

Table 5: miRTarBase 2017<\/strong><\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n
Term<\/strong><\/td>\nOverlap<\/strong><\/td>\nP-value<\/strong><\/td>\nAdjusted P-value<\/strong><\/td>\nOld P-value<\/strong><\/td>\nOld Adjusted P-value<\/strong><\/td>\nOdds Ratio<\/strong><\/td>\nCombined Score<\/strong><\/td>\nGenes<\/strong><\/td>\n<\/tr>\n
mmu-miR-199a-5p<\/td>\n1\/32<\/td>\n0.00479249
\n77347936<\/td>\n		0.0336249
\n419150495<\/td>\n		0<\/td>\n0<\/td>\n322.032258
\n06451616<\/td>\n		1719.8788
\n256956173<\/td>\n		RHOU<\/td>\n<\/tr>\n
hsa-miR-126-3p<\/td>\n1\/58<\/td>\n0.0086751
\n325027492<\/td>\n		0.03362494
\n19150495<\/td>\n		0<\/td>\n0<\/td>\n174.91228
\n070175438<\/td>\n		830.3601
\n39497581<\/td>\n		RHOU<\/td>\n<\/tr>\n
hsa-miR-452-5p<\/td>\n1\/75<\/td>\n0.01120831
\n39716831<\/td>\n		0.033624941
\n9150495<\/td>\n		0<\/td>\n0<\/td>\n134.61486
\n486486487<\/td>\n		604.56874
\n65206931<\/td>\n		RHOU<\/td>\n<\/tr>\n
hsa-miR-374b-5p<\/td>\n1\/234<\/td>\n0.03469244
\n80209845<\/td>\n		0.0642745
\n574039018<\/td>\n		0<\/td>\n0<\/td>\n42.412017
\n167381975<\/td>\n		142.556682
\n38733136<\/td>\n		RHOU<\/td>\n<\/tr>\n
mmu-miR-466l-5p<\/td>\n1\/391<\/td>\n0.05751343
\n62651423<\/td>\n		0.06427455
\n74039018<\/td>\n		0<\/td>\n0<\/td>\n25.1371794
\n87179488<\/td>\n		71.785165
\n60137199<\/td>\n		RHOU<\/td>\n<\/tr>\n
mmu-miR-466d-5p<\/td>\n1\/395<\/td>\n0.05809011459502<\/td>\n0.06427455
\n74039018<\/td>\n		0<\/td>\n0<\/td>\n24.8769035
\n5329949<\/td>\n		70.793691
\n44023838<\/td>\n		RHOU<\/td>\n<\/tr>\n
mmu-miR-466i-5p<\/td>\n1\/395<\/td>\n0.0580901
\n1459502<\/td>\n		0.06427455
\n74039018<\/td>\n		0<\/td>\n0<\/td>\n24.876903
\n55329949<\/td>\n		70.793691
\n44023838<\/td>\n		RHOU<\/td>\n<\/tr>\n
mmu-miR-466k<\/td>\n1\/395<\/td>\n0.058090
\n11459502<\/td>\n		0.0642745
\n574039018<\/td>\n		0<\/td>\n0<\/td>\n24.876903
\n55329949<\/td>\n		70.793691
\n44023838<\/td>\n		RHOU<\/td>\n<\/tr>\n
mmu-miR-7b-5p<\/td>\n1\/438<\/td>\n0.0642745
\n574039018<\/td>\n		0.06427455
\n74039018<\/td>\n		0<\/td>\n0<\/td>\n22.379862
\n700228838<\/td>\n		61.42357896
\n4976976<\/td>\n		RHOU<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

PPI Hub Proteins<\/strong><\/p>\n

Protein-protein interaction analysis (Table 6) revealed significant interactions between RHOU and several hub proteins, including PTK2 (p=0.0242, adjusted p=0.0542, odds ratio=61.21), PAK1 (p=0.0261, adjusted p=0.0542, odds ratio=56.63), NCK1 (p=0.0361, adjusted p=0.0542, odds ratio=40.64), and PLCG1 (p=0.0361, adjusted p=0.0542, odds ratio=40.64).<\/p>\n

