Application of Prognostic Model based on Histone Phosphorylation Modification Gene in Lung Adenocarcinoma

Application of Prognostic Model based on Histone Phosphorylation Modification Gene 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 Application of Prognostic Model based on Histone Phosphorylation Modification Gene in Lung Adenocarcinoma Peng Zhang, Sen Li, Zhanliang Ren, Bo Wang, Hang Chen, Wenmiao Wang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7953290/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background : The incidence rate of lung adenocarcinoma (LUAD) is gradually increasing and the prognosis is poor. Recent studies have reported that histone phosphorylation plays an important role in the occurrence and development of tumors; however, the role of histone phosphorylation in LUAD remains unclear. Methods To investigate the role of histone phosphorylation in LUAD regulation, we first curated data on 42 genes related to this process from the published literature. This data served as the foundation for our subsequent analyses. Next,we downloaded expression data for LUAD patients from The Cancer Genome Atlas (TCGA) public database. We employed differential expression analysis (FDR 0.5) to identify genes exhibiting significant differences in expression between LUAD tissues and normal controls. This analysis yielded a set of differentially expressed genes (DEGs). From the identified DEGs, we utilized univariate Cox analysis to select 11 genes with prognostic potential. These genes were then subjected to least absolute shrinkage and selection operator regression（LASSO）analysis for the construction of a prognostic model. LUAD patients within the TCGA cohort were categorized into high- and low-risk groups based on a predefined optimal median value derived from the prognostic model. The accuracy and generalizability of the model were subsequently evaluated using data from three independent Gene Expression Omnibus（GEO） datasets. We conducted a comprehensive comparison between the high- and low-risk groups defined by the prognostic model. This comparison encompassed analyses of prognosis, clinical relevance, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment of DEGs, tumor mutational burden (TMB), and immune cell infiltration. Furthermore, we employed the "estimate" package to calculate and compare the immune score, stromal score, and tumor purity across the different risk groups. Finally, to validate the expression of PBK in LUAD cell lines. Results By constructing a 6-gene prognostic model using LASSO, LUAD patients in the TCGA database were divided into high-risk and low-risk groups. Through comparison, it was found that the high-risk and low-risk groups showed significant differences in survival, immune cell infiltration, TMB, immunotherapy, and other aspects. Univariate and multivariate Cox regression analysis showed that risk score can be used as an independent prognostic factor for LUAD. Conclusion The present study is the first to use histone phosphorylation genes for the reliable prognosis of LUAD in patients. The constructed prognostic model can significantly distinguish high-risk and low-risk populations, predict the prognosis of LUAD patients, TMB, and may also guide future immunotherapy interventions for these patients. Lung adenocarcinoma immunotherapy prognostic model tumor microenvironment PBK Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Introduction The high incidence and mortality rates associated with lung cancer have always been challenging to overcome [1] .Lung cancer is mainly divided into two categories: non-small cell lung cancer (NSCLC, accounting for about 85%) and small cell lung cancer (SCLC, accounting for about 15% [2] .LUAD is the most common subtype of NSCLC [3] .Although radiotherapy, chemotherapy, and immunotherapy have improved lung cancer treatment outcomes in recent years, the overall survival rate (OS) of LUAD patients remains significantly low due to the lack of effective early diagnosis and personalized treatment [4] .Therefore, improvisation of the prognosis and treatment of LUAD patients necessitates urgent study of the pathogenesis of LUAD and effective therapeutic targets. Post-translational modification (PTM) of histones plays a vital role in regulating various processes related to the chromatin structure. Changes in the mode of PTM of histones are widely associated with the occurrence and development of cancer [5] .The level of histone modification is associated with the invasion and poor prognosis of various cancer cells, such as lung cancer, kidney cancer, and prostate cancer [6] .Methylation, acetylation, phosphorylation, glycosylation, ubiquitination, and citrullination [7] are common ways of histone PTM. Histone phosphorylation is a dynamic PTM regulated by the competitive activities of protein kinases and protein phosphorylases. The phosphorylation sites are involved in transcriptional regulation and chromatin condensation [8] .However, the role of histone phosphorylation in the occurrence and development of LUAD is unclear. We constructed a prognostic model based on histone phosphorylation involved in gene regulation and discovered the key gene PBK, also known as T-LAK cell-derived protein kinase (TOPK) [9] ，There are few reports in LUAD, so we will include PBK in subsequent studies. PBK is significantly expressed in various types of tumor tissues and can promote cervical cancer [10] ，breast cancer [11] ，Schwannoma [12] ，lymphma [13] and other cancers, leading to poor prognosis. However, the role of PBK in LUAD is unclear; therefore, the potential role of PBK in LUAD is worth exploring. Methods Data collection To investigate the role of histone phosphorylation in LUAD regulation, we first curated data on 42 genes related to this process from the published literature（Supplement Table）.Download gene expression matrix data (59 normal tissues and 539 LUAD tissues) and clinical information of LUAD patients from the Cancer Genome Atlas(TCGA) public data platform, remove samples with incomplete clinical information, and include all remaining patients in further analysis The Gene Expression Omnibus (GEO) database is a public data platform, and the 5 lung adenocarcinoma datasets we used for validation (GSE31210, GSE50081, GSE30219, GSE13213, GSE72094) were downloaded from the GEO database. Both TCGA and GEO databases are publicly available. Therefore, this study does not require approval from the local ethics committee. Construction and validation of prognostic models Perform Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis with 10 fold cross validation on prognostic genes, and run 1000 cycles using the "glmnet" package. Based on the optimal lambda value, a 6-gene prognostic model was established to divide LUAD patients in the TCGA dataset into two groups, with significant differences in survival analysis between the different groups (P=0.007). Three LUAD datasets were downloaded from the GEO data platform to verify the accuracy of the model. Establishment and verification of column chart We constructed a column chart by integrating traditional clinical variables such as age, gender, T stage, M stage, and N stage, as well as risk scores derived from prognostic features, to analyze the possible 1-year, 3-year, and 5-year overall survival rates of LUAD patients. In addition, generate a calibration curve for the column chart to check the consistency between the predicted survival rate after deviation correction and the observed survival rate. Enrichment analysis Identification of differentially expressed genes among different risk groups of LUAD patients in the TCGA dataset using the "limma" package (| log2FC |>1, P<0.05). The biological processes, molecular functions, and cellular components of differentially expressed genes were enriched using Gene Ontology (GO). Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis identified signaling pathways Calculate tumor purity and immune cell infiltration The R software package "estimate" was used to calculate the immune score、stromal score and tumor purity [14] . Quantify the scores of 22 immune cells in LUAD samples using the CIBERSORT algorithm, and visualize the infiltration distribution of 22 immune cells in the samples using the "ggplot2" software package. Cell culture and real-time polymerase chain reaction Human pulmonary bronchial epithelial cells 16-HBE and LUAD cell lines (NCI-H1299, NCI-1975 and A549, purchased from the Institute of Cell Biology, Chinese Academy of Sciences) were cultured in RPMI-1640 medium (containing 10% fetal bovine serum) and placed in a humid environment containing 5% CO 2 at 37 ℃. Then, the expression of PBK in LUAD cell lines was verified by real-time polymerase chain reaction （RTPCR.）. PCR primers and probes were designed using Primer 5.0 and synthesized by Sangon Biotechnology Co. Ltd. (Shanghai, China). Expression of target genes was determined relative to β-actin, and analysis was calculated by the 2 -ΔΔCt method. The following primer sequences were used: PBK, F: 5′- GACTGCTCCTGCCTTCATAACCATC -3′ and R: 5′- ACCATTCTCCTCCACAGCTTCTTG - 3′; β-actin, F: 5′- ACTTAGTTGCGTTACACCCTTTC -3′ and R: 5′- TCACCTTCACCGTTCCAGTTT - 3′. Statistical analysis This study used R language (version 4.2.0), Graphpad Prism 8.0, and SPSS 20.0 for data statistical analysis. Kaplan Meier method was used to plot survival curves, and Wilcoxon test and Kruskal Wallis test were used to compare the significance of patient differences The Pearman test is used for correlation analysis. A statistical result with P<0.05 indicates statistical significance (* P 0.05, * * P <0.01, * * * P <0.001, ns is not significant). Results Extraction of DEGs and prognostic related genes Through differential expression analysis, we identified a total of 32 differentially expressed genes from 42 histone phosphorylation modification related genes. The volcano plot was used to visualize the regulation of differentially expressed genes (Figure 1A, green and red represent downregulation and upregulation respectively), and we found that most of these genes are upregulated,We conducted GO enrichment analysis and KEGG pathway analysis on DEGs. GO enrichment analysis (Figure 1B) showed that in biological process（BP）, cellular component（CC）and molecular function（MF）, DEGs were mainly involved in mitochondrial cell cycle phase transition, chromosomal region, and protein serine tyrosine kinase activity. KEGG analysis showed (Figure 1C) that DEGs were mainly involved in cell cycle, viral carcinogenesis, p53 signaling pathway, etc. Eleven prognostic related genes were identified from differentially expressed genes through univariate COX analysis (Figure 1D). The investigation of copy number variation (CNV) frequency (Figure 1E) showed that 11 prognostic related genes had copy number variations. Figure 1: Extraction and screening of DEGs and prognostic related genes. (A) Volcanic diagram showing the regulation of DEGs in LUAD tissue and normal tissue (green: downward regulation; red: upward regulation); (B) The foam map shows the GO analysis based on the differential genes in TCGA database. (C) The histogram displays the analysis of differentially expressed genes based on the KEGG using the TCGA database. (D) Using univariate Cox analysis, 11 genes related to patient survival were screened in LUAD patients with TCGA; (E) CNV frequency of prognostic related genes. The height of the column represents the frequency of change. The blue dots indicate the frequency of loss, The red dots represent the amplification frequency. DEGs, differentially expressed genes; LUAD, lung adenocarcinoma; GO, gene ontology; TCGA, the Cancer Genome Atlas; KEGG, Kyoto Encyclopedia of Genes and Genomes; BP, biological process; CC, cellular component; MF, molecular function; CNV, Copy number variation. Construction of a prognostic model in the TCGA cohort Using LASSO regression analysis on 11 prognostic related genes to avoid overfitting of results. A total of 1000 iterations were conducted to identify the genes with the strongest correlation, calculate the relative coefficients of the genes, and establish LASSO coefficient spectra for 6 gene prognostic models (Figure 2A). Risk score= (0.0015 × expression value of AURKA) + (0.01599 × expression value of CHEK1) + (0.00314 × expression value of PBK) + (0.00514 × expression value of PRKDC) + (0.00313 × expression value of PKM) + (0.00832 × expression value of RUVBL1). Then, ten fold cross validation was used to determine the optimal penalty parameters for the model (Figure 2B). The expression of six genes in the prognostic model was validated (Figure 2C), and it was