Comprehensive pan-cancer analysis identifies H2AFX associated with poor prognosis in lung adenocarcinoma

Comprehensive pan-cancer analysis identifies H2AFX associated with poor prognosis in lung adenocarcinoma | 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 Comprehensive pan-cancer analysis identifies H2AFX associated with poor prognosis in lung adenocarcinoma Tiankai Yuan, Dingguo Wang, yong bing wu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8161785/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Objective: Lung adenocarcinoma (LUAD), the most prevalent histological subtype of lung cancer, develops through complex molecular regulatory networks. Despite significant advances in targeted therapies, there remains a critical shortage of LUAD-specific biomarkers successfully translated to clinical practice. This study aims to investigate the role of H2AFX, an essential histone H2A variant involved in maintaining genomic stability and chromatin remodeling, in LUAD pathogenesis and its clinical relevance. Methods: We employed an integrated bioinformatics approach using R programming and multiple public databases. Genetic alterations and expression profiles of H2AFX were analyzed through cBioPortal, TIMER2, Sangboxer, and TCGA databases. Advanced bioinformatics tools including TISIDB, ESTIMATE, and CIBERSORT were utilized to assess H2AFX's clinical correlations, prognostic value, and impact on the tumor immune microenvironment. Results: Our analysis revealed H2AFX's significant involvement in regulating the tumor microenvironment and immune modulation. Clinical investigations demonstrated that H2AFX overexpression strongly correlates with poor clinical outcomes in LUAD patients, establishing its independent prognostic significance. Conclusion: H2AFX shows substantial therapeutic potential in precision medicine and represents a promising dual-purpose biomarker for both prognostic assessment and immunological characterization in LUAD. H2AFX LUAD Pan-Cancer Immune Subtypes Immunity-Related Analysis Analysis of Clinical Correlates Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Cancer remains a paramount global health challenge, with IARC (International Agency for Research on Cancer, IARC) reporting 20 million new cases and 9.7 million deaths in 2022. Lung cancer, accounting for 18% of cancer mortality, demonstrates particular clinical urgency. (Pérez-Díez等 2021)NSCLC represents 85% of cases, of which LUAD constitutes 40-50% and shows increasing incidence. (Bray等 2024; Molina等 2009)The evolution of LUAD treatment from conventional therapies to precision approaches (targeted/immunotherapy) underscores the critical need for reliable prognostic biomarkers.(Oser等 2015; Schabath和Cote 2019; Song等 2019; S. Wang等 2019; Zulfiqar等 2022) H2AFX (H2A histone family member X) is an evolutionarily highly conserved histone variant that plays an indispensable role in maintaining genomic stability and DNA damage repair. When cells experience DNA double-strand breaks, the serine 139 residue of H2AFX undergoes rapid phosphorylation to form γ-H2AX.(Bonner等 2008b; Mah, El-Osta和Karagiannis 2010) (Dibitetto等 2024)This characteristic molecular event serves as a sensitive biomarker for DNA damage response (DDR), which specifically recruits repair protein complexes to the damage site to initiate complete repair processes.(Bergink等 2006; Lai和Chan 2024) (Andrea Kinner等 2008; Mah, El-Osta和Karagiannis 2010)Beyond its canonical function, recent studies have revealed that H2AFX extensively participates in critical biological processes, such as chromatin dynamic remodeling and gene transcriptional regulation, through diverse post-translational modifications, including phosphorylation, ubiquitination, and methylation.(Barski等 2007; Giaimo等 2019; Jeffery等 2021; Joo等 2007; A. Kinner等 2008) (Bonner等 2008a)Particularly noteworthy is the dual role H2AFX exhibits in tumorigenesis: on one hand.(Prabhu等 2024a) In various malignant tumors, clinical studies have confirmed that H2AFX overexpression is significantly associated with poor patient prognosis. The potential mechanisms may involve remodeling of the tumor immune microenvironment, thereby promoting tumor immune escape. Recent studies have also found that H2AFX, as a core member of lactation modification-related gene networks, with its expression characteristics exhibiting significant correlations with immunotherapy responsiveness.(L. Chen等 2022; Yu等 2021; D. Zhang等 2019; Y. Zhang等 2024) This study integrates bioinformatics and experimental approaches to elucidate H2AFX's pan-cancer roles. Utilizing multi-omics databases and R-based analytics, we systematically characterize H2AFX expression and prognostic significance. Functional enrichment analyses further delineate its immunomodulatory effects in LUAD. Materials and Methods Genetic Mutation and Expression Analysis cBioPortal ( https://www.cbioportal.org/ ) was employed to analyze the mutation profile of H2AFX . We investigated mutations, copy number alterations (CNAs), and gene fusion events in H2AFX using the standardized pan-cancer dataset and LUAD-specific datasets from the cBioPortal database. Subsequently, TIMER2.0 ( http://timer.comp-genomics.org/ ) and GEPIA2 ( http://gepia2.cancer-pku.cn/ ) were utilized to compare H2AFX mRNA expression levels between tumor tissues and adjacent normal tissues across multiple cancer types. The Xena Browser ( https://xenabrowser.net/datapages/ ) was used to download H2AFX expression data and corresponding clinical information from diverse patient cohorts. (Xu等 2024)A pan-cancer radar chart was generated using the R package fmsb. Furthermore, the R package TCGAbiolinks was applied to retrieve the LUAD expression matrix from the Cancer Genome Atlas (TCGA) database, followed by differential expression analysis of H2AFX between tumor and normal tissues.(De Braekeleer等 2017; Wu等 2021; Zoabi和Shomron 2021) Finally, GEPIA2 was used to correlate H2AFX gene expression with mutational landscapes in LUAD. Prognostic Analysis of H2AFX in Pan-Cancer We conducted univariate survival analysis using GEPIA2 to investigate the association between H2AFX expression levels and clinical outcomes across multiple cancer types, including overall survival (OS), disease-free interval (DFI), disease-specific survival (DSS), and progression-free interval (PFI). The Kaplan-Meier method was employed to compare survival rates between high and low H2AFX expression groups based on survival map results derived from the GEPIA2 database. Furthermore, we performed both univariate and multivariate Cox proportional hazards regression analyses using the R packages survival, rms, and timeROC. A prognostic nomogram model was developed and validated through calibration curves. (Balachandran等 2015; X. Wang等 2022)To evaluate the predictive performance, time-dependent receiver operating characteristic (ROC) curves were generated for different follow-up years. Distribution of H2AFX Expression Across Molecular and Immune Subtypes in Human Cancers The TISIDB database ( http://cis.hku.hk/TISIDB/ ), a web portal for tumor-immune system interactions integrating diverse heterogeneous data types, was utilized to investigate the association between H2AFX expression and both molecular subtypes and pan-cancer immune subtypes. (Ru等 2019)Molecular subtypes were tumor-specific, while immune subtypes were categorized as follows: C1 (wound healing), C2 (IFN-γ dominant), C3 (inflammatory), C4 (lymphocyte depleted), C5 (immunologically quiet), and C6 (TGF-β dominant). Enrichment Analysis Differential gene expression analysis between H2AFX expression groups was performed using the R package limma. Functional enrichment analyses, including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses, were conducted using the R package clusterProfiler. (M. Kanehisa和Goto 2000; Minoru Kanehisa 2002 ; Minoru Kanehisa等 2016, 2017, 2023)Gene Set Enrichment Analysis (GSEA) was additionally performed to identify significantly enriched pathways.(Subramanian等 2005) Gene sets (H, C2, and C5 collections) were obtained from the Molecular Signatures Database (MSigDB; http://www.gsea-msigdb.org/gsea/msigdb/index.jsp ). Samples were stratified into two subgroups based on median H2AFX expression levels to investigate associated pathways and molecular mechanisms.(Liberzon等 2015) Immune Infiltration Analysis We first performed ESTIMATE analysis to evaluate the immune and stromal components associated with H2AFX expression in LUAD using the SangerBox platform ( http://vip.sangerbox.com/home.html ). Subsequently, we employed the R package CIBERSORT to quantify immune cell infiltration patterns based on H2AFX expression levels.(D. Chen等 2024; Shen等 2022) To validate and extend these findings, we conducted comprehensive correlation analyses between H2AFX expression and tumor-associated immune cells (including B cells, CD8 + T cells, CD4 + T cells, macrophages, neutrophils, and dendritic cells) using the TIMER 2.0 database. Immunohistochemical (IHC) Analysis First, we performed immunohistochemical staining on both LUAD tissues and normal tissues, followed by grayscale value quantification and comparative analysis. To validate the immune infiltration results, we initially conducted IHC staining on a tissue microarray containing samples from 60 LUAD patients. Based on H2AFX expression levels, these samples were categorized into high-expression and low-expression groups. Subsequently, we performed IHC staining for classical antigens of various immune cells on blank tissue microarrays from the same sample set, with subsequent grayscale value quantification. Comparative analyses were then conducted between the high- and low-expression groups. Western Blot Analysis To validate the aberrant expression of H2AFX in LUAD, we extracted proteins from normal lung epithelial cells (BEAS-2B RRID: CVCL_0168) and three lung cancer cell lines (A549 RRID: CVCL_0023, HCC827 RRID: CVCL_2063, and H1299 RRID: CVCL_0060). Protein expression levels were determined by Western blot using antibodies from Proteintech Group. Quantitative analysis of band intensities was performed using ImageJ-win64 software. Statistical Analysis Statistical analyses were performed using R software (v4.4.2) and multiple online databases. The Wilcoxon rank-sum test was employed to determine the significance between two groups. For non-normally distributed variables, the Wilcoxon test was used for two-group comparisons, while the Kruskal-Wallis test was applied for differences among three or more groups. Spearman's correlation analysis was utilized to assess associations. Survival time differences between risk groups were estimated using Kaplan-Meier curves with log-rank tests. Results H2AFX Alterations and Expression Patterns Across Cancer Types Building upon these findings that establish H2AFX overexpression across multiple malignancies. In subsequent analyses. Our pan-cancer analysis reveals H2AFX copy number variations (CNVs) as fundamental genomic hallmarks that significantly contribute to tumorigenesis through somatic alterations. Comprehensive characterization of H2AFX genetic alterations across diverse