Integrated Multi-Omics and Transcriptomic Validation Analysis of TP53, ATM, BRCA1, EGFR, and KRAS Reveals Prognostic Significance in Lung Cancer

Integrated Multi-Omics and Transcriptomic Validation Analysis of TP53, ATM, BRCA1, EGFR, and KRAS Reveals Prognostic Significance in Lung Cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Integrated Multi-Omics and Transcriptomic Validation Analysis of TP53, ATM, BRCA1, EGFR, and KRAS Reveals Prognostic Significance in Lung Cancer Janani Vijayaraj¹, Jyoti Bala² This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9683939/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Lung cancer remains one of the leading causes of cancer-related mortality worldwide, necessitating improved understanding of its molecular mechanisms for effective diagnosis and therapy. In the present study, an integrated multi-omics bioinformatics approach combined with independent transcriptomic validation was employed to investigate the prognostic significance of five key genes—TP53, ATM, BRCA1, EGFR, and KRAS—using The Cancer Genome Atlas (TCGA) datasets for lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC). Mutation analysis revealed that TP53 exhibited the highest alteration frequency, followed by KRAS and EGFR, indicating their critical involvement in lung tumorigenesis. Gene expression analysis demonstrated significant differential regulation between tumor and normal tissues, with TP53, BRCA1, and KRAS showing upregulated expression, whereas EGFR displayed subtype-specific variation. Kaplan–Meier survival analysis indicated that elevated expression of TP53 and BRCA1 was significantly associated with poor overall survival, while ATM and EGFR were associated with relatively favorable prognosis. Protein–protein interaction analysis further revealed strong functional connectivity among these genes, particularly in pathways related to DNA damage response, cell cycle regulation, and oncogenic signaling. To strengthen the reliability of the findings, external validation was performed using independent GEO datasets (GSE19188 and GSE19804), which confirmed the differential expression patterns of the selected genes. Overall, the integration of genomic, transcriptomic, and clinical data provides a comprehensive understanding of the molecular landscape of lung cancer and highlights TP53, ATM, BRCA1, EGFR, and KRAS as potential prognostic biomarkers and therapeutic targets. These findings may contribute to future precision oncology and translational lung cancer research. Lung cancer Multi-omics analysis TP53 EGFR KRAS BRCA1 GEO validation Prognostic biomarkers Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Lung cancer remains one of the leading causes of cancer-related mortality worldwide, accounting for approximately 1.8 million deaths annually [ 1 , 2 ]. It is broadly classified into two major histological subtypes: lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), which differ in their molecular characteristics and clinical outcomes. Despite advances in diagnostic and therapeutic strategies, the overall survival rate of lung cancer patients remains low, primarily due to late-stage diagnosis and molecular heterogeneity of tumors. Recent advances in high-throughput sequencing technologies and large-scale cancer genomics projects such as The Cancer Genome Atlas have enabled comprehensive profiling of genetic and molecular alterations in various cancers. These datasets provide valuable insights into the mutational landscape, gene expression patterns, and clinical associations of cancer-related genes, facilitating the identification of biomarkers for prognosis and targeted therapy. Large-scale integrative analyses across multiple tumor types have revealed conserved oncogenic pathways and molecular subtypes, emphasizing the importance of multi-omics approaches in cancer research [ 3 – 5 ]. Among the key genes implicated in lung cancer, TP53 is one of the most frequently mutated tumor suppressor genes and plays a crucial role in maintaining genomic stability by regulating cell cycle arrest, apoptosis, and DNA repair mechanisms. Mutations in TP53 are reported in a significant proportion of lung cancer cases and are associated with poor clinical outcomes [ 6 ]. Similarly, EGFR mutations are well-established drivers in lung cancer, particularly in LUAD, and have been extensively targeted in precision therapies using tyrosine kinase inhibitors [ 7 ]. The oncogene KRAS is another critical driver mutation involved in abnormal cell signaling and tumor progression, although its prognostic significance varies across studies [ 8 ]. In addition to these oncogenic drivers, genes involved in DNA damage response pathways such as ATM and BRCA1 have gained attention for their roles in maintaining genomic integrity. ATM is a key regulator of the DNA damage response and coordinates repair mechanisms following double-strand breaks [ 9 ]. BRCA1 is essential for homologous recombination repair and has been implicated in cancer susceptibility and progression [ 10 ]. Dysregulation of these genes can lead to genomic instability, a hallmark of cancer. While previous studies have investigated these genes individually, there remains a lack of comprehensive studies integrating mutation analysis, transcriptomic profiling, survival outcomes, protein interaction networks, and independent external validation in lung cancer. Such integrative approaches are essential for understanding the complex molecular interplay underlying tumor progression and for identifying robust clinically relevant biomarkers with potential translational applicability. These advances underscore the transition from single-gene studies to integrative genomic frameworks for biomarker discovery and precision oncology [ 3 , 11 ]. Therefore, the present study aimed to perform an integrated multi-omics and transcriptomic validation analysis of TP53, ATM, BRCA1, EGFR, and KRAS using TCGA datasets combined with independent GEO validation cohorts. By integrating genomic, transcriptomic, survival, and protein interaction data, the study sought to elucidate the roles of these genes in lung cancer progression, identify potential prognostic biomarkers, and provide insights into clinically relevant therapeutic targets for precision oncology. 2. Literature Review Lung cancer is characterized by extensive genetic heterogeneity and complex molecular alterations that influence tumor progression, treatment response, and patient survival. The heterogeneity of somatic mutations across cancer types and patients has been extensively characterized, highlighting the complexity of identifying driver mutations and therapeutic targets [ 11 – 13 ]. Large-scale genomic studies such as The Cancer Genome Atlas have significantly contributed to understanding the molecular landscape of lung cancer, identifying recurrent mutations in key genes including TP53, EGFR, and KRAS [ 14 , 15 ]. These studies have demonstrated that lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) exhibit distinct genomic profiles, emphasizing the need for subtype-specific analyses [ 14 ]. Among the most extensively studied genes, TP53 is one of the most frequently mutated tumor suppressor genes in lung cancer, with mutation frequencies exceeding 50% in non-small cell lung cancer (NSCLC). TP53 mutations are strongly associated with poor prognosis, therapeutic resistance, and increased genomic instability in non-small cell lung cancer (NSCLC). Several studies have reported that TP53 mutations often co-occur with other oncogenic alterations, influencing tumor evolution and treatment outcomes [ 16 ]. Additionally, TP53 mutations have been shown to contribute to genomic instability, a hallmark of cancer, thereby promoting tumor progression [ 6 ]. The epidermal growth factor receptor (EGFR) gene is another key driver in lung cancer, particularly in LUAD, where activating mutations are associated with sensitivity to tyrosine kinase inhibitors (TKIs). EGFR mutations are among the most clinically actionable alterations in lung cancer, and targeted therapies have significantly improved patient outcomes [ 7 , 17 ]. However, resistance to EGFR-targeted therapies remains a major challenge, often involving secondary mutations or co-occurring genetic alterations such as TP53 mutations. KRAS mutations are among the most frequently reported oncogenic alterations in lung cancer and are associated with dysregulated MAPK and PI3K-AKT signaling pathways involved in tumor progression and therapeutic resistance [ 18 ]. Recent advances in KRAS-targeted therapies have renewed interest in understanding its biological and clinical significance [ 8 ]. Studies have also reported that KRAS mutations are often mutually exclusive with EGFR mutations, suggesting distinct oncogenic pathways in lung cancer development. In addition to oncogenic drivers, alterations in DNA damage response genes such as ATM and BRCA1 have been increasingly implicated in lung cancer progression and genomic instability. These genes are essential components of DNA repair pathways and contribute to the maintenance of genomic integrity and cellular homeostasis. Previous studies have reported that dysregulation of ATM and BRCA1 may influence tumor progression, therapeutic responsiveness, and sensitivity to immunotherapy in lung cancer patients [ 10 , 19 ]. Emerging evidence further suggests that abnormalities in DNA repair pathways may serve as clinically relevant biomarkers and potential therapeutic targets in precision oncology. Gene expression profiling has further enhanced the understanding of molecular alterations in lung cancer. Differential expression of oncogenes and tumor suppressor genes has been linked to tumor progression and clinical outcomes. For instance, overexpression of TP53 and KRAS has been associated with aggressive tumor behavior, while variations in EGFR expression have been shown to differ between LUAD and LUSC subtypes [ 20 ]. High-throughput platforms such as RNA sequencing have enabled large-scale analysis of gene expression