found that they were all highly expressed in tumor tissues. The risk curve (Figure 2D) showed that Luad patients were divided into high-risk and low-risk groups based on the median risk score. As the risk value increases, the number of deaths increases. Survival analysis showed (Figure 2E) that patients in the high-risk group had a worse prognosis (P=0.007). The areas under the Receiver Operating Characteristic (ROC) curve for one year, three years, and five years were 0.62, 0.627 and 0.558, respectively (Figure 2F), indicating that the model performed well in predicting the one-year, three-year, and five-year survival rates of patients. The results of Principal Component Analysis (PCA) showed that patients with different risks can be divided into two groups (Figure 2G). To evaluate the reliability of the model, we plotted a heatmap of risk scores with age, gender, clinical stage, T, M, and N (Figure 2H). Among them, clinical T stage (P<0.01), N stage (P<0.05), and Stage stage (P<0.01) had significant significance. In both univariate (Figure 2I) and multivariate Cox regression analyses (Figure 2J), the P-values of risk scores were less than 0.001, indicating that the model has good predictive ability. Figure 2: Construction of prognostic model and clinical correlation analysis. A. LASSO regression analysis constructs a prognostic model by obtaining 6 genes with the minimum λ. B. LASSO pedigree diagram of 6 prognostic genes. C. The expression of various genes in the prognostic model. D. Risk score distribution and survival status of each patient. E. The survival curve shows that patients in the high-risk and low-risk groups have different prognoses. F. The ROC curve displays the one-year, three-year, and five-year survival rates of patients. G. PCA shows that the high-risk and low-risk groups can be divided into two groups. H. Stage staging (P<0.01), T staging (P<0.01), and N staging (P<0.01) all have statistical significance in the heatmap of clinical correlation analysis. I-J. The risk score has significant significance in independently predicting the prognosis of LUAD (P<0.001). LASSO, least absolute shrinkage and selection operator regression; ROC, receiver Operating Characteristic; PCA, principal component analysis. Validation of risk model To verify the accuracy of the model, we used three GEO datasets for validation. In GSE30219, the survival curve suggests that high-risk patients have poor prognosis (Figure 3A, P=0.017), the area under the ROC curve (Figure 3D) indicated that the one-year, three-year, and five-year survival rates were 0.646, 0.715, 0.721. In GSE31210, the survival curve showed that patients in the high-risk group had a poorer prognosis than those in the low-risk group (Figure 3B, P<0.001), the area under the ROC curve (Figure 3E) indicates that the one-year, three-year, and five-year survival rates are 0.74, 0.663 and 0.695. In GSE50081, the survival curve indicates poor prognosis in high-risk patients (Figure 3C, P<0.001), the area under the ROC curve (Figure 3F) indicates that the survival rates at one, three, and five years are 0.752, 0.696, 0.661. PCA analysis can clearly distinguish high-risk and low-risk groups in the GEO dataset (Figure 3G, H, I). The results of these validation sets indicate that our prognostic model has good accuracy. Figure 3: Validation of prognostic models in three GEO datasets. A-C. The survival curves indicate that the risk score is statistically significant in all three validation sets. D-F. ROC curves predict and validate the one-year, three-year, and five-year survival rates of patients. G-I. The PCA plot in the GEO cohort. GEO, Gene Expression Omnibus; ROC, receiver Operating Characteristic; PCA, principal component analysis. Establishment and validation of nomogram In order to better utilize the constructed prognostic model, we established column charts (Figure 4A) for the overall survival（OS） of LUAD patients at 1, 3, and 5 years to accurately predict their OS. Visualize the performance of the column chart using calibration charts for 1-year, 3-year, and 5-year OS (Figure 4B). The sensitivity of the column chart model was evaluated using ROC curves, and the results showed that the area under the curve（AUC）values for predicting 1-year, 3-year, and 5-year survival rates were 0.708, 0.729, 0.684 (Figure 4C). Then, through univariate Cox analysis, the risk score was found to be a risk factor for hazard ratio （HR）>1 in LUAD patients, with P<0.001 (Figure 4D). Multivariate Cox (Figure 4E) analysis showed that risk score was proven to be an independent prognostic factor for OS in LUAD patients (risk score HR=1.94, 95% CI: 1.313-2.868, P<0.001). These results indicate that the model performs well in predicting OS. Figure 4: Construction of nomograms. A. Nomograms used to predict the 1, 3, and 5-year overall survival of LUAD patients in the TCGA cohort. B. Calibration chart for predicting recurrence in 1, 3 and 5 years. C. ROC curve evaluates the sensitivity of nomograph model. D. Univariate Cox regression analysis in TCGA cohort. E. Multivariate Cox regression analysis in TCGA cohort. OS, overall survival; LUAD, lung adenocarcinoma; TCGA; The Cancer Genome Atlas; AUC, area under the curve; ROC, receiver operating characteristic; HR, hazard ratio; CI, confidence interval. Functional analysis between different risk groups We used GO and KEGG enrichment analysis to investigate the potential functions of DEGs between two risk groups and the main pathways involved in regulation. GO enrichment analysis showed (Figure 5A) that in biological processes (BP), differentially expressed genes were mainly enriched in organelle division and nuclear division. Chromosomal regions and spindles are mainly enriched in the cellular component (CC). In molecular function (MF), differentially expressed genes are mainly enriched in microtubules and microtubule protein binding. KEGG enrichment analysis showed (Figure 5B) that DEGs were mainly involved in cell cycle, progesterone mediated oocyte maturation, oocyte meiosis, linoleic acid metabolism, arachidonic acid metabolism, IL-17 signaling pathway, P53 signaling pathway, etc. GSVA prompted CELL_CYCLE, DNA_REPLICATION, GLYCOLYSIS_GLUCONEOGENESIS and others were mainly enriched in high-risk groups, PRIMARY_BILE_ACID_BIOSYNTHESIS,ARACHIDONIC_ACID_METABOLISM ALPHA_LINOLENIC_ACID_METABOLISM and others were mainly enriched in low-risk groups (Figure 5C). Figure 5: Enrichment analysis of DEGs between high-risk and low-risk groups. A. GO analysis of DEGs. B. KEGG analysis of DEGs. C. GSVA analysis of DEGs. GO, gene ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; DEGs, differentially expressed genes; BP, biological process; CC, cellular component; MF, molecular function; GSVA, Gene Set Variation Analysis. Correlation analysis between risk score and tumor mutation burden The distribution of LUAD gene mutations in the high-risk and low-risk groups was revealed by drawing waterfall plots (Figure 6A and Figure 6B). TP53 (52%) and MUC16 (36%) were the most common mutated genes in the high-risk and low-risk groups, respectively, with a higher proportion of mutations in high-risk populations. Comparing the tumor mutational burden (TMB) of different risk groups, it can be found that the TMB of the high-risk group is higher than that of the low-risk group (Figure 6C, P=2.2e-05), and there was a significant positive correlation between TMB and risk score (Figure 6D, R=0.22, P=6.9e-07). When introducing risk scores into TMB, we found that there was a statistically significant difference in survival among patients in each group (Figure 6E, P=0.007). Among them, the high tumor mutation low risk score group has the best prognosis, while the low tumor mutation high risk score group has the worst prognosis. Figure 6: Gene mutations and TMB status between different groups. The waterfall plot (A, B) showed the mutation status of 20 highly mutated genes in the high-risk and low-risk groups. C. TMB between different risk groups. D. The correlation between risk score and TMB. E. Survival status of patients with high and low risk scores combined with high and low tumor mutations. LUAD, lung adenocarcinoma; TMB, tumor mutational burden. Correlation analysis between immune cell infiltration and immune function Based on CIBERSORT analysis to evaluate the immune cell infiltration between different risk groups (Figure 7A), we found that different immune cell infiltrations were observed between the two groups. M0 macrophages (P<0.001), neutrophils (P<0.01), activated CD4 memory T cells (P<0.001), and resting natural killer cells (P<0.001) were highly expressed in the high-risk group, and the survival curve showed high expression of M0 macrophages (Figure 7C, P=0.009) is associated with poor prognosis. In the analysis of immune function correlation (Figure 7B), we found that human leukocyte antigen (HLA) (P<0.001), type II interferon response (Type-III IFN-Reponse) (P<0.001), and checkpoint (P<0.05) were highly expressed in the low-risk group. Patients with high expression of human leukocyte antigen (Figure 7D, P=0.003) and high expression of type II interferon response (Figure 7E, P=0.016) had better prognosis. This may be the reason why low-risk patients have a better prognosis. By comparing the immune cell infiltration and immune function differences among patients in different risk groups, we infer that they play an important role in the occurrence and development of LUAD, and the prognosis of patients in the high-risk group is poor, consistent with previous studies. We used the ESTIMATE algorithm to evaluate the Immunescore and Stromalscore in the tumor microenvironment (TME), and added the two scores to obtain the ESTIMATEscore index (Figure 7F), which can be used to infer the purity of the tumor. We found that the matrix score (P<0.001), immune score (P<0.001), and ESTIMATEscore (P<0.001) of the high-risk group were generally reduced, but the tumor purity was abnormally increased (Figure 7G, P=8.8e-05). The increase in tumor purity is associated with poor prognosis (Figure 7H, P=0.005). Figure 7: Correlation analysis of immune cells and immune function among different risk groups. A. The infiltration of immune cells between high-risk and low-risk populations. B. Analysis of different risk scoring groups and immune function. C. Differential expression of macrophage M0 and analysis of patient survival curve. D. Differential expression of human leukocyte antigens and analysis of patient survival curves. E. Differential expression of type II interferon response and analysis of patient survival curve. F. TME scores between different risk groups. G. There was a significant difference in tumor purity between high and low risk scores. H. Survival curves between different tumor purities. LUAD, lung adenocarcinoma; TME, tumor microenvironment. * P < 0.05.