malignancies demonstrates gene amplification as the predominant variant (Fig. 1 A), exhibiting substantially higher prevalence than other mutation classes, including deletions and point mutations, with copy number amplifications (CNAs) showing particular enrichment in clinically relevant cancers such as ovarian carcinoma, osteosarcoma, and pulmonary malignancies. (Quan等 2025)Systematic investigation of TCGA RNA-seq data (TPM-normalized) reveals consistent H2AFX mRNA upregulation in tumor versus matched normal tissues across multiple cancer types (Fig. 1 B), a finding robustly validated through TIMER2.0 and GEPIA2 platforms which confirm significant H2AFX overexpression in 25 distinct malignancies spanning adrenocortical carcinoma (ACC), bladder urothelial carcinoma (BLCA), breast invasive carcinoma (BRCA), and other major cancer types, while uniquely identifying acute myeloid leukemia (LAML) as exhibiting exclusive downregulation in GEPIA2 analyses, with all platforms consistently demonstrating pronounced H2AFX overexpression in LUAD that underscores its potential clinical significance in oncology (Fig. 1 C-D). Experimental Validation of H2AFX in LUAD To elucidate the role of H2AFX in LUAD tumor progression based on its heterogeneous mRNA expression patterns, we initially performed Western blot analysis to compare H2AFX protein levels between normal lung epithelial cells (BEAS-2B RRID: CVCL_0168) and multiple lung cancer cell lines (A549 RRID: CVCL_0023, HCC827 RRID: CVCL_2063, and H1299 RRID: CVCL_0060). The results revealed a significant upregulation of H2AFX expression in lung cancer cells relative to normal lung epithelial cells (Fig. 2 A). We subsequently performed immunohistochemical (IHC) staining which revealed predominant nuclear localization of H2AFX protein with negligible cytoplasmic staining. Consistent with its mRNA expression profile, H2AFX protein level was significantly elevated in LUAD tissues compared to normal lung tissues (Fig. 2 B). Association between H2AFX Expression and Molecular Subtypes with Prognosis in Various Cancers Our systematic analyses have consistently demonstrated elevated H2AFX expression across multiple cancer types. Given the well-documented heterogeneity of malignancies, where substantial variations exist in disease progression, therapeutic response, and survival outcomes among patients, the paradigm shift from conventional histopathological classification to molecular subtyping provides enhanced diagnostic precision. The Tumor Immune System Interaction Database (TISIDB) enables molecular stratification of tumors, through which we identified significant differential H2AFX expression (P < 0.05) across distinct molecular subtypes, suggesting its potential prognostic relevance (Fig. 3 A). To rigorously evaluate the prognostic significance of H2AFX, we performed pan-cancer analyses using the GEPIA 2 platform. Our data established H2AFX as a robust negative prognostic indicator for lower-grade glioma (LGG), adrenocortical carcinoma (ACC), kidney renal clear cell carcinoma (KIRC), pan-kidney cohort (KIPAN), mesothelioma (MESO), LUAD, liver hepatocellular carcinoma (LIHC), kidney renal papillary cell carcinoma (KIRP), acute myeloid leukemia (LAML), prostate adenocarcinoma (PRAD), neuroblastoma (NB), and sarcoma (SARC) (Fig. 3 B). These findings strongly implicate H2AFX in the regulation of overall survival in cancer patients. Validation studies using GEPIA 2 revealed statistically significant associations (P < 0.05) between H2AFX expression levels and overall survival in adrenocortical carcinoma (ACC), kidney renal clear cell carcinoma (KIRC), cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), head and neck squamous cell carcinoma (HNSC), acute myeloid leukemia (LAML), brain lower-grade glioma (LGG), LUAD, and thyroid carcinoma (THCA) (Fig. 3 C). Notably, elevated H2AFX expression consistently correlated with unfavorable clinical outcomes. Collectively, these results provide compelling evidence that H2AFX serves as a critical determinant of patient survival across diverse malignancies. H2AFX Expression Correlates with Immune Subtypes and Clinical Characteristics in LUAD The tumor immune microenvironment (TIME) and intertumoral mRNA modifications are critical regulators of tumorigenesis and progression. Using the TISIDB database, we assessed whether H2AFX modulates tumor development through TIME regulation. Patients were classified into six immune subtypes (Fig. 4 A): C1 (Wound Healing), C2 (IFN-γ Dominant), C3 (Inflammatory), C4 (Lymphocyte-Depleted), C5 (Immunologically Quiet), and C6 (TGF-β Dominant). Our data confirm H2AFX's tumor-promoting role across multiple cancers and its negative impact on patient survival. Focusing on LUAD , we employed integrated bioinformatics approaches to explore its mechanistic contributions. TCGA transcriptomic analysis verified H2AFX overexpression in LUAD and its association with worse clinical outcomes (Figs. 4 B-C). We hypothesized that H2AFX promotes tumor progression and metastasis, leading to poor prognosis. Indeed, H2AFX levels correlated significantly with advanced T-stage (p = 0.0032), N-stage (p = 0.014), and overall pathological stage (p = 0.0036), but not with M-stage (p = 0.29) (Figs. 4 D-G). To evaluate H2AFX's independent prognostic value in LUAD, we conducted univariate and multivariate Cox regression analyses using Xena database clinical data. H2AFX expression emerged as an independent predictor of poor prognosis (Fig. 4 H). For clinical application, we developed a prognostic nomogram combining H2AFX expression with other significant covariates from multivariate analysis (Fig. 4 J). Model validation included. Calibration curves indicating high concordance between predicted and observed survival and time-dependent ROC analyses confirming strong predictive accuracy (Fig. 4 K). Relationship Between H2AFX and Mutational Patterns We first utilized cBioPortal to identify somatic mutations in H2AFX. Two missense mutation loci were detected in the exonic regions of H2AFX (Fig. 5 A). Additionally, we analyzed 11 LUAD datasets and found the overall mutation frequency of H2AFX to be 1.5% (Fig. 5 B), with amplification being the predominant mutation type (Fig. 5 C). Paradoxically, comparison of prognosis between mutated and non-mutated groups revealed better overall survival in the mutated group (Fig. 5 D), which may be attributed to the limited sample size. Furthermore, we conducted mutational landscape analysis based on H2AFX expression levels. As shown in Fig. 5 F, 15 genes exhibited differential mutation frequencies between H2AFX-high and H2AFX-low groups, suggesting a significant association between H2AFX expression and mutation rates in lung cancer (Fig. 5 F). Comprehensive Investigation of H2AFX in LUAD Pathogenesis and Immune Modulation Based on our previous findings demonstrating that H2AFX promotes tumor growth and metastasis, thereby affecting patient prognosis, this study further investigated the specific biological mechanisms through which H2AFX facilitates LUAD progression. The research began with differential expression analysis of H2AFX-associated signature genes (Fig. 6 A). Subsequent GO and KEGG pathway enrichment analyses revealed significant associations with multiple immune-related activities. Gene Set Enrichment Analysis (GSEA) further confirmed that H2AFX was significantly associated with multiple fundamental biological activities, particularly: Immune-related processes, DNA repair mechanisms, and Cell cycle regulation (Figs. 6 B-E). Considering prior studies have well established H2AFX’s essential roles in cell cycle control and chromatin repair, this study specifically focused on its biological functions in tumor immunity. Initial ESTIMATE analysis demonstrated that H2AFX expression levels were significantly negatively correlated with all three major immune scores (Fig. 7 A). Further analysis using the CIBERSORT algorithm on TCGA LUAD expression profiles elucidated the impact of H2AFX on specific immune cell subsets. The data revealed statistically significant negative correlations between H2AFX expression and most tumor-infiltrating immune cell types (Figs. 7 B-D, 7 F), including naïve B cells, CD8 + T cells, regulatory T cells (Tregs), activated NK cells, M0 macrophages, and other immune cell types. These findings were independently validated using the TIMER2.0 database, showing substantial consistency with the TCGA-based results (Fig. 7 E). Validation of H2AFX's Immunological Relevance To validate our previous immune-related analyses, we performed immunohistochemical (IHC) staining for multiple immune cell markers (CD4, CD8 T cells, dendritic cells, NK cells, and macrophages) on the same tissue microarray (Fig. 8 ). The experimental results demonstrated substantial consistency with the bioinformatics analysis findings Discussion This study analyzed the major mutation types of H2AFX in cancers through cBioPortal, identifying gene amplification as the predominant alteration. Subsequently, by integrating online databases such as UCSC XENA and TIMER2, we examined the mRNA expression of H2AFX and found significant differences in its expression across various tumors. These results are consistent with previous studies in cancers like hepatocellular carcinoma and breast cancer.(Hu, Zhong和Jiang 2023) (Dibitetto等 2024; Yao和Chen 2023)Secondly, we employed online analysis tools such as GEPIA to investigate the relationship between H2AFX expression and prognosis. These findings further support the potential of H2AFX as a biomarker for these tumors. In summary, H2AFX may play diverse roles across multiple cancer types. This study discovered that H2AFX is generally upregulated in pan-cancer analysis and may serve as a poor prognostic marker for multiple cancers, particularly in LUAD, where its overexpression is significantly correlated with advanced T stage, N stage, and overall pathological stage. Through univariate and multivariate COX analyses, we constructed a nomogram model to facilitate clinical translation. To further explore the tumor-promoting mechanisms of H2AFX, we conducted functional enrichment analysis, which showed that H2AFX-related genes are significantly enriched in multiple classical oncogenic signaling pathways. GSEA analysis confirmed H2AFX's involvement in various immune-related activities, DNA damage repair, and cell cycle regulation, consistent with its canonical role in maintaining genomic stability.