patterns, facilitating the identification of potential biomarkers for diagnosis and prognosis. Survival analysis studies have highlighted the prognostic significance of key genetic alterations in lung cancer. Elevated TP53 expression and mutation status have been consistently associated with poor survival outcomes, whereas alterations in genes such as ATM may correlate with improved prognosis depending on the context. Similarly, the prognostic value of EGFR mutations has been extensively studied, with evidence suggesting improved survival in patients receiving targeted therapies [ 7 ]. However, the prognostic role of KRAS remains controversial, with studies reporting variable outcomes depending on co-mutations and tumor subtype. Protein–protein interaction (PPI) network analysis has emerged as a powerful approach to understand the functional relationships among genes and proteins involved in cancer. PPI networks provide insights into molecular pathways and biological processes, revealing how multiple genes interact to regulate tumor progression. Studies have shown that TP53, ATM, and BRCA1 are closely linked within DNA damage response networks, while EGFR and KRAS are associated with signaling pathways that regulate cell proliferation and survival. These interactions highlight the complex molecular interplay underlying lung cancer development [ 3 ]. Pathway enrichment analyses have further demonstrated that key pathways such as the p53 signaling pathway, PI3K-AKT signaling pathway, and cell cycle regulation are frequently dysregulated in lung cancer. Dysregulation of these pathways contributes substantially to tumor progression, therapeutic resistance, and molecular heterogeneity in lung cancer. Additionally, recent studies have emphasized the role of tumor heterogeneity and clonal evolution in lung cancer, where different genetic alterations may arise during tumor progression, influencing treatment response and prognosis [ 11 – 13 ]. Despite significant advancements in lung cancer genomics, many previous studies have primarily focused on individual genes or isolated molecular mechanisms. Comprehensive studies integrating mutation profiling, transcriptomic alterations, survival outcomes, protein–protein interaction networks, and external transcriptomic validation remain comparatively limited. Considering the extensive molecular heterogeneity of lung cancer, integrated multi-omics approaches are increasingly important for identifying robust prognostic biomarkers and clinically relevant therapeutic targets. Therefore, the present study builds upon existing literature by integrating mutation analysis, gene expression profiling, survival analysis, protein interaction network analysis, and independent GEO-based transcriptomic validation to systematically investigate TP53, ATM, BRCA1, EGFR, and KRAS in lung cancer. By combining genomic, transcriptomic, and clinical datasets through multiple complementary analytical platforms, this study aims to provide a comprehensive understanding of the molecular interplay underlying lung cancer progression and prognosis, thereby contributing to the advancement of precision oncology and translational cancer bioinformatics. 3. Materials and Methods 3.1 Data Source and Study Design This study employed an integrated in silico approach utilizing publicly available datasets from cBioPortal. The TCGA PanCancer Atlas datasets for lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) were selected due to their comprehensive genomic and clinical annotations. The LUAD dataset comprised approximately 566 samples, while the LUSC dataset included approximately 504 samples, encompassing mutation, gene expression, and clinical survival data. Five genes TP53, ATM, BRCA1, EGFR, and KRAS, were selected based on their established roles in tumor suppression, oncogenic signaling, and DNA damage response. The overall workflow of the study is illustrated in Fig. 1 . 3.2 Mutation Analysis Mutation analysis was performed using cBioPortal [ 15 ]. The selected genes were queried across LUAD and LUSC datasets to evaluate mutation frequency, alteration types, and distribution patterns. Genomic alterations including missense mutations, truncating mutations, amplifications, and deep deletions were analyzed. OncoPrint visualization was used to represent mutation patterns across patient samples. Mutation frequency was calculated as the percentage of altered cases relative to the total number of samples. 3.3 Gene Expression Analysis Gene expression profiling was conducted using UALCAN [ 21 ], which utilizes TCGA RNA-sequencing data. Expression levels of TP53, ATM, BRCA1, EGFR, and KRAS were compared between tumor and normal tissues in both LUAD and LUSC cohorts. Box plots were generated to visualize expression differences, and statistical significance was assessed using Student’s t-test. A p-value < 0.05 was considered statistically significant. 3.4 Survival Analysis The prognostic significance of gene expression was evaluated using Kaplan-Meier Plotter [ 22 ]. Overall survival (OS) was selected as the clinical endpoint. Patients were stratified into high and low expression groups using the auto-selected best cutoff method. Kaplan–Meier survival curves were generated, and hazard ratios (HR) with 95% confidence intervals were calculated. Statistical significance was determined using the log-rank test. 3.5 Protein–Protein Interaction Network Analysis Protein–protein interaction (PPI) analysis was performed using STRING (version 11.5) [ 23 ]. The selected genes were analyzed as multiple proteins in Homo sapiens. Interaction networks were generated based on known and predicted associations, including direct (physical) and indirect (functional) interactions. The confidence score threshold was set to medium-to-high (≥ 0.4), and network visualization was used to identify key functional relationships among the genes. 3.6 Functional Enrichment Analysis Functional enrichment analysis was conducted using STRING to identify significantly enriched biological pathways and processes. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and Gene Ontology (GO) biological processes were analyzed. Key pathways identified included p53 signaling, DNA damage response, cell cycle regulation, and growth factor signaling pathways relevant to lung cancer progression. These pathways are consistent with previously reported oncogenic signaling networks identified in large-scale cancer genomics studies [ 3 ]. 3.7 External Validation Using GEO Datasets To improve the reliability and reproducibility of the identified biomarkers, external validation was performed using independent Gene Expression Omnibus (GEO) datasets, namely GSE19188 and GSE19804, through the GEO2R online analysis platform provided by the National Center for Biotechnology Information (NCBI). Differential gene expression analysis was conducted between tumor and normal lung tissue samples. Genes were considered significantly differentially expressed based on adjusted p-value, p-value, and log fold change (logFC) values. EGFR, KRAS, and BRCA1 were validated using the GSE19188 dataset, whereas TP53 and ATM were validated using the GSE19804 dataset due to better representation and statistical significance within the respective datasets. The GEO validation analysis was performed to independently confirm the transcriptomic alterations observed in the TCGA-based analyses. 3.8 Statistical Analysis Statistical analyses were performed using the built-in analytical tools available within the respective platforms. Differences in gene expression were evaluated using Student’s t-test, while survival differences were assessed using Kaplan–Meier analysis with log-rank testing. For GEO validation, differential expression significance was assessed using GEO2R-generated adjusted p-values and log fold change values. A p-value < 0.05 was considered statistically significant throughout the study. Schematic representation of the study design, including data acquisition from TCGA datasets, mutation analysis using cBioPortal, gene expression analysis using UALCAN, survival analysis using Kaplan–Meier Plotter, protein–protein interaction analysis using STRING database, and external validation using GEO datasets through GEO2R analysis. The integrated approach enables comprehensive evaluation of genomic, transcriptomic, and clinical data associated with lung cancer. 4. Results 4.1 Mutation Analysis Mutation profiling across TCGA lung cancer cohorts (LUAD and LUSC) revealed a heterogeneous pattern of genomic alterations among the selected genes ( Fig. 2 ). TP53 exhibited the highest mutation frequency (~ 66%), confirming its dominant role as a tumor suppressor frequently disrupted in lung cancer. The mutations observed in TP53 were predominantly missense and truncating mutations, indicating loss of functional protein activity and genomic instability. OncoPrint visualization showing genomic alterations in TP53, ATM, BRCA1, EGFR, and KRAS across TCGA LUAD and LUSC cohorts. KRAS (~ 19%) and EGFR (~ 12%) mutations were observed at moderate frequencies and were primarily associated with oncogenic activation. KRAS mutations were more prominent in LUAD, consistent with its role in driving adenocarcinoma-specific signaling pathways. EGFR alterations, including amplifications and missense mutations, highlight its importance as a therapeutic target in lung cancer. In contrast, ATM (~ 8%) and BRCA1 (~ 4%) exhibited lower mutation frequencies. However, given their critical roles in DNA damage repair pathways, even low-frequency alterations may have significant functional consequences in tumor progression and genomic instability. Overall, the mutation landscape demonstrated a dual pattern characterized by high-frequency tumor suppressor disruption and moderate-frequency oncogenic activation. To investigate whether these genomic alterations translate into transcriptional changes, differential gene expression analysis was performed. 