** P < 0.01. *** P < 0.001. Correlation analysis between risk score, tumor microenvironment, and immune checkpoint Immune checkpoint differential analysis (Figure 8A) showed that CD276 (P<0.001) and TNFSF4 (P<0.01) were highly expressed in the high-risk group, indicating that patients in the high-risk group were more likely to receive targeted therapy with anti-CD276 and anti-TNFSF4. Other immune checkpoints are highly expressed in low-risk patients, such as CTLA4 (P<0.01), CD28 (P<0.001), CD48 (P<0.001), IDO2 (P<0.001), TIGIT (P<0.01), etc., indicating that low-risk patients were more likely to receive targeted therapy against these genes. Use TIDE and IPS analysis to predict the efficacy of immune checkpoint inhibitors in low-risk and high-risk groups. The TIDE score showed that the high-risk group had a higher TIDE score, indicating that immune evasion was more common in immunotherapy patients in the high-risk group compared to the low-risk group (Figure 8B, P<0.05). Therefore, this may also be one of the reasons why patients in the high-risk group have a poorer prognosis compared to those in the low-risk group. The IPS results showed that the CTLA4_neg-PD1_neg score of the low-risk group was significantly higher than that of the high-risk group (Figure 8C), and the IPS CTLA4_neg-PD1_pos score of the low-risk group was significantly higher than that of the high-risk group(Figure 8D). Compared with the high-risk group, the IPS CTLA4_pos-PD1_neg score of the low-risk group was significantly higher than that of the high-risk group (Figure 8E), and the IPS CTLA4_pos-PD1_pos score of the low-risk group was significantly higher than that of the high-risk group (Figure 8F). Half maximal inhibitory concentration(IC50) was an important indicator for evaluating drug efficacy. We studied the risk and sensitivity of anti-tumor drugs and found that the risk is related to many anti-tumor drugs, such as 5-fluorouracil(Figure 8G), cytarabine(Figure 8H), gefitinib(Figure 8I)and irinotecan(Figure 8J), which were more suitable for high-risk patients. These results indicated that risk score can serve as a potential predictor of chemotherapy sensitivity, providing new insights for the treatment of tumors and the prevention of drug resistance. Figure 8: Differential analysis of immune checkpoint differences between high-risk and low-risk groups. A. Analysis of immune checkpoint differences between high-risk and low-risk groups. B. TIDE scores for different risk groups. C-F. The relative probability that the low-risk group and the high-risk group will respond to ICI treatment assessed using immunophenoscore (IPS). G-J. Risk score and anticancer drug sensitivity analysis. TIDE, tumor Immune checkpoint immune dysfunction and exclusion; IPS, immunophenoscore; ICI, Immune checkpoint inhibitor; IC50, half maximal inhibitory concentration. Expression and validation of PBK in lung adenocarcinoma Based on the previous description, we have constructed a 6-gene prognostic model with good accuracy. Then, further research will be conducted based on PBK in the model genes. The expression of PBK in LUAD samples of TCGA database was analyzed, and the differential analysis results showed that the expression of PBK in lung adenocarcinoma was significantly higher than that in normal tissues (Figure 9A, P<0.001). Survival analysis showed that high expression of PBK was associated with poor prognosis in patients (Figure 9B, P=0.003). In order to make the results more reliable, we downloaded three independent LUAD datasets from the GEO data platform to verify the relationship between PBK gene expression in LUAD and patient prognosis. The three validation sets are GSE13213 (Figure 9C, P=0.003), GSE31210 (Figure 9D, P<0.001), and GSE72094 (Figure 9E, P<0.001). The consistent results obtained from the three datasets indicate that high expression of PBK gene is associated with poor prognosis in LUAD patients, suggesting that PBK plays an important role in the occurrence and development of LUAD. The transcription of PBK in LUAD cell lines was validated through RT-PCR experiments, and the results showed (Figure 9F) that compared with human lung bronchial epithelial cell line 16HBE, PBK was highly expressed in LUAD cell lines A549 (P<0.01), NCI-H1975 (P<0.05), and NCI-H1299 (P<0.001). Finally, we evaluated the expression of PBK in TCGA pan cancer. The results showed that PBK was highly expressed in most tumors (Figure 9G). Figure 9: Expression and validation of PBK gene. A. The expression of PBK in LUAD patients in the TCGA cohort. B. The expression of PBK and the survival curve of LUAD patients. C-E. Validate the expression and survival relationship of PBK in the GEO dataset. F. The transcription status of PBK in LUAD cell lines. G. The expression of PBK in pan cancer (blue represents normal samples, red represents tumor samples). LUAD, lung adenocarcinoma; TCGA, the Cancer Genome Atlas; GEO, Gene Expression Omnibus. * P < 0.05，** P < 0.01，*** P < 0.001. Discussion Lung adenocarcinoma (LUAD) is the most prevalent malignant tumor and carries a significant mortality burden. Early diagnosis remains a challenge, often resulting in advanced-stage or metastatic disease at presentation. This, in turn, translates to a poor prognosis for patients. Consequently, there is an urgent need to identify and develop accurate predictive biomarkers alongside novel therapeutic targets. In this study, a prognostic model was established using six genes (AURKA, CHEK1, PBK, PRKDC, PKM, and RUVBL1). LUAD patients were divided into high- and low-risk groups based on the optimal median value. Survival analysis showed that the high-risk group had a poorer prognosis than that of the low-risk group. We have explored potential reasons for this in our subsequent analysis An increasing number of studies indicate that the tumor microenvironment (TME） is closely related to the occurrence of tumors. Non-tumor cells include stromal cells, immune cells, fibroblasts, and normal cells, which, together with tumor cells, participate in the formation and regulation of TME [15] . The content of tumor cells in tumor tissue is defined as tumor purity [16] . Therefore, the purity of tumors decreases with the increase of non-tumor cell components. Through correlation analysis between risk score and TME, the high-risk group was found to have high tumor purity, and survival analysis suggested that high tumor purity was associated with poor prognosis. Furthermore, some reports suggest that tumor purity may be a potential prognostic indicator for gastric cancer [17] 、colon cancer [18] and cervical cancer [19] . Further analysis revealed that in the high-risk group, the expression of neutrophils and M0 macrophages increased. Scholars have found that macrophages play a significant role in maintaining tissue homeostasis and enhancing host defense in TME [20] ，These can be divided into the following two categories: M1 and M2. M1 type is involved in the inflammatory response and is related to anti-tumor immunity, whereas M2 type has tumor-promoting properties [21] . Macrophages account for a large proportion of tumor matrix components and are therefore also known as tumor-associated macrophages (TAMs). In the late stage of tumor progression, TAMs transition to M2 type [22] ，Studies have proven that this type of TAM can provide a favorable environment for tumor metastasis [23] and angiogenesis [24] . In this study, we found that the increase of M0 macrophages in high-risk group patients is associated with poor prognosis in LUAD patients. We speculate that it may be due to the recruitment of a large number of macrophages around the tumor, changing the tumor microenvironment and promoting tumor occurrence and development. Additionally, polarized M2 macrophages increase tumor angiogenesis and invasion. Therefore, TAM may play an important role in the occurrence and development of LUAD. In addition, inhibiting macrophage recruitment at the tumor site and preventing macrophage polarization towards the M2 type may be another approach to tumor treatment. In addition, neutrophils also increase in high-risk patients, and studies have shown that they can affect the development of cancer [25] ，Tumor-associated neutrophils affect TME and cancer progression, possibly by enhancing the recruitment of regulatory T cells (Tregs) in TME. The combined effect of the two weakens anti-tumor immune activity, thereby promoting tumor growth [26] . Neutrophils secrete various cytokines, such as matrix metalloproteinase (MMP), vascular endothelial growth factor, neutrophil elastase, reactive oxygen species, etc. These cytokines can accelerate tumor cell growth and spread by altering TME, leading to poor prognosis [27] . MMP-9 secreted by neutrophils is closely related to tumor angiogenesis [28] and plays an important role in promoting the establishment of tumor-neovascularization network [29] . Neutrophils can also affect cancer progression by recruiting Tregs and macrophages [30] . Therefore, macrophages and neutrophils play an important role in promoting tumor development, which may be one of the reasons for poor prognosis in high-risk patients. Immunotherapy can enhance the natural defense of patients, thereby killing invading tumor cells in the body. Therefore, the therapy is increasingly attracting attention worldwide. In recent years, monoclonal antibody immune checkpoint inhibitors (ICIs), therapeutic antibodies, small molecule inhibitors, and cancer vaccines have gradually emerged as several types of drugs for cancer immunotherapy [31] . Among them, especially ICIs, have made certain progress in the treatment of malignant tumors such as NSCLC, melanoma, prostate cancer, and kidney cancer [32] . Ipilimumab, a monoclonal antibody targeted against cytotoxic T lymphocytes associated with antigen-4 (CTLA-4), was the first one to be put into clinical use [33] .It was approved in 2011 for the treatment of malignant melanoma [34] . In this study, CTLA-4 was highly expressed in the low-risk group. It translocated to the cell surface with the assistance of T cell receptors after receiving a stimulation signal from CD28, competing with CD28 to bind to the stimulatory molecules CD80 and CD86, thereby transmitting the inhibitory signal into T cells, leading to inhibition of proliferation and activation [35] . Furthermore, studies have shown that injecting anti-CTLA-4 antibodies into mice with pre-formed tumors can significantly reduce tumor growth [36] . Studies have shown that cancer with high TMB has a high likelihood of immune therapy response. Thus, it can yield good results in anti-PD-1/PD-L1 immunotherapy against different tumors [37] . In our study, patients in the high-risk group had higher TMB than that in the low-risk group, indicating that patients in the high-risk group may be more likely to benefit from receiving anti-PD-1/PD-L1 treatment. We further studied the key genes in the prognostic model. Among them, Aurora kinase A (AURKA) is an important member of the serine/threonine kinase family, which includes three members: Aurora A (AURKA), Aurora B (AURKB), and Aurora C (AURKC). The aurora kinase family can participate in different steps of mitosis to precisely regulate spatial and temporal functions. These are necessary to maintain the integrity of the genome and chromosomes during cell division [38] . The expression level of AURKA is increased in most tumor tissues reported in the TCGA database [39] . and the increase of AURKA is associated with poor prognosis in patients with gastrointestinal stromal tumors [40] ，colon cancer [41] and NSCLC [42] . Checkpoint kinase (CHEK1) has the function of encoding serine/threonine protein kinases, which activate downstream events through ATR (ATM Rad3 related) dependent phosphorylation, causing cell cycle arrest, maintaining replication fork activity, and activating DNA repair mechanisms [43] . CHEK1 not only participates in monitoring the movement of replication forks but may also regulate other chromosomal activities, such as transcriptional regulation [44] . Compared with the expression levels in normal tissues, CHEK1 is overexpressed in various tumors [45] . Abnormal expression of CHEK1 protein can promote tumor cell proliferation and tumor grading, leading to poor prognosis in breast cancer [46] 、multiple myeloma [47] and NSCLC [48] patients. The catalytic subunit (PRKDC) can encode DNA-dependent protein kinase catalytic subunits (DNA-PKcs) and is also a member of the phosphatidylinositol kinase-associated kinase (PIKK) family [49] . In most types of tumors, the expression of PRKDC is higher than that in adjacent tissues [50] ，which is closely related to high-grade tumors, such as liver cancer [51] 、ovarian cancer [52] ，and lymphoma [53] . The expression is also related to the cancer progression. PRKDC also plays an important role in regulating cell cycle and chromosome segregation, thereby promoting the occurrence and development of tumors [54] . RUVBL1 (also known as Pontin52, Rvb1, TAP54 α, and TIP49) is a highly conserved ATPase in the AAA+ (cell activity-related ATPase) superfamily, exhibiting ATPase activity and DNA helicase activity [55] ，It is mainly located in the nucleus and related to the nuclear matrix or nuclear cytoplasmic sol [56] ，and is involved in chromatin remodeling [57] . The increased expression of RUVBL1 in tumor tissues [58] ，can accelerate tumor cell proliferation and promote tumor cell survival, which is crucial for the development of cance [59] ，such as liver cancer [60] ，oral cancer [61] ，and NSCLC [62] . The muscle type of pyruvate kinase (PKM) participates in the final step of glycolysis and plays an important role [63] in controlling tumor metabolism [64] . Some studies indicate the oncological effects of PKM differentially spliced transcripts, including PKM1 and PKM2 [65] . PKM1 can activate the glucose metabolism pathway and stimulate mitochondrial autophagy, which is related to the development of malignant tumors [66] . PKM2 is highly expressed in malignant tumors [67] ，LUAD patients with increased PKM2 expression have decreased overall survival [68] ，In addition, studies have shown that knocking down the expression of PKM2 can inhibit the growth of tumor cells [69] . PBK is a serine/threonine kinase that can participate in cell mitosis and the cell cycle [70] . However, few studies have reported on PBK promoting the occurrence and development of LUAD; therefore, we will explore the relationship between PBK and