(Prabhu等 2024b; Yan等 2019) However, in LUAD, aberrant H2AFX expression may hijack these pathways to promote tumor progression. Notably, based on the enrichment analysis results, we further investigated and found that H2AFX expression is significantly negatively correlated with ESTIMATE immune scores, suggesting that it may suppress anti-tumor immune responses by regulating immune checkpoints or cytokine signaling pathways. Moreover, this study is the first to reveal the critical role of H2AFX in tumor immune evasion. We found that H2AFX expression is significantly negatively correlated with the infiltration of immune cells such as CD8 + T cells, NK cells, and dendritic cells, indicating its potential involvement in shaping an immunosuppressive tumor microenvironment. Immunohistochemical validation further confirmed that the high H2AFX expression group exhibited reduced levels of multiple immune cell markers. The clinical significance of this study is reflected in the prognostic nomogram based on H2AFX, which demonstrated excellent predictive performance. Targeting H2AFX or its downstream effectors may enhance the efficacy of immune checkpoint inhibitors. However, this study has certain limitations: The findings require experimental validation and are potentially limited by the cohort size. Moreover, future studies with expanded cohorts are needed to address these limitations. Conclusions Pan-cancer analyses establish H2AFX as a promising biomarker for tumor diagnosis, prognosis, and immunity, with particular clinical value in LUAD. Declarations Acknowledgments None. Authors’ contributions T-K Y, D-G W conceived and designed the experiments; T-K Performed the experiments; T-K Y and analyzed the data; T-K Y wrote the paper. All authors have read and approved the final manuscript. Funding This work was supported by the National Natural Science Foundation of China (82360505 and 82360545) and Department of Science and Technology of Jiangxi Province (20224ACB206028 and 2024ZD00). Availability of data and materials All data in our study are available upon request. Ethics approval and consent to participate This study was conducted in accordance with the ethical principles of the Declaration of Helsinki. Publicly available, de-identified data from The Cancer Genome Atlas (TCGA) were used. The use of such data is exempt from additional ethical approval and patient consent. For data and/or samples collected from participants at The Second Affiliated Hospital of Nanchang University, this part of the study was approved by the Ethics Committee of The Second Affiliated Hospital of Nanchang University (Approval No.: IIT-O-2025-056). All procedures performed in these studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. Informed consent was obtained from all individual participants included in this part of the study. Consent for publication Not applicable. Competing interests None. Author details Department of Cardiothoracic Surgery, the Second Affiliated Hospital of Nanchang University, 1 Ming de Road, Nanchang 330000, Jiangxi, People’s Republic of China. Email Address of the Corresponding Author [email protected] City in the affiliation for author(s) NanChang City in the affiliation for author The Second Affiliated Hospital of Nanchang University, Nanchang, Nanchang, Jiangxi Province Country affiliation for the author China References Balachandran, Vinod P., Mithat Gonen, J. Joshua Smith和Ronald P. DeMatteo. 2015. 《Nomograms in Oncology: More than Meets the Eye.》 The Lancet. Oncology 16(4): e173-180. doi:10.1016/S1470-2045(14)71116-7. Barski, Artem, Suresh Cuddapah, Kairong Cui, Tae-Young Roh, Dustin E. Schones, Zhibin Wang, Gang Wei, Iouri Chepelev和Keji Zhao. 2007. 《High-Resolution Profiling of Histone Methylations in the Human Genome》. Cell 129(4): 823～37. doi:10.1016/j.cell.2007.05.009. Bergink, Steven, Florian A. Salomons, Deborah Hoogstraten, Tom A.M. Groothuis, Harm De Waard, Junxin Wu, Li Yuan, 等. 2006. 《DNA Damage Triggers Nucleotide Excision Repair-Dependent Monoubiquitylation of Histone H2A》. Genes & Development 20(10): 1343～52. doi:10.1101/gad.373706. Bonner, William M., Christophe E. Redon, Jennifer S. Dickey, Asako J. Nakamura, Olga A. Sedelnikova, Stéphanie Solier和Yves Pommier. 2008a. 《GammaH2AX and Cancer.》 Nature reviews. Cancer 8(12): 957～67. doi:10.1038/nrc2523. Bonner, William M., Christophe E. Redon, Jennifer S. Dickey, Asako J. Nakamura, Olga A. Sedelnikova, Stéphanie Solier和Yves Pommier. 2008b. 《γH2AX and Cancer》. Nature Reviews Cancer 8(12): 957～67. doi:10.1038/nrc2523. Bray, Freddie, Mathieu Laversanne, Hyuna Sung, Jacques Ferlay, Rebecca L. Siegel, Isabelle Soerjomataram和Ahmedin Jemal. 2024. 《Global Cancer Statistics 2022: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries》. CA: A Cancer Journal for Clinicians 74(3): 229～63. doi:10.3322/caac.21834. Chen, Di, Lixia Xu, Huiwu Xing, Weitao Shen, Ziguang Song, Hongjiang Li, Xuqiang Zhu, 等. 2024. 《Sangerbox 2: Enhanced Functionalities and Update for a Comprehensive Clinical Bioinformatics Data Analysis Platform.》 iMeta 3(5): e238. doi:10.1002/imt2.238. Chen, Lihua, Lixiang Huang, Yu Gu, Wei Cang, Pengming Sun和Yang Xiang. 2022. 《Lactate-Lactylation Hands between Metabolic Reprogramming and Immunosuppression.》 International journal of molecular sciences 23(19). doi:10.3390/ijms231911943. De Braekeleer, Etienne, Jean-Loup Huret, Hossain Mossafa和Philippe Dessen. 2017. 《Cytogenetic Resources and Information.》 Methods in molecular biology (Clifton, N.J.) 1541: 311～31. doi:10.1007/978-1-4939-6703-2_25. Dibitetto, Diego, Martin Liptay, Francesca Vivalda, Hülya Dogan, Ewa Gogola, Martín González Fernández, Alexandra Duarte, 等. 2024. 《H2AX Promotes Replication Fork Degradation and Chemosensitivity in BRCA-Deficient Tumours.》 Nature communications 15(1): 4430. doi:10.1038/s41467-024-48715-1. Giaimo, Benedetto Daniele, Francesca Ferrante, Andreas Herchenröther, Sandra B. Hake和Tilman Borggrefe. 2019. 《The Histone Variant H2A.Z in Gene Regulation》. Epigenetics & Chromatin 12(1): 37. doi:10.1186/s13072-019-0274-9. Hu, Hailiang, Tao Zhong和Suwei Jiang. 2023. 《 H2AFX Might Be a Prognostic Biomarker for Hepatocellular Carcinoma》. Cancer Reports 6(1): e1684. doi:10.1002/cnr2.1684. Jeffery, Nazish N., Christina Davidson, Scott A. Peslak, Paul D. Kingsley, Yukio Nakamura, James Palis和Michael Bulger. 2021. 《Histone H2A.X Phosphorylation and Caspase-Initiated Chromatin Condensation in Late-Stage Erythropoiesis》. Epigenetics & Chromatin 14(1): 37. doi:10.1186/s13072-021-00408-5. Joo, Heui-Yun, Ling Zhai, Chunying Yang, Shuyi Nie, Hediye Erdjument-Bromage, Paul Tempst, Chenbei Chang和Hengbin Wang. 2007. 《Regulation of Cell Cycle Progression and Gene Expression by H2A Deubiquitination》. Nature 449(7165): 1068～72. doi:10.1038/nature06256. Kanehisa, M., 和S. Goto. 2000. 《KEGG: Kyoto Encyclopedia of Genes and Genomes.》 Nucleic acids research 28(1): 27～30. doi:10.1093/nar/28.1.27. Kanehisa, Minoru. 2002. 《The KEGG Database.》 Novartis Foundation symposium 247: 91～101; discussion 101-3, 119～28, 244～52. Kanehisa, Minoru, Miho Furumichi, Yoko Sato, Masayuki Kawashima和Mari Ishiguro-Watanabe. 2023. 《KEGG for Taxonomy-Based Analysis of Pathways and Genomes.》 Nucleic acids research 51(D1): D587～92. doi:10.1093/nar/gkac963. Kanehisa, Minoru, Miho Furumichi, Mao Tanabe, Yoko Sato和Kanae Morishima. 2017. 《KEGG: New Perspectives on Genomes, Pathways, Diseases and Drugs.》 Nucleic acids research 45(D1): D353～61. doi:10.1093/nar/gkw1092. Kanehisa, Minoru, Yoko Sato, Masayuki Kawashima, Miho Furumichi和Mao Tanabe. 2016. 《KEGG as a Reference Resource for Gene and Protein Annotation.》 Nucleic acids research 44(D1): D457-462. doi:10.1093/nar/gkv1070. Kinner, A., W. Wu, C. Staudt和G. Iliakis. 2008. 《-H2AX in Recognition and Signaling of DNA Double-Strand Breaks in the Context of Chromatin》. Nucleic Acids Research 36(17): 5678～94. doi:10.1093/nar/gkn550. Kinner, Andrea, Wenqi Wu, Christian Staudt和George Iliakis. 2008. 《Gamma-H2AX in Recognition and Signaling of DNA Double-Strand Breaks in the Context of Chromatin.》 Nucleic acids research 36(17): 5678～94. doi:10.1093/nar/gkn550. Lai, Po Man, 和Kui Ming Chan. 2024. 《Roles of Histone H2A Variants in Cancer Development, Prognosis, and Treatment》. International Journal of Molecular Sciences 25(6): 3144. doi:10.3390/ijms25063144. Liberzon, Arthur, Chet Birger, Helga Thorvaldsdóttir, Mahmoud Ghandi, Jill P. Mesirov和Pablo Tamayo. 2015. 《The Molecular Signatures Database (MSigDB) Hallmark Gene Set Collection.》 Cell systems 1(6): 417～25. doi:10.1016/j.cels.2015.12.004. Mah, L.-J., A. El-Osta和T. C. Karagiannis. 2010. 《gammaH2AX: A Sensitive Molecular Marker of DNA Damage and Repair.》 Leukemia 24(4): 679～86. doi:10.1038/leu.2010.6. Molina, Julian R, Ping Yang, Stephen D Cassivi, Steven E Schild和Alex A Adjei. 2009. 《Non–Small Cell Lung Cancer: Epidemiology, Risk Factors, Treatment, and Survivorship》. Oser, Matthew G, Matthew J Niederst, Lecia V Sequist和Jeffrey A Engelman. 2015. 《Transformation from Non-Small-Cell Lung Cancer to Small-Cell Lung Cancer: Molecular Drivers and Cells of Origin》. The Lancet Oncology 16(4): e165～72. doi:10.1016/S1470-2045(14)71180-5. Pérez-Díez, Irene, Marta R. Hidalgo, Pablo Malmierca-Merlo, Zoraida Andreu, Sergio Romera-Giner, Rosa Farràs, María De La Iglesia-Vayá, 等. 2021. 《Functional Signatures in Non-Small-Cell Lung Cancer: A Systematic Review and Meta-Analysis of Sex-Based Differences in Transcriptomic Studies》. Cancers 13(1): 143. doi:10.3390/cancers13010143. Prabhu, Kirti S., Shilpa Kuttikrishnan, Nuha Ahmad, Ummu Habeeba, Zahwa Mariyam, Muhammad Suleman, Ajaz A. Bhat和Shahab Uddin. 2024a. 《H2AX: A Key Player in DNA Damage Response and a Promising Target for Cancer Therapy.》 Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie 175: 116663. doi:10.1016/j.biopha.2024.116663. Prabhu, Kirti S., Shilpa Kuttikrishnan, Nuha Ahmad, Ummu Habeeba, Zahwa Mariyam, Muhammad Suleman, Ajaz A. Bhat和Shahab Uddin. 2024b. 《H2AX: A Key Player in DNA Damage Response and a Promising Target for Cancer Therapy.》 Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie 175: 116663. doi:10.1016/j.biopha.2024.116663. Quan, Zihan, Songqing Fan, Hongmei Zheng, Yue Ning和Yang Yang. 2025. 《A Pan-Cancer Analysis of MARCH8: Molecular Characteristics, Clinical Relevance, and Immuno-Oncology Features.》 Cancer biology & therapy 26(1): 2458773. doi:10.1080/15384047.2025.2458773. Ru, Beibei, Ching Ngar Wong, Yin Tong, Jia Yi Zhong, Sophia Shek Wa Zhong, Wai Chung Wu, Ka Chi Chu, 等. 2019. 《TISIDB: An Integrated Repository Portal for Tumor-Immune System Interactions.》 Bioinformatics (Oxford, England) 35(20): 4200～4202. doi:10.1093/bioinformatics/btz210. Schabath, Matthew B., 和Michele L. Cote. 2019. 《Cancer Progress and Priorities: Lung Cancer》. Cancer Epidemiology, Biomarkers & Prevention 28(10): 1563～79. doi:10.1158/1055-9965.EPI-19-0221. Shen, Weitao, Ziguang Song, Xiao Zhong, Mei Huang, Danting Shen, Pingping Gao, Xiaoqian Qian, 等. 2022. 《Sangerbox: A Comprehensive, Interaction-Friendly Clinical Bioinformatics Analysis Platform.》 iMeta 1(3): e36. doi:10.1002/imt2.36. Song, Qian, Jun Shang, Zuyi Yang, Lanlin Zhang, Chufan Zhang, Jianing Chen和Xianghua Wu. 2019. 《Identification of an Immune Signature Predicting Prognosis Risk of Patients in Lung Adenocarcinoma》. Journal of Translational Medicine 17(1): 70. doi:10.1186/s12967-019-1824-4. Subramanian, Aravind, Pablo Tamayo, Vamsi K. Mootha, Sayan Mukherjee, Benjamin L. Ebert, Michael A. Gillette, Amanda Paulovich, 等. 2005. 《Gene Set Enrichment Analysis: A Knowledge-Based Approach for Interpreting Genome-Wide Expression Profiles.》 Proceedings of the National Academy of Sciences of the United States of America 102(43): 15545～50. doi:10.1073/pnas.0506580102. Wang, Si-si, Wei Liu, Dalam Ly, Hao Xu, Limei Qu和Li Zhang. 2019. 