4.2 Gene Expression Analysis Differential gene expression analysis demonstrated significant dysregulation of the selected genes between tumor and normal tissues. TP53, BRCA1, and KRAS were markedly upregulated in tumor samples, suggesting their active involvement in tumor progression and cellular proliferation. Box plot representation of mRNA expression levels of TP53, ATM, BRCA1, EGFR, and KRAS comparing normal and tumor tissues. The panels on the left represent lung adenocarcinoma (LUAD), while the panels on the right represent lung squamous cell carcinoma (LUSC). The results demonstrate distinct expression patterns between normal and tumor samples, highlighting subtype-specific molecular alterations in lung cancer. The elevated expression of TP53, despite its high mutation rate, may reflect accumulation of dysfunctional mutant protein, a phenomenon commonly observed in cancers with TP53 mutations. Similarly, BRCA1 upregulation may indicate compensatory activation of DNA repair mechanisms in response to increased genomic stress in tumor cells. KRAS overexpression further supports its role in promoting oncogenic signaling pathways, particularly those related to cell growth and survival. ATM expression exhibited moderate variation between tumor and normal tissues, indicating a context-dependent role that may vary across cancer subtypes. EGFR displayed subtype-specific expression patterns, with differential regulation observed between LUAD and LUSC. This indicates that EGFR-mediated signaling may contribute differently to tumor biology depending on histological subtype. Collectively, these findings highlight significant transcriptional alterations contributing to lung cancer pathogenesis. Given the observed alterations in gene expression, their potential clinical relevance was further evaluated through survival analysis. 4.3 Survival Analysis Kaplan–Meier survival analysis revealed distinct prognostic implications for the selected genes (Fig. 4 ). Elevated expression of TP53 (HR = 1.37, p < 0.001) and BRCA1 (HR = 1.30, p < 0.001) was significantly associated with reduced overall survival, indicating their potential roles as negative prognostic biomarkers. Kaplan–Meier survival curves illustrating overall survival differences based on gene expression levels for TP53, ATM, BRCA1, EGFR, and KRAS. High expression of TP53 and BRCA1 is associated with reduced survival, whereas ATM and EGFR show improved survival outcomes. KRAS does not exhibit a statistically significant association. The association of high TP53 expression with poor prognosis is consistent with the presence of dysfunctional mutant TP53 protein, which contributes to tumor progression and resistance to apoptosis. Similarly, increased BRCA1 expression may reflect enhanced DNA repair activity in aggressive tumor phenotypes. In contrast, higher expression levels of ATM (HR = 0.59, p < 0.001) and EGFR (HR = 0.78, p < 0.001) were associated with improved survival outcomes. This suggests that intact ATM-mediated DNA repair mechanisms may contribute to better genomic stability and prognosis. The protective association of EGFR expression may reflect its role in early-stage tumors or its responsiveness to targeted therapies. KRAS expression did not demonstrate a statistically significant impact on survival (p > 0.05), indicating that its prognostic relevance may depend more on mutation status rather than expression levels alone. These results emphasize the heterogeneous prognostic roles of the selected genes in lung cancer. To further understand the functional relationships underlying these prognostic differences, protein–protein interaction network analysis was conducted. 4.4 Protein–Protein Interaction Network Analysis Protein–protein interaction (PPI) analysis revealed a highly interconnected network among the selected genes (Fig. 5 ), indicating coordinated functional roles in lung cancer biology. STRING-generated network showing functional associations among TP53, ATM, BRCA1, EGFR, and KRAS, highlighting interactions between DNA repair and signaling pathways. TP53, ATM, and BRCA1 formed a tightly connected cluster, reflecting their central involvement in DNA damage response and genomic stability pathways. This cluster highlights the critical role of DNA repair mechanisms in maintaining cellular integrity and preventing tumor progression. Disruption within this network, particularly through TP53 mutations, may lead to impaired DNA repair and increased mutational burden. EGFR and KRAS were positioned within signaling-related clusters, associated with pathways regulating cell proliferation, differentiation, and survival. Their interactions suggest activation of downstream oncogenic signaling cascades such as MAPK and PI3K pathways. Additionally, the presence of intermediary nodes (e.g., MLH1, PMS2) suggests broader involvement of mismatch repair pathways and genomic maintenance systems in lung cancer biology. Overall, the PPI network demonstrates a functional link between: DNA repair pathways (TP53–ATM–BRCA1 axis) Oncogenic signaling pathways (EGFR–KRAS axis) This integrated interaction framework provides deeper insight into the molecular mechanisms underlying lung cancer progression. 4.5 External Validation of Hub Genes Using GEO Datasets To further validate the reliability of the identified biomarkers, external transcriptomic validation was performed using independent GEO datasets GSE19188 and GSE19804. Differential expression analysis confirmed reproducible dysregulation patterns for TP53, ATM, EGFR, KRAS, and BRCA1 across independent lung cancer cohorts. Table 1 External validation of identified hub genes using GEO datasets Gene GEO Dataset Probe ID adj.P.Val P.Value logFC TP53 GSE19804 201746_at 6.21E-02 9.42E-03 0.478 ATM GSE19804 210858_x_at 1.60E-01 3.93E-02 0.496 EGFR GSE19188 211607_x_at 1.16E-01 3.35E-02 0.267 KRAS GSE19188 204009_s_at 5.79E-02 1.26E-02 0.262 BRCA1 GSE19188 204531_s_at 1.41E-05 1.25E-07 1.448 Table 1 summarizes the validation results obtained from GEO2R analysis. Among the validated genes, BRCA1 demonstrated the strongest differential expression with the highest logFC value and highly significant adjusted p-value, indicating robust transcriptomic validation. KRAS and TP53 also exhibited notable differential expression patterns across datasets. Although EGFR and ATM showed comparatively moderate statistical significance, their expression trends remained consistent with the primary TCGA-based analyses, supporting their potential biological relevance in lung cancer progression. Figure 6 . External validation of identified hub genes using GEO datasets GSE19188 and GSE19804. The bar graph represents the log fold change (logFC) values of TP53, ATM, EGFR, KRAS, and BRCA1 between lung cancer and normal tissue samples. BRCA1 demonstrated the strongest differential expression among the validated genes. 5. Discussion The present study provides an integrated analysis of mutation patterns, gene expression profiles, survival outcomes, protein–protein interaction networks, and external transcriptomic validation for five key genes TP53, ATM, BRCA1, EGFR, and KRAS, in lung cancer. The findings are largely consistent with previously reported studies while also providing additional insights through a multi-dimensional analytical framework. TP53 was identified as the most frequently mutated gene, which aligns with large-scale genomic studies reporting high TP53 mutation frequencies in non-small cell lung cancer (NSCLC). The association between elevated TP53 expression and poor survival further supports its established role in tumor progression, genomic instability, and therapeutic resistance. EGFR and KRAS demonstrated distinct but complementary oncogenic roles in lung cancer progression. EGFR exhibited subtype-specific expression patterns consistent with its clinical relevance in lung adenocarcinoma and its importance as a therapeutic target for tyrosine kinase inhibitors. Interestingly, higher EGFR expression was associated with improved survival in the present analysis, potentially reflecting the beneficial impact of targeted therapies in EGFR-driven tumors. In contrast, KRAS alterations were associated with aggressive tumor behavior and poor prognostic characteristics, although a statistically significant survival association was not observed in this dataset, consistent with the context-dependent prognostic role of KRAS reported in previous studies. The DNA damage response genes ATM and BRCA1 also demonstrated important prognostic implications. ATM, a central regulator of genomic stability and DNA repair signaling, was associated with improved survival, supporting earlier reports that functional ATM activity contributes to enhanced treatment responsiveness and maintenance of genomic integrity. Conversely, BRCA1 overexpression was associated with poorer survival outcomes, suggesting a complex role in lung cancer progression beyond its classical DNA repair functions. Among the validated genes, BRCA1 demonstrated the strongest differential expression across independent GEO datasets, further supporting its potential prognostic relevance. Protein–protein interaction analysis revealed a tightly interconnected network among TP53, ATM, and BRCA1, highlighting their coordinated involvement in DNA damage response pathways. EGFR and KRAS were primarily associated with signaling pathways regulating cell proliferation, survival, and oncogenic activation. The integration of mutation, expression, survival, and interaction analyses provides a more comprehensive understanding of lung cancer biology compared to studies focused on a single molecular dimension. Such multi-omics integration reflects the growing importance of systems-level cancer analysis in translational bioinformatics and precision medicine. To improve the robustness of the findings, the identified hub genes were further validated using independent GEO datasets, including GSE19188 and GSE19804. The external validation demonstrated reproducible expression trends for TP53, ATM, EGFR, KRAS, and BRCA1 across multiple patient cohorts. Although the degree of statistical significance varied among genes, the overall consistency of expression patterns strengthened the reliability and potential prognostic relevance of the identified biomarkers in lung cancer. Despite these findings, several limitations should be acknowledged. The study relied primarily on publicly available transcriptomic datasets and computational analyses without direct experimental validation. Variability in sample size, clinical annotation, and platform differences across datasets may also influence the observed results. Although independent GEO datasets were incorporated for external validation, additional in vitro, in vivo, and large-scale clinical studies are required to further confirm the biological and translational significance of these biomarkers. Overall, the present study highlights TP53, ATM, EGFR, KRAS, and BRCA1 as important molecular contributors to lung cancer progression and prognosis. The integration of multi-omics analysis with external GEO validation strengthens the reliability of these findings and supports the application of integrative bioinformatics approaches for identifying clinically relevant biomarkers and potential therapeutic targets in lung cancer. 