Table 6: PPI Hub Proteins<\/strong><\/p>\n\n\n\n\n\n\n\n\n\n
Term<\/td>\nOverlap<\/td>\nP-value<\/td>\nAdjusted P-value<\/td>\nOld P-value<\/td>\nOld Adjusted P-value<\/td>\nOdds Ratio<\/td>\nCombined Score<\/td>\nGenes<\/td>\n<\/tr>\n
PTK2<\/td>\n1\/163<\/td>\n0.0242523
\n103555676<\/td>\n		0.054235319
\n7780439<\/td>\n		0<\/td>\n0<\/td>\n61.219135
\n80246913<\/td>\n		227.688866
\n40394177<\/td>\n		RHOU<\/td>\n<\/tr>\n
PAK1<\/td>\n1\/176<\/td>\n0.02616948
\n57476556<\/td>\n		0.0542353
\n197780439<\/td>\n		0<\/td>\n0<\/td>\n56.634285
\n71428572<\/td>\n		206.3278
\n3304510207<\/td>\n		RHOU<\/td>\n<\/tr>\n
NCK1<\/td>\n1\/244<\/td>\n0.03615687
\n98520293<\/td>\n		0.05423531
\n97780439<\/td>\n		0<\/td>\n0<\/td>\n40.6460905
\n3497942<\/td>\n		134.940469
\n61461834<\/td>\n		RHOU<\/td>\n<\/tr>\n
PLCG1<\/td>\n1\/244<\/td>\n0.03615687
\n98520293<\/td>\n		0.05423531
\n97780439<\/td>\n		0<\/td>\n0<\/td>\n40.6460905
\n3497942<\/td>\n		134.9404696
\n1461834<\/td>\n		RHOU<\/td>\n<\/tr>\n
SRC<\/td>\n1\/513<\/td>\n0.074996485
\n8813518<\/td>\n		0.08999578
\n30576222<\/td>\n		0<\/td>\n0<\/td>\n19.0283
\n203125<\/td>\n		49.289324
\n9102792<\/td>\n		RHOU<\/td>\n<\/tr>\n
GRB2<\/td>\n1\/767<\/td>\n0.11069923
\n7301363<\/td>\n		0.1106992
\n37301363<\/td>\n		0<\/td>\n0<\/td>\n12.55287206
\n2663186<\/td>\n		27.6280972
\n6264245<\/td>\n		RHOU<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Reactome Pathways 2024<\/strong><\/p>\n

Pathway analysis using Reactome database (Table 7) showed significant enrichment of RHOU in the RHOU GTPase Cycle (p=0.0059, adjusted p=0.0481, odds ratio=255.87) and Interleukin-4 and Interleukin-13 Signaling (p=0.0167, adjusted p=0.0584, odds ratio=89.57). PADI6 was significantly associated with M-decay Degradation of Maternal mRNAs by Maternally Stored Factors (p=0.0068, adjusted p=0.0481, odds ratio=221.68), Maternal to Zygotic Transition (p=0.0162, adjusted p=0.0584, odds ratio=92.07), and Chromatin Modifying Enzymes\/Organization (p=0.0352, adjusted p=0.0823, odds ratio=41.68).<\/p>\n