《Tumor-Infiltrating B Cells: Their Role and Application in Anti-Tumor Immunity in Lung Cancer》. Cellular & Molecular Immunology 16(1): 6～18. doi:10.1038/s41423-018-0027-x. Wang, Xiaoxue, Jingliang Lu, Zixuan Song, Yangzi Zhou, Tong Liu和Dandan Zhang. 2022. 《From Past to Future: Bibliometric Analysis of Global Research Productivity on Nomogram (2000-2021).》 Frontiers in public health 10: 997713. doi:10.3389/fpubh.2022.997713. Wu, Wen-Tao, Yuan-Jie Li, Ao-Zi Feng, Li Li, Tao Huang, An-Ding Xu和Jun Lyu. 2021. 《Data Mining in Clinical Big Data: The Frequently Used Databases, Steps, and Methodological Models.》 Military Medical Research 8(1): 44. doi:10.1186/s40779-021-00338-z. Xu, Rongjian, Fengyi Han, Yandong Zhao, Ao Liu, Ning An, Baogang Wang, Patrick Zardo, 等. 2024. 《Role of CENPL, DARS2, and PAICS in Determining the Prognosis of Patients with Lung Adenocarcinoma.》 Translational lung cancer research 13(10): 2729～45. doi:10.21037/tlcr-24-696. Yan, Junfang, Yi Xie, Qianjing Zhang, Lu Gan, Fang Wang, Hongyan Li, Jing Si和Hong Zhang. 2019. 《Dynamic Recognition and Repair of DNA Complex Damage.》 Journal of cellular physiology 234(8): 13014～20. doi:10.1002/jcp.27971. Yao, Deshun, 和Xin Chen. 2023. 《MiRNA-145-5p Restrains Malignant Behaviors of Breast Cancer Cells Via Downregulating H2AFX Expression.》 Iranian journal of biotechnology 21(3): e3433. doi:10.30498/ijb.2023.349450.3433. Yu, Jie, Peiwei Chai, Minyue Xie, Shengfang Ge, Jing Ruan, Xianqun Fan和Renbing Jia. 2021. 《Histone Lactylation Drives Oncogenesis by Facilitating m(6)A Reader Protein YTHDF2 Expression in Ocular Melanoma.》 Genome biology 22(1): 85. doi:10.1186/s13059-021-02308-z. Zhang, Di, Zhanyun Tang, He Huang, Guolin Zhou, Chang Cui, Yejing Weng, Wenchao Liu, 等. 2019. 《Metabolic Regulation of Gene Expression by Histone Lactylation.》 Nature 574(7779): 575～80. doi:10.1038/s41586-019-1678-1. Zhang, Yu, Hang Song, Meili Li和Peirong Lu. 2024. 《Histone Lactylation Bridges Metabolic Reprogramming and Epigenetic Rewiring in Driving Carcinogenesis: Oncometabolite Fuels Oncogenic Transcription.》 Clinical and translational medicine 14(3): e1614. doi:10.1002/ctm2.1614. Zoabi, Yazeed, 和Noam Shomron. 2021. 《Processing and Analysis of RNA-Seq Data from Public Resources.》 Methods in molecular biology (Clifton, N.J.) 2243: 81～94. doi:10.1007/978-1-0716-1103-6_4. Zulfiqar, Bilal, Asim Farooq, Shahzina Kanwal和Kashif Asghar. 2022. 《Immunotherapy and Targeted Therapy for Lung Cancer: Current Status and Future Perspectives》. Frontiers in Pharmacology 13: 1035171. doi:10.3389/fphar.2022.1035171. Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterial.docx SupplementaryFile1OriginalUncroppedBlots.pdf.pdf Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 24 Feb, 2026 Reviewers agreed at journal 24 Feb, 2026 Reviewers agreed at journal 19 Feb, 2026 Reviewers agreed at journal 17 Feb, 2026 Reviewers invited by journal 17 Feb, 2026 Editor assigned by journal 09 Dec, 2025 Editor invited by journal 08 Dec, 2025 Submission checks completed at journal 05 Dec, 2025 First submitted to journal 05 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8161785","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":593015156,"identity":"26434ad4-701c-4941-95c8-729e358bd0e5","order_by":0,"name":"Tiankai Yuan","email":"","orcid":"","institution":"Nanchang University","correspondingAuthor":false,"prefix":"","firstName":"Tiankai","middleName":"","lastName":"Yuan","suffix":""},{"id":593015161,"identity":"7ee1bb7f-3933-4781-ba7d-7ee97f49de8a","order_by":1,"name":"Dingguo Wang","email":"","orcid":"","institution":"Nanchang University","correspondingAuthor":false,"prefix":"","firstName":"Dingguo","middleName":"","lastName":"Wang","suffix":""},{"id":593015167,"identity":"1fdf31d4-02a1-4696-b5e1-532eaa29b740","order_by":2,"name":"yong bing wu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8ElEQVRIiWNgGAWjYPCCAzwMDPwfH3yokJCTJ0ELg7HhjDMWxoYNRGoBEWbSvG0ViRA2HmBwI8fwccGvOzLm/AuSjXnnSSQwNjA/fHQDvxZj45l9z3gsZzw4+HDuNok8dgY2Y+McPFrMbuQA3dNzmMfgxsFmg7fbJIoZG3jYpInUcphNgneORGLDAWK08PwAajnfxibJ20CEFvszz4qNeRueAW3hYTaccUzC2LCZgF8k25M3Pub5c8fe4PwZxgcfaurk5NmbHz7Gp4WBgcOAgbENSEskQAWY8SoHAfYHDAx/gDT/AYJKR8EoGAWjYIQCALPEUfJrj7UGAAAAAElFTkSuQmCC","orcid":"","institution":"Nanchang University","correspondingAuthor":true,"prefix":"","firstName":"yong","middleName":"bing","lastName":"wu","suffix":""}],"badges":[],"createdAt":"2025-11-20 07:53:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8161785/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8161785/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103176432,"identity":"408ea185-dd7c-4965-b4cb-c626eb99a434","added_by":"auto","created_at":"2026-02-22 16:37:39","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1424057,"visible":true,"origin":"","legend":"\u003cp\u003ePan-cancer analysis of H2AFX: investigating its alteration and expression in Human Cancers.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8161785/v1/41e2b9587e8660a0e1b1fafc.jpeg"},{"id":103504733,"identity":"8ea4779d-e216-4f8f-a155-a6d57b395056","added_by":"auto","created_at":"2026-02-26 13:21:08","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":756951,"visible":true,"origin":"","legend":"\u003cp\u003eExperimental validation of H2AFX in lung adenocarcinoma (LUAD).\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8161785/v1/09d1900aed04e4ba42680d4c.jpeg"},{"id":103176438,"identity":"4a4e540f-4148-4b2b-af28-30e25122b1f0","added_by":"auto","created_at":"2026-02-22 16:37:39","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1683695,"visible":true,"origin":"","legend":"\u003cp\u003eH2AFX Expression is associated with various subtypes across multiple cancers and demonstrates prognostic potential across cancer types.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8161785/v1/3cf9cfa1e9fad5b905cf2f21.jpeg"},{"id":103176436,"identity":"c89a2ce3-462e-4743-93a5-4c4280e62041","added_by":"auto","created_at":"2026-02-22 16:37:39","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1131139,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between H2AFX expression and the tumor microenvironment, and prognostic correlation analysis.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8161785/v1/279569d7853d24ebb161cf71.jpeg"},{"id":103176434,"identity":"dcf0470c-5ff7-4a7c-9989-0f5f66204fb9","added_by":"auto","created_at":"2026-02-22 16:37:39","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":860239,"visible":true,"origin":"","legend":"\u003cp\u003eSomatic mutations of H2AFX and their association with mutations in LUAD.\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8161785/v1/e16b15d5035a0a7a0000a915.jpeg"},{"id":103176439,"identity":"3f66605c-0586-40b9-ab9b-799d134f0d83","added_by":"auto","created_at":"2026-02-22 16:37:39","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":952220,"visible":true,"origin":"","legend":"\u003cp\u003eEnrichment analysis of differentially expressed genes (DEGs) based on H2AFX expression levels.\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8161785/v1/06fa7f1af5ca9c6e746f8772.jpeg"},{"id":103176440,"identity":"11e9436a-4593-480e-853b-5d3490f1bfbc","added_by":"auto","created_at":"2026-02-22 16:37:40","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":487406,"visible":true,"origin":"","legend":"\u003cp\u003eImmune-related analysis based on H2AFX expression levels.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-8161785/v1/6b497db9140cd3ee29bd785b.png"},{"id":103504505,"identity":"776bd1c1-31e8-4efc-b445-75f09c839438","added_by":"auto","created_at":"2026-02-26 13:20:19","extension":"jpeg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":869117,"visible":true,"origin":"","legend":"\u003cp\u003eExperimental validation of H2AFX expression levels and immune-related analyses.\u003c/p\u003e","description":"","filename":"floatimage8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8161785/v1/a7d418a35029eb2ceba11b16.jpeg"},{"id":103509431,"identity":"6218f660-3e45-4d46-a3d5-fe9bffbd0bd6","added_by":"auto","created_at":"2026-02-26 13:58:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":8878422,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8161785/v1/c0b102ff-9d85-4296-ba44-72f875731908.pdf"},{"id":103176431,"identity":"901ba31b-ac8a-4ed7-a702-4f8eb22c11c8","added_by":"auto","created_at":"2026-02-22 16:37:39","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":17566,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-8161785/v1/fc19e1498c5d257afac44fdd.docx"},{"id":103504854,"identity":"64e84d30-fab5-46ef-9307-3b047f72abb5","added_by":"auto","created_at":"2026-02-26 13:21:47","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":944520,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFile1OriginalUncroppedBlots.pdf.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8161785/v1/f1dd364677510a30b972ef87.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Comprehensive pan-cancer analysis identifies H2AFX associated with poor prognosis in lung adenocarcinoma","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCancer remains a paramount global health challenge, with IARC (International Agency for Research on Cancer, IARC) reporting 20 million new cases and 9.7 million deaths in 2022. Lung cancer, accounting for 18% of cancer mortality, demonstrates particular clinical urgency. (P\u0026eacute;rez-D\u0026iacute;ez等\u0026nbsp;2021)NSCLC represents 85% of cases, of which LUAD constitutes 40-50% and shows increasing incidence.\u0026nbsp;(Bray等\u0026nbsp;2024; Molina等\u0026nbsp;2009)The evolution of LUAD treatment from conventional therapies to precision approaches (targeted/immunotherapy) underscores the critical need for reliable prognostic biomarkers.(Oser等\u0026nbsp;2015; Schabath和Cote 2019; Song等\u0026nbsp;2019; S. Wang等\u0026nbsp;2019; Zulfiqar等\u0026nbsp;2022)\u003c/p\u003e\n\u003cp\u003eH2AFX (H2A histone family member X) is an evolutionarily highly conserved histone variant that plays an indispensable role in maintaining genomic stability and DNA damage repair. When cells experience DNA double-strand breaks, the serine 139 residue of H2AFX undergoes rapid phosphorylation to form \u0026gamma;-H2AX.(Bonner等\u0026nbsp;2008b; Mah, El-Osta和Karagiannis 2010)\u0026nbsp;(Dibitetto等\u0026nbsp;2024)This characteristic molecular event serves as a sensitive biomarker for DNA damage response (DDR), which specifically recruits repair protein complexes to the damage site to initiate complete repair processes.(Bergink等\u0026nbsp;2006; Lai和Chan 2024)\u0026nbsp;(Andrea Kinner等\u0026nbsp;2008; Mah, El-Osta和Karagiannis 2010)Beyond its canonical function, recent studies have revealed that H2AFX extensively participates in critical biological processes, such as chromatin dynamic remodeling and gene transcriptional regulation, through diverse post-translational modifications, including phosphorylation, ubiquitination, and methylation.(Barski等\u0026nbsp;2007; Giaimo等\u0026nbsp;2019; Jeffery等\u0026nbsp;2021; Joo等\u0026nbsp;2007; A. Kinner等\u0026nbsp;2008)\u0026nbsp;(Bonner等\u0026nbsp;2008a)Particularly noteworthy is the dual role H2AFX exhibits in tumorigenesis: on one hand.