LUAD in subsequent studies. We found that in the TCGA dataset, the expression level of PBK was significantly increased in LUAD patients compared to that in the normal lung tissue, and high expression of PBK was associated with poor prognosis in LUAD patients. Then, we validated the correlation between high expression of PBK and poor prognosis in LUAD patients using three GEO datasets and validated the expression of PBK gene in three LUAD cell lines, all of which showed high expression. Therefore, based on our research results, we strongly speculate that PBK may play an important role in the development of LUAD. Our study has certain limitations. The study majorly involved bioinformatics analysis of a small dataset. This demands validation of the results using larger datasets and prospective studies. Conclusion The constructed prognostic model can effectively distinguish high-risk and low-risk populations, predict the prognosis of LUAD patients, and respond to TMB and immunotherapy. Based on the TCGA data, high expression of PBK is associated with poor prognosis in LUAD patients, which was validated using three GEO datasets. The expression was also demonstrated to be upregulated in LUAD cell lines. Therefore, PBK may be a potential target for LUAD treatment. Declarations Contributors Peng Zhang conducted the database search, screened and extracted data for the analysis, prepared extracted data for the procedures, and was responsible for writing this article. Sen Li and Zhanliang Ren conducted the database search and screening, as well as the collection and analysis of data. Bo Wang and Hang Chen performed statistical analysis and interpretation of data. Wenmiao Wang and Yong Zhang contributed to the discussion and editing. Peng Zhang and Yunhao Liu were responsible for the study's concept, design, and analysis, as well as revising the manuscript. All authors read and provided consent for the final version of the manuscript. Data Availability The study is in accordance with relevant guidelines and regulations. All data were publicly available from TCGA ( https://portal.gdc.cancer.gov/ ) and GEO ( https://www.ncbi.nlm.nih.gov/geo/ ) datasets. The datasets used and/or analysed during the current study available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding The authors received no specific funding for this study. Acknowledgements We thank Bullet Edits Limited for the linguistic editing and proofreading of the manuscript. Ethics approval and consent to participate Not applicable. Patient consent for publication Not applicable. References RL S, KD M, A J, et al. - Cancer statistics, 2018 [J]. CA Cancer J Clin, 2018, 68(1): 7-30. S B K, PA C, H B, et al. - Progress and prospects of early detection in lung cancer [J]. Open Biol, 2017, 7(9): 170070. S S, JR E, R M, et al. - Development of a RNA-Seq Based Prognostic Signature in Lung Adenocarcinoma [J]. J Natl Cancer Inst, 2016, 109(1). KD M, RL S, CC L, et al. - Cancer treatment and survivorship statistics, 2016 [J]. CA Cancer J Clin, 2016, 66(4): 271-89. 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S G, D B, RA L. - Characterization of PDZ-binding kinase, a mitotic kinase [J]. Proc Natl Acad Sci U S A, 2000, 97(10): 5167-72. Additional Declarations No competing interests reported. Supplementary Files SupplementTable.xlsx Cite Share Download PDF Status: Posted Version 1 posted 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-7953290","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":549421801,"identity":"43a8906e-7314-48ff-9fc9-bffdfba3a6f1","order_by":0,"name":"Peng Zhang","email":"","orcid":"","institution":"Shaanxi University of Traditional Chinese Medicine Affiliated Hospital","correspondingAuthor":false,"prefix":"","firstName":"Peng","middleName":"","lastName":"Zhang","suffix":""},{"id":549421802,"identity":"10343f76-03ce-406c-a3ab-66fba67eea3a","order_by":1,"name":"Sen Li","email":"","orcid":"","institution":"Shaanxi University of Traditional Chinese Medicine Affiliated Hospital","correspondingAuthor":false,"prefix":"","firstName":"Sen","middleName":"","lastName":"Li","suffix":""},{"id":549421803,"identity":"d186763e-e128-4ca6-91c0-99173dd8e8cb","order_by":2,"name":"Zhanliang Ren","email":"","orcid":"","institution":"Shaanxi University of Traditional Chinese Medicine Affiliated Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zhanliang","middleName":"","lastName":"Ren","suffix":""},{"id":549421804,"identity":"2a3479c2-b223-4411-a514-05901cc65fba","order_by":3,"name":"Bo Wang","email":"","orcid":"","institution":"Shaanxi University of Traditional Chinese Medicine Affiliated Hospital","correspondingAuthor":false,"prefix":"","firstName":"Bo","middleName":"","lastName":"Wang","suffix":""},{"id":549421805,"identity":"4a6e10dc-c740-4c8e-808a-b19ed8b33ac2","order_by":4,"name":"Hang Chen","email":"","orcid":"","institution":"Shaanxi University of Traditional Chinese Medicine Affiliated Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hang","middleName":"","lastName":"Chen","suffix":""},{"id":549421806,"identity":"f5bc031d-8a54-4f59-842d-d4d3f218466b","order_by":5,"name":"Wenmiao Wang","email":"","orcid":"","institution":"Second Affiliated Hospital of Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Wenmiao","middleName":"","lastName":"Wang","suffix":""},{"id":549421807,"identity":"40a714f6-5070-452b-946d-7224218e566e","order_by":6,"name":"Yong Zhang","email":"","orcid":"","institution":"Shaanxi University of Traditional Chinese Medicine Affiliated Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yong","middleName":"","lastName":"Zhang","suffix":""},{"id":549421808,"identity":"b103968b-36a7-4e9f-ac25-cd4665bf3c59","order_by":7,"name":"Yunhao Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtUlEQVRIiWNgGAWjYBACPgYexgcSFTY8/PwNRGphY+BhNrA4kyYjOeMA8VrYBCrbDtsYNCQQq0Ui9xjDjTPneQwYDjB++JhDlJa8tIczKm7zmDM3MEvO3EaUlhxzY4kzt3ksGw6wMfMSqcVM+m/bOR6DAwkkaJGQbDtAihaeN8YGEmeSeSRnHGwmzi/87DmGwKi0s+fnbz744SMxWhgEEmAsxgZi1IOsOUCkwlEwCkbBKBi5AACTNzMCuNuj/AAAAABJRU5ErkJggg==","orcid":"","institution":"Shaanxi University of Traditional Chinese Medicine Affiliated Hospital","correspondingAuthor":true,"prefix":"","firstName":"Yunhao","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2025-10-27 11:30:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7953290/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7953290/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":96790158,"identity":"53ed5f78-0269-4d4d-b44a-a4c9e2f8263e","added_by":"auto","created_at":"2025-11-26 06:52:39","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":4768304,"visible":true,"origin":"","legend":"\u003cp\u003eExtraction and screening of DEGs and prognostic related genes. (A) Volcanic diagram showing the regulation of DEGs in LUAD tissue and normal tissue (green: downward regulation; red: upward regulation); (B) The foam map shows the GO analysis based on the differential genes in TCGA database. (C) The histogram displays the analysis of differentially expressed genes based on the KEGG using the TCGA database. (D) Using univariate Cox analysis, 11 genes related to patient survival were screened in LUAD patients with TCGA; (E) CNV frequency of prognostic related genes. The height of the column represents the frequency of change. The blue dots indicate the frequency of loss, The red dots represent the amplification frequency. DEGs, differentially expressed genes; LUAD, lung adenocarcinoma; GO, gene ontology; TCGA, the Cancer Genome Atlas; KEGG, Kyoto Encyclopedia of Genes and Genomes; BP, biological process; CC, cellular component; MF, molecular function; CNV, Copy number variation.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7953290/v1/3efcb4734812f9f23860f9bf.png"},{"id":96790165,"identity":"fe703d0f-5faf-46a9-bb77-cc36dc3107fb","added_by":"auto","created_at":"2025-11-26 06:52:39","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":16129273,"visible":true,"origin":"","legend":"\u003cp\u003eConstruction of prognostic model and clinical correlation analysis. A. LASSO regression analysis constructs a prognostic model by obtaining 6 genes with the minimum λ. B. LASSO pedigree diagram of 6 prognostic genes. C. The expression of various genes in the prognostic model. D. Risk score distribution and survival status of each patient. E. The survival curve shows that patients in the high-risk and low-risk groups have different prognoses. F. The ROC curve displays the one-year, three-year, and five-year survival rates of patients. G. PCA shows that the high-risk and low-risk groups can be divided into two groups. H. Stage staging (P\u0026lt;0.01), T staging (P\u0026lt;0.01), and N staging (P\u0026lt;0.01) all have statistical significance in the heatmap of clinical correlation analysis. I-J. The risk score has significant significance in independently predicting the prognosis of LUAD (P\u0026lt;0.001). LASSO, least absolute shrinkage and selection operator regression; ROC, receiver Operating Characteristic; PCA, principal component analysis.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7953290/v1/a3129313b9649dfce0fbfdbb.png"},{"id":96790159,"identity":"c58b41c9-b6d6-4ccb-b248-a26bc36f10fe","added_by":"auto","created_at":"2025-11-26 06:52:39","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":9208389,"visible":true,"origin":"","legend":"\u003cp\u003eValidation of prognostic models in three GEO datasets. A-C. The survival curves indicate that the risk score is statistically significant in all three validation sets. D-F. ROC curves predict and validate the one-year, three-year, and five-year survival rates of patients. G-I. The PCA plot in the GEO cohort. GEO, Gene Expression Omnibus; ROC, receiver Operating Characteristic; PCA, principal component analysis.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7953290/v1/5f5eb765e11fc23bb068f579.png"},{"id":96916385,"identity":"d9c3c9a0-289a-4f57-9ccd-c46174e17adf","added_by":"auto","created_at":"2025-11-27 14:08:32","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":3315936,"visible":true,"origin":"","legend":"\u003cp\u003eConstruction of nomograms. A. Nomograms used to predict the 1, 3, and 5-year overall survival of LUAD patients in the TCGA cohort. B. Calibration chart for predicting recurrence in 1, 3 and 5 years. C. ROC curve evaluates the sensitivity of nomograph model. D. Univariate Cox regression analysis in TCGA cohort. E. Multivariate Cox regression analysis in TCGA cohort. OS, overall survival; LUAD, lung adenocarcinoma; TCGA; The Cancer Genome Atlas; AUC, area under the curve; ROC, receiver operating characteristic; HR, hazard ratio; CI, confidence interval.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-7953290/v1/16d37e2fae0d7737022e323c.png"},{"id":96790162,"identity":"32064c0a-22f2-4aed-bfe1-a6b33f8d0601","added_by":"auto","created_at":"2025-11-26 06:52:39","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":11164703,"visible":true,"origin":"","legend":"\u003cp\u003eEnrichment analysis of DEGs between high-risk and low-risk groups. A. GO analysis of DEGs. B. KEGG analysis of DEGs. C. GSVA analysis of DEGs. GO, gene ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; DEGs, differentially expressed genes; BP, biological process; CC, cellular component; MF, molecular function; GSVA, Gene Set Variation Analysis.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-7953290/v1/2c349742171d160ee469e927.png"},{"id":96915677,"identity":"f4a041a6-03eb-4aca-9390-d763453d287b","added_by":"auto","created_at":"2025-11-27 14:07:31","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":12990498,"visible":true,"origin":"","legend":"\u003cp\u003eGene mutations and TMB status between different groups. The waterfall plot (A, B) showed the mutation status of 20 highly mutated genes in the high-risk and low-risk groups. C. TMB between different risk groups. D. The correlation between risk score and TMB. E. Survival status of patients with high and low risk scores combined with high and low tumor mutations. LUAD, lung adenocarcinoma; TMB, tumor mutational burden.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-7953290/v1/01b752c6e4f921a6ceaa6a26.png"},{"id":96790164,"identity":"c20d864f-e862-41ec-a953-c82cd903b709","added_by":"auto","created_at":"2025-11-26 06:52:39","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":4500277,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation analysis of immune cells and immune function among different risk groups. A. The infiltration of immune cells between high-risk and low-risk populations. B. Analysis of different risk scoring groups and immune function. C. Differential expression of macrophage M0 and analysis of patient survival curve. D. Differential expression of human leukocyte antigens and analysis of patient survival curves. E. Differential expression of type II interferon response and analysis of patient survival curve. F. TME scores between different risk groups. G. There was a significant difference in tumor purity between high and low risk scores. H. Survival curves between different tumor purities. LUAD, lung adenocarcinoma; TME, tumor microenvironment. * \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05.