Table 7: Reactome Pathways 2024<\/strong><\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Term<\/strong><\/td>\nOverlap<\/strong><\/td>\nP-value<\/strong><\/td>\nAdjusted P-value<\/strong><\/td>\nOld P-value<\/strong><\/td>\nOld Adjusted P-value<\/strong><\/td>\nOdds Ratio<\/strong><\/td>\nCombined Score<\/strong><\/td>\nGenes<\/strong><\/td>\n<\/tr>\n
RHOU GTPase Cycle<\/td>\n1\/40<\/td>\n0.00598823
\n14019791<\/td>\n		0.04819082
\n41377977<\/td>\n		0<\/td>\n0<\/td>\n255.87179
\n48717949<\/td>\n		1309.54139
\n8623503<\/td>\n		RHOU<\/td>\n<\/tr>\n
M-decay Degradation of Maternal mRNAs by Maternally Stored Factors<\/td>\n1\/46<\/td>\n0.0068844
\n034482568<\/td>\n		0.04819082
\n41377977<\/td>\n		0<\/td>\n0<\/td>\n221.68888
\n88888889<\/td>\n		1103.67742
\n29731402<\/td>\n		PADI6<\/td>\n<\/tr>\n
Maternal to Zygotic Transition (MZT)<\/td>\n1\/109<\/td>\n0.0162617
\n282000212<\/td>\n		0.05847376
\n31112347<\/td>\n		0<\/td>\n0<\/td>\n92.0787037
\n0370372<\/td>\n		379.266738
\n25723503<\/td>\n		PADI6<\/td>\n<\/tr>\n
Interleukin-4 and Interleukin-13 Signaling<\/td>\n1\/112<\/td>\n0.01670678
\n94603527<\/td>\n		0.05847376
\n31112347<\/td>\n		0<\/td>\n0<\/td>\n89.576576
\n57657657<\/td>\n		366.54198
\n4609772<\/td>\n		RHOU<\/td>\n<\/tr>\n
Chromatin Modifying Enzymes<\/td>\n1\/238<\/td>\n0.03527839
\n86095116<\/td>\n		0.0823162
\n634221937<\/td>\n		0<\/td>\n0<\/td>\n41.6877637
\n13080166<\/td>\n		139.42407
\n70668133<\/td>\n		PADI6<\/td>\n<\/tr>\n
Chromatin Organization<\/td>\n1\/238<\/td>\n0.0352783
\n986095116<\/td>\n		0.082316263
\n4221937<\/td>\n		0<\/td>\n0<\/td>\n41.6877637
\n13080166<\/td>\n		139.42407
\n70668133<\/td>\n		PADI6<\/td>\n<\/tr>\n
RHO GTPase Cycle<\/td>\n1\/450<\/td>\n0.0659956
\n039535799<\/td>\n		0.11599391
\n95015217<\/td>\n		0<\/td>\n0<\/td>\n21.768374
\n16481069<\/td>\n		59.170079
\n47527356<\/td>\n		RHOU<\/td>\n<\/tr>\n
Signaling by Interleukins<\/td>\n1\/452<\/td>\n0.0662822
\n397151552<\/td>\n		0.11599391
\n95015217<\/td>\n		0<\/td>\n0<\/td>\n21.669623
\n05986696<\/td>\n		58.8077445
\n55934576<\/td>\n		RHOU<\/td>\n<\/tr>\n
Signaling by Rho GTPases<\/td>\n1\/672<\/td>\n0.09745545
\n68631971<\/td>\n		0.1395731
\n862121099<\/td>\n		0<\/td>\n0<\/td>\n14.4008941
\n87779434<\/td>\n		33.5304639
\n4688685<\/td>\n		RHOU<\/td>\n<\/tr>\n
Signaling by Rho GTPases, Miro GTPases and RHOBTB3<\/td>\n1\/688<\/td>\n0.0996951
\n330086499<\/td>\n		0.13957318
\n62121099<\/td>\n		0<\/td>\n0<\/td>\n14.0538573
\n50800582<\/td>\n		32.4031134
\n51140854<\/td>\n		RHOU<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Target Scan microRNA 2017<\/strong><\/p>\n

Table 8 indicates potential microRNA targeting of RHOU by hsa-miR-126 (p=0.0227, odds ratio=65.27), supporting the findings from miRTarBase. Both PADI6 and RHOU were targeted by hsa-miR-4684-3p (p=0.0267, odds ratio=18.49).<\/p>\n