(Prabhu等\u0026nbsp;2024a)\u003c/p\u003e\n\u003cp\u003eIn various malignant tumors, clinical studies have confirmed that H2AFX overexpression is significantly associated with poor patient prognosis. The potential mechanisms may involve remodeling of the tumor immune microenvironment, thereby promoting tumor immune escape. Recent studies have also found that H2AFX, as a core member of lactation modification-related gene networks, with its expression characteristics exhibiting significant correlations with immunotherapy responsiveness.(L. Chen等\u0026nbsp;2022; Yu等\u0026nbsp;2021; D. Zhang等\u0026nbsp;2019; Y. Zhang等\u0026nbsp;2024)\u003c/p\u003e\n\u003cp\u003eThis study integrates bioinformatics and experimental approaches to elucidate H2AFX\u0026apos;s pan-cancer roles. Utilizing multi-omics databases and R-based analytics, we systematically characterize H2AFX expression and prognostic significance. Functional enrichment analyses further delineate its immunomodulatory effects in LUAD.\u0026nbsp;\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003eGenetic Mutation and Expression Analysis\u003c/h2\u003e \u003cp\u003ecBioPortal (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cbioportal.org/\u003c/span\u003e\u003cspan address=\"https://www.cbioportal.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was employed to analyze the mutation profile of \u003cem\u003eH2AFX\u003c/em\u003e. We investigated mutations, copy number alterations (CNAs), and gene fusion events in \u003cem\u003eH2AFX\u003c/em\u003e using the standardized pan-cancer dataset and LUAD-specific datasets from the cBioPortal database. Subsequently, TIMER2.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://timer.comp-genomics.org/\u003c/span\u003e\u003cspan address=\"http://timer.comp-genomics.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and GEPIA2 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://gepia2.cancer-pku.cn/\u003c/span\u003e\u003cspan address=\"http://gepia2.cancer-pku.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) were utilized to compare \u003cem\u003eH2AFX\u003c/em\u003e mRNA expression levels between tumor tissues and adjacent normal tissues across multiple cancer types. The Xena Browser (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://xenabrowser.net/datapages/\u003c/span\u003e\u003cspan address=\"https://xenabrowser.net/datapages/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to download \u003cem\u003eH2AFX\u003c/em\u003e expression data and corresponding clinical information from diverse patient cohorts. (Xu等 2024)A pan-cancer radar chart was generated using the R package fmsb. Furthermore, the R package TCGAbiolinks was applied to retrieve the LUAD expression matrix from the Cancer Genome Atlas (TCGA) database, followed by differential expression analysis of \u003cem\u003eH2AFX\u003c/em\u003e between tumor and normal tissues.(De Braekeleer等 2017; Wu等 2021; Zoabi和Shomron 2021) Finally, GEPIA2 was used to correlate \u003cem\u003eH2AFX\u003c/em\u003e gene expression with mutational landscapes in LUAD.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePrognostic Analysis of H2AFX in Pan-Cancer\u003c/h2\u003e \u003cp\u003eWe conducted univariate survival analysis using GEPIA2 to investigate the association between H2AFX expression levels and clinical outcomes across multiple cancer types, including overall survival (OS), disease-free interval (DFI), disease-specific survival (DSS), and progression-free interval (PFI). The Kaplan-Meier method was employed to compare survival rates between high and low H2AFX expression groups based on survival map results derived from the GEPIA2 database. Furthermore, we performed both univariate and multivariate Cox proportional hazards regression analyses using the R packages survival, rms, and timeROC. A prognostic nomogram model was developed and validated through calibration curves. (Balachandran等 2015; X. Wang等 2022)To evaluate the predictive performance, time-dependent receiver operating characteristic (ROC) curves were generated for different follow-up years.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDistribution of H2AFX Expression Across Molecular and Immune Subtypes in Human Cancers\u003c/h3\u003e\n\u003cp\u003eThe TISIDB database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://cis.hku.hk/TISIDB/\u003c/span\u003e\u003cspan address=\"http://cis.hku.hk/TISIDB/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), a web portal for tumor-immune system interactions integrating diverse heterogeneous data types, was utilized to investigate the association between H2AFX expression and both molecular subtypes and pan-cancer immune subtypes. (Ru等 2019)Molecular subtypes were tumor-specific, while immune subtypes were categorized as follows: C1 (wound healing), C2 (IFN-γ dominant), C3 (inflammatory), C4 (lymphocyte depleted), C5 (immunologically quiet), and C6 (TGF-β dominant).\u003c/p\u003e\n\u003ch3\u003eEnrichment Analysis\u003c/h3\u003e\n\u003cp\u003eDifferential gene expression analysis between H2AFX expression groups was performed using the R package limma. Functional enrichment analyses, including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses, were conducted using the R package clusterProfiler. (M. Kanehisa和Goto 2000; Minoru Kanehisa \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Minoru Kanehisa等 2016, 2017, 2023)Gene Set Enrichment Analysis (GSEA) was additionally performed to identify significantly enriched pathways.(Subramanian等 2005) Gene sets (H, C2, and C5 collections) were obtained from the Molecular Signatures Database (MSigDB; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.gsea-msigdb.org/gsea/msigdb/index.jsp\u003c/span\u003e\u003cspan address=\"http://www.gsea-msigdb.org/gsea/msigdb/index.jsp\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Samples were stratified into two subgroups based on median H2AFX expression levels to investigate associated pathways and molecular mechanisms.(Liberzon等 2015)\u003c/p\u003e\n\u003ch3\u003eImmune Infiltration Analysis\u003c/h3\u003e\n\u003cp\u003eWe first performed ESTIMATE analysis to evaluate the immune and stromal components associated with H2AFX expression in LUAD using the SangerBox platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://vip.sangerbox.com/home.html\u003c/span\u003e\u003cspan address=\"http://vip.sangerbox.com/home.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Subsequently, we employed the R package CIBERSORT to quantify immune cell infiltration patterns based on H2AFX expression levels.(D. Chen等 2024; Shen等 2022) To validate and extend these findings, we conducted comprehensive correlation analyses between H2AFX expression and tumor-associated immune cells (including B cells, CD8\u0026thinsp;+\u0026thinsp;T cells, CD4\u0026thinsp;+\u0026thinsp;T cells, macrophages, neutrophils, and dendritic cells) using the TIMER 2.0 database.\u003c/p\u003e\n\u003ch3\u003eImmunohistochemical (IHC) Analysis\u003c/h3\u003e\n\u003cp\u003eFirst, we performed immunohistochemical staining on both LUAD tissues and normal tissues, followed by grayscale value quantification and comparative analysis. To validate the immune infiltration results, we initially conducted IHC staining on a tissue microarray containing samples from 60 LUAD patients. Based on H2AFX expression levels, these samples were categorized into high-expression and low-expression groups. Subsequently, we performed IHC staining for classical antigens of various immune cells on blank tissue microarrays from the same sample set, with subsequent grayscale value quantification. Comparative analyses were then conducted between the high- and low-expression groups.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eWestern Blot Analysis\u003c/h2\u003e \u003cp\u003eTo validate the aberrant expression of H2AFX in LUAD, we extracted proteins from normal lung epithelial cells (BEAS-2B RRID: CVCL_0168) and three lung cancer cell lines (A549 RRID: CVCL_0023, HCC827 RRID: CVCL_2063, and H1299 RRID: CVCL_0060). Protein expression levels were determined by Western blot using antibodies from Proteintech Group. Quantitative analysis of band intensities was performed using ImageJ-win64 software.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were performed using R software (v4.4.2) and multiple online databases. The Wilcoxon rank-sum test was employed to determine the significance between two groups. For non-normally distributed variables, the Wilcoxon test was used for two-group comparisons, while the Kruskal-Wallis test was applied for differences among three or more groups. Spearman's correlation analysis was utilized to assess associations. Survival time differences between risk groups were estimated using Kaplan-Meier curves with log-rank tests.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eH2AFX Alterations and Expression Patterns Across Cancer Types\u003c/h2\u003e \u003cp\u003eBuilding upon these findings that establish H2AFX overexpression across multiple malignancies. In subsequent analyses. Our pan-cancer analysis reveals H2AFX copy number variations (CNVs) as fundamental genomic hallmarks that significantly contribute to tumorigenesis through somatic alterations. Comprehensive characterization of H2AFX genetic alterations across diverse malignancies demonstrates gene amplification as the predominant variant (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e1\u003c/span\u003eA), exhibiting substantially higher prevalence than other mutation classes, including deletions and point mutations, with copy number amplifications (CNAs) showing particular enrichment in clinically relevant cancers such as ovarian carcinoma, osteosarcoma, and pulmonary malignancies. (Quan等 2025)Systematic investigation of TCGA RNA-seq data (TPM-normalized) reveals consistent H2AFX mRNA upregulation in tumor versus matched normal tissues across multiple cancer types (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e1\u003c/span\u003eB), a finding robustly validated through TIMER2.0 and GEPIA2 platforms which confirm significant H2AFX overexpression in 25 distinct malignancies spanning adrenocortical carcinoma (ACC), bladder urothelial carcinoma (BLCA), breast invasive carcinoma (BRCA), and other major cancer types, while uniquely identifying acute myeloid leukemia (LAML) as exhibiting exclusive downregulation in GEPIA2 analyses, with all platforms consistently demonstrating pronounced H2AFX overexpression in LUAD that underscores its potential clinical significance in oncology (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e1\u003c/span\u003eC-D).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eExperimental Validation of H2AFX in LUAD\u003c/h2\u003e \u003cp\u003eTo elucidate the role of H2AFX in LUAD tumor progression based on its heterogeneous mRNA expression patterns, we initially performed Western blot analysis to compare H2AFX protein levels between normal lung epithelial cells (BEAS-2B RRID: CVCL_0168) and multiple lung cancer cell lines (A549 RRID: CVCL_0023, HCC827 RRID: CVCL_2063, and H1299 RRID: CVCL_0060). The results revealed a significant upregulation of H2AFX expression in lung cancer cells relative to normal lung epithelial cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e2\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003eWe subsequently performed immunohistochemical (IHC) staining which revealed predominant nuclear localization of H2AFX protein with negligible cytoplasmic staining. Consistent with its mRNA expression profile, H2AFX protein level was significantly elevated in LUAD tissues compared to normal lung tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e2\u003c/span\u003eB).