** \u003cem\u003eP \u003c/em\u003e\u0026lt; 0.01. *** \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-7953290/v1/c7ce750c4f620c7c46ac8413.png"},{"id":96790166,"identity":"ef43d15a-bfb9-49b3-9a12-fc2ce083541a","added_by":"auto","created_at":"2025-11-26 06:52:39","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":9381523,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential analysis of immune checkpoint differences between high-risk and low-risk groups. A. Analysis of immune checkpoint differences between high-risk and low-risk groups. B. TIDE scores for different risk groups. C-F. The relative probability that the low-risk group and the high-risk group will respond to ICI treatment assessed using immunophenoscore (IPS). G-J. Risk score and anticancer drug sensitivity analysis. TIDE, tumor Immune checkpoint immune dysfunction and exclusion; IPS, immunophenoscore; ICI, Immune checkpoint inhibitor; IC50, half maximal inhibitory concentration.\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-7953290/v1/ada3c1c00347f4dafd6ec86e.png"},{"id":96916989,"identity":"387fd372-080a-423a-824a-df5bffb7446a","added_by":"auto","created_at":"2025-11-27 14:09:06","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":25302633,"visible":true,"origin":"","legend":"\u003cp\u003eExpression and validation of PBK gene. A. The expression of PBK in LUAD patients in the TCGA cohort. B. The expression of PBK and the survival curve of LUAD patients. C-E. Validate the expression and survival relationship of PBK in the GEO dataset. F. The transcription status of PBK in LUAD cell lines. G. The expression of PBK in pan cancer (blue represents normal samples, red represents tumor samples). LUAD, lung adenocarcinoma; TCGA, the Cancer Genome Atlas; GEO, Gene Expression Omnibus. * \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05，** \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01，*** \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Figure9.png","url":"https://assets-eu.researchsquare.com/files/rs-7953290/v1/4853741aaca6674c1fa49274.png"},{"id":101296532,"identity":"47cecdaa-bbe8-4679-9734-85abebaebf70","added_by":"auto","created_at":"2026-01-28 09:12:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":71958894,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7953290/v1/3828875b-e245-4c6e-9e54-5c8079ac3770.pdf"},{"id":96790157,"identity":"f628e54c-5caa-4023-939e-cdb43324cc56","added_by":"auto","created_at":"2025-11-26 06:52:39","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":11395,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementTable.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7953290/v1/315ec56fc7aea93944e723fe.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Application of Prognostic Model based on Histone Phosphorylation Modification Gene in Lung Adenocarcinoma","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe high incidence and mortality rates associated with lung cancer have always been challenging to overcome \u003csup\u003e[1]\u003c/sup\u003e.Lung cancer is mainly divided into two categories: non-small cell lung cancer (NSCLC, accounting for about 85%) and small cell lung cancer (SCLC, accounting for about 15%\u003csup\u003e[2]\u003c/sup\u003e.LUAD is the most common subtype of NSCLC \u003csup\u003e[3]\u003c/sup\u003e.Although radiotherapy, chemotherapy, and immunotherapy have improved lung cancer treatment outcomes in recent years, the overall survival rate (OS) of LUAD patients remains significantly low due to the lack of effective early diagnosis and personalized treatment\u003csup\u003e[4]\u003c/sup\u003e.Therefore, improvisation of the prognosis and treatment of LUAD patients necessitates urgent study of the pathogenesis of LUAD and effective therapeutic targets.\u003c/p\u003e\n\u003cp\u003ePost-translational modification (PTM) of histones plays a vital role in regulating various processes related to the chromatin structure. Changes in the mode of PTM of histones are widely associated with the occurrence and development of cancer\u003csup\u003e[5]\u003c/sup\u003e.The level of histone modification is associated with the invasion and poor prognosis of various cancer cells, such as lung cancer, kidney cancer, and prostate cancer \u003csup\u003e[6]\u003c/sup\u003e.Methylation, acetylation, phosphorylation, glycosylation, ubiquitination, and citrullination \u003csup\u003e[7]\u003c/sup\u003e are common ways of histone PTM. Histone phosphorylation is a dynamic PTM regulated by the competitive activities of protein kinases and protein phosphorylases. The phosphorylation sites are involved in transcriptional regulation and chromatin condensation\u003csup\u003e[8]\u003c/sup\u003e.However, the role of histone phosphorylation in the occurrence and development of LUAD is unclear.\u003c/p\u003e\n\u003cp\u003eWe constructed a prognostic model based on histone phosphorylation involved in gene regulation and discovered the key gene PBK, also known as T-LAK cell-derived protein kinase (TOPK)\u003csup\u003e[9]\u003c/sup\u003e，There are few reports in LUAD, so we will include PBK in subsequent studies. PBK is significantly expressed in various types of tumor tissues and can promote cervical cancer\u003csup\u003e[10]\u003c/sup\u003e，breast cancer\u003csup\u003e[11]\u003c/sup\u003e，Schwannoma \u003csup\u003e[12]\u003c/sup\u003e，lymphma \u003csup\u003e[13]\u003c/sup\u003e and other cancers, leading to poor prognosis. However, the role of PBK in LUAD is unclear; therefore, the potential role of PBK in LUAD is worth exploring.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eData collection\u003c/p\u003e\n\u003cp id=\"_Toc22834\"\u003eTo investigate the role of histone phosphorylation in LUAD regulation, we first curated data on 42 genes related to this process from the published literature（Supplement Table）.Download gene expression matrix data (59 normal tissues and 539 LUAD tissues) and clinical information of LUAD patients from the Cancer Genome Atlas(TCGA) public data platform, remove samples with incomplete clinical information, and include all remaining patients in further analysis The Gene Expression Omnibus (GEO) database is a public data platform, and the 5 lung adenocarcinoma datasets we used for validation (GSE31210, GSE50081, GSE30219, GSE13213, GSE72094) were downloaded from the GEO database.\u0026nbsp;Both TCGA and GEO databases are publicly available. Therefore, this study does not require approval from the local ethics committee.\u003c/p\u003e\n\u003cp id=\"_Toc26765\"\u003eConstruction and validation of prognostic models\u003c/p\u003e\n\u003cp\u003ePerform Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis with 10 fold cross validation on prognostic genes, and run 1000 cycles using the \u0026quot;glmnet\u0026quot; package. Based on the optimal lambda value, a 6-gene prognostic model was established to divide LUAD patients in the TCGA dataset into two groups, with significant differences in survival analysis between the different groups (P=0.007). Three LUAD datasets were downloaded from the GEO data platform to verify the accuracy of the model.\u003c/p\u003e\n\u003cp\u003eEstablishment and verification of column chart\u003c/p\u003e\n\u003cp\u003eWe constructed a column chart by integrating traditional clinical variables such as age, gender, T stage, M stage, and N stage, as well as risk scores derived from prognostic features, to analyze the possible 1-year, 3-year, and 5-year overall survival rates of LUAD patients. In addition, generate a calibration curve for the column chart to check the consistency between the predicted survival rate after deviation correction and the observed survival rate.\u003c/p\u003e\n\u003cp id=\"_Toc5316\"\u003eEnrichment analysis\u003c/p\u003e\n\u003cp\u003eIdentification of differentially expressed genes among different risk groups of LUAD patients in the TCGA dataset using the \u0026quot;limma\u0026quot; package (| log2FC |\u0026gt;1, P\u0026lt;0.05). The biological processes, molecular functions, and cellular components of differentially expressed genes were enriched using Gene Ontology (GO). Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis identified signaling pathways\u003c/p\u003e\n\u003cp\u003eCalculate tumor purity and immune cell infiltration\u003c/p\u003e\n\u003cp\u003eThe R software package \u0026quot;estimate\u0026quot; was used to calculate the immune score、stromal score and tumor purity\u003csup\u003e[14]\u003c/sup\u003e. Quantify the scores of 22 immune cells in LUAD samples using the CIBERSORT algorithm, and visualize the infiltration distribution of 22 immune cells in the samples using the \u0026quot;ggplot2\u0026quot; software package.\u003c/p\u003e\n\u003cp id=\"_Toc248291306\"\u003eCell culture and real-time polymerase chain reaction\u003c/p\u003e\n\u003cp\u003eHuman pulmonary bronchial epithelial cells 16-HBE and LUAD cell lines (NCI-H1299, NCI-1975 and A549, purchased from the Institute of Cell Biology, Chinese Academy of Sciences) were cultured in RPMI-1640 medium (containing 10% fetal bovine serum) and placed in a humid environment containing 5% CO 2 at 37 ℃. Then, the expression of PBK in LUAD cell lines was verified by real-time polymerase chain reaction （RTPCR.）. PCR primers and probes were designed using Primer 5.0 and synthesized by Sangon Biotechnology Co. Ltd. (Shanghai, China). Expression of target genes was determined relative to \u0026beta;-actin, and analysis was calculated by the 2\u003csup\u003e-\u0026Delta;\u0026Delta;Ct\u003c/sup\u003e method. The following primer sequences were used:\u003c/p\u003e\n\u003cp\u003ePBK, F: 5\u0026prime;- GACTGCTCCTGCCTTCATAACCATC -3\u0026prime; and R: 5\u0026prime;- ACCATTCTCCTCCACAGCTTCTTG - 3\u0026prime;;\u003c/p\u003e\n\u003cp\u003e\u0026beta;-actin, F: 5\u0026prime;- ACTTAGTTGCGTTACACCCTTTC -3\u0026prime; and R: 5\u0026prime;- TCACCTTCACCGTTCCAGTTT - 3\u0026prime;.\u003c/p\u003e\n\u003cp\u003eStatistical analysis\u003c/p\u003e\n\u003cp\u003eThis study used R language (version 4.2.0), Graphpad Prism 8.0, and SPSS 20.0 for data statistical analysis. Kaplan Meier method was used to plot survival curves, and Wilcoxon test and Kruskal Wallis test were used to compare the significance of patient differences The Pearman test is used for correlation analysis. A statistical result with P\u0026lt;0.05 indicates statistical significance (* \u003cem\u003eP\u003c/em\u003e0.05, * * \u003cem\u003eP\u003c/em\u003e\u0026lt;0.01, * * * \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001, ns is not significant).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eExtraction of DEGs and prognostic related genes\u003c/p\u003e\n\u003cp\u003eThrough differential expression analysis, we identified a total of 32 differentially expressed genes from 42 histone phosphorylation modification related genes. The volcano plot was used to visualize the regulation of differentially expressed genes (Figure 1A, green and red represent downregulation and upregulation respectively), and we found that most of these genes are upregulated,We conducted GO enrichment analysis and KEGG pathway analysis on DEGs. GO enrichment analysis (Figure 1B) showed that in biological process（BP）, cellular component（CC）and molecular function（MF）,\u0026nbsp;DEGs were mainly involved in mitochondrial cell cycle phase transition, chromosomal region, and protein serine tyrosine kinase activity. KEGG analysis showed (Figure 1C) that DEGs were mainly involved in cell cycle, viral carcinogenesis, p53 signaling pathway, etc. Eleven prognostic related genes were identified from differentially expressed genes through univariate COX analysis (Figure 1D). The investigation of copy number variation (CNV) frequency (Figure 1E) showed that 11 prognostic related genes had copy number variations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 1:\u003c/strong\u003e Extraction and screening of DEGs and prognostic related genes. (A) Volcanic diagram showing the regulation of DEGs in LUAD tissue and normal tissue (green: downward regulation; red: upward regulation); (B) The foam map shows the GO analysis based on the differential genes in TCGA database. (C) The histogram displays the analysis of differentially expressed genes based on the KEGG using the TCGA database. (D) Using univariate Cox analysis, 11 genes related to patient survival were screened in LUAD patients with TCGA; (E) CNV frequency of prognostic related genes. The height of the column represents the frequency of change. The blue dots indicate the frequency of loss, The red dots represent the amplification frequency. DEGs, differentially expressed genes; LUAD, lung adenocarcinoma; GO, gene ontology; TCGA, the Cancer Genome Atlas; KEGG, Kyoto Encyclopedia of Genes and Genomes; BP, biological process; CC, cellular component; MF, molecular function; CNV, Copy number variation.