Table 8: Target Scan microRNA 2017<\/strong><\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Term<\/strong><\/td>\nOverlap<\/strong><\/td>\nP-value<\/strong><\/td>\nAdjusted P-value<\/strong><\/td>\nOld P-value<\/strong><\/td>\nOld Adjusted P-value<\/strong><\/td>\nOdds Ratio<\/strong><\/td>\nCombined Score<\/strong><\/td>\nGenes<\/strong><\/td>\n<\/tr>\n
hsa-miR-126<\/td>\n1\/153<\/td>\n0.02277584
\n92784422<\/td>\n		0.2706471
\n536354705<\/td>\n		0<\/td>\n0<\/td>\n65.279605
\n26315789<\/td>\n		246.891027
\n8850476<\/td>\n		RHOU<\/td>\n<\/tr>\n
hsa-miR-4684-3p<\/td>\n2\/1953<\/td>\n0.02673345
\n66800765<\/td>\n		0.27064715
\n36354705<\/td>\n		0<\/td>\n0<\/td>\n18.499231
\n163505893<\/td>\n		67.001245
\n01584236<\/td>\n		PADI6;RHOU<\/td>\n<\/tr>\n
mmu-miR-450a<\/td>\n1\/241<\/td>\n0.035717705
\n9161049<\/td>\n		0.27064715
\n36354705<\/td>\n		0<\/td>\n0<\/td>\n41.160416
\n66666667<\/td>\n		137.1509844
\n9199263<\/td>\n		RHOU<\/td>\n<\/tr>\n
hsa-miR-487b<\/td>\n1\/262<\/td>\n0.03878912
\n36974251<\/td>\n		0.27064715
\n36354705<\/td>\n		0<\/td>\n0<\/td>\n37.8084291
\n1877394<\/td>\n		122.86285
\n30879148<\/td>\n		RHOU<\/td>\n<\/tr>\n
mmu-miR-652<\/td>\n1\/315<\/td>\n0.0465117
\n822885065<\/td>\n		0.2706471
\n536354705<\/td>\n		0<\/td>\n0<\/td>\n31.342356
\n68789809<\/td>\n		96.15990
\n539884224<\/td>\n		RHOU<\/td>\n<\/tr>\n
hsa-miR-4479<\/td>\n1\/337<\/td>\n0.04970522
\n71622002<\/td>\n		0.27064715
\n36354705<\/td>\n		0<\/td>\n0<\/td>\n29.2574404
\n76190474<\/td>\n		87.820455
\n10023957<\/td>\n		RHOU<\/td>\n<\/tr>\n
hsa-miR-4665-3p<\/td>\n1\/356<\/td>\n0.052457457
\n6559142<\/td>\n		0.27064715
\n36354705<\/td>\n		0<\/td>\n0<\/td>\n27.6647887
\n32394367<\/td>\n		81.548957
\n56877927<\/td>\n		RHOU<\/td>\n<\/tr>\n
hsa-miR-4304<\/td>\n1\/487<\/td>\n0.07128887
\n73429607<\/td>\n		0.27064715
\n36354705<\/td>\n		0<\/td>\n0<\/td>\n20.073045
\n26748971<\/td>\n		53.01321
\n28788392<\/td>\n		RHOU<\/td>\n<\/tr>\n
hsa-miR-3917<\/td>\n1\/514<\/td>\n0.07513888
\n88058125<\/td>\n		0.27064715
\n36354705<\/td>\n		0<\/td>\n0<\/td>\n18.99025341
\n1306043<\/td>\n		49.154695
\n28186957<\/td>\n		RHOU<\/td>\n<\/tr>\n
mmu-miR-1963<\/td>\n1\/525<\/td>\n0.076704356
\n5059508<\/td>\n		0.27064715
\n36354705<\/td>\n		0<\/td>\n0<\/td>\n18.581106
\n70229007<\/td>\n		47.712506
\n25877883<\/td>\n		RHOU<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