6. Conclusion This study presents an integrated multi-omics analysis of TP53, ATM, BRCA1, EGFR, and KRAS in lung cancer using TCGA datasets by combining mutation profiling, gene expression analysis, survival assessment, and protein–protein interaction network analysis. The findings demonstrated that TP53 exhibited the highest mutation frequency and was strongly associated with poor prognosis, reinforcing its critical role as a major tumor suppressor involved in lung cancer pathogenesis. BRCA1 also showed a significant negative prognostic association, suggesting its contribution to tumor progression beyond its established function in DNA damage repair. In contrast, ATM and EGFR were associated with relatively favorable survival outcomes, indicating their potential utility as prognostic biomarkers and therapeutic targets. Although KRAS showed frequent genetic alterations in lung cancer, its prognostic significance at the expression level appeared comparatively limited, highlighting the complexity of its biological role in tumor progression. The protein–protein interaction network further demonstrated the coordinated interplay between DNA damage response pathways involving TP53, ATM, and BRCA1, and oncogenic signaling pathways associated with EGFR and KRAS. These observations emphasize the importance of integrated molecular mechanisms in lung cancer development and progression. Furthermore, external validation using independent GEO datasets (GSE19188 and GSE19804) strengthened the reliability and reproducibility of the identified biomarkers, thereby improving the robustness of the study findings. Overall, this integrative bioinformatics approach provides a comprehensive understanding of the molecular landscape of lung cancer and highlights the significance of combining multi-dimensional genomic datasets for biomarker discovery and precision oncology research. Nevertheless, the present study is limited by its reliance on publicly available transcriptomic datasets and the absence of experimental and clinical validation. Therefore, future studies involving in vitro, in vivo, and clinical investigations are necessary to further confirm the translational applicability of TP53, ATM, BRCA1, EGFR, and KRAS as diagnostic, prognostic, and therapeutic biomarkers in lung cancer. Declarations Author Contribution JV. conceptualized the study, performed data collection and bioinformatics analyses, interpreted the results, prepared the figures and tables, and wrote the main manuscript text. JB. contributed to data interpretation, manuscript review, scientific editing, and overall supervision of the study. All authors reviewed and approved the final manuscript. Data Availability The datasets analyzed during the current study are publicly available in The Cancer Genome Atlas (TCGA) database and the Gene Expression Omnibus (GEO) repository. GEO datasets used for external validation include GSE19188 and GSE19804, which are accessible through the National Center for Biotechnology Information (NCBI) GEO database. All data utilized in this study are publicly available and can be accessed from the respective repositories. References Bray F et al (2018) Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: A Cancer Journal for Clinicians. 68(6):394–424. https://doi.org/10.3322/caac.21492 Siegel RL et al (2020) Cancer statistics, 2020. 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Vijayaraj¹","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA50lEQVRIiWNgGAWjYBACxgYQaWBTzyb/+ACQJSFDrJa0BD6GtASQFh5iLTucIMeQYwBiEdbC3H724OeCgrQ8NoYzn1/dqLHgYWA/fHQDXof15CVLzzCwKWZj7N1mnXMM6DCetLQb+P2SYyDNY5DG2MbMu804hw2oRYLHDL+W/jfGv3kMDjO2sfE8M875R4yWGTlmQFsOJ7bx8DA/zm0jSssbM2ugw4zZJNjMmHP7JHjYCPnFsD/H+DbPHxs5+RnMjz/nfKuT42c/fAy/lgYEm00CTOJTDgLySGzmD4RUj4JRMApGwcgEAOp8QMbjoYc4AAAAAElFTkSuQmCC","orcid":"","institution":"SRM Institute of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Janani","middleName":"","lastName":"Vijayaraj¹","suffix":""},{"id":638748044,"identity":"64ca526b-f492-4b73-8d29-8903314b1799","order_by":1,"name":"Jyoti Bala²","email":"","orcid":"","institution":"Molelixir Informatics","correspondingAuthor":false,"prefix":"","firstName":"Jyoti","middleName":"","lastName":"Bala²","suffix":""}],"badges":[],"createdAt":"2026-05-11 20:23:34","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9683939/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9683939/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109186663,"identity":"7dc2230e-c39f-4ba6-9a44-98f105bff3c2","added_by":"auto","created_at":"2026-05-13 11:14:25","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":279925,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eWorkflow of the integrated bioinformatics analysis.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9683939/v1/2366ab242d74ebd93c813bbd.png"},{"id":109186654,"identity":"fe4afff1-511b-42d5-82dd-cd19c487d907","added_by":"auto","created_at":"2026-05-13 11:14:14","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":127585,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMutation landscape of selected genes across TCGA lung cancer cohorts (LUAD and LUSC).\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9683939/v1/6ec971258edd03df8f20a442.png"},{"id":109186708,"identity":"d531008d-043f-430c-8151-07a83d286a59","added_by":"auto","created_at":"2026-05-13 11:15:02","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":64208,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferential gene expression analysis in LUAD and LUSC.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9683939/v1/e3a693e8b222b31a6b1da056.png"},{"id":109186651,"identity":"9afe4e40-ddba-4044-9eaf-9977dd158398","added_by":"auto","created_at":"2026-05-13 11:14:13","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":187127,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan–Meier survival analysis of selected genes.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9683939/v1/b493ddd50bccde24b2098769.png"},{"id":109186655,"identity":"08f331b6-c29c-4665-a3fc-8aa4f573ab49","added_by":"auto","created_at":"2026-05-13 11:14:14","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":76263,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProtein–protein interaction (PPI) network of selected genes.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-9683939/v1/121f25eb6bf9a5f98727debc.png"},{"id":109186657,"identity":"7a7784bb-f696-4e2c-938b-1fcb998efc73","added_by":"auto","created_at":"2026-05-13 11:14:15","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":23257,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExternal validation of hub genes using independent GEO datasets.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-9683939/v1/94c7fe7ad70caab29a22619f.png"},{"id":109186715,"identity":"a85488e9-c86c-482e-9842-f2738eacd261","added_by":"auto","created_at":"2026-05-13 11:15:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":917000,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9683939/v1/e5583dd9-153d-4531-a7e4-46348f2ad55f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integrated Multi-Omics and Transcriptomic Validation Analysis of TP53, ATM, BRCA1, EGFR, and KRAS Reveals Prognostic Significance in Lung Cancer","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eLung cancer remains one of the leading causes of cancer-related mortality worldwide, accounting for approximately 1.8\u0026nbsp;million deaths annually [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. It is broadly classified into two major histological subtypes: lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), which differ in their molecular characteristics and clinical outcomes. Despite advances in diagnostic and therapeutic strategies, the overall survival rate of lung cancer patients remains low, primarily due to late-stage diagnosis and molecular heterogeneity of tumors.