\u003c/p\u003e\n\u003cp\u003eConstruction of a prognostic model in the TCGA cohort\u003c/p\u003e\n\u003cp\u003eUsing LASSO regression analysis on 11 prognostic related genes to avoid overfitting of results. A total of 1000 iterations were conducted to identify the genes with the strongest correlation, calculate the relative coefficients of the genes, and establish LASSO coefficient spectra for 6 gene prognostic models (Figure 2A). Risk score=\u0026nbsp;(0.0015 \u0026times; expression value of AURKA) + (0.01599 \u0026times; expression value of CHEK1) + (0.00314 \u0026times; expression value of PBK) + (0.00514 \u0026times; expression value of PRKDC) + (0.00313 \u0026times; expression value of PKM) + (0.00832 \u0026times; expression value of RUVBL1). Then, ten fold cross validation was used to determine the optimal penalty parameters for the model (Figure 2B). The expression of six genes in the prognostic model was validated (Figure 2C), and it was found that they were all highly expressed in tumor tissues. The risk curve (Figure 2D) showed that Luad patients were divided into high-risk and low-risk groups based on the median risk score. As the risk value increases, the number of deaths increases. Survival analysis showed (Figure 2E) that patients in the high-risk group had a worse prognosis (P=0.007). The areas under the Receiver Operating Characteristic (ROC) curve for one year, three years, and five years were 0.62, 0.627 and 0.558, respectively (Figure 2F), indicating that the model performed well in predicting the one-year, three-year, and five-year survival rates of patients. The results of Principal Component Analysis (PCA) showed that patients with different risks can be divided into two groups (Figure 2G). To evaluate the reliability of the model, we plotted a heatmap of risk scores with age, gender, clinical stage, T, M, and N (Figure 2H). Among them, clinical T stage (P\u0026lt;0.01), N stage (P\u0026lt;0.05), and Stage stage (P\u0026lt;0.01) had significant significance. In both univariate (Figure 2I) and multivariate Cox regression analyses (Figure 2J), the P-values of risk scores were less than 0.001, indicating that the model has good predictive ability.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 2:\u003c/strong\u003e Construction of prognostic model and clinical correlation analysis.\u0026nbsp;A. LASSO regression analysis constructs a prognostic model by obtaining 6 genes with the minimum \u0026lambda;. B. LASSO pedigree diagram of 6 prognostic genes. C. The expression of various genes in the prognostic model. D. Risk score distribution and survival status of each patient. E. The survival curve shows that patients in the high-risk and low-risk groups have different prognoses. F. The ROC curve displays the one-year, three-year, and five-year survival rates of patients. G. PCA shows that the high-risk and low-risk groups can be divided into two groups. H. Stage staging (P\u0026lt;0.01), T staging (P\u0026lt;0.01), and N staging (P\u0026lt;0.01) all have statistical significance in the heatmap of clinical correlation analysis. I-J. The risk score has significant significance in independently predicting the prognosis of LUAD (P\u0026lt;0.001). LASSO, least absolute shrinkage and selection operator regression; ROC, receiver Operating Characteristic; PCA, principal component analysis.\u003c/p\u003e\n\u003cp\u003eValidation of risk model\u003c/p\u003e\n\u003cp\u003eTo verify the accuracy of the model, we used three GEO datasets for validation. In GSE30219, the survival curve suggests that high-risk patients have poor prognosis (Figure 3A, P=0.017), the area under the ROC curve (Figure 3D) indicated that the one-year, three-year, and five-year survival rates were 0.646, 0.715, 0.721. In GSE31210, the survival curve showed that patients in the high-risk group had a poorer prognosis than those in the low-risk group (Figure 3B, P\u0026lt;0.001), the area under the ROC curve (Figure 3E) indicates that the one-year, three-year, and five-year survival rates are 0.74, 0.663 and 0.695. In GSE50081, the survival curve indicates poor prognosis in high-risk patients (Figure 3C, P\u0026lt;0.001), the area under the ROC curve (Figure 3F) indicates that the survival rates at one, three, and five years are 0.752, 0.696, 0.661. PCA analysis can clearly distinguish high-risk and low-risk groups in the GEO dataset (Figure 3G, H, I). The results of these validation sets indicate that our prognostic model has good accuracy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 3:\u0026nbsp;\u003c/strong\u003eValidation of prognostic models in three GEO datasets. A-C. The survival curves indicate that the risk score is statistically significant in all three validation sets. D-F. ROC curves predict and validate the one-year, three-year, and five-year survival rates of patients. G-I. The PCA plot in the GEO cohort. GEO, Gene Expression Omnibus; ROC, receiver Operating Characteristic; PCA, principal component analysis.\u003c/p\u003e\n\u003cp\u003eEstablishment and validation of nomogram\u003c/p\u003e\n\u003cp id=\"_Toc12821\"\u003eIn order to better utilize the constructed prognostic model, we established column charts (Figure 4A) for the overall survival（OS） of LUAD patients at 1, 3, and 5 years to accurately predict their OS. Visualize the performance of the column chart using calibration charts for 1-year, 3-year, and 5-year OS (Figure 4B). The sensitivity of the column chart model was evaluated using ROC curves, and the results showed that the area under the curve（AUC）values for predicting 1-year, 3-year, and 5-year survival rates were 0.708, 0.729, 0.684 (Figure 4C). Then, through univariate Cox analysis, the risk score was found to be a risk factor for hazard ratio （HR）\u0026gt;1 in LUAD patients, with P\u0026lt;0.001 (Figure 4D). Multivariate Cox (Figure 4E) analysis showed that risk score was proven to be an independent prognostic factor for OS in LUAD patients (risk score HR=1.94, 95% CI: 1.313-2.868, P\u0026lt;0.001). These results indicate that the model performs well in predicting OS.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 4:\u0026nbsp;\u003c/strong\u003eConstruction of nomograms. A. Nomograms used to predict the 1, 3, and 5-year overall survival of LUAD patients in the TCGA cohort. B. Calibration chart for predicting recurrence in 1, 3 and 5\u0026nbsp;years. C. ROC curve evaluates the sensitivity of nomograph model. D. Univariate Cox regression analysis in TCGA cohort. E. Multivariate Cox regression analysis in TCGA cohort. OS, overall survival; LUAD, lung adenocarcinoma; TCGA; The Cancer Genome Atlas; AUC, area under the curve; ROC, receiver operating characteristic; HR, hazard ratio; CI, confidence interval.\u003c/p\u003e\n\u003cp\u003eFunctional analysis between different risk groups\u003c/p\u003e\n\u003cp\u003eWe used GO and KEGG enrichment analysis to investigate the potential functions of DEGs between two risk groups and the main pathways involved in regulation. GO enrichment analysis showed (Figure 5A) that in biological processes (BP), differentially expressed genes were mainly enriched in organelle division and nuclear division. Chromosomal regions and spindles are mainly enriched in the cellular component (CC). In molecular function (MF), differentially expressed genes are mainly enriched in microtubules and microtubule protein binding. KEGG enrichment analysis showed (Figure 5B) that DEGs were mainly involved in cell cycle, progesterone mediated oocyte maturation, oocyte meiosis, linoleic acid metabolism, arachidonic acid metabolism, IL-17 signaling pathway, P53 signaling pathway, etc. GSVA prompted CELL_CYCLE, DNA_REPLICATION, GLYCOLYSIS_GLUCONEOGENESIS and others were mainly enriched in high-risk groups, PRIMARY_BILE_ACID_BIOSYNTHESIS,ARACHIDONIC_ACID_METABOLISM ALPHA_LINOLENIC_ACID_METABOLISM and others were mainly enriched in low-risk groups (Figure 5C).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 5:\u003c/strong\u003e Enrichment analysis of DEGs between high-risk and low-risk groups. A. GO analysis of DEGs. B. KEGG analysis of DEGs. C. GSVA analysis of DEGs. GO, gene ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; DEGs, differentially expressed genes; BP, biological process; CC, cellular component; MF, molecular function; GSVA, Gene Set Variation Analysis.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCorrelation analysis between risk score and tumor mutation burden\u003c/p\u003e\n\u003cp\u003eThe distribution of LUAD gene mutations in the high-risk and low-risk groups was revealed by drawing waterfall plots (Figure 6A and Figure 6B). TP53 (52%) and MUC16 (36%) were the most common mutated genes in the high-risk and low-risk groups, respectively, with a higher proportion of mutations in high-risk populations. Comparing the tumor mutational burden (TMB) of different risk groups, it can be found that the TMB of the high-risk group is higher than that of the low-risk group (Figure 6C, P=2.2e-05), and there was a significant positive correlation between TMB and risk score (Figure 6D, R=0.22, P=6.9e-07). When introducing risk scores into TMB, we found that there was a statistically significant difference in survival among patients in each group (Figure 6E, P=0.007). Among them, the high tumor mutation low risk score group has the best prognosis, while the low tumor mutation high risk score group has the worst prognosis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 6:\u003c/strong\u003e Gene mutations and TMB status between different groups. The waterfall plot (A, B) showed the mutation status of 20 highly mutated genes in the high-risk and low-risk groups. C. TMB between different risk groups. D. The correlation between risk score and TMB. E. Survival status of patients with high and low risk scores combined with high and low tumor mutations. LUAD, lung adenocarcinoma; TMB, tumor mutational burden.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCorrelation analysis between immune cell infiltration and immune function\u003c/p\u003e\n\u003cp\u003eBased on CIBERSORT analysis to evaluate the immune cell infiltration between different risk groups (Figure 7A), we found that different immune cell infiltrations were observed between the two groups. M0 macrophages (P\u0026lt;0.001), neutrophils (P\u0026lt;0.01), activated CD4 memory T cells (P\u0026lt;0.001), and resting natural killer cells (P\u0026lt;0.001) were highly expressed in the high-risk group, and the survival curve showed high expression of M0 macrophages (Figure 7C, P=0.009) is associated with poor prognosis. In the analysis of immune function correlation (Figure 7B), we found that human leukocyte antigen (HLA) (P\u0026lt;0.001), type II interferon response (Type-III IFN-Reponse) (P\u0026lt;0.001), and checkpoint (P\u0026lt;0.05) were highly expressed in the low-risk group. Patients with high expression of human leukocyte antigen (Figure 7D, P=0.003) and high expression of type II interferon response (Figure 7E, P=0.016) had better prognosis. This may be the reason why low-risk patients have a better prognosis.\u0026nbsp;By comparing the immune cell infiltration and immune function differences among patients in different risk groups, we infer that they play an important role in the occurrence and development of LUAD, and the prognosis of patients in the high-risk group is poor, consistent with previous studies. We used the ESTIMATE algorithm to evaluate the Immunescore and Stromalscore in the tumor microenvironment (TME), and added the two scores to obtain the ESTIMATEscore index (Figure 7F), which can be used to infer the purity of the tumor. We found that the matrix score (P\u0026lt;0.001), immune score (P\u0026lt;0.001), and ESTIMATEscore (P\u0026lt;0.001) of the high-risk group were generally reduced, but the tumor purity was abnormally increased (Figure 7G, P=8.8e-05). The increase in tumor purity is associated with poor prognosis (Figure 7H, P=0.005).