RNAseq Automatic GEO Signatures Human Down\/Up<\/strong><\/p>\n

Tables 9 and 10 show differential expression of RHOU across various experimental conditions and disease states in GEO datasets, with consistent p-values of 0.0370 and odds ratios of 39.65 across multiple signatures, indicating the potential involvement of RHOU in diverse biological contexts beyond BCC.<\/p>\n

Table 9: RNA seq Automatic GEO Signatures Human Down<\/strong><\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Term<\/td>\nOverlap<\/td>\nP-value<\/td>\nAdjusted
\nP-value<\/td>\n		Old P-value<\/td>\nOld Adjusted P-value<\/td>\nOdds Ratio<\/td>\nCombined
\nScore<\/td>\n		Genes<\/td>\n<\/tr>\n
Deep Gsk-126 Her2 Tumours GSE136300 1<\/td>\n1\/250<\/td>\n0.03703482
\n76752191<\/td>\n		0.0370348
\n276752191<\/td>\n		0<\/td>\n0<\/td>\n39.654618
\n47389558<\/td>\n		130.697519
\n05195764<\/td>\n		RHOU<\/td>\n<\/tr>\n
Circrna Gallbladder Compared Matched GSE100363 1<\/td>\n1\/250<\/td>\n0.0370348
\n276752191<\/td>\n		0.0370348
\n276752191<\/td>\n		0<\/td>\n0<\/td>\n39.654618
\n47389558<\/td>\n		130.697519
\n05195764<\/td>\n		RHOU<\/td>\n<\/tr>\n
Cyclooxygenase-2 Cox-2 Er? 5?-Reductase GSE80979 1<\/td>\n1\/250<\/td>\n0.0370348
\n276752191<\/td>\n		0.03703482
\n76752191<\/td>\n		0<\/td>\n0<\/td>\n39.6546184
\n7389558<\/td>\n		130.6975190
\n5195764<\/td>\n		RHOU<\/td>\n<\/tr>\n
Follicular Helper Effector Lymphocytes GSE58596 1<\/td>\n1\/250<\/td>\n0.037034
\n8276752191<\/td>\n		0.037034
\n8276752191<\/td>\n		0<\/td>\n0<\/td>\n39.654618
\n47389558<\/td>\n		130.69751
\n905195764<\/td>\n		RHOU<\/td>\n<\/tr>\n
Ppar? Prevents Infectivity Boosting GSE128121 1<\/td>\n1\/250<\/td>\n0.03703482
\n76752191<\/td>\n		0.03703482
\n76752191<\/td>\n		0<\/td>\n0<\/td>\n39.6546184
\n7389558<\/td>\n		130.697519
\n05195764<\/td>\n		RHOU<\/td>\n<\/tr>\n
Protein Casp8Ap2 Improvement G0 GSE143808 1<\/td>\n1\/250<\/td>\n0.0370348
\n276752191<\/td>\n		0.0370348
\n276752191<\/td>\n		0<\/td>\n0<\/td>\n39.6546184
\n7389558<\/td>\n		130.697519
\n05195764<\/td>\n		RHOU<\/td>\n<\/tr>\n
Moderate Dysplasia Mucosa Conversion GSE72627 1<\/td>\n1\/250<\/td>\n0.037034
\n8276752191<\/td>\n		0.0370348
\n276752191<\/td>\n		0<\/td>\n0<\/td>\n39.654618
\n47389558<\/td>\n		130.697519
\n05195764<\/td>\n		RHOU<\/td>\n<\/tr>\n
Arginine Integrating E2F Set GSE111960 3<\/td>\n1\/250<\/td>\n0.037034
\n8276752191<\/td>\n		0.03703482
\n76752191<\/td>\n		0<\/td>\n0<\/td>\n39.654618
\n47389558<\/td>\n		130.6975190
\n5195764<\/td>\n		RHOU<\/td>\n<\/tr>\n
Gtf2I Williams Behavioural Rescuable GSE128840 1<\/td>\n1\/250<\/td>\n0.03703482
\n76752191<\/td>\n		0.03703482
\n76752191<\/td>\n		0<\/td>\n0<\/td>\n39.6546184
\n7389558<\/td>\n		130.69751
\n905195764<\/td>\n		RHOU<\/td>\n<\/tr>\n
Cdk9 Degrader Multi-Targeted Cdk GSE89385 3<\/td>\n1\/250<\/td>\n0.03703482
\n76752191<\/td>\n		0.03703482
\n76752191<\/td>\n		0<\/td>\n0<\/td>\n39.654618