\u003c/p\u003e \u003cp\u003eRecent advances in high-throughput sequencing technologies and large-scale cancer genomics projects such as The Cancer Genome Atlas have enabled comprehensive profiling of genetic and molecular alterations in various cancers. These datasets provide valuable insights into the mutational landscape, gene expression patterns, and clinical associations of cancer-related genes, facilitating the identification of biomarkers for prognosis and targeted therapy. Large-scale integrative analyses across multiple tumor types have revealed conserved oncogenic pathways and molecular subtypes, emphasizing the importance of multi-omics approaches in cancer research [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAmong the key genes implicated in lung cancer, TP53 is one of the most frequently mutated tumor suppressor genes and plays a crucial role in maintaining genomic stability by regulating cell cycle arrest, apoptosis, and DNA repair mechanisms. Mutations in TP53 are reported in a significant proportion of lung cancer cases and are associated with poor clinical outcomes [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Similarly, EGFR mutations are well-established drivers in lung cancer, particularly in LUAD, and have been extensively targeted in precision therapies using tyrosine kinase inhibitors [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The oncogene KRAS is another critical driver mutation involved in abnormal cell signaling and tumor progression, although its prognostic significance varies across studies [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn addition to these oncogenic drivers, genes involved in DNA damage response pathways such as ATM and BRCA1 have gained attention for their roles in maintaining genomic integrity. ATM is a key regulator of the DNA damage response and coordinates repair mechanisms following double-strand breaks [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. BRCA1 is essential for homologous recombination repair and has been implicated in cancer susceptibility and progression [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Dysregulation of these genes can lead to genomic instability, a hallmark of cancer.\u003c/p\u003e \u003cp\u003eWhile previous studies have investigated these genes individually, there remains a lack of comprehensive studies integrating mutation analysis, transcriptomic profiling, survival outcomes, protein interaction networks, and independent external validation in lung cancer. Such integrative approaches are essential for understanding the complex molecular interplay underlying tumor progression and for identifying robust clinically relevant biomarkers with potential translational applicability.\u003c/p\u003e \u003cp\u003eThese advances underscore the transition from single-gene studies to integrative genomic frameworks for biomarker discovery and precision oncology [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Therefore, the present study aimed to perform an integrated multi-omics and transcriptomic validation analysis of TP53, ATM, BRCA1, EGFR, and KRAS using TCGA datasets combined with independent GEO validation cohorts. By integrating genomic, transcriptomic, survival, and protein interaction data, the study sought to elucidate the roles of these genes in lung cancer progression, identify potential prognostic biomarkers, and provide insights into clinically relevant therapeutic targets for precision oncology.\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cp\u003eLung cancer is characterized by extensive genetic heterogeneity and complex molecular alterations that influence tumor progression, treatment response, and patient survival. The heterogeneity of somatic mutations across cancer types and patients has been extensively characterized, highlighting the complexity of identifying driver mutations and therapeutic targets [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Large-scale genomic studies such as The Cancer Genome Atlas have significantly contributed to understanding the molecular landscape of lung cancer, identifying recurrent mutations in key genes including TP53, EGFR, and KRAS [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. These studies have demonstrated that lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) exhibit distinct genomic profiles, emphasizing the need for subtype-specific analyses [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAmong the most extensively studied genes, TP53 is one of the most frequently mutated tumor suppressor genes in lung cancer, with mutation frequencies exceeding 50% in non-small cell lung cancer (NSCLC). TP53 mutations are strongly associated with poor prognosis, therapeutic resistance, and increased genomic instability in non-small cell lung cancer (NSCLC). Several studies have reported that TP53 mutations often co-occur with other oncogenic alterations, influencing tumor evolution and treatment outcomes [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Additionally, TP53 mutations have been shown to contribute to genomic instability, a hallmark of cancer, thereby promoting tumor progression [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe epidermal growth factor receptor (EGFR) gene is another key driver in lung cancer, particularly in LUAD, where activating mutations are associated with sensitivity to tyrosine kinase inhibitors (TKIs). EGFR mutations are among the most clinically actionable alterations in lung cancer, and targeted therapies have significantly improved patient outcomes [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. However, resistance to EGFR-targeted therapies remains a major challenge, often involving secondary mutations or co-occurring genetic alterations such as TP53 mutations.\u003c/p\u003e \u003cp\u003eKRAS mutations are among the most frequently reported oncogenic alterations in lung cancer and are associated with dysregulated MAPK and PI3K-AKT signaling pathways involved in tumor progression and therapeutic resistance [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Recent advances in KRAS-targeted therapies have renewed interest in understanding its biological and clinical significance [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Studies have also reported that KRAS mutations are often mutually exclusive with EGFR mutations, suggesting distinct oncogenic pathways in lung cancer development.\u003c/p\u003e \u003cp\u003eIn addition to oncogenic drivers, alterations in DNA damage response genes such as ATM and BRCA1 have been increasingly implicated in lung cancer progression and genomic instability. These genes are essential components of DNA repair pathways and contribute to the maintenance of genomic integrity and cellular homeostasis. Previous studies have reported that dysregulation of ATM and BRCA1 may influence tumor progression, therapeutic responsiveness, and sensitivity to immunotherapy in lung cancer patients [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Emerging evidence further suggests that abnormalities in DNA repair pathways may serve as clinically relevant biomarkers and potential therapeutic targets in precision oncology.\u003c/p\u003e \u003cp\u003eGene expression profiling has further enhanced the understanding of molecular alterations in lung cancer. Differential expression of oncogenes and tumor suppressor genes has been linked to tumor progression and clinical outcomes. For instance, overexpression of TP53 and KRAS has been associated with aggressive tumor behavior, while variations in EGFR expression have been shown to differ between LUAD and LUSC subtypes [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. High-throughput platforms such as RNA sequencing have enabled large-scale analysis of gene expression patterns, facilitating the identification of potential biomarkers for diagnosis and prognosis.\u003c/p\u003e \u003cp\u003eSurvival analysis studies have highlighted the prognostic significance of key genetic alterations in lung cancer. Elevated TP53 expression and mutation status have been consistently associated with poor survival outcomes, whereas alterations in genes such as ATM may correlate with improved prognosis depending on the context. Similarly, the prognostic value of EGFR mutations has been extensively studied, with evidence suggesting improved survival in patients receiving targeted therapies [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. However, the prognostic role of KRAS remains controversial, with studies reporting variable outcomes depending on co-mutations and tumor subtype.\u003c/p\u003e \u003cp\u003eProtein\u0026ndash;protein interaction (PPI) network analysis has emerged as a powerful approach to understand the functional relationships among genes and proteins involved in cancer. PPI networks provide insights into molecular pathways and biological processes, revealing how multiple genes interact to regulate tumor progression. Studies have shown that TP53, ATM, and BRCA1 are closely linked within DNA damage response networks, while EGFR and KRAS are associated with signaling pathways that regulate cell proliferation and survival. These interactions highlight the complex molecular interplay underlying lung cancer development [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePathway enrichment analyses have further demonstrated that key pathways such as the p53 signaling pathway, PI3K-AKT signaling pathway, and cell cycle regulation are frequently dysregulated in lung cancer. Dysregulation of these pathways contributes substantially to tumor progression, therapeutic resistance, and molecular heterogeneity in lung cancer. Additionally, recent studies have emphasized the role of tumor heterogeneity and clonal evolution in lung cancer, where different genetic alterations may arise during tumor progression, influencing treatment response and prognosis [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite significant advancements in lung cancer genomics, many previous studies have primarily focused on individual genes or isolated molecular mechanisms. Comprehensive studies integrating mutation profiling, transcriptomic alterations, survival outcomes, protein\u0026ndash;protein interaction networks, and external transcriptomic validation remain comparatively limited. Considering the extensive molecular heterogeneity of lung cancer, integrated multi-omics approaches are increasingly important for identifying robust prognostic biomarkers and clinically relevant therapeutic targets.\u003c/p\u003e \u003cp\u003eTherefore, the present study builds upon existing literature by integrating mutation analysis, gene expression profiling, survival analysis, protein interaction network analysis, and independent GEO-based transcriptomic validation to systematically investigate TP53, ATM, BRCA1, EGFR, and KRAS in lung cancer. By combining genomic, transcriptomic, and clinical datasets through multiple complementary analytical platforms, this study aims to provide a comprehensive understanding of the molecular interplay underlying lung cancer progression and prognosis, thereby contributing to the advancement of precision oncology and translational cancer bioinformatics.