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 7:\u003c/strong\u003e Correlation analysis of immune cells and immune function among different risk groups. A. The infiltration of immune cells between high-risk and low-risk populations. B. Analysis of different risk scoring groups and immune function. C. Differential expression of macrophage M0 and analysis of patient survival curve. D. Differential expression of human leukocyte antigens and analysis of patient survival curves. E. Differential expression of type II interferon response and analysis of patient survival curve. F. TME scores between different risk groups. G. There was a significant difference in tumor purity between high and low risk scores. H. Survival curves between different tumor purities. LUAD, lung adenocarcinoma; TME, tumor microenvironment. * \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05.** \u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.01. *** \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCorrelation analysis between risk score, tumor microenvironment, and immune checkpoint\u003c/p\u003e\n\u003cp\u003eImmune checkpoint differential analysis (Figure 8A) showed that CD276 (P\u0026lt;0.001) and TNFSF4 (P\u0026lt;0.01) were highly expressed in the high-risk group, indicating that patients in the high-risk group were more likely to receive targeted therapy with anti-CD276 and anti-TNFSF4. Other immune checkpoints are highly expressed in low-risk patients, such as CTLA4 (P\u0026lt;0.01), CD28 (P\u0026lt;0.001), CD48 (P\u0026lt;0.001), IDO2 (P\u0026lt;0.001), TIGIT (P\u0026lt;0.01), etc., indicating that low-risk patients were more likely to receive targeted therapy against these genes.\u0026nbsp;Use TIDE and IPS analysis to predict the efficacy of immune checkpoint inhibitors in low-risk and high-risk groups. The TIDE score showed that the high-risk group had a higher TIDE score, indicating that immune evasion was more common in immunotherapy patients in the high-risk group compared to the low-risk group (Figure 8B, P\u0026lt;0.05). Therefore, this may also be one of the reasons why patients in the high-risk group have a poorer prognosis compared to those in the low-risk group. The IPS results showed that the CTLA4_neg-PD1_neg score of the low-risk group was significantly higher than that of the high-risk group (Figure 8C), and the IPS CTLA4_neg-PD1_pos score of the low-risk group was significantly higher than that of the high-risk group(Figure 8D). Compared with the high-risk group, the IPS CTLA4_pos-PD1_neg score of the low-risk group was significantly higher than that of the high-risk group (Figure 8E), and the IPS CTLA4_pos-PD1_pos score of the low-risk group was significantly higher than that of the high-risk group (Figure 8F). Half maximal inhibitory concentration(IC50) was an important indicator for evaluating drug efficacy. We studied the risk and sensitivity of anti-tumor drugs and found that the risk is related to many anti-tumor drugs, such as 5-fluorouracil(Figure 8G), cytarabine(Figure 8H), gefitinib(Figure 8I)and irinotecan(Figure 8J), which were more suitable for high-risk patients. These results indicated that risk score can serve as a potential predictor of chemotherapy sensitivity, providing new insights for the treatment of tumors and the prevention of drug resistance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 8:\u003c/strong\u003e Differential analysis of immune checkpoint differences between high-risk and low-risk groups. A. Analysis of immune checkpoint differences between high-risk and low-risk groups. B. TIDE scores for different risk groups. C-F. The relative probability that the low-risk group and the high-risk group will respond to ICI treatment assessed using immunophenoscore (IPS). G-J. Risk score and anticancer drug sensitivity analysis. TIDE, tumor Immune checkpoint immune dysfunction and exclusion; IPS, immunophenoscore; ICI, Immune checkpoint inhibitor; IC50, half maximal inhibitory concentration.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eExpression and validation of PBK in lung adenocarcinoma\u003c/p\u003e\n\u003cp\u003eBased on the previous description, we have constructed a 6-gene prognostic model with good accuracy. Then, further research will be conducted based on PBK in the model genes. The expression of PBK in LUAD samples of TCGA database was analyzed, and the differential analysis results showed that the expression of PBK in lung adenocarcinoma was significantly higher than that in normal tissues (Figure 9A, P\u0026lt;0.001). Survival analysis showed that high expression of PBK was associated with poor prognosis in patients (Figure 9B, P=0.003). In order to make the results more reliable, we downloaded three independent LUAD datasets from the GEO data platform to verify the relationship between PBK gene expression in LUAD and patient prognosis. The three validation sets are GSE13213 (Figure 9C, P=0.003), GSE31210 (Figure 9D, P\u0026lt;0.001), and GSE72094 (Figure 9E, P\u0026lt;0.001). The consistent results obtained from the three datasets indicate that high expression of PBK gene is associated with poor prognosis in LUAD patients, suggesting that PBK plays an important role in the occurrence and development of LUAD. The transcription of PBK in LUAD cell lines was validated through RT-PCR experiments, and the results showed (Figure 9F) that compared with human lung bronchial epithelial cell line 16HBE, PBK was highly expressed in LUAD cell lines A549 (P\u0026lt;0.01), NCI-H1975 (P\u0026lt;0.05), and NCI-H1299 (P\u0026lt;0.001). Finally, we evaluated the expression of PBK in TCGA pan cancer. The results showed that PBK was highly expressed in most tumors (Figure 9G).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 9:\u0026nbsp;\u003c/strong\u003eExpression and validation of PBK gene. A. The expression of PBK in LUAD patients in the TCGA cohort. B. The expression of PBK and the survival curve of LUAD patients. C-E. Validate the expression and survival relationship of PBK in the GEO dataset. F. The transcription status of PBK in LUAD cell lines. G. The expression of PBK in pan cancer (blue represents normal samples, red represents tumor samples). LUAD, lung adenocarcinoma; TCGA, the Cancer Genome Atlas; GEO, Gene Expression Omnibus. * \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05，** \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01，*** \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eLung adenocarcinoma (LUAD) is the most prevalent malignant tumor and carries a significant mortality burden. Early diagnosis remains a challenge, often resulting in advanced-stage or metastatic disease at presentation. This, in turn, translates to a poor prognosis for patients. Consequently, there is an urgent need to identify and develop accurate predictive biomarkers alongside novel therapeutic targets.\u003c/p\u003e\n\u003cp\u003eIn this study, a prognostic model was established using six genes (AURKA, CHEK1, PBK, PRKDC, PKM, and RUVBL1). LUAD patients were divided into high- and low-risk groups based on the optimal median value. Survival analysis showed that the high-risk group had a poorer prognosis than that of the low-risk group. We have explored potential reasons for this in our subsequent analysis\u003c/p\u003e\n\u003cp\u003eAn increasing number of studies indicate that the tumor microenvironment (TME）\u0026nbsp;is closely related to the occurrence of tumors. Non-tumor cells include stromal cells, immune cells, fibroblasts, and normal cells, which, together with tumor cells, participate in the formation and regulation of TME\u003csup\u003e[15]\u003c/sup\u003e.\u0026nbsp;The content of tumor cells in tumor tissue is defined as tumor purity\u0026nbsp;\u003csup\u003e[16]\u003c/sup\u003e. Therefore, the purity of tumors decreases with the increase of non-tumor cell components. Through correlation analysis between risk score and TME, the high-risk group was found to have high tumor purity, and survival analysis suggested that high tumor purity was associated with poor prognosis. Furthermore, some reports suggest that tumor purity may be a potential prognostic indicator for gastric cancer\u003csup\u003e[17]\u003c/sup\u003e、colon cancer\u0026nbsp;\u003csup\u003e[18]\u003c/sup\u003e and cervical cancer\u0026nbsp;\u003csup\u003e[19]\u003c/sup\u003e.\u0026nbsp;Further analysis revealed that in the high-risk group, the expression of neutrophils and M0 macrophages increased. Scholars have found that macrophages play a significant role in maintaining tissue homeostasis and enhancing host defense in TME\u003csup\u003e[20]\u003c/sup\u003e，These can be divided into the following two categories: M1 and M2. M1 type is involved in the inflammatory response and is related to anti-tumor immunity, whereas M2 type has tumor-promoting properties\u003csup\u003e[21]\u003c/sup\u003e.\u0026nbsp;Macrophages account for a large proportion of tumor matrix components and are therefore also known as tumor-associated macrophages (TAMs). In the late stage of tumor progression, TAMs transition to M2 type\u003csup\u003e[22]\u003c/sup\u003e，Studies have proven that this type of TAM can provide a favorable environment for tumor metastasis\u0026nbsp;\u003csup\u003e[23]\u003c/sup\u003e and angiogenesis\u0026nbsp;\u003csup\u003e[24]\u003c/sup\u003e. In this study, we found that the increase of M0 macrophages in high-risk group patients is associated with poor prognosis in LUAD patients. We speculate that it may be due to the recruitment of a large number of macrophages around the tumor, changing the tumor microenvironment and promoting tumor occurrence and development. Additionally, polarized M2 macrophages increase tumor angiogenesis and invasion. Therefore, TAM may play an important role in the occurrence and development of LUAD. In addition, inhibiting macrophage recruitment at the tumor site and preventing macrophage polarization towards the M2 type may be another approach to tumor treatment. In addition, neutrophils also increase in high-risk patients, and studies have shown that they can affect the development of cancer\u0026nbsp;\u003csup\u003e[25]\u003c/sup\u003e，Tumor-associated neutrophils affect TME and cancer progression, possibly by enhancing the recruitment of regulatory T cells (Tregs) in TME. The combined effect of the two weakens anti-tumor immune activity, thereby promoting tumor growth\u003csup\u003e[26]\u003c/sup\u003e.\u0026nbsp;Neutrophils secrete various cytokines, such as matrix metalloproteinase (MMP), vascular endothelial growth factor, neutrophil elastase, reactive oxygen species, etc. These cytokines can accelerate tumor cell growth and spread by altering TME, leading to poor prognosis\u003csup\u003e[27]\u003c/sup\u003e.\u0026nbsp;MMP-9 secreted by neutrophils is closely related to tumor angiogenesis\u003csup\u003e[28]\u003c/sup\u003eand plays an important role in promoting the establishment of tumor-neovascularization network\u0026nbsp;\u003csup\u003e[29]\u003c/sup\u003e.\u0026nbsp;Neutrophils can also affect cancer progression by recruiting Tregs and macrophages\u0026nbsp;\u003csup\u003e[30]\u003c/sup\u003e.\u0026nbsp;Therefore, macrophages and neutrophils play an important role in promoting tumor development, which may be one of the reasons for poor prognosis in high-risk patients.\u003c/p\u003e\n\u003cp\u003eImmunotherapy can enhance the natural defense of patients, thereby killing invading tumor cells in the body. Therefore, the therapy is increasingly attracting attention worldwide. In recent years, monoclonal antibody immune checkpoint inhibitors (ICIs), therapeutic antibodies, small molecule inhibitors, and cancer vaccines have gradually emerged as several types of drugs for cancer immunotherapy\u003csup\u003e[31]\u003c/sup\u003e.\u0026nbsp;Among them, especially ICIs, have made certain progress in the treatment of malignant tumors such as NSCLC, melanoma, prostate cancer, and kidney cancer\u0026nbsp;\u003csup\u003e[32]\u003c/sup\u003e.\u0026nbsp;Ipilimumab, a monoclonal antibody targeted against cytotoxic T lymphocytes associated with antigen-4 (CTLA-4), was the first one to be put into clinical use\u0026nbsp;\u003csup\u003e[33]\u003c/sup\u003e.It\u0026nbsp;was approved in 2011 for the treatment of malignant melanoma\u0026nbsp;\u003csup\u003e[34]\u003c/sup\u003e.\u0026nbsp;In this study, CTLA-4 was highly expressed in the low-risk group. It translocated to the cell surface with the assistance of T cell receptors after receiving a stimulation signal from CD28, competing with CD28 to bind to the stimulatory molecules CD80 and CD86, thereby transmitting the inhibitory signal into T cells, leading to inhibition of proliferation and activation\u003csup\u003e[35]\u003c/sup\u003e.