\n47389558<\/td>\n		130.69751
\n905195764<\/td>\n		RHOU<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Table 10: RNA seq Automatic GEO Signatures Human Up<\/strong><\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Term<\/strong><\/td>\nOverlap<\/strong><\/td>\nP-value<\/strong><\/td>\nAdjusted P-value<\/strong><\/td>\nOld P-value<\/strong><\/td>\nOld Adjusted P-value<\/strong><\/td>\nOdds Ratio<\/strong><\/td>\nCombined Score<\/strong><\/td>\nGenes<\/strong><\/td>\n<\/tr>\n
Transcriptional Cell-Derived Polarized Hepatocytes GSE123462 1<\/td>\n1\/250<\/td>\n0.037034827
\n6752191<\/td>\n		0.037034827
\n6752191<\/td>\n		0<\/td>\n0<\/td>\n39.65461847
\n389558<\/td>\n		130.697519
\n05195764<\/td>\n		RHOU<\/td>\n<\/tr>\n
Age-Induced Methylmalonic Acid Accumulation GSE127001 1<\/td>\n1\/250<\/td>\n0.03703482
\n76752191<\/td>\n		0.03703482
\n76752191<\/td>\n		0<\/td>\n0<\/td>\n39.6546184
\n7389558<\/td>\n		130.697519
\n05195764<\/td>\n		RHOU<\/td>\n<\/tr>\n
Metallo-Endopeptidase Neprilysin White Preadipocytes GSE117270 1<\/td>\n1\/250<\/td>\n0.0370348
\n276752191<\/td>\n		0.0370348
\n276752191<\/td>\n		0<\/td>\n0<\/td>\n39.6546184
\n7389558<\/td>\n		130.6975190
\n5195764<\/td>\n		RHOU<\/td>\n<\/tr>\n
Ehmt1 Ehmt2 Fetal Hemoglobin GSE71421 1<\/td>\n1\/250<\/td>\n0.0370348
\n276752191<\/td>\n		0.03703482
\n76752191<\/td>\n		0<\/td>\n0<\/td>\n39.6546184
\n7389558<\/td>\n		130.697519
\n05195764<\/td>\n		RHOU<\/td>\n<\/tr>\n
Polybrominated Diphenyl Ethers Pbdes GSE111203 4<\/td>\n1\/250<\/td>\n0.0370348
\n276752191<\/td>\n		0.03703482
\n76752191<\/td>\n		0<\/td>\n0<\/td>\n39.6546184
\n7389558<\/td>\n		130.697519
\n05195764<\/td>\n		RHOU<\/td>\n<\/tr>\n
Directed Embryonic Corneal Cell-Like GSE81474 1<\/td>\n1\/250<\/td>\n0.0370348
\n276752191<\/td>\n		0.0370348
\n276752191<\/td>\n		0<\/td>\n0<\/td>\n39.654618
\n47389558<\/td>\n		130.6975190
\n5195764<\/td>\n		RHOU<\/td>\n<\/tr>\n
Leader Recruitment Upstream Reading GSE81802 1<\/td>\n1\/250<\/td>\n0.0370348
\n276752191<\/td>\n		0.03703482
\n76752191<\/td>\n		0<\/td>\n0<\/td>\n39.654618
\n47389558<\/td>\n		130.697519
\n05195764<\/td>\n		RHOU<\/td>\n<\/tr>\n
Maintaining Iron Lysosomal Acidity GSE141507 1<\/td>\n1\/250<\/td>\n0.03703482
\n76752191<\/td>\n		0.03703482
\n76752191<\/td>\n		0<\/td>\n0<\/td>\n39.6546184
\n7389558<\/td>\n		130.697519
\n05195764<\/td>\n		RHOU<\/td>\n<\/tr>\n
U87Mg A3 Adenosine Mrs1220 GSE100146 1<\/td>\n1\/250<\/td>\n0.03703482
\n76752191<\/td>\n		0.03703482
\n76752191<\/td>\n		0<\/td>\n0<\/td>\n39.6546184
\n7389558<\/td>\n		130.69751
\n905195764<\/td>\n		RHOU<\/td>\n<\/tr>\n
Strand-Oriented Culture 24H Presence GSE102305 1<\/td>\n1\/250<\/td>\n0.0370348
\n276752191<\/td>\n		0.03703482
\n76752191<\/td>\n		0<\/td>\n0<\/td>\n39.654618
\n47389558<\/td>\n		130.697519
\n05195764<\/td>\n		RHOU<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Discussion<\/strong><\/p>\n