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eAssociation between H2AFX Expression and Molecular Subtypes with Prognosis in Various Cancers\u003c/h2\u003e \u003cp\u003eOur systematic analyses have consistently demonstrated elevated H2AFX expression across multiple cancer types. Given the well-documented heterogeneity of malignancies, where substantial variations exist in disease progression, therapeutic response, and survival outcomes among patients, the paradigm shift from conventional histopathological classification to molecular subtyping provides enhanced diagnostic precision. The Tumor Immune System Interaction Database (TISIDB) enables molecular stratification of tumors, through which we identified significant differential H2AFX expression (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) across distinct molecular subtypes, suggesting its potential prognostic relevance (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e3\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003eTo rigorously evaluate the prognostic significance of H2AFX, we performed pan-cancer analyses using the GEPIA 2 platform. Our data established H2AFX as a robust negative prognostic indicator for lower-grade glioma (LGG), adrenocortical carcinoma (ACC), kidney renal clear cell carcinoma (KIRC), pan-kidney cohort (KIPAN), mesothelioma (MESO), LUAD, liver hepatocellular carcinoma (LIHC), kidney renal papillary cell carcinoma (KIRP), acute myeloid leukemia (LAML), prostate adenocarcinoma (PRAD), neuroblastoma (NB), and sarcoma (SARC) (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). These findings strongly implicate H2AFX in the regulation of overall survival in cancer patients. Validation studies using GEPIA 2 revealed statistically significant associations (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) between H2AFX expression levels and overall survival in adrenocortical carcinoma (ACC), kidney renal clear cell carcinoma (KIRC), cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), head and neck squamous cell carcinoma (HNSC), acute myeloid leukemia (LAML), brain lower-grade glioma (LGG), LUAD, and thyroid carcinoma (THCA) (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). Notably, elevated H2AFX expression consistently correlated with unfavorable clinical outcomes. Collectively, these results provide compelling evidence that H2AFX serves as a critical determinant of patient survival across diverse malignancies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eH2AFX Expression Correlates with Immune Subtypes and Clinical Characteristics in LUAD\u003c/h2\u003e \u003cp\u003eThe tumor immune microenvironment (TIME) and intertumoral mRNA modifications are critical regulators of tumorigenesis and progression. Using the TISIDB database, we assessed whether H2AFX modulates tumor development through TIME regulation. Patients were classified into six immune subtypes (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e4\u003c/span\u003eA): C1 (Wound Healing), C2 (IFN-γ Dominant), C3 (Inflammatory), C4 (Lymphocyte-Depleted), C5 (Immunologically Quiet), and C6 (TGF-β Dominant).\u003c/p\u003e \u003cp\u003eOur data confirm H2AFX's tumor-promoting role across multiple cancers and its negative impact on patient survival. Focusing on \u003cb\u003eLUAD\u003c/b\u003e, we employed integrated bioinformatics approaches to explore its mechanistic contributions. TCGA transcriptomic analysis verified H2AFX overexpression in LUAD and its association with worse clinical outcomes (Figs.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e4\u003c/span\u003eB-C). We hypothesized that H2AFX promotes tumor progression and metastasis, leading to poor prognosis. Indeed, H2AFX levels correlated significantly with advanced T-stage (p\u0026thinsp;=\u0026thinsp;0.0032), N-stage (p\u0026thinsp;=\u0026thinsp;0.014), and overall pathological stage (p\u0026thinsp;=\u0026thinsp;0.0036), but not with M-stage (p\u0026thinsp;=\u0026thinsp;0.29) (Figs.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e4\u003c/span\u003eD-G). To evaluate H2AFX's independent prognostic value in LUAD, we conducted univariate and multivariate Cox regression analyses using Xena database clinical data. H2AFX expression emerged as an independent predictor of poor prognosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e4\u003c/span\u003eH). For clinical application, we developed a prognostic nomogram combining H2AFX expression with other significant covariates from multivariate analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e4\u003c/span\u003eJ). Model validation included. Calibration curves indicating high concordance between predicted and observed survival and time-dependent ROC analyses confirming strong predictive accuracy (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e4\u003c/span\u003eK).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eRelationship Between H2AFX and Mutational Patterns\u003c/h2\u003e \u003cp\u003eWe first utilized cBioPortal to identify somatic mutations in H2AFX. Two missense mutation loci were detected in the exonic regions of H2AFX (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Additionally, we analyzed 11 LUAD datasets and found the overall mutation frequency of H2AFX to be 1.5% (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e5\u003c/span\u003eB), with amplification being the predominant mutation type (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). Paradoxically, comparison of prognosis between mutated and non-mutated groups revealed better overall survival in the mutated group (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e5\u003c/span\u003eD), which may be attributed to the limited sample size. Furthermore, we conducted mutational landscape analysis based on H2AFX expression levels. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e5\u003c/span\u003eF, 15 genes exhibited differential mutation frequencies between H2AFX-high and H2AFX-low groups, suggesting a significant association between H2AFX expression and mutation rates in lung cancer (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e5\u003c/span\u003eF).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eComprehensive Investigation of H2AFX in LUAD Pathogenesis and Immune Modulation\u003c/h2\u003e \u003cp\u003eBased on our previous findings demonstrating that H2AFX promotes tumor growth and metastasis, thereby affecting patient prognosis, this study further investigated the specific biological mechanisms through which H2AFX facilitates LUAD progression. The research began with differential expression analysis of H2AFX-associated signature genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). Subsequent GO and KEGG pathway enrichment analyses revealed significant associations with multiple immune-related activities. Gene Set Enrichment Analysis (GSEA) further confirmed that H2AFX was significantly associated with multiple fundamental biological activities, particularly: Immune-related processes, DNA repair mechanisms, and Cell cycle regulation (Figs.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e6\u003c/span\u003eB-E).\u003c/p\u003e \u003cp\u003eConsidering prior studies have well established H2AFX\u0026rsquo;s essential roles in cell cycle control and chromatin repair, this study specifically focused on its biological functions in tumor immunity. Initial ESTIMATE analysis demonstrated that H2AFX expression levels were significantly negatively correlated with all three major immune scores (Fig.\u0026nbsp;\u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). Further analysis using the CIBERSORT algorithm on TCGA LUAD expression profiles elucidated the impact of H2AFX on specific immune cell subsets. The data revealed statistically significant negative correlations between H2AFX expression and most tumor-infiltrating immune cell types (Figs.\u0026nbsp;\u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e7\u003c/span\u003eB-D, \u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e7\u003c/span\u003eF), including na\u0026iuml;ve B cells, CD8\u0026thinsp;+\u0026thinsp;T cells, regulatory T cells (Tregs), activated NK cells, M0 macrophages, and other immune cell types. These findings were independently validated using the TIMER2.0 database, showing substantial consistency with the TCGA-based results (Fig.\u0026nbsp;\u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e7\u003c/span\u003eE).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eValidation of H2AFX's Immunological Relevance\u003c/h2\u003e \u003cp\u003eTo validate our previous immune-related analyses, we performed immunohistochemical (IHC) staining for multiple immune cell markers (CD4, CD8 T cells, dendritic cells, NK cells, and macrophages) on the same tissue microarray (Fig.\u0026nbsp;\u003cspan refid=\"Fig16\" class=\"InternalRef\"\u003e8\u003c/span\u003e). The experimental results demonstrated substantial consistency with the bioinformatics analysis findings\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study analyzed the major mutation types of H2AFX in cancers through cBioPortal, identifying gene amplification as the predominant alteration. Subsequently, by integrating online databases such as UCSC XENA and TIMER2, we examined the mRNA expression of H2AFX and found significant differences in its expression across various tumors. These results are consistent with previous studies in cancers like hepatocellular carcinoma and breast cancer.(Hu, Zhong和Jiang 2023) (Dibitetto等 2024; Yao和Chen 2023)Secondly, we employed online analysis tools such as GEPIA to investigate the relationship between H2AFX expression and prognosis. These findings further support the potential of H2AFX as a biomarker for these tumors. In summary, H2AFX may play diverse roles across multiple cancer types.\u003c/p\u003e \u003cp\u003eThis study discovered that H2AFX is generally upregulated in pan-cancer analysis and may serve as a poor prognostic marker for multiple cancers, particularly in LUAD, where its overexpression is significantly correlated with advanced T stage, N stage, and overall pathological stage. Through univariate and multivariate COX analyses, we constructed a nomogram model to facilitate clinical translation. To further explore the tumor-promoting mechanisms of H2AFX, we conducted functional enrichment analysis, which showed that H2AFX-related genes are significantly enriched in multiple classical oncogenic signaling pathways. GSEA analysis confirmed H2AFX's involvement in various immune-related activities, DNA damage repair, and cell cycle regulation, consistent with its canonical role in maintaining genomic stability.