\u003c/p\u003e"},{"header":"3. Materials and Methods","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Data Source and Study Design\u003c/h2\u003e \u003cp\u003eThis study employed an integrated in silico approach utilizing publicly available datasets from cBioPortal. The TCGA PanCancer Atlas datasets for lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) were selected due to their comprehensive genomic and clinical annotations.\u003c/p\u003e \u003cp\u003eThe LUAD dataset comprised approximately 566 samples, while the LUSC dataset included approximately 504 samples, encompassing mutation, gene expression, and clinical survival data. Five genes TP53, ATM, BRCA1, EGFR, and KRAS, were selected based on their established roles in tumor suppression, oncogenic signaling, and DNA damage response. The overall workflow of the study is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Mutation Analysis\u003c/h2\u003e \u003cp\u003eMutation analysis was performed using cBioPortal [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The selected genes were queried across LUAD and LUSC datasets to evaluate mutation frequency, alteration types, and distribution patterns. Genomic alterations including missense mutations, truncating mutations, amplifications, and deep deletions were analyzed. OncoPrint visualization was used to represent mutation patterns across patient samples. Mutation frequency was calculated as the percentage of altered cases relative to the total number of samples.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Gene Expression Analysis\u003c/h2\u003e \u003cp\u003eGene expression profiling was conducted using UALCAN [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], which utilizes TCGA RNA-sequencing data. Expression levels of TP53, ATM, BRCA1, EGFR, and KRAS were compared between tumor and normal tissues in both LUAD and LUSC cohorts. Box plots were generated to visualize expression differences, and statistical significance was assessed using Student\u0026rsquo;s t-test. A p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Survival Analysis\u003c/h2\u003e \u003cp\u003eThe prognostic significance of gene expression was evaluated using Kaplan-Meier Plotter [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Overall survival (OS) was selected as the clinical endpoint. Patients were stratified into high and low expression groups using the auto-selected best cutoff method. Kaplan\u0026ndash;Meier survival curves were generated, and hazard ratios (HR) with 95% confidence intervals were calculated. Statistical significance was determined using the log-rank test.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Protein\u0026ndash;Protein Interaction Network Analysis\u003c/h2\u003e \u003cp\u003eProtein\u0026ndash;protein interaction (PPI) analysis was performed using STRING (version 11.5) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The selected genes were analyzed as multiple proteins in Homo sapiens. Interaction networks were generated based on known and predicted associations, including direct (physical) and indirect (functional) interactions. The confidence score threshold was set to medium-to-high (\u0026ge;\u0026thinsp;0.4), and network visualization was used to identify key functional relationships among the genes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Functional Enrichment Analysis\u003c/h2\u003e \u003cp\u003eFunctional enrichment analysis was conducted using STRING to identify significantly enriched biological pathways and processes. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and Gene Ontology (GO) biological processes were analyzed. Key pathways identified included p53 signaling, DNA damage response, cell cycle regulation, and growth factor signaling pathways relevant to lung cancer progression. These pathways are consistent with previously reported oncogenic signaling networks identified in large-scale cancer genomics studies [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.7 External Validation Using GEO Datasets\u003c/h2\u003e \u003cp\u003eTo improve the reliability and reproducibility of the identified biomarkers, external validation was performed using independent Gene Expression Omnibus (GEO) datasets, namely GSE19188 and GSE19804, through the GEO2R online analysis platform provided by the National Center for Biotechnology Information (NCBI). Differential gene expression analysis was conducted between tumor and normal lung tissue samples.\u003c/p\u003e \u003cp\u003eGenes were considered significantly differentially expressed based on adjusted p-value, p-value, and log fold change (logFC) values. EGFR, KRAS, and BRCA1 were validated using the GSE19188 dataset, whereas TP53 and ATM were validated using the GSE19804 dataset due to better representation and statistical significance within the respective datasets. The GEO validation analysis was performed to independently confirm the transcriptomic alterations observed in the TCGA-based analyses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.8 Statistical Analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were performed using the built-in analytical tools available within the respective platforms. Differences in gene expression were evaluated using Student\u0026rsquo;s t-test, while survival differences were assessed using Kaplan\u0026ndash;Meier analysis with log-rank testing. For GEO validation, differential expression significance was assessed using GEO2R-generated adjusted p-values and log fold change values. A p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant throughout the study.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSchematic representation of the study design, including data acquisition from TCGA datasets, mutation analysis using cBioPortal, gene expression analysis using UALCAN, survival analysis using Kaplan\u0026ndash;Meier Plotter, protein\u0026ndash;protein interaction analysis using STRING database, and external validation using GEO datasets through GEO2R analysis. The integrated approach enables comprehensive evaluation of genomic, transcriptomic, and clinical data associated with lung cancer.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Mutation Analysis\u003c/h2\u003e \u003cp\u003eMutation profiling across TCGA lung cancer cohorts (LUAD and LUSC) revealed a heterogeneous pattern of genomic alterations among the selected genes \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e TP53 exhibited the highest mutation frequency (~\u0026thinsp;66%), confirming its dominant role as a tumor suppressor frequently disrupted in lung cancer. The mutations observed in TP53 were predominantly missense and truncating mutations, indicating loss of functional protein activity and genomic instability.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eOncoPrint visualization showing genomic alterations in TP53, ATM, BRCA1, EGFR, and KRAS across TCGA LUAD and LUSC cohorts.\u003c/p\u003e \u003cp\u003eKRAS (~\u0026thinsp;19%) and EGFR (~\u0026thinsp;12%) mutations were observed at moderate frequencies and were primarily associated with oncogenic activation. KRAS mutations were more prominent in LUAD, consistent with its role in driving adenocarcinoma-specific signaling pathways. EGFR alterations, including amplifications and missense mutations, highlight its importance as a therapeutic target in lung cancer.\u003c/p\u003e \u003cp\u003eIn contrast, ATM (~\u0026thinsp;8%) and BRCA1 (~\u0026thinsp;4%) exhibited lower mutation frequencies. However, given their critical roles in DNA damage repair pathways, even low-frequency alterations may have significant functional consequences in tumor progression and genomic instability.\u003c/p\u003e \u003cp\u003eOverall, the mutation landscape demonstrated a dual pattern characterized by high-frequency tumor suppressor disruption and moderate-frequency oncogenic activation. To investigate whether these genomic alterations translate into transcriptional changes, differential gene expression analysis was performed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Gene Expression Analysis\u003c/h2\u003e \u003cp\u003eDifferential gene expression analysis demonstrated significant dysregulation of the selected genes between tumor and normal tissues. TP53, BRCA1, and KRAS were markedly upregulated in tumor samples, suggesting their active involvement in tumor progression and cellular proliferation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBox plot representation of mRNA expression levels of TP53, ATM, BRCA1, EGFR, and KRAS comparing normal and tumor tissues. The panels on the left represent lung adenocarcinoma (LUAD), while the panels on the right represent lung squamous cell carcinoma (LUSC). The results demonstrate distinct expression patterns between normal and tumor samples, highlighting subtype-specific molecular alterations in lung cancer.\u003c/p\u003e \u003cp\u003eThe elevated expression of TP53, despite its high mutation rate, may reflect accumulation of dysfunctional mutant protein, a phenomenon commonly observed in cancers with TP53 mutations. Similarly, BRCA1 upregulation may indicate compensatory activation of DNA repair mechanisms in response to increased genomic stress in tumor cells.\u003c/p\u003e \u003cp\u003eKRAS overexpression further supports its role in promoting oncogenic signaling pathways, particularly those related to cell growth and survival. ATM expression exhibited moderate variation between tumor and normal tissues, indicating a context-dependent role that may vary across cancer subtypes.\u003c/p\u003e \u003cp\u003eEGFR displayed subtype-specific expression patterns, with differential regulation observed between LUAD and LUSC. This indicates that EGFR-mediated signaling may contribute differently to tumor biology depending on histological subtype.