Our comprehensive bioinformatic analysis of GWAS-identified genes associated with basal cell carcinoma has revealed significant functional implications for RHOU and PADI6 in disease pathogenesis.<\/p>\n

RHOU is not yet an established clinical biomarker for any cancer, including BCC. However, several studies have reported RHOU overexpression in malignancies such as breast and colorectal cancers, with correlations to aggressive tumor behavior. Its expression could potentially serve as a prognostic indicator or part of a molecular signature when combined with other markers, especially if further validated in BCC-specific studies. With GWAS implicating RHOU in BCC susceptibility, future studies may explore its expression levels in patient samples as a non-invasive diagnostic or risk stratification tool.<\/p>\n

The results highlight several molecular mechanisms through which these genes may influence BCC development and progression, providing novel insights into the underlying biology of this common skin malignancy.<\/p>\n

RHOU, a Rho GTPase family member, is strongly linked to GTPase signaling, cytoskeletal organization, and endocytosis, aligning with its known role in cell migration and morphology. 29,30<\/sup> Its association with Cdc42 signaling\u2014a pathway involved in cancer invasion\u2014suggests a role in BCC malignancy. RHOU also appears to influence mitochondrial function and energy metabolism, as indicated by its involvement in protein localization to mitochondria and interactions with energy-related metabolites (GTP, magnesium). Protein-protein interaction (PPI) analysis shows RHOU connects with key signaling molecules like PTK2 (FAK), PAK1, NCK1, and PLCG1, implicating it in focal adhesion, cytoskeleton remodeling, and cell invasion pathways. 31,32<\/sup> Collectively, these findings position RHOU as a potential driver of BCC pathogenesis through its effects on cell signaling, metabolism, and motility. Our microRNA interaction analysis identified several microRNAs targeting RHOU, with particularly strong associations with miR-199a-5p, miR-126-3p, and miR-452-5p. 33<\/sup><\/p>\n

These microRNAs have been previously implicated in cancer biology, with miR-199a-5p shown to regulate cell migration and invasion in various malignancies,34<\/sup> miR-126-3p known to modulate angiogenesis and metastasis,35<\/sup> and miR-452-5p involved in epithelial-mesenchymal transition.36<\/sup> The identification of these regulatory relationships provides insight into the post-transcriptional control of RHOU expression and suggests potential mechanisms through which dysregulation of these microRNAs could contribute to BCC pathogenesis.<\/p>\n

The Reactome pathway analysis further confirmed the involvement of RHOU in GTPase cycle regulation and revealed an unexpected association with interleukin-4 and interleukin-13 signaling. These cytokines play roles in immune regulation and have been implicated in the tumor microenvironment of various cancers.37<\/sup> This finding suggests a potential link between RHOU and immune-related processes in BCC, which warrants further investigation given the emerging importance of immunotherapy in skin cancer treatment.<\/p>\n