(Prabhu等 2024b; Yan等 2019) However, in LUAD, aberrant H2AFX expression may hijack these pathways to promote tumor progression. Notably, based on the enrichment analysis results, we further investigated and found that H2AFX expression is significantly negatively correlated with ESTIMATE immune scores, suggesting that it may suppress anti-tumor immune responses by regulating immune checkpoints or cytokine signaling pathways. Moreover, this study is the first to reveal the critical role of H2AFX in tumor immune evasion. We found that H2AFX expression is significantly negatively correlated with the infiltration of immune cells such as CD8\u0026thinsp;+\u0026thinsp;T cells, NK cells, and dendritic cells, indicating its potential involvement in shaping an immunosuppressive tumor microenvironment. Immunohistochemical validation further confirmed that the high H2AFX expression group exhibited reduced levels of multiple immune cell markers.\u003c/p\u003e \u003cp\u003eThe clinical significance of this study is reflected in the prognostic nomogram based on H2AFX, which demonstrated excellent predictive performance. Targeting H2AFX or its downstream effectors may enhance the efficacy of immune checkpoint inhibitors. However, this study has certain limitations: The findings require experimental validation and are potentially limited by the cohort size. Moreover, future studies with expanded cohorts are needed to address these limitations.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003ePan-cancer analyses establish H2AFX as a promising biomarker for tumor diagnosis, prognosis, and immunity, with particular clinical value in LUAD.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAcknowledgments\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e\n\u003cp\u003eAuthors\u0026rsquo; contributions\u003cbr\u003e\u0026nbsp;T-K Y, D-G W conceived and designed the experiments; T-K Performed the experiments; T-K Y and\u003c/p\u003e\n\u003cp\u003eanalyzed the data; T-K Y wrote the paper. All authors have read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Natural Science Foundation of China (82360505 and 82360545) and Department of Science and Technology of Jiangxi Province (20224ACB206028 and 2024ZD00).\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials\u003c/p\u003e\n\u003cp\u003eAll data in our study are available upon request.\u003c/p\u003e\n\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the ethical principles of the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003ePublicly available, de-identified data from The Cancer Genome Atlas (TCGA) were used. The use of such data is exempt from additional ethical approval and patient consent.\u003c/p\u003e\n\u003cp\u003eFor data and/or samples collected from participants at The Second Affiliated Hospital of Nanchang University, this part of the study was approved by the\u0026nbsp;Ethics Committee of The Second Affiliated Hospital of Nanchang University\u0026nbsp;(Approval No.: IIT-O-2025-056). All procedures performed in these studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. Informed consent was obtained from all individual participants included in this part of the study.\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e\n\u003cp\u003eAuthor details\u003c/p\u003e\n\u003cp\u003eDepartment of Cardiothoracic Surgery, the Second Affiliated Hospital of Nanchang University, 1 Ming de Road, Nanchang 330000, Jiangxi, People\u0026rsquo;s Republic of China.\u003c/p\u003e\n\u003cp\u003eEmail Address of the Corresponding Author\u003c/p\u003e\n\u003cp\[email protected]\u003c/p\u003e\n\u003cp\u003eCity in the affiliation for author(s)\u003c/p\u003e\n\u003cp\u003eNanChang\u003c/p\u003e\n\u003cp\u003eCity in the affiliation for author\u003c/p\u003e\n\u003cp\u003eThe Second Affiliated Hospital of Nanchang University, Nanchang,\u0026nbsp;Nanchang, Jiangxi Province\u003c/p\u003e\n\u003cp\u003eCountry affiliation for the author\u003c/p\u003e\n\u003cp\u003eChina\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eBalachandran, Vinod P., Mithat Gonen, J. Joshua Smith和Ronald P. DeMatteo. 2015. 《Nomograms in Oncology: More than Meets the Eye.》 \u003cem\u003eThe Lancet. Oncology\u003c/em\u003e 16(4): e173-180. doi:10.1016/S1470-2045(14)71116-7.\u003c/li\u003e\n \u003cli\u003eBarski, Artem, Suresh Cuddapah, Kairong Cui, Tae-Young Roh, Dustin E. Schones, Zhibin Wang, Gang Wei, Iouri Chepelev和Keji Zhao. 2007.\u0026nbsp;《High-Resolution Profiling of Histone Methylations in the Human Genome》. \u003cem\u003eCell\u003c/em\u003e 129(4): 823～37. doi:10.1016/j.cell.2007.05.009.\u003c/li\u003e\n \u003cli\u003eBergink, Steven, Florian A. Salomons, Deborah Hoogstraten, Tom A.M. Groothuis, Harm De Waard, Junxin Wu, Li Yuan,\u0026nbsp;等. 2006.\u0026nbsp;《DNA Damage Triggers Nucleotide Excision Repair-Dependent Monoubiquitylation of Histone H2A》. \u003cem\u003eGenes \u0026amp; Development\u003c/em\u003e 20(10): 1343～52. doi:10.1101/gad.373706.\u003c/li\u003e\n \u003cli\u003eBonner, William M., Christophe E. Redon, Jennifer S. Dickey, Asako J. Nakamura, Olga A. Sedelnikova, St\u0026eacute;phanie Solier和Yves Pommier. 2008a.\u0026nbsp;《GammaH2AX and Cancer.》\u0026nbsp;\u003cem\u003eNature reviews. Cancer\u003c/em\u003e 8(12): 957～67. doi:10.1038/nrc2523.\u003c/li\u003e\n \u003cli\u003eBonner, William M., Christophe E. Redon, Jennifer S. Dickey, Asako J. Nakamura, Olga A. Sedelnikova, St\u0026eacute;phanie Solier和Yves Pommier. 2008b.\u0026nbsp;《\u0026gamma;H2AX and Cancer》. \u003cem\u003eNature Reviews Cancer\u003c/em\u003e 8(12): 957～67. doi:10.1038/nrc2523.\u003c/li\u003e\n \u003cli\u003eBray, Freddie, Mathieu Laversanne, Hyuna Sung, Jacques Ferlay, Rebecca L. Siegel, Isabelle Soerjomataram和Ahmedin Jemal. 2024.\u0026nbsp;《Global Cancer Statistics 2022: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries》. \u003cem\u003eCA: A Cancer Journal for Clinicians\u003c/em\u003e 74(3): 229～63. doi:10.3322/caac.21834.\u003c/li\u003e\n \u003cli\u003eChen, Di, Lixia Xu, Huiwu Xing, Weitao Shen, Ziguang Song, Hongjiang Li, Xuqiang Zhu,\u0026nbsp;等. 2024.\u0026nbsp;《Sangerbox 2: Enhanced Functionalities and Update for a Comprehensive Clinical Bioinformatics Data Analysis Platform.》\u0026nbsp;\u003cem\u003eiMeta\u003c/em\u003e 3(5): e238. doi:10.1002/imt2.238.\u003c/li\u003e\n \u003cli\u003eChen, Lihua, Lixiang Huang, Yu Gu, Wei Cang, Pengming Sun和Yang Xiang. 2022.\u0026nbsp;《Lactate-Lactylation Hands between Metabolic Reprogramming and Immunosuppression.》\u0026nbsp;\u003cem\u003eInternational journal of molecular sciences\u003c/em\u003e 23(19). doi:10.3390/ijms231911943.\u003c/li\u003e\n \u003cli\u003eDe Braekeleer, Etienne, Jean-Loup Huret, Hossain Mossafa和Philippe Dessen. 2017.\u0026nbsp;《Cytogenetic Resources and Information.》\u0026nbsp;\u003cem\u003eMethods in molecular biology (Clifton, N.J.)\u003c/em\u003e 1541: 311～31. doi:10.1007/978-1-4939-6703-2_25.\u003c/li\u003e\n \u003cli\u003eDibitetto, Diego, Martin Liptay, Francesca Vivalda, H\u0026uuml;lya Dogan, Ewa Gogola, Mart\u0026iacute;n Gonz\u0026aacute;lez Fern\u0026aacute;ndez, Alexandra Duarte,\u0026nbsp;等. 2024.\u0026nbsp;《H2AX Promotes Replication Fork Degradation and Chemosensitivity in BRCA-Deficient Tumours.》\u0026nbsp;\u003cem\u003eNature communications\u003c/em\u003e 15(1): 4430. doi:10.1038/s41467-024-48715-1.\u003c/li\u003e\n \u003cli\u003eGiaimo, Benedetto Daniele, Francesca Ferrante, Andreas Herchenr\u0026ouml;ther, Sandra B. Hake和Tilman Borggrefe. 2019.\u0026nbsp;《The Histone Variant H2A.Z in Gene Regulation》. \u003cem\u003eEpigenetics \u0026amp; Chromatin\u003c/em\u003e 12(1): 37. doi:10.1186/s13072-019-0274-9.\u003c/li\u003e\n \u003cli\u003eHu, Hailiang, Tao Zhong和Suwei Jiang. 2023.\u0026nbsp;《\u0026nbsp;\u003cem\u003eH2AFX\u003c/em\u003e \u0026nbsp;Might Be a Prognostic Biomarker for Hepatocellular Carcinoma》. \u003cem\u003eCancer Reports\u003c/em\u003e 6(1): e1684. doi:10.1002/cnr2.1684.\u003c/li\u003e\n \u003cli\u003eJeffery, Nazish N., Christina Davidson, Scott A. Peslak, Paul D. Kingsley, Yukio Nakamura, James Palis和Michael Bulger. 2021.\u0026nbsp;《Histone H2A.X Phosphorylation and Caspase-Initiated Chromatin Condensation in Late-Stage Erythropoiesis》. \u003cem\u003eEpigenetics \u0026amp; Chromatin\u003c/em\u003e 14(1): 37. doi:10.1186/s13072-021-00408-5.\u003c/li\u003e\n \u003cli\u003eJoo, Heui-Yun, Ling Zhai, Chunying Yang, Shuyi Nie, Hediye Erdjument-Bromage, Paul Tempst, Chenbei Chang和Hengbin Wang. 2007.\u0026nbsp;《Regulation of Cell Cycle Progression and Gene Expression by H2A Deubiquitination》. \u003cem\u003eNature\u003c/em\u003e 449(7165): 1068～72. doi:10.1038/nature06256.\u003c/li\u003e\n \u003cli\u003eKanehisa, M.,\u0026nbsp;和S. Goto. 2000.\u0026nbsp;《KEGG: Kyoto Encyclopedia of Genes and Genomes.》\u0026nbsp;\u003cem\u003eNucleic acids research\u003c/em\u003e 28(1): 27～30. doi:10.1093/nar/28.1.27.\u003c/li\u003e\n \u003cli\u003eKanehisa, Minoru. 2002.\u0026nbsp;《The KEGG Database.》\u0026nbsp;\u003cem\u003eNovartis Foundation symposium\u003c/em\u003e 247: 91～101; discussion 101-3, 119～28, 244～52.\u003c/li\u003e\n \u003cli\u003eKanehisa, Minoru, Miho Furumichi, Yoko Sato, Masayuki Kawashima和Mari Ishiguro-Watanabe. 2023.\u0026nbsp;《KEGG for Taxonomy-Based Analysis of Pathways and Genomes.》\u0026nbsp;\u003cem\u003eNucleic acids research\u003c/em\u003e 51(D1): D587～92. doi:10.1093/nar/gkac963.\u003c/li\u003e\n \u003cli\u003eKanehisa, Minoru, Miho Furumichi, Mao Tanabe, Yoko Sato和Kanae Morishima. 2017.\u0026nbsp;《KEGG: New Perspectives on Genomes, Pathways, Diseases and Drugs.》\u0026nbsp;\u003cem\u003eNucleic acids research\u003c/em\u003e 45(D1): D353～61. doi:10.1093/nar/gkw1092.\u003c/li\u003e\n \u003cli\u003eKanehisa, Minoru, Yoko Sato, Masayuki Kawashima, Miho Furumichi和Mao Tanabe. 2016.\u0026nbsp;《KEGG as a Reference Resource for Gene and Protein Annotation.》\u0026nbsp;\u003cem\u003eNucleic acids research\u003c/em\u003e 44(D1): D457-462. doi:10.1093/nar/gkv1070.\u003c/li\u003e\n \u003cli\u003eKinner, A., W. Wu, C. Staudt和G. Iliakis. 2008.\u0026nbsp;《-H2AX in Recognition and Signaling of DNA Double-Strand Breaks in the Context of Chromatin》. \u003cem\u003eNucleic Acids Research\u003c/em\u003e 36(17): 5678～94. doi:10.1093/nar/gkn550.\u003c/li\u003e\n \u003cli\u003eKinner, Andrea, Wenqi Wu, Christian Staudt和George Iliakis. 2008.\u0026nbsp;《Gamma-H2AX in Recognition and Signaling of DNA Double-Strand Breaks in the Context of Chromatin.》\u0026nbsp;\u003cem\u003eNucleic acids research\u003c/em\u003e 36(17): 5678～94. doi:10.1093/nar/gkn550.\u003c/li\u003e\n \u003cli\u003eLai, Po Man,\u0026nbsp;和Kui Ming Chan. 2024.\u0026nbsp;《Roles of Histone H2A Variants in Cancer Development, Prognosis, and Treatment》. \u003cem\u003eInternational Journal of Molecular Sciences\u003c/em\u003e 25(6): 3144. doi:10.3390/ijms25063144.\u003c/li\u003e\n \u003cli\u003eLiberzon, Arthur, Chet Birger, Helga Thorvaldsd\u0026oacute;ttir, Mahmoud Ghandi, Jill P. Mesirov和Pablo Tamayo. 2015.\u0026nbsp;《The Molecular Signatures Database (MSigDB) Hallmark Gene Set Collection.》\u0026nbsp;\u003cem\u003eCell systems\u003c/em\u003e 1(6): 417～25. doi:10.1016/j.cels.2015.12.004.