\u0026nbsp;Furthermore, studies have shown that injecting anti-CTLA-4 antibodies into mice with pre-formed tumors can significantly reduce tumor growth\u0026nbsp;\u003csup\u003e[36]\u003c/sup\u003e.\u0026nbsp;Studies have shown that cancer with high TMB has a high likelihood of immune therapy response. Thus, it can yield good results in anti-PD-1/PD-L1 immunotherapy against different tumors\u003csup\u003e[37]\u003c/sup\u003e.\u0026nbsp;In our study, patients in the high-risk group had higher TMB than that in the low-risk group, indicating that patients in the high-risk group may be more likely to benefit from receiving anti-PD-1/PD-L1 treatment.\u003c/p\u003e\n\u003cp\u003eWe further studied the key genes in the prognostic model. Among them, Aurora kinase A (AURKA) is an important member of the serine/threonine kinase family, which includes three members: Aurora A (AURKA), Aurora B (AURKB), and Aurora C (AURKC). The aurora kinase family can participate in different steps of mitosis to precisely regulate spatial and temporal functions. These are necessary to maintain the integrity of the genome and chromosomes during cell division\u003csup\u003e[38]\u003c/sup\u003e.\u0026nbsp;The expression level of AURKA is increased in most tumor tissues reported in the TCGA database\u0026nbsp;\u003csup\u003e[39]\u003c/sup\u003e.\u0026nbsp;and the increase of AURKA is associated with poor prognosis in patients with gastrointestinal stromal tumors\u0026nbsp;\u003csup\u003e[40]\u003c/sup\u003e，colon cancer\u0026nbsp;\u003csup\u003e[41]\u003c/sup\u003eand NSCLC \u003csup\u003e[42]\u003c/sup\u003e.\u0026nbsp;Checkpoint kinase (CHEK1) has the function of encoding serine/threonine protein kinases, which activate downstream events through ATR (ATM Rad3 related) dependent phosphorylation, causing cell cycle arrest, maintaining replication fork activity, and activating DNA repair mechanisms\u003csup\u003e[43]\u003c/sup\u003e.\u0026nbsp;CHEK1 not only participates in monitoring the movement of replication forks but may also regulate other chromosomal activities, such as transcriptional regulation\u0026nbsp;\u003csup\u003e[44]\u003c/sup\u003e.\u0026nbsp;Compared with the expression levels in normal tissues, CHEK1 is overexpressed in various tumors\u0026nbsp;\u003csup\u003e[45]\u003c/sup\u003e.\u0026nbsp;Abnormal expression of CHEK1 protein can promote tumor cell proliferation and tumor grading, leading to poor prognosis in breast cancer\u0026nbsp;\u003csup\u003e[46]\u003c/sup\u003e、multiple myeloma\u0026nbsp;\u003csup\u003e[47]\u003c/sup\u003eand NSCLC\u0026nbsp;\u003csup\u003e[48]\u003c/sup\u003e patients.\u0026nbsp;The catalytic subunit (PRKDC) can encode DNA-dependent protein kinase catalytic subunits (DNA-PKcs) and is also a member of the phosphatidylinositol kinase-associated kinase (PIKK) family\u0026nbsp;\u003csup\u003e[49]\u003c/sup\u003e.\u0026nbsp;In most types of tumors, the expression of PRKDC is higher than that in adjacent tissues\u0026nbsp;\u003csup\u003e[50]\u003c/sup\u003e，which is closely related to high-grade tumors, such as liver cancer\u0026nbsp;\u003csup\u003e[51]\u003c/sup\u003e、ovarian cancer\u0026nbsp;\u003csup\u003e[52]\u003c/sup\u003e ，and lymphoma\u0026nbsp;\u003csup\u003e[53]\u003c/sup\u003e.\u0026nbsp;The expression is also related to the cancer progression.\u0026nbsp;PRKDC also plays an important role in regulating cell cycle and chromosome segregation, thereby promoting the occurrence and development of tumors\u003csup\u003e[54]\u003c/sup\u003e.\u0026nbsp;RUVBL1 (also known as Pontin52, Rvb1, TAP54 \u0026alpha;, and TIP49) is a highly conserved ATPase in the AAA+ (cell activity-related ATPase) superfamily, exhibiting ATPase activity and DNA helicase activity \u003csup\u003e[55]\u003c/sup\u003e，It is\u0026nbsp;mainly located in the nucleus and related to the nuclear matrix or nuclear cytoplasmic sol\u0026nbsp;\u003csup\u003e[56]\u003c/sup\u003e，and is\u0026nbsp;involved in chromatin remodeling\u0026nbsp;\u003csup\u003e[57]\u003c/sup\u003e.\u0026nbsp;The increased expression of RUVBL1 in tumor tissues\u0026nbsp;\u003csup\u003e[58]\u003c/sup\u003e，can accelerate tumor cell proliferation and promote tumor cell survival, which is crucial for the development of cance\u003csup\u003e[59]\u003c/sup\u003e，such as liver cancer\u0026nbsp;\u003csup\u003e[60]\u003c/sup\u003e，oral cancer\u0026nbsp;\u003csup\u003e[61]\u003c/sup\u003e，and\u0026nbsp;NSCLC\u0026nbsp;\u003csup\u003e[62]\u003c/sup\u003e.\u0026nbsp;The muscle type of pyruvate kinase (PKM) participates in the final step of glycolysis and plays an important role\u003csup\u003e[63]\u003c/sup\u003e in controlling tumor metabolism\u0026nbsp;\u003csup\u003e[64]\u003c/sup\u003e.\u0026nbsp;Some studies indicate the oncological effects of PKM differentially spliced transcripts, including PKM1 and PKM2\u003csup\u003e[65]\u003c/sup\u003e.\u0026nbsp;PKM1 can activate the glucose metabolism pathway and stimulate mitochondrial autophagy, which is related to the development of malignant tumors\u0026nbsp;\u003csup\u003e[66]\u003c/sup\u003e.\u0026nbsp;PKM2 is highly expressed in malignant tumors\u0026nbsp;\u003csup\u003e[67]\u003c/sup\u003e，LUAD patients with increased PKM2 expression have decreased overall survival\u0026nbsp;\u003csup\u003e[68]\u003c/sup\u003e，In addition, studies have shown that knocking down the expression of PKM2 can inhibit the growth of tumor cells\u0026nbsp;\u003csup\u003e[69]\u003c/sup\u003e.\u0026nbsp;PBK is a serine/threonine kinase that can participate in cell mitosis and the cell cycle\u0026nbsp;\u003csup\u003e[70]\u003c/sup\u003e.\u0026nbsp;However, few studies have reported on PBK promoting the occurrence and development of LUAD; therefore, we will explore the relationship between PBK and LUAD in subsequent studies. We found that in the TCGA dataset, the expression level of PBK was significantly increased in LUAD patients compared to that in the normal lung tissue, and high expression of PBK was associated with poor prognosis in LUAD patients. Then, we validated the correlation between high expression of PBK and poor prognosis in LUAD patients using three GEO datasets and validated the expression of PBK gene in three LUAD cell lines, all of which showed high expression. Therefore, based on our research results, we strongly speculate that PBK may play an important role in the development of LUAD.\u003c/p\u003e\n\u003cp\u003eOur study has certain limitations. The study majorly involved bioinformatics analysis of a small dataset. This demands validation of the results using larger datasets and prospective studies.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe constructed prognostic model can effectively distinguish high-risk and low-risk populations, predict the prognosis of LUAD patients, and respond to TMB and immunotherapy. Based on the TCGA data, high expression of PBK is associated with poor prognosis in LUAD patients, which was validated using three GEO datasets. The expression was also demonstrated to be upregulated in LUAD cell lines. Therefore, PBK may be a potential target for LUAD treatment.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eContributors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePeng Zhang conducted the database search, screened and extracted data for the analysis, prepared extracted data for the procedures, and was responsible for writing this article. Sen Li and Zhanliang Ren conducted the database search and screening, as well as the collection and analysis of data. Bo Wang and Hang Chen performed statistical analysis and interpretation of data. Wenmiao Wang and Yong Zhang contributed to the discussion and editing. Peng Zhang and Yunhao Liu were responsible for the study\u0026apos;s concept, design, and analysis, as well as revising the manuscript. All authors read and provided consent for the final version of the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study is in accordance with relevant guidelines and regulations. All data were publicly available from TCGA (\u003ca href=\"https://portal.gdc.cancer.gov/\" target=\"_blank\"\u003ehttps://portal.gdc.cancer.gov/\u003c/a\u003e) and GEO (\u003ca href=\"https://www.ncbi.nlm.nih.gov/geo/\" target=\"_blank\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/a\u003e) datasets. The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors received no specific funding for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank Bullet Edits Limited for the linguistic editing and proofreading of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatient consent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eRL S, KD M, A J, et al. - Cancer statistics, 2018 [J]. 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Proc Natl Acad Sci U S A, 2000, 97(10): 5167-72.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Lung adenocarcinoma, immunotherapy, prognostic model, tumor microenvironment, PBK","lastPublishedDoi":"10.21203/rs.3.rs-7953290/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7953290/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe incidence rate of lung adenocarcinoma (LUAD) is gradually increasing and the prognosis is poor. Recent studies have reported that histone phosphorylation plays an important role in the occurrence and development of tumors; however, the role of histone phosphorylation in LUAD remains unclear.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo investigate the role of histone phosphorylation in LUAD regulation, we first curated data on 42 genes related to this process from the published literature. This data served as the foundation for our subsequent analyses. Next,we downloaded expression data for LUAD patients from The Cancer Genome Atlas (TCGA) public database. We employed differential expression analysis (FDR \u0026lt; 0.05, |logFC| \u0026gt; 0.5) to identify genes exhibiting significant differences in expression between LUAD tissues and normal controls. This analysis yielded a set of differentially expressed genes (DEGs). From the identified DEGs, we utilized univariate Cox analysis to select 11 genes with prognostic potential. These genes were then subjected to least absolute shrinkage and selection operator regression（LASSO）analysis for the construction of a prognostic model. LUAD patients within the TCGA cohort were categorized into high- and low-risk groups based on a predefined optimal median value derived from the prognostic model. The accuracy and generalizability of the model were subsequently evaluated using data from three independent Gene Expression Omnibus（GEO） datasets. We conducted a comprehensive comparison between the high- and low-risk groups defined by the prognostic model. This comparison encompassed analyses of prognosis, clinical relevance, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment of DEGs, tumor mutational burden (TMB), and immune cell infiltration. Furthermore, we employed the \"estimate\" package to calculate and compare the immune score, stromal score, and tumor purity across the different risk groups. Finally, to validate the expression of PBK in LUAD cell lines.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBy constructing a 6-gene prognostic model using LASSO, LUAD patients in the TCGA database were divided into high-risk and low-risk groups. Through comparison, it was found that the high-risk and low-risk groups showed significant differences in survival, immune cell infiltration, TMB, immunotherapy, and other aspects. Univariate and multivariate Cox regression analysis showed that risk score can be used as an independent prognostic factor for LUAD.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe present study is the first to use histone phosphorylation genes for the reliable prognosis of LUAD in patients. The constructed prognostic model can significantly distinguish high-risk and low-risk populations, predict the prognosis of LUAD patients, TMB, and may also guide future immunotherapy interventions for these patients.\u003c/p\u003e","manuscriptTitle":"Application of Prognostic Model based on Histone Phosphorylation Modification Gene in Lung Adenocarcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-26 06:52:34","doi":"10.21203/rs.3.rs-7953290/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c62972b5-b3ab-47d5-90dc-b71a73038bae","owner":[],"postedDate":"November 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-24T12:09:56+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-26 06:52:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7953290","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7953290","identity":"rs-7953290","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}