PADI6, while less extensively characterized in the context of cancer, showed significant associations with cellular components related to cortical granules and molecular functions involving hydrolase activity. The most striking findings for PADI6 came from the Reactome pathway analysis, which revealed strong associations with maternal mRNA degradation, maternal-to-zygotic transition, and chromatin organization. PADI6 has been primarily studied in the context of early embryonic development, where it participates in epigenetic regulation and cytoplasmic lattice formation.38<\/sup> The significant enrichment in chromatin modifying enzymes suggests that PADI6 may influence BCC pathogenesis through epigenetic mechanisms, potentially affecting gene expression patterns critical for cell identity and differentiation.<\/p>\n

The association of both RHOU and PADI6 with hsa-miR-4684-3p in the Target Scan analysis suggests a potential co-regulation of these genes, which could be relevant to their roles in BCC. While the functional significance of this microRNA in cancer remains to be elucidated, this finding points to a possible regulatory network involving both genes of interest.<\/p>\n

RHOU shows variable expression across disease states, indicating it may impact multiple biological processes relevant to cancer. 39<\/sup> Its involvement in GTPase signaling and cytoskeletal regulation suggests it could be a promising therapeutic target in BCC, especially as Rho GTPase inhibitors are being explored in other cancers. Additionally, microRNAs regulating RHOU may serve as useful biomarkers. 40 <\/sup>Meanwhile, PADI6 is linked to chromatin modification and epigenetic dysregulation, highlighting its potential role in BCC and the relevance of epigenetic therapies like HDAC and DNA methyltransferase inhibitors in treating such cases.<\/p>\n

Limitations of our study<\/strong><\/p>\n

The analyses are based on computational approaches and statistical associations, which require experimental validation. The functional implications we have identified represent testable hypotheses rather than definitive mechanisms. Additionally, the analyses are constrained by the current state of knowledge represented in the databases used, which may contain biases or gaps.<\/p>\n

Conclusion<\/strong><\/p>\n

Our comprehensive functional analysis of GWAS-identified genes offers important insights into the molecular underpinnings of basal cell carcinoma (BCC) and lays the groundwork for numerous future research directions. By elucidating the potential involvement of RHOU in GTPase signaling, cytoskeletal remodeling, and mitochondrial function, as well as the role of PADI6 in epigenetic regulation, this study enhances our understanding of BCC pathogenesis. These findings may ultimately contribute to the identification of novel therapeutic targets and support the development of more precise treatment strategies for this prevalent skin malignancy.<\/p>\n

Acknowledgement<\/strong><\/p>\n

Authors thank Central Research Laboratory for Molecular Genetics, Bioinformatics and Machine Learning at Apollo Institute of Medical Sciences and Research Chittoor Murukamabttu – 571727, Andhra Pradesh, India for the infrastructure.<\/p>\n

Funding Sources<\/strong><\/p>\n

The author(s) received no financial support for the research, authorship, and\/or publication of this article.<\/p>\n

Conflict of Interest<\/strong><\/p>\n

The author(s) do not have any conflict of interest.<\/p>\n

Data Availability Statement<\/strong><\/p>\n

This statement does not apply to this article.<\/p>\n

Ethics Statement<\/strong><\/p>\n

This research did not involve human participants, animal subjects, or any material that requires ethical approval.<\/p>\n

Informed Consent Statement<\/strong><\/p>\n

This study did not involve human participants, and therefore, informed consent was not required.<\/p>\n

Clinical Trial Registration<\/strong><\/p>\n

This research does not involve any clinical trials.<\/p>\n

Permission to reproduce material from other sources\u00a0<\/strong><\/p>\n

Not Applicable<\/p>\n

Author contributions: <\/strong><\/p>\n