\u003c/li\u003e\n \u003cli\u003eMah, L.-J., A. El-Osta和T. C. Karagiannis. 2010.\u0026nbsp;《gammaH2AX: A Sensitive Molecular Marker of DNA Damage and Repair.》\u0026nbsp;\u003cem\u003eLeukemia\u003c/em\u003e 24(4): 679～86. doi:10.1038/leu.2010.6.\u003c/li\u003e\n \u003cli\u003eMolina, Julian R, Ping Yang, Stephen D Cassivi, Steven E Schild和Alex A Adjei. 2009.\u0026nbsp;《Non\u0026ndash;Small Cell Lung Cancer: Epidemiology, Risk Factors, Treatment, and Survivorship》.\u003c/li\u003e\n \u003cli\u003eOser, Matthew G, Matthew J Niederst, Lecia V Sequist和Jeffrey A Engelman. 2015.\u0026nbsp;《Transformation from Non-Small-Cell Lung Cancer to Small-Cell Lung Cancer: Molecular Drivers and Cells of Origin》.\u0026nbsp;\u003cem\u003eThe Lancet Oncology\u003c/em\u003e 16(4): e165～72. doi:10.1016/S1470-2045(14)71180-5.\u003c/li\u003e\n \u003cli\u003eP\u0026eacute;rez-D\u0026iacute;ez, Irene, Marta R. Hidalgo, Pablo Malmierca-Merlo, Zoraida Andreu, Sergio Romera-Giner, Rosa Farr\u0026agrave;s, Mar\u0026iacute;a De La Iglesia-Vay\u0026aacute;,\u0026nbsp;等. 2021.\u0026nbsp;《Functional Signatures in Non-Small-Cell Lung Cancer: A Systematic Review and Meta-Analysis of Sex-Based Differences in Transcriptomic Studies》. \u003cem\u003eCancers\u003c/em\u003e 13(1): 143. doi:10.3390/cancers13010143.\u003c/li\u003e\n \u003cli\u003ePrabhu, Kirti S., Shilpa Kuttikrishnan, Nuha Ahmad, Ummu Habeeba, Zahwa Mariyam, Muhammad Suleman, Ajaz A. Bhat和Shahab Uddin. 2024a.\u0026nbsp;《H2AX: A Key Player in DNA Damage Response and a Promising Target for Cancer Therapy.》\u0026nbsp;\u003cem\u003eBiomedicine \u0026amp; pharmacotherapy = Biomedecine \u0026amp; pharmacotherapie\u003c/em\u003e 175: 116663. doi:10.1016/j.biopha.2024.116663.\u003c/li\u003e\n \u003cli\u003ePrabhu, Kirti S., Shilpa Kuttikrishnan, Nuha Ahmad, Ummu Habeeba, Zahwa Mariyam, Muhammad Suleman, Ajaz A. Bhat和Shahab Uddin. 2024b.\u0026nbsp;《H2AX: A Key Player in DNA Damage Response and a Promising Target for Cancer Therapy.》\u0026nbsp;\u003cem\u003eBiomedicine \u0026amp; pharmacotherapy = Biomedecine \u0026amp; pharmacotherapie\u003c/em\u003e 175: 116663. doi:10.1016/j.biopha.2024.116663.\u003c/li\u003e\n \u003cli\u003eQuan, Zihan, Songqing Fan, Hongmei Zheng, Yue Ning和Yang Yang. 2025.\u0026nbsp;《A Pan-Cancer Analysis of MARCH8: Molecular Characteristics, Clinical Relevance, and Immuno-Oncology Features.》\u0026nbsp;\u003cem\u003eCancer biology \u0026amp; therapy\u003c/em\u003e 26(1): 2458773. doi:10.1080/15384047.2025.2458773.\u003c/li\u003e\n \u003cli\u003eRu, Beibei, Ching Ngar Wong, Yin Tong, Jia Yi Zhong, Sophia Shek Wa Zhong, Wai Chung Wu, Ka Chi Chu,\u0026nbsp;等. 2019.\u0026nbsp;《TISIDB: An Integrated Repository Portal for Tumor-Immune System Interactions.》\u0026nbsp;\u003cem\u003eBioinformatics (Oxford, England)\u003c/em\u003e 35(20): 4200～4202. doi:10.1093/bioinformatics/btz210.\u003c/li\u003e\n \u003cli\u003eSchabath, Matthew B.,\u0026nbsp;和Michele L. Cote. 2019.\u0026nbsp;《Cancer Progress and Priorities: Lung Cancer》. \u003cem\u003eCancer Epidemiology, Biomarkers \u0026amp; Prevention\u003c/em\u003e 28(10): 1563～79. doi:10.1158/1055-9965.EPI-19-0221.\u003c/li\u003e\n \u003cli\u003eShen, Weitao, Ziguang Song, Xiao Zhong, Mei Huang, Danting Shen, Pingping Gao, Xiaoqian Qian,\u0026nbsp;等. 2022.\u0026nbsp;《Sangerbox: A Comprehensive, Interaction-Friendly Clinical Bioinformatics Analysis Platform.》\u0026nbsp;\u003cem\u003eiMeta\u003c/em\u003e 1(3): e36. doi:10.1002/imt2.36.\u003c/li\u003e\n \u003cli\u003eSong, Qian, Jun Shang, Zuyi Yang, Lanlin Zhang, Chufan Zhang, Jianing Chen和Xianghua Wu. 2019.\u0026nbsp;《Identification of an Immune Signature Predicting Prognosis Risk of Patients in Lung Adenocarcinoma》. \u003cem\u003eJournal of Translational Medicine\u003c/em\u003e 17(1): 70. doi:10.1186/s12967-019-1824-4.\u003c/li\u003e\n \u003cli\u003eSubramanian, Aravind, Pablo Tamayo, Vamsi K. Mootha, Sayan Mukherjee, Benjamin L. Ebert, Michael A. Gillette, Amanda Paulovich,\u0026nbsp;等. 2005.\u0026nbsp;《Gene Set Enrichment Analysis: A Knowledge-Based Approach for Interpreting Genome-Wide Expression Profiles.》\u0026nbsp;\u003cem\u003eProceedings of the National Academy of Sciences of the United States of America\u003c/em\u003e 102(43): 15545～50. doi:10.1073/pnas.0506580102.\u003c/li\u003e\n \u003cli\u003eWang, Si-si, Wei Liu, Dalam Ly, Hao Xu, Limei Qu和Li Zhang. 2019.\u0026nbsp;《Tumor-Infiltrating B Cells: Their Role and Application in Anti-Tumor Immunity in Lung Cancer》. \u003cem\u003eCellular \u0026amp; Molecular Immunology\u003c/em\u003e 16(1): 6～18. doi:10.1038/s41423-018-0027-x.\u003c/li\u003e\n \u003cli\u003eWang, Xiaoxue, Jingliang Lu, Zixuan Song, Yangzi Zhou, Tong Liu和Dandan Zhang. 2022.\u0026nbsp;《From Past to Future: Bibliometric Analysis of Global Research Productivity on Nomogram (2000-2021).》\u0026nbsp;\u003cem\u003eFrontiers in public health\u003c/em\u003e 10: 997713. doi:10.3389/fpubh.2022.997713.\u003c/li\u003e\n \u003cli\u003eWu, Wen-Tao, Yuan-Jie Li, Ao-Zi Feng, Li Li, Tao Huang, An-Ding Xu和Jun Lyu. 2021.\u0026nbsp;《Data Mining in Clinical Big Data: The Frequently Used Databases, Steps, and Methodological Models.》\u0026nbsp;\u003cem\u003eMilitary Medical Research\u003c/em\u003e 8(1): 44. doi:10.1186/s40779-021-00338-z.\u003c/li\u003e\n \u003cli\u003eXu, Rongjian, Fengyi Han, Yandong Zhao, Ao Liu, Ning An, Baogang Wang, Patrick Zardo,\u0026nbsp;等. 2024.\u0026nbsp;《Role of CENPL, DARS2, and PAICS in Determining the Prognosis of Patients with Lung Adenocarcinoma.》\u0026nbsp;\u003cem\u003eTranslational lung cancer research\u003c/em\u003e 13(10): 2729～45. doi:10.21037/tlcr-24-696.\u003c/li\u003e\n \u003cli\u003eYan, Junfang, Yi Xie, Qianjing Zhang, Lu Gan, Fang Wang, Hongyan Li, Jing Si和Hong Zhang. 2019.\u0026nbsp;《Dynamic Recognition and Repair of DNA Complex Damage.》\u0026nbsp;\u003cem\u003eJournal of cellular physiology\u003c/em\u003e 234(8): 13014～20. doi:10.1002/jcp.27971.\u003c/li\u003e\n \u003cli\u003eYao, Deshun,\u0026nbsp;和Xin Chen. 2023.\u0026nbsp;《MiRNA-145-5p Restrains Malignant Behaviors of Breast Cancer Cells Via Downregulating H2AFX Expression.》\u0026nbsp;\u003cem\u003eIranian journal of biotechnology\u003c/em\u003e 21(3): e3433. doi:10.30498/ijb.2023.349450.3433.\u003c/li\u003e\n \u003cli\u003eYu, Jie, Peiwei Chai, Minyue Xie, Shengfang Ge, Jing Ruan, Xianqun Fan和Renbing Jia. 2021.\u0026nbsp;《Histone Lactylation Drives Oncogenesis by Facilitating m(6)A Reader Protein YTHDF2 Expression in Ocular Melanoma.》\u0026nbsp;\u003cem\u003eGenome biology\u003c/em\u003e 22(1): 85. doi:10.1186/s13059-021-02308-z.\u003c/li\u003e\n \u003cli\u003eZhang, Di, Zhanyun Tang, He Huang, Guolin Zhou, Chang Cui, Yejing Weng, Wenchao Liu,\u0026nbsp;等. 2019.\u0026nbsp;《Metabolic Regulation of Gene Expression by Histone Lactylation.》\u0026nbsp;\u003cem\u003eNature\u003c/em\u003e 574(7779): 575～80. doi:10.1038/s41586-019-1678-1.\u003c/li\u003e\n \u003cli\u003eZhang, Yu, Hang Song, Meili Li和Peirong Lu. 2024.\u0026nbsp;《Histone Lactylation Bridges Metabolic Reprogramming and Epigenetic Rewiring in Driving Carcinogenesis: Oncometabolite Fuels Oncogenic Transcription.》\u0026nbsp;\u003cem\u003eClinical and translational medicine\u003c/em\u003e 14(3): e1614. doi:10.1002/ctm2.1614.\u003c/li\u003e\n \u003cli\u003eZoabi, Yazeed,\u0026nbsp;和Noam Shomron. 2021.\u0026nbsp;《Processing and Analysis of RNA-Seq Data from Public Resources.》\u0026nbsp;\u003cem\u003eMethods in molecular biology (Clifton, N.J.)\u003c/em\u003e 2243: 81～94. doi:10.1007/978-1-0716-1103-6_4.\u003c/li\u003e\n \u003cli\u003eZulfiqar, Bilal, Asim Farooq, Shahzina Kanwal和Kashif Asghar. 2022.\u0026nbsp;《Immunotherapy and Targeted Therapy for Lung Cancer: Current Status and Future Perspectives》. \u003cem\u003eFrontiers in Pharmacology\u003c/em\u003e 13: 1035171. doi:10.3389/fphar.2022.1035171.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"H2AFX, LUAD, Pan-Cancer, Immune Subtypes, Immunity-Related Analysis, Analysis of Clinical Correlates","lastPublishedDoi":"10.21203/rs.3.rs-8161785/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8161785/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective:\u003c/strong\u003e Lung adenocarcinoma (LUAD), the most prevalent histological subtype of lung cancer, develops through complex molecular regulatory networks. Despite significant advances in targeted therapies, there remains a critical shortage of LUAD-specific biomarkers successfully translated to clinical practice. This study aims to investigate the role of H2AFX, an essential histone H2A variant involved in maintaining genomic stability and chromatin remodeling, in LUAD pathogenesis and its clinical relevance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e We employed an integrated bioinformatics approach using R programming and multiple public databases. Genetic alterations and expression profiles of H2AFX were analyzed through cBioPortal, TIMER2, Sangboxer, and TCGA databases. Advanced bioinformatics tools including TISIDB, ESTIMATE, and CIBERSORT were utilized to assess H2AFX's clinical correlations, prognostic value, and impact on the tumor immune microenvironment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Our analysis revealed H2AFX's significant involvement in regulating the tumor microenvironment and immune modulation. Clinical investigations demonstrated that H2AFX overexpression strongly correlates with poor clinical outcomes in LUAD patients, establishing its independent prognostic significance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e H2AFX shows substantial therapeutic potential in precision medicine and represents a promising dual-purpose biomarker for both prognostic assessment and immunological characterization in LUAD.\u003c/p\u003e","manuscriptTitle":"Comprehensive pan-cancer analysis identifies H2AFX associated with poor prognosis in lung adenocarcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-22 16:37:29","doi":"10.21203/rs.3.rs-8161785/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-02-24T12:09:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"137891995102183435027947279916430437740","date":"2026-02-24T06:47:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"242617060457164420463044307436672400038","date":"2026-02-20T02:44:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"149091251058953596208513509719475761200","date":"2026-02-18T00:44:41+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-17T18:59:59+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-09T06:29:57+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-12-08T05:54:01+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-05T15:34:18+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cancer","date":"2025-12-05T15:23:22+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fe389a84-a67a-457b-9c66-c5afcfbabf4f","owner":[],"postedDate":"February 22nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-02-22T16:37:30+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-22 16:37:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8161785","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8161785","identity":"rs-8161785","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}