\u003c/p\u003e \u003cp\u003eCollectively, these findings highlight significant transcriptional alterations contributing to lung cancer pathogenesis.\u003c/p\u003e \u003cp\u003eGiven the observed alterations in gene expression, their potential clinical relevance was further evaluated through survival analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Survival Analysis\u003c/h2\u003e \u003cp\u003eKaplan\u0026ndash;Meier survival analysis revealed distinct prognostic implications for the selected genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Elevated expression of TP53 (HR\u0026thinsp;=\u0026thinsp;1.37, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and BRCA1 (HR\u0026thinsp;=\u0026thinsp;1.30, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) was significantly associated with reduced overall survival, indicating their potential roles as negative prognostic biomarkers.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eKaplan\u0026ndash;Meier survival curves illustrating overall survival differences based on gene expression levels for TP53, ATM, BRCA1, EGFR, and KRAS. High expression of TP53 and BRCA1 is associated with reduced survival, whereas ATM and EGFR show improved survival outcomes. KRAS does not exhibit a statistically significant association.\u003c/p\u003e \u003cp\u003eThe association of high TP53 expression with poor prognosis is consistent with the presence of dysfunctional mutant TP53 protein, which contributes to tumor progression and resistance to apoptosis. Similarly, increased BRCA1 expression may reflect enhanced DNA repair activity in aggressive tumor phenotypes.\u003c/p\u003e \u003cp\u003eIn contrast, higher expression levels of ATM (HR\u0026thinsp;=\u0026thinsp;0.59, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and EGFR (HR\u0026thinsp;=\u0026thinsp;0.78, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were associated with improved survival outcomes. This suggests that intact ATM-mediated DNA repair mechanisms may contribute to better genomic stability and prognosis. The protective association of EGFR expression may reflect its role in early-stage tumors or its responsiveness to targeted therapies.\u003c/p\u003e \u003cp\u003eKRAS expression did not demonstrate a statistically significant impact on survival (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), indicating that its prognostic relevance may depend more on mutation status rather than expression levels alone.\u003c/p\u003e \u003cp\u003eThese results emphasize the heterogeneous prognostic roles of the selected genes in lung cancer. To further understand the functional relationships underlying these prognostic differences, protein\u0026ndash;protein interaction network analysis was conducted.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Protein\u0026ndash;Protein Interaction Network Analysis\u003c/h2\u003e \u003cp\u003eProtein\u0026ndash;protein interaction (PPI) analysis revealed a highly interconnected network among the selected genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), indicating coordinated functional roles in lung cancer biology.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSTRING-generated network showing functional associations among TP53, ATM, BRCA1, EGFR, and KRAS, highlighting interactions between DNA repair and signaling pathways.\u003c/p\u003e \u003cp\u003eTP53, ATM, and BRCA1 formed a tightly connected cluster, reflecting their central involvement in DNA damage response and genomic stability pathways. This cluster highlights the critical role of DNA repair mechanisms in maintaining cellular integrity and preventing tumor progression. Disruption within this network, particularly through TP53 mutations, may lead to impaired DNA repair and increased mutational burden.\u003c/p\u003e \u003cp\u003eEGFR and KRAS were positioned within signaling-related clusters, associated with pathways regulating cell proliferation, differentiation, and survival. Their interactions suggest activation of downstream oncogenic signaling cascades such as MAPK and PI3K pathways.\u003c/p\u003e \u003cp\u003eAdditionally, the presence of intermediary nodes (e.g., MLH1, PMS2) suggests broader involvement of mismatch repair pathways and genomic maintenance systems in lung cancer biology.\u003c/p\u003e \u003cp\u003eOverall, the PPI network demonstrates a functional link between:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eDNA repair pathways (TP53\u0026ndash;ATM\u0026ndash;BRCA1 axis)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eOncogenic signaling pathways (EGFR\u0026ndash;KRAS axis)\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThis integrated interaction framework provides deeper insight into the molecular mechanisms underlying lung cancer progression.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.5 External Validation of Hub Genes Using GEO Datasets\u003c/h2\u003e \u003cp\u003eTo further validate the reliability of the identified biomarkers, external transcriptomic validation was performed using independent GEO datasets GSE19188 and GSE19804. Differential expression analysis confirmed reproducible dysregulation patterns for TP53, ATM, EGFR, KRAS, and BRCA1 across independent lung cancer cohorts.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eExternal validation of identified hub genes using GEO datasets\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGEO Dataset\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProbe ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eadj.P.Val\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP.Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003elogFC\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTP53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGSE19804\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e201746_at\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.21E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.42E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.478\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eATM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGSE19804\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e210858_x_at\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.60E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.93E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.496\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEGFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGSE19188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e211607_x_at\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.16E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.35E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.267\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKRAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGSE19188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e204009_s_at\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.79E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.26E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.262\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBRCA1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGSE19188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e204531_s_at\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.41E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.25E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.448\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the validation results obtained from GEO2R analysis. Among the validated genes, BRCA1 demonstrated the strongest differential expression with the highest logFC value and highly significant adjusted p-value, indicating robust transcriptomic validation. KRAS and TP53 also exhibited notable differential expression patterns across datasets. Although EGFR and ATM showed comparatively moderate statistical significance, their expression trends remained consistent with the primary TCGA-based analyses, supporting their potential biological relevance in lung cancer progression.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. External validation of identified hub genes using GEO datasets GSE19188 and GSE19804. The bar graph represents the log fold change (logFC) values of TP53, ATM, EGFR, KRAS, and BRCA1 between lung cancer and normal tissue samples. BRCA1 demonstrated the strongest differential expression among the validated genes.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Discussion","content":"\u003cp\u003eThe present study provides an integrated analysis of mutation patterns, gene expression profiles, survival outcomes, protein\u0026ndash;protein interaction networks, and external transcriptomic validation for five key genes TP53, ATM, BRCA1, EGFR, and KRAS, in lung cancer. The findings are largely consistent with previously reported studies while also providing additional insights through a multi-dimensional analytical framework.\u003c/p\u003e \u003cp\u003eTP53 was identified as the most frequently mutated gene, which aligns with large-scale genomic studies reporting high TP53 mutation frequencies in non-small cell lung cancer (NSCLC). The association between elevated TP53 expression and poor survival further supports its established role in tumor progression, genomic instability, and therapeutic resistance. EGFR and KRAS demonstrated distinct but complementary oncogenic roles in lung cancer progression. EGFR exhibited subtype-specific expression patterns consistent with its clinical relevance in lung adenocarcinoma and its importance as a therapeutic target for tyrosine kinase inhibitors. Interestingly, higher EGFR expression was associated with improved survival in the present analysis, potentially reflecting the beneficial impact of targeted therapies in EGFR-driven tumors. In contrast, KRAS alterations were associated with aggressive tumor behavior and poor prognostic characteristics, although a statistically significant survival association was not observed in this dataset, consistent with the context-dependent prognostic role of KRAS reported in previous studies.\u003c/p\u003e \u003cp\u003eThe DNA damage response genes ATM and BRCA1 also demonstrated important prognostic implications. ATM, a central regulator of genomic stability and DNA repair signaling, was associated with improved survival, supporting earlier reports that functional ATM activity contributes to enhanced treatment responsiveness and maintenance of genomic integrity. Conversely, BRCA1 overexpression was associated with poorer survival outcomes, suggesting a complex role in lung cancer progression beyond its classical DNA repair functions. Among the validated genes, BRCA1 demonstrated the strongest differential expression across independent GEO datasets, further supporting its potential prognostic relevance.\u003c/p\u003e \u003cp\u003eProtein\u0026ndash;protein interaction analysis revealed a tightly interconnected network among TP53, ATM, and BRCA1, highlighting their coordinated involvement in DNA damage response pathways. EGFR and KRAS were primarily associated with signaling pathways regulating cell proliferation, survival, and oncogenic activation. The integration of mutation, expression, survival, and interaction analyses provides a more comprehensive understanding of lung cancer biology compared to studies focused on a single molecular dimension. Such multi-omics integration reflects the growing importance of systems-level cancer analysis in translational bioinformatics and precision medicine.\u003c/p\u003e \u003cp\u003eTo improve the robustness of the findings, the identified hub genes were further validated using independent GEO datasets, including GSE19188 and GSE19804. The external validation demonstrated reproducible expression trends for TP53, ATM, EGFR, KRAS, and BRCA1 across multiple patient cohorts. Although the degree of statistical significance varied among genes, the overall consistency of expression patterns strengthened the reliability and potential prognostic relevance of the identified biomarkers in lung cancer.\u003c/p\u003e \u003cp\u003eDespite these findings, several limitations should be acknowledged. The study relied primarily on publicly available transcriptomic datasets and computational analyses without direct experimental validation. Variability in sample size, clinical annotation, and platform differences across datasets may also influence the observed results. Although independent GEO datasets were incorporated for external validation, additional in vitro, in vivo, and large-scale clinical studies are required to further confirm the biological and translational significance of these biomarkers.\u003c/p\u003e \u003cp\u003eOverall, the present study highlights TP53, ATM, EGFR, KRAS, and BRCA1 as important molecular contributors to lung cancer progression and prognosis. The integration of multi-omics analysis with external GEO validation strengthens the reliability of these findings and supports the application of integrative bioinformatics approaches for identifying clinically relevant biomarkers and potential therapeutic targets in lung cancer.\u003c/p\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eThis study presents an integrated multi-omics analysis of TP53, ATM, BRCA1, EGFR, and KRAS in lung cancer using TCGA datasets by combining mutation profiling, gene expression analysis, survival assessment, and protein\u0026ndash;protein interaction network analysis. The findings demonstrated that TP53 exhibited the highest mutation frequency and was strongly associated with poor prognosis, reinforcing its critical role as a major tumor suppressor involved in lung cancer pathogenesis. BRCA1 also showed a significant negative prognostic association, suggesting its contribution to tumor progression beyond its established function in DNA damage repair. In contrast, ATM and EGFR were associated with relatively favorable survival outcomes, indicating their potential utility as prognostic biomarkers and therapeutic targets. Although KRAS showed frequent genetic alterations in lung cancer, its prognostic significance at the expression level appeared comparatively limited, highlighting the complexity of its biological role in tumor progression.\u003c/p\u003e \u003cp\u003eThe protein\u0026ndash;protein interaction network further demonstrated the coordinated interplay between DNA damage response pathways involving TP53, ATM, and BRCA1, and oncogenic signaling pathways associated with EGFR and KRAS. These observations emphasize the importance of integrated molecular mechanisms in lung cancer development and progression. Furthermore, external validation using independent GEO datasets (GSE19188 and GSE19804) strengthened the reliability and reproducibility of the identified biomarkers, thereby improving the robustness of the study findings.\u003c/p\u003e \u003cp\u003eOverall, this integrative bioinformatics approach provides a comprehensive understanding of the molecular landscape of lung cancer and highlights the significance of combining multi-dimensional genomic datasets for biomarker discovery and precision oncology research. Nevertheless, the present study is limited by its reliance on publicly available transcriptomic datasets and the absence of experimental and clinical validation. Therefore, future studies involving in vitro, in vivo, and clinical investigations are necessary to further confirm the translational applicability of TP53, ATM, BRCA1, EGFR, and KRAS as diagnostic, prognostic, and therapeutic biomarkers in lung cancer.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eJV. conceptualized the study, performed data collection and bioinformatics analyses, interpreted the results, prepared the figures and tables, and wrote the main manuscript text. JB. contributed to data interpretation, manuscript review, scientific editing, and overall supervision of the study. All authors reviewed and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets analyzed during the current study are publicly available in The Cancer Genome Atlas (TCGA) database and the Gene Expression Omnibus (GEO) repository. GEO datasets used for external validation include GSE19188 and GSE19804, which are accessible through the National Center for Biotechnology Information (NCBI) GEO database. All data utilized in this study are publicly available and can be accessed from the respective repositories.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBray F et al (2018) Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: A Cancer Journal for Clinicians. 68(6):394\u0026ndash;424. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3322/caac.21492\u003c/span\u003e\u003cspan address=\"10.3322/caac.21492\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSiegel RL et al (2020) Cancer statistics, 2020. 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Nucleic Acids Res 49(D1):D605\u0026ndash;D612. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/nar/gkaa1074\u003c/span\u003e\u003cspan address=\"10.1093/nar/gkaa1074\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Lung cancer, Multi-omics analysis, TP53, EGFR, KRAS, BRCA1, GEO validation, Prognostic biomarkers","lastPublishedDoi":"10.21203/rs.3.rs-9683939/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9683939/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLung cancer remains one of the leading causes of cancer-related mortality worldwide, necessitating improved understanding of its molecular mechanisms for effective diagnosis and therapy. In the present study, an integrated multi-omics bioinformatics approach combined with independent transcriptomic validation was employed to investigate the prognostic significance of five key genes\u0026mdash;TP53, ATM, BRCA1, EGFR, and KRAS\u0026mdash;using The Cancer Genome Atlas (TCGA) datasets for lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC). Mutation analysis revealed that TP53 exhibited the highest alteration frequency, followed by KRAS and EGFR, indicating their critical involvement in lung tumorigenesis. Gene expression analysis demonstrated significant differential regulation between tumor and normal tissues, with TP53, BRCA1, and KRAS showing upregulated expression, whereas EGFR displayed subtype-specific variation. Kaplan\u0026ndash;Meier survival analysis indicated that elevated expression of TP53 and BRCA1 was significantly associated with poor overall survival, while ATM and EGFR were associated with relatively favorable prognosis. Protein\u0026ndash;protein interaction analysis further revealed strong functional connectivity among these genes, particularly in pathways related to DNA damage response, cell cycle regulation, and oncogenic signaling. To strengthen the reliability of the findings, external validation was performed using independent GEO datasets (GSE19188 and GSE19804), which confirmed the differential expression patterns of the selected genes. Overall, the integration of genomic, transcriptomic, and clinical data provides a comprehensive understanding of the molecular landscape of lung cancer and highlights TP53, ATM, BRCA1, EGFR, and KRAS as potential prognostic biomarkers and therapeutic targets. These findings may contribute to future precision oncology and translational lung cancer research.\u003c/p\u003e","manuscriptTitle":"Integrated Multi-Omics and Transcriptomic Validation Analysis of TP53, ATM, BRCA1, EGFR, and KRAS Reveals Prognostic Significance in Lung Cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-13 11:11:00","doi":"10.21203/rs.3.rs-9683939/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"dd576c16-2fc9-4a61-99dd-094577c5aa3a","owner":[],"postedDate":"May 13th, 2026","published":true,"recentEditorialEvents":[{"type":"submitted","content":"Functional \u0026 Integrative Genomics","date":"2026-05-11T20:14:34+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-13T11:11:01+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-13 11:11:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9683939","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9683939","identity":"rs-9683939","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}